diff --git a/PAIRS_TRADING_BACKTEST_USAGE.md b/PAIRS_TRADING_BACKTEST_USAGE.md index e07a6ef..4a575d4 100644 --- a/PAIRS_TRADING_BACKTEST_USAGE.md +++ b/PAIRS_TRADING_BACKTEST_USAGE.md @@ -11,6 +11,7 @@ The enhanced `pt_backtest.py` script now supports multi-day and multi-instrument - Support for wildcard patterns in configuration files - CLI override for data file specification + ### 2. Dynamic Instrument Selection - Auto-detection of instruments from database - CLI override for instrument specification diff --git a/README.md b/README.md index bc35b76..e4ac969 100644 --- a/README.md +++ b/README.md @@ -38,15 +38,12 @@ CONFIG = EQT_CONFIG # For equity data ``` Each configuration dictionary specifies: -- `security_type`: "CRYPTO" or "EQUITY". - `data_directory`: Path to the data files. - `datafiles`: A list of database files to process. You can comment/uncomment specific files to include/exclude them from the backtest. - `db_table_name`: The name of the table within the SQLite database. - `instruments`: A list of symbols to consider for forming trading pairs. - `trading_hours`: Defines the session start and end times, crucial for equity markets. -- `price_column`: The column in the data to be used as the price (e.g., "close"). -- `min_required_points`: Minimum data points needed for statistical calculations. -- `zero_threshold`: A small value to handle potential division by zero. +- `stat_model_price`: The column in the data to be used as the price (e.g., "close"). - `dis-equilibrium_open_trshld`: The threshold (in standard deviations) of the dis-equilibrium for opening a trade. - `dis-equilibrium_close_trshld`: The threshold (in standard deviations) of the dis-equilibrium for closing an open trade. - `training_minutes`: The length of the rolling window (in minutes) used to train the model (e.g., calculate cointegration, mean, and standard deviation of the dis-equilibrium). diff --git a/configuration/crypto.cfg b/configuration/crypto.cfg deleted file mode 100644 index 4acea64..0000000 --- a/configuration/crypto.cfg +++ /dev/null @@ -1,31 +0,0 @@ -{ - "security_type": "CRYPTO", - "data_directory": "./data/crypto", - "datafiles": [ - "2025*.mktdata.ohlcv.db" - ], - "db_table_name": "md_1min_bars", - "exchange_id": "BNBSPOT", - "instrument_id_pfx": "PAIR-", - "trading_hours": { - "begin_session": "00:00:00", - "end_session": "23:59:00", - "timezone": "UTC" - }, - "price_column": "close", - "min_required_points": 30, - "zero_threshold": 1e-10, - "dis-equilibrium_open_trshld": 2.0, - "dis-equilibrium_close_trshld": 0.5, - "training_minutes": 120, - "funding_per_pair": 2000.0, - "fit_method_class": "pt_trading.sliding_fit.SlidingFit", - # "fit_method_class": "pt_trading.static_fit.StaticFit", - "close_outstanding_positions": true, - "trading_hours": { - "begin_session": "06:00:00", - "end_session": "16:00:00", - "timezone": "America/New_York" - } - -} \ No newline at end of file diff --git a/configuration/equity_single.cfg b/configuration/equity_single.cfg deleted file mode 100644 index be60667..0000000 --- a/configuration/equity_single.cfg +++ /dev/null @@ -1,27 +0,0 @@ -{ - "security_type": "EQUITY", - "data_directory": "./data/equity", - # "datafiles": [ - # "20250604.mktdata.ohlcv.db", - # ], - "db_table_name": "md_1min_bars", - "exchange_id": "ALPACA", - "instrument_id_pfx": "STOCK-", - "trading_hours": { - "begin_session": "9:30:00", - "end_session": "16:00:00", - "timezone": "America/New_York" - }, - "price_column": "close", - "min_required_points": 30, - "zero_threshold": 1e-10, - "dis-equilibrium_open_trshld": 2.0, - "dis-equilibrium_close_trshld": 1.0, - "training_minutes": 120, - "funding_per_pair": 2000.0, - "fit_method_class": "pt_trading.sliding_fit.SlidingFit", - # "fit_method_class": "pt_trading.static_fit.StaticFit", - "exclude_instruments": ["CAN"], - "close_outstanding_positions": false - -} \ No newline at end of file diff --git a/configuration/vecm.cfg b/configuration/vecm.cfg new file mode 100644 index 0000000..536fa4f --- /dev/null +++ b/configuration/vecm.cfg @@ -0,0 +1,43 @@ +{ + "market_data_loading": { + "CRYPTO": { + "data_directory": "./data/crypto", + "db_table_name": "md_1min_bars", + "instrument_id_pfx": "PAIR-", + }, + "EQUITY": { + "data_directory": "./data/equity", + "db_table_name": "md_1min_bars", + "instrument_id_pfx": "STOCK-", + } + }, + + # ====== Funding ====== + "funding_per_pair": 2000.0, + + # ====== Trading Parameters ====== + "stat_model_price": "close", # "vwap" + "execution_price": { + "column": "vwap", + "shift": 1, + }, + "dis-equilibrium_open_trshld": 2.0, + "dis-equilibrium_close_trshld": 1.0, + "training_minutes": 120, + "fit_method_class": "pt_trading.vecm_rolling_fit.VECMRollingFit", + + # ====== Stop Conditions ====== + "stop_close_conditions": { + "profit": 2.0, + "loss": -0.5 + } + + # ====== End of Session Closeout ====== + "close_outstanding_positions": true, + # "close_outstanding_positions": false, + "trading_hours": { + "timezone": "America/New_York", + "begin_session": "9:30:00", + "end_session": "18:30:00", + } +} \ No newline at end of file diff --git a/configuration/zscore.cfg b/configuration/zscore.cfg new file mode 100644 index 0000000..92b5187 --- /dev/null +++ b/configuration/zscore.cfg @@ -0,0 +1,42 @@ +{ + "market_data_loading": { + "CRYPTO": { + "data_directory": "./data/crypto", + "db_table_name": "md_1min_bars", + "instrument_id_pfx": "PAIR-", + }, + "EQUITY": { + "data_directory": "./data/equity", + "db_table_name": "md_1min_bars", + "instrument_id_pfx": "STOCK-", + } + }, + + # ====== Funding ====== + "funding_per_pair": 2000.0, + # ====== Trading Parameters ====== + "stat_model_price": "close", + "execution_price": { + "column": "vwap", + "shift": 1, + }, + "dis-equilibrium_open_trshld": 2.0, + "dis-equilibrium_close_trshld": 0.5, + "training_minutes": 120, + "fit_method_class": "pt_trading.z-score_rolling_fit.ZScoreRollingFit", + + # ====== Stop Conditions ====== + "stop_close_conditions": { + "profit": 2.0, + "loss": -0.5 + } + + # ====== End of Session Closeout ====== + "close_outstanding_positions": true, + # "close_outstanding_positions": false, + "trading_hours": { + "timezone": "America/New_York", + "begin_session": "9:30:00", + "end_session": "18:30:00", + } +} \ No newline at end of file diff --git a/lib/pt_trading/fit_method.py b/lib/pt_trading/fit_method.py index c7022ca..934da1b 100644 --- a/lib/pt_trading/fit_method.py +++ b/lib/pt_trading/fit_method.py @@ -1,8 +1,10 @@ +from __future__ import annotations + from abc import ABC, abstractmethod from enum import Enum from typing import Dict, Optional, cast -import pandas as pd # type: ignore[import] +import pandas as pd from pt_trading.results import BacktestResult from pt_trading.trading_pair import TradingPair @@ -12,13 +14,24 @@ NanoPerMin = 1e9 class PairsTradingFitMethod(ABC): TRADES_COLUMNS = [ "time", - "action", "symbol", + "side", + "action", "price", "disequilibrium", "scaled_disequilibrium", + "signed_scaled_disequilibrium", "pair", ] + @staticmethod + def create(config: Dict) -> PairsTradingFitMethod: + import importlib + fit_method_class_name = config.get("fit_method_class", None) + assert fit_method_class_name is not None + module_name, class_name = fit_method_class_name.rsplit(".", 1) + module = importlib.import_module(module_name) + fit_method = getattr(module, class_name)() + return cast(PairsTradingFitMethod, fit_method) @abstractmethod def run_pair( @@ -28,9 +41,12 @@ class PairsTradingFitMethod(ABC): @abstractmethod def reset(self) -> None: ... + @abstractmethod + def create_trading_pair( + self, + config: Dict, + market_data: pd.DataFrame, + symbol_a: str, + symbol_b: str, + ) -> TradingPair: ... -class PairState(Enum): - INITIAL = 1 - OPEN = 2 - CLOSED = 3 - CLOSED_POSITIONS = 4 diff --git a/lib/pt_trading/results.py b/lib/pt_trading/results.py index 3d4e229..c30bfa7 100644 --- a/lib/pt_trading/results.py +++ b/lib/pt_trading/results.py @@ -46,7 +46,7 @@ def create_result_database(db_path: str) -> None: if db_dir and not os.path.exists(db_dir): os.makedirs(db_dir, exist_ok=True) print(f"Created directory: {db_dir}") - + conn = sqlite3.connect(db_path) cursor = conn.cursor() @@ -68,7 +68,8 @@ def create_result_database(db_path: str) -> None: close_quantity INTEGER, close_disequilibrium REAL, symbol_return REAL, - pair_return REAL + pair_return REAL, + close_condition TEXT ) """ ) @@ -120,8 +121,8 @@ def store_config_in_database( config_file_path: str, config: Dict, fit_method_class: str, - datafiles: List[str], - instruments: List[str], + datafiles: List[Tuple[str, str]], + instruments: List[Dict[str, str]], ) -> None: """ Store configuration information in the database for reference. @@ -139,8 +140,13 @@ def store_config_in_database( config_json = json.dumps(config, indent=2, default=str) # Convert lists to comma-separated strings for storage - datafiles_str = ", ".join(datafiles) - instruments_str = ", ".join(instruments) + datafiles_str = ", ".join([f"{datafile}" for _, datafile in datafiles]) + instruments_str = ", ".join( + [ + f"{inst['symbol']}:{inst['instrument_type']}:{inst['exchange_id']}" + for inst in instruments + ] + ) # Insert configuration record cursor.execute( @@ -171,251 +177,23 @@ def store_config_in_database( traceback.print_exc() -def store_results_in_database( - db_path: str, datafile: str, bt_result: "BacktestResult" -) -> None: - """ - Store backtest results in the SQLite database. - """ - if db_path.upper() == "NONE": - return - - def convert_timestamp(timestamp: Any) -> Optional[datetime]: - """Convert pandas Timestamp to Python datetime object for SQLite compatibility.""" - if timestamp is None: - return None - if hasattr(timestamp, "to_pydatetime"): - return timestamp.to_pydatetime() +def convert_timestamp(timestamp: Any) -> Optional[datetime]: + """Convert pandas Timestamp to Python datetime object for SQLite compatibility.""" + if timestamp is None: + return None + if isinstance(timestamp, pd.Timestamp): + return timestamp.to_pydatetime() + elif isinstance(timestamp, datetime): return timestamp + elif isinstance(timestamp, date): + return datetime.combine(timestamp, datetime.min.time()) + elif isinstance(timestamp, str): + return datetime.strptime(timestamp, "%Y-%m-%d %H:%M:%S") + elif isinstance(timestamp, int): + return datetime.fromtimestamp(timestamp) + else: + raise ValueError(f"Unsupported timestamp type: {type(timestamp)}") - try: - # Extract date from datafile name (assuming format like 20250528.mktdata.ohlcv.db) - filename = os.path.basename(datafile) - date_str = filename.split(".")[0] # Extract date part - - # Convert to proper date format - try: - date_obj = datetime.strptime(date_str, "%Y%m%d").date() - except ValueError: - # If date parsing fails, use current date - date_obj = datetime.now().date() - - conn = sqlite3.connect(db_path) - cursor = conn.cursor() - - # Process each trade from bt_result - trades = bt_result.get_trades() - - for pair_name, symbols in trades.items(): - # Calculate pair return for this pair - pair_return = 0.0 - pair_trades = [] - - # First pass: collect all trades and calculate returns - for symbol, symbol_trades in symbols.items(): - if len(symbol_trades) == 0: # No trades for this symbol - print( - f"Warning: No trades found for symbol {symbol} in pair {pair_name}" - ) - continue - - elif len(symbol_trades) >= 2: # Completed trades (entry + exit) - # Handle both old and new tuple formats - if len(symbol_trades[0]) == 2: # Old format: (action, price) - entry_action, entry_price = symbol_trades[0] - exit_action, exit_price = symbol_trades[1] - open_disequilibrium = 0.0 # Fallback for old format - open_scaled_disequilibrium = 0.0 - close_disequilibrium = 0.0 - close_scaled_disequilibrium = 0.0 - open_time = datetime.now() - close_time = datetime.now() - else: # New format: (action, price, disequilibrium, scaled_disequilibrium, timestamp) - ( - entry_action, - entry_price, - open_disequilibrium, - open_scaled_disequilibrium, - open_time, - ) = symbol_trades[0] - ( - exit_action, - exit_price, - close_disequilibrium, - close_scaled_disequilibrium, - close_time, - ) = symbol_trades[1] - - # Handle None values - open_disequilibrium = ( - open_disequilibrium - if open_disequilibrium is not None - else 0.0 - ) - open_scaled_disequilibrium = ( - open_scaled_disequilibrium - if open_scaled_disequilibrium is not None - else 0.0 - ) - close_disequilibrium = ( - close_disequilibrium - if close_disequilibrium is not None - else 0.0 - ) - close_scaled_disequilibrium = ( - close_scaled_disequilibrium - if close_scaled_disequilibrium is not None - else 0.0 - ) - - # Convert pandas Timestamps to Python datetime objects - open_time = convert_timestamp(open_time) or datetime.now() - close_time = convert_timestamp(close_time) or datetime.now() - - # Calculate actual share quantities based on funding per pair - # Split funding equally between the two positions - funding_per_position = bt_result.config["funding_per_pair"] / 2 - shares = funding_per_position / entry_price - - # Calculate symbol return - symbol_return = 0.0 - if entry_action == "BUY" and exit_action == "SELL": - symbol_return = (exit_price - entry_price) / entry_price * 100 - elif entry_action == "SELL" and exit_action == "BUY": - symbol_return = (entry_price - exit_price) / entry_price * 100 - - pair_return += symbol_return - - pair_trades.append( - { - "symbol": symbol, - "entry_action": entry_action, - "entry_price": entry_price, - "exit_action": exit_action, - "exit_price": exit_price, - "symbol_return": symbol_return, - "open_disequilibrium": open_disequilibrium, - "open_scaled_disequilibrium": open_scaled_disequilibrium, - "close_disequilibrium": close_disequilibrium, - "close_scaled_disequilibrium": close_scaled_disequilibrium, - "open_time": open_time, - "close_time": close_time, - "shares": shares, - "is_completed": True, - } - ) - - # Skip one-sided trades - they will be handled by outstanding_positions table - elif len(symbol_trades) == 1: - print( - f"Skipping one-sided trade for {symbol} in pair {pair_name} - will be stored in outstanding_positions table" - ) - continue - - else: - # This should not happen, but handle unexpected cases - print( - f"Warning: Unexpected number of trades ({len(symbol_trades)}) for symbol {symbol} in pair {pair_name}" - ) - continue - - # Second pass: insert completed trade records into database - for trade in pair_trades: - # Only store completed trades in pt_bt_results table - cursor.execute( - """ - INSERT INTO pt_bt_results ( - date, pair, symbol, open_time, open_side, open_price, - open_quantity, open_disequilibrium, close_time, close_side, - close_price, close_quantity, close_disequilibrium, - symbol_return, pair_return - ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) - """, - ( - date_obj, - pair_name, - trade["symbol"], - trade["open_time"], - trade["entry_action"], - trade["entry_price"], - trade["shares"], - trade["open_scaled_disequilibrium"], - trade["close_time"], - trade["exit_action"], - trade["exit_price"], - trade["shares"], - trade["close_scaled_disequilibrium"], - trade["symbol_return"], - pair_return, - ), - ) - - # Store outstanding positions in separate table - outstanding_positions = bt_result.get_outstanding_positions() - for pos in outstanding_positions: - # Calculate position quantity (negative for SELL positions) - position_qty_a = ( - pos["shares_a"] if pos["side_a"] == "BUY" else -pos["shares_a"] - ) - position_qty_b = ( - pos["shares_b"] if pos["side_b"] == "BUY" else -pos["shares_b"] - ) - - # Calculate unrealized returns - # For symbol A: (current_price - open_price) / open_price * 100 * position_direction - unrealized_return_a = ( - (pos["current_px_a"] - pos["open_px_a"]) / pos["open_px_a"] * 100 - ) * (1 if pos["side_a"] == "BUY" else -1) - unrealized_return_b = ( - (pos["current_px_b"] - pos["open_px_b"]) / pos["open_px_b"] * 100 - ) * (1 if pos["side_b"] == "BUY" else -1) - - # Store outstanding position for symbol A - cursor.execute( - """ - INSERT INTO outstanding_positions ( - date, pair, symbol, position_quantity, last_price, unrealized_return, open_price, open_side - ) VALUES (?, ?, ?, ?, ?, ?, ?, ?) - """, - ( - date_obj, - pos["pair"], - pos["symbol_a"], - position_qty_a, - pos["current_px_a"], - unrealized_return_a, - pos["open_px_a"], - pos["side_a"], - ), - ) - - # Store outstanding position for symbol B - cursor.execute( - """ - INSERT INTO outstanding_positions ( - date, pair, symbol, position_quantity, last_price, unrealized_return, open_price, open_side - ) VALUES (?, ?, ?, ?, ?, ?, ?, ?) - """, - ( - date_obj, - pos["pair"], - pos["symbol_b"], - position_qty_b, - pos["current_px_b"], - unrealized_return_b, - pos["open_px_b"], - pos["side_b"], - ), - ) - - conn.commit() - conn.close() - - except Exception as e: - print(f"Error storing results in database: {str(e)}") - import traceback - - traceback.print_exc() class BacktestResult: @@ -428,16 +206,19 @@ class BacktestResult: self.trades: Dict[str, Dict[str, Any]] = {} self.total_realized_pnl = 0.0 self.outstanding_positions: List[Dict[str, Any]] = [] - + self.pairs_trades_: Dict[str, List[Dict[str, Any]]] = {} + def add_trade( self, pair_nm: str, symbol: str, + side: str, action: str, price: Any, disequilibrium: Optional[float] = None, scaled_disequilibrium: Optional[float] = None, timestamp: Optional[datetime] = None, + status: Optional[str] = None, ) -> None: """Add a trade to the results tracking.""" pair_nm = str(pair_nm) @@ -447,7 +228,16 @@ class BacktestResult: if symbol not in self.trades[pair_nm]: self.trades[pair_nm][symbol] = [] self.trades[pair_nm][symbol].append( - (action, price, disequilibrium, scaled_disequilibrium, timestamp) + { + "symbol": symbol, + "side": side, + "action": action, + "price": price, + "disequilibrium": disequilibrium, + "scaled_disequilibrium": scaled_disequilibrium, + "timestamp": timestamp, + "status": status, + } ) def add_outstanding_position(self, position: Dict[str, Any]) -> None: @@ -484,20 +274,27 @@ class BacktestResult: print(result) for row in result.itertuples(): + side = row.side action = row.action symbol = row.symbol price = row.price disequilibrium = getattr(row, "disequilibrium", None) scaled_disequilibrium = getattr(row, "scaled_disequilibrium", None) - timestamp = getattr(row, "time", None) + if hasattr(row, "time"): + timestamp = getattr(row, "time") + else: + timestamp = convert_timestamp(row.Index) + status = row.status self.add_trade( pair_nm=str(row.pair), - action=str(action), symbol=str(symbol), + side=str(side), + action=str(action), price=float(str(price)), disequilibrium=disequilibrium, scaled_disequilibrium=scaled_disequilibrium, timestamp=timestamp, + status=str(status) if status is not None else "?", ) def print_single_day_results(self) -> None: @@ -523,105 +320,126 @@ class BacktestResult: def calculate_returns(self, all_results: Dict[str, Dict[str, Any]]) -> None: """Calculate and print returns by day and pair.""" + def _symbol_return(trade1_side: str, trade1_px: float, trade2_side: str, trade2_px: float) -> float: + if trade1_side == "BUY" and trade2_side == "SELL": + return (trade2_px - trade1_px) / trade1_px * 100 + elif trade1_side == "SELL" and trade2_side == "BUY": + return (trade1_px - trade2_px) / trade1_px * 100 + else: + return 0 + print("\n====== Returns By Day and Pair ======") + trades = [] for filename, data in all_results.items(): - day_return = 0 + pairs = list(data["trades"].keys()) + for pair in pairs: + self.pairs_trades_[pair] = [] + trades_dict = data["trades"][pair] + for symbol in trades_dict.keys(): + trades.extend(trades_dict[symbol]) + trades = sorted(trades, key=lambda x: (x["timestamp"], x["symbol"])) + print(f"\n--- {filename} ---") self.outstanding_positions = data["outstanding_positions"] + + day_return = 0.0 + for idx in range(0, len(trades), 4): + symbol_a = trades[idx]["symbol"] + trade_a_1 = trades[idx] + trade_a_2 = trades[idx + 2] - # Process each pair - for pair, symbols in data["trades"].items(): - pair_return = 0 - pair_trades = [] + symbol_b = trades[idx + 1]["symbol"] + trade_b_1 = trades[idx + 1] + trade_b_2 = trades[idx + 3] - # Calculate individual symbol returns in the pair - for symbol, trades in symbols.items(): - if len(trades) == 0: - continue - - symbol_return = 0 - symbol_trades = [] - - # Process all trades sequentially for this symbol - for i, trade in enumerate(trades): - # Handle both old and new tuple formats - if len(trade) == 2: # Old format: (action, price) - action, price = trade - disequilibrium = None - scaled_disequilibrium = None - timestamp = None - else: # New format: (action, price, disequilibrium, scaled_disequilibrium, timestamp) - action, price = trade[:2] - disequilibrium = trade[2] if len(trade) > 2 else None - scaled_disequilibrium = trade[3] if len(trade) > 3 else None - timestamp = trade[4] if len(trade) > 4 else None - - symbol_trades.append((action, price, disequilibrium, scaled_disequilibrium, timestamp)) - - # Calculate returns for all trade combinations - for i in range(len(symbol_trades) - 1): - trade1 = symbol_trades[i] - trade2 = symbol_trades[i + 1] - - action1, price1, diseq1, scaled_diseq1, ts1 = trade1 - action2, price2, diseq2, scaled_diseq2, ts2 = trade2 - - # Calculate return based on action combination - trade_return = 0 - if action1 == "BUY" and action2 == "SELL": - # Long position - trade_return = (price2 - price1) / price1 * 100 - elif action1 == "SELL" and action2 == "BUY": - # Short position - trade_return = (price1 - price2) / price1 * 100 - - symbol_return += trade_return - - # Store trade details for reporting - pair_trades.append( - ( - symbol, - action1, - price1, - action2, - price2, - trade_return, - scaled_diseq1, - scaled_diseq2, - i + 1, # Trade sequence number - ) - ) - - pair_return += symbol_return + symbol_return = 0 + assert ( + trade_a_1["timestamp"] < trade_a_2["timestamp"] + ), f"Trade 1: {trade_a_1['timestamp']} is not less than Trade 2: {trade_a_2['timestamp']}" + assert ( + trade_a_1["action"] == "OPEN" and trade_a_2["action"] == "CLOSE" + ), f"Trade 1: {trade_a_1['action']} and Trade 2: {trade_a_2['action']} are the same" - # Print pair returns with disequilibrium information - if pair_trades: - print(f" {pair}:") - for ( - symbol, - action1, - price1, - action2, - price2, - trade_return, - scaled_diseq1, - scaled_diseq2, - trade_num, - ) in pair_trades: - disequil_info = "" - if ( - scaled_diseq1 is not None - and scaled_diseq2 is not None - ): - disequil_info = f" | Open Dis-eq: {scaled_diseq1:.2f}, Close Dis-eq: {scaled_diseq2:.2f}" + # Calculate return based on action combination + trade_return = 0 + symbol_a_return = _symbol_return(trade_a_1["side"], trade_a_1["price"], trade_a_2["side"], trade_a_2["price"]) + symbol_b_return = _symbol_return(trade_b_1["side"], trade_b_1["price"], trade_b_2["side"], trade_b_2["price"]) - print( - f" {symbol} (Trade #{trade_num}): {action1} @ ${price1:.2f}, {action2} @ ${price2:.2f}, Return: {trade_return:.2f}%{disequil_info}" - ) - print(f" Pair Total Return: {pair_return:.2f}%") - day_return += pair_return + pair_return = symbol_a_return + symbol_b_return + + self.pairs_trades_[pair].append( + { + "symbol": symbol_a, + "open_side": trade_a_1["side"], + "open_action": trade_a_1["action"], + "open_price": trade_a_1["price"], + "close_side": trade_a_2["side"], + "close_action": trade_a_2["action"], + "close_price": trade_a_2["price"], + "symbol_return": symbol_a_return, + "open_disequilibrium": trade_a_1["disequilibrium"], + "open_scaled_disequilibrium": trade_a_1["scaled_disequilibrium"], + "close_disequilibrium": trade_a_2["disequilibrium"], + "close_scaled_disequilibrium": trade_a_2["scaled_disequilibrium"], + "open_time": trade_a_1["timestamp"], + "close_time": trade_a_2["timestamp"], + "shares": self.config["funding_per_pair"] / 2 / trade_a_1["price"], + "is_completed": True, + "close_condition": trade_a_2["status"], + "pair_return": pair_return + } + ) + self.pairs_trades_[pair].append( + { + "symbol": symbol_b, + "open_side": trade_b_1["side"], + "open_action": trade_b_1["action"], + "open_price": trade_b_1["price"], + "close_side": trade_b_2["side"], + "close_action": trade_b_2["action"], + "close_price": trade_b_2["price"], + "symbol_return": symbol_b_return, + "open_disequilibrium": trade_b_1["disequilibrium"], + "open_scaled_disequilibrium": trade_b_1["scaled_disequilibrium"], + "close_disequilibrium": trade_b_2["disequilibrium"], + "close_scaled_disequilibrium": trade_b_2["scaled_disequilibrium"], + "open_time": trade_b_1["timestamp"], + "close_time": trade_b_2["timestamp"], + "shares": self.config["funding_per_pair"] / 2 / trade_b_1["price"], + "is_completed": True, + "close_condition": trade_b_2["status"], + "pair_return": pair_return + } + ) + + + # Print pair returns with disequilibrium information + day_return = 0.0 + if pair in self.pairs_trades_: + + print(f"{pair}:") + pair_return = 0.0 + for trd in self.pairs_trades_[pair]: + disequil_info = "" + if ( + trd["open_scaled_disequilibrium"] is not None + and trd["open_scaled_disequilibrium"] is not None + ): + disequil_info = f" | Open Dis-eq: {trd['open_scaled_disequilibrium']:.2f}," + f" Close Dis-eq: {trd['open_scaled_disequilibrium']:.2f}" + + print( + f" {trd['open_time'].time()}-{trd['close_time'].time()} {trd['symbol']}: " + f" {trd['open_side']} @ ${trd['open_price']:.2f}," + f" {trd["close_side"]} @ ${trd["close_price"]:.2f}," + f" Return: {trd['symbol_return']:.2f}%{disequil_info}" + ) + pair_return += trd["symbol_return"] + + print(f" Pair Total Return: {pair_return:.2f}%") + day_return += pair_return # Print day total return and add to global realized PnL if day_return != 0: @@ -698,7 +516,7 @@ class BacktestResult: print("-" * 100) - total_value += pos["total_current_value"] + total_value += pos["total_current_value"] print(f"{'TOTAL OUTSTANDING VALUE':<80} ${total_value:<12.2f}") @@ -734,7 +552,7 @@ class BacktestResult: last_row = pair_result_df.loc[last_row_index] last_tstamp = last_row["tstamp"] - colname_a, colname_b = pair.colnames() + colname_a, colname_b = pair.exec_prices_colnames() last_px_a = last_row[colname_a] last_px_b = last_row[colname_b] @@ -793,3 +611,131 @@ class BacktestResult: ) return current_value_a, current_value_b, total_current_value + + def store_results_in_database( + self, db_path: str, day: str + ) -> None: + """ + Store backtest results in the SQLite database. + """ + if db_path.upper() == "NONE": + return + + try: + # Extract date from datafile name (assuming format like 20250528.mktdata.ohlcv.db) + date_str = day + + # Convert to proper date format + try: + date_obj = datetime.strptime(date_str, "%Y%m%d").date() + except ValueError: + # If date parsing fails, use current date + date_obj = datetime.now().date() + + conn = sqlite3.connect(db_path) + cursor = conn.cursor() + + # Process each trade from bt_result + trades = self.get_trades() + + for pair_name, _ in trades.items(): + + # Second pass: insert completed trade records into database + for trade_pair in sorted(self.pairs_trades_[pair_name], key=lambda x: x["open_time"]): + # Only store completed trades in pt_bt_results table + cursor.execute( + """ + INSERT INTO pt_bt_results ( + date, pair, symbol, open_time, open_side, open_price, + open_quantity, open_disequilibrium, close_time, close_side, + close_price, close_quantity, close_disequilibrium, + symbol_return, pair_return, close_condition + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + date_obj, + pair_name, + trade_pair["symbol"], + trade_pair["open_time"], + trade_pair["open_side"], + trade_pair["open_price"], + trade_pair["shares"], + trade_pair["open_scaled_disequilibrium"], + trade_pair["close_time"], + trade_pair["close_side"], + trade_pair["close_price"], + trade_pair["shares"], + trade_pair["close_scaled_disequilibrium"], + trade_pair["symbol_return"], + trade_pair["pair_return"], + trade_pair["close_condition"] + ), + ) + + # Store outstanding positions in separate table + outstanding_positions = self.get_outstanding_positions() + for pos in outstanding_positions: + # Calculate position quantity (negative for SELL positions) + position_qty_a = ( + pos["shares_a"] if pos["side_a"] == "BUY" else -pos["shares_a"] + ) + position_qty_b = ( + pos["shares_b"] if pos["side_b"] == "BUY" else -pos["shares_b"] + ) + + # Calculate unrealized returns + # For symbol A: (current_price - open_price) / open_price * 100 * position_direction + unrealized_return_a = ( + (pos["current_px_a"] - pos["open_px_a"]) / pos["open_px_a"] * 100 + ) * (1 if pos["side_a"] == "BUY" else -1) + unrealized_return_b = ( + (pos["current_px_b"] - pos["open_px_b"]) / pos["open_px_b"] * 100 + ) * (1 if pos["side_b"] == "BUY" else -1) + + # Store outstanding position for symbol A + cursor.execute( + """ + INSERT INTO outstanding_positions ( + date, pair, symbol, position_quantity, last_price, unrealized_return, open_price, open_side + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + date_obj, + pos["pair"], + pos["symbol_a"], + position_qty_a, + pos["current_px_a"], + unrealized_return_a, + pos["open_px_a"], + pos["side_a"], + ), + ) + + # Store outstanding position for symbol B + cursor.execute( + """ + INSERT INTO outstanding_positions ( + date, pair, symbol, position_quantity, last_price, unrealized_return, open_price, open_side + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + date_obj, + pos["pair"], + pos["symbol_b"], + position_qty_b, + pos["current_px_b"], + unrealized_return_b, + pos["open_px_b"], + pos["side_b"], + ), + ) + + conn.commit() + conn.close() + + except Exception as e: + print(f"Error storing results in database: {str(e)}") + import traceback + + traceback.print_exc() + diff --git a/lib/pt_trading/rolling_window_fit.py b/lib/pt_trading/rolling_window_fit.py new file mode 100644 index 0000000..debf63e --- /dev/null +++ b/lib/pt_trading/rolling_window_fit.py @@ -0,0 +1,319 @@ +from abc import ABC, abstractmethod +from enum import Enum +from typing import Any, Dict, Optional, cast + +import pandas as pd # type: ignore[import] +from pt_trading.fit_method import PairsTradingFitMethod +from pt_trading.results import BacktestResult +from pt_trading.trading_pair import PairState, TradingPair +from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults + +NanoPerMin = 1e9 + + +class RollingFit(PairsTradingFitMethod): + """ + N O T E: + ========= + - This class remains to be abstract + - The following methods are to be implemented in the subclass: + - create_trading_pair() + ========= + """ + + def __init__(self) -> None: + super().__init__() + + def run_pair( + self, pair: TradingPair, bt_result: BacktestResult + ) -> Optional[pd.DataFrame]: + print(f"***{pair}*** STARTING....") + config = pair.config_ + + curr_training_start_idx = pair.get_begin_index() + end_index = pair.get_end_index() + + pair.user_data_["state"] = PairState.INITIAL + # Initialize trades DataFrame with proper dtypes to avoid concatenation warnings + pair.user_data_["trades"] = pd.DataFrame(columns=self.TRADES_COLUMNS).astype( + { + "time": "datetime64[ns]", + "symbol": "string", + "side": "string", + "action": "string", + "price": "float64", + "disequilibrium": "float64", + "scaled_disequilibrium": "float64", + "pair": "object", + } + ) + + training_minutes = config["training_minutes"] + curr_predicted_row_idx = 0 + while True: + print(curr_training_start_idx, end="\r") + pair.get_datasets( + training_minutes=training_minutes, + training_start_index=curr_training_start_idx, + testing_size=1, + ) + + if len(pair.training_df_) < training_minutes: + print( + f"{pair}: current offset={curr_training_start_idx}" + f" * Training data length={len(pair.training_df_)} < {training_minutes}" + " * Not enough training data. Completing the job." + ) + break + + try: + # ================================ PREDICTION ================================ + self.pair_predict_result_ = pair.predict() + except Exception as e: + raise RuntimeError( + f"{pair}: TrainingPrediction failed: {str(e)}" + ) from e + + # break + + curr_training_start_idx += 1 + if curr_training_start_idx > end_index: + break + curr_predicted_row_idx += 1 + + self._create_trading_signals(pair, config, bt_result) + print(f"***{pair}*** FINISHED *** Num Trades:{len(pair.user_data_['trades'])}") + + return pair.get_trades() + + def _create_trading_signals( + self, pair: TradingPair, config: Dict, bt_result: BacktestResult + ) -> None: + + predicted_df = self.pair_predict_result_ + assert predicted_df is not None + + open_threshold = config["dis-equilibrium_open_trshld"] + close_threshold = config["dis-equilibrium_close_trshld"] + for curr_predicted_row_idx in range(len(predicted_df)): + pred_row = predicted_df.iloc[curr_predicted_row_idx] + scaled_disequilibrium = pred_row["scaled_disequilibrium"] + + if pair.user_data_["state"] in [ + PairState.INITIAL, + PairState.CLOSE, + PairState.CLOSE_POSITION, + PairState.CLOSE_STOP_LOSS, + PairState.CLOSE_STOP_PROFIT, + ]: + if scaled_disequilibrium >= open_threshold: + open_trades = self._get_open_trades( + pair, row=pred_row, open_threshold=open_threshold + ) + if open_trades is not None: + open_trades["status"] = PairState.OPEN.name + print(f"OPEN TRADES:\n{open_trades}") + pair.add_trades(open_trades) + pair.user_data_["state"] = PairState.OPEN + pair.on_open_trades(open_trades) + + elif pair.user_data_["state"] == PairState.OPEN: + if scaled_disequilibrium <= close_threshold: + close_trades = self._get_close_trades( + pair, row=pred_row, close_threshold=close_threshold + ) + if close_trades is not None: + close_trades["status"] = PairState.CLOSE.name + print(f"CLOSE TRADES:\n{close_trades}") + pair.add_trades(close_trades) + pair.user_data_["state"] = PairState.CLOSE + pair.on_close_trades(close_trades) + elif pair.to_stop_close_conditions(predicted_row=pred_row): + close_trades = self._get_close_trades( + pair, row=pred_row, close_threshold=close_threshold + ) + if close_trades is not None: + close_trades["status"] = pair.user_data_[ + "stop_close_state" + ].name + print(f"STOP CLOSE TRADES:\n{close_trades}") + pair.add_trades(close_trades) + pair.user_data_["state"] = pair.user_data_["stop_close_state"] + pair.on_close_trades(close_trades) + + # Outstanding positions + if pair.user_data_["state"] == PairState.OPEN: + print(f"{pair}: *** Position is NOT CLOSED. ***") + # outstanding positions + if config["close_outstanding_positions"]: + close_position_row = pd.Series(pair.market_data_.iloc[-2]) + close_position_row["disequilibrium"] = 0.0 + close_position_row["scaled_disequilibrium"] = 0.0 + close_position_row["signed_scaled_disequilibrium"] = 0.0 + + close_position_trades = self._get_close_trades( + pair=pair, row=close_position_row, close_threshold=close_threshold + ) + if close_position_trades is not None: + close_position_trades["status"] = PairState.CLOSE_POSITION.name + print(f"CLOSE_POSITION TRADES:\n{close_position_trades}") + pair.add_trades(close_position_trades) + pair.user_data_["state"] = PairState.CLOSE_POSITION + pair.on_close_trades(close_position_trades) + else: + if predicted_df is not None: + bt_result.handle_outstanding_position( + pair=pair, + pair_result_df=predicted_df, + last_row_index=0, + open_side_a=pair.user_data_["open_side_a"], + open_side_b=pair.user_data_["open_side_b"], + open_px_a=pair.user_data_["open_px_a"], + open_px_b=pair.user_data_["open_px_b"], + open_tstamp=pair.user_data_["open_tstamp"], + ) + + def _get_open_trades( + self, pair: TradingPair, row: pd.Series, open_threshold: float + ) -> Optional[pd.DataFrame]: + colname_a, colname_b = pair.exec_prices_colnames() + + open_row = row + + open_tstamp = open_row["tstamp"] + open_disequilibrium = open_row["disequilibrium"] + open_scaled_disequilibrium = open_row["scaled_disequilibrium"] + signed_scaled_disequilibrium = open_row["signed_scaled_disequilibrium"] + open_px_a = open_row[f"{colname_a}"] + open_px_b = open_row[f"{colname_b}"] + + # creating the trades + print(f"OPEN_TRADES: {row["tstamp"]} {open_scaled_disequilibrium=}") + if open_disequilibrium > 0: + open_side_a = "SELL" + open_side_b = "BUY" + close_side_a = "BUY" + close_side_b = "SELL" + else: + open_side_a = "BUY" + open_side_b = "SELL" + close_side_a = "SELL" + close_side_b = "BUY" + + # save closing sides + pair.user_data_["open_side_a"] = open_side_a + pair.user_data_["open_side_b"] = open_side_b + pair.user_data_["open_px_a"] = open_px_a + pair.user_data_["open_px_b"] = open_px_b + + pair.user_data_["open_tstamp"] = open_tstamp + + pair.user_data_["close_side_a"] = close_side_a + pair.user_data_["close_side_b"] = close_side_b + + # create opening trades + trd_signal_tuples = [ + ( + open_tstamp, + pair.symbol_a_, + open_side_a, + "OPEN", + open_px_a, + open_disequilibrium, + open_scaled_disequilibrium, + signed_scaled_disequilibrium, + pair, + ), + ( + open_tstamp, + pair.symbol_b_, + open_side_b, + "OPEN", + open_px_b, + open_disequilibrium, + open_scaled_disequilibrium, + signed_scaled_disequilibrium, + pair, + ), + ] + # Create DataFrame with explicit dtypes to avoid concatenation warnings + df = pd.DataFrame( + trd_signal_tuples, + columns=self.TRADES_COLUMNS, + ) + # Ensure consistent dtypes + return df.astype( + { + "time": "datetime64[ns]", + "action": "string", + "symbol": "string", + "price": "float64", + "disequilibrium": "float64", + "scaled_disequilibrium": "float64", + "signed_scaled_disequilibrium": "float64", + "pair": "object", + } + ) + + def _get_close_trades( + self, pair: TradingPair, row: pd.Series, close_threshold: float + ) -> Optional[pd.DataFrame]: + colname_a, colname_b = pair.exec_prices_colnames() + + close_row = row + close_tstamp = close_row["tstamp"] + close_disequilibrium = close_row["disequilibrium"] + close_scaled_disequilibrium = close_row["scaled_disequilibrium"] + signed_scaled_disequilibrium = close_row["signed_scaled_disequilibrium"] + close_px_a = close_row[f"{colname_a}"] + close_px_b = close_row[f"{colname_b}"] + + close_side_a = pair.user_data_["close_side_a"] + close_side_b = pair.user_data_["close_side_b"] + + trd_signal_tuples = [ + ( + close_tstamp, + pair.symbol_a_, + close_side_a, + "CLOSE", + close_px_a, + close_disequilibrium, + close_scaled_disequilibrium, + signed_scaled_disequilibrium, + pair, + ), + ( + close_tstamp, + pair.symbol_b_, + close_side_b, + "CLOSE", + close_px_b, + close_disequilibrium, + close_scaled_disequilibrium, + signed_scaled_disequilibrium, + pair, + ), + ] + + # Add tuples to data frame with explicit dtypes to avoid concatenation warnings + df = pd.DataFrame( + trd_signal_tuples, + columns=self.TRADES_COLUMNS, + ) + # Ensure consistent dtypes + return df.astype( + { + "time": "datetime64[ns]", + "action": "string", + "symbol": "string", + "price": "float64", + "disequilibrium": "float64", + "scaled_disequilibrium": "float64", + "signed_scaled_disequilibrium": "float64", + "pair": "object", + } + ) + + def reset(self) -> None: + curr_training_start_idx = 0 diff --git a/lib/pt_trading/sliding_fit.py b/lib/pt_trading/sliding_fit.py deleted file mode 100644 index 9488af3..0000000 --- a/lib/pt_trading/sliding_fit.py +++ /dev/null @@ -1,362 +0,0 @@ -from abc import ABC, abstractmethod -from enum import Enum -from typing import Dict, Optional, cast - -import pandas as pd # type: ignore[import] -from pt_trading.fit_method import PairState, PairsTradingFitMethod -from pt_trading.results import BacktestResult -from pt_trading.trading_pair import TradingPair - -NanoPerMin = 1e9 - - - - - -class SlidingFit(PairsTradingFitMethod): - def __init__(self) -> None: - super().__init__() - - def run_pair( - self, pair: TradingPair, bt_result: BacktestResult - ) -> Optional[pd.DataFrame]: - print(f"***{pair}*** STARTING....") - config = pair.config_ - - curr_training_start_idx = pair.get_begin_index() - end_index = pair.get_end_index() - - pair.user_data_["state"] = PairState.INITIAL - # Initialize trades DataFrame with proper dtypes to avoid concatenation warnings - pair.user_data_["trades"] = pd.DataFrame(columns=self.TRADES_COLUMNS).astype({ - "time": "datetime64[ns]", - "action": "string", - "symbol": "string", - "price": "float64", - "disequilibrium": "float64", - "scaled_disequilibrium": "float64", - "pair": "object" - }) - pair.user_data_["is_cointegrated"] = False - - training_minutes = config["training_minutes"] - curr_predicted_row_idx = 0 - while True: - print(curr_training_start_idx, end="\r") - pair.get_datasets( - training_minutes=training_minutes, - training_start_index=curr_training_start_idx, - testing_size=1, - ) - - if len(pair.training_df_) < training_minutes: - print( - f"{pair}: current offset={curr_training_start_idx}" - f" * Training data length={len(pair.training_df_)} < {training_minutes}" - " * Not enough training data. Completing the job." - ) - break - - try: - # ================================ TRAINING ================================ - is_cointegrated = pair.train_pair() - except Exception as e: - raise RuntimeError(f"{pair}: Training failed: {str(e)}") from e - - if pair.user_data_["is_cointegrated"] != is_cointegrated: - pair.user_data_["is_cointegrated"] = is_cointegrated - if not is_cointegrated: - if pair.user_data_["state"] == PairState.OPEN: - print( - f"{pair} {curr_training_start_idx} LOST COINTEGRATION. Consider closing positions..." - ) - else: - print( - f"{pair} {curr_training_start_idx} IS NOT COINTEGRATED. Moving on" - ) - else: - print("*" * 80) - print( - f"Pair {pair} ({curr_training_start_idx}) IS COINTEGRATED" - ) - print("*" * 80) - if not is_cointegrated: - curr_training_start_idx += 1 - continue - - try: - # ================================ PREDICTION ================================ - pair.predict() - except Exception as e: - raise RuntimeError(f"{pair}: Prediction failed: {str(e)}") from e - - # break - - curr_training_start_idx += 1 - if curr_training_start_idx > end_index: - break - curr_predicted_row_idx += 1 - - self._create_trading_signals(pair, config, bt_result) - print(f"***{pair}*** FINISHED ... {len(pair.user_data_['trades'])}") - return pair.get_trades() - - def _create_trading_signals( - self, pair: TradingPair, config: Dict, bt_result: BacktestResult - ) -> None: - if pair.predicted_df_ is None: - print(f"{pair.market_data_.iloc[0]['tstamp']} {pair}: No predicted data") - return - - open_threshold = config["dis-equilibrium_open_trshld"] - close_threshold = config["dis-equilibrium_close_trshld"] - for curr_predicted_row_idx in range(len(pair.predicted_df_)): - pred_row = pair.predicted_df_.iloc[curr_predicted_row_idx] - if pair.user_data_["state"] in [PairState.INITIAL, PairState.CLOSED, PairState.CLOSED_POSITIONS]: - open_trades = self._get_open_trades( - pair, row=pred_row, open_threshold=open_threshold - ) - if open_trades is not None: - open_trades["status"] = "OPEN" - print(f"OPEN TRADES:\n{open_trades}") - pair.add_trades(open_trades) - pair.user_data_["state"] = PairState.OPEN - elif pair.user_data_["state"] == PairState.OPEN: - close_trades = self._get_close_trades( - pair, row=pred_row, close_threshold=close_threshold - ) - if close_trades is not None: - close_trades["status"] = "CLOSE" - print(f"CLOSE TRADES:\n{close_trades}") - pair.add_trades(close_trades) - pair.user_data_["state"] = PairState.CLOSED - - # Outstanding positions - if pair.user_data_["state"] == PairState.OPEN: - print( - f"{pair}: *** Position is NOT CLOSED. ***" - ) - # outstanding positions - if config["close_outstanding_positions"]: - close_position_trades = self._get_close_position_trades( - pair=pair, - row=pred_row, - close_threshold=close_threshold, - ) - if close_position_trades is not None: - close_position_trades["status"] = "CLOSE_POSITION" - print(f"CLOSE_POSITION TRADES:\n{close_position_trades}") - pair.add_trades(close_position_trades) - pair.user_data_["state"] = PairState.CLOSED_POSITIONS - else: - if pair.predicted_df_ is not None: - bt_result.handle_outstanding_position( - pair=pair, - pair_result_df=pair.predicted_df_, - last_row_index=0, - open_side_a=pair.user_data_["open_side_a"], - open_side_b=pair.user_data_["open_side_b"], - open_px_a=pair.user_data_["open_px_a"], - open_px_b=pair.user_data_["open_px_b"], - open_tstamp=pair.user_data_["open_tstamp"], - ) - - def _get_open_trades( - self, pair: TradingPair, row: pd.Series, open_threshold: float - ) -> Optional[pd.DataFrame]: - colname_a, colname_b = pair.colnames() - - assert pair.predicted_df_ is not None - predicted_df = pair.predicted_df_ - - # Check if we have any data to work with - if len(predicted_df) == 0: - return None - - open_row = row - open_tstamp = open_row["tstamp"] - open_disequilibrium = open_row["disequilibrium"] - open_scaled_disequilibrium = open_row["scaled_disequilibrium"] - open_px_a = open_row[f"{colname_a}"] - open_px_b = open_row[f"{colname_b}"] - - if open_scaled_disequilibrium < open_threshold: - return None - - # creating the trades - print(f"OPEN_TRADES: {row["tstamp"]} {open_scaled_disequilibrium=}") - if open_disequilibrium > 0: - open_side_a = "SELL" - open_side_b = "BUY" - close_side_a = "BUY" - close_side_b = "SELL" - else: - open_side_a = "BUY" - open_side_b = "SELL" - close_side_a = "SELL" - close_side_b = "BUY" - - # save closing sides - pair.user_data_["open_side_a"] = open_side_a - pair.user_data_["open_side_b"] = open_side_b - pair.user_data_["open_px_a"] = open_px_a - pair.user_data_["open_px_b"] = open_px_b - - pair.user_data_["open_tstamp"] = open_tstamp - - pair.user_data_["close_side_a"] = close_side_a - pair.user_data_["close_side_b"] = close_side_b - - # create opening trades - trd_signal_tuples = [ - ( - open_tstamp, - open_side_a, - pair.symbol_a_, - open_px_a, - open_disequilibrium, - open_scaled_disequilibrium, - pair, - ), - ( - open_tstamp, - open_side_b, - pair.symbol_b_, - open_px_b, - open_disequilibrium, - open_scaled_disequilibrium, - pair, - ), - ] - # Create DataFrame with explicit dtypes to avoid concatenation warnings - df = pd.DataFrame( - trd_signal_tuples, - columns=self.TRADES_COLUMNS, - ) - # Ensure consistent dtypes - return df.astype({ - "time": "datetime64[ns]", - "action": "string", - "symbol": "string", - "price": "float64", - "disequilibrium": "float64", - "scaled_disequilibrium": "float64", - "pair": "object" - }) - - def _get_close_trades( - self, pair: TradingPair, row: pd.Series, close_threshold: float - ) -> Optional[pd.DataFrame]: - colname_a, colname_b = pair.colnames() - - assert pair.predicted_df_ is not None - if len(pair.predicted_df_) == 0: - return None - - close_row = row - close_tstamp = close_row["tstamp"] - close_disequilibrium = close_row["disequilibrium"] - close_scaled_disequilibrium = close_row["scaled_disequilibrium"] - close_px_a = close_row[f"{colname_a}"] - close_px_b = close_row[f"{colname_b}"] - - close_side_a = pair.user_data_["close_side_a"] - close_side_b = pair.user_data_["close_side_b"] - - if close_scaled_disequilibrium > close_threshold: - return None - trd_signal_tuples = [ - ( - close_tstamp, - close_side_a, - pair.symbol_a_, - close_px_a, - close_disequilibrium, - close_scaled_disequilibrium, - pair, - ), - ( - close_tstamp, - close_side_b, - pair.symbol_b_, - close_px_b, - close_disequilibrium, - close_scaled_disequilibrium, - pair, - ), - ] - - # Add tuples to data frame with explicit dtypes to avoid concatenation warnings - df = pd.DataFrame( - trd_signal_tuples, - columns=self.TRADES_COLUMNS, - ) - # Ensure consistent dtypes - return df.astype({ - "time": "datetime64[ns]", - "action": "string", - "symbol": "string", - "price": "float64", - "disequilibrium": "float64", - "scaled_disequilibrium": "float64", - "pair": "object" - }) - - def _get_close_position_trades( - self, pair: TradingPair, row: pd.Series, close_threshold: float - ) -> Optional[pd.DataFrame]: - colname_a, colname_b = pair.colnames() - - assert pair.predicted_df_ is not None - if len(pair.predicted_df_) == 0: - return None - - close_position_row = row - close_position_tstamp = close_position_row["tstamp"] - close_position_disequilibrium = close_position_row["disequilibrium"] - close_position_scaled_disequilibrium = close_position_row["scaled_disequilibrium"] - close_position_px_a = close_position_row[f"{colname_a}"] - close_position_px_b = close_position_row[f"{colname_b}"] - - close_position_side_a = pair.user_data_["close_side_a"] - close_position_side_b = pair.user_data_["close_side_b"] - - trd_signal_tuples = [ - ( - close_position_tstamp, - close_position_side_a, - pair.symbol_a_, - close_position_px_a, - close_position_disequilibrium, - close_position_scaled_disequilibrium, - pair, - ), - ( - close_position_tstamp, - close_position_side_b, - pair.symbol_b_, - close_position_px_b, - close_position_disequilibrium, - close_position_scaled_disequilibrium, - pair, - ), - ] - - # Add tuples to data frame with explicit dtypes to avoid concatenation warnings - df = pd.DataFrame( - trd_signal_tuples, - columns=self.TRADES_COLUMNS, - ) - # Ensure consistent dtypes - return df.astype({ - "time": "datetime64[ns]", - "action": "string", - "symbol": "string", - "price": "float64", - "disequilibrium": "float64", - "scaled_disequilibrium": "float64", - "pair": "object" - }) - - def reset(self) -> None: - curr_training_start_idx = 0 diff --git a/lib/pt_trading/static_fit.py b/lib/pt_trading/static_fit.py deleted file mode 100644 index e182930..0000000 --- a/lib/pt_trading/static_fit.py +++ /dev/null @@ -1,220 +0,0 @@ -from abc import ABC, abstractmethod -from enum import Enum -from typing import Dict, Optional, cast - -import pandas as pd # type: ignore[import] -from pt_trading.results import BacktestResult -from pt_trading.trading_pair import TradingPair -from pt_trading.fit_method import PairsTradingFitMethod - -NanoPerMin = 1e9 - - - -class StaticFit(PairsTradingFitMethod): - - def run_pair( - self, pair: TradingPair, bt_result: BacktestResult - ) -> Optional[pd.DataFrame]: # abstractmethod - config = pair.config_ - pair.get_datasets(training_minutes=config["training_minutes"]) - try: - is_cointegrated = pair.train_pair() - if not is_cointegrated: - print(f"{pair} IS NOT COINTEGRATED") - return None - except Exception as e: - print(f"{pair}: Training failed: {str(e)}") - return None - - try: - pair.predict() - except Exception as e: - print(f"{pair}: Prediction failed: {str(e)}") - return None - - pair_trades = self.create_trading_signals( - pair=pair, config=config, result=bt_result - ) - - return pair_trades - - def create_trading_signals( - self, pair: TradingPair, config: Dict, result: BacktestResult - ) -> pd.DataFrame: - beta = pair.vecm_fit_.beta # type: ignore - colname_a, colname_b = pair.colnames() - - predicted_df = pair.predicted_df_ - if predicted_df is None: - # Return empty DataFrame with correct columns and dtypes - return pd.DataFrame(columns=self.TRADES_COLUMNS).astype({ - "time": "datetime64[ns]", - "action": "string", - "symbol": "string", - "price": "float64", - "disequilibrium": "float64", - "scaled_disequilibrium": "float64", - "pair": "object" - }) - - open_threshold = config["dis-equilibrium_open_trshld"] - close_threshold = config["dis-equilibrium_close_trshld"] - - # Iterate through the testing dataset to find the first trading opportunity - open_row_index = None - for row_idx in range(len(predicted_df)): - curr_disequilibrium = predicted_df["scaled_disequilibrium"][row_idx] - - # Check if current row has sufficient disequilibrium (not near-zero) - if curr_disequilibrium >= open_threshold: - open_row_index = row_idx - break - - # If no row with sufficient disequilibrium found, skip this pair - if open_row_index is None: - print(f"{pair}: Insufficient disequilibrium in testing dataset. Skipping.") - return pd.DataFrame() - - # Look for close signal starting from the open position - trading_signals_df = ( - predicted_df["scaled_disequilibrium"][open_row_index:] < close_threshold - ) - - # Adjust indices to account for the offset from open_row_index - close_row_index = None - for idx, value in trading_signals_df.items(): - if value: - close_row_index = idx - break - - open_row = predicted_df.loc[open_row_index] - open_px_a = predicted_df.at[open_row_index, f"{colname_a}"] - open_px_b = predicted_df.at[open_row_index, f"{colname_b}"] - open_tstamp = predicted_df.at[open_row_index, "tstamp"] - open_disequilibrium = open_row["disequilibrium"] - open_scaled_disequilibrium = open_row["scaled_disequilibrium"] - - abs_beta = abs(beta[1]) - pred_px_b = predicted_df.loc[open_row_index][f"{colname_b}_pred"] - pred_px_a = predicted_df.loc[open_row_index][f"{colname_a}_pred"] - - if pred_px_b * abs_beta - pred_px_a > 0: - open_side_a = "BUY" - open_side_b = "SELL" - close_side_a = "SELL" - close_side_b = "BUY" - else: - open_side_b = "BUY" - open_side_a = "SELL" - close_side_b = "SELL" - close_side_a = "BUY" - - # If no close signal found, print position and unrealized PnL - if close_row_index is None: - - last_row_index = len(predicted_df) - 1 - - # Use the new method from BacktestResult to handle outstanding positions - result.handle_outstanding_position( - pair=pair, - pair_result_df=predicted_df, - last_row_index=last_row_index, - open_side_a=open_side_a, - open_side_b=open_side_b, - open_px_a=float(open_px_a), - open_px_b=float(open_px_b), - open_tstamp=pd.Timestamp(open_tstamp), - ) - - # Return only open trades (no close trades) - trd_signal_tuples = [ - ( - open_tstamp, - open_side_a, - pair.symbol_a_, - open_px_a, - open_disequilibrium, - open_scaled_disequilibrium, - pair, - ), - ( - open_tstamp, - open_side_b, - pair.symbol_b_, - open_px_b, - open_disequilibrium, - open_scaled_disequilibrium, - pair, - ), - ] - else: - # Close signal found - create complete trade - close_row = predicted_df.loc[close_row_index] - close_tstamp = close_row["tstamp"] - close_disequilibrium = close_row["disequilibrium"] - close_scaled_disequilibrium = close_row["scaled_disequilibrium"] - close_px_a = close_row[f"{colname_a}"] - close_px_b = close_row[f"{colname_b}"] - - print(f"{pair}: Close signal found at index {close_row_index}") - - trd_signal_tuples = [ - ( - open_tstamp, - open_side_a, - pair.symbol_a_, - open_px_a, - open_disequilibrium, - open_scaled_disequilibrium, - pair, - ), - ( - open_tstamp, - open_side_b, - pair.symbol_b_, - open_px_b, - open_disequilibrium, - open_scaled_disequilibrium, - pair, - ), - ( - close_tstamp, - close_side_a, - pair.symbol_a_, - close_px_a, - close_disequilibrium, - close_scaled_disequilibrium, - pair, - ), - ( - close_tstamp, - close_side_b, - pair.symbol_b_, - close_px_b, - close_disequilibrium, - close_scaled_disequilibrium, - pair, - ), - ] - - # Add tuples to data frame with explicit dtypes to avoid concatenation warnings - df = pd.DataFrame( - trd_signal_tuples, - columns=self.TRADES_COLUMNS, - ) - # Ensure consistent dtypes - return df.astype({ - "time": "datetime64[ns]", - "action": "string", - "symbol": "string", - "price": "float64", - "disequilibrium": "float64", - "scaled_disequilibrium": "float64", - "pair": "object" - }) - - def reset(self) -> None: - pass - - diff --git a/lib/pt_trading/trading_pair.py b/lib/pt_trading/trading_pair.py index e3e0645..33d53ea 100644 --- a/lib/pt_trading/trading_pair.py +++ b/lib/pt_trading/trading_pair.py @@ -1,14 +1,79 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod +from enum import Enum from typing import Any, Dict, List, Optional import pandas as pd # type:ignore -from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults # type:ignore -class TradingPair: +class PairState(Enum): + INITIAL = 1 + OPEN = 2 + CLOSE = 3 + CLOSE_POSITION = 4 + CLOSE_STOP_LOSS = 5 + CLOSE_STOP_PROFIT = 6 + +class CointegrationData: + EG_PVALUE_THRESHOLD = 0.05 + + tstamp_: pd.Timestamp + pair_: str + eg_pvalue_: float + johansen_lr1_: float + johansen_cvt_: float + eg_is_cointegrated_: bool + johansen_is_cointegrated_: bool + + def __init__(self, pair: TradingPair): + training_df = pair.training_df_ + + assert training_df is not None + from statsmodels.tsa.vector_ar.vecm import coint_johansen + + df = training_df[pair.colnames()].reset_index(drop=True) + + # Run Johansen cointegration test + result = coint_johansen(df, det_order=0, k_ar_diff=1) + self.johansen_lr1_ = result.lr1[0] + self.johansen_cvt_ = result.cvt[0, 1] + self.johansen_is_cointegrated_ = self.johansen_lr1_ > self.johansen_cvt_ + + # Run Engle-Granger cointegration test + from statsmodels.tsa.stattools import coint # type: ignore + + col1, col2 = pair.colnames() + assert training_df is not None + series1 = training_df[col1].reset_index(drop=True) + series2 = training_df[col2].reset_index(drop=True) + + self.eg_pvalue_ = float(coint(series1, series2)[1]) + self.eg_is_cointegrated_ = bool(self.eg_pvalue_ < self.EG_PVALUE_THRESHOLD) + + self.tstamp_ = training_df.index[-1] + self.pair_ = pair.name() + + def to_dict(self) -> Dict[str, Any]: + return { + "tstamp": self.tstamp_, + "pair": self.pair_, + "eg_pvalue": self.eg_pvalue_, + "johansen_lr1": self.johansen_lr1_, + "johansen_cvt": self.johansen_cvt_, + "eg_is_cointegrated": self.eg_is_cointegrated_, + "johansen_is_cointegrated": self.johansen_is_cointegrated_, + } + + def __repr__(self) -> str: + return f"CointegrationData(tstamp={self.tstamp_}, pair={self.pair_}, eg_pvalue={self.eg_pvalue_}, johansen_lr1={self.johansen_lr1_}, johansen_cvt={self.johansen_cvt_}, eg_is_cointegrated={self.eg_is_cointegrated_}, johansen_is_cointegrated={self.johansen_is_cointegrated_})" + + +class TradingPair(ABC): market_data_: pd.DataFrame symbol_a_: str symbol_b_: str - price_column_: str + stat_model_price_: str training_mu_: float training_std_: float @@ -16,54 +81,81 @@ class TradingPair: training_df_: pd.DataFrame testing_df_: pd.DataFrame - vecm_fit_: VECMResults - user_data_: Dict[str, Any] - predicted_df_: Optional[pd.DataFrame] + # predicted_df_: Optional[pd.DataFrame] def __init__( - self, config: Dict[str, Any], market_data: pd.DataFrame, symbol_a: str, symbol_b: str, price_column: str + self, + config: Dict[str, Any], + market_data: pd.DataFrame, + symbol_a: str, + symbol_b: str, ): self.symbol_a_ = symbol_a self.symbol_b_ = symbol_b - self.price_column_ = price_column - self.set_market_data(market_data) + self.stat_model_price_ = config["stat_model_price"] self.user_data_ = {} self.predicted_df_ = None self.config_ = config - def set_market_data(self, market_data: pd.DataFrame) -> None: + self._set_market_data(market_data) + + def _set_market_data(self, market_data: pd.DataFrame) -> None: self.market_data_ = pd.DataFrame( self._transform_dataframe(market_data)[["tstamp"] + self.colnames()] ) self.market_data_ = self.market_data_.dropna().reset_index(drop=True) - self.market_data_['tstamp'] = pd.to_datetime(self.market_data_['tstamp']) - self.market_data_ = self.market_data_.sort_values('tstamp') + self.market_data_["tstamp"] = pd.to_datetime(self.market_data_["tstamp"]) + self.market_data_ = self.market_data_.sort_values("tstamp") + self._set_execution_price_data() + pass + + def _set_execution_price_data(self) -> None: + if "execution_price" not in self.config_: + self.market_data_[f"exec_price_{self.symbol_a_}"] = self.market_data_[f"{self.stat_model_price_}_{self.symbol_a_}"] + self.market_data_[f"exec_price_{self.symbol_b_}"] = self.market_data_[f"{self.stat_model_price_}_{self.symbol_b_}"] + return + execution_price_column = self.config_["execution_price"]["column"] + execution_price_shift = self.config_["execution_price"]["shift"] + self.market_data_[f"exec_price_{self.symbol_a_}"] = self.market_data_[f"{self.stat_model_price_}_{self.symbol_a_}"].shift(-execution_price_shift) + self.market_data_[f"exec_price_{self.symbol_b_}"] = self.market_data_[f"{self.stat_model_price_}_{self.symbol_b_}"].shift(-execution_price_shift) + self.market_data_ = self.market_data_.dropna().reset_index(drop=True) + + + def get_begin_index(self) -> int: if "trading_hours" not in self.config_: return 0 - assert "timezone" in self.config_["trading_hours"] + assert "timezone" in self.config_["trading_hours"] assert "begin_session" in self.config_["trading_hours"] - start_time = pd.to_datetime(self.config_["trading_hours"]["begin_session"]).tz_localize(self.config_["trading_hours"]["timezone"]).time() - mask = self.market_data_['tstamp'].dt.time >= start_time + start_time = ( + pd.to_datetime(self.config_["trading_hours"]["begin_session"]) + .tz_localize(self.config_["trading_hours"]["timezone"]) + .time() + ) + mask = self.market_data_["tstamp"].dt.time >= start_time return int(self.market_data_.index[mask].min()) def get_end_index(self) -> int: if "trading_hours" not in self.config_: return 0 - assert "timezone" in self.config_["trading_hours"] + assert "timezone" in self.config_["trading_hours"] assert "end_session" in self.config_["trading_hours"] - end_time = pd.to_datetime(self.config_["trading_hours"]["end_session"]).tz_localize(self.config_["trading_hours"]["timezone"]).time() - mask = self.market_data_['tstamp'].dt.time <= end_time + end_time = ( + pd.to_datetime(self.config_["trading_hours"]["end_session"]) + .tz_localize(self.config_["trading_hours"]["timezone"]) + .time() + ) + mask = self.market_data_["tstamp"].dt.time <= end_time return int(self.market_data_.index[mask].max()) def _transform_dataframe(self, df: pd.DataFrame) -> pd.DataFrame: # Select only the columns we need df_selected: pd.DataFrame = pd.DataFrame( - df[["tstamp", "symbol", self.price_column_]] + df[["tstamp", "symbol", self.stat_model_price_]] ) # Start with unique timestamps @@ -81,13 +173,13 @@ class TradingPair: ) # Create column name like "close-COIN" - new_price_column = f"{self.price_column_}_{symbol}" + new_price_column = f"{self.stat_model_price_}_{symbol}" # Create temporary dataframe with timestamp and price temp_df = pd.DataFrame( { "tstamp": df_symbol["tstamp"], - new_price_column: df_symbol[self.price_column_], + new_price_column: df_symbol[self.stat_model_price_], } ) @@ -108,7 +200,7 @@ class TradingPair: testing_start_index = training_start_index + training_minutes self.training_df_ = self.market_data_.iloc[ - training_start_index:testing_start_index, : training_minutes + training_start_index:testing_start_index, :training_minutes ].copy() assert self.training_df_ is not None self.training_df_ = self.training_df_.dropna().reset_index(drop=True) @@ -125,82 +217,15 @@ class TradingPair: def colnames(self) -> List[str]: return [ - f"{self.price_column_}_{self.symbol_a_}", - f"{self.price_column_}_{self.symbol_b_}", + f"{self.stat_model_price_}_{self.symbol_a_}", + f"{self.stat_model_price_}_{self.symbol_b_}", ] - def fit_VECM(self) -> None: - assert self.training_df_ is not None - vecm_df = self.training_df_[self.colnames()].reset_index(drop=True) - vecm_model = VECM(vecm_df, coint_rank=1) - vecm_fit = vecm_model.fit() - - assert vecm_fit is not None - - # URGENT check beta and alpha - - # Check if the model converged properly - if not hasattr(vecm_fit, "beta") or vecm_fit.beta is None: - print(f"{self}: VECM model failed to converge properly") - - self.vecm_fit_ = vecm_fit - # print(f"{self}: beta={self.vecm_fit_.beta} alpha={self.vecm_fit_.alpha}" ) - # print(f"{self}: {self.vecm_fit_.summary()}") - pass - - def check_cointegration_johansen(self) -> bool: - assert self.training_df_ is not None - from statsmodels.tsa.vector_ar.vecm import coint_johansen - - df = self.training_df_[self.colnames()].reset_index(drop=True) - result = coint_johansen(df, det_order=0, k_ar_diff=1) - # print( - # f"{self}: lr1={result.lr1[0]} > cvt={result.cvt[0, 1]}? {result.lr1[0] > result.cvt[0, 1]}" - # ) - is_cointegrated: bool = bool(result.lr1[0] > result.cvt[0, 1]) - - return is_cointegrated - - def check_cointegration_engle_granger(self) -> bool: - from statsmodels.tsa.stattools import coint - - col1, col2 = self.colnames() - assert self.training_df_ is not None - series1 = self.training_df_[col1].reset_index(drop=True) - series2 = self.training_df_[col2].reset_index(drop=True) - - # Run Engle-Granger cointegration test - pvalue = coint(series1, series2)[1] - # Define cointegration if p-value < 0.05 (i.e., reject null of no cointegration) - is_cointegrated: bool = bool(pvalue < 0.05) - # print(f"{self}: is_cointegrated={is_cointegrated} pvalue={pvalue}") - return is_cointegrated - - def check_cointegration(self) -> bool: - is_cointegrated_johansen = self.check_cointegration_johansen() - is_cointegrated_engle_granger = self.check_cointegration_engle_granger() - result = is_cointegrated_johansen or is_cointegrated_engle_granger - return result or True # TODO: remove this - - def train_pair(self) -> bool: - result = self.check_cointegration() - # print('*' * 80 + '\n' + f"**************** {self} IS COINTEGRATED ****************\n" + '*' * 80) - self.fit_VECM() - assert self.training_df_ is not None and self.vecm_fit_ is not None - diseq_series = self.training_df_[self.colnames()] @ self.vecm_fit_.beta - # print(diseq_series.shape) - self.training_mu_ = float(diseq_series[0].mean()) - self.training_std_ = float(diseq_series[0].std()) - - self.training_df_["dis-equilibrium"] = ( - self.training_df_[self.colnames()] @ self.vecm_fit_.beta - ) - # Normalize the dis-equilibrium - self.training_df_["scaled_dis-equilibrium"] = ( - diseq_series - self.training_mu_ - ) / self.training_std_ - - return result + def exec_prices_colnames(self) -> List[str]: + return [ + f"exec_price_{self.symbol_a_}", + f"exec_price_{self.symbol_b_}", + ] def add_trades(self, trades: pd.DataFrame) -> None: if self.user_data_["trades"] is None or len(self.user_data_["trades"]) == 0: @@ -209,7 +234,7 @@ class TradingPair: else: # Ensure both DataFrames have the same columns and dtypes before concatenation existing_trades = self.user_data_["trades"] - + # If existing trades is empty, just assign the new trades if len(existing_trades) == 0: self.user_data_["trades"] = trades.copy() @@ -223,68 +248,123 @@ class TradingPair: trades[col] = pd.Timestamp.now() elif col in ["action", "symbol"]: trades[col] = "" - elif col in ["price", "disequilibrium", "scaled_disequilibrium"]: + elif col in [ + "price", + "disequilibrium", + "scaled_disequilibrium", + ]: trades[col] = 0.0 elif col == "pair": trades[col] = None else: trades[col] = None - + # Concatenate with explicit dtypes to avoid warnings self.user_data_["trades"] = pd.concat( - [existing_trades, trades], - ignore_index=True, - copy=False + [existing_trades, trades], ignore_index=True, copy=False ) def get_trades(self) -> pd.DataFrame: - return self.user_data_["trades"] if "trades" in self.user_data_ else pd.DataFrame() - - def predict(self) -> pd.DataFrame: - assert self.testing_df_ is not None - assert self.vecm_fit_ is not None - predicted_prices = self.vecm_fit_.predict(steps=len(self.testing_df_)) - - # Convert prediction to a DataFrame for readability - predicted_df = pd.DataFrame( - predicted_prices, columns=pd.Index(self.colnames()), dtype=float + return ( + self.user_data_["trades"] if "trades" in self.user_data_ else pd.DataFrame() ) + def cointegration_check(self) -> Optional[pd.DataFrame]: + print(f"***{self}*** STARTING....") + config = self.config_ - predicted_df = pd.merge( - self.testing_df_.reset_index(drop=True), - pd.DataFrame( - predicted_prices, columns=pd.Index(self.colnames()), dtype=float - ), - left_index=True, - right_index=True, - suffixes=("", "_pred"), - ).dropna() + curr_training_start_idx = 0 - predicted_df["disequilibrium"] = ( - predicted_df[self.colnames()] @ self.vecm_fit_.beta - ) + COINTEGRATION_DATA_COLUMNS = { + "tstamp": "datetime64[ns]", + "pair": "string", + "eg_pvalue": "float64", + "johansen_lr1": "float64", + "johansen_cvt": "float64", + "eg_is_cointegrated": "bool", + "johansen_is_cointegrated": "bool", + } + # Initialize trades DataFrame with proper dtypes to avoid concatenation warnings + result: pd.DataFrame = pd.DataFrame( + columns=[col for col in COINTEGRATION_DATA_COLUMNS.keys()] + ) # .astype(COINTEGRATION_DATA_COLUMNS) - predicted_df["scaled_disequilibrium"] = ( - abs(predicted_df["disequilibrium"] - self.training_mu_) - / self.training_std_ - ) - - # print("*** PREDICTED DF") - # print(predicted_df) - # print("*" * 80) - # print("*** SELF.PREDICTED_DF") - # print(self.predicted_df_) - # print("*" * 80) + training_minutes = config["training_minutes"] + while True: + print(curr_training_start_idx, end="\r") + self.get_datasets( + training_minutes=training_minutes, + training_start_index=curr_training_start_idx, + testing_size=1, + ) - predicted_df = predicted_df.reset_index(drop=True) - if self.predicted_df_ is None: - self.predicted_df_ = predicted_df - else: - self.predicted_df_ = pd.concat([self.predicted_df_, predicted_df], ignore_index=True) - # Reset index to ensure proper indexing - self.predicted_df_ = self.predicted_df_.reset_index(drop=True) - return self.predicted_df_ + if len(self.training_df_) < training_minutes: + print( + f"{self}: current offset={curr_training_start_idx}" + f" * Training data length={len(self.training_df_)} < {training_minutes}" + " * Not enough training data. Completing the job." + ) + break + new_row = pd.Series(CointegrationData(self).to_dict()) + result.loc[len(result)] = new_row + curr_training_start_idx += 1 + return result + + def to_stop_close_conditions(self, predicted_row: pd.Series) -> bool: + config = self.config_ + if ( + "stop_close_conditions" not in config + or config["stop_close_conditions"] is None + ): + return False + if "profit" in config["stop_close_conditions"]: + current_return = self._current_return(predicted_row) + # + # print(f"time={predicted_row['tstamp']} current_return={current_return}") + # + if current_return >= config["stop_close_conditions"]["profit"]: + print(f"STOP PROFIT: {current_return}") + self.user_data_["stop_close_state"] = PairState.CLOSE_STOP_PROFIT + return True + if "loss" in config["stop_close_conditions"]: + if current_return <= config["stop_close_conditions"]["loss"]: + print(f"STOP LOSS: {current_return}") + self.user_data_["stop_close_state"] = PairState.CLOSE_STOP_LOSS + return True + return False + + def on_open_trades(self, trades: pd.DataFrame) -> None: + if "close_trades" in self.user_data_: + del self.user_data_["close_trades"] + self.user_data_["open_trades"] = trades + + def on_close_trades(self, trades: pd.DataFrame) -> None: + del self.user_data_["open_trades"] + self.user_data_["close_trades"] = trades + + def _current_return(self, predicted_row: pd.Series) -> float: + if "open_trades" in self.user_data_: + open_trades = self.user_data_["open_trades"] + if len(open_trades) == 0: + return 0.0 + + def _single_instrument_return(symbol: str) -> float: + instrument_open_trades = open_trades[open_trades["symbol"] == symbol] + instrument_open_price = instrument_open_trades["price"].iloc[0] + + sign = -1 if instrument_open_trades["side"].iloc[0] == "SELL" else 1 + instrument_price = predicted_row[f"{self.stat_model_price_}_{symbol}"] + instrument_return = ( + sign + * (instrument_price - instrument_open_price) + / instrument_open_price + ) + return float(instrument_return) * 100.0 + + instrument_a_return = _single_instrument_return(self.symbol_a_) + instrument_b_return = _single_instrument_return(self.symbol_b_) + return instrument_a_return + instrument_b_return + return 0.0 def __repr__(self) -> str: return self.name() @@ -292,3 +372,9 @@ class TradingPair: def name(self) -> str: return f"{self.symbol_a_} & {self.symbol_b_}" # return f"{self.symbol_a_} & {self.symbol_b_}" + + @abstractmethod + def predict(self) -> pd.DataFrame: ... + + # @abstractmethod + # def predicted_df(self) -> Optional[pd.DataFrame]: ... diff --git a/lib/pt_trading/vecm_rolling_fit.py b/lib/pt_trading/vecm_rolling_fit.py new file mode 100644 index 0000000..be97299 --- /dev/null +++ b/lib/pt_trading/vecm_rolling_fit.py @@ -0,0 +1,122 @@ +from typing import Any, Dict, Optional, cast + +import pandas as pd +from pt_trading.results import BacktestResult +from pt_trading.rolling_window_fit import RollingFit +from pt_trading.trading_pair import TradingPair +from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults + +NanoPerMin = 1e9 + + +class VECMTradingPair(TradingPair): + vecm_fit_: Optional[VECMResults] + pair_predict_result_: Optional[pd.DataFrame] + + def __init__( + self, + config: Dict[str, Any], + market_data: pd.DataFrame, + symbol_a: str, + symbol_b: str, + ): + super().__init__(config, market_data, symbol_a, symbol_b) + self.vecm_fit_ = None + self.pair_predict_result_ = None + + def _train_pair(self) -> None: + self._fit_VECM() + assert self.vecm_fit_ is not None + diseq_series = self.training_df_[self.colnames()] @ self.vecm_fit_.beta + # print(diseq_series.shape) + self.training_mu_ = float(diseq_series[0].mean()) + self.training_std_ = float(diseq_series[0].std()) + + self.training_df_["dis-equilibrium"] = ( + self.training_df_[self.colnames()] @ self.vecm_fit_.beta + ) + # Normalize the dis-equilibrium + self.training_df_["scaled_dis-equilibrium"] = ( + diseq_series - self.training_mu_ + ) / self.training_std_ + + def _fit_VECM(self) -> None: + assert self.training_df_ is not None + vecm_df = self.training_df_[self.colnames()].reset_index(drop=True) + vecm_model = VECM(vecm_df, coint_rank=1) + vecm_fit = vecm_model.fit() + + assert vecm_fit is not None + + # URGENT check beta and alpha + + # Check if the model converged properly + if not hasattr(vecm_fit, "beta") or vecm_fit.beta is None: + print(f"{self}: VECM model failed to converge properly") + + self.vecm_fit_ = vecm_fit + pass + + def predict(self) -> pd.DataFrame: + self._train_pair() + + assert self.testing_df_ is not None + assert self.vecm_fit_ is not None + predicted_prices = self.vecm_fit_.predict(steps=len(self.testing_df_)) + + # Convert prediction to a DataFrame for readability + predicted_df = pd.DataFrame( + predicted_prices, columns=pd.Index(self.colnames()), dtype=float + ) + + predicted_df = pd.merge( + self.testing_df_.reset_index(drop=True), + pd.DataFrame( + predicted_prices, columns=pd.Index(self.colnames()), dtype=float + ), + left_index=True, + right_index=True, + suffixes=("", "_pred"), + ).dropna() + + predicted_df["disequilibrium"] = ( + predicted_df[self.colnames()] @ self.vecm_fit_.beta + ) + + predicted_df["signed_scaled_disequilibrium"] = ( + predicted_df["disequilibrium"] - self.training_mu_ + ) / self.training_std_ + + predicted_df["scaled_disequilibrium"] = abs( + predicted_df["signed_scaled_disequilibrium"] + ) + + predicted_df = predicted_df.reset_index(drop=True) + if self.pair_predict_result_ is None: + self.pair_predict_result_ = predicted_df + else: + self.pair_predict_result_ = pd.concat( + [self.pair_predict_result_, predicted_df], ignore_index=True + ) + # Reset index to ensure proper indexing + self.pair_predict_result_ = self.pair_predict_result_.reset_index(drop=True) + return self.pair_predict_result_ + + +class VECMRollingFit(RollingFit): + def __init__(self) -> None: + super().__init__() + + def create_trading_pair( + self, + config: Dict, + market_data: pd.DataFrame, + symbol_a: str, + symbol_b: str, + ) -> TradingPair: + return VECMTradingPair( + config=config, + market_data=market_data, + symbol_a=symbol_a, + symbol_b=symbol_b, + ) diff --git a/lib/pt_trading/z-score_rolling_fit.py b/lib/pt_trading/z-score_rolling_fit.py new file mode 100644 index 0000000..33011fc --- /dev/null +++ b/lib/pt_trading/z-score_rolling_fit.py @@ -0,0 +1,85 @@ +from typing import Any, Dict, Optional, cast + +import pandas as pd +from pt_trading.results import BacktestResult +from pt_trading.rolling_window_fit import RollingFit +from pt_trading.trading_pair import TradingPair +import statsmodels.api as sm + +NanoPerMin = 1e9 + + +class ZScoreTradingPair(TradingPair): + zscore_model_: Optional[sm.regression.linear_model.RegressionResultsWrapper] + pair_predict_result_: Optional[pd.DataFrame] + zscore_df_: Optional[pd.DataFrame] + + def __init__( + self, + config: Dict[str, Any], + market_data: pd.DataFrame, + symbol_a: str, + symbol_b: str, + ): + super().__init__(config, market_data, symbol_a, symbol_b) + self.zscore_model_ = None + self.pair_predict_result_ = None + self.zscore_df_ = None + + def _fit_zscore(self) -> None: + assert self.training_df_ is not None + symbol_a_px_series = self.training_df_[self.colnames()].iloc[:, 0] + symbol_b_px_series = self.training_df_[self.colnames()].iloc[:, 1] + + symbol_a_px_series, symbol_b_px_series = symbol_a_px_series.align( + symbol_b_px_series, axis=0 + ) + + X = sm.add_constant(symbol_b_px_series) + self.zscore_model_ = sm.OLS(symbol_a_px_series, X).fit() + assert self.zscore_model_ is not None + hedge_ratio = self.zscore_model_.params.iloc[1] + + # Calculate spread and Z-score + spread = symbol_a_px_series - hedge_ratio * symbol_b_px_series + self.zscore_df_ = (spread - spread.mean()) / spread.std() + + def predict(self) -> pd.DataFrame: + self._fit_zscore() + assert self.zscore_df_ is not None + self.training_df_["dis-equilibrium"] = self.zscore_df_ + self.training_df_["scaled_dis-equilibrium"] = abs(self.zscore_df_) + + assert self.testing_df_ is not None + assert self.zscore_df_ is not None + predicted_df = self.testing_df_ + + predicted_df["disequilibrium"] = self.zscore_df_ + predicted_df["signed_scaled_disequilibrium"] = self.zscore_df_ + predicted_df["scaled_disequilibrium"] = abs(self.zscore_df_) + + predicted_df = predicted_df.reset_index(drop=True) + if self.pair_predict_result_ is None: + self.pair_predict_result_ = predicted_df + else: + self.pair_predict_result_ = pd.concat( + [self.pair_predict_result_, predicted_df], ignore_index=True + ) + # Reset index to ensure proper indexing + self.pair_predict_result_ = self.pair_predict_result_.reset_index(drop=True) + return self.pair_predict_result_.dropna() + + +class ZScoreRollingFit(RollingFit): + def __init__(self) -> None: + super().__init__() + + def create_trading_pair( + self, config: Dict, market_data: pd.DataFrame, symbol_a: str, symbol_b: str + ) -> TradingPair: + return ZScoreTradingPair( + config=config, + market_data=market_data, + symbol_a=symbol_a, + symbol_b=symbol_b, + ) diff --git a/lib/tools/data_loader.py b/lib/tools/data_loader.py index 08a5f28..b137dcc 100644 --- a/lib/tools/data_loader.py +++ b/lib/tools/data_loader.py @@ -1,10 +1,17 @@ +from __future__ import annotations + import sqlite3 from typing import Dict, List, cast import pandas as pd +def load_sqlite_to_dataframe(db_path:str, query:str) -> pd.DataFrame: + df: pd.DataFrame = pd.DataFrame() + import os + if not os.path.exists(db_path): + print(f"WARNING: database file {db_path} does not exist") + return df -def load_sqlite_to_dataframe(db_path, query): try: conn = sqlite3.connect(db_path) @@ -21,13 +28,14 @@ def load_sqlite_to_dataframe(db_path, query): conn.close() -def convert_time_to_UTC(value: str, timezone: str) -> str: +def convert_time_to_UTC(value: str, timezone: str, extra_minutes: int = 0) -> str: from zoneinfo import ZoneInfo - from datetime import datetime + from datetime import datetime, timedelta # Parse it to naive datetime object local_dt = datetime.strptime(value, "%Y-%m-%d %H:%M:%S") + local_dt = local_dt + timedelta(minutes=extra_minutes) zinfo = ZoneInfo(timezone) result: datetime = local_dt.replace(tzinfo=zinfo).astimezone(ZoneInfo("UTC")) @@ -35,25 +43,28 @@ def convert_time_to_UTC(value: str, timezone: str) -> str: return result.strftime("%Y-%m-%d %H:%M:%S") -def load_market_data(datafile: str, config: Dict) -> pd.DataFrame: - from tools.data_loader import load_sqlite_to_dataframe +def load_market_data( + datafile: str, + instruments: List[Dict[str, str]], + db_table_name: str, + trading_hours: Dict = {}, + extra_minutes: int = 0, +) -> pd.DataFrame: - instrument_ids = [ - '"' + config["instrument_id_pfx"] + instrument + '"' - for instrument in config["instruments"] + insts = [ + '"' + instrument["instrument_id_pfx"] + instrument["symbol"] + '"' + for instrument in instruments ] - security_type = config["security_type"] - exchange_id = config["exchange_id"] + instrument_ids = list(set(insts)) + exchange_ids = list( + set(['"' + instrument["exchange_id"] + '"' for instrument in instruments]) + ) query = "select" - if security_type == "CRYPTO": - query += " strftime('%Y-%m-%d %H:%M:%S', tstamp_ns/1000000000, 'unixepoch') as tstamp" - query += ", tstamp as time_ns" - else: - query += " tstamp" - query += ", tstamp_ns as time_ns" + query += " tstamp" + query += ", tstamp_ns as time_ns" - query += f", substr(instrument_id, {len(config['instrument_id_pfx']) + 1}) as symbol" + query += f", substr(instrument_id, instr(instrument_id, '-') + 1) as symbol" query += ", open" query += ", high" query += ", low" @@ -62,74 +73,76 @@ def load_market_data(datafile: str, config: Dict) -> pd.DataFrame: query += ", num_trades" query += ", vwap" - query += f" from {config['db_table_name']}" - query += f" where exchange_id ='{exchange_id}'" + query += f" from {db_table_name}" + query += f" where exchange_id in ({','.join(exchange_ids)})" query += f" and instrument_id in ({','.join(instrument_ids)})" df = load_sqlite_to_dataframe(db_path=datafile, query=query) # Trading Hours - date_str = df["tstamp"][0][0:10] - trading_hours = config["trading_hours"] + if len(df) > 0 and len(trading_hours) > 0: + date_str = df["tstamp"][0][0:10] - start_time = convert_time_to_UTC( - f"{date_str} {trading_hours['begin_session']}", trading_hours["timezone"] - ) - end_time = convert_time_to_UTC( - f"{date_str} {trading_hours['end_session']}", trading_hours["timezone"] - ) + start_time = convert_time_to_UTC( + f"{date_str} {trading_hours['begin_session']}", trading_hours["timezone"] + ) + end_time = convert_time_to_UTC( + f"{date_str} {trading_hours['end_session']}", trading_hours["timezone"], extra_minutes=extra_minutes # to get execution price + ) - # Perform boolean selection - df = df[(df["tstamp"] >= start_time) & (df["tstamp"] <= end_time)] - df["tstamp"] = pd.to_datetime(df["tstamp"]) + # Perform boolean selection + df = df[(df["tstamp"] >= start_time) & (df["tstamp"] <= end_time)] + df["tstamp"] = pd.to_datetime(df["tstamp"]) return cast(pd.DataFrame, df) -def get_available_instruments_from_db(datafile: str, config: Dict) -> List[str]: - """ - Auto-detect available instruments from the database by querying distinct instrument_id values. - Returns instruments without the configured prefix. - """ - try: - conn = sqlite3.connect(datafile) +# def get_available_instruments_from_db(datafile: str, config: Dict) -> List[str]: +# """ +# Auto-detect available instruments from the database by querying distinct instrument_id values. +# Returns instruments without the configured prefix. +# """ +# try: +# conn = sqlite3.connect(datafile) - # Build exclusion list with full instrument_ids - exclude_instruments = config.get("exclude_instruments", []) - prefix = config.get("instrument_id_pfx", "") - exclude_instrument_ids = [f"{prefix}{inst}" for inst in exclude_instruments] - - # Query to get distinct instrument_ids - query = f""" - SELECT DISTINCT instrument_id - FROM {config['db_table_name']} - WHERE exchange_id = ? - """ - - # Add exclusion clause if there are instruments to exclude - if exclude_instrument_ids: - placeholders = ','.join(['?' for _ in exclude_instrument_ids]) - query += f" AND instrument_id NOT IN ({placeholders})" - cursor = conn.execute(query, (config["exchange_id"],) + tuple(exclude_instrument_ids)) - else: - cursor = conn.execute(query, (config["exchange_id"],)) - instrument_ids = [row[0] for row in cursor.fetchall()] - conn.close() +# # Build exclusion list with full instrument_ids +# exclude_instruments = config.get("exclude_instruments", []) +# prefix = config.get("instrument_id_pfx", "") +# exclude_instrument_ids = [f"{prefix}{inst}" for inst in exclude_instruments] - # Remove the configured prefix to get instrument symbols - instruments = [] - for instrument_id in instrument_ids: - if instrument_id.startswith(prefix): - symbol = instrument_id[len(prefix) :] - instruments.append(symbol) - else: - instruments.append(instrument_id) +# # Query to get distinct instrument_ids +# query = f""" +# SELECT DISTINCT instrument_id +# FROM {config['db_table_name']} +# WHERE exchange_id = ? +# """ - return sorted(instruments) +# # Add exclusion clause if there are instruments to exclude +# if exclude_instrument_ids: +# placeholders = ",".join(["?" for _ in exclude_instrument_ids]) +# query += f" AND instrument_id NOT IN ({placeholders})" +# cursor = conn.execute( +# query, (config["exchange_id"],) + tuple(exclude_instrument_ids) +# ) +# else: +# cursor = conn.execute(query, (config["exchange_id"],)) +# instrument_ids = [row[0] for row in cursor.fetchall()] +# conn.close() - except Exception as e: - print(f"Error auto-detecting instruments from {datafile}: {str(e)}") - return [] +# # Remove the configured prefix to get instrument symbols +# instruments = [] +# for instrument_id in instrument_ids: +# if instrument_id.startswith(prefix): +# symbol = instrument_id[len(prefix) :] +# instruments.append(symbol) +# else: +# instruments.append(instrument_id) + +# return sorted(instruments) + +# except Exception as e: +# print(f"Error auto-detecting instruments from {datafile}: {str(e)}") +# return [] # if __name__ == "__main__": diff --git a/requirements.txt b/requirements.txt index c1208f7..57f7220 100644 --- a/requirements.txt +++ b/requirements.txt @@ -74,6 +74,7 @@ PyYAML>=6.0 reportlab>=3.6.8 requests>=2.25.1 requests-file>=1.5.1 +scipy<1.13.0 seaborn>=0.13.2 SecretStorage>=3.3.1 setproctitle>=1.2.2 diff --git a/research/cointegration_test.py b/research/cointegration_test.py new file mode 100644 index 0000000..262c736 --- /dev/null +++ b/research/cointegration_test.py @@ -0,0 +1,126 @@ +import argparse +import glob +import importlib +import os +from datetime import date, datetime +from typing import Any, Dict, List, Optional + +import pandas as pd + +from tools.config import expand_filename, load_config +from tools.data_loader import get_available_instruments_from_db +from pt_trading.results import ( + BacktestResult, + create_result_database, + store_config_in_database, + store_results_in_database, +) +from pt_trading.fit_method import PairsTradingFitMethod +from pt_trading.trading_pair import TradingPair + +from research.research_tools import create_pairs, resolve_datafiles + + +def main() -> None: + parser = argparse.ArgumentParser(description="Run pairs trading backtest.") + parser.add_argument( + "--config", type=str, required=True, help="Path to the configuration file." + ) + parser.add_argument( + "--datafile", + type=str, + required=False, + help="Market data file to process.", + ) + parser.add_argument( + "--instruments", + type=str, + required=False, + help="Comma-separated list of instrument symbols (e.g., COIN,GBTC). If not provided, auto-detects from database.", + ) + args = parser.parse_args() + + config: Dict = load_config(args.config) + + # Resolve data files (CLI takes priority over config) + datafile = resolve_datafiles(config, args.datafile)[0] + + if not datafile: + print("No data files found to process.") + return + + print(f"Found {datafile} data files to process:") + + # # Create result database if needed + # if args.result_db.upper() != "NONE": + # args.result_db = expand_filename(args.result_db) + # create_result_database(args.result_db) + + # # Initialize a dictionary to store all trade results + # all_results: Dict[str, Dict[str, Any]] = {} + + # # Store configuration in database for reference + # if args.result_db.upper() != "NONE": + # # Get list of all instruments for storage + # all_instruments = [] + # for datafile in datafiles: + # if args.instruments: + # file_instruments = [ + # inst.strip() for inst in args.instruments.split(",") + # ] + # else: + # file_instruments = get_available_instruments_from_db(datafile, config) + # all_instruments.extend(file_instruments) + + # # Remove duplicates while preserving order + # unique_instruments = list(dict.fromkeys(all_instruments)) + + # store_config_in_database( + # db_path=args.result_db, + # config_file_path=args.config, + # config=config, + # fit_method_class=fit_method_class_name, + # datafiles=datafiles, + # instruments=unique_instruments, + # ) + + # Process each data file + stat_model_price = config["stat_model_price"] + + print(f"\n====== Processing {os.path.basename(datafile)} ======") + + # Determine instruments to use + if args.instruments: + # Use CLI-specified instruments + instruments = [inst.strip() for inst in args.instruments.split(",")] + print(f"Using CLI-specified instruments: {instruments}") + else: + # Auto-detect instruments from database + instruments = get_available_instruments_from_db(datafile, config) + print(f"Auto-detected instruments: {instruments}") + + if not instruments: + print(f"No instruments found in {datafile}...") + return + # Process data for this file + try: + cointegration_data: pd.DataFrame = pd.DataFrame() + for pair in create_pairs(datafile, stat_model_price, config, instruments): + cointegration_data = pd.concat([cointegration_data, pair.cointegration_check()]) + + pd.set_option('display.width', 400) + pd.set_option('display.max_colwidth', None) + pd.set_option('display.max_columns', None) + with pd.option_context('display.max_rows', None, 'display.max_columns', None): + print(f"cointegration_data:\n{cointegration_data}") + + except Exception as err: + print(f"Error processing {datafile}: {str(err)}") + import traceback + + traceback.print_exc() + + + +if __name__ == "__main__": + main() diff --git a/research/notebooks/__DEPRECATED__/pt_pair_backtest.ipynb b/research/notebooks/__DEPRECATED__/pt_pair_backtest.ipynb deleted file mode 100644 index d849d05..0000000 --- a/research/notebooks/__DEPRECATED__/pt_pair_backtest.ipynb +++ /dev/null @@ -1,4433 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "vscode": { - "languageId": "raw" - } - }, - "source": [ - "# Pairs Trading Backtest Notebook\n", - "\n", - "This comprehensive notebook supports both StaticFit and SlidingFit.\n", - "It automatically adapts its analysis and visualization based on the strategy specified in the configuration file.\n", - "\n", - "## Key Features:\n", - "\n", - "1. **Configuration-Driven**: Loads strategy and parameters from HJSON configuration files\n", - "2. **Dual Model Support**: Works with both StaticFit and SlidingFit\n", - "3. **Adaptive Visualization**: Different visualizations based on selected strategy\n", - "4. **Comprehensive Analysis**: Deep analysis of trading pairs and dis-equilibrium\n", - "5. **Interactive Configuration**: Easy parameter adjustment and re-running\n", - "\n", - "## Usage:\n", - "\n", - "1. **Configure Parameters**: Set CONFIG_FILE, SYMBOL_A, SYMBOL_B, and TRADING_DATE\n", - "2. **Run Analysis**: Execute cells step by step\n", - "3. **View Results**: Comprehensive visualizations and trading signals\n", - "4. **Experiment**: Modify parameters and re-run for different scenarios\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "vscode": { - "languageId": "raw" - } - }, - "source": [ - "\n", - "# Settings" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Trading Parameters Configuration\n", - "# Specify your configuration file, trading symbols and date here\n", - "\n", - "# Configuration file selection\n", - "CONFIG_FILE = \"equity\" # Options: \"equity\", \"crypto\", or custom filename (without .cfg extension)\n", - "\n", - "# Trading pair symbols\n", - "SYMBOL_A = \"COIN\" # Change this to your desired symbol A\n", - "SYMBOL_B = \"MSTR\" # Change this to your desired symbol B\n", - "\n", - "# Date for data file selection (format: YYYYMMDD)\n", - "TRADING_DATE = \"20250605\" # Change this to your desired date\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Setup and Configuration" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Code Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Setup complete!\n" - ] - } - ], - "source": [ - "import sys\n", - "import os\n", - "sys.path.append('/home/oleg/develop/pairs_trading/lib')\n", - "sys.path.append('/home/coder/pairs_trading/lib')\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import importlib\n", - "from typing import Dict, List, Optional\n", - "from IPython.display import clear_output\n", - "\n", - "# Import our modules\n", - "from pt_trading.fit_methods import StaticFit, SlidingFit, PairState\n", - "from tools.data_loader import load_market_data\n", - "from pt_trading.trading_pair import TradingPair\n", - "from pt_trading.results import BacktestResult\n", - "\n", - "# Set plotting style\n", - "plt.style.use('seaborn-v0_8')\n", - "sns.set_palette(\"husl\")\n", - "plt.rcParams['figure.figsize'] = (15, 10)\n", - "\n", - "print(\"Setup complete!\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "vscode": { - "languageId": "raw" - } - }, - "source": [ - "## Load Configuration\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Load Configuration from Configuration Files using HJSON\n", - "import hjson\n", - "import os\n", - "\n", - "def load_config_from_file(config_type) -> Optional[Dict]:\n", - " \"\"\"Load configuration from configuration files using HJSON\"\"\"\n", - " config_file = f\"../../configuration/{config_type}.cfg\"\n", - " \n", - " try:\n", - " with open(config_file, 'r') as f:\n", - " # HJSON handles comments, trailing commas, and other human-friendly features\n", - " config = hjson.load(f)\n", - " \n", - " # Convert relative paths to absolute paths from notebook perspective\n", - " if 'data_directory' in config:\n", - " data_dir = config['data_directory']\n", - " if data_dir.startswith('./'):\n", - " # Convert relative path to absolute path from notebook's perspective\n", - " config['data_directory'] = os.path.abspath(f\"../../{data_dir[2:]}\")\n", - " \n", - " return config\n", - " \n", - " except FileNotFoundError:\n", - " print(f\"Configuration file not found: {config_file}\")\n", - " return None\n", - " except hjson.HjsonDecodeError as e:\n", - " print(f\"HJSON parsing error in {config_file}: {e}\")\n", - " return None\n", - " except Exception as e:\n", - " print(f\"Unexpected error loading config from {config_file}: {e}\")\n", - " return None\n", - "\n", - "def instantiate_fit_method_from_config(config: Dict):\n", - " \"\"\"Dynamically instantiate strategy from config\"\"\"\n", - " fit_method_class_name = config.get(\"fit_method_class\", None)\n", - " assert fit_method_class_name is not None\n", - " try:\n", - " # Split module and class name\n", - " if '.' in fit_method_class_name:\n", - " module_name, class_name = fit_method_class_name.rsplit('.', 1)\n", - " else:\n", - " module_name = \"fit_methods\"\n", - " class_name = fit_method_class_name\n", - " \n", - " # Import module and get class\n", - " module = importlib.import_module(module_name)\n", - " fit_method_class = getattr(module, class_name)\n", - " \n", - " # Instantiate strategy\n", - " return fit_method_class()\n", - " \n", - " except Exception as e:\n", - " print(f\"Error instantiating strategy {fit_method_class_name}: {e}\")\n", - " raise Exception(f\"Error instantiating strategy {fit_method_class_name}: {e}\") from e\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Print Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Trading Parameters:\n", - " Configuration: equity\n", - " Symbol A: COIN\n", - " Symbol B: MSTR\n", - " Trading Date: 20250605\n", - "\n", - "Loading equity configuration using HJSON...\n", - "✓ Successfully loaded EQUITY configuration\n", - " Data directory: /home/oleg/develop/pairs_trading/data/equity\n", - " Database table: md_1min_bars\n", - " Exchange: ALPACA\n", - " Training window: 120 minutes\n", - " Open threshold: 2\n", - " Close threshold: 1\n", - " Strategy: SlidingFit\n", - "\n", - "Data Configuration:\n", - " Data File: 20250605.mktdata.ohlcv.db\n", - " Security Type: EQUITY\n", - " ✓ Data file found: /home/oleg/develop/pairs_trading/data/equity/20250605.mktdata.ohlcv.db\n" - ] - } - ], - "source": [ - "print(f\"Trading Parameters:\")\n", - "print(f\" Configuration: {CONFIG_FILE}\")\n", - "print(f\" Symbol A: {SYMBOL_A}\")\n", - "print(f\" Symbol B: {SYMBOL_B}\")\n", - "print(f\" Trading Date: {TRADING_DATE}\")\n", - "\n", - "# Load the specified configuration\n", - "print(f\"\\nLoading {CONFIG_FILE} configuration using HJSON...\")\n", - "\n", - "CONFIG = load_config_from_file(CONFIG_FILE)\n", - "assert CONFIG is not None\n", - "pt_bt_config: Dict = dict(CONFIG)\n", - "\n", - "if pt_bt_config:\n", - " print(f\"✓ Successfully loaded {pt_bt_config['security_type']} configuration\")\n", - " print(f\" Data directory: {pt_bt_config['data_directory']}\")\n", - " print(f\" Database table: {pt_bt_config['db_table_name']}\")\n", - " print(f\" Exchange: {pt_bt_config['exchange_id']}\")\n", - " print(f\" Training window: {pt_bt_config['training_minutes']} minutes\")\n", - " print(f\" Open threshold: {pt_bt_config['dis-equilibrium_open_trshld']}\")\n", - " print(f\" Close threshold: {pt_bt_config['dis-equilibrium_close_trshld']}\")\n", - " \n", - " # Instantiate strategy from config\n", - " FIT_MODEL = instantiate_fit_method_from_config(pt_bt_config)\n", - " print(f\" Strategy: {type(FIT_MODEL).__name__}\")\n", - " \n", - " # Automatically construct data file name based on date and config type\n", - " DATA_FILE = f\"{TRADING_DATE}.mktdata.ohlcv.db\"\n", - "\n", - " # Update CONFIG with the specific data file and instruments\n", - " pt_bt_config[\"datafiles\"] = [DATA_FILE]\n", - " pt_bt_config[\"instruments\"] = [SYMBOL_A, SYMBOL_B]\n", - " \n", - " print(f\"\\nData Configuration:\")\n", - " print(f\" Data File: {DATA_FILE}\")\n", - " print(f\" Security Type: {pt_bt_config['security_type']}\")\n", - " \n", - " # Verify data file exists\n", - " data_file_path = f\"{pt_bt_config['data_directory']}/{DATA_FILE}\"\n", - " if os.path.exists(data_file_path):\n", - " print(f\" ✓ Data file found: {data_file_path}\")\n", - " else:\n", - " print(f\" ⚠ Data file not found: {data_file_path}\")\n", - " print(f\" Please check if the date and file exist in the data directory\")\n", - " \n", - " # List available files in the data directory\n", - " try:\n", - " data_dir = pt_bt_config['data_directory']\n", - " if os.path.exists(data_dir):\n", - " available_files = [f for f in os.listdir(data_dir) if f.endswith('.db')]\n", - " print(f\" Available files in {data_dir}:\")\n", - " for file in sorted(available_files)[:5]: # Show first 5 files\n", - " print(f\" - {file}\")\n", - " if len(available_files) > 5:\n", - " print(f\" ... and {len(available_files)-5} more files\")\n", - " except Exception as e:\n", - " print(f\" Could not list files in data directory: {e}\")\n", - "else:\n", - " print(\"⚠ Failed to load configuration. Please check the configuration file.\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "vscode": { - "languageId": "raw" - } - }, - "source": [ - "## Load and Prepare Market Data\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading data from: /home/oleg/develop/pairs_trading/data/equity/20250605.mktdata.ohlcv.db\n", - "Loaded 782 rows of market data\n", - "Symbols in data: ['COIN' 'MSTR']\n", - "Time range: 2025-06-05 13:30:00 to 2025-06-05 20:00:00\n", - "\n", - "Created trading pair: COIN & MSTR\n", - "Market data shape: (391, 3)\n", - "Column names: ['close_COIN', 'close_MSTR']\n", - "\n", - "Sample data:\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " tstamp close_COIN close_MSTR\n", - "0 2025-06-05 13:30:00 263.380 384.7700\n", - "1 2025-06-05 13:31:00 265.385 382.7806\n", - "2 2025-06-05 13:32:00 263.735 379.8300\n", - "3 2025-06-05 13:33:00 264.250 380.0400\n", - "4 2025-06-05 13:34:00 262.230 379.6400" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Load market data\n", - "datafile_path = f\"{pt_bt_config['data_directory']}/{DATA_FILE}\"\n", - "print(f\"Loading data from: {datafile_path}\")\n", - "\n", - "market_data_df = load_market_data(datafile_path, config=pt_bt_config)\n", - "\n", - "print(f\"Loaded {len(market_data_df)} rows of market data\")\n", - "print(f\"Symbols in data: {market_data_df['symbol'].unique()}\")\n", - "print(f\"Time range: {market_data_df['tstamp'].min()} to {market_data_df['tstamp'].max()}\")\n", - "\n", - "# Create trading pair\n", - "pair = TradingPair(\n", - " market_data=market_data_df,\n", - " symbol_a=SYMBOL_A,\n", - " symbol_b=SYMBOL_B,\n", - " price_column=pt_bt_config[\"price_column\"]\n", - ")\n", - "\n", - "print(f\"\\nCreated trading pair: {pair}\")\n", - "print(f\"Market data shape: {pair.market_data_.shape}\")\n", - "print(f\"Column names: {pair.colnames()}\")\n", - "\n", - "# Display sample data\n", - "print(f\"\\nSample data:\")\n", - "display(pair.market_data_.head())\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fixed draw_symbol_trades function created successfully!\n", - "This function correctly filters trades data rather than trying to filter price data with trade conditions.\n" - ] - } - ], - "source": [ - "# Fixed draw_symbol_trades function\n", - "def draw_symbol_trades_fixed(fig, symbol_name, color, symbol_data, colname):\n", - " # Add Symbol price data to row 4 (subplot 4)\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=symbol_data['tstamp'],\n", - " y=symbol_data[colname],\n", - " name=f'{symbol_name} Price',\n", - " line=dict(color=color, width=2),\n", - " opacity=0.8\n", - " ),\n", - " row=4, col=1\n", - " )\n", - " \n", - " # Add trading signals for Symbol if available\n", - " if pair_trades is not None and len(pair_trades) > 0:\n", - " # Filter trades for this symbol\n", - " symbol_trades = pair_trades[pair_trades['symbol'] == symbol_name].copy()\n", - " \n", - " if len(symbol_trades) > 0:\n", - " # Separate trades by action and status - filter the trades, not the price data\n", - " buy_open_trades = symbol_trades[(symbol_trades['action'].str.contains('BUY', na=False)) & \n", - " (symbol_trades['status'] == 'OPEN')]\n", - " buy_close_trades = symbol_trades[(symbol_trades['action'].str.contains('BUY', na=False)) & \n", - " (symbol_trades['status'] == 'CLOSE')]\n", - " sell_open_trades = symbol_trades[(symbol_trades['action'].str.contains('SELL', na=False)) & \n", - " (symbol_trades['status'] == 'OPEN')]\n", - " sell_close_trades = symbol_trades[(symbol_trades['action'].str.contains('SELL', na=False)) & \n", - " (symbol_trades['status'] == 'CLOSE')]\n", - " \n", - " # Add BUY OPEN signals\n", - " if len(buy_open_trades) > 0:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=buy_open_trades['time'],\n", - " y=buy_open_trades['price'],\n", - " mode='markers',\n", - " name=f'{symbol_name} BUY OPEN',\n", - " marker=dict(color='red', size=12, symbol='triangle-up'),\n", - " showlegend=True\n", - " ),\n", - " row=4, col=1\n", - " )\n", - " \n", - " # Add BUY CLOSE signals\n", - " if len(buy_close_trades) > 0:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=buy_close_trades['time'],\n", - " y=buy_close_trades['price'],\n", - " mode='markers',\n", - " name=f'{symbol_name} BUY CLOSE',\n", - " marker=dict(color='red', size=12, symbol='triangle-down'),\n", - " showlegend=True\n", - " ),\n", - " row=4, col=1\n", - " )\n", - " \n", - " # Add SELL OPEN signals\n", - " if len(sell_open_trades) > 0:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=sell_open_trades['time'],\n", - " y=sell_open_trades['price'],\n", - " mode='markers',\n", - " name=f'{symbol_name} SELL OPEN',\n", - " marker=dict(color='blue', size=12, symbol='triangle-up'),\n", - " showlegend=True\n", - " ),\n", - " row=4, col=1\n", - " )\n", - " \n", - " # Add SELL CLOSE signals\n", - " if len(sell_close_trades) > 0:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=sell_close_trades['time'],\n", - " y=sell_close_trades['price'],\n", - " mode='markers',\n", - " name=f'{symbol_name} SELL CLOSE',\n", - " marker=dict(color='blue', size=12, symbol='triangle-down'),\n", - " showlegend=True\n", - " ),\n", - " row=4, col=1\n", - " )\n", - "\n", - "print(\"Fixed draw_symbol_trades function created successfully!\")\n", - "print(\"This function correctly filters trades data rather than trying to filter price data with trade conditions.\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fit Method Functions" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def run_static_fit(config: Dict, pair: TradingPair, bt_result: BacktestResult) -> bool:\n", - " is_cointegrated = False\n", - " print(\"\\n=== STATIC FIT ANALYSIS ===\")\n", - " \n", - " # For StaticFit, we do traditional training/testing split\n", - " training_minutes = pt_bt_config[\"training_minutes\"]\n", - " pair.get_datasets(training_minutes=training_minutes)\n", - " \n", - " print(f\"Training data: {len(pair.training_df_)} rows\")\n", - " print(f\"Testing data: {len(pair.testing_df_)} rows\")\n", - " print(f\"Training period: {pair.training_df_['tstamp'].iloc[0]} to {pair.training_df_['tstamp'].iloc[-1]}\")\n", - " print(f\"Testing period: {pair.testing_df_['tstamp'].iloc[0]} to {pair.testing_df_['tstamp'].iloc[-1]}\")\n", - " \n", - " # Train and test cointegration\n", - " is_cointegrated = pair.train_pair()\n", - " print(f\"Pair cointegration status: {is_cointegrated}\")\n", - " \n", - " if is_cointegrated:\n", - " print(f\"VECM Beta coefficients: {pair.vecm_fit_.beta.flatten()}\")\n", - " print(f\"Training dis-equilibrium mean: {pair.training_mu_:.6f}\")\n", - " print(f\"Training dis-equilibrium std: {pair.training_std_:.6f}\")\n", - " \n", - " # Generate predictions and run strategy\n", - " pair.predict()\n", - " pair_trades = FIT_MODEL.run_pair(config=pt_bt_config, pair=pair, bt_result=bt_result)\n", - " \n", - " if pair_trades is not None and len(pair_trades) > 0:\n", - " print(f\"Generated {len(pair_trades)} trading signals\")\n", - " else:\n", - " print(\"No trading signals generated\")\n", - " else:\n", - " print(\"Pair is not cointegrated - cannot proceed with strategy\")\n", - "\n", - " return is_cointegrated\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "vscode": { - "languageId": "raw" - } - }, - "source": [ - "## Print Strategy Specifics\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Analysis for SlidingFit...\n", - "\n", - "=== SLIDING FIT FIT_MODEL ANALYSIS ===\n", - "This strategy:\n", - " - Re-fits cointegration model using sliding window\n", - " - Adapts to changing market conditions\n", - " - Dynamic parameter updates every minute\n", - "\n", - "Sliding window analysis parameters:\n", - " Training window size: 120 minutes\n", - " Maximum iterations: 271\n", - " Total analysis time: ~271 minutes\n", - "\n", - "Strategy Configuration:\n", - " Open threshold: 2\n", - " Close threshold: 1\n", - " Training minutes: 120\n", - " Funding per pair: $2000\n" - ] - } - ], - "source": [ - "# Determine analysis approach based on strategy type\n", - "FIT_METHOD_TYPE = type(FIT_MODEL).__name__\n", - "print(f\"Analysis for {FIT_METHOD_TYPE}...\")\n", - "\n", - "if FIT_METHOD_TYPE == \"StaticFit\":\n", - " print(\"\\n=== STATIC FIT FIT_MODEL ANALYSIS ===\")\n", - " print(\"This strategy:\")\n", - " print(\" - Fits cointegration model once using training data\")\n", - " print(\" - Uses fixed parameters for entire trading period\")\n", - " print(\" - Generates trading signals based on static thresholds\")\n", - " \n", - "elif FIT_METHOD_TYPE == \"SlidingFit\":\n", - " print(\"\\n=== SLIDING FIT FIT_MODEL ANALYSIS ===\")\n", - " print(\"This strategy:\")\n", - " print(\" - Re-fits cointegration model using sliding window\")\n", - " print(\" - Adapts to changing market conditions\")\n", - " print(\" - Dynamic parameter updates every minute\")\n", - " \n", - " # Calculate maximum possible iterations for sliding window\n", - " training_minutes = pt_bt_config[\"training_minutes\"]\n", - " max_iterations = len(pair.market_data_) - training_minutes\n", - " print(f\"\\nSliding window analysis parameters:\")\n", - " print(f\" Training window size: {training_minutes} minutes\")\n", - " print(f\" Maximum iterations: {max_iterations}\")\n", - " print(f\" Total analysis time: ~{max_iterations} minutes\")\n", - "\n", - "print(f\"\\nStrategy Configuration:\")\n", - "print(f\" Open threshold: {pt_bt_config['dis-equilibrium_open_trshld']}\")\n", - "print(f\" Close threshold: {pt_bt_config['dis-equilibrium_close_trshld']}\")\n", - "print(f\" Training minutes: {pt_bt_config['training_minutes']}\")\n", - "print(f\" Funding per pair: ${pt_bt_config['funding_per_pair']}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "vscode": { - "languageId": "raw" - } - }, - "source": [ - "## Visualize Raw Price Data\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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m7elJTMzY3d9RHfwNHjxY3377rT744AOVLFky5fGkpCRJUpUqVVJtX6VKFW3ZskWSVLx4ce3evTvV87Zta9++fSmvzwjX9eS6rHuSFyQkGDp6VNq719WMGQG1aGHL73dl/6NjWoECkmV52rHDk22f+ENl1SpD06b5FBPjqkuXzK2fdDZzHPekX1cAeU92zdvNmw2NGBHQpk2GrroqrA4dbL3+ekDPPefXjTeG1KGDIymyxtj33/u0dq2pvn1DJ2wNmVN+/tmSZXm6556gXnwxoFdf9evpp49malybNxuaOtXUlVeGVayYk+Zn0YncdFNQAwfGauRIS9deG9a331r67jufDh40VLy4q0qVPF18cVgVK7qqVy/tz7ho1KNHSGXL+rR5s6mtWw0tXWqqbFlX9evbmXp/5cpJ5co5uvzyyHqB+eFr83f16rkaMsTW0KExeuUVn554wpXff3rzdswYvxYtMtS/f1AlS578+6hKFal375DeeCOgkiVdde6cz76wQA7hHBmIPsxbIPowb4How7zNHlEZ/HmepyeeeEJff/21xowZo3LlyqV6vmzZsipRooTWrl2b6vF169apdevWkqQGDRpo//79WrJkierUqSNJmjt3rlzXVb169XLmjSBLFS4cCWBnzbK0d6+h9u3TvzBlGFLRop527TIlOSfc36JFkUWdli0z1aVLlg8XQBQ4ckTautVQ5crc4JFZCxeaevXVgIoX9/Sf/wRVqVLka/h//xfShx/69d57Af3xh61t20ytW2cqPt6T60qjRwfUr18Wl7Bl0qJFpurWdRUbK91xR0gPPRSr118PaODAUEq1k+NIpql0q59sW3r77YCSkrxMhyQJCdItt4T00ksxWrjQp/h4T61b2zr/fEdlyuTP78MCBaROnRyd7GcyIvx+6bbbwnrkkYA+/NBSr16Znytff+3T9OmR19arl7Ffrlq1crR5c1jjx/tVsqSrRo34pQwAAAAAgLwqKoO/QYMG6fPPP9eIESNUsGDBlDX5ChUqpNjYWBmGod69e+u1115TjRo1VLNmTX366adas2aNXn31VUmR6r/zzjtPjz76qAYNGqRwOKwnnnhCnTt3VnJycm6+PZymokUjF0SnTbNUpYqrChVOfIG0eHFPu3advFfVb7/5ZFmeVq40FQpJgUCWDhdAFPjmG0sTJlh65ZWjrAmaCXPn+jRihF8NGri6886QYmKOP2eaUs+eYZUo4erTT/2qXt3VFVcEde65rn7+2afhwwP65Rcz14KFgwelFSt8uummSKCSmBgJ/557LkZDhgRk29LOnab27DFUtaqr/v2DadbY++ADv9asMfTww6HT+tnRqJGrW24JKTZWatTIkT9rl6lDlCtRQrrpJunVV31q1CgSUmfUmjWG3n8/oAsvtFMqbjPq6qsjQf2IEQE99VRQJUvmzyA6t3iedOCAVLCg5PNl/f5/+83U0qWmevSgYhMAAAAA8ruoDP7GjRsnSerZs2eqx4cMGaIu/yvN6tWrl0KhkIYMGaJ9+/apRo0aeuedd1S+fPmU7YcOHaonnnhCN954o0zTVMeOHfXII4/k3BtBljpW8XfwoKEePcIn3bZoUU9btpgnfH7vXmntWlMXXxzWF1/4tXKlqdq1ubsdONusXWvKdQ19+62lK6/kYmlGzJ7t09tvR9ot9+kTlnWCM41OnZz/VXkd17y5ox9+cPTuuwHVqHE0TaCWE377zSfPkxo0OD62evVc9ewZ0s8/+5SU5KlGDVsJCZ4+/dSv556L0cCBQcXFRbadPdunb76xdPPNIVWrdvo/N9q0ofoNJ3bRRdLMma7eeiugZ545mvL9dyrTp1sqUcI95XlSegxD6tMnpEceidGwYQE9/njwjEPpXbsM7doVCdHNE5+W5VueJ23caGjePJ/mzfNp2zZThiElJnoqVsxT2bKurrkmrPj4tK/9738tffONT489FjxlG+KdOw0NHx7Q4cOGOnRwVKIEoS0AAAAA5GdRGfz9+eefGdquT58+6tOnzwmfL1KkiF544YWsGhZyWYECUkyMJ59Patbs5BdMixXztHjxiSv+fv/dJ8OQOne2NWeOpeXLCf6As9GGDYYMI9JC+PLL7bPywnRmfPWVT++/H1DbtrZuvjmcbhvMkzEM6aabwho4MEbjx/vVu3fmw4kztXChqUqV3DQVnukFldWquXrmmRg9/3wk/NuyxdC77/rVtq2tdu0I7pB9IiFcWP37BzR2rF+33HLquXLwoDRvnk9du57+Z1mBAlLfviH95z8x+ugjv66//vTn6KFD0tNPB7R9e6TVb6NGjho1clSnjpuvuyykF/bFxXlq3NhRt262Dh+OBHW7dhn6+WefVq0y9cADqcO96dN9+vBDv3w+T+PH+3XbbSf+d/A86c03/YqJkYJBT7/84tNFF3EjCwAAAADkZ1EZ/AHpMQypdGlPtWs7qdrKpad4cU/79hmybaVbjbJokU+VK7sqXFiqWdPVsmVc7QfONkePStu3m2rXztaMGZZ++81UgwbcAHAiU6ZY+vhjvy66yFaPHpkP/Y4pVszTtdeG9d57AbVo4ahWrZz7mtt25MaPTp0ydlG8cmVPAwYE/xf+BbRjh6kKFTzdcEPOB5Y4+yQlSdddF9Y770Qqubp1C6t06RNXcn3/vU+uK7VufWahT6VKnrp3D+uDDwKqVctRw4YnnqPe/4bzz88Dz5NGjQpo/35D994b1MqVphYs8Gn2bEuFC3vq2jWs8893sqXlZW6x7cjn5I8/pg77evYMq3ZtN93z0U2bbD3zTIwGD47Rgw+GVLy4p5kzfRozJqCLLw6rZElP77wTUJs2jqpXT//f4YsvLC1f7tMDDwQ1bZqlX381ddFF2fxmAQAAAAC5iuAP+cqjjwZP2Fbu74oV8+R50u7dRpp2R7YtLVliptwNXbOmow8/9CsY1CkDRQD5x6ZNhjxPatvW1urVpmbMsNSgQSi3h5XneJ40YYKlKVP8uvLKsLp0sU879DumfXtHP/3kaNQov558MqgCBbJmrKeyYoWpw4eNVG0+T6VqVU8DBoT07LMBxcRI99xz5u0PgYxq29aRZYX0ySd+DRwYq9atbV15pa3ixVOf23ieNHOmpcaN3VO2hcyITp0cLVni6M03I+v9HTokLVzo06JFPm3aFLmxynEk1zVUsmRkrc/KlY+PacYMn+bP96lfv6AaNXLVqJGra6+1tWmTocmTLb3zTkBffumqe/ewzj3XPePPlNPhedILLwR00UV2lnR9mDjR0n//a6lly5OHfX9XtqynRx8N6plnAho8OEbt2tmaONGvjh1tde8eOU/99ltX770X+az8Z1C6fr2hjz+2dNFFkeNt2+Zo9Gi/Dh5Uuu1DAQAAAAD5A2VMyFcCAWWofVWxYpGLT7t2pb2StHJl5MJv/fqRC7+1arlyHEMrVjBdgLPJ+vWmTNNTmTKe2rWz9dtvPu3YkQtXn/Mwz5PGjPFryhS/uncPq2vXMw/9pEh10C23hLVvn6E33wykVA1lt0WLTBUp4qlSpcwdsFo1V08+GdTjjwdVtGg2DQ5Ih2FIrVs7Gjr0qK6/PqRff/Xp3/+OSXPOsnKlqS1bTLVtmzUtHg1Duu22kPx+6b77YvTQQ7GaMsVSQoKnyy6zde21Yd1wQ1g33xxSgQLSoEEx+uqryPqZa9caGjPGrwsusNW0aepArWxZT337hjV48FEVLuzphRdi9OGHuZOkr1tnaNEin0aP9ss5w869y5ebmjbNr27dbN12W1j165869DsmOTkS/sXGepo4MdJKuGfPSFW1YUi9eoW0aZOpr75KvcNQSBoxIqDSpT1ddVXk371hQ0euG3lfAAAAAID8i4o/nJWKFj1x8Pfbb6YKFTp+4bdMGU+FCnlavtxU3bq0+QPOFuvWmSpTxpPfL7Vs6WjsWE/ffutLuYCa3x04IA0eHKN9+wwVLOipYEEpPt5TXJz+93dP27dH2vPddFNI7dtn7Zp2pUp5uuOOkF56KUZTpkTWWMxuv/7qU4MGzmmFl6VK5VA6CaTD749U4Z1/vqPnnw/o1VcDevLJoypSJPL8zJk+lSjhqk6drDuPKVRIuvfeoObN86lOHVc1arjpVrued56jceP8ev/9gJYvd7Rhg6Fy5Tz16HHilriVK3t66KGQJk60NG2apSuuCOd4hdqyZT75fJ62bDE1Z45Pbdue3mfcoUPSG28EVL26o86dT+9zrGhR6bHHglq82KfmzVN/RlWuHLk55ZNPLDVvbsvvl2bPtvT11z7t3Wto8ODjVciJiVKlSq5+/dWnVq1YhxQAAAAA8itKmHBWio2NXMBOL/hbtMinc889flHFMI6t85f67mjPkxYsMBUM5sSIAeS0DRtMlS8fuUgeGyv961+Ovv3Wkn125H6aONGvvXsNXXqprWbNHFWq5CouTjp8OBKKzpvn08qVpm67LetDv2MaNXJ1xRVhTZzo16JFqU9Z1q0z9N//Wpo716c1awzt368zqgzcts3Qtm2mzj2Xi+GIXrGx0t13R1oSv/ZaQLYtHTwozZsXCa6yumVm5cqeune3Vbdu+qGfFAklb7ghrHvuCWrpUlP79xu6++7QKVviGobUqZMtz4sEWTlt6VJTNWu6at7c1ief+NOc7+3aZejZZwOaO/fk1XPvv+/X4cPSbbeFM9SV4kTi46UWLdL/N7zqqrD8fmnIkBj16xerjz+2VL26q//8J6jy5VN/MDZu7Oi330yFWYoUAAAAAPItKv5w1ipWzNPOnamvnuzcaWjTJlNXXJH6yn7t2pE1UY4ejVxUk6TPP7f00Ud+NW9u6667wrmy/gyA7OG60saNhpo2PX7BtH17WzNmWPr1VzNNe7pocOiQNHWqpfr1XdWsefLxb9xoaOZMS9deGz7tCpWs0rWrrfXrTY0YEdB//hPU5s2Gpk+39McfkWocxzn+4Zuc7Oqhh0Ip7Zwz4ujRSBvEWbMsWZaXJWt5AbmpSBGpX7+QnnwyoHHj/CpRwpXjSOedl7tzuUkTV1WrHtXRo4aSkzM2RxMSpGbNHM2c6dPFF2dNK+GMsG3pjz9MXXmlraZNHQ0Y4NOXXx6vPD5wQHr22YB27jS0eLFPf/5pq0ePcJow86effPrhB0u33x5SUlL2VQXHx0s33hjWhAmWLr3UVtu2dkq15z81bOhowgS/li0zVb8+n3cAAAAAkB8R/OGsVaxY2oq/33+PrOlVt27qio+aNV25rqE//4xcJFm1ytCECZZq1nQ0d66lGjVcdehAlQiQX2zfbigUMlShwvGLouXLe6pa1dVXX1lq3Dh0RpUbOe2330y9/XZAe/YY+uorTw88EFK1aulf8PU86cMPI2FBp065X95oGNLtt4f0+OMxGjgwVp4nVavmqF+/oBo1cnXkiLRjh6G//jI1bpyl558P6NFHgypY8MT73LNH+uEHS7/8EqkWdBxDCQmeuna1U27uAKJZtWqurr8+rPffD6hgQU+NGrknDIJyUmKiJGUuAOvQwdGPP1pavNhUvXpZF1R5njR+vKVGjdw0n4erV5sKhQzVru0oOdlT+/aOPv/cUrt2tixLeu65GB04YOjpp4NassTUmDF+rV5t6u67QypQwNOyZaaWLfPphx98atrU0b/+lf3niM2bO2re/NTHKVvWU4kSrhYs8BH8AQAAAEA+RfCHs1bRop7++CP1lftFi3yqVs1Nc8G4VClPhQtH1vmrWtXVsGEBVarkaeDAkD780K8xY/yqVMlVlSqs8QTkB+vXRz4b/h78SdLll4f14osxGjYsoDvuOHWrutx26FAkxJszx1Lduo4eeiisUaP8ev75gB56KKhzzkn7ml9/NbVkiU/33ReUlUfOEgoWlPr3D+mbbyy1bGmrcuXjn7Xx8ZHWzZUqOSpb1tWgQTF65ZWABgwIpRp/OCz98otPc+b4tHhxpFqwfn1X119vq1YtV6VLe1RuI1+54AJHq1fb+uGHSGAVrc45x1W5cq6++cZSvXqhLNvv6tWGpk3za/16Rw88kHq/S5eaiovzVLFi5LPmiivCmjPHpwkT/P9rC2zo4YeDKlnSU8mSjipXdvXqqwENGBAj2zbkeZEK5JYtHV11Vd7qCmEYUsOGrubO9cnz8tbYAAAAAABZI49c0gNy3j8r/tatM7R4sakuXdJeHIus8+do6VKf/vrL1OHDhh56KHJRvEePsFavNvXqqzF66qmjio/PyXcBIDusX28oMdFToUKpH2/QwNU99wQ1bFhAzz8f0D33hE5aWbZqlaFly3yqXt1VlSpujgZpW7dG1p86dMjQLbeEdP75kbWh7r8/pKefjtGzz8boP/8J/68CJ8K2pbFj/apTx1GDBnmrEqRkSU/XX3/yRanKlPF0330hDRkS0Jtv+nXHHWHt3SvNmGFpxgxLBw4YqlrV1U03hdS0qcPnNfI1w5B69w7rX/9yVKdO3prPmWEYUocOtt57L9Jas3jxrLnJ6ssvLZmmpyVLfPrrL0MlShzf79KlpmrVclMquxMSpEsvtTVhgl9+f+TGr2OhoBRZ6/DJJ4OaPt1S8eKeatVys7W155lq1MjRl19aWrPG4KY1AAAAAMiHCP5w1ipe3NORI4YOHZJ27TL0zDMxKlvWO+Fd8bVquZo719LatVLfvqGUC0R+f2QtnUceidGIEQH9+98h7p4Gotz69Waaar9jGjd29cADIb34YkBPPRWjf/87mCo8O2bPHmno0BgdOhSp/vD7PVWv7qpNm4y1YzsTq1YZGjo0RgkJnh5+OJjqAnSBAtLAgUE9+WSMnn7arxtvlHw+UwkJnn77LXJzw733Ho3az7EaNVzdcUdYw4YFtH27qbVrDfn9UuvWji64wFbp0lzkxtkjEFCWtsfMLS1bOho71tOsWT5dddWZVy/u3i3Nn+9Tt262pk61NHv28f0ePSqtWmWmudHgwgttrVljql07WzVqpP2axsdH1iSNBtWquYqP9/TLLz5VqRIdYwYAAAAAZBzBH85axYpFLv4uXuzT6NF+FSvmaeDAE68LVbt25CJP27Z2mov2xYt7uvXWkF56KUYrVpiqXj36L7IBZ7MNG0y1bn3ii6E1arh69NGgnnsuRoMHx+jBB0OpqkVcVxoxIiDLkoYNO6JduwwtX+7TokWmhg8PKCYmmG0VdYsWmXr11YAqVPB0//3BdKva4uOlBx4I6qWXYjVihBQK+eV5kb6l7dvbKls2usOx5s0d7dsX0qxZlrp3t9W6tX3SykwAeVuBAlKrVo6+/dbSlVfaZ1w9/c03lvz+SCXhzp2GZs+21KWLLZ9PWrHClOMYqlUr9Wd0TIx0771Z12o0N/l8Uv36jubPjwSe0XqjBwAAAAAgfeapNwHyp2PB34gRfhUp4unBB9O/QH5McrKnRx4J6oYb0m81dywY3LGDqydANNu/X9q711D58icPv8qV8/TYY0GZpvTkkzHauvX43J882dIff/h0110hFS4caQPXubOthx4KqWFDR8OGBbRhQ9Z/Vnz/vU8vvhhQnTruKT/TihSRnnoqpMmTpddfD+rJJ49q4MCgevQ4eTvNaNGpk6NnngnqoosI/YD8oEMHW/v2GVqwwHdG+wmFpJkzLZ1/vqO4uMgNXXv3Gvr998ivRcuWmSpSxMv31cHt2zvats3U/Pln9vUEAAAAAOQ9BH84axUp4snn81Sy5KlDv2Nq1HDl96f/XGysVLCgpz17CP6A3BJprxnQmjWnPw/Xr4/8aKxY8dQVeUlJkRsCYmM9PfFEjDZuNLR8ualPP/XryivDqlkz9T4MQ7rjjpCSkz29+GJA+/ef9jDT2L1bGjXKrxYtHN1zT0iBQMZeZ5qRELBiRU9167qKicm6MQFAVilXzlO1ao5mzjyzoOrHH306dMhQx46Rqu5KlTxVqOBq1qxIGeHSpT7VquXk+yq4atVc1anjaNIkS17+zjgBAAAA4KxD8Iezls8nDRgQ0qOPBpWQkDX7LFrU065d+fxKEZCHLVvm06JFPj3xRIx++intxeGdOw3NmePTypWmjh5Nfx8bNpiKifGUnJyxK6GJidIjjwSVmOjpqadiNHx4QDVqOLriivRbhcbGSvfdF1IoZOill2IUzqICu08/9SsmRrrhhrB8FHAAyIfatHG0bJlP27ef3rmW50nTp1uqX99RyZLHP+PbtrW1aJGpTZsMrVtnpnRxyO+6dQtr82ZT8+bxQwMAAAAA8hOCP5zVatd2VahQ1u0vMdHT7t0Ef0Bu2brVUKFCnpo2dTR8eEAffxypZNi82dDIkX7dd1+M3nwzoEGDYnTrrQU0YECM3n3XryNHju9j/XpD5cp5mar2SEiQHnwwqORkT44j3XlnSOZJfsIWL+7pvvuCWrvW0Ftv+c+42mLbNkOzZ/t06aW0tQSQfzVt6ig21tOcOacXVP3xh6mNG0116pT6xowWLRz5fNJbbwXkeUqzvl9+VbWqp3r1IlV/7tnxlgEAAADgrGDl9gCA/KRYMU/r1pGnA7llyxZT5cq5uv32sMqV8/TRR37Nn+/T9u2RNZu6d7d13nm2du40tHatqXXrTP34o09r1pjq3z+owoUjFX/Vq2f+Cmh8vPToo0EdPaoMtQ6uWtXT7beHNXx4QHFx0o03hk+7tdyECX4VKSJdcEH6VYYAkB/ExkZCuu++s9S1q33SGyzSM326pdKlXdWpk/ozvmBBqXnzyH6Tk10lJZ09vS+7dg3r8cdjNXeuTy1bOrk9HAAAAABAFiChALIQrT6B3LV1q6GSJSPVepdcYuu++4IqUsRT794hvfjiUV10ka34+Mh6dm3bOrrpprAefjio3bsNDR4co02bDG3ZYqhChdMrfbCsjIV+xzRv7uimm0L65htLEyac3r04a9camjfPpy5dwhle1w8AolWbNrZ27za0eHHmfo1Zs8bQggU+XXyxne5NFm3aREKvs6Xa75gqVTydey5VfwAAAACQnxD8AVkoMdHT/v2GbIpugBznupGWl6VLH79y2aCBq0ceCalNG0d+f/qvq1jR0+OPByVJjz0WI9c1VL58zl39bNfOUffuYU2Z4tfnn584/Fu3ztCYMX5NmGClusHg44/9KlnS1XnnUakBIP+rXNlT2bKuvv024zdLeJ40bpxfZcqc+LOyWjVXHTrYatv27DuJ69IlrG3bIhXwAAAAAIDoR6tPIAsVKxZpDbVnj3FWtYkC8oKdOw2Fw4ZKlcr83CtRIhL+Pf98jDZulMqWzdn527mzrcOHpfHj/dq+3VCZMq6KFvVS2gfPmmVp3TpThQt7CgalqVMtNW7sqnp1R4sX+9SvX1A+rtcCOAsYhnT++Y7Gj7e0f39kjdVTWbjQ1PLlPv373yf+rDQMqVevcNYONkpUruypYcNI1V+zZie+UQYAAAAAEB0I/oAsVLRoJCzYvZvgD8hpW7dGquBKlz69uZeQEFmjb9cuQ7GxWTmyjOnWLdJ+7qeffPr+e5/C4cj7MQzp3HMddekSVP36roJB6bvvfPrqK0vz5wdUqZKrJk3ozwbg7NGqla3x4y19/72liy8+eYWebUtjx/pVp46jevX4rDyRa64J68EHY/Tll5YuvfTsq3oEAAAAgPyE4A/IQomJx4M/ADlryxZTfr+n4sVPP3QPBHRaFYNZwTAi4V+3brY8TzpwQNq1y1CRIp4SE49vFxcnderkqGNHR0uXmkpO9tJdrwoA8qtChaRGjVx9+61PF12U/pp9x3z7rU/bt5vq2/con5UnUaaMpw4dHE2ebOm882wVKZLbIwIAAAAAnC7W+AOyUFycFBvrEfwBuWDrVkMlS+aPEMwwIhWIlSqlDv3+uU2dOi7VxQDOSm3a2NqyxdSqVSf+debwYemTT/xq1cpWxYp8Vp5Kly5h+f3SRx/R6xMAAAAAohkVf0AWK1qU4A952/z5pvx+qUGD9Fue7dkjxcZKBQrk8MDO0Natp7e+HwAg+tSp46pYMU+DBsXIND3FxESqthMSIuujFivmac8eQ0ePRqqpcWrx8dJVV4X17rsBXXCBrcqV+ZkKAAAAANGI4A/IYgR/yMu++86nkSMDMgzp+utD6tTJSfX83Lk+jRzpV926ru67L5RLozw9W7eaql6di7sAcDYwTWnAgKBWrzYVCknBoKFQSNq719CuXYZWrDC1Z4+hK6+0VawYAVZGtW3r6JtvXI0eHdB//hPMF1X0AAAAAHC2IfgDsljRop42b6aLLvKe+fNNvflmQG3a2IqL8zRmTEB79oR1zTWRsGzSJEuffupXuXKufv3Vpw0bDJUvHx0XSw8dilzsLV06OsYLADhzZcp4KlPGOfWGyDDTlHr2DOvpp2P0ww8+tWrF1xcAAAAAog3BH5DFihb1tHgxt0cjb1m0yNTw4QE1a2br5pvDMk2pcGFp3Di/9u0zFAwamj/fp6uuCqtzZ1v9+8fos8/8uvvu6Kj627YtMudKlky/fSkAAMiYWrVcNW3q6KOP/Gra1FEgkNsjAgAAAABkBmVJQBYrWtTT3r2GHG6QRg45fFjavTv95zxP+vVXU6+8ElD9+q5uvz0S+klS5862br89pB9+8Om330z93/8FdfnltixLuvRSW/Pn+7R1a3SE2Fu3Rt4UFX8AAJy5a64Ja98+afp07hMFAAAAgGjDb3JAFitWzJPnRdoOsqYMsls4LA0ZEqN160zVr++oQwdb9eq58jzp5599mjbN0tq1purVc9S3b0jWPz71W7VyVLq0qwIFpFKljn+/nneeo08/9TR1qqU+fcI5/K4yb8sWQ0WLeoqNze2RAAAQ/ZKTPbVr52jqVEtt29qKj8/tEQEAAAAAMorgD8hiiYmR8GTPHqlYsVweDPK9ceP82rDBULduYc2f79PQoTFKSnJlGNJff5mqVcvRgAFB1a0beSw9lSunDagDAenii22NH2/pyittJSXl7RB761ZTpUrR5hMAgKxy5ZVhffedT59/bunaa+3cHg4AAAAAIIMI/oAsVrRoJCDZtctU1ar0+0T2mT/f1FdfWbrxxpAuuMDRZZfZWr3a0IwZllxXuvvukCpVOv3Arl07W1OmWPr8c0s33ZS3q/62bDFUsybBHwAAWaVwYemii2x9/rmlCy5w6GQBAAAAAFGCNf6ALFawoBQIeNq9OzrWRkN02r7d0FtvBdS0qaMOHSIBs2FIVat6uu22sO64I3xGoZ8kxcZGLvjNnu3Tnj1ZMers4brStm0GFX8AAGSxiy+2VaCANGkS94sCAAAAQLQg+AOymGFEqv7yclCC6BYOS8OGBVSokKfevUMnbOGZFTp0sOX3S1On+rPvIJmwf7/00UeWDh48/tiOHYYcx1Dp0lQiAACQleLipCuuCGvOHEubNnFTGwAAAABEA27dBLJB0aKedu0iV0f2+PjjyLp+jz8eVMGC2XusggWlSy+1NXGipQ4d7FwN1/bvl55+OkabNpnav9/QrbdG2o9u3Rq5EFmqFMEfAABZrX17R19+6WrQoBj5/ZFKe9eVKlZ0ddNNYX7+AgAAAEAeQzIBZIOiRWn1ieyxfLmpL7+0dM01tipXzpkLbRdeaKtYMU9jx+Ze1d+x0G//fkMXXxzW7NmWVqyI/AjbssVUIOCx9hAAANnAsqT77w/pwgttdexo6+KLbV16qa1duww99FCM/vvfyNrCAAAAAIC8gYo/IBsULerpjz9yexTIb44ckUaO9Kt6dUcXXWTn2HEDAal797BefTVGv/9uql69nL269/fQ75FHgipZ0tMff/j0zjt+PflkUFu3GipVysvWlqcAAJzNypb1VLZs6nOPCy6w9fHHfn34oV/z5/t0++0hJSdzEw4AAAAA5DYq/oBscKziz+PaB7LQ2LF+HTgQaXGZ0yFXkyauatRw9MEHftk5lznqyJHUoV/p0p5MU7r55pA2b45UP0aCP0oNAADISbGx0g03hPXww0Ht2SO9/nogt4cEAAAAABDBH5Atihb15LqG9u3L7ZEgv/jtN1OzZlm67rpwrtxNbxjS9deHtXWrqZkzfTl23EmT/Nq+3dDDDwdTrS9YsaKnjh1tTZpkaf16M1fXHgQA4GxWs6ara6+1tWqVqS1bKL8HAAAAgNxG8Adkg6JFIyEE6/whKxw8KL31VkB16zpq29bJtXFUrOjp/PNtTZrk18GD2X+8DRsMTZ/uU5cutsqUSRvsde0aVlycdORIpNUnAADIHQ0bOipY0NOcOTl3cxAAAAAAIH0Ef0A2IPhDVvrsM79CIemWW3K+xec/XXVVWOGw9NVX2btErOdJ773nV3Kyd8L1DOPipJ49w5Kk8uVp9QkAQG4JBKTmzR19/70llx/JAAAAAJCrCP6AbFCokOTzeQR/OGOOI33/vU+tWzsqViz3q9oKF5bq13e1eHH23tH/3Xc+rVjhU69eYVknyRibNXM0bNiRdCsCAQBAzjn/fFt79xpasoRfMQEAAAAgN/FbGZANDEMqVozgD2du6VJTBw4Yatky/aq33FCrlqPVqw0dPZo9+z94UBo3zq8WLWzVrn3qsoEiRbJnHAAAIOMqVfJUurRLu08AAAAAyGUEf0A2KVqU4A9n7ocffCpZ0lWlSnmnoq1mTVeua2jFiuz5ETJhgl+2LfXoEc6W/QMAgKxnGNL55ztasMCnQ4dyezQAAAAAcPYi+AOySWIiwR/OTDAoLVjg07/+5eT62n5/V7q0p4QET8uWZf2PkA0bDM2caalr17ASE7N89wAAIBu1bGnLdaV586j6AwAAAIDcQvAHZJNixTzt2ZOH0hpEnV9/9SkYNNSypZPbQ0nFMCJVf8uXZ/1FvY8/9qtECVcdOuSt9wwAAE4tMVGqV8/V7NknWaAXAAAAAJCtCP6AbJKY6GnXLkNe3unQiCjzww8+VaniKjk5730T1a7taO1aQ0eOZN0+//jD1KJFPl11lS2L64UAAESl885ztHq1qc2buQEOAAAAAHIDwR+QTYoW9WTbhg4ezO2RIBodOCD9/rupli3t3B5Kuo6t8/fnn1nzY8TzpI8+8qtiRVfNmlHtBwBAtGrY0FHBgp7mzKHdJwAAAADkBoI/IJsUKxap0lqxgmmGzDu2Nk6LFnkzBCtZ0lORIlm3zt+iRaZWrjR1zTXhPLWeIQAAyBy/X2rTxtaMGZYOHMjt0QAAAADA2YdEAsgmlSp5qlPH0ahRAe3bl9ujwZmwbWnLlpxt2/rDD5bq1HGVkJBzx8wMw5Bq1XKyZJ0/141U+9Wq5ahOHTcLRgcAAHJT5862PE+aNo3e3QAAAACQ0wj+gGxiGNLtt4fkedLrrwdY6y8KbdliaOxYS3ffHasBA2L1/PMB/fVX9pej7dhhaOVKUy1b5s1qv2Nq1XK1bp2pQ4eOP+Y40rhxllavzvjX6ccffdq0ydTVV1PtBwBAfpCQIF14oa3p0y3t3ZvbowEAAACAswvBH5CNihSR7rwzpKVLfZo6lTue8zLPk/76y9D8+aY+/tjSoEExGjAgVnPmWPrXvxz16RPSpk2mBg6M0dSpluxsXHrvxx99CgQ8NW6ct4O/mjVdeZ70xx/Hf5RMnGhp2jS/hg8P6OjRU+/DtqVPPrHUuLGjqlVJxwEAyC8uvtiW3y9NmeLP7aEAAAAAwFmFJALIZnXrurr00rAmTrRUo4aratVoZZiX2Lb01VeWpk61dOBApNysSBFPlSu7uuuukBo3duT/3/Wqpk0dTZzo18cf+zV7tk81a7oqVcpT6dKuypXzUtZ1PBOeJ/3wg0+NGjmKjT3j3WWrEiUi73n5cp8aNXK1cKGpqVP9at/e1pw5Pn3yiV/XXRc+6T5+/93Ujh2m7rknAykhAACIGgULRlp+TppkqXNnO0vOkwAAAAAAp0bwB+SArl1tLV/u07BhAT333NE8H+icDTxP+vVXU2PH+vXXX6batLHVqJGjihVdFSmS/mtiY6Xrrw+rZUtbX35pac0aUz/8YCgUigSGZcu6atzY+d9+vNNqW7l+vaEtW0z16HHywCwvOLbO37JlpnbsMPTGGwGde66jXr3CSkry9NFHfjVvbqtKlRNf6Jszx1KFCq4qVuRiIAAA+U3Hjrb++19Lkydb6t0775/bAAAAAEB+QPAH5ACfT7r55pAefDBWK1eaqluXqr/cdPCgNGxYQEuW+FSnjqP/+7+jKlcu48FT5cqe7rwzcvHK86RduwytWWPol198+uorS5Mn+1W6tKuHHgqeMEQ8kR9/9Ck+3lOdOtHxPVKzpqvvvrP08ssBFSjg6fbbQzIM6aKLbM2b59NbbwX05JNBWen8tNm/PxK+nqoqEAAARKcCBaRLL7X10UeWLrnEVnIyN/oAAAAAQHZjjT8gh5Qt6ykmxtO6dUy73HT4sPTsszFat87UffcFNXBgKFOh3z8ZhlS8uKemTV3dcUdYI0Yc1cCBQR06ZGjYsECm1gJ0Xemnnyw1b+6kG5TlRbVqRQLKTZsM9esXUnx85HGfT7rllpC2bDFOuL7ljz/6ZBhSy5Z5ey1DAABw+i64wFZCgvTFF1FycgMAAAAAUY4EAsghhiGVL+9p/frT6P+ILBEMSi++GND27YYeeCCohg3d02rHeTKWFVnX8e67Q1qxwtT48f50x7F9e9oDL19uas8eQ//6V/QEYcWLe2rUyNFNN4VVuXLqALVCBU+XXGJr8mRLGzemfb9z5lhq2NBVoUI5NVoAAJDTAgGpcWNHS5bwqycAAAAA5AR++wJyUMWKLhV/uSQcll55JaA1a0z17x/K9jXlatRw1aNHWF9+aemnn3ySIm1BFywwNWBArAYMiNHatanDsB9/9KlECVdVq0ZHm89j7r03pDZt0g8rr7jCVsmSnoYPDygUOv74unWGNmww1bp1JkoiAQBAVKpVy9H27aZ27eIGOAAAAADIbiQQQA6qUMHV9u2mjh7N7ZGcXTxPGjEioKVLTd17b0jVquVMsNapk6MWLWy99ZZfCxeaeuGFgF5+OUZly7oqWzYShh37XgiFpJ9/9qlFCyfLqxBzUyAg9e0b0vbthsaMOV79OGeOpcKFPdWrF10hJwAAyLxatSJdFpYt49dPAAAAAMhu/OYF5KCKFV15nrRhA1MvJy1YYOrnn326666Q6tbNuaDJMKTevcNKTvb0wgsx2rDB1D33BNW/f0h9+4a0Z4+h0aMjYdiiRaYOH46uNp8ZVa6cp549w5o1y9LcuT7ZdqS6sVUrWz5fbo8OAABkt/h4qVw5l+APAAAAAHIAK6wDOahsWU8+n6cNGwxVq5bbozk7eJ702Wd+1arlqGnTnK8ui42V7r8/pF9+MXX++Y5iYyOPlyrlqVevsEaODKhuXVc//+xTxYquSpfO3hakuaVtW0dLljgaNcqvXbsMHTxoqHXr/BdyAgCA9NWqFTnf8bxwvupuAAAAAAB5DbdcAjnIsqQyZTzW+ctBv/9uat06U5dfnntryRUv7qlTp+Oh3zGtWjlq2dLWO+9EWoG2bJl/g7BI9WNI8fGexo3zq3JlV2XK5M+QEwAApFWrlqNduwz99RepHwAAAABkJ9IHIIdVqOBq/XqmXk7wPOnTT/2qWtVVrVp5by05w5B69QqrcGFPjmOoRYvcCydzQsGCkfX+LMtThw75+70CAIDUqldnnT8AAAAAyAk5/luX53navn27bJuLvjg7VazoasMGQ0yB7LdsmalVq0xdeWXebSkVFyf17x/SnXeGlJiY26PJflWqeBox4ihtPgEAOMsULBg5Dyb4AwAAAIDslWO/dX333Xe6+uqrVbduXbVt21Z//vmnJOnRRx/VlClTcmoYQK6rUCFS3bVlSx5NoqJUKBSp8Pu7zz6zVLGiq3r18l6139+VKuWpRYuzJwiLi8vtEQAAgNxQu7ajZct8ac7ZAAAAAABZJ0eCv88//1x9+vRR2bJl9fjjj8t1j1+EL1eunCZNmpQTwwDyhPLlI9//tPvMOjt2GLrzzlg99liM5s71yXGkFStMLVvm0+WX591qPwAAgLNJzZqu9u0ztHUrJ2cAAAAAkF1yJHkYMWKEbrzxRr344ovq0qVLqufOOeccrVy5MieGAeQJcXFScjLr/GWlCRMsBQJSgQKehg0LqH//GL37rl9lyrhq3DhvV/sBAACcLapXd2WaHu0+AQAAACAbWTlxkI0bN+r8889P97kCBQrowIEDOTEMIM+oUMHTunXc6fx3ixebWrjQp7/+MrR9u6GdOw1dd11YHTqcvAXmmjWGfvzR0s03h9SunaM1awx98YWl+fN96ts3RLUfAABAHhEbG1nvd9ky3ynP8QAAAAAApydHgr+kpCStWbNGLVq0SPPcn3/+qdKlS2dqfyNHjtRXX32lNWvWKDY2Vg0aNFD//v1VuXLllG169uyp+fPnp3rdNddco8GDB6f8vXr16mn2/eKLL6pz586ZGg+QWRUquJo2zZLniWBK0sGD0tChASUmeipb1lO9eq42bzY0aZJfrVs7CgTSf53nSWPHRir7zj8/cvGocmVPffuGFQ6H5ffn4JsAAADAKdWs6WjmTM6DAQAAACC75Ejwd8kll+i1115T5cqV1bRpU0mSYRhasWKF3n77bXXv3j1T+5s/f76uu+461a1bV47j6MUXX1Tv3r01bdo0xcXFpWx39dVXq1+/fil/L1CgQJp9DRkyROedd17K3xMSEjL79oBMq1jR1eHDhnbsMFSihJfbw8l1P/zgk+dJgwcHdWwKbt9uqH//WM2a5VOnTunfEf7rr6b++MOnf/87KJ8v9XOEfgAAAHlP7dqupkwxtHGjofLlOQ8GAAAAgKyWI8Ff3759tXLlSt10000qUqSIJOnWW2/V7t271aZNG/Xp0ydT+xs1alSqvz/zzDNq0aKFli5dqiZNmqQ8Hhsbq6SkpJPuKyEh4ZTbAFmtQoXIunPr1hH8eZ40a5alRo1c/T13T0721LKlrWnT/GrXzkkT5Nm2NG6cX3XqOKpXj3X8AAAAosE557jy+TwtXepT+fJ2bg8HAAAAAPKdHFlVPRAI6PXXX9d7772nq6++WldddZWuvPJKjRo1Sq+//ros68zyx2NrBBYuXDjV41OnTlWzZs10ySWX6IUXXtCRI0fSvHbQoEFq1qyZunXrpokTJ8rzzu4QBjmjSBGpcGFP69fnyBTM09asMbRpk6k2bdJe+LnsMlt79hj67jtfmudmzfJp+3ZT3buHaRMFAAAQJQIBqW5dV99+65PLvVsAAAAAkOVypOLvmObNm6t58+ZZuk/XdfX000+rYcOGqlatWsrjl1xyiUqXLq0SJUrozz//1NChQ7V27VoNGzYsZZt+/fqpefPmKlCggL7//nsNGjRIhw8f1g033JDh45umIdMkdUDmVarkaeNGnywrOq94zJzpk2V5at06Mn6fz0z134z67jtLxYp5atBAMs3Ur61QQWre3NXnn/vVrp2nY/cILF9u6JNPAjr/fEdVqhiSmIPA6TjdeQsg9zBvkR907erosccC+vlnS//6V3SeC2cUcxaIPsxbIPowb4How7zNXjkS/P3000/asmWLunbtmua5SZMmqXTp0qcdCA4aNEgrV67U2LFjUz1+zTXXpPx/9erVlZSUpF69emnDhg0qX768JOmuu+5K2aZWrVo6cuSIRo0alangr2jRgjIoN8JpqF1bmjFDSkwM5PZQMm35cmn06Mg6eq1bS4mJx59LSEi7luaJHD0qzZ8vXXGFVKxY+ovy9eol3X23tHixX23bSlOnSu+8I9WsKfXta6lQoZgzezMAMjVvAeQNzFtEs6ZNpZYtpalTLV10kdKs1ZwfMWeB6MO8BaIP8xaIPszb7JEjwd/LL7+s9u3bp/vc7t279fHHH2v8+PGZ3u/gwYP17bff6oMPPlDJkiVPum39+vUlSevXr08J/tLbZsSIEQqFQgoEMhbG7N59iIo/nJakJFPbt/u1enVQRYvm9mgy7uhRaciQgMqVk7ZuNfTee4569bLl85lKSCig/fuPyHEyduf2t9+aOnjQryZNgtqzJ/1tCheW6tXza8wYQ99/7+rHH326+GJHPXrYsm2d8HUATu105i2A3MW8RX5x6aWGHnwwoClTwmrTJv9+LzNngejDvAWiD/MWiD7M29OTmFgwQ9vlSPC3cuVK3XPPPek+V7t2bb3xxhuZ2p/neXriiSf09ddfa8yYMSpXrtwpX7N8+XJJUlJS0km3KVy4cIZDP0lyXU+uy7qAyLwaNVz5/ZZmzDB15ZVp17fLq0aP9mvPHmnAgKDmzvVp0iRLF17oqWTJyAe047iy7Yx9WM+Y4VetWo4SE13ZJ/kSXHZZSI89FqudO03ddVdIzZs78jyd9DUAMi4z8xZA3sC8RbQrW1Zq0sTRxIk+NW9u6wyXfc/zmLNA9GHeAtGHeQtEH+Zt9siRBqqGYejAgQPpPrdv3z45jpOp/Q0aNEhTpkzRCy+8oIIFC2rHjh3asWOHjh49KknasGGDhg8friVLlmjTpk2aMWOGBg4cqCZNmqhGjRqSpJkzZ2rChAlasWKF1q9fr7Fjx2rkyJG6/vrrz+zNAhkUHy+1auXom2+sqAmwfvvN1KxZlq67LqzkZE+dOtmKi5M+/TTzV2o2bza0cqWpNm1OPf8rV/Z0++0hDR4cVPPmmfu8AAAAQN7UpUtYu3aZ+vbbs6DXJwAAAADkkBy5r7J+/fr68MMP1bFjx1Tr4Xmep7Fjx6a04cyocePGSZJ69uyZ6vEhQ4aoS5cu8vv9+umnn/T+++/r8OHDKlWqlDp27Kg777wzZVvLsvThhx/q6aefliSVL19eDzzwgK6++urTfZtApnXqZGvmTEvz5/vUsuXJA63Nmw19+aWlm28OKzeWlTx4UHrzzYDq1XPUrl1krLGx0mWX2Ro3ztIVV7ip1vo7ldmzfYqP99SoUcaCvFatCPwAAADyk7JlPbVoYWvyZL9at3aUicYrAAAAAIATyJHg7+6779YNN9ygyy67TFdeeaWSkpL0119/afLkyVq3bp3GjBmTqf39+eefJ32+VKlS+uCDD066TevWrdW6detMHRfIamXKeKpTx9H06dYpg78ff/Rp1ixL7drZqlQp59vLTpjgl21Lt94aShU8tm9v64svLE2YYOnxxzO2r3BYmjPHUqtWjvz+7BkvAAAA8r4uXWzNnevTrFk+derEjV4AAAAAcKZypNVngwYN9N5776lgwYIaOnSo/v3vf+uFF15QoUKF9N577+ncc8/NiWEAeVKnTrZWrza1atXJy/hWroxM1yVLcqcV0rJlppo3d9JU9QUC0pVXhjV3rql16zK2r/nzfTp40FD79lHS4xQAAADZomRJT02bOvr6a0seS6cDAAAAwBnLkeBPkho1aqTx48fr119/1ezZs/XLL79o7NixatSoUU4NAciTzj3XVXKyq+nTT1yA67rS6tWR6bp0aY5N2xSHD0vbtpmqUiX9hVZbt3aUlOTpFIW2Kb75xlKtWo5KleLqDgAAwNmubVtH27aZ+vPPnD/PBQAAAID8Jsd/s4qNjVVycrIKFCiQ04cG8iTDkDp0cDRvnk979qS/zcaNhoJBQ40bO/rjD1OhUMb27WRRt6T16015nlSpUvrBn2VJ3brZmjdPp6xc3LDB0MqVptq3p5UTAAAApFq1IjfCzZqVO50tAAAAACA/ybY1/p588kndfPPNKl26tJ588slTbv/II49k11CAPK91a1sTJ1qaMcNSt25p21+uXGnKND1ddllYCxbE6s8/TdWtm34IJ0meJ73zjl+LF5saMiSoM83Z16wxFQh4Kl36xBV6//qXqy+/lD76yNLAgScO9b75xlKRIp4aNSL4AwAAQORGuDZtHE2aZKlnz7Di43N7RAAAAAAQvbKt4m/mzJnav39/yv+f7M+sWbOyaxhAVChYMNIuc8YMS+Fw2udXrjRVoYKnSpU8FS7snbLd5+efW5o1y9KePYamTDnzfH/NGlMVK7ryneQmbNOUrr9eWrLE1PLl6Y/v8GHphx98atPGlpVttx0AAAAg2rRubctxIueKmeWe+H44AAAAADjrZNul95kzZ6b7/wDS166dra+/trR4samGDVNfvVi1ylT9+q4MQ6pTx9HixT5de23aykBJmj/f1Mcf+3X55ZEEcdo0S+3aRdbgO13r1hlq0ODUV1RatJAqVvQ0YYKlRx8NyfhH188ff/QpHI6s4wIAAAAcU7iw1KiRq1mzLHXs6KQ5jzyRw4el/v1jdcMNYTVvzjkmAAAAAGT7Gn/BYFC33367fv755+w+FBDVypXzVKaMq7lzU9/lvH+/tH27qXPOiQRvdeq4Wr/e1P8KalNZs8bQ668H1KSJo27dbF16qa34eGn8eP9pj+vgwcjxT7S+398ZhnTNNbZWrPDp999Tf7x4XqTNZ8OGrooVO/0QEgAAAPlT27a2Nm0ytXp1BlM/SbNnW9q/39CCBTm+fD0AAAAA5EnZ/ttRTEyMfv75ZzkOd18Cp9K8uaNffvEpFDr+2MqVkWlateqx4C8yl5YtSx0Q7tpl6IUXYlS+vKfbb49U28XGSlddFda8eT6tWHF6033t2sjrKlfOWA+l+vVdnXOOq4kT/fL+lu+tWGFq0yZT7dunX6kIAACAs1vduq6KF49U/WWE60rTp/vk83latsyX6twTAAAAAM5WOXJb5L/+9S/98MMPOXEoIKo1b+4oGDS0aNHxqblypakiRTwVLx65kpGYKJUu7Wrx4uPbOI40bFhAPp90331BBQLH99m6taMKFVx98IH/tC6GrF1rqkABTyVLZuzFhiFdfXVYa9eaeucdv8aOtfTOO369955fycmu6tRhERYAAACkZRhSmzaOfvrJp8OHT739ggWmdu401bWrrf37DW3ZkvFKQQAAAADIr3Ik+OvataumTJmiwYMHa/bs2VqyZImWLl2a6g8AqVQpTxUquJo79/hdzqtWRdp8/n2dk7p1XS1Zcvyu5kmTLK1ebahv35AKF069T8OQrr8+rDVrTP3wQ+oqwYxYuzbS5jOj66xIUs2arpo3t7VwoU+//OLTmjWmChb0dM014UztBwAAAGeX1q1thcOGfvrp1OetX35pqXp1Rx072vL5PC1dSrtPAAAAAMhYD5UzdNttt0mSxo4dq7Fjx8r425V/z/NkGIaWL1+eE0MB8rzmzR19+qmlo0cly5JWrzbVrVvq9ph16jiaPt3S9u2Gdu82NGWKX926hVWtWvrVdDVrumrSxNHIkQFNmeKqSpVIO87atV0lJ5+8km/1alMtW2a+PWffvmFJ4Uy/DgAAAGevokWl+vUdzZljqX37Ey8XsWaNoRUrfLrnnqBiYyNt8Zct86ljR5aYAAAAAHB2y5Hg7/3338+JwwD5QtOmjj76yK9Fi3wqUcJVOGzonHNSB3o1argyTU8//eTTjBmWatZ0dOmlJw/nbrstpAYNfFq1ytTq1ZHqP8OQrrzS1iWX2LLS+TTYt0/avdtQxYosmAIAAICc0aqVo2HDAtqyxVDp0umfh375paWkJFeNGkXOk2vVcvXVV5ZcVzIp/AMAAABwFsuR4O+cc85RYmJiThwKiHrJyZ4qVXL1008+1aol+XyeKlZMHfwVKCCdc46rTz7xKz7e0x13hE55gSM2NrLeX+vWkbugjx6VpkyxNGmSpZ9/9unWW0NpAr61ayM7rVyZdfkAAACQMxo2dFSwoKc5c3y69tq0N7ft2SPNnetT9+52yjlwrVquPv3U0Pr1hipV4qY1AAAAAGevbLsX0rZtvfTSS2rUqJFatmyp+vXr69///rf27duXXYcE8o3mzR399pupxYt9qljRUyCQdpu6dSNh3G23hXQ6uXpsrHT11bb+85+gXFd67LEYTZuW+l6ANWtMxcd7Skri4gkAAAByRiAQOR/+4YdIBd8/ff21pUBAOv/846Fg1aqu/H5Py5Zlfk1rAAAAAMhPsi34Gz16tEaOHKm6deuqd+/eatOmjb744gsNHjw4uw4J5BvNmzuybUOLFvnStPk8pmNHWw8+GFSDBmdWjVe5sqcnngjqwgttjRvn17x5xy+WrFljqlIlV39blhMAAADIduedZ2vPHkNLl6b+lXXfPmnGDEvnn+8oLu74436/VL26q2XL6PMJAAAA4OyWbb8Vffrpp+rRo4fee+899e/fX6+88ooGDRqk//73vwoGg9l1WCBfKFbMSwn8qlZNP9iLi5Nq186aFpyWJXXvbqtZM0dvvunX5s2GPC/S6rNSJdp8AgAAIGdVqeKpVClXc+YcvynN86R33w3INKXLLw+neU3t2q7++MOUffKlrwEAAAAgX8u24G/jxo264IILUj124YUXynVdbdq0KbsOC+QbzZvbMgypWjUnR45nGNKtt4ZUrJinl14KaMsWQ/v2GapSheAPAAAAOcswIutTL1jg0+HDkcfmzvVpwQKfbroppISEtK+pVctRMGikrFMNIOO2bzcUTpunAwAAIApl229EwWBQBQsWTPVYgQIFJElHjx7NrsMC+Ub79o4eeyyookVz7pixsdK994a0d6+h55+PLCxIxR8AAAByQ8uWkfb38+f7tGeP9N57fjVvbqtp0/TPTytW9FSggEe7TyCTDhyQHnwwRh9/7M/toQAAACALWNm583nz5mnbtm0pf3ddV4ZhaN68edq8eXOqbTt27JidQwGijmXphOv7ZadSpTzddltIr7wSo8KFPSUm5vgQAAAAABUr5ql2bUdz5vj0yy8++XxSr14nLkny+aQaNVwtXWrq8stzcKBAlPv6a0uhkKGZM326/PKw4uNze0QAAAA4E9ka/L3wwgvpPv7cc8+l+rthGFq+fHl2DgVAJjRp4uraa8MKhyNtlgAAAIDc0Lq1oxEjIp0o7r03eMpAolYtVx99ZCkUkgKBHBggEOWCQemrryw1bero119NzZhh6fLLWSgTAAAgmmVb8Ddjxozs2jWAHHDJJfyyBwAAgNzVqJGj+HhP9es7atTo1N0watd2ZNt+rVxpqnZtWtbj7OR50pQplg4fls4911W1aq58vvS3/f57nw4dMnTNNWHFx1v68ktLF11kE5wDAABEsWwL/sqUKZNduwYAAAAAnAViYqTnnjua4daD5cp5ionxtGYNwR/OXrNn+zRhgl/x8Z6mTTMUF+epXj1Hl1xiq2JFL2U715W++MJSkyaOkpM9de5sa9YsS99951P79k4uvgMAAACcCVY9BwAAAADkWQkJkpnB31wNQypb1tOmTfSrR/7ieZE/p7Jhg6HRo/1q29bW668f1eDBR9Wpk61160w9/XSM1qw5PjcWLDC1fbupzp0ja2cmJ3tq0sTRF19YcsnNAQAAohbBHwAAAAAg3yhXztWmTfyqi/xjyxZDDz0Uo0ceidHmzScOtQ8fll55JaBSpTz17BmWYUiVK3vq2tXW4MFBlSnj6ZlnIuGf50nTpvlVo4ajKlWOJ4qXXBLW9u2m5s8/QW9QAAAA5Hn8NgQAAAAAyDfKlXO1ebMhh06FyCMOHpQ+/NCvN9/0a8+ezL32p598evTRGNm2FA5LjzwSo+nTfWmq/zxPGjUqoH37DPXrF0qzRl9cnPTvfx8P/6ZP92n1alOdO6de271yZU+1azuaNs3KUIUhAAAA8p5sW+MPAAAAAICcVrasJ9s2tG2boTJlSC5werZuNbRkiakOHRwZp9k5NhSSvv7a0mefRVpn+v3SggU+3XBDWP/618n3Gw5LY8f69fXXlpo3t3XLLWGZpjR+vF9jxgS0aJGjjh1tBYOGjhyRNmwwNW+eT337hlSyZPrf98fCv+efj9EHHwRUqpSrc89N29PzkktsPftsjH7/3VT9+vT8BAAAiDYEfwAAAACAfKNcuUhQsXGjqTJlKPtD5q1YYerFFwM6eNBQXFxI//pX5r+P/vzT1Ouv+7V7t6F27Rx16RIJ7t5/36833gho7lxHvXuHlJiY9rU7dhh67bWA1q831KtXSO3bHw8Jb7ghrPr1Hb35ZkAvvBCT8pqYGE+XXRZW8+YnH+ux8O+dd/xq1Sr98LFOHVe1ajl6662Ann76qBISMv32AQAAkIsMz6N5w5nYseNAbg8ByBMsy1RiYkHt2XNIts1doUA0YN4C0Yd5C2TMnXfGql07W9262afeOBsxZ6PPggWmhg8PqEoVV3Fx0sqVpp5//qji4zO+jz17pIceilXJkp5uvTWk0qVTX3b55RdTo0YFFAxGqusuushWbGzkud9+M/X66wHFxnrq1y+kypXTv2QTCkVaiMbGSgUK6LSrEk/1HipUcDVwYCjL95+XMW+B6MO8BaIP8/b0JCUVytB22VbxV6NGDRkZPDM0DEPLli3LrqEAAAAAAM4i5cu72riRJe2ROTNm+PTeewE1aeLo9ttDOnRIGjAgVh995Ffv3uEM7cPzpDfeCMg0pXvvDaZbLdeokavq1Y9qyhRLkydbmjHDUteuYe3YYWjKFL/OPTdy/JOFjYGAVLToab7RDEhMlO68M6TnnovRlCmWLr88d0N0AAAAZFy2BX8PPPDAKYO/WbNmae7cudk1BAAAAADAWahsWU8LFxL84dQ8T/r9d1P//a+lJUt86tTJ1vXXh2UYkXDt6qvDGj06oPPOc1St2qnvRp861dKyZT4NHJh+6HdMfLzUo4etDh0cTZhgadSogAxDuuaasC65xM4TFXZ167q6/PKwJk70q1o1VzVrcjc+AABANMi24K9Xr14nfO6bb77RiBEjtGzZMjVp0kR33nlndg0DAAAAAHCWKVfO1fTplo4eVUoLReDvwmHpu+98+u9/LW3daqpSJVd9+4bUrFnqde/at3c0Z46rd97x68kng7JOchVl5UpTEydauvTSsOrUyVhIVqKEp7vuCqtzZ1uuqxO29swtXbrY+vPPSPvTp546qsKFc3tEAAAAOJUcvQXyyy+/1OWXX667775biYmJ+uCDDzRmzBi1aNEiJ4cBAAAAAMjHypVz5XnSli15oGwKOWLHDkOjR/u1efOp/80XLjT1wAMxevfdgMqW9fToo0ENHhxU8+ZOmko705R69w5p82ZTX3xx4tTv4EFp+HC/Klf21KVL5ttiVqzo5bnQT4q8/zvvDMl1paFDY3TkSG6PCAAAAKeSbRV/x3iep2nTpumNN97QqlWrdP7552vw4MGqX79+dh8aAAAAAHAWKlPGk2FIGzaYqlzZye3hRIWNGw2NG+dXsWKeevYMKxDI7RGltWWLoeRkTz5f6sfXrTP0/PMx2r/f0MyZPnXtaqtzZzvNdtu2GfrgA78WLfKpdm1H9957VGXLnjpsq1jR00UXhfXxx379+KNPzZo5atbMUdGinhYt8mnePJ8WLTLl90sPP3zyqsBolJgoDRgQ1FNPxeiVVwK6//6Q/P7cHhUAAABOJNtOR13X1ZQpU/TGG29o/fr1at++vZ599lnVrl07uw4JAAAAAIBiYqQSJVxt2mRKIvg7mYMHpUmT/PrmG5+SkjwtX25qzRpT99wTUokSmatAO3Qo0lr1n4FbVli+3NRTT8WoZElX3brZKS05f//d1CuvBFS6tKdBg4L65hufJkzw6+effbrhhrAOHJBWrDC1YoWp1atNJSZ66tcvqCZN3Eyto3fttbaqVXM1b55Pn39u6ZNP/PL5PDmOoUqVXHXpYqtlS0fFiuW9qr2sULGip3vvDem55wJ6442A7rorJJNlNAEAAPIkw/O8bDkrveCCC7Rp0yY1a9ZMd955p6pVq3bS7YsUKZIdw8h2O3YcyO0hAHmCZZlKTCyoPXsOybZZ9B2IBsxbIPowb4GMe/nlgI4ckR58MHTCbYLBSHB07rlutlQw5fac/fxzS3v3GrruunC6Idf8+abeeSegcDiyllunTrY2bzb0yisBHTpk6M47Q6pfP2Pj3rNHevDBWMXHe7r66nCmg7VTGTIkoD17DBUv7un3332qWNHVuec6mjLFUr16kfX5jq3nuGqVoTffDGjLlkgyVaSIp2rVXNWs6ah1a0cxMWc2llBIWrzY1I4dhho0cJWcnD/DvvTMn2/qtddi1L69rRtvTP/7yralX381VbeuqwIFcn6MZyq35y2AzGPeAtGHeXt6kpIKZWi7bKv427hxoyRp7ty5mjdv3im3X758eXYNBQAAAABwlilb1tXMmen/yhsKSTNn+jRlil/79xtq0sTR3Xfnrwqm7dsNTZhgyXEMVajg6rzzUlc+rlljaPjwgM4911WvXiElJkYer1DB0+DBQb3xRkBDh8bopptCatfu5FWTnieNGhWQzyclJXl69dUYVa3qqnv3sKpXP/MLOStWmFq61Kd+/YJq2tTV8uWmPvrIr8mT/Wrb1lavXuFUVYZVq3p68smgli83VaqUp6QkL0tDyEBAatTo7LxA1bSpq5tuCumddwLats1Qjx5hlS9/PPj8809To0b5tWWLqeLFXd16a1i1a5+dXysAAIDckm3B35AhQ7Jr1wAAAAAAnFTZsp727ze0f7+UkBB5zPOkWbN8mjTJr/37pfPOc1Sliqv33vPr3Xf9uvnm9CuYotHHH/uVkCBVr25r9Gi/qlU7Xpl26JA0bFhA5ct76ts37Xpt8fHS/feH9Pbbfn34oV8NGjgpwWB6vvvOp0WLfLrvvqAaNnS1ZImp8eP9euKJGN14Y0gXXHBm7VYnT7ZUpoyrJk0iAVLNmq4efzyo7dsja/6l928WCCjD1YrInHbtHBUuHNTYsX49/HCsWrWyddFFtr7+2tKsWZaqVHHVv39Qn39uaciQGLVrZ6t793BUVv8BAABEo2wL/q688srs2jUAAAAAACdVvnwk9Nm40UypOJo716d33gmoZUtbXbvaKUGYaUpvvx1Q4cKeunWzc23MWWXNGkPz5vl0yy0hNW3q6OGHTb3+ekCPPhqUaUaq8w4eNDRwYPCELU4NQ+rePaxffvHpo4/8uv32cLrb7dplaMwYv847z1bDhpGvc506rp54Iqh33vFr/Hi/GjZ0T3vtu9WrDf3+u0933RVKFfAZhlSy5NnTYjOvadTIVf36wZQg/bvvLBUo4KlXr0iFqGlK9euH9M03Po0f79fvv5t68MHQWdUWFQAAILfko0YmAAAAAABEJCd7sixPGzdG0qJDh6QxY/xq0sTRnXeGUwUQbdo4uuaasCZP/n/27jzOxvL/4/j7Ptvswwxjxr5ky07IMmiQVCj6Rt9KKSWppCjaiAotWkS7VNZvpRSJylb2ElmSiGIoZmwzBnOW+/79MT9T01hmmJkz53g9Hw8PzX3u+7o/5+Qzxnmf67qc+uor++mGDAiWJc2c6VT58qbatvUpPFy6+26PfvvNps8/d+ibb+xasyYrFDxbCBMZKfXs6dGyZQ5t25b77QPLkt5+26mQEOnmm3MGg4Yh3XCDR6Gh0tSp576B4uzZTiUkmLr00vObNYiC53BIl1/u07hxJ9S/v1vPPXdCHTv6spfMNYysx0ePzgqcX3vNJW/g5+oAAADFXqHN+Hv66afzdf7jjz9eSJUAAAAAAC40drtUvryl5GSbJJ8+/NApt1vq3dt9yvO7dPHqyBFDU6a4FB7uVmJiYAZNGzbY9PPPdg0enJkdwNSsaeraaz369FOHDEPq1Mmr5s3ztgzmZZf5tGiRqQ8+cGrUqMwcs+4WL7Zr0ya7HnooUxERua+NiMgKBCdOdGndOpsaN87f0pu//25o3Tq7+vcPrv0Xg014uM7YL/HxlgYMcGvUqBB98olDPXuS/gEAABSmQgv+Fi1alOdzDcMg+AMAAAAAFKiKFU3t3m3T9u2GFi1y6Oab3YqNPfW5hiHddJNHx49Lb77pkt3uVsuWgRX+mWbWbL/atX1q1ChnyHbttV79/LNNXq+h//731Mt2norNJt1yi0ejRoVo6VK7LrvMp4MHs/YQXLbMoaQk7xn30mvRwqelS316/32n6tTJVEhI3p/P7NlOxcebAff/AblVr27puuu8+vhjp+rXN3Xxxey/CAAAUFiKRfAHAAAAAEBBq1DB0vffG5o0yaUqVUxdfvmZAyTDkPr29cjnk15/3SnDyAquAsXy5Xbt3m3Tk0+eyDEzT8qaAfnoo+7s/86PmjVNtW7t1f/+51RqqqF58xxyuaTbb3erXbuzv6Z9+ng0bFiIPvssb7O99u0z9L//OfXDD3b16+fOd70onrp29WrTpqz9JkePPqHISH9XBAAAEJwKLfgDAAAAAMCfKlY0lZlpKDnZ0KhRJ/K0XKTNJvXrlxX+vfaaUw6HpaZNA2N20ldfOdSokU/Vq596777zCdBuuMGjH36wa84chzp39qpbN+8pl/c8lYQES926efXZZw6ZZtbegeHhlsLDpRIlLJUsaSk6Oqvmzz93aP58h6KipLvuCtwlV5GbzSb17+/RY4+FaNIklwYOdOcKqAEAAHD+Cj34c7vdmj9/vn744Qf99ddfkqSEhAQ1a9ZMV1xxhVwuV2GXAAAAAAC4AFWqlBXYXXGFV1WrnjoMO5WTAYXPZ+jVV116/vlMlSmT9+v94a+/DO3cadN99516D8PzFRMjDR+eqbAwndNr0aWLV7//btOqVXYdO2bo+HFD1r+GMQzJ6bR0zTVeXXWVV6GhBVQ8io1SpSz17evWK6+EaPlyO8EuAABAISjU4O+HH37QkCFDtG/fPlmWpejoaElSWlqaPvzwQ7300kt6/vnndckllxRmGQAAAACAC1BMjPTYY5m66KL8z9iz26V+/dzq1y9UGzfa1KFD8Q4oli+3KzTUUuPGhVdn5crnHn66XNKDD/4dSlqWdOyYlJZm6PBhQ0eOGDp6VGrSxHfafRgRHJo1M9WypVdTpzrVoIFP//9WEQAAAApIHhY6OTe//fab7rzzTkVEROi5557TDz/8oDVr1mjNmjVau3atXnjhBYWHh+vOO+/Ujh07CqsMAAAAAMAF7OKLTZ3rQjOhoVK1apY2by7em8xZlrRihV3NmvnO+bkWNcOQIiKksmUtXXyxqRYtfOrYkdDvQtG7t0eS9MEHTj9XAgAAEHwKLfh77bXXVKVKFc2aNUtdu3ZV5D92bY6IiFCXLl300UcfqUqVKpo4cWJhlQEAAAAAwDmrU8enLVtsuZalLE527jS0b59NrVoV71mJwEnR0Vnh36pVDq1bV2hvTQEAAFyQCu2nq1WrVqlv374KPcOi/GFhYbr99tu1atWqwioDAAAAAIBzVqeOqfR0Q8nJhr9LOa3lyx0qUcJS3br5X9IU8JdWrXxq0MCnyZNdOnbM39UAAAAEj0Lb4y8tLU0JCQlnPS8hIUFpaWmFVQYAAAAAAOesZk1Tdruln3+2qWLF4jejzueTVq60q1Urn2xMnEIAMQzp9ts9Gjo0RDNnOnX77Z5c55w4kbV/5bp1dtlsktMpuVyW4uIsde/ulVF883gAAAC/KbTgLyEhQVu2bFHTpk3PeN7PP/+cp4AQAAAAAICi5nJJNWqY+vlnu664ovgFfz//bFNamqFWrbz+LgXIt9KlLfXq5dEHH7j088821a9vqn59n+LjLS1dateSJQ4dP26obl2fHA4pPV06ftym776zqUYNU/XrM8sVAADg3wot+OvYsaNef/11tWnTRlWqVDnlOX/88YfeeustXXPNNYVVBgAAAAAA56VOHVMLFjhkmvLbrDq3W1q71q7jx6XERJ9crqzjK1bYFR9vqmrVYrwJIXAGl1/uU+nSmVq3zq5162z6+uust6rCwy0lJXnVsaNPcXF///m2LOnRR0P09dcO1a/v9lfZAAAAxVahBX933323Fi1apGuuuUbXXnutkpKSVLZsWUnSn3/+qSVLlmj27NlKSEhQ//79C6sMAAAAAADOy8UXm/rkE0N//GEUecC2a5ehJUscWr7crowMQ4YhffaZpeuv96hpU5++/96uq65iyUMELsOQmjQx1aSJKcuS9u/P2lOzbl1ToaGnPv/yy72aPNml1FRDpUsTegMAAPxToQV/0dHRmjZtmp588kl9+OGH+vDDD3Od07FjR40YMUJRUVGFVQYAAAAAAOelenVTTqeln3+2q2rVwltSc8cOQ7NmOZWWZig9XUp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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Price Statistics:\n", - " COIN: Mean=$253.37, Std=$5.92\n", - " MSTR: Mean=$375.88, Std=$4.10\n", - " Price Ratio: Mean=0.67, Std=0.01\n", - " Correlation: 0.9498\n" - ] - } - ], - "source": [ - "# Plot raw price data\n", - "\n", - "# Get column names for the trading pair\n", - "colname_a, colname_b = pair.colnames()\n", - "price_data = pair.market_data_.copy()\n", - "\n", - "# # 1. Price data - separate plots for each symbol\n", - "# colname_a, colname_b = pair.colnames()\n", - "# price_data = pair.market_data_.copy()\n", - "\n", - "# Create separate subplots for better visibility\n", - "fig_price, price_axes = plt.subplots(2, 1, figsize=(18, 10))\n", - "\n", - "# Plot SYMBOL_A\n", - "price_axes[0].plot(price_data['tstamp'], price_data[colname_a], alpha=0.7, \n", - " label=f'{SYMBOL_A}', linewidth=1, color='blue')\n", - "price_axes[0].set_title(f'{SYMBOL_A} Price Data')\n", - "price_axes[0].set_ylabel(f'{SYMBOL_A} Price')\n", - "price_axes[0].legend()\n", - "price_axes[0].grid(True)\n", - "\n", - "# Plot SYMBOL_B\n", - "price_axes[1].plot(price_data['tstamp'], price_data[colname_b], alpha=0.7, \n", - " label=f'{SYMBOL_B}', linewidth=1, color='red')\n", - "price_axes[1].set_title(f'{SYMBOL_B} Price Data')\n", - "price_axes[1].set_ylabel(f'{SYMBOL_B} Price')\n", - "price_axes[1].set_xlabel('Time')\n", - "price_axes[1].legend()\n", - "price_axes[1].grid(True)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n", - " \n", - "\n", - "# Plot individual prices\n", - "fig, axes = plt.subplots(2, 1, figsize=(18, 12))\n", - "\n", - "# Normalized prices for comparison\n", - "norm_a = price_data[colname_a] / price_data[colname_a].iloc[0]\n", - "norm_b = price_data[colname_b] / price_data[colname_b].iloc[0]\n", - "\n", - "axes[0].plot(price_data['tstamp'], norm_a, label=f'{SYMBOL_A} (normalized)', alpha=0.8, linewidth=1)\n", - "axes[0].plot(price_data['tstamp'], norm_b, label=f'{SYMBOL_B} (normalized)', alpha=0.8, linewidth=1)\n", - "axes[0].set_title('Normalized Price Comparison (Base = 1.0)')\n", - "axes[0].set_ylabel('Normalized Price')\n", - "axes[0].legend()\n", - "axes[0].grid(True)\n", - "\n", - "# Price ratio\n", - "price_ratio = price_data[colname_a] / price_data[colname_b]\n", - "axes[1].plot(price_data['tstamp'], price_ratio, label=f'{SYMBOL_A}/{SYMBOL_B} Ratio', color='green', alpha=0.8, linewidth=1)\n", - "axes[1].set_title('Price Ratio')\n", - "axes[1].set_ylabel('Ratio')\n", - "axes[1].set_xlabel('Time')\n", - "axes[1].legend()\n", - "axes[1].grid(True)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# Print basic statistics\n", - "print(f\"\\nPrice Statistics:\")\n", - "print(f\" {SYMBOL_A}: Mean=${price_data[colname_a].mean():.2f}, Std=${price_data[colname_a].std():.2f}\")\n", - "print(f\" {SYMBOL_B}: Mean=${price_data[colname_b].mean():.2f}, Std=${price_data[colname_b].std():.2f}\")\n", - "print(f\" Price Ratio: Mean={price_ratio.mean():.2f}, Std={price_ratio.std():.2f}\")\n", - "print(f\" Correlation: {price_data[colname_a].corr(price_data[colname_b]):.4f}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "vscode": { - "languageId": "raw" - } - }, - "source": [ - "# Run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running SlidingFit analysis...\n", - "\n", - "=== SLIDING FIT ANALYSIS ===\n", - "Processing first 200 iterations for demonstration...\n", - "***COIN & MSTR*** STARTING....\n", - "********************************************************************************\n", - "Pair COIN & MSTR (0) IS COINTEGRATED\n", - "********************************************************************************\n", - "COIN & MSTR: 272 Not enough training data. Completing the job.\n", - "OPEN_TRADES: 2025-06-05 15:40:00 open_scaled_disequilibrium=np.float64(2.1021479687626523)\n", - "OPEN TRADES:\n", - " time action symbol price disequilibrium \\\n", - "0 2025-06-05 15:40:00 SELL COIN 260.465 1.991597 \n", - "1 2025-06-05 15:40:00 BUY MSTR 380.530 1.991597 \n", - "\n", - " scaled_disequilibrium pair status \n", - "0 2.102148 COIN & MSTR OPEN \n", - "1 2.102148 COIN & MSTR OPEN \n", - "CLOSE TRADES:\n", - " time action symbol price disequilibrium \\\n", - "0 2025-06-05 16:02:00 BUY COIN 259.3853 0.208324 \n", - "1 2025-06-05 16:02:00 SELL MSTR 379.9023 0.208324 \n", - "\n", - " scaled_disequilibrium pair status \n", - "0 0.744767 COIN & MSTR CLOSE \n", - "1 0.744767 COIN & MSTR CLOSE \n", - "OPEN_TRADES: 2025-06-05 16:31:00 open_scaled_disequilibrium=np.float64(2.0704276873028338)\n", - "OPEN TRADES:\n", - " time action symbol price disequilibrium \\\n", - "0 2025-06-05 16:31:00 SELL COIN 259.62 1.917107 \n", - "1 2025-06-05 16:31:00 BUY MSTR 377.25 1.917107 \n", - "\n", - " scaled_disequilibrium pair status \n", - "0 2.070428 COIN & MSTR OPEN \n", - "1 2.070428 COIN & MSTR OPEN \n", - "CLOSE TRADES:\n", - " time action symbol price disequilibrium \\\n", - "0 2025-06-05 16:42:00 BUY COIN 257.28 0.471149 \n", - "1 2025-06-05 16:42:00 SELL MSTR 375.58 0.471149 \n", - "\n", - " scaled_disequilibrium pair status \n", - "0 0.762836 COIN & MSTR CLOSE \n", - "1 0.762836 COIN & MSTR CLOSE \n", - "OPEN_TRADES: 2025-06-05 16:46:00 open_scaled_disequilibrium=np.float64(2.199766239888042)\n", - "OPEN TRADES:\n", - " time action symbol price disequilibrium \\\n", - "0 2025-06-05 16:46:00 BUY COIN 254.6100 -2.275201 \n", - "1 2025-06-05 16:46:00 SELL MSTR 376.1044 -2.275201 \n", - "\n", - " scaled_disequilibrium pair status \n", - "0 2.199766 COIN & MSTR OPEN \n", - "1 2.199766 COIN & MSTR OPEN \n", - "CLOSE TRADES:\n", - " time action symbol price disequilibrium \\\n", - "0 2025-06-05 17:34:00 SELL COIN 252.83 0.248202 \n", - "1 2025-06-05 17:34:00 BUY MSTR 375.00 0.248202 \n", - "\n", - " scaled_disequilibrium pair status \n", - "0 0.957174 COIN & MSTR CLOSE \n", - "1 0.957174 COIN & MSTR CLOSE \n", - "OPEN_TRADES: 2025-06-05 18:51:00 open_scaled_disequilibrium=np.float64(2.1149913107636116)\n", - "OPEN TRADES:\n", - " time action symbol price disequilibrium \\\n", - "0 2025-06-05 18:51:00 SELL COIN 245.77 61.682717 \n", - "1 2025-06-05 18:51:00 BUY MSTR 372.40 61.682717 \n", - "\n", - " scaled_disequilibrium pair status \n", - "0 2.114991 COIN & MSTR OPEN \n", - "1 2.114991 COIN & MSTR OPEN \n", - "CLOSE TRADES:\n", - " time action symbol price disequilibrium \\\n", - "0 2025-06-05 19:10:00 BUY COIN 245.59 9.682403 \n", - "1 2025-06-05 19:10:00 SELL MSTR 370.66 9.682403 \n", - "\n", - " scaled_disequilibrium pair status \n", - "0 0.979289 COIN & MSTR CLOSE \n", - "1 0.979289 COIN & MSTR CLOSE \n", - "OPEN_TRADES: 2025-06-05 19:15:00 open_scaled_disequilibrium=np.float64(2.006393273424948)\n", - "OPEN TRADES:\n", - " time action symbol price disequilibrium \\\n", - "0 2025-06-05 19:15:00 SELL COIN 244.020 325.962059 \n", - "1 2025-06-05 19:15:00 BUY MSTR 368.225 325.962059 \n", - "\n", - " scaled_disequilibrium pair status \n", - "0 2.006393 COIN & MSTR OPEN \n", - "1 2.006393 COIN & MSTR OPEN \n", - "CLOSE TRADES:\n", - " time action symbol price disequilibrium \\\n", - "0 2025-06-05 19:16:00 BUY COIN 243.27 -22.525948 \n", - "1 2025-06-05 19:16:00 SELL MSTR 367.22 -22.525948 \n", - "\n", - " scaled_disequilibrium pair status \n", - "0 0.701777 COIN & MSTR CLOSE \n", - "1 0.701777 COIN & MSTR CLOSE \n", - "***COIN & MSTR*** FINISHED ... 20\n", - "Generated 20 trading signals\n", - "\n", - "Strategy execution completed!\n", - "\n", - "================================================================================\n", - "BACKTEST RESULTS\n", - "================================================================================\n", - "\n", - "Detailed Trading Signals:\n", - "Time Action Symbol Price Scaled Dis-eq \n", - "--------------------------------------------------------------------------------\n", - "2025-06-05 15:40:00 SELL COIN $260.46 2.102 \n", - "2025-06-05 15:40:00 BUY MSTR $380.53 2.102 \n", - "2025-06-05 16:02:00 BUY COIN $259.39 0.745 \n", - "2025-06-05 16:02:00 SELL MSTR $379.90 0.745 \n", - "2025-06-05 16:31:00 SELL COIN $259.62 2.070 \n", - "2025-06-05 16:31:00 BUY MSTR $377.25 2.070 \n", - "2025-06-05 16:42:00 BUY COIN $257.28 0.763 \n", - "2025-06-05 16:42:00 SELL MSTR $375.58 0.763 \n", - "2025-06-05 16:46:00 BUY COIN $254.61 2.200 \n", - "2025-06-05 16:46:00 SELL MSTR $376.10 2.200 \n", - "... and 10 more trading signals\n", - "\n", - "====== NO OUTSTANDING POSITIONS ======\n", - "\n", - "====== GRAND TOTALS ACROSS ALL PAIRS ======\n", - "Total Realized PnL: 0.00%\n", - "\n", - "================================================================================\n" - ] - } - ], - "source": [ - "# Initialize strategy state and run analysis\n", - "print(f\"Running {FIT_METHOD_TYPE} analysis...\")\n", - "\n", - "# Initialize result tracking\n", - "bt_result = BacktestResult(config=pt_bt_config)\n", - "pair_trades = None\n", - "\n", - "# Run strategy-specific analysis\n", - "if FIT_METHOD_TYPE == \"StaticFit\":\n", - " is_cointegrated = run_static_fit(config=pt_bt_config, pair=pair, bt_result=bt_result)\n", - "elif FIT_METHOD_TYPE == \"SlidingFit\":\n", - " print(\"\\n=== SLIDING FIT ANALYSIS ===\")\n", - " \n", - " # Initialize tracking variables for sliding window analysis\n", - " training_minutes = pt_bt_config[\"training_minutes\"]\n", - " max_iterations = len(pair.market_data_) - training_minutes\n", - " \n", - " # Limit iterations for demonstration (change this for full run)\n", - " max_demo_iterations = min(200, max_iterations)\n", - " print(f\"Processing first {max_demo_iterations} iterations for demonstration...\")\n", - " \n", - " # Initialize pair state for sliding fit method\n", - " pair.user_data_['state'] = PairState.INITIAL\n", - " pair.user_data_[\"trades\"] = pd.DataFrame(columns=pd.Index(FIT_MODEL.TRADES_COLUMNS, dtype=str))\n", - " pair.user_data_[\"is_cointegrated\"] = False\n", - " \n", - " # Run the sliding fit method\n", - " # ==========================================================================\n", - " pair_trades = FIT_MODEL.run_pair(config=pt_bt_config, pair=pair, bt_result=bt_result)\n", - " # ==========================================================================\n", - " \n", - " if pair_trades is not None and len(pair_trades) > 0:\n", - " print(f\"Generated {len(pair_trades)} trading signals\")\n", - " else:\n", - " print(\"No trading signals generated\")\n", - "\n", - "print(\"\\nStrategy execution completed!\")\n", - "\n", - "# Print comprehensive backtest results\n", - "print(\"\\n\" + \"=\"*80)\n", - "print(\"BACKTEST RESULTS\")\n", - "print(\"=\"*80)\n", - "\n", - "assert pair.predicted_df_ is not None\n", - "\n", - "if pair_trades is not None and len(pair_trades) > 0:\n", - " # Print detailed results using BacktestResult methods\n", - " bt_result.print_single_day_results()\n", - " \n", - " # Print trading signal details\n", - " print(f\"\\nDetailed Trading Signals:\")\n", - " print(f\"{'Time':<20} {'Action':<15} {'Symbol':<10} {'Price':<12} {'Scaled Dis-eq':<15}\")\n", - " print(\"-\" * 80)\n", - " \n", - " for _, trade in pair_trades.head(10).iterrows(): # Show first 10 trades\n", - " time_str = str(trade['time'])[:19] \n", - " action_str = str(trade['action'])[:14]\n", - " symbol_str = str(trade['symbol'])[:9]\n", - " price_str = f\"${trade['price']:.2f}\"\n", - " diseq_str = f\"{trade.get('scaled_disequilibrium', 'N/A'):.3f}\" if 'scaled_disequilibrium' in trade else 'N/A'\n", - " \n", - " print(f\"{time_str:<20} {action_str:<15} {symbol_str:<10} {price_str:<12} {diseq_str:<15}\")\n", - " \n", - " if len(pair_trades) > 10:\n", - " print(f\"... and {len(pair_trades)-10} more trading signals\")\n", - " \n", - " # Print outstanding positions\n", - " bt_result.print_outstanding_positions()\n", - " \n", - " # Print grand totals\n", - " bt_result.print_grand_totals()\n", - " \n", - "else:\n", - " print(f\"\\nNo trading signals generated\")\n", - " print(f\"Backtest completed with no trades\")\n", - " \n", - " # Still print any outstanding information\n", - " print(f\"\\nConfiguration Summary:\")\n", - " print(f\" Pair: {SYMBOL_A} & {SYMBOL_B}\")\n", - " print(f\" Strategy: {FIT_METHOD_TYPE}\")\n", - " print(f\" Open threshold: {pt_bt_config['dis-equilibrium_open_trshld']}\")\n", - " print(f\" Close threshold: {pt_bt_config['dis-equilibrium_close_trshld']}\")\n", - " print(f\" Training window: {pt_bt_config['training_minutes']} minutes\")\n", - " \n", - " if FIT_METHOD_TYPE == \"StaticFit\":\n", - " if 'is_cointegrated' in locals() and is_cointegrated:\n", - " print(f\" Cointegration: ✓ Confirmed\")\n", - " if hasattr(pair, 'predicted_df_') and len(pair.predicted_df_) > 0:\n", - " scaled_diseq = pair.predicted_df_['scaled_disequilibrium']\n", - " max_abs_diseq = scaled_diseq.abs().max()\n", - " print(f\" Max absolute scaled dis-equilibrium: {max_abs_diseq:.3f}\")\n", - " if max_abs_diseq < pt_bt_config['dis-equilibrium_open_trshld']:\n", - " print(f\" Note: Max dis-equilibrium ({max_abs_diseq:.3f}) never reached open threshold ({pt_bt_config['dis-equilibrium_open_trshld']})\")\n", - " else:\n", - " print(f\" Cointegration: ✗ Not detected\")\n", - " elif FIT_METHOD_TYPE == \"SlidingFit\":\n", - " pass # TODO: Implement sliding fit cointegration check\n", - "print(\"\\n\" + \"=\"*80)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "vscode": { - "languageId": "raw" - } - }, - "source": [ - "## Visualization\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=== SLIDING FIT FIT_MODEL VISUALIZATION ===\n", - "Note: Sliding strategy visualization requires detailed tracking data\n", - "For full sliding window visualization, run the complete sliding analysis\n", - "Using consistent timeline with 391 timestamps\n", - "Timeline range: 2025-06-05 13:30:00 to 2025-06-05 20:00:00\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Strategy-specific visualization\n", - "from matplotlib.pyplot import pink\n", - "\n", - "\n", - "assert pt_bt_config is not None\n", - "assert pair.predicted_df_ is not None\n", - "\n", - "if FIT_METHOD_TYPE == \"StaticFit\" and hasattr(pair, 'predicted_df_'):\n", - " print(\"=== STATIC FIT FIT_MODEL VISUALIZATION ===\")\n", - " \n", - " fig, axes = plt.subplots(4, 1, figsize=(18, 16))\n", - " \n", - " # 1. Actual vs Predicted Prices\n", - " colname_a, colname_b = pair.colnames()\n", - " \n", - " axes[0].plot(pair.predicted_df_['tstamp'], pair.predicted_df_[colname_a],\n", - " label=f'{SYMBOL_A} Actual', alpha=0.8, linewidth=1)\n", - " axes[0].plot(pair.predicted_df_['tstamp'], pair.predicted_df_[f'{colname_a}_pred'],\n", - " label=f'{SYMBOL_A} Predicted', alpha=0.8, linestyle='--', linewidth=1)\n", - " axes[0].plot(pair.predicted_df_['tstamp'], pair.predicted_df_[colname_b],\n", - " label=f'{SYMBOL_B} Actual', alpha=0.8, linewidth=1)\n", - " axes[0].plot(pair.predicted_df_['tstamp'], pair.predicted_df_[f'{colname_b}_pred'],\n", - " label=f'{SYMBOL_B} Predicted', alpha=0.8, linestyle='--', linewidth=1)\n", - " axes[0].set_title('Actual vs Predicted Prices')\n", - " axes[0].set_ylabel('Price')\n", - " axes[0].legend()\n", - " axes[0].grid(True)\n", - " \n", - " # 2. Raw dis-equilibrium\n", - " axes[1].plot(pair.predicted_df_['tstamp'], pair.predicted_df_['disequilibrium'],\n", - " color='blue', alpha=0.8, label='Dis-equilibrium', linewidth=1)\n", - " axes[1].axhline(y=pair.training_mu_, color='red', linestyle='--', alpha=0.7, label='Training Mean')\n", - " axes[1].set_title('Testing Period: Raw Dis-equilibrium')\n", - " axes[1].set_ylabel('Dis-equilibrium')\n", - " axes[1].legend()\n", - " axes[1].grid(True)\n", - " \n", - " # 3. Scaled dis-equilibrium with thresholds\n", - " axes[2].plot(pair.predicted_df_['tstamp'], pair.predicted_df_['scaled_disequilibrium'],\n", - " color='green', alpha=0.8, label='Scaled Dis-equilibrium', linewidth=1)\n", - " axes[2].axhline(y=pt_bt_config['dis-equilibrium_open_trshld'], color='purple',\n", - " linestyle=':', alpha=0.7, label=f\"Open Threshold ({pt_bt_config['dis-equilibrium_open_trshld']})\")\n", - " axes[2].axhline(y=-pt_bt_config['dis-equilibrium_open_trshld'], color='purple',\n", - " linestyle=':', alpha=0.7)\n", - " axes[2].axhline(y=pt_bt_config['dis-equilibrium_close_trshld'], color='brown',\n", - " linestyle=':', alpha=0.7, label=f\"Close Threshold ({pt_bt_config['dis-equilibrium_close_trshld']})\")\n", - " axes[2].axhline(y=-pt_bt_config['dis-equilibrium_close_trshld'], color='brown',\n", - " linestyle=':', alpha=0.7)\n", - " axes[2].axhline(y=0, color='black', linestyle='-', alpha=0.5, linewidth=0.5)\n", - " axes[2].set_title('Testing Period: Scaled Dis-equilibrium with Trading Thresholds')\n", - " axes[2].set_ylabel('Scaled Dis-equilibrium')\n", - " axes[2].legend()\n", - " axes[2].grid(True)\n", - " \n", - " # 4. Trading signals overlay\n", - " if pair_trades is not None and len(pair_trades) > 0:\n", - " # Create a copy of the scaled dis-equilibrium plot\n", - " axes[3].plot(pair.predicted_df_['tstamp'], pair.predicted_df_['scaled_disequilibrium'],\n", - " color='green', alpha=0.8, label='Scaled Dis-equilibrium', linewidth=1)\n", - " axes[3].axhline(y=pt_bt_config['dis-equilibrium_open_trshld'], color='purple',\n", - " linestyle=':', alpha=0.7, label=f\"Open Threshold\")\n", - " axes[3].axhline(y=-pt_bt_config['dis-equilibrium_open_trshld'], color='purple',\n", - " linestyle=':', alpha=0.7)\n", - " \n", - " # Add trading signals\n", - " for idx, (_, trade) in enumerate(pair_trades.iterrows()):\n", - " color = 'red' if 'BUY' in trade['action'] else 'blue'\n", - " marker = '^' if 'BUY' in trade['action'] else 'v'\n", - " axes[3].scatter(trade['time'], trade['scaled_disequilibrium'],\n", - " color=color, marker=marker, s=100, alpha=0.8,\n", - " label=f\"{trade['action']} {trade['symbol']}\" if idx < 2 else \"\")\n", - " \n", - " axes[3].set_title('Trading Signals on Scaled Dis-equilibrium')\n", - " else:\n", - " axes[3].text(0.5, 0.5, 'No Trading Signals Generated', \n", - " transform=axes[3].transAxes, ha='center', va='center', fontsize=16)\n", - " axes[3].set_title('Trading Signals (None Generated)')\n", - " \n", - " axes[3].set_ylabel('Scaled Dis-equilibrium')\n", - " axes[3].set_xlabel('Time')\n", - " axes[3].legend()\n", - " axes[3].grid(True)\n", - " \n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "elif FIT_METHOD_TYPE == \"SlidingFit\":\n", - " print(\"=== SLIDING FIT FIT_MODEL VISUALIZATION ===\")\n", - " print(\"Note: Sliding strategy visualization requires detailed tracking data\")\n", - " print(\"For full sliding window visualization, run the complete sliding analysis\")\n", - " \n", - " # Create consistent timeline - superset of timestamps from both dataframes\n", - " market_timestamps = set(pair.market_data_['tstamp'])\n", - " predicted_timestamps = set(pair.predicted_df_['tstamp'])\n", - " \n", - " # Create superset of all timestamps\n", - " all_timestamps = sorted(market_timestamps.union(predicted_timestamps))\n", - " \n", - " # Create a unified timeline dataframe for consistent plotting\n", - " timeline_df = pd.DataFrame({'tstamp': all_timestamps})\n", - " \n", - " # Merge with predicted data to get dis-equilibrium values\n", - " timeline_df = timeline_df.merge(pair.predicted_df_[['tstamp', 'disequilibrium', 'scaled_disequilibrium']], \n", - " on='tstamp', how='left')\n", - " \n", - " print(f\"Using consistent timeline with {len(timeline_df)} timestamps\")\n", - " print(f\"Timeline range: {timeline_df['tstamp'].min()} to {timeline_df['tstamp'].max()}\")\n", - " \n", - " fig, axes = plt.subplots(3, 1, figsize=(18, 16))\n", - " \n", - " # 1. Raw dis-equilibrium - using consistent timeline\n", - " axes[0].plot(timeline_df['tstamp'], timeline_df['disequilibrium'],\n", - " color='blue', alpha=0.8, label='Dis-equilibrium', linewidth=1)\n", - " axes[0].axhline(y=pair.training_mu_, color='red', linestyle='--', alpha=0.7, label='Training Mean')\n", - " axes[0].set_title('Testing Period: Raw Dis-equilibrium')\n", - " axes[0].set_ylabel('Dis-equilibrium')\n", - " axes[0].set_xlim(timeline_df['tstamp'].min(), timeline_df['tstamp'].max())\n", - " axes[0].legend()\n", - " axes[0].grid(True)\n", - " \n", - " # 2. Scaled dis-equilibrium with thresholds - using consistent timeline\n", - " axes[1].plot(timeline_df['tstamp'], timeline_df['scaled_disequilibrium'],\n", - " color='green', alpha=0.8, label='Scaled Dis-equilibrium', linewidth=1)\n", - " axes[1].axhline(y=pt_bt_config['dis-equilibrium_open_trshld'], color='purple',\n", - " linestyle=':', alpha=0.7, label=f\"Open Threshold ({pt_bt_config['dis-equilibrium_open_trshld']})\")\n", - " axes[1].axhline(y=-pt_bt_config['dis-equilibrium_open_trshld'], color='purple',\n", - " linestyle=':', alpha=0.7)\n", - " axes[1].axhline(y=pt_bt_config['dis-equilibrium_close_trshld'], color='brown',\n", - " linestyle=':', alpha=0.7, label=f\"Close Threshold ({pt_bt_config['dis-equilibrium_close_trshld']})\")\n", - " axes[1].axhline(y=-pt_bt_config['dis-equilibrium_close_trshld'], color='brown',\n", - " linestyle=':', alpha=0.7)\n", - " axes[1].axhline(y=0, color='black', linestyle='-', alpha=0.5, linewidth=0.5)\n", - " axes[1].set_title('Testing Period: Scaled Dis-equilibrium with Trading Thresholds')\n", - " axes[1].set_ylabel('Scaled Dis-equilibrium')\n", - " axes[1].set_xlim(timeline_df['tstamp'].min(), timeline_df['tstamp'].max())\n", - " axes[1].legend()\n", - " axes[1].grid(True)\n", - "\n", - " # 3. Trading signals if available - using consistent timeline\n", - " if pair_trades is not None and len(pair_trades) > 0:\n", - " # Show trading signals over time\n", - " trade_times = pair_trades['time'].values\n", - " trade_actions = pair_trades['action'].values\n", - " position_statuses = pair_trades['status'].values\n", - " \n", - " for i, (time, action, status) in enumerate(zip(trade_times, trade_actions, position_statuses)):\n", - " if action == \"BUY\":\n", - " if status == \"OPEN\":\n", - " color='red'\n", - " else:\n", - " color='pink'\n", - " else:\n", - " if status == \"OPEN\":\n", - " color='blue'\n", - " else:\n", - " color='purple'\n", - " axes[2].scatter(time, i, color=color, alpha=0.8, s=50)\n", - " \n", - " axes[2].set_title('Trading Signal Timeline')\n", - " axes[2].set_ylabel('Signal Index')\n", - " else:\n", - " axes[2].text(0.5, 0.5, 'No Trading Signals Generated', \n", - " transform=axes[2].transAxes, ha='center', va='center', fontsize=16)\n", - " axes[2].set_title('Trading Signals (None Generated)')\n", - " \n", - " # Set consistent x-axis limits for all charts\n", - " axes[2].set_xlim(timeline_df['tstamp'].min(), timeline_df['tstamp'].max())\n", - " axes[2].set_xlabel('Time')\n", - " axes[2].grid(True)\n", - " \n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "else:\n", - " print(\"No visualization data available - strategy may not have run successfully\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualisation-2 (plotly)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - " \n", - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=== SLIDING FIT INTERACTIVE VISUALIZATION ===\n", - "Note: Sliding strategy visualization with interactive plotly charts\n", - "Using consistent timeline with 391 timestamps\n", - "Timeline range: 2025-06-05 13:30:00 to 2025-06-05 20:00:00\n" - ] - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "linkText": "Export to plot.ly", - "plotlyServerURL": "https://plot.ly", - "showLink": false - }, - "data": [ - { - "line": { - "color": "green", - "width": 2 - }, - "name": "Scaled Dis-equilibrium", - "opacity": 0.8, - "type": "scatter", - "x": [ - "2025-06-05T13:30:00.000000000", - "2025-06-05T13:31:00.000000000", - "2025-06-05T13:32:00.000000000", - "2025-06-05T13:33:00.000000000", - "2025-06-05T13:34:00.000000000", - "2025-06-05T13:35:00.000000000", - "2025-06-05T13:36:00.000000000", - "2025-06-05T13:37:00.000000000", - "2025-06-05T13:38:00.000000000", - "2025-06-05T13:39:00.000000000", - "2025-06-05T13:40:00.000000000", - "2025-06-05T13:41:00.000000000", - "2025-06-05T13:42:00.000000000", - "2025-06-05T13:43:00.000000000", - "2025-06-05T13:44:00.000000000", - "2025-06-05T13:45:00.000000000", - "2025-06-05T13:46:00.000000000", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Interactive Plotly Visualization\n", - "import plotly.graph_objects as go\n", - "from plotly.subplots import make_subplots\n", - "import plotly.express as px\n", - "import plotly.offline as pyo\n", - "from IPython.display import HTML\n", - "\n", - "# Configure plotly for offline mode\n", - "pyo.init_notebook_mode(connected=True)\n", - "\n", - "# Strategy-specific interactive visualization\n", - "assert pt_bt_config is not None\n", - "assert pair.predicted_df_ is not None\n", - "\n", - "if FIT_METHOD_TYPE == \"SlidingFit\":\n", - " print(\"=== SLIDING FIT INTERACTIVE VISUALIZATION ===\")\n", - " print(\"Note: Sliding strategy visualization with interactive plotly charts\")\n", - " \n", - " # Create consistent timeline - superset of timestamps from both dataframes\n", - " market_timestamps = set(pair.market_data_['tstamp'])\n", - " predicted_timestamps = set(pair.predicted_df_['tstamp'])\n", - " \n", - " # Create superset of all timestamps\n", - " all_timestamps = sorted(market_timestamps.union(predicted_timestamps))\n", - " \n", - " # Create a unified timeline dataframe for consistent plotting\n", - " timeline_df = pd.DataFrame({'tstamp': all_timestamps})\n", - " \n", - " # Merge with predicted data to get dis-equilibrium values\n", - " timeline_df = timeline_df.merge(pair.predicted_df_[['tstamp', 'disequilibrium', 'scaled_disequilibrium']], \n", - " on='tstamp', how='left')\n", - " \n", - " # Get Symbol_A and Symbol_B market data\n", - " colname_a, colname_b = pair.colnames()\n", - " symbol_a_data = pair.market_data_[['tstamp', colname_a]].copy()\n", - " symbol_b_data = pair.market_data_[['tstamp', colname_b]].copy()\n", - " \n", - " print(f\"Using consistent timeline with {len(timeline_df)} timestamps\")\n", - " print(f\"Timeline range: {timeline_df['tstamp'].min()} to {timeline_df['tstamp'].max()}\")\n", - " \n", - " # Create subplots with price charts at bottom\n", - " fig = make_subplots(\n", - " rows=4, cols=1,\n", - " subplot_titles=[\n", - " 'Testing Period: Scaled Dis-equilibrium with Trading Thresholds',\n", - " 'Trading Signal Timeline',\n", - " f'{SYMBOL_A} Market Data with Trading Signals',\n", - " f'{SYMBOL_B} Market Data with Trading Signals'\n", - " ],\n", - " vertical_spacing=0.06,\n", - " specs=[[{\"secondary_y\": False}],\n", - " [{\"secondary_y\": False}],\n", - " [{\"secondary_y\": False}],\n", - " [{\"secondary_y\": False}]]\n", - " )\n", - " \n", - " # 1. Scaled dis-equilibrium with thresholds - using consistent timeline\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=timeline_df['tstamp'],\n", - " y=timeline_df['scaled_disequilibrium'],\n", - " name='Scaled Dis-equilibrium',\n", - " line=dict(color='green', width=2),\n", - " opacity=0.8\n", - " ),\n", - " row=1, col=1\n", - " )\n", - " \n", - " # Add threshold lines to first subplot\n", - " fig.add_shape(\n", - " type=\"line\",\n", - " x0=timeline_df['tstamp'].min(),\n", - " x1=timeline_df['tstamp'].max(),\n", - " y0=pt_bt_config['dis-equilibrium_open_trshld'],\n", - " y1=pt_bt_config['dis-equilibrium_open_trshld'],\n", - " line=dict(color=\"purple\", width=2, dash=\"dot\"),\n", - " opacity=0.7,\n", - " row=1, col=1\n", - " )\n", - " \n", - " fig.add_shape(\n", - " type=\"line\",\n", - " x0=timeline_df['tstamp'].min(),\n", - " x1=timeline_df['tstamp'].max(),\n", - " y0=-pt_bt_config['dis-equilibrium_open_trshld'],\n", - " y1=-pt_bt_config['dis-equilibrium_open_trshld'],\n", - " line=dict(color=\"purple\", width=2, dash=\"dot\"),\n", - " opacity=0.7,\n", - " row=1, col=1\n", - " )\n", - " \n", - " fig.add_shape(\n", - " type=\"line\",\n", - " x0=timeline_df['tstamp'].min(),\n", - " x1=timeline_df['tstamp'].max(),\n", - " y0=pt_bt_config['dis-equilibrium_close_trshld'],\n", - " y1=pt_bt_config['dis-equilibrium_close_trshld'],\n", - " line=dict(color=\"brown\", width=2, dash=\"dot\"),\n", - " opacity=0.7,\n", - " row=1, col=1\n", - " )\n", - " \n", - " fig.add_shape(\n", - " type=\"line\",\n", - " x0=timeline_df['tstamp'].min(),\n", - " x1=timeline_df['tstamp'].max(),\n", - " y0=-pt_bt_config['dis-equilibrium_close_trshld'],\n", - " y1=-pt_bt_config['dis-equilibrium_close_trshld'],\n", - " line=dict(color=\"brown\", width=2, dash=\"dot\"),\n", - " opacity=0.7,\n", - " row=1, col=1\n", - " )\n", - " \n", - " fig.add_shape(\n", - " type=\"line\",\n", - " x0=timeline_df['tstamp'].min(),\n", - " x1=timeline_df['tstamp'].max(),\n", - " y0=0,\n", - " y1=0,\n", - " line=dict(color=\"black\", width=1, dash=\"solid\"),\n", - " opacity=0.5,\n", - " row=1, col=1\n", - " )\n", - " \n", - " # 2. Trading signals timeline if available - using consistent timeline\n", - " if pair_trades is not None and len(pair_trades) > 0:\n", - " # Separate trades by action and status for different colors\n", - " buy_open_trades = pair_trades[(pair_trades['action'].str.contains('BUY', na=False)) & \n", - " (pair_trades['status'] == 'OPEN')]\n", - " buy_close_trades = pair_trades[(pair_trades['action'].str.contains('BUY', na=False)) & \n", - " (pair_trades['status'] == 'CLOSE')]\n", - " sell_open_trades = pair_trades[(pair_trades['action'].str.contains('SELL', na=False)) & \n", - " (pair_trades['status'] == 'OPEN')]\n", - " sell_close_trades = pair_trades[(pair_trades['action'].str.contains('SELL', na=False)) & \n", - " (pair_trades['status'] == 'CLOSE')]\n", - " \n", - " # Create y-values for timeline visualization\n", - " trade_indices = list(range(len(pair_trades)))\n", - " \n", - " # Add trading signals with different colors based on action and status\n", - " if len(buy_open_trades) > 0:\n", - " buy_open_indices = [i for i, (_, row) in enumerate(pair_trades.iterrows()) \n", - " if 'BUY' in row['action'] and row['status'] == 'OPEN']\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=buy_open_trades['time'],\n", - " y=buy_open_indices,\n", - " mode='markers',\n", - " name='BUY OPEN',\n", - " marker=dict(color='red', size=10, symbol='circle')\n", - " ),\n", - " row=2, col=1\n", - " )\n", - " \n", - " if len(buy_close_trades) > 0:\n", - " buy_close_indices = [i for i, (_, row) in enumerate(pair_trades.iterrows()) \n", - " if 'BUY' in row['action'] and row['status'] == 'CLOSE']\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=buy_close_trades['time'],\n", - " y=buy_close_indices,\n", - " mode='markers',\n", - " name='BUY CLOSE',\n", - " marker=dict(color='pink', size=10, symbol='circle')\n", - " ),\n", - " row=2, col=1\n", - " )\n", - " \n", - " if len(sell_open_trades) > 0:\n", - " sell_open_indices = [i for i, (_, row) in enumerate(pair_trades.iterrows()) \n", - " if 'SELL' in row['action'] and row['status'] == 'OPEN']\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=sell_open_trades['time'],\n", - " y=sell_open_indices,\n", - " mode='markers',\n", - " name='SELL OPEN',\n", - " marker=dict(color='blue', size=10, symbol='circle')\n", - " ),\n", - " row=2, col=1\n", - " )\n", - " \n", - " if len(sell_close_trades) > 0:\n", - " sell_close_indices = [i for i, (_, row) in enumerate(pair_trades.iterrows()) \n", - " if 'SELL' in row['action'] and row['status'] == 'CLOSE']\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=sell_close_trades['time'],\n", - " y=sell_close_indices,\n", - " mode='markers',\n", - " name='SELL CLOSE',\n", - " marker=dict(color='purple', size=10, symbol='circle')\n", - " ),\n", - " row=2, col=1\n", - " )\n", - " \n", - " # 3. Symbol_A Market Data with Trading Signals (moved to bottom)\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=symbol_a_data['tstamp'],\n", - " y=symbol_a_data[colname_a],\n", - " name=f'{SYMBOL_A} Price',\n", - " line=dict(color='blue', width=2),\n", - " opacity=0.8\n", - " ),\n", - " row=3, col=1\n", - " )\n", - " \n", - " # Add trading signals for Symbol_A if available\n", - " if pair_trades is not None and len(pair_trades) > 0:\n", - " # Filter trades for Symbol_A\n", - " symbol_a_trades = pair_trades[pair_trades['symbol'] == SYMBOL_A]\n", - " \n", - " if len(symbol_a_trades) > 0:\n", - " # Separate trades by action and status for different colors\n", - " buy_open_trades = symbol_a_trades[(symbol_a_trades['action'].str.contains('BUY', na=False)) & \n", - " (symbol_a_trades['status'] == 'OPEN')]\n", - " buy_close_trades = symbol_a_trades[(symbol_a_trades['action'].str.contains('BUY', na=False)) & \n", - " (symbol_a_trades['status'] == 'CLOSE')]\n", - " sell_open_trades = symbol_a_trades[(symbol_a_trades['action'].str.contains('SELL', na=False)) & \n", - " (symbol_a_trades['status'] == 'OPEN')]\n", - " sell_close_trades = symbol_a_trades[(symbol_a_trades['action'].str.contains('SELL', na=False)) & \n", - " (symbol_a_trades['status'] == 'CLOSE')]\n", - " \n", - " # Add BUY OPEN signals\n", - " if len(buy_open_trades) > 0:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=buy_open_trades['time'],\n", - " y=buy_open_trades['price'],\n", - " mode='markers',\n", - " name=f'{SYMBOL_A} BUY OPEN',\n", - " marker=dict(color='red', size=12, symbol='triangle-up'),\n", - " showlegend=True\n", - " ),\n", - " row=3, col=1\n", - " )\n", - " \n", - " # Add BUY CLOSE signals\n", - " if len(buy_close_trades) > 0:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=buy_close_trades['time'],\n", - " y=buy_close_trades['price'],\n", - " mode='markers',\n", - " name=f'{SYMBOL_A} BUY CLOSE',\n", - " marker=dict(color='pink', size=12, symbol='triangle-up'),\n", - " showlegend=True\n", - " ),\n", - " row=3, col=1\n", - " )\n", - " \n", - " # Add SELL OPEN signals\n", - " if len(sell_open_trades) > 0:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=sell_open_trades['time'],\n", - " y=sell_open_trades['price'],\n", - " mode='markers',\n", - " name=f'{SYMBOL_A} SELL OPEN',\n", - " marker=dict(color='blue', size=12, symbol='triangle-down'),\n", - " showlegend=True\n", - " ),\n", - " row=3, col=1\n", - " )\n", - " \n", - " # Add SELL CLOSE signals\n", - " if len(sell_close_trades) > 0:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=sell_close_trades['time'],\n", - " y=sell_close_trades['price'],\n", - " mode='markers',\n", - " name=f'{SYMBOL_A} SELL CLOSE',\n", - " marker=dict(color='purple', size=12, symbol='triangle-down'),\n", - " showlegend=True\n", - " ),\n", - " row=3, col=1\n", - " )\n", - " \n", - " # 4. Symbol_B Market Data with Trading Signals\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=symbol_b_data['tstamp'],\n", - " y=symbol_b_data[colname_b],\n", - " name=f'{SYMBOL_B} Price',\n", - " line=dict(color='orange', width=2),\n", - " opacity=0.8\n", - " ),\n", - " row=4, col=1\n", - " )\n", - " \n", - " # Add trading signals for Symbol_B if available\n", - " if pair_trades is not None and len(pair_trades) > 0:\n", - " # Filter trades for Symbol_B\n", - " symbol_b_trades = pair_trades[pair_trades['symbol'] == SYMBOL_B]\n", - " \n", - " if len(symbol_b_trades) > 0:\n", - " # Separate trades by action and status for different colors\n", - " buy_open_trades = symbol_b_trades[(symbol_b_trades['action'].str.contains('BUY', na=False)) & \n", - " (symbol_b_trades['status'] == 'OPEN')]\n", - " buy_close_trades = symbol_b_trades[(symbol_b_trades['action'].str.contains('BUY', na=False)) & \n", - " (symbol_b_trades['status'] == 'CLOSE')]\n", - " sell_open_trades = symbol_b_trades[(symbol_b_trades['action'].str.contains('SELL', na=False)) & \n", - " (symbol_b_trades['status'] == 'OPEN')]\n", - " sell_close_trades = symbol_b_trades[(symbol_b_trades['action'].str.contains('SELL', na=False)) & \n", - " (symbol_b_trades['status'] == 'CLOSE')]\n", - " \n", - " # Add BUY OPEN signals\n", - " if len(buy_open_trades) > 0:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=buy_open_trades['time'],\n", - " y=buy_open_trades['price'],\n", - " mode='markers',\n", - " name=f'{SYMBOL_B} BUY OPEN',\n", - " marker=dict(color='red', size=12, symbol='triangle-up'),\n", - " showlegend=True\n", - " ),\n", - " row=4, col=1\n", - " )\n", - " \n", - " # Add BUY CLOSE signals\n", - " if len(buy_close_trades) > 0:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=buy_close_trades['time'],\n", - " y=buy_close_trades['price'],\n", - " mode='markers',\n", - " name=f'{SYMBOL_B} BUY CLOSE',\n", - " marker=dict(color='red', size=12, symbol='triangle-up'),\n", - " showlegend=True\n", - " ),\n", - " row=4, col=1\n", - " )\n", - " \n", - " # Add SELL OPEN signals\n", - " if len(sell_open_trades) > 0:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=sell_open_trades['time'],\n", - " y=sell_open_trades['price'],\n", - " mode='markers',\n", - " name=f'{SYMBOL_B} SELL OPEN',\n", - " marker=dict(color='blue', size=12, symbol='triangle-down'),\n", - " showlegend=True\n", - " ),\n", - " row=4, col=1\n", - " )\n", - " \n", - " # Add SELL CLOSE signals\n", - " if len(sell_close_trades) > 0:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=sell_close_trades['time'],\n", - " y=sell_close_trades['price'],\n", - " mode='markers',\n", - " name=f'{SYMBOL_B} SELL CLOSE',\n", - " marker=dict(color='blue', size=12, symbol='triangle-down'),\n", - " showlegend=True\n", - " ),\n", - " row=4, col=1\n", - " )\n", - " \n", - " # Update layout\n", - " fig.update_layout(\n", - " height=1200,\n", - " title_text=f\"Sliding Fit Strategy Analysis - {SYMBOL_A} & {SYMBOL_B}\",\n", - " showlegend=True,\n", - " template=\"plotly_white\"\n", - " )\n", - " \n", - " # Update y-axis labels\n", - " fig.update_yaxes(title_text=\"Scaled Dis-equilibrium\", row=1, col=1)\n", - " fig.update_yaxes(title_text=\"Signal Index\", row=2, col=1)\n", - " fig.update_yaxes(title_text=f\"{SYMBOL_A} Price ($)\", row=3, col=1)\n", - " fig.update_yaxes(title_text=f\"{SYMBOL_B} Price ($)\", row=4, col=1)\n", - " \n", - " # Update x-axis labels and ensure consistent time range\n", - " time_range = [timeline_df['tstamp'].min(), timeline_df['tstamp'].max()]\n", - " fig.update_xaxes(range=time_range, row=1, col=1)\n", - " fig.update_xaxes(range=time_range, row=2, col=1)\n", - " fig.update_xaxes(range=time_range, row=3, col=1)\n", - " fig.update_xaxes(title_text=\"Time\", range=time_range, row=4, col=1)\n", - " \n", - " # Display using plotly offline mode\n", - " pyo.iplot(fig)\n", - "\n", - "else:\n", - " print(\"No interactive visualization data available - strategy may not have run successfully\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "vscode": { - "languageId": "raw" - } - }, - "source": [ - "## Summary and Analysis\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================================================================\n", - "PAIRS TRADING BACKTEST SUMMARY\n", - "================================================================================\n", - "\n", - "Pair: COIN & MSTR\n", - "Strategy: SlidingFit\n", - "Configuration: equity\n", - "Data file: 20250605.mktdata.ohlcv.db\n", - "Trading date: 20250605\n", - "\n", - "Strategy Parameters:\n", - " Training window: 120 minutes\n", - " Open threshold: 2\n", - " Close threshold: 1\n", - " Funding per pair: $2000\n", - "\n", - "Sliding Window Analysis:\n", - " Total data points: 391\n", - " Maximum iterations: 271\n", - " Analysis type: Dynamic sliding window\n", - "\n", - "Trading Signals: 20 generated\n", - " Unique trade times: 10\n", - " BUY signals: 10\n", - " SELL signals: 10\n", - "\n", - "First few trading signals:\n", - " 1. SELL COIN @ $260.46 at 2025-06-05 15:40:00\n", - " 2. BUY MSTR @ $380.53 at 2025-06-05 15:40:00\n", - " 3. BUY COIN @ $259.39 at 2025-06-05 16:02:00\n", - " 4. SELL MSTR @ $379.90 at 2025-06-05 16:02:00\n", - " 5. SELL COIN @ $259.62 at 2025-06-05 16:31:00\n", - " ... and 15 more signals\n", - "\n", - "================================================================================\n" - ] - } - ], - "source": [ - "print(\"=\" * 80)\n", - "print(\"PAIRS TRADING BACKTEST SUMMARY\")\n", - "print(\"=\" * 80)\n", - "\n", - "print(f\"\\nPair: {SYMBOL_A} & {SYMBOL_B}\")\n", - "print(f\"Strategy: {FIT_METHOD_TYPE}\")\n", - "print(f\"Configuration: {CONFIG_FILE}\")\n", - "print(f\"Data file: {DATA_FILE}\")\n", - "print(f\"Trading date: {TRADING_DATE}\")\n", - "\n", - "print(f\"\\nStrategy Parameters:\")\n", - "print(f\" Training window: {pt_bt_config['training_minutes']} minutes\")\n", - "print(f\" Open threshold: {pt_bt_config['dis-equilibrium_open_trshld']}\")\n", - "print(f\" Close threshold: {pt_bt_config['dis-equilibrium_close_trshld']}\")\n", - "print(f\" Funding per pair: ${pt_bt_config['funding_per_pair']}\")\n", - "\n", - "# Strategy-specific summary\n", - "if FIT_METHOD_TYPE == \"StaticFit\":\n", - " if 'is_cointegrated' in locals() and is_cointegrated:\n", - " assert pair.predicted_df_ is not None, \"predicted_df_ is None\"\n", - " print(f\"\\nCointegration Analysis:\")\n", - " print(f\" ✓ Pair is cointegrated\")\n", - " print(f\" VECM Beta coefficients: {pair.vecm_fit_.beta.flatten()}\")\n", - " print(f\" Training mean: {pair.training_mu_:.6f}\")\n", - " print(f\" Training std: {pair.training_std_:.6f}\")\n", - " \n", - " if hasattr(pair, 'predicted_df_'):\n", - " print(f\" Testing predictions: {len(pair.predicted_df_)} data points\")\n", - " else:\n", - " print(f\"\\n✗ Pair is not cointegrated\")\n", - "\n", - "elif FIT_METHOD_TYPE == \"SlidingFit\":\n", - " print(f\"\\nSliding Window Analysis:\")\n", - " training_minutes = pt_bt_config['training_minutes']\n", - " max_iterations = len(pair.market_data_) - training_minutes\n", - " print(f\" Total data points: {len(pair.market_data_)}\")\n", - " print(f\" Maximum iterations: {max_iterations}\")\n", - " print(f\" Analysis type: Dynamic sliding window\")\n", - "\n", - "# Trading signals summary\n", - "if pair_trades is not None and len(pair_trades) > 0:\n", - " print(f\"\\nTrading Signals: {len(pair_trades)} generated\")\n", - " unique_times = pair_trades['time'].unique()\n", - " print(f\" Unique trade times: {len(unique_times)}\")\n", - " \n", - " # Group by action type\n", - " buy_signals = pair_trades[pair_trades['action'].str.contains('BUY', na=False)]\n", - " sell_signals = pair_trades[pair_trades['action'].str.contains('SELL', na=False)]\n", - " \n", - " print(f\" BUY signals: {len(buy_signals)}\")\n", - " print(f\" SELL signals: {len(sell_signals)}\")\n", - " \n", - " # Show first few trades\n", - " print(f\"\\nFirst few trading signals:\")\n", - " for i, (idx, trade) in enumerate(pair_trades.head(5).iterrows()):\n", - " print(f\" {i+1}. {trade['action']} {trade['symbol']} @ ${trade['price']:.2f} at {trade['time']}\")\n", - " \n", - " if len(pair_trades) > 5:\n", - " print(f\" ... and {len(pair_trades)-5} more signals\")\n", - " \n", - "else:\n", - " print(f\"\\nTrading Signals: None generated\")\n", - " print(\" Possible reasons:\")\n", - " print(\" - Dis-equilibrium never exceeded open threshold\")\n", - " print(\" - Pair not cointegrated (for StaticFit)\")\n", - " print(\" - Insufficient data or market conditions\")\n", - "\n", - "print(f\"\\n\" + \"=\" * 80)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "vscode": { - "languageId": "raw" - } - }, - "source": [ - "# Conclusions and Next Steps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "This notebook demonstrates a comprehensive pairs trading backtest framework that supports both StaticFit and SlidingFit. \n", - "\n", - "### Key Insights:\n", - "\n", - "#### StaticFit:\n", - "- **Pros**: Simpler computation, consistent parameters throughout trading period\n", - "- **Cons**: May not adapt to changing market conditions\n", - "- **Best for**: Stable market conditions, strong cointegration relationships\n", - "\n", - "#### SlidingFit:\n", - "- **Pros**: Adaptive to market changes, dynamic parameter updates\n", - "- **Cons**: More computationally intensive, potentially noisy signals\n", - "- **Best for**: Volatile markets, evolving relationships between instruments\n", - "\n", - "### Framework Features:\n", - "\n", - "1. **Configuration-Driven**: Easy switching between strategies and parameters\n", - "2. **Comprehensive Analysis**: From data loading to signal generation\n", - "3. **Rich Visualization**: Strategy-specific charts and analysis\n", - "4. **Interactive Experimentation**: Easy parameter modification and testing\n", - "\n", - "### Recommendations:\n", - "\n", - "1. **Start with StaticFit** for initial pair analysis\n", - "2. **Use SlidingFit** for more sophisticated, adaptive trading\n", - "3. **Experiment with thresholds** based on observed dis-equilibrium statistics\n", - "4. **Test multiple symbol pairs** to find strong cointegration relationships\n", - "5. **Validate results** on different time periods and market conditions\n", - "\n", - "### Next Steps:\n", - "\n", - "- Implement transaction costs and slippage modeling\n", - "- Add risk management features (position sizing, stop-losses)\n", - "- Develop portfolio-level analysis across multiple pairs\n", - "- Create automated parameter optimization routines\n", - "- Implement real-time trading signal generation\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3.12-venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/research/notebooks/__DEPRECATED__/pt_static.ipynb b/research/notebooks/__DEPRECATED__/pt_static.ipynb deleted file mode 100644 index 4c202b4..0000000 --- a/research/notebooks/__DEPRECATED__/pt_static.ipynb +++ /dev/null @@ -1,771 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Pairs Trading Visualization Notebook\n", - "\n", - "This notebook allows you to visualize pairs trading strategies on individual instrument pairs.\n", - "You can examine the relationship between two instruments, their dis-equilibrium, and trading signals." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 🎯 Key Features:\n", - "\n", - "1. **Interactive Configuration**: \n", - " - Easy switching between CRYPTO and EQUITY configurations\n", - " - Simple parameter adjustment for thresholds and training periods\n", - "\n", - "2. **Single Pair Focus**: \n", - " - Instead of running multiple pairs, focuses on one pair at a time\n", - " - Allows deep analysis of the relationship between two instruments\n", - "\n", - "3. **Step-by-Step Visualization**:\n", - " - **Raw price data**: Individual prices, normalized comparison, and price ratios\n", - " - **Training analysis**: Cointegration testing and VECM model fitting\n", - " - **Dis-equilibrium visualization**: Both raw and scaled dis-equilibrium with threshold lines\n", - " - **Strategy execution**: Trading signal generation and visualization\n", - " - **Prediction analysis**: Actual vs predicted prices with trading signals overlaid\n", - "\n", - "4. **Rich Analytics**:\n", - " - Cointegration status and VECM model details\n", - " - Statistical summaries for all stages\n", - " - Threshold crossing analysis\n", - " - Trading signal breakdown\n", - "\n", - "5. **Interactive Experimentation**:\n", - " - Easy parameter modification\n", - " - Re-run capabilities for different configurations\n", - " - Support for both StaticFitStrategy and SlidingFitStrategy\n", - "\n", - "### 🚀 How to Use:\n", - "\n", - "1. **Start Jupyter**:\n", - " ```bash\n", - " cd src/notebooks\n", - " jupyter notebook pairs_trading_visualization.ipynb\n", - " ```\n", - "\n", - "2. **Customize Your Analysis**:\n", - " - Change `SYMBOL_A` and `SYMBOL_B` to your desired trading pair\n", - " - Switch between `CRYPTO_CONFIG` and `EQT_CONFIG`\n", - " - Only **StaticFitStrategy** is supported. \n", - " - Adjust thresholds and parameters as needed\n", - "\n", - "3. **Run and Visualize**:\n", - " - Execute cells step by step to see the analysis unfold\n", - " - Rich matplotlib visualizations show relationships and signals\n", - " - Comprehensive summary at the end\n", - "\n", - "The notebook provides exactly what you requested - a way to visualize the relationship between two instruments and their scaled dis-equilibrium, with all the stages of your pairs trading strategy clearly displayed and analyzed.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup and Imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Setup complete!\n" - ] - } - ], - "source": [ - "import sys\n", - "import os\n", - "sys.path.append('..')\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "from typing import Dict, List, Optional\n", - "\n", - "# Import our modules\n", - "from pt_trading.fit_methods import StaticFit, SlidingFit\n", - "from tools.data_loader import load_market_data\n", - "from pt_trading.trading_pair import TradingPair\n", - "from pt_trading.results import BacktestResult\n", - "\n", - "# Set plotting style\n", - "plt.style.use('seaborn-v0_8')\n", - "sns.set_palette(\"husl\")\n", - "plt.rcParams['figure.figsize'] = (12, 8)\n", - "\n", - "print(\"Setup complete!\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using EQUITY configuration\n", - "Available instruments: ['COIN', 'GBTC', 'HOOD', 'MSTR', 'PYPL']\n" - ] - } - ], - "source": [ - "# Configuration - Choose between CRYPTO_CONFIG or EQT_CONFIG\n", - "\n", - "CRYPTO_CONFIG = {\n", - " \"security_type\": \"CRYPTO\",\n", - " \"data_directory\": \"../../data/crypto\",\n", - " \"datafiles\": [\n", - " \"20250519.mktdata.ohlcv.db\",\n", - " ],\n", - " \"db_table_name\": \"bnbspot_ohlcv_1min\",\n", - " \"exchange_id\": \"BNBSPOT\",\n", - " \"instrument_id_pfx\": \"PAIR-\",\n", - " \"instruments\": [\n", - " \"BTC-USDT\",\n", - " \"BCH-USDT\",\n", - " \"ETH-USDT\",\n", - " \"LTC-USDT\",\n", - " \"XRP-USDT\",\n", - " \"ADA-USDT\",\n", - " \"SOL-USDT\",\n", - " \"DOT-USDT\",\n", - " ],\n", - " \"trading_hours\": {\n", - " \"begin_session\": \"00:00:00\",\n", - " \"end_session\": \"23:59:00\",\n", - " \"timezone\": \"UTC\",\n", - " },\n", - " \"price_column\": \"close\",\n", - " \"min_required_points\": 30,\n", - " \"zero_threshold\": 1e-10,\n", - " \"dis-equilibrium_open_trshld\": 2.0,\n", - " \"dis-equilibrium_close_trshld\": 0.5,\n", - " \"training_minutes\": 120,\n", - " \"funding_per_pair\": 2000.0,\n", - "}\n", - "\n", - "EQT_CONFIG = {\n", - " \"security_type\": \"EQUITY\",\n", - " \"data_directory\": \"../../data/equity\",\n", - " \"datafiles\": {\n", - " \"0508\": \"20250508.alpaca_sim_md.db\",\n", - " \"0509\": \"20250509.alpaca_sim_md.db\",\n", - " \"0510\": \"20250510.alpaca_sim_md.db\",\n", - " \"0511\": \"20250511.alpaca_sim_md.db\",\n", - " \"0512\": \"20250512.alpaca_sim_md.db\",\n", - " \"0513\": \"20250513.alpaca_sim_md.db\",\n", - " \"0514\": \"20250514.alpaca_sim_md.db\",\n", - " \"0515\": \"20250515.alpaca_sim_md.db\",\n", - " \"0516\": \"20250516.alpaca_sim_md.db\",\n", - " \"0517\": \"20250517.alpaca_sim_md.db\",\n", - " \"0518\": \"20250518.alpaca_sim_md.db\",\n", - " \"0519\": \"20250519.alpaca_sim_md.db\",\n", - " \"0520\": \"20250520.alpaca_sim_md.db\",\n", - " \"0521\": \"20250521.alpaca_sim_md.db\",\n", - " \"0522\": \"20250522.alpaca_sim_md.db\",\n", - " },\n", - " \"db_table_name\": \"md_1min_bars\",\n", - " \"exchange_id\": \"ALPACA\",\n", - " \"instrument_id_pfx\": \"STOCK-\",\n", - " \"instruments\": [\n", - " \"COIN\",\n", - " \"GBTC\",\n", - " \"HOOD\",\n", - " \"MSTR\",\n", - " \"PYPL\",\n", - " ],\n", - " \"trading_hours\": {\n", - " \"begin_session\": \"9:30:00\",\n", - " \"end_session\": \"16:00:00\",\n", - " \"timezone\": \"America/New_York\",\n", - " },\n", - " \"price_column\": \"close\",\n", - " \"min_required_points\": 30,\n", - " \"zero_threshold\": 1e-10,\n", - " \"dis-equilibrium_open_trshld\": 2.0,\n", - " \"dis-equilibrium_close_trshld\": 1.0, #0.5,\n", - " \"training_minutes\": 120,\n", - " \"funding_per_pair\": 2000.0,\n", - "}\n", - "\n", - "# Choose your configuration\n", - "CONFIG = EQT_CONFIG # Change to CRYPTO_CONFIG if you want to use crypto data\n", - "\n", - "print(f\"Using {CONFIG['security_type']} configuration\")\n", - "print(f\"Available instruments: {CONFIG['instruments']}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Select Trading Pair and Data File" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selected pair: COIN & GBTC\n", - "Data file: 20250509.alpaca_sim_md.db\n", - "Strategy: StaticFitStrategy\n" - ] - } - ], - "source": [ - "# Select your trading pair and strategy\n", - "SYMBOL_A = \"COIN\" # Change these to your desired symbols\n", - "SYMBOL_B = \"GBTC\"\n", - "DATA_FILE = CONFIG[\"datafiles\"][\"0509\"]\n", - "\n", - "# Choose strategy\n", - "FIT_METHOD = StaticFit()\n", - "\n", - "print(f\"Selected pair: {SYMBOL_A} & {SYMBOL_B}\")\n", - "print(f\"Data file: {DATA_FILE}\")\n", - "print(f\"Strategy: {type(FIT_METHOD).__name__}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load Market Data" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current working directory: /home/oleg/devel/pairs_trading/src/notebooks\n", - "Loading data from: ../../data/equity/20250509.alpaca_sim_md.db\n", - "Error: Execution failed on sql 'select tstamp, tstamp_ns as time_ns, substr(instrument_id, 7) as symbol, open, high, low, close, volume, num_trades, vwap from md_1min_bars where exchange_id ='ALPACA' and instrument_id in (\"STOCK-COIN\",\"STOCK-GBTC\",\"STOCK-HOOD\",\"STOCK-MSTR\",\"STOCK-PYPL\")': no such table: md_1min_bars\n" - ] - }, - { - "ename": "Exception", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mOperationalError\u001b[39m Traceback (most recent call last)", - "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/python3.12-venv/lib/python3.12/site-packages/pandas/io/sql.py:2664\u001b[39m, in \u001b[36mSQLiteDatabase.execute\u001b[39m\u001b[34m(self, sql, params)\u001b[39m\n\u001b[32m 2663\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m2664\u001b[39m \u001b[43mcur\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2665\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m cur\n", - "\u001b[31mOperationalError\u001b[39m: no such table: md_1min_bars", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[31mDatabaseError\u001b[39m Traceback (most recent call last)", - "\u001b[36mFile \u001b[39m\u001b[32m~/devel/pairs_trading/src/notebooks/../tools/data_loader.py:11\u001b[39m, in \u001b[36mload_sqlite_to_dataframe\u001b[39m\u001b[34m(db_path, query)\u001b[39m\n\u001b[32m 9\u001b[39m conn = sqlite3.connect(db_path)\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m df = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_sql_query\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 12\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m df\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/python3.12-venv/lib/python3.12/site-packages/pandas/io/sql.py:528\u001b[39m, in \u001b[36mread_sql_query\u001b[39m\u001b[34m(sql, con, index_col, coerce_float, params, parse_dates, chunksize, dtype, dtype_backend)\u001b[39m\n\u001b[32m 527\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m pandasSQL_builder(con) \u001b[38;5;28;01mas\u001b[39;00m pandas_sql:\n\u001b[32m--> \u001b[39m\u001b[32m528\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpandas_sql\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_query\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 529\u001b[39m \u001b[43m \u001b[49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 530\u001b[39m \u001b[43m \u001b[49m\u001b[43mindex_col\u001b[49m\u001b[43m=\u001b[49m\u001b[43mindex_col\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 531\u001b[39m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m=\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 532\u001b[39m \u001b[43m \u001b[49m\u001b[43mcoerce_float\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcoerce_float\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 533\u001b[39m \u001b[43m \u001b[49m\u001b[43mparse_dates\u001b[49m\u001b[43m=\u001b[49m\u001b[43mparse_dates\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 534\u001b[39m \u001b[43m \u001b[49m\u001b[43mchunksize\u001b[49m\u001b[43m=\u001b[49m\u001b[43mchunksize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 535\u001b[39m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 536\u001b[39m \u001b[43m \u001b[49m\u001b[43mdtype_backend\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdtype_backend\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 537\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/python3.12-venv/lib/python3.12/site-packages/pandas/io/sql.py:2728\u001b[39m, in \u001b[36mSQLiteDatabase.read_query\u001b[39m\u001b[34m(self, sql, index_col, coerce_float, parse_dates, params, chunksize, dtype, dtype_backend)\u001b[39m\n\u001b[32m 2717\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mread_query\u001b[39m(\n\u001b[32m 2718\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 2719\u001b[39m sql,\n\u001b[32m (...)\u001b[39m\u001b[32m 2726\u001b[39m dtype_backend: DtypeBackend | Literal[\u001b[33m\"\u001b[39m\u001b[33mnumpy\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[33m\"\u001b[39m\u001b[33mnumpy\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 2727\u001b[39m ) -> DataFrame | Iterator[DataFrame]:\n\u001b[32m-> \u001b[39m\u001b[32m2728\u001b[39m cursor = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2729\u001b[39m columns = [col_desc[\u001b[32m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m col_desc \u001b[38;5;129;01min\u001b[39;00m cursor.description]\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/python3.12-venv/lib/python3.12/site-packages/pandas/io/sql.py:2676\u001b[39m, in \u001b[36mSQLiteDatabase.execute\u001b[39m\u001b[34m(self, sql, params)\u001b[39m\n\u001b[32m 2675\u001b[39m ex = DatabaseError(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mExecution failed on sql \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msql\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexc\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m2676\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m ex \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mexc\u001b[39;00m\n", - "\u001b[31mDatabaseError\u001b[39m: Execution failed on sql 'select tstamp, tstamp_ns as time_ns, substr(instrument_id, 7) as symbol, open, high, low, close, volume, num_trades, vwap from md_1min_bars where exchange_id ='ALPACA' and instrument_id in (\"STOCK-COIN\",\"STOCK-GBTC\",\"STOCK-HOOD\",\"STOCK-MSTR\",\"STOCK-PYPL\")': no such table: md_1min_bars", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[31mException\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mCurrent working directory: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mos.getcwd()\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 4\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mLoading data from: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdatafile_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m market_data_df = \u001b[43mload_market_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdatafile_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m=\u001b[49m\u001b[43mCONFIG\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 8\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mLoaded \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(market_data_df)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m rows of market data\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 9\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mSymbols in data: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmarket_data_df[\u001b[33m'\u001b[39m\u001b[33msymbol\u001b[39m\u001b[33m'\u001b[39m].unique()\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/devel/pairs_trading/src/notebooks/../tools/data_loader.py:69\u001b[39m, in \u001b[36mload_market_data\u001b[39m\u001b[34m(datafile, config)\u001b[39m\n\u001b[32m 66\u001b[39m query += \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m where exchange_id =\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexchange_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 67\u001b[39m query += \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m and instrument_id in (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m,\u001b[39m\u001b[33m'\u001b[39m.join(instrument_ids)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m)\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m69\u001b[39m df = \u001b[43mload_sqlite_to_dataframe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdb_path\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdatafile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[43m=\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 71\u001b[39m \u001b[38;5;66;03m# Trading Hours\u001b[39;00m\n\u001b[32m 72\u001b[39m date_str = df[\u001b[33m\"\u001b[39m\u001b[33mtstamp\u001b[39m\u001b[33m\"\u001b[39m][\u001b[32m0\u001b[39m][\u001b[32m0\u001b[39m:\u001b[32m10\u001b[39m]\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/devel/pairs_trading/src/notebooks/../tools/data_loader.py:18\u001b[39m, in \u001b[36mload_sqlite_to_dataframe\u001b[39m\u001b[34m(db_path, query)\u001b[39m\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m excpt:\n\u001b[32m 17\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mError: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexcpt\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m18\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m() \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mexcpt\u001b[39;00m\n\u001b[32m 19\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 20\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mconn\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlocals\u001b[39m():\n", - "\u001b[31mException\u001b[39m: " - ] - } - ], - "source": [ - "# Load market data\n", - "datafile_path = f\"{CONFIG['data_directory']}/{DATA_FILE}\"\n", - "print(f\"Current working directory: {os.getcwd()}\")\n", - "print(f\"Loading data from: {datafile_path}\")\n", - "\n", - "market_data_df = load_market_data(datafile_path, config=CONFIG)\n", - "\n", - "print(f\"Loaded {len(market_data_df)} rows of market data\")\n", - "print(f\"Symbols in data: {market_data_df['symbol'].unique()}\")\n", - "print(f\"Time range: {market_data_df['tstamp'].min()} to {market_data_df['tstamp'].max()}\")\n", - "\n", - "# Display first few rows\n", - "market_data_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create Trading Pair and Analyze" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create trading pair\n", - "pair = TradingPair(\n", - " market_data=market_data_df,\n", - " symbol_a=SYMBOL_A,\n", - " symbol_b=SYMBOL_B,\n", - " price_column=CONFIG[\"price_column\"]\n", - ")\n", - "\n", - "print(f\"Created trading pair: {pair}\")\n", - "print(f\"Market data shape: {pair.market_data_.shape}\")\n", - "print(f\"Column names: {pair.colnames()}\")\n", - "\n", - "# Display first few rows of pair data\n", - "pair.market_data_.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Split Data into Training and Testing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get training and testing datasets\n", - "training_minutes = CONFIG[\"training_minutes\"]\n", - "pair.get_datasets(training_minutes=training_minutes)\n", - "\n", - "print(f\"Training data: {len(pair.training_df_)} rows\")\n", - "print(f\"Testing data: {len(pair.testing_df_)} rows\")\n", - "print(f\"Training period: {pair.training_df_['tstamp'].iloc[0]} to {pair.training_df_['tstamp'].iloc[-1]}\")\n", - "print(f\"Testing period: {pair.testing_df_['tstamp'].iloc[0]} to {pair.testing_df_['tstamp'].iloc[-1]}\")\n", - "\n", - "# Check for any missing data\n", - "print(f\"Training data null values: {pair.training_df_.isnull().sum().sum()}\")\n", - "print(f\"Testing data null values: {pair.testing_df_.isnull().sum().sum()}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualize Raw Price Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot raw price data\n", - "fig, axes = plt.subplots(3, 1, figsize=(15, 12))\n", - "\n", - "# Combined price plot\n", - "colname_a, colname_b = pair.colnames()\n", - "all_data = pd.concat([pair.training_df_, pair.testing_df_]).reset_index(drop=True)\n", - "\n", - "# Plot individual prices\n", - "axes[0].plot(all_data['tstamp'], all_data[colname_a], label=f'{SYMBOL_A}', alpha=0.8)\n", - "axes[0].plot(all_data['tstamp'], all_data[colname_b], label=f'{SYMBOL_B}', alpha=0.8)\n", - "axes[0].axvline(x=pair.training_df_['tstamp'].iloc[-1], color='red', linestyle='--', alpha=0.7, label='Train/Test Split')\n", - "axes[0].set_title(f'Price Comparison: {SYMBOL_A} vs {SYMBOL_B}')\n", - "axes[0].set_ylabel('Price')\n", - "axes[0].legend()\n", - "axes[0].grid(True)\n", - "\n", - "# Normalized prices for comparison\n", - "norm_a = all_data[colname_a] / all_data[colname_a].iloc[0]\n", - "norm_b = all_data[colname_b] / all_data[colname_b].iloc[0]\n", - "\n", - "axes[1].plot(all_data['tstamp'], norm_a, label=f'{SYMBOL_A} (normalized)', alpha=0.8)\n", - "axes[1].plot(all_data['tstamp'], norm_b, label=f'{SYMBOL_B} (normalized)', alpha=0.8)\n", - "axes[1].axvline(x=pair.training_df_['tstamp'].iloc[-1], color='red', linestyle='--', alpha=0.7, label='Train/Test Split')\n", - "axes[1].set_title('Normalized Price Comparison')\n", - "axes[1].set_ylabel('Normalized Price')\n", - "axes[1].legend()\n", - "axes[1].grid(True)\n", - "\n", - "# Price ratio\n", - "price_ratio = all_data[colname_a] / all_data[colname_b]\n", - "axes[2].plot(all_data['tstamp'], price_ratio, label=f'{SYMBOL_A}/{SYMBOL_B} Ratio', color='green', alpha=0.8)\n", - "axes[2].axvline(x=pair.training_df_['tstamp'].iloc[-1], color='red', linestyle='--', alpha=0.7, label='Train/Test Split')\n", - "axes[2].set_title('Price Ratio')\n", - "axes[2].set_ylabel('Ratio')\n", - "axes[2].set_xlabel('Time')\n", - "axes[2].legend()\n", - "axes[2].grid(True)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train the Pair and Check Cointegration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Train the pair and check cointegration\n", - "try:\n", - " is_cointegrated = pair.train_pair()\n", - " print(f\"Pair {pair} cointegration status: {is_cointegrated}\")\n", - "\n", - " if is_cointegrated:\n", - " print(f\"VECM Beta coefficients: {pair.vecm_fit_.beta.flatten()}\")\n", - " print(f\"Training dis-equilibrium mean: {pair.training_mu_:.6f}\")\n", - " print(f\"Training dis-equilibrium std: {pair.training_std_:.6f}\")\n", - "\n", - " # Display VECM summary\n", - " print(\"\\nVECM Model Summary:\")\n", - " print(pair.vecm_fit_.summary())\n", - " else:\n", - " print(\"Pair is not cointegrated. Cannot proceed with strategy.\")\n", - "\n", - "except Exception as e:\n", - " print(f\"Training failed: {str(e)}\")\n", - " is_cointegrated = False" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualize Training Period Dis-equilibrium" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if is_cointegrated:\n", - " # fig, axes = plt.subplots(, 1, figsize=(15, 10))\n", - "\n", - " # # Raw dis-equilibrium\n", - " # axes[0].plot(pair.training_df_['tstamp'], pair.training_df_['dis-equilibrium'],\n", - " # color='blue', alpha=0.8, label='Raw Dis-equilibrium')\n", - " # axes[0].axhline(y=pair.training_mu_, color='red', linestyle='--', alpha=0.7, label='Mean')\n", - " # axes[0].axhline(y=pair.training_mu_ + pair.training_std_, color='orange', linestyle='--', alpha=0.5, label='+1 Std')\n", - " # axes[0].axhline(y=pair.training_mu_ - pair.training_std_, color='orange', linestyle='--', alpha=0.5, label='-1 Std')\n", - " # axes[0].set_title('Training Period: Raw Dis-equilibrium')\n", - " # axes[0].set_ylabel('Dis-equilibrium')\n", - " # axes[0].legend()\n", - " # axes[0].grid(True)\n", - "\n", - " # Scaled dis-equilibrium\n", - " fig, axes = plt.subplots(1, 1, figsize=(15, 5))\n", - " axes.plot(pair.training_df_['tstamp'], pair.training_df_['scaled_dis-equilibrium'],\n", - " color='green', alpha=0.8, label='Scaled Dis-equilibrium')\n", - " axes.axhline(y=0, color='red', linestyle='--', alpha=0.7, label='Mean (0)')\n", - " axes.axhline(y=1, color='orange', linestyle='--', alpha=0.5, label='+1 Std')\n", - " axes.axhline(y=-1, color='orange', linestyle='--', alpha=0.5, label='-1 Std')\n", - " axes.axhline(y=CONFIG['dis-equilibrium_open_trshld'], color='purple',\n", - " linestyle=':', alpha=0.7, label=f\"Open Threshold ({CONFIG['dis-equilibrium_open_trshld']})\")\n", - " axes.axhline(y=CONFIG['dis-equilibrium_close_trshld'], color='brown',\n", - " linestyle=':', alpha=0.7, label=f\"Close Threshold ({CONFIG['dis-equilibrium_close_trshld']})\")\n", - " axes.set_title('Training Period: Scaled Dis-equilibrium')\n", - " axes.set_ylabel('Scaled Dis-equilibrium')\n", - " axes.set_xlabel('Time')\n", - " axes.legend()\n", - " axes.grid(True)\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " # Print statistics\n", - " print(f\"Training dis-equilibrium statistics:\")\n", - " print(f\" Mean: {pair.training_df_['dis-equilibrium'].mean():.6f}\")\n", - " print(f\" Std: {pair.training_df_['dis-equilibrium'].std():.6f}\")\n", - " print(f\" Min: {pair.training_df_['dis-equilibrium'].min():.6f}\")\n", - " print(f\" Max: {pair.training_df_['dis-equilibrium'].max():.6f}\")\n", - "\n", - " print(f\"\\nScaled dis-equilibrium statistics:\")\n", - " print(f\" Mean: {pair.training_df_['scaled_dis-equilibrium'].mean():.6f}\")\n", - " print(f\" Std: {pair.training_df_['scaled_dis-equilibrium'].std():.6f}\")\n", - " print(f\" Min: {pair.training_df_['scaled_dis-equilibrium'].min():.6f}\")\n", - " print(f\" Max: {pair.training_df_['scaled_dis-equilibrium'].max():.6f}\")\n", - "else:\n", - " print(\"The pair is not cointegrated\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate Predictions and Run Strategy" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if is_cointegrated:\n", - " try:\n", - " # Generate predictions\n", - " pair.predict()\n", - " print(f\"Generated predictions for {len(pair.predicted_df_)} rows\")\n", - "\n", - " # Display prediction data structure\n", - " print(f\"Prediction columns: {list(pair.predicted_df_.columns)}\")\n", - " print(f\"Prediction period: {pair.predicted_df_['tstamp'].iloc[0]} to {pair.predicted_df_['tstamp'].iloc[-1]}\")\n", - "\n", - " # Run strategy\n", - " bt_result = BacktestResult(config=CONFIG)\n", - " pair_trades = FIT_METHOD.run_pair(config=CONFIG, pair=pair, bt_result=bt_result)\n", - "\n", - " if pair_trades is not None and len(pair_trades) > 0:\n", - " print(f\"\\nGenerated {len(pair_trades)} trading signals:\")\n", - " print(pair_trades)\n", - " else:\n", - " print(\"\\nNo trading signals generated\")\n", - "\n", - " except Exception as e:\n", - " print(f\"Prediction/Strategy failed: {str(e)}\")\n", - " pair_trades = None" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualize Predictions and Dis-equilibrium" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if is_cointegrated and hasattr(pair, 'predicted_df_'):\n", - " fig, axes = plt.subplots(4, 1, figsize=(16, 16))\n", - "\n", - " # Actual vs Predicted Prices\n", - " colname_a, colname_b = pair.colnames()\n", - "\n", - " axes[0].plot(pair.predicted_df_['tstamp'], pair.predicted_df_[colname_a],\n", - " label=f'{SYMBOL_A} Actual', alpha=0.8)\n", - " axes[0].plot(pair.predicted_df_['tstamp'], pair.predicted_df_[f'{colname_a}_pred'],\n", - " label=f'{SYMBOL_A} Predicted', alpha=0.8, linestyle='--')\n", - " axes[0].set_title('Actual vs Predicted Prices - Symbol A')\n", - " axes[0].set_ylabel('Price')\n", - " axes[0].legend()\n", - " axes[0].grid(True)\n", - "\n", - " axes[1].plot(pair.predicted_df_['tstamp'], pair.predicted_df_[colname_b],\n", - " label=f'{SYMBOL_B} Actual', alpha=0.8)\n", - " axes[1].plot(pair.predicted_df_['tstamp'], pair.predicted_df_[f'{colname_b}_pred'],\n", - " label=f'{SYMBOL_B} Predicted', alpha=0.8, linestyle='--')\n", - " axes[1].set_title('Actual vs Predicted Prices - Symbol B')\n", - " axes[1].set_ylabel('Price')\n", - " axes[1].legend()\n", - " axes[1].grid(True)\n", - "\n", - " # Raw dis-equilibrium\n", - " axes[2].plot(pair.predicted_df_['tstamp'], pair.predicted_df_['disequilibrium'],\n", - " color='blue', alpha=0.8, label='Dis-equilibrium')\n", - " axes[2].axhline(y=pair.training_mu_, color='red', linestyle='--', alpha=0.7, label='Training Mean')\n", - " axes[2].set_title('Testing Period: Raw Dis-equilibrium')\n", - " axes[2].set_ylabel('Dis-equilibrium')\n", - " axes[2].legend()\n", - " axes[2].grid(True)\n", - "\n", - " # Scaled dis-equilibrium with trading signals\n", - " axes[3].plot(pair.predicted_df_['tstamp'], pair.predicted_df_['scaled_disequilibrium'],\n", - " color='green', alpha=0.8, label='Scaled Dis-equilibrium')\n", - "\n", - " # Add threshold lines\n", - " axes[3].axhline(y=CONFIG['dis-equilibrium_open_trshld'], color='purple',\n", - " linestyle=':', alpha=0.7, label=f\"Open Threshold ({CONFIG['dis-equilibrium_open_trshld']})\")\n", - " axes[3].axhline(y=CONFIG['dis-equilibrium_close_trshld'], color='brown',\n", - " linestyle=':', alpha=0.7, label=f\"Close Threshold ({CONFIG['dis-equilibrium_close_trshld']})\")\n", - "\n", - " # Add trading signals if they exist\n", - " if pair_trades is not None and len(pair_trades) > 0:\n", - " for _, trade in pair_trades.iterrows():\n", - " color = 'red' if 'BUY' in trade['action'] else 'blue'\n", - " marker = '^' if 'BUY' in trade['action'] else 'v'\n", - " axes[3].scatter(trade['time'], trade['scaled_disequilibrium'],\n", - " color=color, marker=marker, s=100, alpha=0.8,\n", - " label=f\"{trade['action']} {trade['symbol']}\" if _ < 2 else \"\")\n", - "\n", - " axes[3].set_title('Testing Period: Scaled Dis-equilibrium with Trading Signals')\n", - " axes[3].set_ylabel('Scaled Dis-equilibrium')\n", - " axes[3].set_xlabel('Time')\n", - " axes[3].legend()\n", - " axes[3].grid(True)\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " # Print prediction statistics\n", - " print(f\"\\nTesting dis-equilibrium statistics:\")\n", - " print(f\" Mean: {pair.predicted_df_['disequilibrium'].mean():.6f}\")\n", - " print(f\" Std: {pair.predicted_df_['disequilibrium'].std():.6f}\")\n", - " print(f\" Min: {pair.predicted_df_['disequilibrium'].min():.6f}\")\n", - " print(f\" Max: {pair.predicted_df_['disequilibrium'].max():.6f}\")\n", - "\n", - " print(f\"\\nTesting scaled dis-equilibrium statistics:\")\n", - " print(f\" Mean: {pair.predicted_df_['scaled_disequilibrium'].mean():.6f}\")\n", - " print(f\" Std: {pair.predicted_df_['scaled_disequilibrium'].std():.6f}\")\n", - " print(f\" Min: {pair.predicted_df_['scaled_disequilibrium'].min():.6f}\")\n", - " print(f\" Max: {pair.predicted_df_['scaled_disequilibrium'].max():.6f}\")\n", - "\n", - " # Count threshold crossings\n", - " open_crossings = (pair.predicted_df_['scaled_disequilibrium'] >= CONFIG['dis-equilibrium_open_trshld']).sum()\n", - " close_crossings = (pair.predicted_df_['scaled_disequilibrium'] <= CONFIG['dis-equilibrium_close_trshld']).sum()\n", - " print(f\"\\nThreshold crossings:\")\n", - " print(f\" Open threshold ({CONFIG['dis-equilibrium_open_trshld']}): {open_crossings} times\")\n", - " print(f\" Close threshold ({CONFIG['dis-equilibrium_close_trshld']}): {close_crossings} times\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary and Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"=\" * 60)\n", - "print(\"PAIRS TRADING ANALYSIS SUMMARY\")\n", - "print(\"=\" * 60)\n", - "\n", - "print(f\"\\nPair: {SYMBOL_A} & {SYMBOL_B}\")\n", - "print(f\"Strategy: {type(FIT_METHOD).__name__}\")\n", - "print(f\"Data file: {DATA_FILE}\")\n", - "print(f\"Training period: {training_minutes} minutes\")\n", - "\n", - "print(f\"\\nCointegration Status: {'✓ COINTEGRATED' if is_cointegrated else '✗ NOT COINTEGRATED'}\")\n", - "\n", - "if is_cointegrated:\n", - " print(f\"\\nVECM Model:\")\n", - " print(f\" Beta coefficients: {pair.vecm_fit_.beta.flatten()}\")\n", - " print(f\" Training mean: {pair.training_mu_:.6f}\")\n", - " print(f\" Training std: {pair.training_std_:.6f}\")\n", - "\n", - " if pair_trades is not None and len(pair_trades) > 0:\n", - " print(f\"\\nTrading Signals: {len(pair_trades)} generated\")\n", - " unique_times = pair_trades['time'].unique()\n", - " print(f\" Unique trade times: {len(unique_times)}\")\n", - "\n", - " # Group by time to see paired trades\n", - " for trade_time in unique_times:\n", - " trades_at_time = pair_trades[pair_trades['time'] == trade_time]\n", - " print(f\"\\n Trade at {trade_time}:\")\n", - " for _, trade in trades_at_time.iterrows():\n", - " print(f\" {trade['action']} {trade['symbol']} @ ${trade['price']:.2f} (dis-eq: {trade['scaled_disequilibrium']:.2f})\")\n", - " else:\n", - " print(f\"\\nTrading Signals: None generated\")\n", - " print(\" Possible reasons:\")\n", - " print(\" - Dis-equilibrium never exceeded open threshold\")\n", - " print(\" - Insufficient testing data\")\n", - " print(\" - Strategy-specific conditions not met\")\n", - "\n", - "else:\n", - " print(\"\\nCannot proceed with trading strategy - pair is not cointegrated\")\n", - " print(\"Consider:\")\n", - " print(\" - Trying different symbol pairs\")\n", - " print(\" - Adjusting training period length\")\n", - " print(\" - Using different data timeframe\")\n", - "\n", - "print(\"\\n\" + \"=\" * 60)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Interactive Analysis (Optional)\n", - "\n", - "You can modify the parameters below and re-run the analysis:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Interactive parameter adjustment\n", - "print(\"Current parameters:\")\n", - "print(f\" Open threshold: {CONFIG['dis-equilibrium_open_trshld']}\")\n", - "print(f\" Close threshold: {CONFIG['dis-equilibrium_close_trshld']}\")\n", - "print(f\" Training minutes: {CONFIG['training_minutes']}\")\n", - "\n", - "# Uncomment and modify these to experiment:\n", - "# CONFIG['dis-equilibrium_open_trshld'] = 1.5\n", - "# CONFIG['dis-equilibrium_close_trshld'] = 0.3\n", - "# CONFIG['training_minutes'] = 180\n", - "\n", - "print(\"\\nTo re-run with different parameters:\")\n", - "print(\"1. Modify the parameters above\")\n", - "print(\"2. Re-run from the 'Split Data into Training and Testing' cell\")\n", - "print(\"3. Or try different symbol pairs by changing SYMBOL_A and SYMBOL_B\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3.12-venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/research/notebooks/single_pair_test.ipynb b/research/notebooks/single_pair_test.ipynb new file mode 100644 index 0000000..3117e45 --- /dev/null +++ b/research/notebooks/single_pair_test.ipynb @@ -0,0 +1,6838 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + "# Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Trading Parameters Configuration\n", + "# Specify your configuration file, trading symbols and date here\n", + "\n", + "# Configuration file selection\n", + "global CONFIG_FILE\n", + "global SYMBOL_A\n", + "global SYMBOL_B\n", + "global TRADING_DATE\n", + "global TRD_DATE\n", + "global PT_BT_CONFIG\n", + "global DATA_FILE\n", + "global FIT_METHOD_TYPE\n", + "global pair\n", + "global pair_trades\n", + "global bt_result\n", + "global INSTRUMENTS\n", + "\n", + "import os\n", + "\n", + "ROOT_DIR = \"/home/oleg/develop/pairs_trading\"\n", + "os.chdir(ROOT_DIR)\n", + "\n", + "CONFIG_FILE = f\"{ROOT_DIR}/configuration/zscore.cfg\"\n", + "\n", + "# Date for data file selection (format: YYYYMMDD)\n", + "TRADING_DATE = \"20250602\" # Change this to your desired date\n", + "\n", + "# ================================ E Q U I T Y ================================\n", + "INSTRUMENTS = {\n", + " \"A\": {\n", + " \"symbol\": \"COIN\",\n", + " \"exchange_id\": \"ALPACA\",\n", + " \"instrument_type\": \"EQUITY\",\n", + " \"instrument_id_pfx\": \"STOCK-\",\n", + " },\n", + " \"B\": {\n", + " \"symbol\": \"MSTR\",\n", + " \"exchange_id\": \"ALPACA\",\n", + " \"instrument_type\": \"EQUITY\",\n", + " \"instrument_id_pfx\": \"STOCK-\",\n", + " },\n", + "}\n", + "\n", + "# ================================ E Q U I T Y ================================\n", + "\n", + "# ================================ C R Y P T O ================================\n", + "\n", + "INSTRUMENTS = {\n", + " \"A\": {\n", + " \"symbol\": \"ADA-USDT\",\n", + " \"exchange_id\": \"BNBSPOT\",\n", + " \"instrument_type\": \"CRYPTO\",\n", + " \"instrument_id_pfx\": \"PAIR-\",\n", + " },\n", + " \"B\": {\n", + " \"symbol\": \"SOL-USDT\",\n", + " \"exchange_id\": \"BNBSPOT\",\n", + " \"instrument_type\": \"CRYPTO\",\n", + " \"instrument_id_pfx\": \"PAIR-\",\n", + " },\n", + "}\n", + "# Trading pair symbols\n", + "# ================================ C R Y P T O ================================\n", + "\n", + "# ================================ E Q U I T Y VS. C R Y P T O ================================\n", + "# INSTRUMENTS = {\n", + "# \"A\": {\n", + "# \"symbol\": \"MSTR\",\n", + "# \"exchange_id\": \"ALPACA\",\n", + "# \"instrument_type\": \"EQUITY\",\n", + "# \"instrument_id_pfx\": \"STOCK-\",\n", + "# },\n", + "# \"B\": {\n", + "# \"symbol\": \"ETH-USDT\",\n", + "# \"exchange_id\": \"BNBSPOT\",\n", + "# \"instrument_type\": \"CRYPTO\",\n", + "# \"instrument_id_pfx\": \"PAIR-\",\n", + "# },\n", + "# }\n", + "# ================================ E Q U I T Y VS. C R Y P T O ================================\n", + "\n", + "SYMBOL_A = INSTRUMENTS[\"A\"][\"symbol\"]\n", + "SYMBOL_B = INSTRUMENTS[\"B\"][\"symbol\"]\n", + "FIT_METHOD_TYPE = \"RollingFit\"\n", + "TRD_DATE = f\"{TRADING_DATE[0:4]}-{TRADING_DATE[4:6]}-{TRADING_DATE[6:8]}\"\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Setup and Configuration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Code Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def setup() -> None:\n", + " import sys\n", + " import os\n", + " sys.path.append('/home/oleg/develop/pairs_trading/lib')\n", + " sys.path.append('/home/coder/pairs_trading/lib')\n", + " \n", + "\n", + " import pandas as pd\n", + " import numpy as np\n", + " import importlib\n", + " from typing import Dict, List, Optional\n", + " from IPython.display import clear_output\n", + "\n", + " # Import our modules\n", + " from pt_trading.rolling_window_fit import RollingFit\n", + " from pt_trading.trading_pair import TradingPair, PairState\n", + " # from pt_trading.results import BacktestResult\n", + "\n", + " pd.set_option('display.width', 400)\n", + " pd.set_option('display.max_colwidth', None)\n", + " pd.set_option('display.max_columns', None)\n", + "\n", + " print(\"Setup complete!\")\n", + " os.chdir(os.path.abspath(os.path.join(os.getcwd(), \"..\", \"..\")))\n", + " print(f\"Current working directory: {os.getcwd()}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "## Load Configuration\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Load Configuration from Configuration Files using HJSON\n", + "from typing import Dict, Optional\n", + "import hjson\n", + "import os\n", + "import importlib\n", + "\n", + "def load_config_from_file() -> Optional[Dict]:\n", + " global DB_TABLE_NAME\n", + " global PT_BT_CONFIG\n", + " \"\"\"Load configuration from configuration files using HJSON\"\"\"\n", + " config_file = CONFIG_FILE\n", + " config = None\n", + " \n", + " try:\n", + " with open(config_file, 'r') as f:\n", + " # HJSON handles comments, trailing commas, and other human-friendly features\n", + " config = hjson.load(f)\n", + " \n", + " # Convert relative paths to absolute paths from notebook perspective\n", + " if 'data_directory' in config:\n", + " data_dir = config['data_directory']\n", + " if data_dir.startswith('./'):\n", + " # Convert relative path to absolute path from notebook's perspective\n", + " config['data_directory'] = os.path.abspath(f\"../../{data_dir[2:]}\")\n", + " \n", + " \n", + " except FileNotFoundError:\n", + " print(f\"Configuration file not found: {config_file}\")\n", + " except hjson.HjsonDecodeError as e:\n", + " print(f\"HJSON parsing error in {config_file}: {e}\")\n", + " except Exception as e:\n", + " print(f\"Unexpected error loading config from {config_file}: {e}\")\n", + " \n", + " assert config is not None\n", + " PT_BT_CONFIG = dict(config)\n", + " DB_TABLE_NAME = PT_BT_CONFIG[\"market_data_loading\"][INSTRUMENTS[\"A\"][\"instrument_type\"]][\"db_table_name\"]\n", + "\n", + "def instantiate_fit_method_from_config(config: Dict):\n", + " \"\"\"Dynamically instantiate strategy from config\"\"\"\n", + " fit_method_class_name = config.get(\"fit_method_class\", None)\n", + " print(f\"Fit Model: {fit_method_class_name}\")\n", + " \n", + " try:\n", + " # Split module and class name\n", + " if '.' in fit_method_class_name:\n", + " module_name, class_name = fit_method_class_name.rsplit('.', 1)\n", + " else:\n", + " module_name = \"fit_methods\"\n", + " class_name = fit_method_class_name\n", + " \n", + " # Import module and get class\n", + " module = importlib.import_module(module_name)\n", + " fit_method_class = getattr(module, class_name)\n", + " \n", + " print(\"Load configuration SUCCESS\")\n", + " # Instantiate strategy\n", + " return fit_method_class()\n", + " except ValueError as e:\n", + " print(f\"Error instantiating strategy {fit_method_class_name}: {e}\")\n", + " raise Exception(f\"Error instantiating strategy {fit_method_class_name}: {e}\") from e\n", + " \n", + " except Exception as e:\n", + " print(f\"Error instantiating strategy {fit_method_class_name}: {e}\")\n", + " raise Exception(f\"Error instantiating strategy {fit_method_class_name}: {e}\") from e\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Print Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def print_config() -> None:\n", + " global PT_BT_CONFIG\n", + " global CONFIG_FILE\n", + " global SYMBOL_A\n", + " global SYMBOL_B\n", + " global TRD_DATE\n", + " global DATA_FILES\n", + " global FIT_MODEL\n", + "\n", + " print(f\"Trading Parameters:\")\n", + " print(f\" Configuration: {CONFIG_FILE}\")\n", + " print(f\" Symbol A: {SYMBOL_A}\")\n", + " print(f\" Symbol B: {SYMBOL_B}\")\n", + " print(f\" Trading Date: {TRD_DATE}\")\n", + "\n", + " # Load the specified configuration\n", + " print(f\"\\nLoading {CONFIG_FILE} configuration using HJSON...\")\n", + "\n", + " load_config_from_file()\n", + "\n", + " if PT_BT_CONFIG:\n", + " print(f\"✓ Successfully loaded configuration\")\n", + " # print(f\" Data directory: {PT_BT_CONFIG['data_directory']}\")\n", + " # print(f\" Database table: {PT_BT_CONFIG['db_table_name']}\")\n", + " # print(f\" Exchange: {PT_BT_CONFIG['exchange_id']}\")\n", + " print(f\" Training window: {PT_BT_CONFIG['training_minutes']} minutes\")\n", + " print(f\" Open threshold: {PT_BT_CONFIG['dis-equilibrium_open_trshld']}\")\n", + " print(f\" Close threshold: {PT_BT_CONFIG['dis-equilibrium_close_trshld']}\")\n", + " \n", + " # Instantiate strategy from config\n", + " FIT_MODEL = instantiate_fit_method_from_config(PT_BT_CONFIG)\n", + " print(f\" Fit Method: {type(FIT_MODEL).__name__}\")\n", + " \n", + " # Automatically construct data file name based on date and config type\n", + " DATA_FILE = f\"{TRADING_DATE}.mktdata.ohlcv.db\"\n", + " data_directory_a = PT_BT_CONFIG[\"market_data_loading\"][INSTRUMENTS[\"A\"][\"instrument_type\"]][\"data_directory\"]\n", + " data_directory_b = PT_BT_CONFIG[\"market_data_loading\"][INSTRUMENTS[\"B\"][\"instrument_type\"]][\"data_directory\"]\n", + " DATA_FILE_A = f\"{data_directory_a}/{TRADING_DATE}.mktdata.ohlcv.db\"\n", + " DATA_FILE_B = f\"{data_directory_b}/{TRADING_DATE}.mktdata.ohlcv.db\"\n", + "\n", + " PT_BT_CONFIG[\"datafiles\"] = list(set([DATA_FILE_A, DATA_FILE_B]))\n", + " \n", + " print(f\"\\nData Configuration:\")\n", + " print(f\" Data File: {DATA_FILE}\")\n", + " \n", + " # Verify data file exists\n", + " os.chdir(ROOT_DIR)\n", + " for data_file_path in PT_BT_CONFIG[\"datafiles\"]:\n", + " if os.path.exists(data_file_path):\n", + " print(f\" ✓ Data file found: {data_file_path}\")\n", + " else:\n", + " raise FileNotFoundError(\n", + " f\" ⚠ Data file not found: {data_file_path}\\n\"\n", + " f\" Please check if the date and file exist in the data directory\"\n", + " )\n", + " \n", + " else:\n", + " print(\"⚠ Failed to load configuration. Please check the configuration file.\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "## Prepare Market Data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setup complete!\n", + "Current working directory: /home/oleg\n", + "Trading Parameters:\n", + " Configuration: /home/oleg/develop/pairs_trading/configuration/zscore.cfg\n", + " Symbol A: ADA-USDT\n", + " Symbol B: SOL-USDT\n", + " Trading Date: 2025-06-02\n", + "\n", + "Loading /home/oleg/develop/pairs_trading/configuration/zscore.cfg configuration using HJSON...\n", + "✓ Successfully loaded configuration\n", + " Training window: 120 minutes\n", + " Open threshold: 2\n", + " Close threshold: 0.5\n", + "Fit Model: pt_trading.z-score_rolling_fit.ZScoreRollingFit\n", + "Load configuration SUCCESS\n", + " Fit Method: ZScoreRollingFit\n", + "\n", + "Data Configuration:\n", + " Data File: 20250602.mktdata.ohlcv.db\n", + " ✓ Data file found: ./data/crypto/20250602.mktdata.ohlcv.db\n", + "\n", + "Created trading pair: ADA-USDT & SOL-USDT\n", + "Market data shape: (540, 5)\n", + "Column names: ['close_ADA-USDT', 'close_SOL-USDT']\n", + "\n", + "Sample data:\n" + ] + }, + { + "data": { + "text/html": [ + "
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Price data - separate plots for each symbol\n", + " # colname_a, colname_b = pair.colnames()\n", + " # price_data = pair.market_data_.copy()\n", + "\n", + " # Create separate subplots for better visibility\n", + " fig_price, price_axes = plt.subplots(2, 1, figsize=(18, 10))\n", + "\n", + " # Plot SYMBOL_A\n", + " price_axes[0].plot(price_data['tstamp'], price_data[colname_a], alpha=0.7, \n", + " label=f'{SYMBOL_A}', linewidth=1, color='blue')\n", + " price_axes[0].set_title(f'{SYMBOL_A} Price Data ({TRD_DATE})')\n", + " price_axes[0].set_ylabel(f'{SYMBOL_A} Price')\n", + " price_axes[0].legend()\n", + " price_axes[0].grid(True)\n", + "\n", + " # Plot SYMBOL_B\n", + " price_axes[1].plot(price_data['tstamp'], price_data[colname_b], alpha=0.7, \n", + " label=f'{SYMBOL_B}', linewidth=1, color='red')\n", + " price_axes[1].set_title(f'{SYMBOL_B} Price Data ({TRD_DATE})')\n", + " price_axes[1].set_ylabel(f'{SYMBOL_B} Price')\n", + " price_axes[1].set_xlabel('Time')\n", + " price_axes[1].legend()\n", + " price_axes[1].grid(True)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + "\n", + " # Plot individual prices\n", + " fig, axes = plt.subplots(2, 1, figsize=(18, 12))\n", + "\n", + " # Normalized prices for comparison\n", + " norm_a = price_data[colname_a] / price_data[colname_a].iloc[0]\n", + " norm_b = price_data[colname_b] / price_data[colname_b].iloc[0]\n", + "\n", + " axes[0].plot(price_data['tstamp'], norm_a, label=f'{SYMBOL_A} (normalized)', alpha=0.8, linewidth=1)\n", + " axes[0].plot(price_data['tstamp'], norm_b, label=f'{SYMBOL_B} (normalized)', alpha=0.8, linewidth=1)\n", + " axes[0].set_title(f'Normalized Price Comparison (Base = 1.0) ({TRD_DATE})')\n", + " axes[0].set_ylabel('Normalized Price')\n", + " axes[0].legend()\n", + " axes[0].grid(True)\n", + "\n", + " # Price ratio\n", + " price_ratio = price_data[colname_a] / price_data[colname_b]\n", + " axes[1].plot(price_data['tstamp'], price_ratio, label=f'{SYMBOL_A}/{SYMBOL_B} Ratio', color='green', alpha=0.8, linewidth=1)\n", + " axes[1].set_title(f'Price Ratio Px({SYMBOL_A})/Px({SYMBOL_B}) ({TRD_DATE})')\n", + " axes[1].set_ylabel('Ratio')\n", + " axes[1].set_xlabel('Time')\n", + " axes[1].legend()\n", + " axes[1].grid(True)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # Print basic statistics\n", + " print(f\"\\nPrice Statistics:\")\n", + " print(f\" {SYMBOL_A}: Mean=${price_data[colname_a].mean():.2f}, Std=${price_data[colname_a].std():.2f}\")\n", + " print(f\" {SYMBOL_B}: Mean=${price_data[colname_b].mean():.2f}, Std=${price_data[colname_b].std():.2f}\")\n", + " print(f\" Price Ratio: Mean={price_ratio.mean():.2f}, Std={price_ratio.std():.2f}\")\n", + " print(f\" Correlation: {price_data[colname_a].corr(price_data[colname_b]):.4f}\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + " # Initialize strategy state and run analysis\n", + "def run_analysis() -> None:\n", + " global FIT_METHOD_TYPE\n", + " global PT_BT_CONFIG\n", + " global pair\n", + " global FIT_MODEL\n", + " global bt_result\n", + " global pair_trades\n", + " global PREDICTED_RESULT\n", + "\n", + " import pandas as pd\n", + " from pt_trading.results import BacktestResult\n", + " from pt_trading.trading_pair import PairState\n", + "\n", + " print(f\"Running {FIT_METHOD_TYPE} analysis...\")\n", + "\n", + " # Initialize result tracking\n", + " bt_result = BacktestResult(config=PT_BT_CONFIG)\n", + " pair_trades = None\n", + "\n", + " # Run strategy-specific analysis\n", + " print(\"\\n=== SLIDING FIT ANALYSIS ===\")\n", + "\n", + " # Initialize tracking variables for sliding window analysis\n", + " training_minutes = PT_BT_CONFIG[\"training_minutes\"]\n", + " max_iterations = len(pair.market_data_) - training_minutes\n", + "\n", + " # Limit iterations for demonstration (change this for full run)\n", + " max_demo_iterations = min(200, max_iterations)\n", + " print(f\"Processing first {max_demo_iterations} iterations for demonstration...\")\n", + "\n", + " # Initialize pair state for sliding fit method\n", + " pair.user_data_['state'] = PairState.INITIAL\n", + " pair.user_data_[\"trades\"] = pd.DataFrame(columns=pd.Index(FIT_MODEL.TRADES_COLUMNS, dtype=str))\n", + " pair.user_data_[\"is_cointegrated\"] = False\n", + "\n", + " # Run the sliding fit method\n", + " # ==========================================================================\n", + " pair_trades = FIT_MODEL.run_pair(pair=pair, bt_result=bt_result)\n", + " PREDICTED_RESULT = pair.pair_predict_result_ # TODO make abstract function\n", + " # ==========================================================================\n", + "\n", + " if pair_trades is not None and len(pair_trades) > 0:\n", + " print(f\"Generated {len(pair_trades)} trading signals\")\n", + " else:\n", + " print(\"No trading signals generated\")\n", + "\n", + " print(\"\\nStrategy execution completed!\")\n", + "\n", + " # Print comprehensive backtest results\n", + " print(\"\\n\" + \"=\"*80)\n", + " print(\"BACKTEST RESULTS\")\n", + " print(\"=\"*80)\n", + "\n", + "\n", + "# run_analysis()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def visualization() -> None:\n", + " global price_data\n", + " global pair_trades\n", + " global PT_BT_CONFIG\n", + " global pair\n", + " global SYMBOL_A\n", + " global SYMBOL_B\n", + " global TRD_DATE\n", + " global PREDICTED_RESULT\n", + "\n", + " import plotly.graph_objects as go\n", + " from plotly.subplots import make_subplots\n", + " import plotly.express as px\n", + " import plotly.offline as pyo\n", + " from IPython.display import HTML\n", + " import pandas as pd\n", + "\n", + " # Configure plotly for offline mode\n", + " pyo.init_notebook_mode(connected=True)\n", + "\n", + " # Strategy-specific interactive visualization\n", + " assert PT_BT_CONFIG is not None\n", + "\n", + " print(\"=== SLIDING FIT INTERACTIVE VISUALIZATION ===\")\n", + " print(\"Note: Rolling Fit strategy visualization with interactive plotly charts\")\n", + "\n", + " # Create consistent timeline - superset of timestamps from both dataframes\n", + " market_timestamps = set(pair.market_data_['tstamp'])\n", + " predicted_timestamps = set(PREDICTED_RESULT['tstamp'])\n", + "\n", + " # Create superset of all timestamps\n", + " all_timestamps = sorted(market_timestamps.union(predicted_timestamps))\n", + "\n", + " # Create a unified timeline dataframe for consistent plotting\n", + " timeline_df = pd.DataFrame({'tstamp': all_timestamps})\n", + "\n", + " # Merge with predicted data to get dis-equilibrium values\n", + " timeline_df = timeline_df.merge(PREDICTED_RESULT[['tstamp', 'disequilibrium', 'scaled_disequilibrium', 'signed_scaled_disequilibrium']], \n", + " on='tstamp', how='left')\n", + "\n", + " # Get Symbol_A and Symbol_B market data\n", + " colname_a, colname_b = pair.colnames()\n", + " symbol_a_data = pair.market_data_[['tstamp', colname_a]].copy()\n", + " symbol_b_data = pair.market_data_[['tstamp', colname_b]].copy()\n", + "\n", + " norm_a = price_data[colname_a] / price_data[colname_a].iloc[0]\n", + " norm_b = price_data[colname_b] / price_data[colname_b].iloc[0]\n", + "\n", + " print(f\"Using consistent timeline with {len(timeline_df)} timestamps\")\n", + " print(f\"Timeline range: {timeline_df['tstamp'].min()} to {timeline_df['tstamp'].max()}\")\n", + "\n", + " # Create subplots with price charts at bottom\n", + " fig = make_subplots(\n", + " rows=4, cols=1,\n", + " row_heights=[0.3, 0.4, 0.15, 0.15],\n", + " subplot_titles=[\n", + " f'Dis-equilibrium with Trading Thresholds ({TRD_DATE})',\n", + " f'Normalized Price Comparison with BUY/SELL Signals - {SYMBOL_A}&{SYMBOL_B} ({TRD_DATE})',\n", + " f'{SYMBOL_A} Market Data with Trading Signals ({TRD_DATE})',\n", + " f'{SYMBOL_B} Market Data with Trading Signals ({TRD_DATE})',\n", + " ],\n", + " vertical_spacing=0.06,\n", + " specs=[[{\"secondary_y\": False}],\n", + " [{\"secondary_y\": False}],\n", + " [{\"secondary_y\": False}],\n", + " [{\"secondary_y\": False}]]\n", + " )\n", + "\n", + " # 1. Scaled dis-equilibrium with thresholds - using consistent timeline\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=timeline_df['tstamp'],\n", + " y=timeline_df['scaled_disequilibrium'],\n", + " name='Absolute Scaled Dis-equilibrium',\n", + " line=dict(color='green', width=2),\n", + " opacity=0.8\n", + " ),\n", + " row=1, col=1\n", + " )\n", + "\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=timeline_df['tstamp'],\n", + " y=timeline_df['signed_scaled_disequilibrium'],\n", + " name='Scaled Dis-equilibrium',\n", + " line=dict(color='darkmagenta', width=2),\n", + " opacity=0.8\n", + " ),\n", + " row=1, col=1\n", + " )\n", + "\n", + " # Add threshold lines to first subplot\n", + " fig.add_shape(\n", + " type=\"line\",\n", + " x0=timeline_df['tstamp'].min(),\n", + " x1=timeline_df['tstamp'].max(),\n", + " y0=PT_BT_CONFIG['dis-equilibrium_open_trshld'],\n", + " y1=PT_BT_CONFIG['dis-equilibrium_open_trshld'],\n", + " line=dict(color=\"purple\", width=2, dash=\"dot\"),\n", + " opacity=0.7,\n", + " row=1, col=1\n", + " )\n", + "\n", + " fig.add_shape(\n", + " type=\"line\",\n", + " x0=timeline_df['tstamp'].min(),\n", + " x1=timeline_df['tstamp'].max(),\n", + " y0=-PT_BT_CONFIG['dis-equilibrium_open_trshld'],\n", + " y1=-PT_BT_CONFIG['dis-equilibrium_open_trshld'],\n", + " line=dict(color=\"purple\", width=2, dash=\"dot\"),\n", + " opacity=0.7,\n", + " row=1, col=1\n", + " )\n", + "\n", + " fig.add_shape(\n", + " type=\"line\",\n", + " x0=timeline_df['tstamp'].min(),\n", + " x1=timeline_df['tstamp'].max(),\n", + " y0=PT_BT_CONFIG['dis-equilibrium_close_trshld'],\n", + " y1=PT_BT_CONFIG['dis-equilibrium_close_trshld'],\n", + " line=dict(color=\"brown\", width=2, dash=\"dot\"),\n", + " opacity=0.7,\n", + " row=1, col=1\n", + " )\n", + "\n", + " fig.add_shape(\n", + " type=\"line\",\n", + " x0=timeline_df['tstamp'].min(),\n", + " x1=timeline_df['tstamp'].max(),\n", + " y0=-PT_BT_CONFIG['dis-equilibrium_close_trshld'],\n", + " y1=-PT_BT_CONFIG['dis-equilibrium_close_trshld'],\n", + " line=dict(color=\"brown\", width=2, dash=\"dot\"),\n", + " opacity=0.7,\n", + " row=1, col=1\n", + " )\n", + "\n", + " fig.add_shape(\n", + " type=\"line\",\n", + " x0=timeline_df['tstamp'].min(),\n", + " x1=timeline_df['tstamp'].max(),\n", + " y0=0,\n", + " y1=0,\n", + " line=dict(color=\"black\", width=1, dash=\"solid\"),\n", + " opacity=0.5,\n", + " row=1, col=1\n", + " )\n", + "\n", + " # Add normalized price lines\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=price_data['tstamp'],\n", + " y=norm_a,\n", + " name=f'{SYMBOL_A} (Normalized)',\n", + " line=dict(color='blue', width=2),\n", + " opacity=0.8\n", + " ),\n", + " row=2, col=1\n", + " )\n", + "\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=price_data['tstamp'],\n", + " y=norm_b,\n", + " name=f'{SYMBOL_B} (Normalized)',\n", + " line=dict(color='orange', width=2),\n", + " opacity=0.8,\n", + " ),\n", + " row=2, col=1\n", + " )\n", + "\n", + " # Add BUY and SELL signals if available\n", + " if pair_trades is not None and len(pair_trades) > 0:\n", + " # Define signal groups to avoid legend repetition\n", + " signal_groups = {}\n", + " \n", + " # Process all trades and group by signal type (ignore OPEN/CLOSE status)\n", + " for _, trade in pair_trades.iterrows():\n", + " symbol = trade['symbol']\n", + " side = trade['side']\n", + " # status = trade['status']\n", + " action = trade['action']\n", + " \n", + " # Create signal group key (without status to combine OPEN/CLOSE)\n", + " signal_key = f\"{symbol} {side} {action}\"\n", + " \n", + " # Find normalized price for this trade\n", + " trade_time = trade['time']\n", + " if symbol == SYMBOL_A:\n", + " closest_idx = price_data['tstamp'].searchsorted(trade_time)\n", + " if closest_idx < len(norm_a):\n", + " norm_price = norm_a.iloc[closest_idx]\n", + " else:\n", + " norm_price = norm_a.iloc[-1]\n", + " else: # SYMBOL_B\n", + " closest_idx = price_data['tstamp'].searchsorted(trade_time)\n", + " if closest_idx < len(norm_b):\n", + " norm_price = norm_b.iloc[closest_idx]\n", + " else:\n", + " norm_price = norm_b.iloc[-1]\n", + " \n", + " # Initialize group if not exists\n", + " if signal_key not in signal_groups:\n", + " signal_groups[signal_key] = {\n", + " 'times': [],\n", + " 'prices': [],\n", + " 'actual_prices': [],\n", + " 'symbol': symbol,\n", + " 'side': side,\n", + " # 'status': status,\n", + " 'action': trade['action']\n", + " }\n", + " \n", + " # Add to group\n", + " signal_groups[signal_key]['times'].append(trade_time)\n", + " signal_groups[signal_key]['prices'].append(norm_price)\n", + " signal_groups[signal_key]['actual_prices'].append(trade['price'])\n", + " \n", + " # Add each signal group as a single trace\n", + " for signal_key, group_data in signal_groups.items():\n", + " symbol = group_data['symbol']\n", + " side = group_data['side']\n", + " # status = group_data['status']\n", + " \n", + " # Determine marker properties (same for all OPEN/CLOSE of same side)\n", + " is_close: bool = (group_data['action'] == \"CLOSE\")\n", + " \n", + " if 'BUY' in side:\n", + " marker_color = 'green'\n", + " marker_symbol = 'triangle-up'\n", + " marker_size = 14\n", + " else: # SELL\n", + " marker_color = 'red'\n", + " marker_symbol = 'triangle-down'\n", + " marker_size = 14\n", + " \n", + " # Create hover text for each point in the group\n", + " hover_texts = []\n", + " for i, (time, norm_price, actual_price) in enumerate(zip(group_data['times'], \n", + " group_data['prices'], \n", + " group_data['actual_prices'])):\n", + " # Find the corresponding trade to get the status for hover text\n", + " trade_info = pair_trades[(pair_trades['time'] == time) & \n", + " (pair_trades['symbol'] == symbol) & \n", + " (pair_trades['side'] == side)]\n", + " if len(trade_info) > 0:\n", + " action = trade_info.iloc[0]['action']\n", + " hover_texts.append(f'{signal_key} {action}
' +\n", + " f'Time: {time}
' +\n", + " f'Normalized Price: {norm_price:.4f}
' +\n", + " f'Actual Price: ${actual_price:.2f}')\n", + " else:\n", + " hover_texts.append(f'{signal_key}
' +\n", + " f'Time: {time}
' +\n", + " f'Normalized Price: {norm_price:.4f}
' +\n", + " f'Actual Price: ${actual_price:.2f}')\n", + " \n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=group_data['times'],\n", + " y=group_data['prices'],\n", + " mode='markers',\n", + " name=signal_key,\n", + " marker=dict(\n", + " color=marker_color,\n", + " size=marker_size,\n", + " symbol=marker_symbol,\n", + " line=dict(width=2, color='black') if is_close else None\n", + " ),\n", + " showlegend=True,\n", + " hovertemplate='%{text}',\n", + " text=hover_texts\n", + " ),\n", + " row=2, col=1\n", + " )\n", + "\n", + " # ----------------------------- \n", + " \n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=symbol_a_data['tstamp'],\n", + " y=symbol_a_data[colname_a],\n", + " name=f'{SYMBOL_A} Price',\n", + " line=dict(color='blue', width=2),\n", + " opacity=0.8\n", + " ),\n", + " row=3, col=1\n", + " )\n", + "\n", + " # Filter trades for Symbol_A\n", + " symbol_a_trades = pair_trades[pair_trades['symbol'] == SYMBOL_A]\n", + " print(f\"\\nSymbol_A trades:\\n{symbol_a_trades}\")\n", + " \n", + " if len(symbol_a_trades) > 0:\n", + " # Separate trades by action and status for different colors\n", + " buy_open_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('BUY', na=False)) & \n", + " (symbol_a_trades['action'].str.contains('OPEN', na=False))]\n", + " buy_close_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('BUY', na=False)) & \n", + " (symbol_a_trades['action'].str.contains('CLOSE', na=False))]\n", + " \n", + " sell_open_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('SELL', na=False)) & \n", + " (symbol_a_trades['action'].str.contains('OPEN', na=False))]\n", + " sell_close_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('SELL', na=False)) & \n", + " (symbol_a_trades['action'].str.contains('CLOSE', na=False))]\n", + " \n", + " # Add BUY OPEN signals\n", + " if len(buy_open_trades) > 0:\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=buy_open_trades['time'],\n", + " y=buy_open_trades['price'],\n", + " mode='markers',\n", + " name=f'{SYMBOL_A} BUY OPEN',\n", + " marker=dict(color='green', size=12, symbol='triangle-up'),\n", + " showlegend=True\n", + " ),\n", + " row=3, col=1\n", + " )\n", + " \n", + " # Add BUY CLOSE signals\n", + " if len(buy_close_trades) > 0:\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=buy_close_trades['time'],\n", + " y=buy_close_trades['price'],\n", + " mode='markers',\n", + " name=f'{SYMBOL_A} BUY CLOSE',\n", + " marker=dict(color='green', size=12, symbol='triangle-up'),\n", + " line=dict(width=2, color='black'),\n", + " showlegend=True\n", + " ),\n", + " row=3, col=1\n", + " )\n", + " \n", + " # Add SELL OPEN signals\n", + " if len(sell_open_trades) > 0:\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=sell_open_trades['time'],\n", + " y=sell_open_trades['price'],\n", + " mode='markers',\n", + " name=f'{SYMBOL_A} SELL OPEN',\n", + " marker=dict(color='red', size=12, symbol='triangle-down'),\n", + " showlegend=True\n", + " ),\n", + " row=3, col=1\n", + " )\n", + " \n", + " # Add SELL CLOSE signals\n", + " if len(sell_close_trades) > 0:\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=sell_close_trades['time'],\n", + " y=sell_close_trades['price'],\n", + " mode='markers',\n", + " name=f'{SYMBOL_A} SELL CLOSE',\n", + " marker=dict(color='red', size=12, symbol='triangle-down'),\n", + " line=dict(width=2, color='black'),\n", + " showlegend=True\n", + " ),\n", + " row=3, col=1\n", + " )\n", + " \n", + " # 4. Symbol_B Market Data with Trading Signals\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=symbol_b_data['tstamp'],\n", + " y=symbol_b_data[colname_b],\n", + " name=f'{SYMBOL_B} Price',\n", + " line=dict(color='orange', width=2),\n", + " opacity=0.8\n", + " ),\n", + " row=4, col=1\n", + " )\n", + " \n", + " # Add trading signals for Symbol_B if available\n", + " symbol_b_trades = pair_trades[pair_trades['symbol'] == SYMBOL_B]\n", + " print(f\"\\nSymbol_B trades:\\n{symbol_b_trades}\")\n", + " \n", + " if len(symbol_b_trades) > 0:\n", + " # Separate trades by action and status for different colors\n", + " buy_open_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('BUY', na=False)) & \n", + " (symbol_b_trades['action'].str.startswith('OPEN', na=False))]\n", + " buy_close_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('BUY', na=False)) & \n", + " (symbol_b_trades['action'].str.startswith('CLOSE', na=False))]\n", + " \n", + " sell_open_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('SELL', na=False)) & \n", + " (symbol_b_trades['action'].str.contains('OPEN', na=False))]\n", + " sell_close_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('SELL', na=False)) & \n", + " (symbol_b_trades['action'].str.contains('CLOSE', na=False))]\n", + " \n", + " # Add BUY OPEN signals\n", + " if len(buy_open_trades) > 0:\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=buy_open_trades['time'],\n", + " y=buy_open_trades['price'],\n", + " mode='markers',\n", + " name=f'{SYMBOL_B} BUY OPEN',\n", + " marker=dict(color='darkgreen', size=12, symbol='triangle-up'),\n", + " showlegend=True\n", + " ),\n", + " row=4, col=1\n", + " )\n", + " \n", + " # Add BUY CLOSE signals\n", + " if len(buy_close_trades) > 0:\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=buy_close_trades['time'],\n", + " y=buy_close_trades['price'],\n", + " mode='markers',\n", + " name=f'{SYMBOL_B} BUY CLOSE',\n", + " marker=dict(color='green', size=12, symbol='triangle-up'),\n", + " line=dict(width=2, color='black'),\n", + " showlegend=True\n", + " ),\n", + " row=4, col=1\n", + " )\n", + " \n", + " # Add SELL OPEN signals\n", + " if len(sell_open_trades) > 0:\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=sell_open_trades['time'],\n", + " y=sell_open_trades['price'],\n", + " mode='markers',\n", + " name=f'{SYMBOL_B} SELL OPEN',\n", + " marker=dict(color='red', size=12, symbol='triangle-down'),\n", + " showlegend=True\n", + " ),\n", + " row=4, col=1\n", + " )\n", + " \n", + " # Add SELL CLOSE signals\n", + " if len(sell_close_trades) > 0:\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=sell_close_trades['time'],\n", + " y=sell_close_trades['price'],\n", + " mode='markers',\n", + " name=f'{SYMBOL_B} SELL CLOSE',\n", + " marker=dict(color='red', size=12, symbol='triangle-down'),\n", + " line=dict(width=2, color='black'),\n", + " showlegend=True\n", + " ),\n", + " row=4, col=1\n", + " )\n", + " \n", + " # Update layout\n", + " fig.update_layout(\n", + " height=1600,\n", + " title_text=f\"Strategy Analysis - {SYMBOL_A} & {SYMBOL_B} ({TRD_DATE})\",\n", + " showlegend=True,\n", + " template=\"plotly_white\",\n", + " plot_bgcolor='lightgray',\n", + " )\n", + " \n", + " # Update y-axis labels\n", + " fig.update_yaxes(title_text=\"Scaled Dis-equilibrium\", row=1, col=1)\n", + " fig.update_yaxes(title_text=f\"{SYMBOL_A} Price ($)\", row=2, col=1)\n", + " fig.update_yaxes(title_text=f\"{SYMBOL_B} Price ($)\", row=3, col=1)\n", + " fig.update_yaxes(title_text=\"Normalized Price (Base = 1.0)\", row=4, col=1)\n", + " \n", + " # Update x-axis labels and ensure consistent time range\n", + " time_range = [timeline_df['tstamp'].min(), timeline_df['tstamp'].max()]\n", + " fig.update_xaxes(range=time_range, row=1, col=1)\n", + " fig.update_xaxes(range=time_range, row=2, col=1)\n", + " fig.update_xaxes(range=time_range, row=3, col=1)\n", + " fig.update_xaxes(title_text=\"Time\", range=time_range, row=4, col=1)\n", + " \n", + " # Display using plotly offline mode\n", + " # pyo.iplot(fig)\n", + " fig.show()\n", + " \n", + " else:\n", + " print(\"No interactive visualization data available - strategy may not have run successfully\")\n", + "\n", + " print(f\"\\nChart shows:\")\n", + " print(f\"- {SYMBOL_A} and {SYMBOL_B} prices normalized to start at 1.0\")\n", + " print(f\"- BUY signals shown as green triangles pointing up\")\n", + " print(f\"- SELL signals shown as orange triangles pointing down\")\n", + " print(f\"- All BUY signals per symbol grouped together, all SELL signals per symbol grouped together\")\n", + " print(f\"- Hover over markers to see individual trade details (OPEN/CLOSE status)\")\n", + "\n", + " if pair_trades is not None and len(pair_trades) > 0:\n", + " print(f\"- Total signals displayed: {len(pair_trades)}\")\n", + " print(f\"- {SYMBOL_A} signals: {len(pair_trades[pair_trades['symbol'] == SYMBOL_A])}\")\n", + " print(f\"- {SYMBOL_B} signals: {len(pair_trades[pair_trades['symbol'] == SYMBOL_B])}\")\n", + " else:\n", + " print(\"- No trading signals to display\")\n", + "\n", + "# visualization()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "## Summary\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def summary() -> None:\n", + " print(\"=\" * 80)\n", + " print(\"PAIRS TRADING BACKTEST SUMMARY\")\n", + " print(\"=\" * 80)\n", + "\n", + " print(f\"\\nPair: {SYMBOL_A} & {SYMBOL_B}\")\n", + " print(f\"Fit Method: {FIT_METHOD_TYPE}\")\n", + " print(f\"Configuration: {CONFIG_FILE}\")\n", + " # print(f\"Data file: {DATA_FILE}\")\n", + " print(f\"Trading date: {TRD_DATE}\")\n", + "\n", + " print(f\"\\nStrategy Parameters:\")\n", + " print(f\" Training window: {PT_BT_CONFIG['training_minutes']} minutes\")\n", + " print(f\" Open threshold: {PT_BT_CONFIG['dis-equilibrium_open_trshld']}\")\n", + " print(f\" Close threshold: {PT_BT_CONFIG['dis-equilibrium_close_trshld']}\")\n", + " print(f\" Funding per pair: ${PT_BT_CONFIG['funding_per_pair']}\")\n", + "\n", + " # Strategy-specific summary\n", + " print(f\"\\nRolling Window Analysis:\")\n", + " training_minutes = PT_BT_CONFIG['training_minutes']\n", + " max_iterations = len(pair.market_data_) - training_minutes\n", + " print(f\" Total data points: {len(pair.market_data_)}\")\n", + " print(f\" Maximum iterations: {max_iterations}\")\n", + " print(f\" Analysis type: Dynamic rolling window\")\n", + "\n", + " # Trading signals summary\n", + " if pair_trades is not None and len(pair_trades) > 0:\n", + " print(f\"\\nTrading Signals: {len(pair_trades)} generated\")\n", + " unique_times = pair_trades['time'].unique()\n", + " print(f\" Unique trade times: {len(unique_times)}\")\n", + " \n", + " # Group by action type\n", + " buy_signals = pair_trades[pair_trades['side'].str.contains('BUY', na=False)]\n", + " sell_signals = pair_trades[pair_trades['side'].str.contains('SELL', na=False)]\n", + " \n", + " print(f\" BUY signals: {len(buy_signals)}\")\n", + " print(f\" SELL signals: {len(sell_signals)}\")\n", + " \n", + " # Show first few trades\n", + " NTRADES_TO_SHOW = 6\n", + " print(f\"\\nFirst few trading signals:\")\n", + " for ii, (idx, trade) in enumerate(pair_trades.head(NTRADES_TO_SHOW).iterrows()):\n", + " print(f\" {ii+1}. {trade['side']} {trade['symbol']} @ ${trade['price']:.2f} at {trade['time']}\")\n", + " \n", + " if len(pair_trades) > NTRADES_TO_SHOW:\n", + " print(f\" ... and {len(pair_trades) - NTRADES_TO_SHOW} more signals\")\n", + " \n", + " else:\n", + " print(f\"\\nTrading Signals: None generated\")\n", + " print(\" Possible reasons:\")\n", + " print(\" - Dis-equilibrium never exceeded open threshold\")\n", + " print(\" - Pair not cointegrated (for StaticFit)\")\n", + " print(\" - Insufficient data or market conditions\")\n", + "\n", + " print(f\"\\n\" + \"=\" * 80)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def performance_results() -> None:\n", + " global pair_trades\n", + " global bt_result\n", + " global SYMBOL_A\n", + " global SYMBOL_B\n", + " global FIT_METHOD_TYPE\n", + " global PT_BT_CONFIG\n", + "\n", + " from pt_trading.results import BacktestResult\n", + "\n", + " if pair_trades is not None and len(pair_trades) > 0:\n", + " # Print detailed results using BacktestResult methods\n", + " # bt_result.print_single_day_results()\n", + " \n", + " # Print trading signal details\n", + " print(f\"\\nDetailed Trading Signals:\")\n", + " print(f\"{'Time':<20} {'Action':<15} {'Symbol':<10} {'Price':<12} {'Scaled Dis-eq':<15} {'Status':<10}\")\n", + " print(\"-\" * 90)\n", + " \n", + " for _, trade in pair_trades.head(10).iterrows(): # Show first 10 trades\n", + " time_str = str(trade['time'])[:19] \n", + " action_str = str(trade['action'])[:14]\n", + " symbol_str = str(trade['symbol'])[:9]\n", + " price_str = f\"${trade['price']:.2f}\"\n", + " diseq_str = f\"{trade.get('scaled_disequilibrium', 'N/A'):.3f}\" if 'scaled_disequilibrium' in trade else 'N/A'\n", + " status = trade.get('status', 'N/A')\n", + " \n", + " print(f\"{time_str:<20} {action_str:<15} {symbol_str:<10} {price_str:<12} {diseq_str:<15} {status:<10}\")\n", + " \n", + " if len(pair_trades) > 10:\n", + " print(f\"... and {len(pair_trades)-10} more trading signals\")\n", + " \n", + " bt_result.collect_single_day_results([pair_trades])\n", + "\n", + " # bt_result.print_grand_totals()\n", + " # bt_result.print_outstanding_positions() \n", + " else:\n", + " print(f\"\\nNo trading signals generated\")\n", + " print(f\"Backtest completed with no trades\")\n", + " \n", + " # Still print any outstanding information\n", + " print(f\"\\nConfiguration Summary:\")\n", + " print(f\" Pair: {SYMBOL_A} & {SYMBOL_B}\")\n", + " print(f\" Strategy: {FIT_METHOD_TYPE}\")\n", + " print(f\" Open threshold: {PT_BT_CONFIG['dis-equilibrium_open_trshld']}\")\n", + " print(f\" Close threshold: {PT_BT_CONFIG['dis-equilibrium_close_trshld']}\")\n", + " print(f\" Training window: {PT_BT_CONFIG['training_minutes']} minutes\")\n", + " \n", + " print(\"\\n\" + \"=\"*80)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def print_summary():\n", + " global pair_trades\n", + "\n", + " from pt_trading.results import BacktestResult\n", + "\n", + " if pair_trades is not None and len(pair_trades) > 0:\n", + " all_results: Dict[str, Dict[str, Any]] = {}\n", + " all_results[f\"{TRADING_DATE}-{pair.name()}\"] = {\n", + " \"trades\": bt_result.trades.copy(), \n", + " \"outstanding_positions\": bt_result.outstanding_positions.copy()\n", + " }\n", + "\n", + " if all_results:\n", + " aggregate_bt_results = BacktestResult(config=PT_BT_CONFIG)\n", + " aggregate_bt_results.calculate_returns(all_results)\n", + " aggregate_bt_results.print_grand_totals()\n", + " aggregate_bt_results.print_outstanding_positions()\n", + "\n", + "\n", + " \n", + "# performance_results()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setup complete!\n", + "Current working directory: /home/oleg\n", + "Trading Parameters:\n", + " Configuration: /home/oleg/develop/pairs_trading/configuration/zscore.cfg\n", + " Symbol A: ADA-USDT\n", + " Symbol B: SOL-USDT\n", + " Trading Date: 2025-06-02\n", + "\n", + "Loading /home/oleg/develop/pairs_trading/configuration/zscore.cfg configuration using HJSON...\n", + "✓ Successfully loaded configuration\n", + " Training window: 120 minutes\n", + " Open threshold: 2\n", + " Close threshold: 0.5\n", + "Fit Model: pt_trading.z-score_rolling_fit.ZScoreRollingFit\n", + "Load configuration SUCCESS\n", + " Fit Method: ZScoreRollingFit\n", + "\n", + "Data Configuration:\n", + " Data File: 20250602.mktdata.ohlcv.db\n", + " ✓ Data file found: ./data/crypto/20250602.mktdata.ohlcv.db\n", + "\n", + "Created trading pair: ADA-USDT & SOL-USDT\n", + "Market data shape: (540, 5)\n", + "Column names: ['close_ADA-USDT', 'close_SOL-USDT']\n", + "\n", + "Sample data:\n" + ] + }, + { + "data": { + "text/html": [ + "
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tstampclose_ADA-USDTclose_SOL-USDTexec_price_ADA-USDTexec_price_SOL-USDT
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+ " Open threshold: 2\n", + " Close threshold: 0.5\n", + " Training minutes: 120\n", + " Funding per pair: $2000\n" + ] + }, + { + "data": { + "image/png": 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o9R5++GEKCgp48803Qz5Uv/zyy4NVKS+++CLr169n5syZwf29TjrpJNq1a8fUqVM5++yz6dChQ73Xr616aSxRsLVnn32WgoICXn31VYYPHw7AiSeeyFFHHcU999zDQQcdFPLB/Y6sE4Dff/+dt99+O/gh/+GHH84hhxzCY489xhNPPAHA/vvvX6c94QEHHMDf//53Zs+ezTHHHBNyLDk5mZdeeqnRROeCBQsoLS1l6tSpIe/9yiuvDP7vOXPm8P3333PFFVdw4YUXBp/lsssu4+WXX+a0004L2R9t5cqVfPDBB8GxUaNGcfTRR/P+++9z2mmnNRjLqlWrAIIJ6vqOb9uasEePHkydOjVkz8LmvqcvvviC8ePHh/y8bOuuu+6iQ4cOvP3228F7nHLKKZx88sk8+OCDTSb+jj32WDZs2NDoHIBLLrmESy+9tMl52ys/Px8I7N+3rczMTLZs2VJnPCcnhxUrVmzX/VatWkVycjIJCQlNzl27di2ffPIJhxxySMhazc/PD/7sba12f8MtW7Y0+PNcUlLCW2+9xfDhw0P2Q3S5XEycOJH99tuP1NRUVq5cydSpUzn11FN5/fXXm0xUtuQ9rl27FofDwQ033MCUKVPo27cvH3/8MU899RR+v59//OMfTd7L4XCQnp4eMh4VFUVKSkq937OtPffcczgcDiZOnFjnWG371hUrVijxJyIiItKGKPEnIiIiInuknJwcjjrqqOBef1t/qFvryy+/BOCss84KGT/77LN54YUX+PLLL0MSf9nZ2SFJv60dffTRIe31Bg0axHvvvReyF1Xt+LRp0/D5fMEWcFsn/crLy/F6vYwcOZJvvvmG8vLyYNKtpZ588kk+//xzHnvssWBCbe7cuZSVlXH44YcHE3IApmkyePDgYCJzy5YtLFu2jPPOOy/k/mPGjKFnz55UVVU1K4ZtkzCGYbD//vtz9913h8wbMWJEk0k/y7L49NNPOeCAA+qtuqlt+/jRRx+x9957k5SUFPKM++67L88++yw//vgjRx11VL33qG0hGB8f36zn+/LLLxk0aFBI4iE+Pp6///3vPPTQQ3U+MN+RdQIwdOjQkMqejh07ctBBB/H555/j9/txOBwh68nr9eJ2u+ncuTNJSUn8+uuvdRJ/kyZNarK6sXYNfPHFF/Tt27feVpJfffUVDoeDyZMnh4yfffbZzJ49m6+++iokobfvvvuGJAL79u1LQkJCgy0XaxUXFwOEVIxurVOnTsFEvs/nY/Xq1Tz//POce+65vPbaa8Hq1+a+p6SkJP744w/WrFkTTCRvraSkhO+//57LLrsspAUlwNixY3n88cfZvHlzo5XHDzzwADU1NY0+N7Bd++i1RHV1NUBIgrRWdHR0necDgu9re5SUlAT3n2tMVVUVl19+OTExMXUSYbWVvtuqHWvovVqWxdVXX01ZWRm33HJLyLFhw4aFVIcedNBBTJw4kaOOOoqHHnqIqVOnNhpvS95jZWUllmXxj3/8g/POOw+AiRMnUlpayssvv8z555/faGK0urq6wdau0dHRwVjqM2vWLP773/8yZcqUetd27c9Y7c+ciIiIiLQNSvyJiIiIyB7roosu4n//+x/PPvtsvVV/GzZswDTNkOQDBCoykpKS6lTgNFRhBIEEzNZqEyXbVpYlJiZiWRbl5eXB1mrz58/n8ccfZ+HChXUSatub+Pvqq6948sknOf/880MqOdasWQPAGWecUe95tR8wb9y4EYAuXbrUmdOtW7dmf9Bfm4QxDIOoqCi6du1apzIFGn+3tYqKinC73fTq1avReWvXrmX58uV1qr62vk5Dap+/oqKiyXgg8J4GDx5cZ7x79+7B41sn/nZknUD934+uXbtSVVVFUVERmZmZVFdX88wzzzBjxgw2b94csj9XeXl5nfOb8+5HjhzJxIkTeeKJJ3jppZcYOXIkEyZM4MgjjwwmNzZs2EC7du3qJCl69OgRPL61+qouk5OTKSsrazIeoMF9x+Li4kL2jNtvv/3Ye++9Of7443n22We5/vrrAZr9ni677DIuuugiJk6cSO/evRk7dixHH300ffv2BWDdunXYts2jjz7Ko48+Wm9MhYWFjSb+9t5772Y9c2urTYZuu78kBBJoWydLa9m23ey9FuvT1P5xfr+fK6+8khUrVvDcc8/VeY8xMTH1xls7Fh0dXe9177jjDr7++mvuu+++4PeyMV26dOGggw7i448/DibZS0pK8Hq9IbEkJia26D3GxMRQWVnJEUccETLviCOO4Ouvv2bZsmWMGDGCoqKikHawcXFxxMfHExMTExJDY/fa2k8//cRNN93E2LFjQyp367Mj318RERER2fmU+BMRERGRPda2VX8Nae6Hmg19gAo0uA9TQ+O1H3avW7eOM888k+7du3P99dfToUMHXC4XX375JS+99BKWZTUrtq2tX7+ea665hn333Zcrrrii3vvef//99baha86+di2xbRKmIY2925ayLIsxY8YwZcqUeo/XV9lSqzZht3z5ciZMmLDTYqq1veukJe644w5mzJjBGWecwZAhQ0hMTMQwDK688sp6r9dQYmRrhmHw2GOPsXDhQj7//HO+/vprbrzxRl588UXeeOONZldIbq2htdbUM9cmQpubIITA/meJiYn8+OOPwbHmvqcRI0bwySefMGfOHL799lv++9//8p///IfbbruNE088MfgzevbZZzdYEbztLxdsa9ukTkNqkz2tpfbfhPz8/DqJ2fz8fAYNGlTnnLKyskb3ZGxMSkpKk9/Hm2++mS+++IIHH3yw3mR+ZmZmsLXm1mpbXNZX7f3EE0/w2muv8Y9//KNOBWxjsrKy8Hq9VFVVkZCQwKWXXsoPP/wQPH7sscdy7733tug9tmvXjjVr1pCRkREyr7YytbS0FIATTjghJHle2/Y1MzMTv99PYWFhyC9VeDweSkpK6n3+3377jQsvvJBevXrx2GOPhVQVb6323tv7/RURERGR1qHEn4iIiIjs0S688EL+97//BfdP21qnTp2wLIu1a9cGq5IACgoKKCsra/Y+djvis88+w+Px8NRTT4VUg9W23Gyp6upqLr30UhITE3n44YfrJJRqWwWmp6c3mpCrjWXt2rV1jq1evXq7YttRaWlpJCQk8McffzQ6r3PnzlRWVjYr4bitvffem+TkZN5//30uuOCCJhOhHTt2rPd91O5Dt22F346q7/uxZs0aYmNjg4mC2v3paivbIFD5U1+1X0sNGTKEIUOGcOWVVzJr1iyuvvpqPvjgA0488UQ6derEd999h9vtDqn6q30XO+vnqTY5m5ub26Lz/H4/lZWVwa9b8p5SUlI4/vjjOf7446moqOC0007j8ccf58QTTwz+TLlcru1ac1A3qdOQ1t7jr1+/fgAsXrw4JDm1efNm8vLymDRpUp1zcnNzm1UxV5/u3bsza9asBiub77vvPmbMmMGNN95YpyKuVt++fZk/fz6WZYX8e7do0SJiY2Pp1q1byPxXX32Vxx9/nDPOOKPRXwipT25uLtHR0cTFxQFw3XXXhSQua5NsLXmP/fv3Z82aNWzevDmklWtt4rL253rbdrC1c2vvtWTJEsaPHx88vmTJEizLqvO9WbduHVOmTCEtLY3nnnuu0URy7c/Y1v99FBEREZHwq//XRkVERERE9hCdO3fmqKOO4o033qhTFVL7Iel//vOfkPEXX3wx5Hhrqk0sbdtm8O23396u6916662sWbOGJ554ot69s8aNG0dCQgLPPPNMve3hattgtmvXjn79+jFz5syQRMi3337LihUrtiu2HWWaJhMmTODzzz9n8eLFdY7XvsNDDz2UBQsW8PXXX9eZU1ZWhs/na/AesbGxTJkyhZUrV/Lggw/WW3327rvvsmjRIiCwRhYtWsSCBQuCxysrK3nzzTfp1KlTk/sWttSCBQtYunRp8OtNmzYxZ84cxowZE1xL9SUrp02b1qyKsoaUlpbWeRe1CYfadob77bcffr+fV199NWTeSy+9hGEY7Lffftt9/621b9+eDh06sGTJkmaf8/3331NZWRmSBGnue9p2f7P4+Hg6d+4cfO709HRGjhzJG2+8EUzWbK2x1rK1HnjgAV588cUm/7SkOq0pXq+XlStXhsTcq1cvunfvzptvvhnyHqZPn45hGBxyyCEh1ygvL2fdunUMHTp0u2IYMmQItm3X+718/vnneeGFF7jgggsabE0McMghh1BQUMDHH38cHCsqKuKjjz7igAMOCNln74MPPuDOO+/kyCOP5IYbbmjwmvV9z3777Tc+++wzxowZE0wwDhgwgH333Tf4p/bnvSXv8bDDDgPgv//9b3DMsixmzJhBSkpKcE/PvffeO+RetYm/ffbZh5SUFKZPnx4S7/Tp04mNjWX//fcPjuXn53P22WdjGAZTp04NJhUbsnTpUhITE5tsrywiIiIiu5Yq/kRERERkj3fBBRfw7rvvsnr16pAPMPv27cuxxx7LG2+8QVlZGSNGjGDx4sXMnDmTCRMmsM8++7R6bGPGjMHlcnHBBRdw0kknUVFRwVtvvUV6enq97esa88UXX/DOO+8wceJEli9fzvLly4PH4uPjmTBhAgkJCfzzn//k2muv5bjjjuOwww4jLS2NjRs38uWXXzJs2DD+7//+D4CrrrqK888/n1NOOYXjjz+ekpISXnnlFXr16hVSObUrXXXVVXz77bdMnjyZSZMm0aNHD/Lz8/noo4947bXXSEpK4pxzzuGzzz7jggsu4Nhjj6V///5UVVXx+++/M3v2bObMmdPoB95TpkxhxYoVvPDCC8ybN4+JEyeSkZFBQUEBn376KYsWLeL1118H4LzzzuP999/n3HPPZfLkySQnJ/POO++Qm5vL448/3mALz+3Vu3dvzjnnHCZPnkxUVFTww/6tq8D2339/3n33XRISEujZsycLFy5k7ty5pKSkbPd9Z86cyfTp05kwYQKdO3emoqKCN998k4SEhGBC78ADD2TUqFH861//YsOGDfTp04dvv/2WOXPmcMYZZzTZ7rIlDjroID755JN695crLy/n3XffBQJVfqtXr2b69OnExMSEVHg19z0dfvjhjBw5kv79+5OSksLixYuZPXs2p512WnDOrbfeyimnnMKRRx7JpEmTyMnJoaCggIULF5KXl8f//ve/Rp9nZ+7xV5uggkCFaHl5Of/+97+BwL95Bx54IBCoPjvssMOC7SlrXXvttVx44YWcffbZHH744fz++++8+uqrnHjiiXUqv+bOnYtt2xx00EHbFevee+9NSkoK3333XUgbz08++YQHHniArl270r179+D3s9aYMWOCrTEnTpzIkCFDuOGGG1ixYgWpqalMnz4dv98f8nOxaNEirr32WlJSUhg9enSd78mwYcOCybQrrriCmJgYhg4dSnp6OitWrODNN98kJiaGq6++ulnP1tz3eNBBBzF69GieeeYZiouL6dOnD3PmzGH+/PncfvvtIYnL+sTExHDZZZdx++23c9lllzFu3Dh++ukn/ve//3HllVeGrOcpU6awfv16pkyZwvz585k/f37wWEZGBmPGjAm59ty5cznggAO0x5+IiIhIG6PEn4iIiIjs8bp06cJRRx3FzJkz6xy78847yc7OZubMmXz66adkZGRw/vnnc8kll+yS2Lp3785jjz3GI488wn333UdGRgYnn3wyaWlp3HjjjS26Vm2VyuzZs5k9e3bIsU6dOgX3rDvyyCNp164dzz77LFOnTsXj8dC+fXuGDx/OcccdFzxnv/3249FHH+WRRx7hoYceonPnztxzzz3MmTMnZF+rXal9+/a8+eabPProo8yaNQu320379u3Zb7/9gvsExsbGMm3aNJ555hk++ugj3nnnHRISEujatWuwDWpjTNPk/vvv56CDDuLNN9/khRdewO12k5qayogRI7jmmmuCFU4ZGRm8/vrrPPDAA7zyyivU1NTQp08fnn766ZBKm51lxIgRDBkyhCeffJKNGzfSs2dP7rnnnpBKtptuugnTNJk1axY1NTUMGzaMF198scE9D5tj5MiRLF68mA8++ICCggISExMZNGgQDz74YDBZYpomTz31FI899hgffPABM2bMoFOnTlx77bWcffbZO/zsWzv++ON55ZVXmD9/PsOHDw85lpeXx7XXXgsE9iZMTk5mxIgRXHLJJcEqRWj+e5o8eTKfffYZ3377LR6Ph44dO3LFFVdwzjnnBOf07NmTt99+myeeeIKZM2dSUlJCWloae+21FxdffPFOffam/Prrrzz66KMhY7VfH3vsscHEX0MOOOAAnnjiCZ544gnuuOMO0tLSOP/88+t9jo8++oi99957u5O6UVFRHHnkkXz00UdcddVVwfHffvsNCLSxrf1ebu3ll18OJv4cDgfPPvss999/P9OmTaOmpoaBAwdyzz33BNvCAqxYsQKv10tRUVG9/7bec889wbU8YcIEZs2axUsvvRT82T/44IO55JJL6NKlS7Oerbnv0TAMnnzySR555BE+/PBDZsyYQbdu3XjggQc46qijmnWvU089FZfLxQsvvMBnn31Ghw4duOGGG+pUSta+1+eff77ONUaOHBmS+Fu5ciW///57i/87JCIiIiKtz7C3Zzd4ERERERERaVP69OnDqaeeGqzI3NOdccYZtGvXjgceeCDcoeyR8vPzOeigg3j44YeDv1SwPdavX8+hhx7Kc889F1L1J+F111138dNPPzFjxgxV/ImIiIi0MdrjT0RERERERHY7V111FR9++CEbNmwIdyh7pP/85z/07t17h5J+ADk5ORx//PE8++yzOyky2VHFxcX897//5YorrlDST0RERKQNUsWfiIiIiIjIbkAVfyIiIiIiIqKKPxEREREREREREREREZHdgCr+RERERERERERERERERHYDqvgTERERERERERERERER2Q0o8SciIiIiIiIiIiIiIiKyG1DiT0RERERERERERERERGQ34Ax3AAL5+eXhDkH2cKZpkJYWT1FRBZalbT+lbdI6lUigdSqRQOtUIoHWqUQCrVOJBFqnEgm0TiUSaJ1KW5CZmdisear4ExFM08AwDEzTCHcoIg3SOpVIoHUqkUDrVCKB1qlEAq1TiQRapxIJtE4lEmidSiRR4k9ERERERERERERERERkN6DEn4iIiIiIiIiIiIiIiMhuQIk/ERERERERERERERERkd2AEn8iIiIiIiIiIiIiIiIiuwEl/kRERERERERERERERER2A0r8iYiIiIiIiIiIiIiIiOwGlPgTERERERERERERERER2Q0o8SciIiIiIiIiIiIiIiKyG1DiT0RERERERERERERERGQ3oMSfiIiIiIiIiIiIiIiIyG5AiT8RERERERERERERERGR3YASfyJtyHvvvcOVV14c7jB2yCWXnMejjz4U/PqEE47kzTdfa7X7bdq0kbFjh/PHH8sBWL16FcceexhVVVWtdk8RERERERERERERkbbIGe4AZPe2ZMkiLrpoCqNGjeaBBx4NObZp00ZOPPGo4NexsXG0b5/F0KF7M2nSyeTkdG7R9epz113/xO0u5557HgoZ//nnn7jssgv48MPPSUxMxO/389pr0/jww1nk5eURHR1NdnYORx11LEceeUzwWh9++B4ADoeDpKRkevToyYQJEznssCMxTTN43cY89tjTDBs2vM54TU0Nzz33NHfccW+TzxVJnnvuZWJjY3fZ/bp1607//gN4441XOfPMKbvsviIiIiIiIiIiIiIi4abEn7Sq9957l+OP/zvvvfcuBQX5ZGRk1pnzyCP/plu37lRXV7Nq1Qreeut1zjzzZO67718MHz6yxdfbHi+++BzvvjuDK6+8lr59+1FRUcHy5b9SVlYeMm/UqH258cb/w7IsioqKmDdvLo8++hBffDGHe+99mIEDB/Puux8F5z/66ENUVFRw443/FxxLSkquN4YvvphDfHw8gwYN2SnP1BC/349hGJjmrin4TU1N3SX32dphhx3FfffdyWmnnYnTqX/mRERERERERERERGTPoE/EpdVUVlYyZ84nTJ36MkVFBXzwwSxOP/3sOvOSk5NJT88AoFOnbMaM2Y/LL7+Qe++9gzfeeAeHw9Gi622Pb775imOPPYEDD5wQHOvVq3edeVFRrmCsmZnt6NOnL/37D+Tyyy/kww/f48gjjwkeB4iOjsbr9YSMNWTOnI8ZM2a/kLHaisWBA4fwxhuv4PX6OOigv3H55f8IJrTKysp49NEH+fbbr/F6PQwZsjdXXHF1sGLygw9m8dhjD3Hzzbfx9NNPsH79Ol5/fSaXXno+RxxxNOvXr+Orrz4nNTWVK664hn79BnDffXcwf/6PdOzYiRtu+D/69t0LgNLSEh5++H5++WUB5eVldOqUzeTJZ3HwwYc0+FwnnHAkkyadzKRJp/DBB7O4++7b6sw566xzOeec8wGYNesdXn/9FTZt2khWVgdOOOEkjjvuxODcX39dwgMP3M3atWvo1q1HvWtgxIhRlJeXsXDhz3WSxyIiIiIiIiIiIiIiuyvt8Set5rPPPqFLl6507tyVv/3tMN5//3/Ytt3keaZpcuKJJ5OXt4nly5ft8PWaIy0tnZ9//oni4uIWn7v33iPo2bM3X3752Q7FsGjRQvr27Vdn/Oeff2Ljxlwee+wZbrrpn3z44Sw++GBW8Pjdd/+T5cuXcd99D/P00y9i2zbXXHM5Pp8vOKe6uppXX/0P1113M9OmvUFqahoAb745nYEDB/Of/0xn/Pjx3HbbLdx556387W+HMXXqK3TsmM2dd94afM8ej4c+ffrxwAOP8PLLb3DUUcdy55238uuvS5r1jAcddDDvvvtR8M8//3kXDoeDQYMGA/Dxxx/y/PNPc955F/HKK29x/vkX8/zzTwdbrFZWVnLttVfStWt3nn9+GmeffR5PPvlInfu4XC569uzNL78saN7LFxERERERERERERHZDajiL0IZ5W4Mj3eX3c+OcmEnJrTonPfff5e//e1QAEaNGk1FhZsFC+bXu7/dtrp06QrApk2b2GuvATt8vaZceumV3HLLdRx99ES6devOgAGDGDt2PKNHj2nW+V26dGHlyhXbff/y8nLcbne9rUsTE5O48sprcTgcdOnSldGjxzJ//g8cddSxrF+/jm+++YqnnprKwIGB5Nmtt97BcccdzldffRGsYPT5fFx11fV1qhhHj96XY445HqfT5OKLL2b69On067dX8LxTTz2DCy44i6KiQtLTM8jMbMcpp0wOnn/CCSfxww/f89lnnwa/T42Jjo4hOjoGgA0bcnn44fs5//yLGTFiHwCmTn2GSy65gvHjDwSgY8dOrF69inffncGhhx7BJ598hG1bXH/9LURHR9O9ew/y8zfz4IN190XMyMhk8+a8JmMSEREREREREREREdldKPEXiapriH17NuykardmMQwqTzoCYqKbNX3dujX8+utS7r77QQCcTicHHngw77//brMSdbUVZoZhNOt6eXl5TJ78VzvIyZPPalEb0G7duvPyy2+wfPkyFi/+hYULF3D99Vdx6KFHcP31tzQjXgCj2ffbVk1NDQBRUVH1xlbb7hQgPT2DVasCSca1a1fjcDhCkm7JySl07tyFtWtXB8cCFXC96ly7R4+/xjIyAu1Iu3fvGRxLSwtUBhYXF5OenoHf72fatBf57LNPyM/Px+fz4vF4gsm85nK73Vx77RWMHj2GU045HYCqqio2bMjl3nvv4P777wrO9fv9xMcnBJ+3R49eREf/tQ779x9U7z2io6Oprq5uUVwiIiIiIiIiIiIiIpFMib9IFBNN1fETd3nFX3OTfgDvvfcufr+fY4459K9r2DYul4srr7yOhITGqwdrk1YdO3Zs1vUyMjJ48cXXgseSkpIAiI+PJy9vU53ru91uHA4HsbGxwTHTNOnXrz/9+vVn0qRTmD37A+644/84/fSz6dixU5Px1sa6PZKTkzEMg/Ly8jrHavfyq2UYBpZltej60dHRwSRqQ9euPV7fmG0H7vfaa9N4663pXHbZP+jevSexsbE89thD+HzNX4t+v5//+78biIuL59prbwqOV1VVAnDddTfXqR40zZZ3JS4rK6NTp8a/byIiIiIiIiIiIiIiuxMl/iKUnZjALqz3axGfz8dHH33AJZdcwciR+4Qcu+GGq/n004845pgTGjzfsizeeut1OnToRK9efZp9vezsnDrXysnpwqeffozH4wmppvv999/o0KFjnaTa1rp27Q5AdXVVo887f/6PrFy5gkmTTml0XmNcLhddu3ZjzZpVdZ6xMV26dMPv9/Prr0uCrT5LS0tYt24tXbt22+54GrJ48S+MHTueiRMPAwLfq3Xr1tGtW/Pv9fjjD7Nq1Qqef35aSOVeWlo6GRmZbNy4IdjSdVtdunRj9uwPqKmpCZ67dOnieueuXr2SAw44sNlxiYiIiIiIiIiIiIhEOiX+ZKebO/cbysvLOOKIY+pU9o0ffyDvvfe/kMRfaWkphYUFVFdXs3r1St58czrLli3lgQcexeFw8NVXX7Toelv7298O5aWXnufOO2/llFNOJyEhgYULf+bNN6dz0UWXBufdfPO1DBw4mAEDBpOens7GjRt45pknycnpTOfOXYPzPB4vhYUFWJZFUVER8+bNZdq0l9h333EccsjhO/TeRo4czaJFC1uUQMzJ6cy4ceO57767uOaaG4mLi+Ppp58gM7Md48btv0Px1H+/HD7/fA6LF/9CYmISb7zxKsXFhc1O/L3//v+YOfO/3HXXAxgGFBYWABAbG0dcXBznnHM+jzzyAPHxCYwaNRqv18tvv/1KeXkZJ510GgcffAjPPfdv7r//Tk477Szy8jby+uuv1LnPpk0byc/fwvDho3bq84uIiIiIiIiIiIiItGVK/MlO99577zJ8+Mh623nuv/+BvPbay6xY8Qfx8fEAXHHFRQDExMSQldWBoUOHc+21NwUr+Jp7vfr2sEtMTOTJJ5/j6aef4Prrr6Kiwk2nTjlceumVHHHE0cF5I0eO5tNPZzNt2ktUVLhJS0tn771HcPbZ54VUBc6bN5ejjz4Eh8NBYmISPXv24oorrubQQ4/YrnaUWzviiKOZMmUybre7yVaoW7vhhlt59NEHue66K/B6vQwePIwHHni00WrG7XXGGeewceMGrrrqUmJiYjjqqGMZN25/KirczTp/4cKf8fv9XH/9VSHjZ511Lueccz5HHnkM0dExTJ/+Mv/+96PExMTSo0dPTjzxZADi4uK4995/8eCD93D22afStWs3LrzwUm666dqQ63366WxGjNiHrKwOO+fBRURERERERERERCSEUV6Bc9kKvMMGgNMR7nDkT4Zt2221Y+QeIz+/7r5usme6+ebr6NOnL5Mnn7VL7+t0mqSmxlNcXIHP17L9A9sar9fLSScdy6233smgQUPCHY7sRLvTOpXdl9apRAKtU4kEWqcSCbROJRJonUok0DqVSKB1Wj/n0t+J+mER/i6dqDlgHzCMcIe0W8vMTGzWvB0rURKRneriiy8nNjY23GFEtM2b85g8+Swl/URERERERERERERakVlchh0dhWPdRlw/Lgp3OPIntfoUaUM6dOjICSecFO4wIlp2dk6wTayIiIiIiIiIiIiItA6zuBR/TgesjFSivl+InRiPr1/PcIe1x1PFn4iIiIiIiIiIiIiIiDSfbWOUlGGlJuPr1xNf7264fl4K2l0u7JT4ExERERERERERERERkWYzyiswfH6s1CQAfF2zMTxejNLyMEcmSvyJiIiIiIiIiIiIiIhIs5nFpQDYqckAWJlpgfH8orDFJAFK/ImIiIiIiIiIiIiIiEizmcWl2NEu7NiYwECUCyslCYcSf2GnxJ+IiIiIiIiIiIiIiIg0W+3+fhhGcMzKTMPMLwxjVAJK/ImIiIiIiIiIiIiIiEgLmMWlwTaftazMNMziMvD6whSVgBJ/IiIiIiIiIiIiIiIi0lx+P2apGyslKXQ4Mw1sG7OwOEyBCexBib8ff/yRCy64gLFjx9KnTx8+/fTTJs+ZN28exx57LAMGDODggw9mxowZIcdfe+01jjzySIYNG8awYcP4+9//zpdfftlajyC7seeee4r77rsr3GHskBNOOJI333wt+PXYscP56qsvWu1+P//8E2PHDqe8vByA77+fy5lnnoJlWa12TxEREREREREREZE9nVFSDrYdaPW5FTs1GdvpwNQ+f2HlDHcAu0plZSV9+vTh+OOP55JLLmly/vr16zn//PM56aSTePDBB/nuu++4+eabyczMZNy4cQBkZWVx9dVX06VLF2zb5p133uHiiy9m5syZ9OrVq7UfqU0rLi5m6tSnmTv3G4qLi0hMTKJnz16ceeYUBg0aEpy3ePEv/Oc/U1myZDEeTw3Z2TkcdtiRnHjiyTgcjuC8sWOHc/fdD7Lffvs36/6XXHIevXr14fLL/xEy/sEHs3jssYf46KMvAKiuruall57ns88+oaAgn7i4OLp27c7f/34K48btH7zWwoU/A+ByuUhOTqF3774cfviRjB9/YPC6d999W6MxvfXW/+jQoWOd8cLCAt5663Vefvn1Zj1bpHj33Y9ITExqeuJOss8++/L880/z8ccfcsghh++y+4qIiIiIiIiIiIjsScySUoA6FX8YBlZGGo78QtTsM3z2mMTf+PHjGT9+fLPnv/7662RnZ3P99dcD0KNHD+bPn89LL70UTPwdeOCBIedceeWVTJ8+nYULF+7xib+bb74Wr9fLzTffRseOnSgqKmT+/B8pKysNzvnyy8/5v/+7nsMOO4rHH7+YhIREfvrpB/7978dYsmQxd9xxL8ZWG4O2hgceuJtff13ClVdeQ9eu3SktLWXJkl8oLS0NmXfkkccyZcr5+P1+tmzZwldffc6tt97IoYceyXXX3cRBBx3MqFGjg/NvuulaunXrwZQp5wfHUlJS641h1qx3GDBgEFlZHVrnIf/k9XpxuVyteo+tpadn7LJ71Tr00CP473/fUOJPREREREREREREpJWYxWXY8bEQHVXnmJWZhnPFWrBtaOXP96V+e0zir6UWLlzI6NGjQ8bGjh3L3XffXe98v9/PRx99RGVlJUOHDm3RvUzTwDR3nx+A8vJyfvllAf/+93MMG7Y3ANnZnRg0aFBwTlVVFffffxfjxo3npptuCY7n5GSTkZHONddcyRdffMrBB08MHnM4DJzO5nWnNQwD06TO/Nr3XDv+7bdfceWV1zBu3H7B+w8Y0L/OteLiYmjfvh0AHTt2YMiQwXTv3o0777yNgw/+GyNHjiI+Pi54jsvlCjmnMZ999gnHHXdCSKwXXnguPXv2Iioqilmz3sHpdHHsscdz7rkXBOfk5W3ioYfu56effsAwTEaP3perrrqW9PR0AJ577mm++uoLTjjh77z00lTy8jbx3Xfz2WefYVx33Y18881X/PTTT2RlZfF//3cbOTkduP76G/j116X06tWbW2+9g+zsHAByc9fz6KMPs2TJYqqrq+jatRsXXngpI0eOqvN+a59jn32Gcd99DzF+/AE899zTTJ36bJ1nv/nmf3LEEUdhWRbTpr3EO+/MoKiokJyczpx99rkceOCE4Ny5c7/hX/96kC1bNtO//0AOO+yI4Pey9p7jx4/nX/+6n7y8DcHYZffhcJghf4u0RVqnEgm0TiUSaJ1KJNA6lUigdSqRQOtUIoHWaShnaRmkJdf7eb2RlYG5ZDnOmhpIiAs9qGTgLqHEXwMKCgrIyAitWMrIyMDtdlNdXU1MTAwAy5cv56STTqKmpoa4uDiefPJJevbs2aJ7paXFt3pl266UmBhNXFwc8+Z9w7hx+xAVVTfr/9NPcyktLeGCC84jNTU+5NhRRx3Gk08+yhdffMqkSccFxxMSYurMbYjL5SA62lVnfnx8NIZhBMczMzP56afvOeaYI0hISGjRtU499SQef/wRvvvuKyZOPLBZ52yrpKSE1atXMXLk3iFzXS4HH374HmeddRZvvfUWCxcu5Prrr2fMmH0YM2YMlmVx1llXExcXxyuvvILf7+e2227jtttuYtq0aQDExkaxYUMu33zzBf/+95OYphm8x0svTeX666/nlltu5sEHH+T//u9GcnJyuOiiC+nYsSM33ngjjz76IM8//zwAmzfDhAkHcu21VxMVFcU777zDNddcwUcffUTHjoH2pQ6HSWxsVMhz1H7PLr74As466/Tg+KxZs3jssccYNSrw3E899RSzZ3/AHXfcTteuXfnxxx+59dabycnpwMiRI9m0aRPXX381p556KpMmTWLJkiXcd999AKSkxJGUFLhnampPMjIy+OOPXxk4sG+j714iV1JSbLhDEGmS1qlEAq1TiQRapxIJtE4lEmidSiTQOpVIoHUa4HG7MXt3I6Gez79tVzaez02Sqipw5GQGx62iUnwzPsWx9144hvbbleHucZT420HdunXjnXfeoby8nNmzZ3PdddfxyiuvtCj5V1RU0eKKP09lPpavsqXhbjfTGUdUXGbTE/908823ce+9d/D666/Tu3dfhg0bxoQJE+nVqzcAv/76OwDp6R0oLq6oc35OThdWrlwVcsztrq53bn28Xj81Nd468ysqarBtOzh+7bU3cuutNzNq1Ch69erN4MFDOOCACQwePKTJawFkZ+ewZs26OscaO2drv/++Ctu2iY5OCJnr9frp0aMnp556FgDjxx9Mv34v8/nnX7HXXkOYN+97fv/9d2bMmEX79llA4J2ffPIJfPvtD+y1V3+qqjx4PB5uvPGfpKYG2ozW3uOww45k9OhA69uTTjqNKVPO5KKLLmLw4OH4/RbHH/937rzzn8H57dvnMHHiXxV0Z5xxLrNnf8x7733IiSeeBIDfb1FV5Wnwe+Z0Bn67Y8mSRTzyyCPccsttZGZ2YvPmYp5++mkef/wpBgwYDMABB0xk7tx5TJv2Kr169efFF/9Dp07ZnH/+pQCMG9eeRYuWMm3aS5SUVOL3/7UfZFpaBitXrmn2WpHI4XCYJCXFUlZWhd9vhTsckXppnUok0DqVSKB1KpFA61QigdapRAKtU4kEWqdb8XiJLi7HGxOD1cBnsFGxsXhW5OLL/DOnUFVN1LtzoKYG36fz8GJidc3ehUHvHppbGKXEXwMyMjIoKCgIGSsoKCAhISFY7QcQFRVFly5dABgwYACLFy/m5Zdf5vbbb2/2vSzLxrLsZs/3e8pZ+e1NYO/Cf2AMkx5jHsARldis6fvtdwCjRu3LokULWLp0Cd9/P5dXXnmZ6667mcMOOxLLCsTu9frx+eo+h20H3sfWx/x+u87cX35ZwNVXXxb8+pprbuRvfzsU27axLOrMr33PteMDBw7lzTffZenSxSxe/Avz5//IG29M55xzzufMM6cEY6nvWvVdb+v4Gzpna5WVVQCYpitkrm3bdO/eK2QsLS2dwsJCfD6LVatW0a5de9LT2wXn5OR0JSEhkZUrV9K7dz8syyYrqwOJicl14ujWrWdwLCkpkBTs3bs3fr+Fz2eRnJxKTU0NpaVlxMcnUFlZyQsvPMt3331DYWEBfr+fmpoaNm3aFHJty7Ib/Z7l5eVx7bX/4KSTTmP//Sfg81msWbOW6upqLrvsopAYvV4vvXr1+fN5V9OvX/+Qa+2114Dgu996PDo6isrKqibfvUSu2nUq0pZpnUok0DqVSKB1KpFA61QigdapRAKtU4kEWqdgFpZg2+BLSsRq4F2YnbJwLvkDs6oG75C9iP7qB2yfn+qjDsb102Kcn82j+tBorMz0XRz9nkGJvwYMGTKEr776KmRs7ty5DBkypNHzLMvC4/G0YmTgiEqk2z534N+FFX8OZ1yzk361oqOjGTFiH0aM2Iczz5zCvffewdSpz3DYYUeSkxNIlq5du5qBAwfXOXfNmjV069atyXv07duPF198Lfh1WloaAPHx8VRUuOvMd7vLiY8PbenpdDoZPHgogwcP5bTTzuSll57npZee59RTz8DlcjV4b7/fT27uevr126vJOBuSnJwCQHl5WbAqb+u4tmYYRjAh2lwxMfWXnm997do2s/U9a21i88knH+HHH+dx8cVXkJ2dQ3R0NDfffB1er6/ZsVRVVXH99VfRv/9Apky5IGQc4P77HyEzM3RPxMbef0PKyspISUlteqKIiIiIiIiIiIiItIhRGvjc3Uqqf+ssAM8+Q7DSknH9vBTnynXYTgc1h47HTozHs98IzI++IvrTuVSdcAhsx2fA0rg9JvFXUVHBunXrgl/n5uaybNkykpOT6dixIw899BCbN2/m/vvvB+Ckk07i1Vdf5f777+f444/n+++/58MPP+SZZ54JXuOhhx5iv/32o0OHDlRUVPDee+/xww8/MHXq1FZ/HldsJpH249C1aze+/voLAEaO3IekpGRef/2VOom/b775ktzcdZx77gV1rrGt6OgYsrNz6ox37tyVH374vs748uW/kZPTudFrduvWHb/fj8dT02ji6cMP36O8vIz99z+oyTgb0qlTNvHx8axZs5rOnbs0+7yuXbuyZctmNm/OC7b6XL16FW53Od26dd/ueBqyePEvHHbYkYwffwAAlZWV5OVtBPZu1vm2bXP77bdg2xa33HJ7yJ6W3bp1Iyoqis2b8xg6tP7rde3ajW+++TJkbOnSJXXm1dTUsGFDLr1792nmk4mIiIiIiIiIiIhIc5llbuzYmMYTdoaBr093fN1ycC39A3/7DKyMQNEODgfVB+2La9kKMFq2BZo0zx6T+FuyZAmnn3568Ot77rkHgGOPPZZ7772X/Px8Nm3aFDyek5PDM888wz333MPLL79MVlYWd955J+PGjQvOKSws5LrrrmPLli0kJibSp08fpk6dypgxY3bdg7VBpaUl3HLL9Rx++FH06NGLuLg4fvttGa+9No2xYwP7ysXGxnLNNTfwz3/exH333cXxx08iPj6e+fN/4MknH2P//Q/iwAMPDrnupk0b+OOP5SFj2dmdiY2tW9V2zDHH8/bbb/LIIw9wxBHHEBXlYu7cb/j009ncd9+/gvMuueQ8JkyYSN++e5GcnMyaNat45pknGTZseEhlYHV1dbDF5ZYtW/jqq895883XOOaYExg2bPh2vyvTNBk+fCSLFi1kv/32b/Z5w4ePonv3Htx++y1cdtk/8Pt9PPTQfQwZMoy+fbe/ArEh2dmd+fLLzxgzZhxg8PzzT7WoPe0LLzzLTz/9wL/+9QRVVZVUVQWqVRMSEoiLi+ekk07j8ccfxrZtBg0agtvtZvHihcTHJ3DooUdw9NHH8/rrr/Dkk49y5JFH89tvv/Hhh7Pq3Gfp0sW4XFEMGDBoZz26iIiIiIiIiIiIiPzJKCtvtNovRJQL79B6Pq+OicY7tP/ODUyC9pjE36hRo1i+fHmDx++99956z3nnnXcaPOfuu+/eGaHtdmJj49hrrwG88cZrbNyYi8/no1279hx55DGcfvpZwXkHHDCBtLR0/vOfF7j44il4PB6ys3M4/fSzmTTp5JCqMIDHH//XtrfiySefZ/DgIXXGO3XK5sknn+XZZ//NFVdchM/npXPnrtxxx33ss8++wXmjRo3mo4/e59ln/011dTUZGRnsu+84zjprSsj1Zs2ayaxZM3G5XCQlJdOnTz9uu+2eYAXcjjjiiGO4//67uOiiyzBNs1nnGIbBPfc8zCOP3M8ll5yLYZiMGjWaK6+8Zofjqc+ll17JPffczgUXnE1ycgqnnnoGFRX1b9xanwUL5lNVVckFF5wdMn7jjbdy2GFHcu65F5KSksq0aS+yceMGEhIS6d27b3C9BBLv9/P44w/z9ttv0K9ff84772LuuSd0L81PP53N3/52SMg+nCIiIiIiIiIiIiKyc5hlFVhpyeEOQxph2C3dNEx2uvz88nCHIGFk2zbnnXcGkyadwsEHHxKWGJxOk9TUeIqLKyJ2c9qSkhJOOeV4nn/+ZTp27BTucKQV7A7rVHZ/WqcSCbROJRJonUok0DqVSKB1KpFA61Qigdbpn2yb2FffxTuoL75BfcMdzR4nMzOxWfOaV14kIq3GMAyuvfYm/H5/uEOJaHl5G/nHP65T0k9ERERERERERESkCWZBMdEffw0t+Vy6ugbD68NubqtPCYs9ptWnSFvWq1cfevXqE+4wIlrfvnu1yv6GIiIiIiIiIiIiIrsb57IVODZsxiwuxcpIa9Y5ZpkbACupeZVnEh6q+BMREREREREREREREdlT+P041m4AwNxS1OzTjLLAtmV2UnyrhCU7hxJ/IiIiIiIiIiIiIiIiewhHbl6gZWdsDGZ+8xN/ZlkFdlwsONVMsi3Td0dERERERERERERERGQP4Vi9HistGX9WJo71m5p9nlFWjpWs/f3aOlX8iYiIiIiIiIiIiIiI7Am8XpzrNuHrloOVkYZZXgHVNc061SxzYycp8dfWKfEnIiIiIiIiIiIiIiKyB3Cs2wh+P/7uOVjt0gJjzWn3adsYZW6sRCX+2jol/kRERERERERERERERPYAzlXrsdqlYyfEB/7ERGPmFzZ5nlFVjeHzYycn7oIoZUco8SciIiIiIiIiIiIiIhJGjtW5OFasbd2b1HhwbNiMr3tO4GvDwMpMw2xGxZ9R5gZQxV8EUOJPREREREREREREREQkjFxLluP6beXOu6BtB/5sxbG5AGwbf07H4Ji/NvG3zdxtmX8m/uyk+J0Xo7QKJf5ERERERERERERERETCxbIwi0sxKip32iVj3vsM16LfQsbM/CLs2Bjs+Ni/bp2ZhuH1YZSUN3o9o7QcOyEOHI6dFqO0DiX+REREREREREREREREwsQoKQe/hVFZDX5/yDHH+k3g8bbsemVuzIJiHOs2hoyb+YVY7dLAMIJjVkZa4FhB4/v8meVurCS1+YwESvyJiIiIiIiIiIiIiIiEiVlUEvzfRmX1Xwe8XqI//RbnH2tadD3HhrzAdQtL/koa2jZmQTH+zPTQyVEurJQkHE3s82eUubGTElsUh4SHEn8iIiIiIiIiIiIiIiJhYhYWgyOQrtm63adRVhE4Xlzaous51udhJcYHkn1bApV8RnEZhteHlZlWZ76VmRacVy/bxixzY2l/v4igxJ+IiIiIiIiIiIiIiEiYmEUl+Du0A8Bw/5X4M93bkfjz+XDkbcHXtwd2bAyOvHwAHPmFYBhY6al1TvF3ao9ZXIZR2sA+f1U14LewE9XqMxIo8SciIiIiIiIiIiIiIhIOto1ZWIK/fQZ2dFRoxV95IPFnlJSBbTfrco68fPBb+LOz8GdlBBN/Zn4RVmoSuJx1zvHndMB2OXGuXl/vNc0/Y7LiY1v0aBIeSvyJiIiIiIiIiIiIiIiEgVFeEWjBmZaCHR+LWVEVPGbWJv58fow/q/+a4lifh50Qh52ciNWhHWZBMXi9gcRfPW0+AXA68ed0xLF6fb0JxtpkpB0f18Knk3BQ4k9ERERERERERERERCQMzMISAKz0FOz4uG0q/txYGYHWnGZxWdMXs20cuZvw53QAw8DfPuPPsc2YJWX4M9MbPNXfPQezpByjnraiRkUlOBwQHdWyh5OwUOJPREREREREREREREQkDMzCYuy4WIiNwU7YNvFXEWgBGuWqNyG3LaO0HMNdiT87CwA7ORE7JhrXkuUADVf8Af6O7bCjXThX1W33aVRUYSXEgmG09PEkDJT4ExERERERERERERERCQOzqAQrPQUAKz4Ow/1n4s+2Md0V2InxWKnJmFsn/mwbo54KQEduHjhM/FmZgQHDwN8hE7OgGDvKhZ2c2HAgDgf+rtmBff62afdpuivV5jOCKPEnIiIiIiIiIiIiIiKyq9k2ZuFfiT87Pg7D6wOPF6OiCiwbKzEBOzUppNWnc/kqYt/5GMcfa4JjRkUlrqW/4++UBU5ncNz6MwloZaQ1WbHn69YZw12JmV8UMm5UKPEXSZT4ExERERERERERERER2cWMyiqM6hqstBQA7IRAcs2oqMQodwfGaiv+SsvB7wfAuXIdOEyiv52PuXEzeLxEf/ItGCae0UND7lFb/ddYm89aVlYGdlwMjlXrQuNUxV9EcTY9RURERERERERERERERHYms7AEACs9FQA7PjYwXlGJUVkdGEuIw0pJCrT3LHVDlBNzSyE1Y4fjXL2e6M++w0pLxqiopPqwAwL7BW7FTk7E278Xvh6dmw7IMPB3ysKxuRBv7ZjfH0hOJsQ2dqa0Iar4ExERERERERERERER2cXMohLsaFcw4WfHxoBhYFRUYZRXBMYdDqzU5MD84lIcq9b/uR9fJ2oO2Ac7MR5HfhE1B4zGTk2qexPDwDtycOP7+20lUF1YFtznz6ioCsSmir+IoYo/ERERERERERERERGRXcwsLMFKS/1r7z3TxI6LwXAHWn1aiQmB8ego7LhYzJIyHOs34evcAVwuAKoPGR/Yg+/PdqE7ykpNBr+FUebGTk7EqKgElPiLJKr4ExERERERERERERER2cXMwhKs9JSQMSs+DqOiErO8Ajsx/q/x1GQca3Ixi0vxd8v564ToqJ2W9AvcJ1A1aBaXAqr4i0RK/ImIiIiIiIiIiIiIiOxK1TUYFZXB/f1q2QmBxJ9RXoEVkvhLwixzY0e58GdntV5csTHYMdGYxWVAYL9BOyYKnI7Wu6fsVEr8iYiIiIiIiIiIiIiI7EJmUQlAnYq/2paeRo2nTsUfgL9LJ3C0bhLOSk36q+LPXalqvwijxJ+IiIiIiIiIiIiIiMguZBaWYDsd2EkJIeN2QhxGtSfwvxP/OlZbGejr0bnVY7NSkzG2avWpxF9kUeJPRERERERERERERESktdg2zmUrwe8PDpmFxVhpKWAYoVO3SrJt3erTTk2i6sRDsTq0a/1wU5MxyyvA5w+0I42PbfV7ys6jxJ+IiIiIiIiIiIiIiEgrMbcUEvX9Ahyr1v81VlSCvU2bTwDrz8Sf7XJCdFTIMTshvs781mClJoFtY5aWYVSo1WekUeJPRERERERERERERESklZil5QA4axN/Xi9mqRt/PYk/O+HPxF9ifJ1qwF3FSkkCwNxcgOH1BWOSyOAMdwAiIiIiIiIiIiIiIiK7K6OkDADHpi1QVY1Z5gbASkutOznKFdj7LzGh7rFdxeXCTojDsT4PQBV/EUYVfyIiIiIiIiIiIiIiIq3ELC3HapcOgHNNLmZhCZgmdkpi3cmGgZ2WUm814K5kpSbjyMsHwNYefxFFFX8iIiIiIiIiIiIiIiKtxCwpw9c1GzvKhWPVeuzkxMA+eg5HvfOrDx0ftjaftazUZBzrNwUSkXFK/EUSVfyJiIiIiIiIiIiIiIi0Bp8fw12JlZKIr1sOji2FODbkYaXX0+azlmm2gcRfYJ8/Oz427LFIyyjxJyIiIiIiIiIiIiIisqNqPMS89xnOX1cEh4zScgDs5CT8XTqCw8SorMZKSwlTkM1jpSQH/tb+fhFHiT8REREREREREREREZEd4fcT/dlczPwinKvXB4fNPxN/VnIiuFz4cjoGvg7zHn5NsZMTAm0+lfiLOEr8iYiIiIiIiIiIiIiIbC/bJuqb+Tjyi/D16IyZXwReLxBI/Nkx0RAdBYCvTzfshDistORwRtw0hwN/p/ZY7dLDHYm0kDPcAYiIiIiIiIiIiIiIiEQq55Lfca5aR834UVhpyThXrsPcUojVKQujtAwrJTE41+rYnqoTDwtjtM1Xc/DYcIcg20EVfyIiIiIiIiIiIiIiItvJkbsJf5dO+LvnYCcnYsdE48grAMAsKcdOTgpzhLInUeJPRERERERERERERERkO5llFYE9/AAMA39WJo68fLBtzLLyv46J7AJK/ImIiIiIiIiIiIiIiGwPvx+jsgorKT44ZGVlYhYUYZSUgd8KafUp0tqU+BMREREREREREREREdkOhrsSADshITjmz8oEy8b5x5rAMbX6lF1IiT8REREREREREREREZHtYJa7AbAT/6r4s1MSsWOicK5Yg+10YMfHhis82QMp8SciIiIiIiIiIiIiIrIdjPIKMM3Q5J5hYLXPxKjxYicngmGEL0DZ4yjxJyIiIiIiIiIiIiIish2M8gqshLg6yT1/h0wArGTt7ye7lhJ/IiIiIiIiIiIiIiIi28Esr8BOTKgz7m8fSPzZKdrfT3YtJf5ERERERERERERERES2g1HuDtnfr5admoSvZxd8OR3CEJXsyZT4ExERERERERERERERaSnbDrT6rCfxh2HgGTcCOy1ll4clezYl/kRERERERERERERERFqqugbD56+34k8kXJT4ExERERERERERERERaSGzvAKg/oo/kTBR4k9ERERERERERERERKSFjHI3gCr+pE1R4k9ERERERERERERERKSFzPIK7JhocLnCHYpIkBJ/IiIiIiIiIiIiIiIiLWSUV6jaT9ocJf5ERERERERERERERESa4vXiWJMLtg0EEn/a30/aGiX+REREREREREREREREmmCu2UD059/jWLcx8HV5BXZiQpijEgmlxJ+IiIiIiIiIiIiIiEgTjPIKAFw/LoIaD0ZllSr+pM1R4k9ERERERERERERERKQJRnkFdnwspruSqHkLAbTHn7Q5znAHICIiIiIiIiIiIiIi0tYZ7gr87dKx42JxLf0DUOJP2h5V/ImIiIiIiIiIiIiIiDTBKK/ETojHO7gfdkwUOEzsuNhwhyUSQhV/IiIiIiIiIiIiIiIijbAtC6OiMlDhFx2FZ/QwHJvywTDCHZpICCX+REREREREREREREREGuOuBNvGio8DwN81G3/X7DAHJVKXWn2KiIiIiIiIiIiIiIg0wi5zB/5OiAtzJCKNU+JPRERERERERERERESkEXZZReDvhPgwRyLSOCX+REREREREREREREREGmGXurFjY8DpCHcoIo1S4k9ERERERERERERERKQxZW5V+0lEUOJPRERERERERERERESkEXZ5BXai9veTtk+JPxERERERERERERERkUbYpW7sBCX+pO1T4k9ERERERERERERERKQhth2o+FOrT4kASvyJiIiIiIiIiIiIiIg0pKIKLAsSlfiTtk+JPxERERERERERERERkQYY5RUAqviTiKDEn4iIiIiIiIiIiIiISAMMd23iT3v8SdunxJ+IiIiIiIiIiIiIiEgDDHclRlwMuJzhDkWkSUr8iYiIiIiIiIiIiIiINMAor4CkhHCHIdIsSvyJiIiIiIiIiIiIiIg0wHBXYijxJxFCiT8REREREREREREREZEGGOUVSvxJxFDiT0REREREREREREREpD62jVFRiZEUH+5IRJpFiT8RERERERERERERiUx+P64fF0FVTbgjkd2UUVkFlqWKP4kYSvyJiIiIiIiIiIiISEQy84twLfmdqPmLwx2K7KYMd2XgfyQr8SeRQYk/EREREREREREREYlIZmExAM4/1mAWFAXHXfOXEP3hF2GKSnYnhrsi8HeiWn1KZFDiT0REREREREREREQikllYgpWegpWaTNS8X8C2cS5bgWvRbzjyCtQCVHaY4a7Ejo7CiHKFOxSRZlHiT0REREREREREREQikllUgpWRimfUYMwthUR9O5+oeb/g79Lpz+PFYY5QIp3prsRWtZ9EECX+RERERERERERERCTy+PyYJeVY6alYHdrh79IJ5x9r8HfuSM3+o7BdTszCknBHKRHOcFdgJyjxJ5HDGe4ARERERERERERERERayiwuBdvGSk8BwLPPEJypSXgH9gHTxEpLwSwqCWuMEvkMdyV2Rmq4wxBpNlX8iYiIiIiIiIiIiEjEMYtKwDCwUpMBsONi8Q7tD85AvYuVnqKKP9kxto3proSEuHBHItJsSvyJiIiIiIiIiIiISMQxC4uxUpLA4aj3uJWWglnmBo93F0cmuwujqhosS60+JaIo8SciIiIiIiIiIiIiEccsLMFKS27wuJUeaM+odp+yvQx3JQB2oir+JHIo8SciIiIiIiIiIiIikcWyMItLg8m9+tgpieAw1e5TtpvhrgBQxZ9EFCX+RERERERERERERCSiGCXl4Lew0lManmSaWKnJmIXFuywu2b0Y7krsaBdEucIdikizKfEnIiIiIiIiIiIiIhGlNplnpaU0Os9KT8EsKt0FEcnuyHRXqNpPIo4SfyIiIiIiIiIiIiISUcyiEqykhCYrsaz0VMySMvD5d9q9HavWYeYX7rTrSdtllFdiJ2h/P4ksSvyJiIiIiIiIiIiISEQxC0sab/P5JystBWwbs2QnVf3ZNlHfLcC1ePnOuZ60aUZFBZYq/iTCKPEnIiIiIiIiIiIiIpHD4w1U/DXR5hPASk0Gw8AsLNkptzaKSjE8XswtRWDbO+Wa0kbZNqZbFX8SeZT4ExEREREREREREZGI4fplGdg2/h6dm57sdGClJAb3BNxRjrwtABhV1RiVVTvlmtJGVdWA39IefxJxlPgTERERERERERERkYhglJbj+vUPfAP7YMc3rxLLykgLVOjtBI68AqyUJICddk1pm0x3BQBWM9eZSFuhxJ+IiIiIiIiIiIiIRISoHxdhx8XiHdCn2ef4O7bHLC7FqKjcsZvbNubmfPxds7ET4jDzC3fsetKmGe7AelGrT4k0e0zi78cff+SCCy5g7Nix9OnTh08//bTJc+bNm8exxx7LgAEDOPjgg5kxY0bI8WeeeYbjjz+eoUOHMnr0aC666CJWrVrVWo8gIiIiIiIiIiIisscyc/NwrN+EZ8QgcDqafZ6/U3swDBy5eTt0f6OoFKPGiz8rE39mGma+Kv52Z4a7AjvKBdFR4Q5FpEX2mMRfZWUlffr04dZbb23W/PXr13P++eczatQo3n33Xc444wxuvvlmvv766+CcH374gVNPPZU333yTF198EZ/PxznnnENl5Q7+5oiIiIiIiIiIiIiIhIhasBR/Vgb+Lp1admJ0FP52aTuc+HNszgfTxMpMw8pMw1FYDJa1Q9eUtst0V6raTyKSM9wB7Crjx49n/PjxzZ7/+uuvk52dzfXXXw9Ajx49mD9/Pi+99BLjxo0DYOrUqSHn3HvvvYwePZqlS5cyYsSInRe8iIiIiIiIiIiIyB7MqKjELCimZr+RYBgtPt+f3QHXL8vA7wdH86oFjYpKzOJS/NkdAHDk5eNvlwZOB1ZmOvgtzKJSrIzUFscjbZ/hrsROiA93GCIttsdU/LXUwoULGT16dMjY2LFjWbhwYYPnlJeXA5CcnNyaoYmIiIiIiIiIiIjsURwbNoNhBNp2bgd/dhaGz4+5uaB5J1TVEP3hl0R/8i2OP9YE9vfLy8dqnwmAlZ4CpqF9/nZjhrsCSxV/EoH2mIq/liooKCAjIyNkLCMjA7fbTXV1NTExMSHHLMvi7rvvZtiwYfTu3btF9zJNA9Ns+W+piOwsDocZ8rdIW6R1KpFA61QigdapRAKtU4kEWqcSCbROJRI0d506N+Zht0vDmRC7fTfKTIWEWKI2bsbXuUPjc31+XJ/PxfD7sbrnEDN3Pj6fD9Pjhex2OJ0mOE3s9FSchcXg1M/Y7sbYuBmzohI7KQGn09S/pzuB5feyYdHTpGTvR2Lm4HCHs1tT4m8nue222/jjjz947bXXWnxuWlo8xnaUp4vsbElJ2/l/nER2Ia1TiQRapxIJtE4lEmidSiTQOpVIoHUqkaCxdWr7/Xg2F+AYMYCE1O1vvejr1QUrdzOJ9VzDLi7DrvEA4P9xCVZZOa4TJ2JkpuF79zMcPy2CKBfJvTtjuAIfq/u6ZGGt3Vjv9SQy2aXl+L78CWvFOsyO7XDu3Rcj/q+1qX9Pt1/h+u+oKl6Cp3wFmVm3EZvUwr06pdmU+GtARkYGBQWhZd8FBQUkJCTUqfa7/fbb+eKLL3jllVfIyspq8b2KiipU8Sdh5XCYJCXFUlZWhd+vDYmlbdI6lUigdSqRQOtUIoHWqUQCrVOJBFqnEgmas06NjZuJqqqhKi0Nu7hiu+9lZqTjWvgbFWs3Q1LCX9cvKSPqvx9tdUMD74QxVEbHQlkVjBmOq7gc4mKodNcANYHrJSbiKiihYlMRxERvd1zSdrje/gTD48G330is7jngscBToX9Pm+D3VVG8/nNsy09mjyPrnbN++Se44jtj+z0s/epBuo68EYdLrVRbIrWZv2SgxF8DhgwZwldffRUyNnfuXIYMGRL82rZt7rjjDj755BOmTZtGTk7Odt3Lsmwsy96RcEV2Cr/fwufTf7ikbdM6lUigdSqRQOtUIoHWqUQCrVOJBFqnEgkaW6euNRuxYmLwJifBjqzl9hk4DQPWbMS3V8/gsHN9HrZhUH34AdiGCTFR2PFxf93LdOA74kDwWyH3N9JScdpg5xXgz26ifai0fdU1RBWVUrPfSPxdssFvA6Gf2+vf01C27ado7WxK1n+C31sBhklSpwNwOEMTep7KzVQULiNrr7OJSerGup/uJnfxVDoOvBDDUPvUnW2PeaMVFRUsW7aMZcuWAZCbm8uyZcvYuHEjAA899BDXXnttcP5JJ53E+vXruf/++1m5ciWvvvoqH374IWeeeWZwzm233cb//vc/HnroIeLj48nPzyc/P5/q6upd+mwiIiIiIiIiIiIiuyvHhjz82Vmwo9sluVz422fiyM0LGTbz8vFnpGFlpGGnpwSSftsyTXCF1tHYifHYLidGcdmOxSVtgqOgCACrXVqYI4kc5VvmU7jqHRLbjyR72DVgW1SV/FFnXunGb3C44knIHEZUXHs69J9CdemKQLJQdro9puJvyZIlnH766cGv77nnHgCOPfZY7r33XvLz89m0aVPweE5ODs888wz33HMPL7/8MllZWdx5552MGzcuOGf69OkATJ48OeRe99xzD8cdd1xrPo6IiIiIiIiIiIjIbs8or8AsKcc7tP9OuZ4/J4uo+UvA5wOnE2wbR14Bvt7dtiM4AzshHtNduVNik/Ay84uwY6KxE7RnY3N5K/JwRiXTrvfJALhiM6ksWkZCxuDgHNvyUZY3l8SsfTAdUQDEpw+k+9iHVO3XSvaYxN+oUaNYvnx5g8fvvffees955513GjynseuJiIiIiIiIiIiIyI5x5G4Cw8Dfsf1OuZ4/uwP8sAjHpnz8OR0wSsoxqmvwZ2Vu1/XshDgM9x5YtVRZjWFb9VdHRihzSxFWZtqOV5buQbzVBbhi//rZiUvtR2XxspA57oKF+D3lJHccFzKupF/r0ZsVERERERERERERkTbJsWkL/vbpEOXaKdezkxKwEuOD7T4deflgGFjt0rfrelZCHMYeWPEX/dU8oj/7Ltxh7Dy2jVlQhD9TbT5bwlOVjys2I/h1XFpfPBWb8FYXB8dKN3xNbHJPouM7hiPEPZISfyIiIiIiIiIiIiLSJpmFJVgZOzEZYxj4s7MClYS2jZmXH6jycm1fczw7IT5Q8WfbOy/GNs6oqMSxKR+zoBiqqnf5/c38Qly/LGt6YgsYpeUYHm9gLUizeau2qfhL6QMYVBX/BkBV6Soqi5eRkn1AmCLcMynxJyIiIiIiIiIiIiJtT40Hw12JlZayUy/rz+6A4a7EKCnHkZe/3W0+4c9Wnz4/1Hh2YoRtm2NNLpiBdpi1lZO7knPFWlw/Lw0kb3cSM78IYOcmmXdzlr8Gv6cUV8xfFX+OqESiE3OC7T6L1rxHVHwHEtrtHa4w90hK/ImIiIiIiIiIiIhIm2MWBtoFWukpO/W6VlYmOBy4lv4e2N+vfUbTJzXATogHwNyD9vlzrlqPP7sDVkYqjg27PvFnlJYD4PrhF/D7d8o1HfmFWClJO62l7J7AW10IENLqE/7c569oGdVlq6koXEJa18O1n98uprctIiIiIiIiIiIiIm2OWViC7XRgJyfu3As7Hfg7ZOJcsTawv1/77dvfDwJ7/AG77z5/Xh+OtRuCrUyNMjdmQTG+bjn4szvg2LAZLGuXhmSWlOPv3BGzrALnspWBQdvG3LgFo7hs+66ZX6Q2ny3krcoHCGn1CRCX1g+fp5S8Zf8hKi6LxHbDwxHeHk2JPxERERERERERERFpc8yiEuy0FDCMnX5tf3YHsG2sjFRw7UCVV3QUtsuJUb57Vvw51m4g+rPvgnvqOVevx3Y68HfugD8nC8PjxdxSuOsCqvFgVFXj65aNr083XAt/xdywmeiPviRm9lfEvvsJUXPnQ1VN86/p9WEWlynx10LeqgIM04UjKilkPDa5J4bpwlOxUdV+YaI3LiIiIiIiIiIiIiJtjllYstPbfNbyZ2cF/t6B/f0AMAzshDjMit2z4s8s+7Ot5oJfcaxYi2PVevydO4LTiZWeih0TvUv3+TP/bPNpJSfiGdofTIOYj7/GqKqhZsIYPCMG4lidS+yMjzDz8pt3zcJisG38Svy1iLcqH1dsRp3EnumIIja5Z6Dar/2IMEW3Z3OGOwARERERERERERERkRBeH2ZpOb4BvVvl8nZiPJ59huDP6bDj10qI320r/owyN/6sDOzEBKK/+QlsG+/wAX8eNPBnZ+HIzcM7fOCuiefPxJ+dnAhOJzXjR2G6K/H16gqmCXTA16MLMZ9+S9R3C6g+esKf4w0ztxQGWsqmJrf+A+xGvNUFuGLq3x+zfb8zAVvVfmGity4iIiIiIiIiIiIibYpZXAqAv5Uq/gB8/XpiJ8Tv8HXshDiM3bXir9SNnZSIZ99h+Du0w46Nwd+xffC4PzsLs7h0lz2/WVKGnRAHzkBNk9UpC1+f7qHJvZhoakYPxSwpw/nbqsYvaNs412/CykxvlZayuzNvVUGd/f1quWJSccWogjJclPgTERERERERERERkTbFLCwG08BOSWp6cphZ8XEY7kqw7XCHsnPZNka5GyspAUyTmoPHUHXMweBwBKf4O7YHw9hl7T7N0nKs5KbXhJ2eiq93N1wLlkJ1w/v9OVbnYm4pxDuoz84Mc7dn23aw1ae0PUr8iYiIiIiIiIiIiEibYhaWYKUkhySZ2io7MR7D64MaT7hD2bmqajC8PuykhMDXpgkx0aFzoqPwZ6Zhbty8S0IySsuxUhKbNdczLNCSNGrBr/VP8PmI+mkR/s4dsbaqYpSm+T2l2Ja3wYo/CS8l/kRERERERERERESkTTELS7Basc3nzmQnxAFgunevdp9mWWA/PSup8USblZWJI6+g9Sse/X7M8orA/n7NERuNd0g/nMtX4Vy8HPz+kMOuxb9jVNXgGTGoFYLdvXmrCgAa3ONPwkuJPxEREREREREREREJL9vGKC4NJI/8fsyS0ohJ/FnxgX0CDXdFmCPZuYxyNwB2UuP7IPo7ZGJU12CUlLduPGUVYNtYzU38EdjH0duvB1HzlxAz82McK9Zi5ubhWLsB1+LlePv3+quiUZrNW/1n4k+tPtskZ7gDEBEREREREREREZE9m7l+E1Gzv8GflYGvZ1ewbKz01HCH1TwxUdhOR2Cfv92IWerGjo9rst2qlZke2OcvLx9fauvtyWiWlgXu14w9/v46ycQ7agi+3t2J+vEXor/+MXjITojDO7jvzg5zj+CtyscRlYTpiG56suxySvyJiIiIiIiIiIiISFgZJeXgMDGqaoj+5icArNTkMEfVTIaBnRC/21X8mWVurOZUw7mcWJlpmJvzoV+PVovHKC3HjnZBTFSLz7VTk6g5eGxg30LbCoxFR4Oz7e8h2RZ5qwrU5rMNU+JPRERERERERERERMLKcFdgJSZQffQEnL+txKiuAVfkfHxtJ8Ttdnv8GWVurHbpzZrrz8rE+fvqQKtWw2iVeMyScuzkpO2/vmFAXAytvBPhHsFbla82n22Y9vgTERERERERERERkbAy3JXYCXFgmvj26oV32IBwh9QigYq/3SjxZ9sY5c2s+AP8WRmBff5KW2+fP7O0vEX7+0nr8VYX4IrNDHcY0gAl/kRERERERERERKRNMzfkgWWFOwxpTeUV2Inx4Y5iu1kJcYFWn3br1pMZRSUYRSWteg8Ao7IKw+fHbmbiz2r31z5/rcK2MUrLlPhrAyy/B19NiVp9tmFK/ImIiIiIiIiIiEibZZSUEfPxNziXrwp3KNJKbNsOtPqMjwt3KNvNTojD8PrA422dG1RWE/XNT8S++ykxH3yBUVjSOvf5k1HmBmh2xR8uF1ZGKmYrJf6CiciUpFa5vjSft7oAQBV/bVjkNEkWERERERERERGRPY5ZVAqA89cV+Pr2aLX9wySMajwYXh92QuRW/NXGbhaXYmVtZ0LE4yVq7ny8wweGvAszN4/oL74H08AzajDOFeuI+fQbqo84ELuRZKlRUoZrye949hkCzpalAswyNxhGi6ow/VmZOFesbXSfPzM3D+eqdXgH98Oup3rPKCwm6pdlUF0TOu71Aajirw3wuDcCEBXfIcyRSEOU+BMREREREREREZE2yywuBdPALHNjbtiMlZ3V+Al+fyDpYKrZWaSwSwPVZXZi5Fb8WWnJWClJRH/9I1VHHAixMS2+hiN3E87VuRiWTc2BowODXh/R387Hykij5oB9IDoKf9dsYt77nOhPvqVm/1HgMME0Q5OAtk3U3Pk4Nhdix8fiHdq/RbEYZW6shDhwOJp9jj8rE9fi5Rhl7nqTerXPYlRV41y9Hm+/nvj6dAfTAJ+Fa+nvOP9Yg5WciJWRGnKqDfg7ZUV0O9jdRY07F2dUMs4oJWHbKiX+REREREREREREpM0yS8rwZ2Vi1Hhx/foHNU0k/qI//ho7NgbP/vvsoghlR9m1bSXjIzip43BQc/BYYt77jJhPv6X60PEtrrJz5OaBw4Fj7QbMTVuwOrTDtWQ5RnUNnsPGQ3QUAHZcLNUHjyXmg8+Jnflx8HzvXj3xjhwc2GtvdS6OzYX4O7TDtXg5vl5dW1RRaZa5m72/Xy2r/V/7/PnqSfy5Fv+GUVND1TEH41y7Aeei33At/SN43I524dlnyJ/JQCXu26qaig1EJWSHOwxphBJ/IiIiIiIiIiIi0mYZxaX4O3fESksh+usfMUrKGt7ny7JwbCkCy8LXJ5A4CY6vXIu/aza4XLsueGmesgpspxNiosIdyQ6xE+KoOXgM0R98QfSXPwSq9prbmta2cWzIw9u/F468fKLm/ULNgaNxLV6Od0Bv7MTQJJydmkT1MQdj/FktaRYUETV/CXZCHL4+3Yn6aRH+nA7UjB9J7Nuzcf24GM8BzU+GG2VurA4tbFnqcmGlpwT2+evTPfR65RW4lvweeJaUJLwpSXh7d8MsLgvOsdJTgslNabtq3OtJbDci3GFII5Q2FxERERERERERkbbJ68Msr8BKTcbfLRs7JhrXshUNTjdKysCysGOiiZr3C1jWny0Pfyb6m/m4Fvy6C4OX5rLL3NgJcbvF/o1WeiqeMcNxrNuIUVTS7PPM/CKMag/+7Cw8owZjFpcS8+GX2FFReAf1qfccOz4Oq2M7rI7t8A3qi3dgH6J+WET0nO8wqmrwjBwMLhee4QNxrskNJOSaw7Yxy91YLaz4g0C7T0defmCfv61E/bgIOzoa78C+fw3GxgTjtzq2U9IvAvi9lfiqi4hWxV+bpsSfiIiIiIiIiIiItElmSaAayE5NAocDX78eOFasBa+3/vmFJQDUjB+FWVyK8/fVuH5ZhvOPNYGWh7+uwCgt375gajxEz/4Kc3PB9p0vDbLL3JAQufv7bcvfpSO2yxlo3dlMjtw87GgXVmYaVkYavl5dMSqr8Awf2OwqVe/eA/B1y8axcTPe/r2CrTr9PTpjZaYR9cMvdRJy9TEqKsFvYSW1fA83q0MmRmU1xp/tWwHMLYU41m7AM3wAuNSEMJLVVGwAIDqhU5gjkcYo8SciIiIiIiIiIiJtklFSCoD1Z2tPX9dsDJ8fM7+o3vlmUQlWUkKgAqpXV1w/LsK14Fe8w/pTc/AY7PjYQPJjOzhXr8excQvRc77d/uSh1Msuc2MnRvD+fttyOLA6tsfZwsSfv2P74N52nhGDqBk3An+Pzs2/r2HgGTcCz5i98Q7pFzLuHdwXs7AEo7i06cu4K4FA69KW8rfLCO7zV8v5+2rshDj83VvwLNImedy5GIaDqLjG91qV8FLiT0RERERERERERNoks7gs0G7QGagSspMTsaNcDSf+CksC+4QBnmEDwGHi690N76C+4HDgGTkYR24ejtxNLY7FsXo9/nbp2LGxRH/yDVRVb/dzSSi7rAI7YTdK/AH+7KzAOq2uaXpyZTVmYTH+7A5/jUVH4e/ZpeXtTx0OfL27BX9mgvF0bI8d7cK5an2Tlwgm/uK3owozqnafvz8rY/1+HGs34Oveebdo5bqnq3HnEhXfEcNU5WZbpsSfiIiIiIiIiIiItElmUWmgzWctw8DKSMNRX+LPtgMVf2kpga/jYqg68XA8+w4LJhz8nTvi75CJq3b/v2YyKipx5BXg692NmgljMLw+YubMbVbbRGlCjQdqPLtXxR+BxB+2jWPj5ibnOjYEKgP9ndq3XkAOB/4u2ThXr29y3RoVldjRru1uy7n1Pn+ODZsxPF583XO261rSejwVm9j82yvYtr/Z59S4c9XmMwIo8SciIiIiIiIiIiJtkllcipWSHDJmZaZh5hfWSV4YZW4Mrw8rPfWvQZcztMrIMPAOG4BZ5sbcUtjsOByrc8Fh4u/SCTsxHs/Y4Zj5RRglavm5o3akrWRbZsfFYqUlN2ufP0duHlZGKsTGtGpMvu45GO7KBitma5kVVdtX7fcnKysTo7IKo7wCx6p1WKlJ2KnJTZ8ou1T+yhmUbvyKqtJVzZpv2xY1FRuITshu5chkR6keU0RERERERERERNqeqhqM6hqsbRIGVrs0jF+WYbgrsBMTguNmUUng+J+tPhtiZaZhx0TjWL8JKyuzWaE4V60PtGGMcgHgz0gL3LO0DP/WFYnSYkZ5BcBuV/EH4M/ugHP5qkCSeusEtG0T/ck3ODZuCX7tHbpXq8djZWVix8YEknHt0hucZ1RU7lDiz9/+z33+cjfhWL8p0GpX2pTq8nVUFPwCGFQULCQupVeT53ir8rH9HqKU+GvzVPEnIiIiIiIiIiIibY5ZUgqAtU1iLZh02xJatWQWlmDHx0JMdOMXNgz82VnNqsQCMErLMQuLQ1sVxkZjR0dhlqrib0cZ5RWB/eia+r5FIH9OB4waT50KO8eKNTg2bMYzrD+efYbg2XcY3n49Wz8gw8DXPQfn6txG230aFZU7VoEZ5cJKS8H1yzIMnx//HtDm0/J7sPzN2M+xjSha/R5Rce1J6rAv7vxfsJvRtrjGnQugir8IoMSfiIiIiIiIiIiItDlmcRmYJnZSQuiBmGispIRAu8+t5xdutb9fE/w5HTBLyjDcFU3Oda5ej+1yBir+tmIlJ2KUlDXrftIwo6ISIyk+tCJuN2FlpmFHu0KTzB4vUT8twde9M75BffH17YGvT3eIjtolMfm75WBU12Bu2tLgHKOiCmsHKv4A/FkZGNWewDtITGj6hAi3+beX2bT0+XCH0Sw15etxFywkrcthJGQOxVu1BW9l078IUePOxRGVhDNKVc5tnRJ/IiIiIiIiIiIi0uYE9vdLBLPuR5hWZhqOrauobBuzqDh0f79G+Du2/7MVYRMfdvv9OFauw9+5IzgdIYfslCRV/O0M5RUY2yZ3dxeGgb9TFo7V64MtTV2/LAOfD+/wAWEJycpIxUqMD1T91cfjxfB4d6jVJxBso+vrtvtX+9m2TWXRr1QVL8e2rXCH06TCNe/him1HYtZI4lL7YjiicBf80uR5Hrf294sUSvyJiIiIiIiIiIhIm2OWlNXZ36+WlZkW2NPP7wfAqKwKVBc1sb9fUJQLf/v0xhN/tk3UNz9hVlTi26vu/ldWciJGaXmjLROlacbunPgDfP17Yfh8xM6cjev7Bbh+/QPvoL47nFjbboaB1bEdZkFR/YcrKgECbXN3gL9je7z9e+Hr2WWHrhMJvFWb8XvdWP5qPBWbwh1Oo7xVBbjzF5DW5VAMw4HpiCI+ba9mJf5q3LlK/EUIJf5ERERERERERESk7fD5cC38FbOgqMHWnVZmGlg2ZmEJQPDvZif+AH92h0C7wz+Th9ty/bwU56r11IwbiZVRt5LQTknE8PkxKqqafU+py3BXYiTvvok/KyONquMOwTugD67fV2PHxeLr3zu8MSUmBCoQ60la167nHdrjD8DpwDty8C5rYRpOVSUrwDDBMKkuXRXucBpVU7ERgLi0vYJj8emDqS5dhc/TcOtiy1eNt7qA6PhOrR6j7Dgl/kRERERERERERCT8bBvHqvXEzpiN65dlePfqha9vj3qnWqnJ4DCD+/yZWwqxY6Kw45pfpeTPzsLw+TE35dc55vx9Na5Fv+EZPhB/t/orXKzkRADt87cjPF4MjweS4sMdSetyOfEO60/VCYdSfdj+ddrG7mp2YjyG1wc1njrHzIpKMAzs2JgwRBaZqkpXEB3fiej4TlSVte3En7cqH8N04Yz+q5o6PmMQABUFixs8z1O5GYCo+KzWDVB2Cme4AxAREREREREREZE9m1lQjGveQhxbCvF37ohnxCDsxto/Ohz401NxrN+EY3MhjrUb8PXuBobR7HvaKUnY8bE4NuRhZf/1Yba5IY+ouT/j69sd34CGK7PshPhA8rG0POR8aT5zSyBxa6TV39J1d9OSxHRrshIDiVazvAIrJjrkmOGuxI6LqXdvTalfVckK4tP7Y9sWVSW/hzucRnmrC3DFpGMYf31/nVGJxCb3wF2wkOSOY+o9z1MZaIvsimu/S+KUHaOfXhEREREREREREQkb14JfiZk1B8PrpXriOGoO2rfxpN+frMw0HJvyMfOLqNlvBJ59h7XsxoaBP7sDztW5fyWgCkuI/vx7/J3a4xk1pPFEomFgJSVilqrib3s5V6/HTkrEqKeVqrQeOzHw82WUV9Q5ZlRUYoVr/8EI5KspxVu1hdjknsQmdcdTsQm/t+57bSu8VQW4YjPrjCe0H05F4WJqGtij0Fu5GUdUEg6n1kYkUMWfiIiIiIiIiIiIhIXzt5W4Fv6Kd8heeAf3bVGVkW+vntiJ8fh6dgXX9n3M6R3QGzO/iJj3P8fXPQdHXgF2UgI1+49qVixWSiJGafl23XuP5/fjWLsB/6A+GC2o1JSdIMqFHe3CKHfXOWRUVO74/n57kKrSlQDEJPfEtgKtU6vL1hCf3j+cYTXIW5VPXGqfOuPJHcdRsn4OBSveotPgy+oc91RuJipOlc2RQhV/IiIiIiIiIiIisss5cjcR9f1CvHv1xDt0rxa3FrQT4vH167ndST8AOymB6qMOwjNmbxwbt4BhUDNhDLhczTs/OQmzRIm/7eFYvwnD68PqnhPuUPZIdmICZj0Vf6a7ss20JI0E1aUrcMVk4IpJxRXbDocrgeo2us+fbdsNVvyZpouMHsdTUbiEisIldY57KvOU+IsgqvgTERERERERERGRXcrcsJmoz7/Hn9MB78jB4Q3GMPD17oavew5YNkQ1L+kHYCUnYlTXQI0HoqNaMcjdj2P1eqy0FOyUpHCHskeyEuMx3Nsk/mwbo7JKFX8tUFW6gtiUngAYhkFMUjeqSttm4s/vKcO2PLhiMuo9npA5lNiU3uSveIu41L4YZiB9ZNsWnqrNJGXtsyvDlR2gij8RERERERERERHZJYwyN9Fz5hLz8ddYmWnU7Dey8X30diWns0VJPwgk/gBMtftsGY8X5/pNgWSrhIWdmIBZFpr4M6qqwbKxtcdfs1i+aqrL1xGT3DM4FpPcg+qyVdi2FcbI6uetLgDAFVt/4s8wDDJ7TcJTkUfpxq+D476aYmy/B1dc+10Sp+w4Jf5ERERERERERESk1TnWbSR25seYhcXUjB9FzcT9dqhNZ1tg/5n4M0rLwhxJZHGs2wh+C383Jf7CxU6Mx6ioBL8/OGZUVAJgKfHXLNVlq8G2ghV/ALFJ3bB8VXgq88IYWf28VfkADVb8AcQkdiax3fCQxJ+ncjOAWn1GkMj+L6uIiIiIiIiIiIi0eWZ+EdFfzMOf04Ga/UYEqut2B04HdkIcZkk5/qZnt5hR7sZ2uSAmuunJXi+GuxI7NbkVItmJbBvnqnX426erpWQYWQnxAIE1U5vArqgC0PelEZbfQ0nu11QWL6eq5HccUUkhCbGYpG5gmFSXriY6vmMYI63LW1WAIyoJ0xnT6Lz4jIGU//ojPk8ZzqgkvJWbMUwnrtj0XRSp7Kg2X/G3cuVK3nnnHZ5++mny8wMZ6bVr1+J2u8McmYiIiIiIiIiIiDTFKK8g+tNvsdJTAq09d5ek35+s5KTWafVp20R//A1R8xY2PdfnJ+bjb4h991PM3LZXaVTLKC4j+uOvcWzYjK9Xt3CHs0ezk/5M/JX/1e7TcFdiOx0tbnm7JynfMp8tv0/H7y0nudN4sodehWH8lWYxnTFExbWnxr0+jFHWz1uV32i1X6241L4AVBUvB8BTmYcrth2G4WjV+GTnabP/la2qquLmm2/mww8/xDAMLMti3LhxZGZm8tBDD5Gdnc21114b7jBFRERERERERESkIT4/0Z98gx3lovqgfcG5+31wbKUkBlpX7mRGmRuzzI1RUwOWBWYDNRy2TdTXP2AWluBvl0b0F99Tfej+2OkpOz2mHeH8ZRlRC37FSoyn5qB98ed0CHdIezQ7Pg4MA7O8gtrd6IyKyuC41K/GvRFndBo5w65pcI4zOhVfTcmuC6qZvNUFDe7vtzVndApR8R2pLFpGYvsReCo3E6X9/SJKm634u++++/j+++959tlnmT9/PrZtB4+NHz+er7/+upGzRUREREREREREJNwc6zdhlpZTc8Do5rWrjEBWRipmeUVwf7SdxZG7CQCjxouZX9TgPNdPi3Gu2UDN+JHUHDwWOymBmE+/3enx7AijopKoBb/i7deD6mMOxt+5o5JL4WYYWInxGOV/ddYzK6oCiT9pUHOSYM6oZHye0l0UUcPcBYuoqdgU/NpbVYArNrNZ58al9aOi+Fds28ZTmafEX4Rps4m/2bNnc/XVVzN27FhcrtDS4k6dOrFhw4YwRSYiIiIiIiIiIiLN4Vy9DisjFTutje87twP8HduDYeDYsHmnXtexPg9/x/bYMdE4GmjfaW7Iw7XkdzwjB+Pv0glcLmomjAHDIOqrH3dqPDvC+dsqbKcD79D+4Nj9qj4jlZ0Qj+neqtVnRaX292uCp2IzUfFZjc5xRifjrwlv4s/yVbNpybMUrPhv4GvLi6+muFmtPgHiUvvhqy7C487FV12EK67xZ5a2pc0m/iorK8nMrD/7XFVVtYujERERERERERERkRbxeHHk5uHrnhPuSFpXTDRWZlqwQm+n8HpxbM7Hn9MBf6f2DV7buWItVkoSvv69gmN2XCyekYNw5OVjFhbvvJi2l8+Pc/kqfL27ae+4NsZOiq+7x198bBgjatts28JTtYWoJpJgjugUfJ6SkC6Gu5o7fwG25aGiaCk+Txm+6kKA5lf8pfQGw6Rkw5cATT6ztC1tNvHXp08fPv7443qPffHFFwwYMGAXRyQiIiIiIiIiIiLN5Vi7AfwW/m67eeIP8GVnYW7cAn7/TrmeY+MWsGz8OVn4sztgFpXWbd3p8+FYt7HexKq/SyfshDicS//YKfHsCOeqdRg1Hnz9eoQ7FNmGlfBn4s+2MfPyMaprsFJ33+rcHVVTkY9t+XA1o9WnbfmwfOFrt1uW9z3RiZ0xMCjf/CPeqgIAXLHpzTrfdMYQm9Sd8s3z/p+9Pw+v7KwOfP/vu/c+o47muUpSlWqeXS7b5RnbYGOGEAiEIeSSdJJOdyCkO+ncPCHd/OgL6ZtAuulMEBrurxOSdBOSSyAJECAYbIzncpVrnqtUpXmeztGZ9vDeP7akKlmzdKRzJK3P8/hx6Zw9LFWdca93rQUgrT7XmIJN/H3kIx/ha1/7Gr/1W7/FM888g1KK06dP85nPfIa///u/58Mf/nC+QxRCCCGEEEIIIYQQQszCut6KW1eNjq7/CiK3oR5lOxg9Azk5ntnejVcaQxfHcDePtxJ9XbtPs7UL5bgzJ1aVwt6zHaulHZLpnMS0JFpjnb+C21iPLo7lLw4xI11chLIdSGUIvnwSr6rcn78oZpSOdwLzJ8GsUBkATmZ43mOOdr1Ax+nPLze0Kez0EMmhi5RtfpSiyoPEe17GTvWhlIkVKl/wcaIVe/HcDGawGDNQlNMYxcoq2MTfo48+yn//7/+d48eP86u/+qtorfnkJz/Jd77zHf7bf/tv3H///fkOUQghhBBCCCGEEEIIMcF2blW8pdKYXX24673N5zhdUYqOhHPT7lNrzPZu3IZ6/+dQELemYlriz2pp8+cnlsycUHN2NaMNReDS9XnPRya7/LhnYHT1YgyNYu/bOf/GYtV548nY4ImzGIMjZO87DErlN6gClkp0oYzAvMkzK+hXTTrZ+ef8jfYcY2zgDNpzchIjQLz3GMqwiNUcobjuPtKjNxgbOEsgUoVSC08JRcv3ABCMSLXfWmPlO4C5vOUtb+Etb3kLLS0tDA0NUVpayvbtUhIuhBBCCCGEEEIIIUShCf3wBYzhUbJ3HURlbQCcLZvzHNUqUQq3oQ6zvRv76B3LO9TQCCqZwm24NVPLbagncPqin1g1TchkMdu7yd49xzikUBB3xxasi9ewD+3293sdo3+IwMsnMQeGSL737RAJLSv217OuteKVFePVL2yumFhdutiv4rKu3MDZ3oRXvbA2kBtVOt5NsKhu3uSZGfITf+48FX/ac0iPXAXtYacHCUZrchJnvPslYlV3YFpRiqoOYlhRxgbOEK3Yv6jjhEuaMcywzPdbgwq24u92zc3NHDlyRJJ+QgghhBBCCCGEEEIUKGN4FIDQj48RfOWU36IynNtEUiFzG+sxRuKoeGJZxzHbu9EBC6+26taxN9ehbH+mH4DZ2gHe/PMT7b07UOkMZkv71Ds8j+Dzxwl/8weoVBpcD2NweFlxz0TFx/AqyqWKrFAFA+hwEG2Z2HcfzHc0BS8d7yS0gCSYYQQwA0XzVvyl4614bgYAO9WbkxgziXYyiXaK6+6djKW45i4AApGquXadRhkWtXv/FWVNj+ckNrF6Cjbx94d/+Id84hOfmPG+T3ziE/zxH//xKkckhBBCCCGEEEIIIYSYkeehkmnsw/tIv/UR3Ppq7P278h3VqnLra8CYPotvsczOHry66ikVerqiFLe2itAzLxN89hUCl2/g1s8/P1GXleDWV2Ndb51yu9Hdh3W5hezRQ6R/6s1gmhjD87clXCxjLImORXN+XJE7zvYt2Efv2BCzOJcrlegiWLSwtpdmsBQnM/dzKjV8GcMMoYwA2WUm/pzsKPGeV+m78v9iBmIU3VbdV1x3HwCByOIrb4trjhAqkrmPa03BJv6+9a1vceTIkRnvu+uuu/j2t7+9yhEJIYQQQgghhBBCCCFmolJpALyiKF5dNZkn34C3KTdt69aMYAC3tmp5iT/XxewdwK173QV6pci89RGyDxzB7OjB6B2Yt9pv8pCN9Zjdff4MxnFmezc6GsHZtxNME6+sBGNodOlxz0Rr1FgKXSQJpUJmH70DZ/e2fIdR8Fw7iZ0eWXDbSytYOm/FX2r4MuHSHQQiVdjJpSX+PCdN9/k/5/pz/ydd576Ekxmkeuf7UMatKW+R0u2UNz1JrGp5bYjF2lGwM/56e3upr6+f8b66ujq6u5e3ckYIIYQQQgghhBBCCJEbaiwFsOGrhtxNtf4sPs8DY/E1F0b/ELjezDPxlMLZvQ2nuRHzZgfutgUm/hrq4ZXTmN29uI1+5Y7Z3uXPEBxvwemVl2AM5bbiTyVToDW6SCr+xNqXTfYALLjizwqVkk31zXq/1i6p4atUbH0rqZHrS2r1mUm003X2SziZYWp2f5BY1WGsUNm07ZQyqN7xnkUfX6xdBVvxV1FRwZUrV2a878qVK5SWlq5yREIIIYQQQgghhBBCiJmo5Hjib4NXd3l11SjbWfK8PLO7Dx0M4FWUzb5RMIC7c+uUVqBz0SUxvOKiyUpENZrAGEn4ib+JuMtLUcOjoPWS4p7JRDLYk8SfWAeySf/5s5AZfwBmqAx3jlafmXgrnpsmUrabYKQGe44k4UxSI9dpffXTKMOi6Z7/SNnmR2dM+omNqWATf48//jh/+qd/yunTp6fcfvr0aT7/+c/zxBNP5CkyIYQQQgghhBBCCCHE7dRY0k9EBQP5DiWvvKpytGVidC3uIv4Eo6sPr7ZqshIvJ5TCa6jzE39a+/83FO5trVi9shKU46LiY7k7bSIJSDJYrA+ZsW6CkQoMK7yg7SdaferxZLrWmrGBs3ieDUByyJ/vFy5uIhCtxk71o7W74HgGb/4zgUgVjXd9bMHtR8XGUbCtPn/913+dEydO8P73v5/t27dTU1NDb28v165dY+/evfzGb/xGvkMUQgghhBBCCCGEEELgV3d5RZHcJqzWIsPAq6nE7O7DObh7cfuOz/fLHtmf87CchnqsC9dQw3HM9m7c2moI3ErS6gq/u5oxPIpbEsvJOdVYEh2wNnwyWKwP2bFuwsULT7BZoVK0Z+M5ScxAEemRa3Sc+hOKKg9Sf/BXSA1fIly6HWVYBCO1aO1ipwYIRuefjZpN9jDWf5raPT+PYQaX82uJdapgK/6Ki4v527/9Wz75yU+ya9cuAHbt2sWnPvUpvvrVr1JcXJznCIUQQgghhBBCCCGEEOC3+pTKLp9bV43R0+/P+VsEf76fi1dXlfOYvLpqME2sm+3js/6mJjB0JIwOBXI650+NJf35fhs9GSzWhWyyh0isfsHbW0E/me5k/edUerQFZVgkhy7SdeZ/kBq+SrTMXxwQiPgzPWeb82en+tCeM/nzcNsPMIMlFNcdXdLvIta/gq34AwgGg7zvfe/jfe97X75DEUIIIYQQQgghhBBCzEKNpdDFRfkOoyB4ddWoE+cwBofxqioWvJ/R3YcOWHPP91sqy8Str8Y6exlcD7fhdQkMpfw5fzlM/BljKT/xJ8Qap7VHNtlDuPhNC95nYt6emxmBok2kR28QLt5KRfPb6Tz9Z2jPJlLuFzxZ4QqUYc045891Utx4+f8iUrqDTYd+Fe3ZjHS/QEXTkxiGVNOKmRVsxZ8QQgghhBBCCCGEEGJtMJKS5JngVZWDufg5f2b3+Hw/Y2Uu2boN9SjbwSsuQs/QzlOXl2IMjebsfGosiY5JFahY+5z0INpzCC+i4s+cqPjLDAN+xV+4ZCtFFfvZdOhXKa69l3DxFgCUMgiEq8jOUPGXid9Eezap4ct0nvkzhtt+AFpTuvmR5f9iYt0qqIq/I0eO8Fd/9VccOHCAO++8EzVHGbhSiuPHj69idEIIIYQQQgghhBBCiGm09lt9RsP5jqQwmCZuTSVmT78/589xCL50Eqe5AW/zLDPCPA+jdwD78L4VC8tt8M/tNdTN2H7TKyvBungdXBdMc9nnU2NJvC2bl30cIfLNTg8AEC6qIeXMs/E4wwxiWFGc7AhuNo6d7idUshWAoop9FFVMfa4HojXYyemJv/ToDQwzzKaDH6HjzOdIDp6npP5BrGDJsn4nsb4VVOLvF3/xF6murp7881yJPyGEEEIIIYQQQgghRAFIZ8DTUvF3G6++GuvMJfA8Qs++gnmzE7OljcxbH/UrAl/H6B9COa4/i2+F6OIisvccwm3aNHPM5aV+Enckjl5uu1HHQaWz8pgQ64KTGQIgEKkgFV9g5g+wQqU4mWHS8ZsAhIu3zrptMFLD2MCZabenR28QKm4iWrGHzYd+jb4rf0tF05sX9wuIDaegEn8f/ehHAdBa86EPfYhoNEowGMxzVEIIIYQQQgghhBBCiNkYYykAdFTaOk5wa6sInDhH6KkXMDt7yDxyL4Fzlwk99Tzpn3gMHZs6D9G80e7P96ssW9G4nAO7Zr3PK/MriIyhUdxlJv7UxGNCEn9iHbDTg5iBIkwrBCwi8Rcsw8mOkB69gRkoIhCZPbEfiNRgp/rR2kWpWxW36dEbFNfeA0C0fDdbjn5iyb+H2DgKcsafbds88MADvPDCC/kORQghhBBCCCGEEEIIMQeV9JM8XpEk/iZ41RVgGpgd3WTvvQN3WyPpxx8E0yD0/echk53cVo3ECVy46iflVmi+34KEguiiCMbQyLIPpcaSADLjT6wLTnoQK1yx6P2sUCluZoT0aAuh4q1zdjgMRGvQ2sVJD946b2YEJzNIeLxFqBALVZCJv2AwSF1dHa7r5jsUIYQQQgghhBBCCCHEHNRYEgwF4VC+Qykcpom9dwf2kf04e3f4t0XCpJ94CJVKEXr6RX+WHhA8dhodjWAf2J3HgH1eWSnG8OiyjzOZ+JMqULEOOJkhAktI/JnBW60+50veBSM1AGRTt+b8peM3gLlbhAoxk4JM/AF88IMf5Mtf/jKZTCbfoQghhBBCCCGEEEIIIWahkmk/wTNHNctGZN9zCPuOvVNu02UlZN74AGbPAMEXTmC0d2O2dZG95xBY5ixHWj1eeQnGwDBovazjGIkUOhIGM/+/kxDL5WQGl5T4s0Kl2OkB3Owo4ZLmubcNl6OUiZ28LfE3egMzWLKkakOxsRXUjL/bdXV10dLSwqOPPsrRo0epqqqaVgr78Y9/PE/RCSGEEEIIIYQQQgghwK/u8qSya8G8umoyD91N6NlXMG+049ZV4W7ZnO+wAHAb6wmcvYzR0YPXULfk46ixJFpav4p1wk4PEQiXL3o/K1gG+En0+Sr+lDIJRKqxU32Tt6VHWwiXzN0iVIiZFGzi7+mnnyYYDAJw5syZafcrpSTxJ4QQQgghhBBCCCFEnqlkSpI8i+RubyI7liR48jzZo4cLplrSq63CqygjcP4qmeUk/hJJdFE0h5EJkR+ek8ZzklihJbT6DJUCYIUrsIIl824fiNaSGrmK1h6gyMRvUtbwpkWfV4iCTfz98Ic/zHcIQgghhBBCCCGEEEKIeaixFF5FWb7DWHOcQ3tw9u0Aq4Au0SqFvX8noR8fQw2PosvmT1YAkMpgXWnBObALDAM1lsStqF/ZWIVYBXZmCGCJFX9+4m++Np8TyhvfRPtr/52Rjh8RrdiPa4/NWykoxEwK6F3Fl8lk+NGPfkR7ezu1tbXcf//9VFRID1shhBBCCCGEEEIIIQqO1hhjKRyp+FuaQkr6jXObG9DHThO4cI3s/XcuaJ/AmYsEzl0B08DZt1MeE2LdcDKDAEuas2eNV/yFi7cuaPto+R5KNz9C/7WvU+6k/H0l8SeWoKDeWTo7O/mFX/gFWltb0eMDZEtLS/nc5z7HPffck+fohBBCCCGEEEKIwmC0dxM8fob0Tz5eMO3hhBAbVNYG15W2juuJaeLs2Y519hIc2Y/Z20/g+Dmc5gacO/ZO3962sS63oEMBAicv4G6uk8eEWDec9CCgCITKFr2vYYbYdPDDRMp2L3if6u3vITlwloHr/0ggUo0ZiC36vEIY+Q7gdp/97GcZGRnh05/+NN/+9rf54he/SFVVFf/5P//nfIcmhBBCCCGEEEIUDKu1A2NwBJVM5TsUIcQGp8b81yEdlequ9cTevQ3leUT+6SlCT72ASiYJXLwG48Uat7Ou3kQ5LuknHwEg9NyrAOiYJP7E2uekh7BCpShjaTVUseo7MQMLfy4YVpjaPT8H6AVXCgrxegVV8XfixAl+4zd+g3e+850AbN++ncrKSt773vcyODgoLT+FEEIIIYQQQgjA6PPbTqnRhFRUCCHyamIBgpa2jutLNIyzextGZw/ZNz2ADocIf/tpjO5+vPrqW9tpjXX+Ks7WzejKMuw79xF8+RQAnrw/iXXAzgxhhRY/3285ohV7qd39IULFDat6XrF+FFTir7u7m127dk25bffu3Wit6e3tlcSfEEIIIYQQQghhOxhDowAYowm8+po8BySE2MiMsSQohY6E8x2KyLHsfbfN99MaHYtitbSSvS3xZ7Z3Y4wmyD7sj2ly9mzHunQdY3QMwqHVDlmInHPSg0ua77dcpZsfXvVzivWjoFp9aq0xTXPKbYbhh+h5Xj5CEkIIIYQQQgghCooxMOS3WjMM1Ggi3+EIITY4lUyhIyEwCuoyo8g1pXCaGzFvtIPrTt5snb+CV1WOVz2eGDEMsg8fJXtkv8ygFeuCkxkisMoVf0IsV0FV/AF85jOfobi4eNrtv/d7v0csdmuQpVKKL3zhC6sZmhBCCCGEEEIIkXdG3yDaMvGqKzFG4vkORwixwalEUub7bRDOtkYCZy5hdvbiNtZjtnVidvaSecM9U5J8XlU5XpUkSsTap7XGzuSn4k+I5SioxN899/gl4WNjYwu6XQghhBBCCCGE2GjMvgG86gp0WTFGV2++wxFCbHDG4DBepSR5NgJdXopXVoJ5vRUdCRN85mXcpk2425ryHZoQK8Jzkmg3u+oz/oRYroJK/P31X/91vkMQQgghhBBCCCEKl9YYvYM4O7ago2GsSy1+209ppyaEyAfXxRgexdnVnO9IxGpQCndbI9bpi5hdfeiyEjKPHJX3ILFuOelBAEn8iTVHmm8LIYQQQgghhBBrhEqmUKk0Xk0lXkkxeB4qkcx3WEKIDcoYHgVPS8XfBuI0N6IcF0yD9OMPglVQdSVC5JSd8RN/AWn1KdYYeWUWQgghhBBCCCEKQdYGBQQCs25i9PoXoNyqCpTjAKBGE+jiotWIUAghpjAGhgHwykvzG4hYNbokRuYN9+DVVEEknO9whFhRTnoIpUzMYEm+QxFiUaTiTwghhBBCCCGEKAChZ18h+MKJObcx+gbQsShEw/7/lcIYTaxShEIIMZUxOIxXVgwBqS3YSNztW2TBidgQnMwgVqgcpSSNItYWeVcWQgghhBBCCCEKgBqJYzjunNsYfYO41ePtpgwDr7gIFZfEnxAiP4yBYbyKsnyHIYQQK8JOD2GFpZWxWHskVS2EEEIIIYQQQuSb1v78vmQKNTbLzD7PwxwYwqu+NWdGl8QwRuKrFKQQQtxGa9TgsMz3E0KsW05mCCskr3Fi7ZHEnxBCCCGEEEIIkW9ZGzVe7Wf0DU6/33YIHD8LrodXXTl5s1dajJJWn0KIPFAjcZTjSsWfEGLdctKDWOGK+TcUosAUVKvPz33uc4va/qMf/egKRSKEEEIIIYQQQqweNZaa/LPRN4i7tWHyZ/N6G8Fjp1GZDPYde6dW/BXHMOJj4HlgyNpeIcTqMQaGAfAqy/IahxBCrATtOTiZIQJS8SfWoIJK/P3lX/7llJ9t2yadTgMQCoXIZDIAhMNhgsGgJP6EEEIIIYQQQqwLKukn/ryaSoy+gVu3D48S+tHLuE2byB69A11cNGU/ryTmt9tLJNElsVWNWQixsRkDQ+hYFELBfIcihBA5lxq5htYu4dJt+Q5FiEUrqMTfsWPHJv985swZfv3Xf52PfOQjPPnkk8RiMRKJBN/97nf5whe+wB/+4R/mMVIhhBBCCCGEECJ3jGQKlMJp2kTwtXOTFXxWSxs6YJF59F4wzWn7TST71GhCEn9CiFVlyHw/IcQ6NjZwFjNYQijWmO9QhFi0gu0D8ru/+7v80i/9Eu95z3uIxfwvL7FYjJ/+6Z/mF3/xF/nUpz6V5wiFEEIIIYQQQojcUGMpdDiEV1sFrocxNAJaY15vw92yecakH+BX2xgGxmh8lSMWQmxoWmMMDEubTyHEupUcPEtRxX6UKtgUihCzKthH7cWLF2loaJjxvsbGRq5cubLKEQkhhBBCCCGEECtDjSXRRRG8ijIwFEbvIMbAMMZoAmfbHCvNlcIrKcIYTaxarEIIoRJJVNaWxJ8QYl2y00NkEh0UVR7IdyhCLElBtfq83ebNm/nqV7/Kww8/jFJq8natNV/5ylfYtGlTHqMTQgghhBBCCCFyRyVT6KIIWCZeRRlG3wAqnvCrAOtr5txXF8dQkvgTQqw0x8W6esOvSh7xq4y9irK8hiSEECshOXgWlEG0Yl++QxFiSQo28febv/mb/Pt//+9585vfzGOPPUZlZSUDAwM8/fTTdHZ28sd//Mf5DlEIIYQQQgghhMgJNZbCra8GwKuuwOjoQTkubnMDGHM363FrKgmeOIfR3o3XULca4QohNiDr4jWCx06jLb/1sFddgY5G8hyVEELk3tjAOcIlzZiBonyHIsSSFGzi7/HHH+drX/saX/rSl/jBD35AX18f1dXVHDp0iD/5kz9h7969+Q5RCCGEEEIIIYTICb/iLwqAW12BdeEaAE7zHG0+xzkHdmH29BN65iXSb30ULa33hBC55nkELlzF2d5E9g1H8x2NEEKsGO05JIcuUN74RL5DEWLJCjbxB7B3717+8A//MN9hCCGEEEIIIYQQK8e2UVkbHQ0D4FVXAqCLong1lfPvbxhkHr2X8D//iPBTz5P+iccmk4hz0ho1HAet/R9jUQgGlvxrCCHWL7O1E5VI4rxxZ75DEUKIFZUauY7npGS+n1jTCjrxN6Grq4uuri727NlDNLqALy9CCCGEEEIIIcQaoZJpgMlknS4uQkcjONub4LaZ93MKBMg88SDhb/2Q4LPHyLzlDXPua3T2EnzlFMbQyORtOhwi/RNvRBdLWyshxFTW+au4tVV4leX5DkUIIVbU2OBZzGAJoeKmfIcixJLNPSggz/72b/+Whx9+mMcee4yf/dmfpaWlBYBf/dVf5S//8i/zHJ0QQgghhBBCCLF8aiwJcGtWllKkfvJN2IcXN+JCRyNkHrgLs7sP82bHzBvZNqEfvkj4e89CwCL9xEOkf+KNpN/2KDpgEXrqOchkl/PriHmYV1oIvHLK/+/4Wfn7FgVPDQxh9vTj7NuR71CEEGLFJQfOUFSxD6UKOnUixJwK9tH75S9/md/93d/lXe96F3/+53+OHm89AnD06FG++93vLup4x44d41d+5Vd46KGH2L17N0899dS8+7z88sv81E/9FAcOHOCJJ57g61//+rKPKYQQQgghhBBC3E6NpQDQRZFbN0bCYJqLPpbXUIfbWE/w2Glw3Gn3WxevY7Z1kXnDUdJvexSvoQ6vugKvtorMEw+hUmlCP3wR3On7ihzIZAk9fwKrpR2zvYvA6YuYbV35jkqIOQXOX0UXRXGbNuU7FCGEACAz1slI5/M5P2422UMm0UFR1eGcH1uI1VSwib//9b/+Fx/5yEf4zd/8Te69994p9zU3N09W/y1UMplk9+7d/Of//J8XtH1bWxv/9t/+W+69917+8R//kZ//+Z/n4x//OD/+8Y+XfEwhhBBCCCGEEOL1VDKFDgeXlOibSfaeQ6hkisDZS1Pv8DwCF67hNDfgztBGVJcWk3njA5i9AwROnMtJLGIqs6MHtCb9E4+Rfvdb0JEwxmgi32EJMbtMFut6G/be7WAU7GVEIcQGM9jybXou/TVOZmT+jRch3nscwwzJfD+x5hXsjL+enh7uvPPOGe8LBAIkk8lFHe+RRx7hkUceWfD2X/3qV2loaOBjH/sYANu3b+f48eN8+ctf5uGHH17SMYUQQgghhBBCiNczxlK32nzmgC4txt63k8DpSzg7t07ODjRbO1FjSZz9O2fd16urxt6zzb/Qf/fBhc8YFAtitnfhlZdO/pt4JTHUaDzPUQnhU2NJtGVBKDh5m9k3AJ6Hu7Uhj5EJIcQt2nMYGzwL2iPee4zyxsdzduxE73GKKg9hmMH5NxaigBXsUp1NmzZx5syZGe87deoUW7duXdHznzx5kvvvv3/KbQ899BAnT55c0fMKIYQQQgghhNhYVDK3iT8A+4696GCA4DMvT7bttM5fwa2twqssn3Nft6EelUyhhnK7in7D0xqzoxu3oe7WTSUxjNGxPAYlxC3BH71C8OWTU24zegfR4RA6Fs1PUEII8Tqp4St4TopgUT2j3S/l7LjZZC+ZRBuxmrtydkwh8qVgK/7e97738bnPfY7y8nLe/OY3A+A4Ds888wz/83/+T3791399Rc/f399PVVXVlNuqqqpIJBKk02nC4XDOzmUYCsOQVZQif0zTmPJ/IQqRPE7FWiCPU7EWyONUrAUb7XFqpFLo6gosK4e/rxXCeeJBAv/8DOHnX8U9uBurdwD7TQ/Mf56GGghYBLt6cGsq/Ns8D/PVs7gHdkKOk5Rr1WIfp6p3ACOTha2bJv8NVHkJxs0OLFNJdaVYEYt5nJrJFMQTeLc9Hq2BIaitxArkphWxEDPZaO/7YnmSg6cJRCqo3flTtJ/6Am66m1Bs+TNIhwdOYFghSmsPYszwWJTHqVhLCjbx90u/9Et0dXXxiU98YnKG3s/8zM8A8MEPfpCf/dmfzWd4OVVRUYSSD/iiAJSUyBd4UfjkcSrWAnmcirVAHqdiLdgoj9NsNotRXY5VXpTbA5cX4ZqP4HzrR6i+QSgvIXJ4J2oBc7rsbZuhp5/AeEzupRacc5ex6isxN+/KbZxr3GyPU631lO/6zsUreNEwkd1Nk/8G7uYqnBMu0ZCJKtoYj3eRHwt5Pc24DmRtospDlZegPU12aBjz7v3Ecv36JMQMNsr7vlg4z7XpvPRP1O14EisYQ2vNzeGz1DQdpWHHffRd/l/YIyeoa5y9jflCdZw4SXXDXVRWVcy5nTxOxVpQsIk/gI9//OP8/M//PC+88AJDQ0OUlpZy//33r3ibT/Cr+/r7+6fc1t/fTywWy2m1H8Dg4JhU/Im8Mk2DkpIIo6MpXNfLdzhCzEgep2ItkMepWAvkcSrWgg31OHVdQvEktjLwhlag5WNVFeZdB7FePolz9A7GRlIL2s2oriLwwgkS3UMQChJ46QyG65Ht6MNp3Jz7ONeg+R6n5qkLGDc7sd/yMASDBC7eRNdVT/k3UIZF0PUYae1F11VNO4YQy7Xg11PXJZTKADB64Qbe3u2ooVGCyTSpohh6JV6fhBi3od73xaIk+s/QdvbvGe5vZ/PBf0063kYy3kvVzn2MjGaJVB6h6/qzFG16G0otvRIvm+xldKCF4s1PMjTL6508TkUhKF/gQpyCTvwBNDY28v73v3/Vz3v48GGeffbZKbe98MILHD58OOfn8jyN5+mcH1eIxXJdD8eRNy5R2ORxKtYCeZyKtUAep2It2AiPUxUfQ2twwmG8FfpdnT3bsSvL8arKYYHnUJtqsTyNbu1Cx4pQPQN4oQB6cHTd/5ss1myPU+t6O6p/CPNfXiD78N0E+wbJ7tmOe/u20SgBDd7QKO48FQZCLMe8r6fJDMGJS1OdvTg7mzG7+9Ea7PKyBb92CLEcG+F9XyxOYuAKGsVI10sUVR0hk2hHmWGCxTtwHI9Yzb0MtT1DvP8SVrCE1Mg1wiVbCcUaFnWeoc5XQAUIl+2b9zEoj1OxFhRs4m/v3r00Nzfz+c9/nubm5in3nTp1ig984ANcuHBhwccbGxujtbV18uf29nYuXLhAaWkpmzZt4rOf/Sw9PT38wR/8AQAf+MAH+N//+3/zB3/wB7znPe/hpZde4jvf+Q5f/OIXF3xMIYQQQgghhBBiLmrMr/7SKzk3Tym8mspF7aKLonjlpZhtXaA1OhbF2bIZ62bHCgW5zrguxuAwTnMD1s0OQt/1Fxa7m2unbmdZ6GgEYzSBm4cwhZigMlkA3JpKzO4+0BqzbwCvrASCgTxHJ4TYqFIjV4lVHUJrj55L/xszUERRxQGU4ac1wiXbCESq6Tj5R2jtv5Mqw6J6x3sp3fzogsZrjQ2eY/DGtympuxfDDK3o7yPEainYSZRaa2zb5r3vfS/PPPPMso939uxZ3vWud/Gud70LgN///d/nXe96F3/yJ38CQF9fH11dXZPbNzY28sUvfpEXXniBd77znfzFX/wF/+W//BcefvjhBR9TCCGEEEIIIYSYy2TirwDnu7kNdZjtXVgt7dj7duCVl6ASSXCcfIdW8IyBYfA0zoFdZB66G2M04VdcRqaPDvFKY6jRxOoHKcRtVMZv8+lu3YxKplGjCYy+QbxqqUQVQuSH9hzSoy1ESndSs+tn0V6W7FgnRVWHJrdRSlGz6wOUNz3J5sP/nu0P/yGlm95A7+W/oevcl/DczJznGBs8T+fpPyNavofqXT+z0r+SEKumYCv+AD7zmc/wrW99i4985CN89KMf5SMf+ciSj3Xvvfdy6dKlWe//9Kc/PeM+//AP/7DkYwohhBBCCCGEEHNRyRQ6YEGg8Cpq3IY6AmcuoQMWzs5mjOFRANRIAl1Zlt/gCpzRNwCmgVdeClUVZJSBjk5P+gHokhhG3+AqRyjEVCo9nvhr2gzHzmC2d2MMjeLs2Z7nyIQoTKNdLxKI1hAplefISknHW9GeTaRsB4FwOTW7foa+K39HUeXBKdsVVR6cclvNrg8QKdtF9/n/yXDbD6nY+tZpx9baZbTrJXovf4Vo+R7qD/4KhlF4n8WEWKqCTvxZlsUnPvEJ9uzZw+/+7u9y8eLFGRN0QgghhBBCCCHEWmSMxNGxaL7DmJFXU4kOB3G2NUEwgFdaDIAxMoorib85GX2DuJXlYJoAuNsaZ93WK45hXmsFrWEBLcmEWAkTrT51LIpXWUbg/BXQGrd6cW2ChVhvtPbwnBRmoGjytky8je6Lf0kgXMnW+z6FUmYeI1y/UiNXUUaQUMx/Dy2pu4/i2nsX1L6zuOYIid5Xife+OiXxp7Um3vMygze+TTbZQ3HtUWr3/rwk/cS6U7CtPm/3vve9j7/4i7/g+PHjvP/97+fmzZv5DkkIIYQQQgghhFgez8Ns7cTdXJfvSGZmGKR/8nHsu8dX0YeC6EgYYySe37jWAHMRLRJ1aTHKcVGp9ApHJcQcMll0KAhK4dZVoxJJdMBCl5fkOzIh8mro5vdoeeF3yIx1An7iqO/q/4sVLMFO9RHvfjnPEa5f6eGrREqbJ+f5AQtK+k2I1dxFJtFGNtk7edto53N0n/9zAtE6mu75OPX7/7Uk/cS6tCYSfwB33303X/va17Asi//4H/9jvsMRQgghhBBCCCGWxezsQWWyfkXdImmt0VqvQFSvO09RdLJqDcArLUYNS+JvTsk0KpHEW2CllFccA5A5fyKvVDrjJ/4Ar67K/39VuVShig0v0X8Kz03TdeYLuE6SsYHTJIcuUrP7/yBWfZiBG99GazffYa47WmtSI1cJl+5Y8jGKKg+gzCCJ3uPjx/QYavs+seojbD70q4SLF//5S4i1omATfx/96Eepra2dclt9fT1/8zd/w7vf/W7uvvvuPEUmhBBCCCGEEEIsn3m9Da+0GF1Ruuh9E32v0fLCx9DaW4HIZqfLiqXibx5mvz+vz6tZYMVfid8+zpDEn8gjlclCOASAW1MFSuFVLewxLMR65TpJ0vEblDc9iZON033uf9J35f8lWrGPosqDVGz9ifGqv1fyHeq6Yye7ce0EkbKlJ/4MM0RR5UHifX7ib2zgLNlkN+WNj+cqTCEKVsHO+PvoRz864+3hcJhPfepTqxyNEEIIIYQQQgiRQ46D2dqJc2DXkipqkkMXcDJDOJkRAuHyFQhwZl5pMdaVGzKPbg5G3yA6EkZHIwvbwTTRsahU/Im8UhOtPgFCQTKP3otXI/P9xMaWGroM2qNs8xuIlu+i49TnQCk2HfwwSinCxU3Eqg4zePOfKa47KrP+cig1cg2UQbhk27KOU1xzF11nv0Q22ctw2w8Il2wlXLo9R1EKUbgKKvF37tw5tm/fTjgc5ty5c/Nuv3///lWISgghhBBCCCGEyC2zrRtlOzjNjUvaP5NoB8BO96964g/XQ8XH0CWxVTvvWmL0DfjVfotIjHolMYxRqaQU+aMyWbzbntPu1oY8RiPE4jiZEdpP/hGbDvwbgkX182+fjWOYQQwzNOd2yaELBMJVBCLVBCLV1O75OTwvQyi2eXKbiuafoPXYfyHRd4rimiPL/l02GtdJgfYwA0VTbk+NXCUUa8C0FriIZhZFlQdRRpCBln8iOXSB+v2/vKg5gUKsVQWV+HvPe97D3/3d33Ho0CHe8573zPok1FqjlOLChQurHKEQQgghhBBCCLF8VksbXmU5urR40ftq7ZFJtAHgpPqgbGeuw5v93GUlABgjcdySGGok7lcuHtw96z7WxWu4tVXo8sW3NF1ztMboH8I+tGdxu5UUY/T0rVBQQixAOoOWCj+xRsV7XyU71kG85xiV235yzm211rSf+K9EynZRu+f/mHPb5OAFohV7J38u3fTgtG3CxU1YwVKyYx2AJP4WQ2uPjpN/TDbZTd3ef0Ws+jAArp0kNXSJoqo7ln0OwwxRVHWQeM8rWKFyYtV3LvuYQqwFBZX4+6u/+iu2b98++WchhBBCCCGEEGLdydqY7V1kjxxY0u52qhftZsf/3J/LyOaloxG0ZaKGR6GhjuBzr2L2DuDs2AKR8PQd0hmCL53E2bmV7IN3rWqs+aCGRlG2s+gWiV5JEdaVFmmhKvJGZTK3Wn0KscYkev0Zbon+U/Mm/jKJVrLJbjw3PVlcMhM7PUQ22U1l89zHA7DCFdjpwcUHvsHFe46RHr1OpHQHnWf+jPLGJzCsMENtT6E9l+Kau3NynuLqu0j0Hqes4Y0oo6DSIUKsmIJ6pB89enTGPwshhBBCCCGEEOuF2dYJrofbvLRWepm4X+0XLKonm1rlKjGl0KUlGCNxzJZ2zN4BAMy+QdymTdM2Nzt6/Cq47o1RzWb2D4JSeJVli9pPT7RQHU0sqQpUiGXRGpWxQRJ/Yg1yMsOkRq5RVHmQsYEz2KkBApHZF1/4SUKFkxkmm2gnVOy33NZakxq+TLh0G4YRIDXkd5qLls9e0T7BClfgSOJvUTw3Q/+1rxOrPkL9gX/LcPsP6L/696AMyjY/QnnTk1ih3HQKKKq+g6ptP0XZ5kdycjwh1gIj3wEIIYQQQgghhBAbidkzgFdWgi6KLmn/dKINK1RBuHgrdnp1K/7An/NnDAwTPHYat2kTOhLG6Jv5gqfZ3g1KYYwmUGPJVY509anBYX9OWiCwqP3c2mp0MIB1uWWFIhNiDhm/gliH5553JkQhivedQCmDmt0fRCmTxMCpWbfVWhPvPU5J3b0YZoixwXOT9yUHz9P+2mfpOvslPM8mOXSRUHETZnD+xRiBcAVOZuMl/hL9pxjtemFJ+w61/guuHadq+7tRSlHe+Dhb7/8vND/w+1TvfF/Okn4AhhGgYutbMawZOhMIsU4VVMXfnXfeuajhmidOnFjBaIQQQgghhBBCiNwz+gbwqiuWvH8m3kqouJFApGrKRcvV4pUVY11vBcMge/QQwVdOY/QNTN9Qa8yObpydW7Eut2B09+Nub1r1eFeTMTSKV16y+B0DFs6uZqzLLdiH9y46cSjEcqiJxJ9U/Ik1KNF7nGjFXgLhSiLluxnrO0V5wxsB0J6Dk40TCJcDkEm0Yaf6qNn1QVwnxdjAWSq2vAWAkc4fY4XKSA6ep+vsl0iP3qCk7r4FxWCF/Fafc7UOXY+G235AauQq4dIdBKM1C97PTg8x2Po9yhreNGW/QFjmjAqRKwWV+PvFX/zFDfXiKIQQQgghhBBig7EdjKFRnD3bl7S71ppMoo2yTY8QiFTjZkfx3AyGuXqVOhOtKO0Du9DFMdzqCgKnL06bT2f0DaIyWZydWzF6BzC7+9Z34k9rjKERnH07lrS7s3cHgXNXsK624uz1Hx/m9VZUOoOzb2cuIxViiluJP6n4E2uLkxkmNXyV2j0/B0Cs6jB9V/4W105iWGE6z3yB5NBFGu/6bcLFTSR6j2MGioiW78ZO99F3+W9xnRTazTDWf4qqne8lGKmh88yfoT2HaMWeBcURCFegPRvXTmAtoEJwvcgme9CeQ/+1r7Pp4K8seL/h9qcmq/CEECujoBJ/v/Zrv5bvEIQQQgghhBBCiBVjDAyB1rhLrPhzsyO42Tih4kbMwHgCLtVPKLY5l2HOHcOmWuwDu7AP+XOPvJpKlO2ghkbRFbdac5ntXehQAK+6Aq+uCqOzd9VizItUBpXJ4pUtoeIP0LEozpZNWBeu4OzZhtnWRejZY2jTwNm9DUwzxwEL4VPpDAA6LBV/Ym1J9L2GUgax6sMAFFUdovfyV0gOniMz1snYwFkCkSq6znyBprv/I/He4xRVHUYZFkUVB+jVXyE5eAE72QPKoKT2XsxAEZsOfoThjh8RKV3Yogsr5L+nO+nBDZP489wMTmaIaMU+En0nSA5dJlq+a979JtqtxmruxrSW1vJcCDE/mfEnhBBCCCGEEEKsEqNvEG2Z6PKlza7JJNoACMX8Vp/A6s/5Cwaw7zk02Y7SqywDpTBf1+7TbO/G3VwHSuHWVftz/pKp1Y11FRnDIwB4S/y3BXD27cQYSWCdvkjwRy/jVZWjHBejuy9XYQoxXcZP/CGtPsUaEx9v82kGigC/8i5U3MRAyz8xeOPbVG1/Fw2H/wOem6H95H/HTvVSXHOXv22kimC0jrGBM4x0/pjimrsnj1NUeYDNh34Vw1zYc8IKjyf+NtCcPzvpL+ap3PoThEua6bv6d2jtzbtferQFJz04+e8ghFgZBVXx93o3b97k61//Ojdu3CAz8SHkNv/jf/yPPEQlhBBCCCGEEEIsjdk3iFdVMaUl5mKk420YVhRrfA6OMgLYqaUlhTKJdkZ7XqFq208tb+xGIIBXXoLRNwi7t/lxJVMYA8PY+/1qCbeuGgCjuw93WxPYDlZLG86Wzesm2WAMjYBpoktiSz6GV1OJV1lG8MQ5vOoK0m95A5Gvfw+zvRtvc10OoxXiFpXOogMWGFIfINYOz82QGrlKza6fmXJ7rOoOBlq+SazmLsqb3oJSivoD/4b2k3803ubzVvvOosoDDLc/jdYudZt+ccmxmIGY/36c3jiJv2zKT/wFo7VU73w/bcc/zWj3S5TWPzDnfom+45jBEiJl81cHCiGWrmATf6dPn+ZDH/oQmzZt4saNG+zevZt4PE5HRwd1dXU0Na3juQBCCCGEEEIIIdYfrTF6B3B2bFnyITKJNkKxxslEXSBSjZ1aWsVf35W/Izl0kdL6hwhGa5YcE4BXXYHRc6viz+joBvAr/gAiYbzSYn/O39YGQs+8hNneTeD4Gew79+Psal7zSQdjaNRv87mcJKpSZI8cIHDmIplH7wPLwm2ow2zvxr43d7EKcTuVyaLDMt9PrC2ZRDtoj3Dx1im3l256GM9JU9n8jsn3ymj5Hur3/Wu0Z6OMW5fDo5X7GWp7imDRJsKlS5u9C6CUIhCuwNlIib9kD2agCCMQIxIspqjqECMdz8yZ+Jts81l9J0qt7fd8IQpdwT7D/ut//a+89a1v5Vvf+hZaa/7v//v/5gc/+AFf+cpXUErxy7/8y/kOUQghhBBCCCGEWDCVTKFSabyayiUfIxNvJVzcOPlzIFK1pIq/5PAVkkMX/T+P/3853OoKjOFRyNqgNda1VrzqCrgtmeDVVWN09RF8+SRmRw+Zh+/Bbagn+OJrhL/zI9B62XHkkzE0gle+tPl+t/Ma6si89VGIhAFwG+r9Nqkj8WUfW4iZqEx23VTeio0jk2hDKZNgbNOU261QGdU734thhafcXlx7NyX190+5LVK2CzMQo6zhseVVvuPP+bM3VKvPHgKRmsm/t5K6+0mP3iCb7J51H2nzKcTqKdjE36VLl3j729+OMb7ib6LV55EjR/joRz/KZz/72XyGJ4QQQgghhBBCLIrR518QdKsqFrWfk42TTfaSSXRgp/oIxW5L/IWrljTjb7DlW4RimwmXbic5dGHR+7+eV+0nM43+QQKvnsHs6sM+tGfKNhNz/qyL18k+cAR3xxayD99D5rH7MHoHMAaHlx1H3miNGh5d1ny/2bj11WAYmO2zX0wVYjlUOoMOScWfWFsy8TaCRfUYRmDJxzCMAM0P/D6lm96w7HisjVbxl+olGK2d/Lmo6hCGFWG0++VZ95E2n0KsnoJN/CmlCAQCKKWorKyks7Nz8r66ujpu3LiRv+CEEEIIIYQQQohFMnoH0LEoRMPzbzzOycZpeeFj3Hjp49x85ZMAhEputQqdaPWptbfgY6aGr5IcukDF1ndQVLGP1NDFRe0/E11ajA4GCB47Q+DsZbJHD+E2Ta3CcOur0ZaJfWiP39pz4vbGenTAWtOJLZUYQzmu3+oz1wIB3LrqNf33IwpcJosOS8WfWFsmWl8vl2GGll3tBxAIV6zojL/MWBfJ4SsrdvzFmqj4m2AYAYpr7iLe/fKMnym01iR6T0ibTyFWScE+y7Zv305bWxsAhw8f5s///M+5fPky169f50tf+hKNjct/YRdCCCGEEEIIIVaL0T+IW724ar/U0EW0Z7Pp0K/ScOd/oOnu/0So6FZCLRCpQns2bnZ0wcccuPEtgkWbiVUfJlq+B9ceIxNvW1Rc0yiFV1WOMTiMvW8Hzv4ZVvNHwqQ+8BPYdx2Yertp4m2qxWzrmnpzaydG9+LbmOaDMeT//esctPqcidtQh9ndB7azIscXG5vKZNHS6lOsIVq7ZBIdhIoL5/qwFarAzY7geXZOj5sZ66Lr7P/DzZf/LzpO/hGek87p8ZfCtcdw7cSUij+A4tr7sNP9pEauTdsnPdqCnR6guFrafAqxGgo28fe+972Pvj7/A/5/+A//gYGBAd75znfy9re/nTNnzvDbv/3beY5QCCGEEEIIIYRYIM/D7B/y594tQnLwAsGiTcSq7iBavofwbdV+4Ff8AQue85eOt5IcPE9l89tRyiBc0oxhhnPS7tPZsx17/07so3fMvlFg5pZsTmOd3wo17Y/5wLYJ/vgYwWOnlx3XalBDI+hQAB2NrMjx3YY6/zHU1bsixxcbm0pnpszjFKLQZZM9aM/OScVfrgTC/vu7kx7K2TGH25/m5iufJDVyjapt70J7NomB/L8vZpM9AARel/iLlO0gEK4k3v3StH2GWv+FQLiKSPnOVYlRiI3OyncAs3nXu941+eft27fzz//8z7z22mtkMhkOHz5MZeXSh6ELMR/zZgfWuStk3vZovkMRQgghhBCrQA2PEvrBC2Te8gZ0UTTf4Yh1yOgdANebnIW3EFprxobOE6u+c9ZtAmH/eHaqn0jZ/BfTxgbOYphhYlWHAVCGRaRsJ8nBC1RsecuCY5uJu2Uz7pbNS9rX21wHgNnRjbt9C9bVVlTWRvUPQSoNkYW3R7XOXsbsHSDz2H2Qg/ZtC2EMjeCVla7Y+XRpMV5JDLOjZ1oLVSGWRWup+BNrTibeClBQiT9rIvGXGSQYrZln6/kNtz9N7+W/oazxTVRtfzeGESDRf5JE73FKao8u+/jLYaf8RSjByNTfUymD4tp7Ge54mupdH5icv5hJtJPoO0Htnp9DKXPV4xViIyrYir/XKyoq4qGHHuJNb3qTJP3EirMuXMXs6Qc7t+X5Yg3TGqN/4wxpFkIIITaawLkrGKMJjK610VZQrDFaE3z1DF5F6ZwVfwMt32Lw5vcmf7ZTfTjpQaLle2fdxzBDmMFS7HT/gkJJDV0gUr4bZdxaBxyt2Etq5Cqem13QMVaCjkbwKsr8OXZaY124gltXBYDZ0bPwA9k2gVMXMG92YF5vXaFopzOGRtHlpSt6Dq+qHDU0vKLnEBuQ7YDWkvgTa0om0UYgXIUZKJzFWlaoHABnmXP+tNaTSb/yxsep3vG+yQRarOYuxgbO5r3dZzbZgxUsxbCmL8opqbsXz0kx2vnc5G0DN75NIFxFSd19qxmmEBtawVb8AaRSKV588UW6urrIZqd+AVFK8a/+1b/KT2BiXVPJFOb4BR81lkKXzdyKRmws5o12Qs+8TOrdT6JLi/MdjhBCCCFyKZ3BunYTALNvEHfHlnl2EGJxzGutGH2DpN/6yJwVYYm+18imeijd9BBmoMhvv6kMomUzzMu7TSBStaBWn56b8duFbX/PlNuj5XvQnk165BrRitmTjCvNbajDunQNs70LYyRB+sG7Ua+cwmzvWvDz0rrairId3Loqgq+eIdW0adb2orkL3MUYiePs3b6ip9FlJZidi0iCCrEAary9rg5Jq0+xdmTibQU13w/AMIOYwRLsJSb+4j2vEu87TmroEq6doLzxcap2vBd12+eG4uoj9F/9GmMDZyiuvSdXoS+aneyd1uZzQrContLNj9B7+asYVoRQrJFE73Fq93xoyqIjIcTKKthn2yuvvMKv/dqvMTIyMuP9kvgTK8VsuTXUXiWS6LKVGc4u1paJWRpmdx+OJP6EEEKIdcW63AL4SQejTyr8RY7ZNsFXz+BsbcCrq55zUyc7gnazjHQ+R8WWJ0kOXiBSsm3GFfW3C0aqScdb8dwMhjn7xfvUyFW050xL7gWLNmMGS0gOXchv4q+xnsDpiwRfPIlXWY5XU4nbUI91/gp4HhjzNC0arxR0tmzCvvsQkW98j8DpS9h3HVjRuNVIHLTGW+mKv9JiVDrrz0GUeWwiR1TGX2gvFX9irdBak0m0UdbwpnyHMk0gXLGkir/k8BW6zn2JcEkzpZveQLRiL5GyXVOSfuAv9AmXbCXeezyvib9sqodw8ewLcmp2/Qx4Lt3n/4JgUf14td/9qxihEKJgW31+6lOfYvfu3Xzzm9/kzJkzXLx4ccp/Fy4sf/C4EDOxrrf5g9OVwhhL5jscUSCM7r4p/88prf3/hBBCCLH6PI/AhWs425pwG+sxBofBcfIdlVhHAqcvobJZ7HsOzrmd1i5uNo5hRRhu/yGeZ5MavrSgRFxJ3f3Y6X5aX/09MmOds26XHLyIFSwlGK2fcrtSimj5HsYGzy/sl1ohXnUFOhRAjSWx9+0ApXAb6lBZe0FJeaOjB2MkgbNvJ7q4CPvAbgLnLqPiYysat+oZAKXwKlY+8QdgjMRX9DxigxlP/BGWxJ9YG5zMIK49VnAVfwBWqAI7s7jEn9Ye/Vf+jnDJVhrv+m2qtr+LaPnuaUm/CbHquxgbOJO3dp9aa+xkz6wVf+DP+qvZ839QUv8A2bFOKra8Var9hFhlBZv46+jo4Fd+5VfYuXMngZVuyyHEODWawOgfwtm+BR0Jo8ZS+Q5JFACVTGGMJPCKi/w2sDlO0gWOnSb0/edzekwhhBBCLIx5owOVTGHv24lbXeHP9R0YzndYYp1Q8QSBs5exD+5Gx4rm3NbJjAKaiqa34GSGGLj+D7j2GNHyPfOeJ1qxl6a7/xOgaH3190j0vTbjdhMVfTNdTIxV3UEm3rqglqErRinchnp0JIzb3AD4c+10OOTP/ptH4PxVvMoyvJpKAP/vPRQicGplFw4bXX14lWUr3lJUl8T8BarDoyt6HrGxSKtPsdZkEu0AhGKFl/hbSsVfvPsl0vGbVO94L0rNf6m+uOYutGczNnBmqWEui5sdwXMzBCM1c26nlEHtng/RcOf/ScmmB1cpOiHEhIJN/B05coSWlpZ8hyE2GLOlDW2ZuE316FgUJRV/AjC6+wGw79iDSqVRo4mcHt/s6sXs6JbHmxBCCJEHgfNXcOur0RWl6PJSME1p9ylyJvjKaXQ4hH1g97zbull/zEW0cj/R8r0MtX4fwwwTLmle0LlCRfU03f07RMv30nPxr3HtqVVubjZOJt5KtHzmCsKiyoMoI0i898SCzrdSskfvIP22R8E0/RuUwt1ci9neNed+amAIs6Mbe9/OW3MUAxbOji2YbZ0r1mFDa43R3Yc7TxvXnDBNvOIiv7WoEDmiMhm0ZYJl5jsUIRYkE2/FDMSwQuX5DmUaK1yBnR5EL/A9x3PS9F/7BsU1dxMp27mgfQKRKsLFWxjueAbtrU6XikyindZXf4/hjh9NdhaYq+JvglIG0fJdC0poCiFyq2CfdZ/61Kf4xje+wd/93d/R1tbG8PDwtP+EyDXrehtu0yawLLyiiCRiBODP9fNKYrhbGkApzFy2+3RdjCF/xa55vW2ejYUQQgiRS2poFKNvEGfPdv8Gw8CtKsfoG8hvYGJdMDp7MFs7yd5zCALzt7dyxhN/VrCUskZ/blGkbOeiWmMZZoja3T+L9hwGb3x7yn3JoUv+MWepIDSsMEWVB0j0HV/w+VZEOORXtt3GbajHGByZ+fuZbRN49QyRbz3tf2YfrxS8tW8dKp3F6B9amXiH46hkCq9+FRJ/gC4txhiWxJ/IHZXJgsz3E2tIJtFGKNY4ayvMfLJCFWgvi2cvbMH4YOv3cJ0kVdvfvajzVG1/N+nRG3Se/eKCk3+uPUai7zXGBs4t6lwA8d7jZOJt9F76Cp2nPw/KIBCpWvRxhBCrp2Cb65aUlLBp0yY+8YlPzPpCLnP+RC6poVGM4VHsu/3B77ooKm2eBABGT5//RT4YwKss8+f87d6Wm2MPj4LW6FgUq6UN5+D8q8GFmMK2CT17jMz9RyAaznc0QgixpljXW9HBAG7jrXlnXnUFVossxhHL5HkEXz6FW1M5LRE1GyczAsrADBZTVHmAosoDlNTdt+hTW6Eyyre8hcGWb1K6+RGC4yvyk0MXCBbVEwjPXiFRXHMXXef+H+xUf0Fd0HM31/oL8Fo7cfbumLxdjcQJf+dH/gzFQ7v9ykpzatXSxNxAs70br7oi57F57T1+VWLN6vx9eaXFWDfaV+VcYmNQowm0JP7EGmGnhxgbOE/FlrfkO5QZBcL++0w22U0kWDzntlp7jHT+mNJNDy/6PTdasZdNBz9M55k/o+vslyjf8iTJoUtkRm8QKd9F6aY3YJhBtOcw0vUCIx0/Gm+RqlFmkB1v+JNFVeGlR65SVHmQyu3vYrDl22g8DENGcwlRyAo28fdbv/VbnDhxgl/4hV+gublZ5vyJFWf0+y2d3Dq/R7UuimKMJf2WMAW4ikisklQaYziOfYffEsmtq8a63pazx4UxMAxKkb1zP6EfH0ONxNGlc384FOJ2xsAwZmsn5tbNuNu35DscIYRYO7TGbGnD3bJ5SqLAq65Anb2MSqbQ0UgeAxRrmXXpOsbwKOl3vGnBnxndzDBWsGTyQtzmO/7dks9f3vg4o50/pu/q19h86Fdxs3GSg+cpqrpjzv38dp8B4r3Hqdjy5JLPn3OhIM7WzQROXcTZscWfpac1wZdPgWmQeveTs89QNAzcTX6rUPvOfTkPzWvvxqssh+DqXLPwyopRiSQ4bv5aM9o2KENaQ64D5pUWrJZ2skcP5TsUIRZk4Po3MKzwZGV8oQnGNhOM1tFz6X/TdNfHMKzZF+dmxzpxs6PEqg4v6VxFlQfYdPAjdJ75MxL9JzHMMKFYA31Xv8bgze9RUncv8d5XcdJDxKrvpKzxTSig+8KXsZM9BIvq5z0HgPYcUqMtVDb/JKGiTdQf+OUlxSuEWF0Fm/h76aWX+OQnP8k73/nOfIciNghjJI6ORSfb8OhYFFwP0hmISBXNRmWOz/fzxmd2eHXV/sXA+Ni0FkRLYQwM45XGcLc2oF96Det624pckBDrl4r783uMoVHcPMcihBBridE/hBEfI/vAkSm3T1QEGX2DflJQiMXI2gROnidw4SrOrma8qoXPH3KyI1ihspyEYZhBqra/h65zX+LGS58gm+wGZRCrPjL3flaYosqDJPoKLPEH2HcdxPrG9wicvoR91wHM9m7Mjm4yb7x/9qTfOLehHuvHxyCVzu13O63RbT14TZtyd8z5TllaAvjVjrqybNXOC4DrYp27QuD0RdytDWQfunt1zy9yyujoIfT8CZzdzTj7FjZbTIh8So+2MNr9ErV7PoRpFebiLMMIsOngh2l99ffpvvBl6g/821k72SUHL6CMAOGy7Us+X1HlAbYc/QSekyJU3IRSJnaqj4Eb/8xw+9PEqg5TccfbCRX571PueAvSTKJ9wYm/TKId7WaJlO6Yf2MhRMEo2Bl/tbW1FBdL1YtYPcbwKN5tlVZekf8hwpA5fxvaxHy/iRX/bm1VTuf8GQNDeBXlYJm4TZswW1r9akIhFsiYTPyN5DkSIYQoEKk01sVr876fmi1t6EgYr75myu26KIqORjB6Zc6fWBzzRjuRv/8u1qXr2HfsJXvf4UXt72SGMYOlOYsnVnMXpZsfIVy6nbp9v0jz/b9PtHzXvPsV19xFevQGdqo/Z7Hkgi4uwj6wm8DZy6jhUQKvnMKtr/FntM/D3ey3OzXbu3MbVHwMnRhDr9J8P2DyO6sxsrpz/ozuPsLf+BeCJ86hoxHMjm753rKGqeFRQk+/iLuplux9d0qXI1HwtNb0Xvk7QrFGSuofzHc4cwoW1VO37xdI9J1gqPW7s26XHLpApGznsltmBqN1hEuaUcqvwg5Eqqnb+/PsfPTz1B/45cmkH4AZiGGFKkjHWxd8/NTIVT9BWdy0rDiFEKurYBN//+7f/Tu+9KUvMTIiFzLF6lAjcbyyksmfdVHUvz0hib+NzOjum6z2A/w5fxXjc/6WS2uMwRG88ZW6zrYmjJEEanB4+ccWG8btFX9CCCEg+Np5gi++RuDY6dk30hrrehtOc8OMFzvdmorJNvBCLITR3k3omZfxaqtIv/tJ7MP7ps2am4+THcHKYeJPKUXt7p+lbu/PU1J335yz/W432e6z70TOYskV++BudDhE+Ds/8it2771jYQmLSBivqtxPVuWQ0dULSuHVruI8xFAQHQ6tauJPDQwTeup5dDRC6l1PYN9zCJVMT34OFWtP4OxlCAXJPHYvGAV7aVCISYneV0mPXKN653sXNZsuX2LVd1Kx5W30X/9H7PT0xWTac0gOXyZavnfVYwsVN5JJLHyedWr4qp9YNAq2caAQYgYF+4z95je/SWdnJ4899hh79+6dVv2nlOILX/hCnqIT647rYsTHcG6frRYKgmmipOJv40pnMIZHsQ/unnKzW1eF1dK+7Dl/ajgOrjuZ+PM21aDDIQLnrpB9+B5ZdSkWxIgn0Nb4a1XWXrX5MkIIUZAyWayrN/Gqygmcu4KOFeHsm96WyOjuQ6XSuM2NMx7Gq6ogcPI8eJ5cEF1DjPZugifPY+/dvqpzb9XAkF8501BH5rH7lvwZzsmMYFXlLvG3VIYVJlqxj+TAWSqa3pzvcKYKWGTvOUjoR6/g7N2OLl/435fbUI91/kpOn9dGVx+qpsL/7uh4OTnmQnhlJaiR1Vn0pcaShJ96Dl1STOaJhyBg4UYjk11QnByMPxCrzHUxb3bg7N3uz8sUosBprRm8+R2KKg8SLd+T73AWrHzLkwy1fZ9E7wnKm56Ycl9q5DrazRKtWP3fJxxrZLjzR2itZ21DOkFrTWrkKqX1D61SdEKIXCnYb7FjY2Ns2bKF/fv3YxgGY2NjU/5LJBL5DlGsI2p0DLSe0uoTpfCKIqixVP4CE3k18W+vy6YuPHA31aKSKdTw8r5sG4NDAHgVZeM3GGTvPoB1rRXr7OVlHVtsHCo+hjfevspY5mNSCCHWOutyC2hN+vEHsffvJPjKKczWzunbXW9Dx6KT8/xez6urQjkuxsDQSocsckDFE4S+/xzh7z+HGhohcP7q7Bu7LmpgOHfnHksSfup5dFkJmUfuXXLST2sX145jBctyFttyREp3kB5tQXtOvkOZxm1uJPPG+8nedWBx+zXWobI21oVrmG1dGB09fhJwqbTG6OrDaKhd+jGWeurSYozhVaj4y9qEvv88KIPM4w9AYHzteDCAV5mjLihi1ZkdPaisjdMsbfvE2pCJ3yCTaKes4bF8h7IophUhWrGPeO/xafclhy5gBooIxWZehLaSQsWNuNk4bnb+Lnt2qhc3O0qkTOb7CbHWFGTFn9aaP/3TPyUSiRAKhfIdjtgAjPHVklMSf/jtPqXib+NS6TQAOjz1dcirr0ZbJmZ7N84iVhm/njEwjI5F/RXC49ydzdijYwRfPYOOFeE2Nyz5+GIDsG1UOoPbUI/Z2oUxNIJXU5nvqIQQIj88j8CFqzjbGiESxr7nEMZI3J8D9roZYGZ7t7/dLEkar7IcbZkYXX141fK6WuiCP34VIz7mV9tpTeiZl1EjcXTp9JnxgVfPEDh/FXdzHdmjh9C3tfpfisDxs4Ai/aYHbyVFlsDNxkF7mKH8V/wBRMp24LkZMol2wiVb8x3OVErhbtm86N28ynJ0LErwlVOTt2Uevgd3x9KqQ43uPtRYEmMBMwZzzSsrxrp6Y9kdSOY+iUfo6RdRY0nSb3tscub5BLc2N11QxOozr7filZeiy5f3+ifEahnp+DFWuIJoxb58h7JoxTV30X3+z7HTgwTCtxacJYcuEinfk5e2pRPJxnS8lViobM5tU8NXAUW4dNvKByaEyKmCrPizbZsHHniAF198Md+hiA1CjcTRoQC8LsGjY1EMmfG3YalUBgAdDk+9wzTx6msw27uWdXxjcBivcvqsFfvIfpxtjYR+/IrMFxJzUnH/9ckrK8EriWEMyVxcIcTGZbZ2osZS2BOtPceTA0YiCa57a0PbRiVTeHMt3jEMvNoqzO7+lQ1aLJ/tYPYOYB/ag7u1AbexHh2wsFqmz65RQ6MELlzD2daIMTJK5B++j3Xm0tLP7Th+u7w92yAann/7uQ6V8d/DrQJJ/IWKm1BGgNTIHNWTa41SpH7qzaTe/3ZS7387XmXZ0j/Pex7Bl0/h1VSituYh8VdaDK6HSuRoxp7nETh2GqOz1/9Za4IvnMDs7iPz2P0zJoi8+mq/C8pcMWiN2dKOdepCbuIUy2fbWK1d/uIXIdYA10kx2vsKpfUPrYnZfq9XVHUIZVgkbpub6zpJ0qMteZnvB2CFKzGsKJlE+7zbpkauEoo1YFrRVYhMCJFLBfmKGQwGqaurw739C7oQK8gYjqNLS6atVPQr/qTV50al0hl0wALLnHaf21CH2TPgz1RbCq0xBoZxx+f7TT2xIvvQ3XhlJQRfOOGvohViBsb4hRavuAivvBQ1JK0+hRAbl3XuCm5dFfq2RTVeSQy0RsVvXZhWo/6f9Txzqdy6aoze/uW1AhQrzugdAK1x66r9GywLt3ETZkvb1M9QWhN85RReLEr2obtJvftJnO1NBE5dWPJnLbOtC+W4OLPMilwMJzvshx8sjMSfYQQIlzSPr/RfRywLHY2goxHcxnrMzqW1+7Qut2AMjeDcf+e885FWgi71E3G5avdptncTOHuZ8PeeJfTDFwgcO4115QaZB+/G21Qz4z5uTZUfw+3tPj0PHBccF6N/kNB3fkTomZcInjgnn1MLhNnaBa4764xbIQpNvOcVtOdQUv9gvkNZEtOKEq3YP6XdZ2r4CmiPoor8JP6UUoRijWTirfNumx65Km0+hVijCjLxB/DBD36QL3/5y2QymXyHIjYAYyQ+rc0ngBeLoFLpqavExYahUulpbT4nuA11/grWjp6lHTuRRGVt9EyJPwDTJHvfnRgDw1hXbizpHGL9U6MJtGVCOIQuL/Er/iRRLITYgNTAMGbvAM7eqRcmJpJ7xuit+eATf/bmSfx5ddUo28HI4Tw4kXtmdx86HJoyk9nd1ogxHEcN3qqEN9u6MDt7sI/eAaYJpomzvQllO6glJk+s6214VeXzJpEXwsmMgDIwg4XTei9SuoPUyFX0Ov1s4TbUozI2Rt8iO2xksgROnMXZuRU9y5zQlaaLImjLXPbM8QlmSxteWQmZR45i9A0ROHcF+/C+udughoJ+1WSXn/gzegeI/M03if71N4j+9TcIf/OHqEyWzOMPoIMBrJb5LzCLlWe1tOHVVKKLi/IdihDz0loz0vEsRZUHCYSnd0taK4pr7iI9cg07PYT2HEY7nycQriIQqc5bTOHiRjKJ6d0Rbudk42STPURKJfEnxFpUkDP+ALq6umhpaeHRRx/l6NGjVFVVTVtJ9/GPfzxP0Yl1RWvUyCjeDLPUdJFfyq6SKXTx8r/Qi7VFpTMQmbltk44V4ZWVYLZ3zTiHz+ju81uI3Ta/b8r9A0MAuBWzf3j1aipxtjUROH4WZ2sDBANL+C3EemYkxvwv7Ur5FX+ZLKQyy243tm64LmZnL+7mWjDmWeukNWZbl18xIs81IXJKjfhJlZnmreWKdb0VHQpOm+WnI2F0wJqMAUCNztzi/fW8qvE5f919eHm6uC8A18Xo7MOrr55xjpjR3YdbVzXlPndTDToUwGppw64sg6xN4Nhp3Poa3Mb6ye28Kv/f1egfwF3srKtMFrO9m+zdB5b0a72emx3BChQXVBuzSNkOBm/+M3aqj2B05qqvtcyrKkeHQ5jt3Xi1fvUamazfjr9+9t838No58DTZIwfyd0FFKXRFGcZgDtq82w5ma6ffLndbE6mmTRi9A3P+HUxw66qxbnSgRhOEfvA8uqyE7J7xOVCBgL9Y0jBwt2zGvN6Gfed+mQeYT+mM/7p19I58RyLEgqRHrpFJtFG57Z35DmVZJtp9Drc/RWr4Kpl4K7V7fz6vMYVijQy1PYXrJGdt45kd89thB2PTr3kJIQpf4XyreJ2nn36aYDBIJBLhzJkzPP300/zwhz+c/O/pp5/Od4hinVDJFMpx0WXTv+xPJv4S0u5zI1KpzKwVfzDe7rO9e3qFVSpN+LvPEjg/e2sko28QHQ3Pm6Cx7z4AjkPg5PlFxS42BjU6ho75q3W98dcwmfN3i9naSeip5wn/w/cx2rtn3c7o6iP8T08R+sELBF8+uXoBCrFBBF84TujHx1buBFpjtbThbm2YnuRXCl1chBGfWvG3oAVdhoFXU4l5exs7saq01lg/fpXw956d2k5wguNg9g/i1b1uxbxp4m5twGppw7zSQuTvv4tKpsjee3hq0iEY8BdyLbbiCzBbO8DzctYuz8mMYIXKcnKsXAmXbAPU+przdzulcDfXTpnzF3zpNcLf+zGkZ+k8ZDsELrfgHNiV94VWXkUZxuDQso8z0bJ28rFsWXibaheUoPPqqlFjScLf+RE6GCT9pgdwt2/x/2vaNPma7GxrxIiPTS5+FPlhtHaB1v6iUiEKmJ0eoOfi/6L9tc8SjNZRVJmbRTb54rf73MdQ6/dxs3Ea7/ptSuruy2tMoWL/NT+T6Jh1Gzvlf/YKRKpWJSYhRG4VbMXfD3/4w3yHIDaIifYoM7X61EURf5ux5KrGJAqDSmfwqspmvd9trCdw9jLGwDBe1a3KPetGuz/Dr7sX2DfjvmZP/61ZNHPQRVHsQ3sIvnYeZ+8OackiplCJBG6DX7mgS2JgmhjDI3iba/McWWFQyRSYBjoaJvz953A315E9emhyoYeKJwgeO4N5swOvugJ7/04C567g7N0+WQUihFim8Zm2ynb8iuTI3FV2S2H0DqDGUjjbZk7AeCXFqNtafarRBF7JwqoP3bpqAmcu+XOrlPLnwWWy2HfsnbdiUCyf++IpzKs38SwTs61rWgWS0TsAnp7xM5XT3IR1qYXQc8dxtjVi331wclHf7bzqCozexSf+rOttuHXV6Ghk0fvOxMkOY4YKY77fBDMQJRTbTHr4KqX1DwBgp4ewQmV5mWu3EtyGeqxrraixJCqRxLrutz0zO3pwtzdN297s6gXXy8lcx+XyKsuwLl0H24HA0i/tWNdbl9yy1p2olPQ8Mk88Muvroldf41dXXm+Tz1h5ZLR1+d9bpTuIKGCpkRvcOPZpDDNM5bZ3UdbwaEFVwy9VxZa3EghXUtn8k5iB/F/XCUbrUEaATLyVaNnOGbex0/1YoXIMQzriCLEWrf1XTiGWyRiJ+xeGZ0qoWBY6HJTE3wal0rPP+AO/FacOWFNWCYN/IQil/NXjM82HtG2M/qHpq9Nn4ezd4V847R1YVPxindMaI5689dqlFF55CcZQbma9rAcqnUVHwmSefAOZx+7DGBkl8g/fJ/DKKQLHzxL5xr9g9A2SecM9pN/+GPY9h/DKSwm+dFJmJYq1y/P8i8ALZdsrFwug4mN+0g8wO2avvF0O63obOhq51arvdXRJDGPkdRV/C7zAPTnnb3AY6/wVAq+dx7rsV5BZ5y77f99iRRiXWnBfOoVzz0Hc5sYZHz/+fL/gjJ07vLoqskf2k37bo2QfuXfGpB/4n+eM4dHFPRdSacyuPtxZks1L4WZGsIKFlfiDW3P+AIY7nqHlhd8mOXg2z1HljrvZr2wz27sJvnwSr7Icr6LU7+oxA7O9G6+4KCdzHZfLqyzzPw8up9tDJovZ0T3rwol5hYJkH7yL9JsfnvvvRCmcZr8KVz5j5Yf2PIyO7slFg0IUqpGuFzADJTTf/3tUbHkSw1wfC60ipdup2fUzBZH0A1CGRahoE5lE+6zb2Kk+qfYTYg0r6MRfT08Pn/nMZ3j/+9/Pk08+yfvf/37+4A/+gJ6ennyHJtYRYzjur/qeZdWqjkYxJPG38Wg93upzjtWQhoG7uQ7zeuvkhT+VGMPoHcDevxNcD2OG1lFGzwDomVenzygYQIdDGLdVKwihkinwvCmLFryyEmn1eRuVGk/eK4W7tYHUu58ke2Q/1uUWAucuYx/cTerdT+Ju3+K/ByhF9t7DGH2DmNda8x2+EEsSeO084W/9YEHbqsFhov/7nwj98EVUfGXeYybauulYdGUSf56HeaPdv2g9y2c5ryTmv2Y6DmRtv6J/oYm/qnIwTQLHzxJ85TT2gV2kfvptuM0NBI+dIXDqYi5/GzFOjSUJPH8c8+BO3EN7cBvqMIbjqPjYlO2M7n682pln/6EUzh17Z00IT3CrKvzkSf/wguOzWjsBcLZsXvA+83EywwXX6hMgXLaDbLKbgRvfpvfSVwBIx9fRe2QoiFtTQeD4WYyBYbL3HcZtqPdfr16foNIas73bn1tXABWPXlkJKIUxMLzkY1g3O/yq2WVUMDq7mtGVZfNu525rQiXTGN39Sz6XWDrd2YfK2riNdfkORYhZaa1J9J0iVn0HhiWVqSstVNxIZo73dDvVTyAsiT8h1qqCTfxdvnyZd7zjHXz1q1+lurqa++67j+rqar761a/ykz/5k1y5ciXfIYp1Qo3EZ2zzOUHHokua8afGkpjjrWLEGmQ7flJlnpZkzqHdGCMJv80O+P/mpol9x150KIDZNX0ejb86PbSolcJeSWxKmzIhJi5+erHbEn/lpX77YllJDfjtenXkti+MpolzaA+p976N5Pvejn3n/mmtsbz6apytmwkeP5O7SqhUGutyi/y7iJWnNdbVG36CJDE27+ZGWzcYCqNvkMjX/4XAa+dy/jg1BobR0QjO9qaZL6Qv9/idvah0Zs7Kq4n3WzU6hjEa928rXeB7sGni1lRidvbiNDdg330QIiGyD9yFs60R8+bsc1HE7NTwKGZb16z3WxeuoS0T85F7/MUbm25VZU1yXMy+wYUvpJqFLi9BByyMvoV3VjB6B/AqSnPW7lVrD8eOYxZoxR/AwPV/pKzhjYRLt2Mn19dCXLehHpXJ4mxrwqupxG30f379Aj41HEeNJQunYso0/W4Pg8NLP8T1Ntz63LWsnYtXXYGORbGur6PE8RritXSgwyG8yvL5NxYiT5IjrdjpQYqq7sh3KBtCKNZIdqwT7c3cLcRO9xOILO9zlhAifwo28feZz3yGxsZGnnnmGT73uc/xyU9+ks997nM8/fTTNDQ08JnPfCbfIYp1whiJo8tmT/x5JTG/gmYxF6q0JvjjVwk9+4pc6F2jVCoNMHfFH+BVluPsavYvlqYzWC1tOE31EAzg1VZjdM+c+HPrZ1mdPgtdEpu8WCkEMFmdM6Xir6YS5bgYnevrgtxSTVb8vV4oOOfFWvuug6hkes6L0gtm24S//xzB549L22ix4ozuflQyPfnn+bfvw62pIvXuJ7EP7CJw8kLOE1nGwDBeZdn4hXV7xkr45bBa2vBKYngVZbNuM1HdZ4zGJxfReMULX3zj7N6Gs62R7MP3THnvdhvqMYZG5Lm9BMETZwk9/SKkM9PvtB2sy9dxd29DBcdnygQDuHVVU9qrG30D4Hm4dctcia4UXlW536J9gYz+Qbzq3M0pc7Nx0B5Wgc34AwiEK4iW76a86Qmqd76fYLSObHJl2vbmi9vcgFtTiX33AWA8QRUKTGv3abZ3+cm2ZSabc8mrLJ+srF4sNZrA7OrFmWGW4YpQCnv3NqzLLZjjVbNi9Xgt7XgFUq0qxGyGu45jWGGiZbvyHcqGECpuQmuX7Nj0772ek8bNjkqrTyHWsIJN/J04cYIPf/jDlJZO/fJTWlrKhz/8YY4fP56nyMS6oTXmlRuoVHrOij+vrgqVTE1rLTQXs7XTH/yutd9aSqw5avxC1Fwz/iZkj+wHTxN69hWMwZHJVjluXRVm38DUOX+LnO83YbLiTxLJYpwRH/NXZ5vm5G1edQVeRSmB81fzGFnhmFbxt0C6JDbnfJ/bGb0DhL777GTV7xSeR+iZl/3ZUSDzF8WKs1pa0bEoXnkp5gwLT26nPY3q7vcXogQs7LsO4DbUETx2eub5tEuhNcbgsD8zq7oCHQrmJqE+wfMwb3bgbmua+0JmOIQOWKjRhD/fLxzyFwAskNvcQPaRe6e83sLU2WBiEVwXo7MXXM+vhn4d63orKuvg7tsxdbfNdX4nBcd/fFo32tGhALp8+ckyr7rST0ov5HNWJosxHMetrlz2eSc42WGAgpzxB9Bw529SveO9KKUIRmvJjnWj19FnUl0cI/P2x27NgVTKf7y9bo632d7lv2Za5gxHyQ+vssz/fLGE123rwlV0KIjbvEqJP8A5uBt3y2b/81F/bheCiDkkkuj+IbzGAqlWFWIWQ12vEas8iDKs+TcWyxYq2gyoGVt422l/EaFU/AmxdhVs4s80TbLZ7Iz3ZbNZTLNwPmyLtcfoHyT8rR8Seu5VnK0NuE2bZt3WrfFXtxg9c19Au7WDS/CVU5Ory9WYJP7WosmKv3lafQIQCWMf3ofZ0YMOBvy5H+C3nnrdnD+jd5Hz/cbpkmJU1obMzK+LYuNR8TG8ktcNBlcKe99OzPZu1IhUiJLOoMMLv7h/u1nn+0xIpgk++wrhbz+N0TdA4LXzUy+6aU3w5VOYHT2k3/iAn3SQ+YtiJbmuP+uuuRG3vtpfgDQH3TeIsu0pC1GyR+9AjaWwzl7OSUgqmfLn6VWW3XYhPXdJMpVKo2wHd77KK6XQJcUYownUaGJRrbbnND4bTBJ/i2P0DKBsB6+qnMCFa5NzkgG/Xe35K7hN9VA89T3Obaj3k4bdfViXW7AuXsc+tDcn1StedYX/eFpA9eZEsiKXFX9Oxn9/KMQZf68XjNbiuWnc7Pp+T3Mb6jAGhm8t4szamD0DuAWWOPEqysDzUMOL/NyXtbGu3MDZvW11E5lKkXnDUbzKMkLffx6zrQujsxejqzd3LdbFNEZbl1/d3JC/+X5aewy1/QDXkSp5MTM7PcTYUAuxamnzuVoMK0wwWkMmMX1MkZ0aT/yFc7fQSQixugo28ffAAw/wR3/0R7S0TF0FeuPGDf74j/+YBx54IE+RiTXP8wh978fgeaTf+gjZx+4Da47VRKEgXmXZjLPaZmKdu4IaS/ntoACVkA+2a5FKZ/wLSQusCHD2bscrK/Gr/cYXJuiKMnQwMKXqwuzu9+f7zVFlOpNbbcpkzp/wGfExdKxo2u3utkZ0OETgwgav+rNtlOMuqeIP/At+Kp2ddTV66IXjmB3dZB+8i/Tb34hKpTFvtE/eb9zsxLp4jez9d+I11KHLS6XiT6wos7MXlbFxmhvxaqtQieScn0F0WzfaNPGqbs360aXF2Pt2EDh9MSftK42BYcCvSIHxC+mDwznrhjBxHB2d/3nulRSh4n7Fn5erxB/j7T47e3JXJbkBmO1d6EiYzAN3oZIpzBu32ssanT0Yw3HsfTun7afLitGxKMGT5wm+cAJnzzac/dO3W4qJ5PFCWtGafYN+pWEuH0eZEUBhBhf3+TAfglE/cZBdZ3P+Xs/d7P+eRoef2Dc7e/zFe5vzlziZiVfhV4kag4tr92lduYFyXJw921YirHlObvqLooIBQk89T/h7zxL+7rNE/v57mFdvSoeTFWC0d2FsqllUtXuuZeKt9F35Wwau/2PeYhCFLdF3CmUYxKoO5DuUDSUUa5w58ZfuRxmBgpw/LIRYmIJN/H3sYx/DcRze/va38853vpNf+qVf4l3vehdve9vbcByH3/md38l3iGKNMvqHUFmb7AN3zdpu0bWTJIdurXZ366oxu/vn/xKSTBM4dQF73w68mkq/wkPmvqxJKp3xW4EtdBW5aZJ+x5vI3nf4toMovNqqKXP+zO4+v9pvkavTJy4uKUn8iXEqPjZlvt8k08TZsw3zyo11UyFq9A34FUiLuBC0mHa9M5ltvg8AjoPZ2YN9aA/OrmZ0RSnuphq/xarWaMfFevkk7uY6fyU94JWXYAyv7+oIkV/m9Ta8smL/8Tj++WamObMTvI4edG3ltPaV9uF9YFkEjp+dfo4rLYuaJWUMDKNDQb8tMeOtMSFnFXIT8wwnjj8XXVKMMZJAjcZzm/jbXOfPVu2Zf6ai8Jkd3bgNdejKMtz6agLnr/h3ZLIETl3EqyjFq51hnoxSfvK4bxB3cy3Zew/nblZVJIyORf0Zl7dXIM7A6BvEq6rM6ZwsJzuCGSxBqcLvahOIVIMy1n3ij3AIr7qCwJlLBJ99hcDJ83hlJTN/9sqnQACvNIY5vtBiQbTGunAVp7nhVnvT1RYJkX7XE6R++i3+fz/5OG5tFaEfHyP8rR+iEgsfsyHm4boYHb2o5s15DSMdvwnAcMePyIzJjEcxXbzvJMVVezADBfY6u86FihvJxNvQeurnHzvVRyBSjZK5oEKsWQWb+Nu0aRPf/OY3+djHPsbWrVvxPI+tW7fyO7/zO/zTP/0T9fWF1WJDrB1Gdx86YE2uPp9J/7Wv03H6TyZ/9uqqUWNzr5wHf9aI8jzsO/YCoIuiGJL4W5NUKrP4hIFlgjH1ZdWtq8bsHZ/zZzv+xaJFtvkEIGCho2GMUWnfKADX9dvnzXKxxt69HeV5WFdurG5cK0ANjRL6l+cIHjtN4OSFhe+Xmkj8La3iD8PA3VQ7Y4LC7OoD1/Pbzo1z9u30F5b0DuCeOI9KJMkePTR5v1deijEcn/eCshBL4jiYrR1+1blS/gXr8pLZ5/xpjdfeg1dfM/2+YAD78F6s621T5hur+Bih544TfPqlBVe3+fP9ym4lSMIhvIrSBVVVLYRKpsBYWHW+VxLzWzlm7JxWaumKUnQ0Iu0+F0jFx/z5eOPtEp19OzH6BgkcO03k77+LMThM9q6DsybV7D07cHY1k3n03mmfuZbL3rsDq6Wd8D98H2O2f0+t/c9yOWzzCf6q+kC4fP4NC4AyLIKRauzk+n/M2wd2ocMhVGIMHQxgH9iV75Bm5FWUoRaR+DPbujDiYzgzVNauKtNEF8f8/yrLyD52H+m3PoJKZwh9//l1s4At34yBYZTjYDTl9xpaJn6TYFE9gXAl/Ve/ltdYROFxnRTJoUuU19+V71A2nFCsCc9NT7b2nGCn+glEZliIJYRYMwp6WmpRURE/93M/x8/93M/lOxSxjpjd/Xg1VbNeLHCzcUa7X0R7Np5nYxgB3PFVx0Z3H+4cqzzN9m7c2urJC1C6KCqtPteqdHrJlUK38+r9OX/Rv/rG5G2Lne83eaySmFT8CeC2pNZsbSyjYZzmRgIXrvpt0NbqKr1kmvBTz6GLojh76v3V9rEo7s6t8+46WfG3kDmds3Ab6rF+fAySabitlaDZ3o1XXDQleeA21OGVxLCOn8UdHMHdvxNdVjJ5v1dW6s/gGU1MuV2IXDA7evyWbc2Nk7d5ddWzJi/UwDBksrMuRHF2bCVw4hzWxWvY9/gJbOvCVXTAwkgksc5dwTm0B/Db4AVfOknmLW+YVjliDAzjNDdMuc0rLc7ZDFKVTPvVfgt4jbv9+ZrLir+JKjSzrQv7qMykmY/Z3gWGwh1POruN9XjFRQTOXsbZuRX7yP45Kzh1eQnZB1fmoqBzYBdufQ3BV04S/v5zuA11ZI/eMaU9u4qPoTLZ+edKLlIm3ka4ZGtOj7mSAtFashsg8edubcDd2jD/hnnmVZYTaOvyOyMs4PXQungdr7oi5wnsXPDqqkm/+WHC3/4hoadfJPPEQ9Mq08XiGH0DaNNEVVfAaG5abS9FOt5KuGQbsapDdJ75AmMDZymqPICdHkB7DsFobd5iE/k32vUCWntUbL6HsXS+o9lYQsX+94dMop1g9NaiQDvVR7RiX77CEkLkQMFW/AmxIjwPo7cft272VSvDnc+iPX+wuOeMfzAOBfEqSmdfOQ/+avvuXtzbBmbrWAQ1lr8P12LpVDqz5Nlgt/Mqysg8dh/ZB+8i++BdZN54P7p8aRf9dUmxzPgTAKjUeHu7OR6jzs5mVCKZs8qaVWc7hH/wPLgemScexD6yH2dXM6Hnj89eiXGbib8jlpHAn2xL2HHb+bTGbO/Ca6ibenFNKZx9OzA6e1GWiXPn1C9J3vjz3hiSdp8i94y+QXQ0MiVB4dZVY8THZmw5bnT1gWWia2a56BuwcHY1Y11uAdsG28a6cgNnz3bsvdv9GYDJFGpgmNDTL2GMJvxj3i6dQY0l8SqnVjHpshKMkdzMu1TJ1ILafMLUZF8uK/5gfHbhaEIW5yyAv0iuCoIB/walyDz+IKl3Pk72obsX/O+5UnRlGZm3PELmsfswhkeJfONfCLxyChwHAKN3ACCnCRPtOWTHOgnFGuffuEAEo7Xrv9XnGuJVlqEcd2GvQbaN2dWDs61p5QNbIl1aTOaND2D2DBB8/rjM/Fsmo28QXVWOMvN3+c/zbLKJDsLFTRRVHSZStoueC39Jywv/kZYXfofWV39vWptBsXFo7THc9gNKau8mGCm8BQnrnRUswQqWkom3Tt6mtYedHvDbewsh1qyCqvh74xvfuODewUopnnrqqRWOSKw3xsAQynZmXeHueTbD7c8QLKonO9blJ/6C/sVat74G62bHrMeebP3WeKuFhlcUxbwx+z6icKlUetrFyqUdSOVspbBXEsNsaVvwal6xfi0k8efVVaGjYazrrWRrKlcrtJyxLlzFGBwh9ROPTVYRZe+/EzWWIvz953C2N2HfdWD22TSLndM5k0gYr6rcv1A9XmWohuOoRBKnYXq7JGfHFoKXrmM+eNiv/HZuu4ARDqEjYYyhUdzmpYckxEyMgeFpLcyndCvYvmXKfaq7F6O+2q+icGa+0Obs3U7g3BWsq62ARtkOzt7taMvCutZK8IUT/gy/4hhkMhiDw7jcOo8xOAwwLS6vtBiVzkI6s6zEPPiJPy+6wEU64RA6FADDhEBgWed9PXdTDSiF2dGDk+Ok4rriuJhdvWSPHJhyc8FVQY9/dks11mOdvUzw1EWM0QSZNz2A2T/oJ5EX0F52oTJjnWjtTq64XwuC0Trs9MBkdxSRX15lOZgG1rWb2K97fr2e2dkLnsZtrJtzu3zz6qrJ3neY4AsnsA/tKbzXiTXE7BvE25bf15dsomP8dW4LSilqdv0MvZe/QijWiBkoYqDlm9jJHoJFMtJnvdKeg9Yehjn9/XOs/xR2up+Kpl/JQ2QCxuf8Jdomf3YyI2jPllafQqxxBZX4e9Ob3jRv4u/SpUu8/PLLMlxULInR3Y+2TLyqmRM6iZ5XcbMjVO98L93n/v+3Kv4Yn/N37goqPjbjUHezvWta6zddFEVlsv4qYaugnm5iHiq9hBl/K0yXxFC2418szUE1oli7blWzzXHhUSmc5kasa61w9I6cz0JaUZ5H4MI1nO1N6NsT8IZB5okHsS63EDhxDvNmB9n7j+Du2DLtELl6DrsN9Vjnr/hzZkJBv02dac68gCQQIPueJymqiMHQ2LS7vfISqfgTuac1xuAQzp4dU2+PhPHKSjDbuqYm/mwbo6sfdXTui8M6VoSzZZP/+AecLZsnE+32kf0EX3wNXRQh88SDBF86ifG6+VLGwDA6YE2rrvNKx6tfR+J4y078pfEWcTFYF8fQK9EyLhDAqyrH6OmDvdtzf/x1wuzsGZ+PWtgJh0mmiXPHXrzKcsJPPU/w5ZMYvbmf75eJtwGKUNHmnB53JQWidaA97FQfoaJN+Q5HhILY+3eNt8xtnvG76gSzvRuv1J+rV+icxk0EOYExPIorib+lSaZRiSQ6z21d0/FWUAahmL8gNhTbTOOR3wLAtRMMtHzTbzMoib91q/fK35IaukTj3R/DtKYu3Bxq/T6R0h1ESrfmJzhBKNbEaPcLkz/baX/en1T8CbG2FVQm4j/9p/80630XLlzg85//PK+88gpNTU38m3/zb1YxMrFemF19s87301oz1PYURZUHiJT45Ri3J/7mnPOntV8R0lg/pbpEx/wPNGosNaX9lihwrovK2MuaDbYSJtqUGSNxPEn8bWiTSa15knnutkYC565gdPXhbV47czPMmx2oZAp7747pdyqFs3sbTnMjwZdeI/Tcq6TDIb/15u2bpdI5eQ47O7ZgXbgyOWfG7OjGra8Ga5bkwRwLk7zyUszWzpnvdF3/31MWNolFUskUKp2dVlkH4OzZRvClk3j113F2bwPPI/T0y/7nlj3zl546+3YS/udnAMg+dPet23dvQ6UyOM0N6GgEt7KMwNnLUyrSzZ5+f6HV6x7TujQGSvnvZbXLW0WsUqlFteW279i7Ys8xt64a68qNjV2VP9frmNYEXjuPV1W+5j4Tew11ZO+/k+ALJwBwdm3N6fEziTaC0VoMa+18tpuYxZUd65bEX4GwD+3GunKDwKunyT52/8wbjX9nff3s1YIVCaGDgZzNhd2IzH6/5b+X5+4fmXgroaL6Gau9zEAMK1RBOt5Kce09eYhOrIbsWBfZZDfd5/+CTQc/jFL+99j06A1SI1epPyDVfvkUKm7AuTmMkx3FCpbgpPwW/oHw2uscJIS4peCX/585c4Zf+ZVf4d3vfjfXrl3j05/+NN/97nf56Z/+6XyHJtaaifl+9TOvWEmPXieTaKOs8XEMy58v4t6W+CMU9C/a9vRP23e21m+6yD+OSkyfryMKl8pk/T+EC+sCzMTKXBWXGUIbnZ/Umv/x6VWW4xUXYbW0zbttIQmcv4pbV42eIZExKRgg+9DduA11hJ55CfW6aqNcVfzp4qJbc2Z+fAyzux93hjafC+GVl2DEx8B2pt0X/vYzBE5dWG64YgOaqLSbOfG3HWfvdoIvvobR3k3wpdcwO3uwH38AtYB5s15NJV5Vuf/f7RcNlcK+c99k6zWvshyVtVGJ8UpXrTF6+2eujDVNvFgUNbzMOX/O+CKdRcyEc5s2TWnJnktuXTUqndm4F6i1JvyPTxE4OfPrmHW5BWNwmOy9h9dkYtTZvQ374G4A3Jrctr3KxFvX1Hw/ADNQjGFFySb9Gbjac7BTc8xCFysvECB790GsGx3TZ66OU0MjqGRq7VTdKoUuLcbYqK+rOWD0DqCjYSjK7/zUTPwmoeLpHTomvL7N4FqQGW9fKhbGSQ8SLmlmrP80gzf+GQDPzTB487sEwlXEqg/nN8ANLhTz575OPA/tVD9msBTDLKzF8EKIxSmoir/bnTx5ks997nM8//zz7Nixg89+9rO89a1vlRafYsmMweE55/ulhq9gmGGi5XtgfLD07RV/AF5tJUb39MTfbK3fJi5GGWNJZFR14VGDI5htt6pv3KZN6PJSmJyfVmAfciwTXRTBGEkgXzE2uIVWsymFu60R68I1uP9Of57XfLL2eGvApuXHuQRG/yBG7wCZN86yWn3KxgaZR44S/s6PCD/1HOl3vGnydVelMnjlpTmJyaurJvPQ3YSefQVgyRfM9Hg8xsgoXtWtlktqNIExMFR4rzliTTAGhtGh4MwJMKXIHr0DlUgSfup50JrMQ3ehFloBrBTpxx+c/PNsvIqyyVjc4hhqcASVsXFn+cyViwu5Kul/RtMLnfG3wrzaSn/OX08/zkRLukwWs6s3Z7N+C5mKj2GMxFEXrmAf3DW1xX0mS+DEOZztTXmvOlkO+64DOLubc9oiUWuPTKKdoqpDOTvmalBK+XP+kj1o7dF17kuMDZxj20P/DdPKb4JhI3O3N+FdvEbwxRM445/jdHERbnOj//rU3u2PvVhmtfVq8sqkTfpyGH3j7YnzeB1New6ZsQ5K6h+YdZtwcRPDHc+gtV4T1/yyY13cfOWTREp3ULf/lwmEZx4lky/ZZA/JwfOUbn5ksrIun7T2cDJDlDc9QVHVHQxc/wfGBs6QibeitUvt7g8VRJwbWSBShWGGSQ1fpahiP3a6X+b7CbEOFNwr67Fjx/iFX/gFPvCBDzA4OMif/Mmf8M1vfpO3ve1ta+IDgChcRlffnPP90iPXCZc0o5SBMiyUGcRzpyb+3KoKjOFRf9bTbWZt/Waa6EgYNTb1OKIwBE+cJXjqAoHzVwmeukDw5ZOAnzAACm7GH4BXUowxKhV/G51KZRbc3s5pbkJlbcz27gVtH3z1NKFnX0Hl6XFmnbuKjkVxmxbYOiwQIPP4g37C8urNyZtzPafT3d5E9ughv7XhHLNz5jIx20y97gKW2eH/2xjLrYASG5IxMORX+832OXk8Qe5uqiV71wHcnfO3+JwiEp5/rmw0jI6EJ6sPze5eMI1ZZ6F5ZSXLroy7lfgrkCTDxJy/rt5bN508T+jplyBr5zGw1WH0+BVGKmP7s2VvEzh1ARwH+66550oWPKVyPhfNTvXjuek1V/EHfrvPbLKbwZvfJdF3Eu3ZpIav5DusjU0psvff6bfWPX+VwPmrhH70yuSsVrO9G29T7cIWghUIr7TYf7/QOt+hrD1aY/QP4lbnuc3nWAfacwgVz76oMBRrwM3GcbNrI8mbGrkKysBOD9B67HcZGziX75Cm6L/2dXov/w29l76C1vlfgu5m42jtYoXKqdjyFsoaHsMKlVO9831suff/onTzw/kOccNTyqC49iiDN75N39W/J5vsISjz/YRY8wqq4u9DH/oQr776KocOHeKLX/wijzzySL5DEuuI2dHjrzKeZb5favQ6pfUP3drejEyv+BtfpWz0D92al5W1Mbv7/dZFM9BFEdSYtPosRMbAMPbeHdj3HMK63ELw+ePjs5ImEn+FUUVwO10Sw+gdyHcYIs9UKj3rIobX0+UleOWlWBev+8m0ieRAOkPo2WM4u7ZOVqOogWGsSy2AXyXtluT2Auftgi++Bmiyh/f5SQWtMa+1Yt1oI3v3wUWtTNbRCF5lGcbg8PgN2n8e5zh57+zftbwDBCy84iKModEpVbtmWzco5beFdtzZ5wcKMQNjcBineZ6kQSBA5s0Pzb3NMnmVZbcl/vr9C42zXFzWpcV+29tlPN5Vcrw6v1ASf4zP+bt6079AbTv+zD/8lu+6IjcVyIXK7OrDqyxDF0Wxzl/F2dXsz3LsHyJw/irZO/ehi6L5DrPgTLTUChWvxcRfHfGeV0iP3qCy+R2Mdr1IcugCsTVWvbjeeJXlpN/zlsmfA8dOE3zlNDoYwOwd8BODa4guLUbZDiqZkteQRVJDIyjHxauuyOuK/8xoKyiDUGz26veJNoPpeCuxUNkqRbZ06ZEWQkWbaTj863Rf+As6Tv8pjUd+i0jp9hU7Z3LoIum4v7BGKYNY9Z0zzl9zMsMk+k9RVHmAkc4fA1Cz+4N5rahzMkMAWOEKlDKo2fUzeYtFzK5m988SjNbQf+0baO1SVLE/3yEJIZapoBJ/x44dA+Dy5cv8xm/8xpzbKqU4fvz4aoQl1gGjsxezq5fMG47OeL+THsDNjhIu3XZrH2t64k+XxPwvTf2Dk4k/s7MHtJ619ZsXi8qMv0KUyqCSKbxKP3nibNlM8MUTmC1toEEHrIK8+O6VxDCvjV9UvD0xYtvgejlPdIjCtNAZfxPsI/sJ/eAFAq+ewb7nELguoR++gNkzgNndSzoSxqupJPjKSbyyYlTG9lv2rVB7OjU4jHXxGhgG5rVWnP27MNu7MPqHcJobcHZtm/8gr+NVlt+qapxI3i/i72i16PLSqS2rHAezuxe3aRPmzQ7USHzu2YZiw1NjST/ZpRSkM6ixW+9l+eRVlmFdvuFXGPT04ezdOfu2460w1WgcPd4mdLFUKoW2TAgUztcZt66awJlLqNEEZns3anyep5EYw13PiT+tMbv7cbZuxm3cRPi7P8Lo6kWXxAg99TxeZRnOgWUunFinMvE2rGApVnD+mZuFJhCt9S8MVt1Bxda342SGSA7KrNpCY999EDWWJPScf+1kzcz3G+eVFgP4n48k8bcoZt8gKIVXWZ7XxF86fpNgtH7OWWFWuAIzUEQm0bYmFg+kRq8TKduJGSxm06GP0nb8D+i58GWa7vn/YZjBnJ9vtPtlui/8BYYRBGWgPZvBG9+mdu+/IlZ1x5RtR7peQCmTun3/mkT/SXou/CVGIEr19nfnPK6FstODAARC+f+8KmanlKK86c2ES7fTd/mrRMr35DskIcQyFc43ZeCjH/1ovkMQ65Hn+Rezaypxt828mjY1cg2AyOsSf+7rEn8o5a+Yu63iymzvxisrnrX1my6KYgx2LfOXELlmDPqrzryJC+yhIO7mOqzrbbh11QXZ5hP85LNy3GmrXoMvn8Js7yb9jjfKl+L1znFRtrOopJbbtIns0UP+iu/iIozuPsz+IdJvecRvRfeD57H378Ls7if9xEMELl6brNxZCYHzV9HRCKl3vMlvt3vqAl55Cem3PjLrHNb5eBVlBM5fBdsu7Ha9lWVYZy/7yclwCLO7D1wP+8AuzJsdGCNxXEn8idnYNpG//y7ZO/bi3LF38nnqFcBjxqsoQ6XSGB09c873g1sXco2ROO5SE3/J9K0EaIHwasbn/HX1Yl24itPcgHWzc90vAFOJJGosiVdXjVdXhVdeSuD0Rf+12DT8OZFrqLXgasok2uZsf1fIohV7KG96MxVb34ZSBtHyvYx0PoeTGcZaAxU7G4ZSZB++x+9q4uk19z1BFxeBYWAMx/02pWLBjL5BvPKSvC+QySRaCc/zOqeUIhRrIBNvW6Wols61k2THOqloejPgV9/V7v15Wo/9FwZa/onqHT+d0/NNJP1K6u6ndo8/C8+1x+i58Jd0nv485U1vpmr7T6GUidYeo53PUVxzN2YgSmn9A9jJbobbn6Gy+R0YRiCnsS2UkxlEGQGMwMp1kxG5EyndTtM9/ynfYQghckASf2Ldsy61YAyNkn7Hm2a9OJQevU4wWot52weRmSr+ALzqSqwLVyfnDJjt3ZPD02eii6IYY8npFVoir4yBYXTAQt/WytDZ1kToRy+jTWP+eUZ54o3Hq0bHpnxxN4ZGUKk0oe8/R/ptj0EwPx/qxcpTqfH2dot8jDr7dqLiY+MtNiHz2H149dVkKu4n/O2nCZ44h9tYj9dQh9c3gHXx+sq8bqUzWNdb/Raf0TDZ++8ke9cB/6LEMs7ljScPjMERcP1GmjpSeIk/e/d2rHNXCJ44R/aBI5jt3ehYFK+6Ah0O+YmQfAcpCpYxNAquR+D0RdwdWzAGp7+X5ctE1WHg/BUwZp/vB0Ao6D/eh5f+eFfJFDpaYO/VwQBeZRmBUxdRyRTZR+7FGBhGJcbyHdmKMrr9uYZubRUohb1/B6HnjqNDAf8zSYF+pioEmXgrJfUP5juMJTGt6JQL3BOVAcmhi5TU3ZevsMRMTJPMWx/12yuvNYaBVxLDWOZc2I3CvN7mLyoDzLYu3C2b8xqPkxkmE29b0OtcKNZIov/UKkS1POm4PxYhfFtbz1BRPZXNP0n/9W8Qqz4yZUH5ciT6To4n/e6bTPoBmIEi6g9+mOH2H9B/9e9x0kPU7f9FkkMXsdP91G3+pcljFNfdz+DN75IcOEes+nBO4losJz1EIFyBkuthQgixqvJZ8S/EystkCbx2Fmfn1jnnYaVHWwiXTP1wNlviz62uQGWyqPgYanAYlUrP2TJFF0X8Fozp7NJ/D5FzxuAwXkXplESD21iPtkzMnoGCrBSC8VWvSmGM3vblV2vUaAJnexNqLEXo6RfBy/8Qb7EyVHoi8bfIx6hS2Pcextm7newDR2618QwFyTzxEO6WzWTv9VvFeBVlqHRmMsmYS9al6wD+/KcJwcCyE4y6vMRfET4wdNuczgJ8HkdC2If3Yl1uQQ0M+xdlGuv9ivKyYtTIaL4jFAXMGB71nyuWReDVMxgDQ9Pey/JFx6LoUACzowe3pmLedtnLfbz7ib/Cme83wa2r9luJV1f4Cf1YFGOdV/yZ3f3+4ouQ397M3daEs62JzJseRJetvRaWq8XJjuJkR9bkfL+ZWMFiQrHGGdt9au3RcfpzJPpey0NkAvDfJwqoNfJiyOejBXJdf3RFRzdG/xA6FsWZpePRahlufwZlBCiuvWfebUPFTdipPlxn9vdM1x6j9dVPk032Ljkm1x77/9j77/DIrsS8E/6dG+pWLuSMzmRHks2chxwOORqNNMqSJXkUHOSR1l577d3P1ifba++uLcnetVdrf2utba0tyR4rzWgkcYI0mcM8zGQ3Oze6GzmjcrjhfH9coBpoVAEFoAoFoM/vefjM9K17bx0AFe4973nflxtv/QuS469s6vhC8iq6GcEMda3Y3rrvWYKx/Uye+884pfq8XhdGvkUocYTuYz+7qqNPCEHr4LP0nPwFMtNvM3H2P5EcfYFApG/F3JYV6SUQ6SM91byqJLs4h6FiPhUKhWLbUcKfYk8TeOcseNJ3k1TBc0sU08MEEwdXbNeNEJ5bwfG3KCBq07O+U8M0/GinKsio78rScnt70me3oc0urO5EMg3cfX3AznQKAaDryEgIkcrc3FYsIUo27r4+is88ij4xjfnmB80bo6KhlGMsN+OgEILSI/fiHF250EHGIhSfeRQZ811DS7GBdY/79DzM81dwDu+vfx+lpuG1Jnx3Tb6w2P21M52vzvEjePEo1guvITK58uIRmYipFe2KNRHzSbxYhNIDpzCuDvtx4zug3w/wxetF563XvX5k71Zf7+Wozx2G1+v/7PYJv+NQRiN7PupTn6SqPqYAAQAASURBVJjG7V32N9d1Sk89hNfd0bxB7QKWIu2s6N4Q/sCP/8zNn0cuJqMskZl6i+zM+6Qn32zSyBS7GZmIoS2o66P10EcnESWbwrNPUPiBj1H41Mc2HaFfDzy3SHLsO8T7Hkc31o+YtaL+osRieqTqPtnZMxRSV8nNb75PNDP1NoXkFSbP/TYT534bzy1u6Ph88irB+KFV7jW/V++v4TkFRt7511sW/6T0KKSvEW47sUr0W06s6z56Tv4C6em3yEy/S6LvyVVji3XdT3bmPTzP3tKYNotTmFfCn0KhUDQBJfwp9ixiPolx/ir2PcfXjBgqpK8jpUsofnjF9mqOP4IWXjyKPj2HMTLhdw2s0VviLcYx7vVJn11FyUZLZcoTlMtxD/qTLzvSKbSIF4+hLRP+lv6/F4vi9XZRuvck5oeXEfN7a2WsmF1A2k6zh9F0RL7gr9pu4GtURnznjjY7X9fz6tdGELkC9vEjdT3vEl57iy/8FUoN/f1sGU2j9NA9aMkM6Fq5C81bEkJumTBVKJbQ5pPI1gTukQN47a0I26n4XdYslsayVr9fed+tvN6l3JlRn4Db30PxmUdxD/oTmF40vPeiPgtFxOLCEJHOIjK5pk4u71ZKuXGEZmKG9o5AGm49jlOcx85NlLdJ6TF77UuAX6+gUGwULxH3r39LzREtdgv61Rt4rQk/BWMHkJp4HdfO0jrwTE37B8I9CM2kmKne85ebOwtAMVNdHFyP9PRbhFuP0XP850lPvsnwW/+iZkFMSs9Pi6oS5RkIdzFw3/+Ia2e3LP7Z+Sk8J08ofnDdfWNd99F78m8QTByuGLUc7bofzy2Qm/tw0+PZCk5xHiO4RgS8QqFQKBqCEv4UexMpCbz+Hl4sgnNi7QnmQvIqmm4RiPat2K7pIdxKwh/gdbahjU6iTc3iDFaP+QT8yWddQ2SV8LdT0OYWgJuupuW4/d1+31ZrYnsHtQFkIrrC8bf0/5c6npyTd+DFIgS+++7eERCKJQJ/+nW8Dy42eyRNR+QLSCvQ2Gg/IfDaWsvvlXphXLmB292BbGvM+8trb0FbSCEyuR0t3gN4Az04B/px9vWB4UdveYk4uJ5aKKKoijaf8r+fhKD0yGk/dWAHuarcvm5k0MLrWn9yZ0uvd9tBOC4ytPMcfwjhdyotfkbLaBhRLIG9RyaspcT69muE/uzrBL79GsaV68Biv59iQ9iFWcxg+5pOjt1GqOUOhNDJzZ8vb8tMvUUpO0brvuewC7M4xYXmDVCxK5EtMQA0FfdZHdvGuDHe9GjPJaT0WBj+OtHOezFDtS0MEZqBFemrKvxJ6ZHdovDnltLk5s8T7bqfeO9j9N/ztyhmRiimb9R0fCk3iefkCMWrd/gFwj0M3vv3cO0sk+f/y6bGCVBIXQPAiu2vaf9Y133su/8foJuRVY9ZkT4CkT4yTYj7lJ6DU0oqx59CoVA0gb1zl6G4vSkUCbzyFtrMHOAXWevjU9gP3bOmGw/8ladW7ABCrNyvquMP8Drbb7qs+tcR/oTAi4QR2crnUmw/2twC6Frl3hldJ/9j31t2/u1EvHgULZ0pi3paKu27HpZ6O3Qd+8G70cem0IfHmzjS+qGPTYHnIesdPbkLEfnitrhcltxzdUNKtOk5vN6u9ffdJF57C0iJPj61uSjUbab09COUnnq4/O9aJ7a0qVmM98+vuY9iD5IvIApFvMWV/F5XO/m//IPlRR87AW+gh/xPfaosZq/FViZyRc6/ptqJjr9bkVF/Ak5kduF1YLFE4OW3IHez71W/Poo+Po198g70iRnMdz70eyYX+/0UteMUZvecA0LTLYKJw6Qn38C1M2W3X6T9FC0DzwJ+t7pCsRG8uP99IVTcZ1X0G+PgujvmHjY39yGl3AStgx/b0HGhljtIT71Jbn71Ys9i+gZuKU2k/S5KmVGk3HinfWbmXQCinff6z5c4gtAMCunrNR1fSF4FBFb8wJr7BSK9tPQ/RSF5eVX0ca0UUtcIhLvRzfVjUmsh1nU/mSbEfTrFJEgPc4993ykUCsVuQAl/ij2BPj6NcWGI4PPfJPDiG5jffQ+3v6fcm1QNKSX55FVCFaIadCOE9GyktzpacGklu9fWUlO/jIyElONvB6HNLviOCa3KR2AjnVR1wIvHfJfE4mtKpDJ4t0z8uoO9uH3dmN99D1y3GcOsK/qIL2DKuWSTR9J8RL6ADG6P8CcyOSiW6nI+kc4iiiXczsbd9C05ofzf0c52/AH+Z82yzxsZCSMNfd2JLePydQJvn13tIJKyvi7f9c5V7+dTrIm2GN+8wpG+w7+v1kJGwqDriE30/N0U/nag4+8Wyl3PuzDuUx+fwrg4RPDrL4PtgOMSeON93IEe7IfuIf+j34N930ns0yeaPdRdiV2YwwxW7wnfrbQOfoxiZpihV36F8TP/gVJ2jLYD348ZbMWwWsknVdynYoOYBjISVj3I4MedFor+f8vu8YyhYbyudmRstdurGSyMfptg/ADBxMbi/dsP/SCh+GFG3/83q8S/7OwZNCNEov8jeG4BpzC37vkmz/9XZq58ofzv9NRbhFvuwAj4i6h8l2E/xVSNwl/qKla0D91Y//rDig3i2tlNu5wLqSGCNcR81kq06348J09ubvP9iJvBKfp/p7220EWhUCh2A0r4U+wJRCaLNA1Kj96LPjyOlslReujuNSfEcvMXGX33X+OWkoRaj656XFu8mKsU9+m1JpCmgTvYW9P4ZDSCltp9Ez57FW12YUd1Im2UJXfHUsSnlsogF1fClhHC7xDL5NCv1hZdsmOREn1kAmkYSvgDyBeQocaLWl67H8dSr54/bWrWP29HA2NeDAMv4b8XdoPjbxVCIOOxdSe2RMp3/GqTsyu2B7/4TV/srwe2Tej3v4h+rXqUkvHhJYJ/8jUl/m0T2nzSd6vvIIfflhACL7656yOx6EDbFcJfOASa2JU9f9pCCmkaiFQa64XXMc9cQGTzlB66x9/BNLHvOe5Hmyo2jO/423vCX7TzXg48+qsk+p8iO3eGSPup8iLLYOKQ6vlTbAqvZf3ro72ONjFN+LN/Svj3nif8e88T+r3nMd4/j8jl0UcncHaI2w+glB0n1HIUscEFSppu0Xf3f0cofpix9/8t+YVL5ceyc2cJtx4nuBh9uVYXIPjRoOnJ7zJ3/Sukxl/BtTPlmM/lWLH9FGqI+vQXjV8huEbM54rzRvzvxlJ247Gk0nMoZoZrjvmshUC4l0Ckj9TEq3U7Zy3YRf9eUkV9KhQKxfajhD/FnkDL5JDRCM6xw+R/9BPkf/DZyjGO+BdsY2f+PSPv/B+4do6+u36JSNvqlcpLwl/FuE9No/D9z2DfvVowrITXsdiVtVf6XXYzrou2kKrY77dbkNEwCOHHzUpZ0fEHIFvjeLFI2SWyW9Fm5/14uzsPIPOLK1xvY0S+sC2iloxHkYZet7hPfWbOf5022Im39N6Wwd0ZO+e1xBDrRX0uiv76xHR5m0hn0WbmMc9dQdThb6bfGEMUiuhD1Scr9NFJtIUU2vT6K64VW0dbSPm9eLvY5XcrMh5FpDbn+JOWCcbace47gqXI913Y3Snmk3jtrRSffgR9ZALznQ+xTxxBJmLrH6xYE88p4NrZPRt9ZgRidB75UQ49/i/pPfWZ8vZQ/BCF1PWKiSoKxVp4iRhiYXff02wV/fooMhyk+MyjFJ95FOeOAwTePkvwj/8CJDgHB5o9RMCfb3GKSQyrZVPHL4l/VuwAYx/8JnZ+FtfOUkgNEWk/iR5IoJtRipnRNc9j56fx3AJWdJDJC59lduh5AKKd963Yz4rto5Qbx3Or32NKKZm58jlK2TEiHffU9HMYwXY0I7SpPsJiZhTpOXV1/AkhaOl/isz0O9iF2Yr75JNXGD/7W3hOoeLjm8EpzKMZoZpckgqFQqGoL0r4U+wJRCZbjlLCCiCXx2DdQiF1lczUW3Qd/cvse/AflvPdb2VN4Q98YbGGDhsAt6fTd2dMVb7AUmwf2nwSpNzVwh+ahheL+K6fQhFhO1UdIDIe3fWrY/WRCWTAxD3qr64Uu1zI3CqiUNweN5sQyLYWf9FCHdCm5/AaGPO5xJKbdzviUBuBTMTQ1or6dF0/5lfXVgh/+siELzDEIgS++96WXXjGVX8VtT42CV6FDhUpy726xm53Fe8StPlkud9vr+DFomUheyP4CyB2zwSSjEZ2p+NvPoXXGscb6KH4+H14Ha0q1rNO2OXos73n+FuOboTR9JsLfoKJQ0ivVJ4Il9Ijn7yy6Q4sxe2D192BlspgXLh9HaP6yIRf57C/H3d/P/bDp8n/0HN4PZ04R/bDDkm78Jw80ithBKrPyayHplv03fUZND3I2JnfJDPzHkiPcNsphBBY0YF1BbWl3r7+e/57rGg/CyPfIpS4GfO5RDC2H6RX9Xy+6Pd55m98ja47f4pox901/QxCCD9GNL1x4a+QHkIIHStWXxdnvOdRND3Iwsi3Vj2WX7jM6Lv/F+nJ77Iw+kLdntMpzim3n0KhUDQJJfwp9gQik8OL1lZ6nBx7ETPYQaLvSYSo/hbQ9LWFv40gEzFk0EKfmNnyuRRbQ5uZ9yfHW1uaPZQ1kZ7D7NDzlLLjlR+P+5OlSxOmlRx/sLg6dhOTqjsJfWQCt68b2RLz+9t2uZC5JWzHF3q3IeoTfPdcXaI+HdeP2O1s/ATnUkTpdv2O6o2XiCOKparOVrEYi+js7/c/zxad5PrIOG5PB6WHT6NPTKNfX3sV9JoUiuijkzhH9iNKdsVFKyKVQRRtvLaE7wqsJA4q6oeUiIXUyn6/PYBMxBDZ/Ia7aLVsHhneGROctSCjYbTd5vhzXT9KfPE1595xkMKnPgYBs8kD2xssdVPtVcdfNazYPoTQKaSGAJi79mWG3/oXpMZeavLIFDsdd38/zvHDBF59B21kotnD2XZEMo2WyuAOrKwakS1xis8+TumJB5o0stU4Jb+awbC2ds2im1H67volStkJpi58Fivajxn0r/NrEf6K6euYwQ4Mq4XeU79EINxNS/9HVu0XiPYhhE6xStzn3LUvMn/jq3Te+ZO0DHx0Qz+DFR2gmN34NXkhdQ0rOoCm1fc7VzOCJPqeJDn24gpXXz55hdH3/g1WbB/xnkeZv/EXdXP9OYX52+67TqFQKHYKSvhT7H6kXHT8rV9k7do50pNvEu97Yk3RDyhHEXju1oU/hMDt6VzhzlA0ASkxLl3zHZg7OB5Meg7jZ/8Ds0PPM33ljyvu4y3Goy1FpMlYFcdfLIqWzu7eSfl8EW16DnegB3QdkYih3cYxP6Kw2Gu1TSt63a4OtGTGd5hVI19c112mzS34TtuubXD8dbdTevBuvO6Ohj9XI/Ba/Ai9ak5dbfE979x56KaT3HHRx6dx+3vwBnpwB3sJvPE+OBsTU5YwFkXD0v13+YtWhlcvQFgSA0sP3o0oFNHG1fdbVYqlDQtbtyIyOYTt7Dnhz4tH/eu49MbccCJf2BX9fkvI6O6L+hQLaf9ze4+95nYKdmEWhLbpKLzdiqaZWLF95JNXKKaHmbv2JQyrlenLf1Q1ek6hAPz+8odP4w70YH3rVUSdOqh3C/rIBGgabm9ns4eyLm5xAQB9C46/JazYIN3Hfgbp2YTbTpW3ByL9fpTnGuJUIXUDK7YPADPYyv6H/1di3Q+u2k/TTALR/oo9f55bYv7GV2nd93FaB57Z8PgD0QFKuQk8b2OVL4XUNaz4gQ0/Xy20DHwUzy2SHH8ZgPTU24y++39hxfbRf89/T/vBH8Bz8nVz/dnK8adQKBRNQwl/it1PsYRw3JtRn2uQnnwdKR0SvY+tu+9S1KdbB8cfgNfT6fcg2arTollo03NoM/M4J+9o9lCq4ot+/5Hs7BniPY+SnXmfUm5y9X5xX9DTFtLISKiqkFmeVN1lE45L6KP+il63vwcA0Z64rfs9RN53gW2b8NffDUL4kw2VyBcJfe7LGOevrHkebWoWdG17JpA1DefUnaDvXHF/LWQ86nd4ziUrPi5SGaRp4PV0lJ3k2sQ0uG55FXjpwbsRuTyB19/dVOSnfvUGbm8XhIO4Az3l9+GKfWbm8FpieL1deLEIxtDwhp/ndiH45y9gvvPhls6hzfuvB7nHoj6XYqo3EvcpFlKIuYWaFnztFLxoBFEo7qprwKXXnFelM1uxNZzCLKbVihC787tqK4QShykkrzBx/ncwwz3se/AfoRkhJs//FxX5qVgbISg+9TAyHsN69Z1mj2Zb0UcmcHs6wNz5rut6Of6WiPc8TP/pv0Pbge8tb7NiA4CkmB2reIyUHsXMDT/GcxGxRkdyMLaf4mI06HJyc2fx3CKJvic2NXYrOgDSo1RlnJXwnAKl7DjB2IFNPed6mME2Yl0PsDDyTaYu/j7jZ/4fwm0n6L/7b6HpFmaonXjv48wPf3XN3sNacQpzGMrxp1AoFE1BCX+KXY+22JlSaQJISg8p3cX/L0mOvUi0456aVtcKzUBogbpEfcKynr9ptZp1W5AS48xF3420iPHhJbx41HeP7SCkdMknrzB77csMv/0vyc68T++pX6Tr2KfRAzEWhr+x6hgvHgVPoo9N4cVj1c+9OKm6W+M+9dEJP7pxMdJNtCZ8F8JtisgvOv62q7/OCuB2tVUV/syLVxGOi3F59Y3ycrSZOdyOVtDUZce66DpeRyvaZGUHnZbK+A5fIXB7fSe5MTKOjIb9OFz8+MTiY/djXBzC+ODChp5eZHPoEzM4h/xOEXegB20+tWrxgDY150e3CoF7aNCPFt2iq21PIiVaMr216FVAzCeRAXNXudxqQYaCSNOo/TsqXyD4tZeQ0Qj2iSONHVwdWVqcttVFONrMvB+tuw1o80l/3CrasyHYhbk93+9XjWD8IHZhlmJmhJ7jP4cRiNF97GfIzX1IatGBolBUxTRw7jyANrtw+1x32A76xPSOu4ethlNMohmhFf2eWyXSdhLduLnQOxDpA6FRylS+vvLdgHms+L6azm/F9lPMjuG5pRXb01NvYUX7CYQ397u3In2AoLhsnEvzU7fi2lmcYpLc/DlAEkwc3NRz1kLr4LPY+WmSoy/QdedP0XvqM2jGzfvLtv2fwLNzFV1/UrpIWT1NKDP9DqPv/984pRSeW8K1M5jK8adQKBRNQc3AKXY9S5MolTr+pi/9IVdf+p+Yu/YV8vPnKWZGSPQ9WfO5NSNUN+FPtsSQwQC6ikPbFsRCmsAb7xP8xsvguIhsDuPaKM7xI7DGar9mMHP58wy/9S+Yv/7n6GaM/nv+NtGOu9E0k5b+p0hOvIJrr4xBk4tinza3UBb3KiGjYdC1cjzgbkObnvNXty4i2uL+e36TEYa7HZEv+K/fYGDbntMd6EUbm1w9ueK6GOeu+N1VM/NrTtzrSyKRoib8aOiZim49kcrgJfz3vNftO8n1G2P+ZNCyzzb3jgPYp48TeOsM+tXKnSWV0IdGQNdw9/f75+lbdH0ud/05Dtp8Eq/TX73rHNyHKNnVnaG3M8USuB5aKrOlBRjaQgqvNb7jvr+2jBDIWKS27yjbIfj1l8H1KD73BFjb9zm4VWTMX5y2ZmzyOoiFFNZffMd38m4D2nxKuf0aiF2YvW07j4KJI4Cgbd/3EFyMsou0nyLe+xjTlz9XdVJcoVjCa2sBz7ttFgPq41Pgeav6/XYqbinZ8BhjTTMJhLspZisvhlnq61vu+FuLYGwfSG9Fb6DnlsjOvE+08/7Nj9MIYoY6KS2e17VzDL3yK8xd+8qK/VITr3Plpf+Rqy//fxj74DfRjNCmxcZaCMYP0HX0pxm8/5dpGfjoKjekGeog3vso8ze+ivRWphUMv/1/cO21f0xq/JUVn9fSc5i+9IeMffCb5GbPMH7mP5QjnA3r9vy+UygUimZjNHsACsVWEeks0jRWTQB5nk1q4lXMYDuz155Heg5msJ1w24maz63XUfhDCH+StoqLQ1FfRN7/u2kz81jf+S5eLII0dJw7arv4306K2XEi7afou/tvrop8SvQ/xdz1r5Ace5G2/Z8ob5fRsO+e8jzf/VcNIfBi0Q3FqO0YPA8tk8NZ9vOJtgQgEck0sr2laUNrFiJfRAatbZ38dwd6CLx1Bm1yBq+vu7xdvzaKyBcofOoZrK+8gD40jHPPcQC08SkCr72LfeoOvL5uRDZXFokU6+P2dGJ+cMGP9UysdPRqqQxOV3t5P6REZPMVJ4Ps0ycQ6SzWi29SCIfwetbphbEdjItDvoi45PKxArhd7ejD4zhHD/ljmJkHKXE7/L+pbI3jtSbQr42UBUOFj8jevIbQR8ZxTmwialpKtOk5vP7u9ffdhXjxWE2iaOCN9xALKQqffLqmePedhAyH/AjfTJZNNe4uOh2F40LJ9he/NLirWFtI4hyqzSmh2DhOYY5wy9FmD6MpmMFW9j/4jwhEV35fxHseITX+CqXc5KJLRqGojNfmR0hqc/O4e+V+QEoCL76BDFrYD9694lpfH5nw72XXuufbQTjFBYw69PuthxUZWCHULaeQvo4ZbEc3a/udBaL9CKFTTN8glPCvd/2YzwKxrs0Lf+DHfS6Nc2HkmzjFeWaH/oxI+yms2CBOcYGpi79HtOM08cVKGjPYjhCN9Wm09D+95uOJ/qdJjr1Ebv4ckfa7AChmRigkrxCMH2Di3G8ze+1LWFE/JaSUm8TOTdB5x1/Ciu1j9J1/zeS53wHACCrHn0KhUDQD5fhT7Hq0bM6fALplIjw78x6ek6f31Gc48Mg/o3XwWTqO/PiGLqDq6fgD/Fi26Tlwdk/Hy25F5PxIxOJTD6FfH8U8cxHnzoM7shfBLSUxgx0Ve16MQJxY98MsjHxr5Wo7IfDivoNgvZtAGY/uyqhPkc2DlHixmzG+YulGP3l7rPC9FZEvbFu/3xKyNYEMh1a6uaTE/PASbm8XXkcb7r4+jKuLHW+eR+DVdxCFItZLbxF8/pv+ZiX81YzX7Udo6hO3LBSxHUQuXxb7fSe55Tv0eiuIekJQevx+3O52rG++glj+vimWVv5bSqwXXkfk8pTuWblAxh3oQRufKrs+tek5pKGv6Jtz+7t9l6JiBVpuMZWgJb5pR6Rx6RpaOotzeOctXKkHMh5FS67zHSUl+vUxnONHkO27cPJICGQkhEj77n2RzcFidPO6OMucjk/4k49L52kYxRIim/ddpoq6Iz0Hp7iAeZtGfQJYscFV92RLk8fFtOqMVayDaeIlouizC80eSd3Qh8cxrtzAPHsJ4/3zNx+Q0u/3G+zdNa5/Zxscf3BTUKvUDVpMX8eq0e0Hiw7CaD+FZT1/6am3CET6CUS25rRcGqfr5Jgf/jqJ/qcIRHqYOPfbSM9h8sJ/RWgm3cd+hmjH3UQ77saKNn8hnRUdJBDpIzX+WnlbauJ1dDPK4H1/n30P/iNCicN4bhHPLRIIdTJ4/9+ndfBjhFvuoPOOn6CQugqAoaI+FQqFoiko4U+x6xGZHDKyeuV3auJ1gvGDBMLdmME2Ou/4CWJd923o3JoRwnXrKPz1dIIn0aZUz1+jEbk80jJxDw5SeuQ0MmDiHD/c7GFVxCkl0dcoP28ZeBqnOE9+4dKK7TK2GPe3jvDn7VbhL+2PeennBBBBCxkK3r7CX2H7hT+EwB3sQR8eL2/SpufQZuZxTvruJffQINpCCjGXxDh3BS2VofDxJyl830eRkRBeS7zi57SiCqaJ196Cdovwt/Q+LrsAhcAd7MUZ7AWjSoiDrlP86KPIUAjray/5scdnLxL63FcI/fFfEPj2a4hMjsDr76KPTFB8+pFVblp3Xx/CcTHf+dA/5fQc3i2djV5nGyKX31KU4V5E5Px4XueOA76Qa29w4U+xhPnWGZzD+/C69qZI4MWjiFx+zUVRIpVBFIr+ddQuxYtF0OZTmN99j9AffQXr1bdrOs58+yzafIric4+XXbtaprHf6dpCCgCvtfGOjdsRpzgPSIyQWhCzHN2MYAbbKWaU8KdYH6+tBbFXhD/Xxfzue7j93dinTxB4+yz6lRto07MEv/QtRDa3qxIVnOIC+jY4/oKJw3hOnuG3fp3s7JmyACilpJC+gRXbmGs9GNtHIXkF18njeTbZmfe37PYDsKL9uHaGmStfQHo27Qe+j+5jP08xO8rIu79BduZ9uo99Gt2MrH+ybUQIQbznYTIz7+I6eaT0SE9+l1j3gwjNIBjbR8+Jv8rA6b/DwOm/Q9/d/x3B+M1ewkT/08R7H8cMdqDpuyeeXaFQKPYSKupTsesRmeyq+DK3lCY3e4aOO358S+eut+NPtsSRVgB9dHJFZJ6i/mi5vB+tBTjHj/gRddrOW+sgPQe3lF4zDsWKDvjl5flpwhwvb/fiUfTFfqQ1nyMeRcvkfLeO3thosHqipbO+S+KWSDfZEkMkU00aVXMR+eK6Qm8jcAd6MS4M+dGTVgDz7bN48agfCYnfAyctE/PcZfRrIzh3HkS2tyCBwvc/s+3j3Qu4PZ0YV2/4PX+LK7y1RTHcWyaGlx6vYULCClB89nGCX/wmoc99BSQ4dx7Aa2/FfPssxue/Ap6k9Nh9eAOr+0RkS5zSg3cTeON9v49teg7n8MrJlCVRSpuew1UibxmRzSFDQX+l/hvvo09M4Q7WHmFnvncOHAf7/lMNHGVzWXKti1QW2Vb5u1CfmF6MTN+94qeMRnz35uQ0XkscMZdc9xiRTGN+eJnSvSfw2lv9zwNda7jjT5tP+t+/t0QNK+rDUufR7ez4q4YVG1SOP0VNeO2tmMPjK66TdgtiPoVx+RrOiSPISBjj7CW0TI78s48jEzFEJov14ht+8klbgsInnlo/rn2HIKVcdPw1XvgLt95J/+n/gdmh5xl9798QarmDnuN/FSldPCdfc7/fEtGuB0hNvM61V3+FcOsJPLdAtC7C3wAAydEXaBl4BsNqwbBaaNv3Ceauf5lY90NEO+7Z8vM0glj3w8xc+RMy0+9gWq04xXli3Q/XdKwQgu5jP4t0iw0epUKhUCiqoYQ/xe5GSkQmh3fLJGN66k0AYl0Pbun0uh7CKcxt6RwrWFz1b569hNvXhdffuMLm2x2RKyBDoZsbdqDoB+CUfOfaWjdHQuiYwXbs/Er3j3tonx9duo6Y58Wj/nslnUW27J7YLpHOIiOhVX87mYij7aVIwVonLEo22uw8ThNW/Lq9naAJAt99z3csex6ljzx0c9y6jrt/AOPiEDJgUrrv5M2Dd9lkzE7B6+1EnLm4oudPpDLIgAnBZatma/z9yliEwsefwDx7CfvknWVXn3NoEPODi74rerHDrxLOyTvQMlkCr77jT0TdEt0qwyFkOOQLfwcGNvbD7mFENo+MhJDxKF4sshjXVZvwJxZSZdFnLztmlxYzaKk0bhXhTxuf8l2mOzCuu1acw/v999mpO9FHJgi8/Na6C3IC330PGQnhnLrT3yAEXiyyDcJfCi8R21WLhXYTS/cWhqUcf7diRQf9eHspEer6QbEGXnsLwnEr9iH7O3j+f+DfS+yge0Hz7EWMS9cwzl/BOXEHxoeXsE8cKd+nlR67D2kayNaEX1Wxi94LnptHuqVt6fgDiLSdINx6nNzch0xd+Cw33vxnRDv9lKeNOv4ibSc4+Og/Z+76n5McexEr2o+1xZhPACPYjqYHkdKldd/3lLe3Hfw+dCtOvEYhrRmYwTbCrXeSnngNw2olEO5e4epbDyEEwtjmtBqFQqFQlFHCn2J3UywhbGeV4yk18Trh9pMYga2tVK634w/Avv8U2kIK61uvUfjk08i2lrqeX+Ejcnl/0mqH45b8Ff/rxaGYwQ7swkqxy+to9SdC10GWJ1UzuLtM+PMquBllSxz94tCuXOF7KyKdJfj81yl+9DG8Sh1ty9Cvj4Lr4R5sgqhimrg9XejD4zh3HKB03ykIr7yJcw7tw7g4hH3vCQha2z/GPYbb1eH3/E3O4Cx+lmmpjP9+3uTrXra3+oLtckwTe7lQWw0hKD18GpHJoY9MVOxsdLva0GbquFhmDyCyebxIyI9lHejB2IA7wXznw5Wiz14laCFNo3oktZToEzM4R3Z3x6HX21n+nF+6PhHJdNXrQH1k3I/f/egjKwQ4GY34jvgGIhaSqt+vgdiFOfRAXEWfVcCK7cO1M4sdiKoTSlEdb/GzU5tdwL3lnk+bnPHjzRfjtaWhU/jRT5TTYJrKYmefc+dBZMDEPHMBGTCx77mZ6oKuYz9yb/PGuAXcYm33tvVECEGk/ST7HvyHTJ77HZJjL2JYbZuaCzKsFrru/Ena9n8vsLo7cHPj04h03I0Z7FjxuaZpJq0DOz8ZJdb9CJPnfxehm7Tt+4RalKFQKBS7iJ2z7Emh2ATaYpfQ8ijAUm6SQupqXVZOaUYIt87CH5pG8emHkbEowa+9rPqQGoTIFXbGzd06OIvC33qrIs1Q5yrHX63IcAh0fdf1/Gnp7Ip+vyW8lhi4HiLT2InP7cC4egNRtAm8/s7NVcnV9h0axu3paJrzp/TE/eR/6OOUnnhglegH4PV0UPjep3COH2nC6PYggcWev/Gb73uRSjcl6vXmAATFjz5C/lMfq/j56nW2o0/Pr/tavp0Qy2Kn3YFeRCaHWKiho7RkYwyPYR8/svddV0Ig4zG0Kt9RIpVB5Au7ut/vVpaEv6p9tUt9T72dq3qdGu74kxJtPolU/X4Nwy7MYgaV268SS5F4xcyNJo9EseMJWshIGG12fsVmkUxjfeMVvLYExY88RPHJBxGuhz4y0aSBrkTMJRH5As7BAewH7yb/I5+g8MmPgrU3FgKU722tlm1/bt2M0HvXL9F19C/TfvBTWzqXYSXq+jP0nvzrdBz+obqdbzuJdt2H0AykWyLW/dD6BygUCoVix6CEP8WuZmniw4vcdAXNXf8KmhEmUoec9EY4/gAwTYrPPQ4CrK+/DLZd/+e4nZESkc/vDuGvmAShoa+zItEMdWDnNxlvKQRePFp1UnWnIjKZyo6/pRW+k7PbPKI6IyX61WG8jla0+RTGhavV980X0cemcA8Obt/4bkFGwsi1HCBC+P0jahVo3XB7Ov1uM+mvONaSmbKDt2noejkm9Fa8zjZwXcT8+t1ltwvLhT+vx4/M1Sem1j2uqQ7fJuDFI4h05e+ovdDvt4qghQxaaFVEYH14HC2ZofTgPas+U2Us4vd9yvo4EW7F+OAComj7rmNFQ3AKcxiq368ihtWGbkZUz5+iJrz2FrTZhZsbCkWsr7+MDAYofuxx3MP7cI/sx+1qQx8eb9o4l6OPjCNNA6/b/4yVscie6lN1ikvCX3MWjwghaOl/ikTf4015/r2IboSIdT9IuPUYgXBXs4ejUCgUig2ghD/FrkZkckhDL/cdZWc/IDX+Cp1Hfrwu8TmaEUJ6JaTnbPlctyLDIQrPPYFIZ7G+9bpySNSTQhE8iazgStppuKUkhhlDiLU/js1QB56Tw7U3t8pfxqO7y/FXLCGK9qoYXwDCQdzudoyru3tSSMwn0RZS2KeP+92f75z1X7sVMK6NAOCo7rTbCq+nA5HLo1+9ASUbUSg21/G3Dl57ix9POq3iPgH/b2Y7N126ho7Xklg5SVkF42pzHb7bjUzE0JKVv6O0iWn/tbWL+/0q4SViiGSq4mPG1WG89taKIruMRX3Xe75Q9zHpV4cJvHUG+57j68ZP72aklHhO/X9/taIcf9URQmBF91HM7O5rPMX24LW3oM3N+wshHJfgN15BlGyKzz2xwkHnDvSijU/5vapNxhiZwOvr2rNufqeURDNCaLqK/d9LdB/7Gfrv+dvNHoZCoVAoNogS/hS7GpHJIqMRvzvHzjF5/r8SbjtJvPexupxfNxZX6TfC9QfI1gTFZx5FH5sk8Oo7DVu9fbshcv5kzm5x/Ok1rIg0g/4E3GZdf7vN8bfUX1RR+APcg/vQxyarCmXbRmnzbl3j6jDSMnH7uindfwo8SeDdDyvuqw/dwO3rVt15txluXzfuYC/Wd94g+OVvASDjO3hVuGHgtbWg7UThr1ha/R0rJSKdQSTTiGS67u57kfWvHWTk5neR706Yr3aIT76APj6Fe2hfXcezk/FiUV/IuvVvICX6xPSeivlcQrbEKkd9lmz0kXGcQ5Ud3t5iBHa94z61yRmsF9/AObzP72rdw8xd/zJXXvp7LIy+gNzma28pPZyicvythRUbUI4/RU147S2Ioo3I5Ai8+Aba7ALFZx9fVRXg9vcgbAdtcpPpKfWiUESbnsMd6G3uOBqIW1xYt8JCsfsQQkdoRrOHoVAoFIoNooQ/xa5Gy+TK/X7Tl/8Iz8nTfexn6lY4rOmLvTwNEv4AvL5uio/fh3FxCG2iyTcjewQtvzjZugscf04pWdPNkRny42DswiZ7/uJRv09yB6x0rYVyjG+Fjj+46XxbcsI1A5HNEf5vf4Y2Ornxg6X0O/v2D/grfkNB7HuOY5y/uqr3U2Ry6JOzVSeBFXsYXaf47OMUnnvCF610bUc7/sCP+9Smd1gMb75A6A+/hHFxaMVm/fI1Qp/7c0J//Bf+f5/787o6o0VusYc4fIvwt5Ba87O47PC9pdttL+Mt9slZ33ljReSnSGcRucKedJ95Sy7HW4Sn9WJeZcy/7q2n8Od3Yr2M29VO6fH7d3Vks5SS2aEvkk9Wjs8upoeZG/oigXAvUxc+y8TZ39pW959bSiE9B1MJf1WxovuwCzO4tupBV6yNtxj/b73wOsa1EYpPPeTHjt+CbEsgw8Gm9/zpY5MgJW5/d1PH0UicYrJpMZ8KhUKhUChWooQ/xa5GZHIUQyUmzv02qfGX6bzjx+sanaMtOf7cxgl/AO7h/SAEWpXIJ8XGKDv+Qo0R/lw7Rym3fkdTTecqpWoS/nQzgmaEt+T4AxCp+joEGoVIZ5ABEwJVot1CFm5fF/pQ81aEi3QWpMQ8c3HDx2rTc4hMboWY5xw95Pd/Da0UM/WhYdA13P19Wx6zYnfiDfRQ+MHnyP/IJ1ZEV+1E3M42X8wolsC2Mc5dXiVmbzfm+asIx8U4c/GmyCIl5plLuH3dFL73KQqfeAppmVhfe6myk1hK9EtDkK/dZSxyqxeheO2t4Elf/KuCfnXYnxS8jRy+sr2F4lMPo83ME/rjrxJ45S3Mt84QePMDP9VhD/bNeYm434mZWfn+MIbWiXk1DGQoWHbGb5l8EetrLyFDQYrPPLoj4+eklDU7wLKzHzA79GdMX/r9VW4+6TlMnPttzHAPgw/8Mr0n/wbZ2Q8Y++A3tzS+2aHnufjNv1H+b/LCZ6vuW8yMAmBaKuqzGlbMvzYqZpq3uEuxO5DhkN+XOj1H6aF7cKstmBECd6C3+cLfyAReW2JPx3g7pSS6cvwpFAqFQrEjUMKfYtciPZexwre5lPxdsrNn6LzzJ4n3PlHX59AaHPV584k0ZCS0avJHsTlELo8MWqDV9yPOtXPMXv0zhl79/3LjzX+OlFt3z9Ua9Qm+62/Twl9LHABtfmFTx283Wjrrx3yu4TpwDg6iT8w0TVRYEpj1sUnE/MZEe/3qDWQoiLc8vi5g4g70YCwXM6XEuDiEs69vz/VbKTaIppUd7juZpZX2gbfPEvr8XxB47V2sr77kC4HNwHUxzl/xnYipTNmhq41P+R2bdx/D6+nE6+2k+NwTiJKN9Y1XVjnyzLfOYL30FoHX3qn5qbXs4nfRMiFlydlWredPZHLoU7M4B28/h697aJD8j3wP9t3H0ManMa7eQJudxzm8r/oikF2MTPixvWJ53Ge+gD62fsyrjEVWOCM3jeMS/MbLCNuh+OwTO3ZhwczVL3L9jf+tLJpVQ3oOM5c/h2G1Ukhdo5C8vOLxuet/TjE7Ss/xn0PTTGLdD9B++IfIJy9tus/bcwrMD3+daNf9dB/7GRK9T5Aaewm7sDrSV0qP2aE/JRjbTyB6+zh6N0og3I3QTIqZG80eimKnIwTOsUPY957AOXnHmru6Az1oyXR9Pjs3g5TooxN7OuYTFvvrrZZmD0OhUCgUCgVK+FPsYoqzV5jTLtLR9QwHH/3ntA48U7eIzyW2TfgDvGgEkdkdbqydjsjl697vV8yMMvTqrzB34y8Itx7Fc/KUsuNbOqeU0r85qnFVpBnqxM5vLuqToIUXi+zM7i1AHxrB+uarZTeOSGfxqvT7LeHu7wddW+WQ2y5EPg+6hgwFMc9dqv1AKTGGRny33y2fWc7BfWgz8+WJYH10Ai2VwTmx9mSGQrFTkPEo0jIxzl/B7emg8PEnEbk81rdebUrUsD40gigUKT7xAF57K+aH/nvV/PAyXmsCr+emk0zGohSffQx9dh7rqy8hFl15xoWrmB9c8IX5ayNo47V9DotcfkW/n//EBl5LrKrwp1+9cXs7fE0D+94TFH70E+R//JPkf/yTlJ58sNmjaggyGgZdX5H2UGvMqxeLbD3qU0qs73wXMZ+k+NzjVTt1m838+DvMXP0zgHWvuxZGX6CUm6T/7r9FINzD/PA3yo8VUteZu/Yl2vZ9gmD8QHl7MLoP6TmbvqZLjr+MdEt0HvkJEn1P0nHHjyM0k+Tot1ftm554nULqGh13/ARCqNvwagihY0X7Vc+foibse09in16/l9Tt6/KTNZrk+tNm5hCFEu5AT1Oef7twVMefQqFQKBQ7BnXHodi1FEffR6DTescPoOmNicPSt1H4k9Ew2k51/FWIotrJiFxh9WTrFlkY/gaaEeTgo79Kz/G/CkKjkLq2pXN6dgYp3Zp7EMxgB3Zh8z2QXmcb+k4U/oolAq++jX599KYbJ51BRteZhAyYuAO9GFebMzEk8kVkKIhz7BDG5Rs1O5rEfBJRKFa88XcHe5GmUXb9GWcv4XW0VuwrUSh2JEJQfPoRCt/3UUpPP4LX303xY4+hT84SeOXtVX1mDUVKzLOXcPt7kC1x7BNH0Ecn0W+MoQ+PY588skp89zrbKTz7BCKbI/QnXyPwwusEXn0H5/hhis8+jtfZRuD1d2v6OUS28iIUr60FbW5h9QGeh3n+qt9hqhy+ex8h8BJRtIWbjr9aY15lLLqxqE8pVzoLAfON99Gvj1J66mG8ju3/jrHzM+QW1l40U8pOcuWNf0e08x50M0IpXz1m3bWzzF37Iom+J7Big7QMPktm5l3s/DROKc3Ymd/Eig3SdvD7VhxnRQcAQTGz8WsJKT0WRr5JtOt+zGAr4N87JPqeIDn2HTz3ZjSw5xaZufoFol33E25Ri3nWw4rtJ79wCSm9Zg9FsVcwTdzuTvThJgl/49NI09jT1/SeU8Bzi8rxp1AoFArFDkEJf4pdS376HGGtCxKNW1EmNAOhmbjbIvxFdqy4Zpy/QvDPvr69E7ZbwHf81a/fz3XypKa+S6LvSQwrgWYECYR7tiz8OaUkQM09CGaoE7swu+mIUa+z3XeZNMF1sxbmux+C5+G1xHw3juf5E+aLvYRr4RwYQJudb0rc55Kz1D56qBzJWQv6xAxoGl5X++oHDR13Xx/61WHEfAp9bAr7+GpxQqHYyXh93Ste315PJ8XH7sW4fL2y4NUgtMkZtLkF7BNHAHAPDiCDFoEXXkcGA1XjFL2+Lgo//HFK959CHx7HHeih9PBpEILSw6fR5pM1vd9FNl+xx8drb0XMLaz6TtVvjCGyuXXjyhR7B68lXhbktJk5P+Z1nZhPWHT85Qvg1BZPaVwcIvTHf4H1jVcQqQzGucuYZy9Revg07r7muEsnz/8Xxt77t7hO5e9vKV1G3vt3mMEW+k79NcxwD3Zusur55q59Gem5tB/6QQDiPQ+jGyHmh7/OxNn/iPRsek/9Ipq2UlT3r+m6KGxC+MvOvIedn6Z18NkV21sGnvGvHSdeK2+bv/FVXDtL5+Ef3fDz3I7Eex7FLsyQnf2g2UNR7CHcwV708ammxH3qE9N4XR11r6LYSTilBaD2e1uFQqFQKBSNZe9edSj2NFJK8pkhQrFDDZ8Q14zQNkV9hv1JnB0mygBoc0lEsdS8jqYNInIFZKh+jr/05OtIzyHe81h5WzB+kEL62pbOuyT8GYF4TfuboQ6QHk6F3pha8DrbwPO2deJ9PcR8CvPcFey7j2Hfdcx344xOgpR46zn+oCwuVIvNayQiX0CGghAK4hwaxPzwck0TCfrENG5nKxhGxcfdQ4NoyTSBV99ChoK4BwfqPXSFYttZ6rQR2cZ/ny5hnLuMl4ji9Xf7G3Qd5/hhhOPiHD20ontvFbqOc9dR8n/p+yl+7LHytYbX2YZzZD/mW2egZK/5/CKXw6vgPvfaWhCOu8qBZXx4Cbe7A6+9dWM/qGLXIhMxP+pTSgKvv4fXmqjpM38pllOka1v0oo1N+skSs/OEvvBVAq+/h33yDpxFUXy7KWZGyM2fw3MLpMZeqrhPPnmVYnaMQ/f/AroRIhDqorSG8JeZfod472PlaypNt0j0PcXCyLfILVyk9+RnMIOVnTaB6MCmYiXnh79OKHFkRXQo+Ndr0Y7TLAx/HaeUYvry55m7/hVaBz/mX8sp1iWUOEQwfoiF4a83eyiKPYRz50F/AdAb72/vE3se2uQMbs/efv87xcV72xrTbBQKhUKhUDQWJfwpdiV2agzXyRDsWj/Pf6voRgjP3R7HH7AjXX/aYs/Rdk7YbhrP8wWZOjn+pJQkR18k0n5XOcYJIBg/QDEzguduXgx1F2+O9A1EfQKb7vnz2hKga2hTOyTuU0oC330XLxrGOXlH2Y1jLt6My/j6wp+MhJBWoEnCX7H8OrPvPgZA6I+/6gsCdhVBQEq0yWm8ns6q53X7upFWAH1yFufYOuKEQrFbsAIAiEJxnR3rhJToo5M4h/evWCBkHzuMc3DAd9LWgmmsWmBk33cSUSyhD49VP85xEUW7ctRnewuwcsGCNjvvv+ebJMQomoOXiCMKJYxzl9GmZik9dE9NbpAl4U+rxbUiJfrENM6hfeR/5HuwTx/HPnEE+8G7tzr8TTM//A0Mq5VY90PMj3yzYpJBbvYMuhkl2ua/J8xwN3a+svBnF+axCzOEWu5csb1l4Gn0QIyuO/4S4dY7Kx4Lfs9fMTOM3ECyRSF1nfzCJVpucfst0brvOUq5SYZe/gckR1+gdfDjtB38VM3nV0DrvmfJzV9QXX+K+mEalB68C/36GNpY9YUE9UabnUc47prX/3sBp7gAoDr+FAqFQqHYISjhT7ErKd54GyQE9p1u+HNtl+NPRv04MJHZQGfLdrCsF0ZrQpziRhH5AkDFydbNUExfp5gZJtH35IrtwdgBkN6mOmGWcEpJdDOyKnaqGmawDYSGnd9kz5+u47W3os3sDOFPG530oywfuscXtxbdOFoyDUJUjMhbhRDV+7IajMjn8UK+8CcTMX9C9a6jmGcvEvzyC5WPWUghCiXctW78NQ33wABomh8jqlDsBTQNGQzA4md0oxHpLMJ28Dpucc8FLUpPPwKhzS8OkZEwXnsL+kj1niCRyy/uW+G7yAqU3VdLGGcvIaPhpsUuKpqDl4gBEHjjA9z9/Xh9XTUdJ0NB0DVEDT1/YiHtf+/0doJhYN9z3P/ebVKEtFNMkp58nZaBZ2jb9z04hTkyU++s2i87e4ZI+0mE8G9XA+EuXDuLa68WOwvJywCEEodXbDesFg49/r/TMvDRNcdkxQbxnDzOBnqUs7MfoJsRop2nKz4ejB+ipf9pWvd9Dwcf+1U6Dv9Qzdd7Cp9o572YwXbmletPUUfcg4O4Xe0EvvseeNvTIamNTyMNffU1yR7DLSXR9CCaUb/KDYVCoVAoFJtHCX+KXUlh8kOCog2trfGr5jQ9tD0df5EQCIG2wxx/Il9A2H6HTDN61DbKkvDn1cnxlxz7DobVSqT91IrtVrQfoZlb6vlzSqkNdSAIzcC02rALm3P8AbidbehTs5s+vp6YZy/htbfiDvaWt9lHD/kCQSRUcweG196y/Y4/10UUSiudpaaBfd9Jik88iDa3UJ74X44+MQ2awOus0O+3jNJ9Jyl88qktiRMKxU5DBoPb5vhbEtW8tsZMsrkDPeijEyt7+oolxOJn0ZJDvtoiFK/95oIFkc1hDA1jHzu8p7t/FKuRiagvwAkobcSBJwReLFpzvDRi/e+d7SI5+gIIjUTfE1ixQcKtR1cJO05xgWJmmGjHzWuvQMiP7C3lpladM5+8jBnqqhgvtyQcroUVHQSgsAFnWSF9HSu2v+r5hRB0Hf1pOg7/ELq5fmexYjVC6LQMfJT05HfLTiKFYssIgf3wabT5FMaFq9vylPrEzJ7v9wN/UauK+VQoFAqFYuewt688FHuWfPoqodjBbVmtvF2OvyWxY6dFfYqFxQ4iTdtxY6vEepOtG8Fzi6Qn3yDR98SqiR2hGVjRQQqpoU2f3y0mNxyFYoY61nX8rRVV5XW0+X/HbXLdVEMspNDHJrFPHFn5Pg4FsY8dWtsRdwtee6svSm9XhCA34wordUl63Yu9g9OrnZXaxDRuR5sfH7gWQWvHTNIqFPVChqzy4oxGo80u+N8DIash53cHehFFe8X7PPDK24Se/wba6GR5oUxFxx/+55Y2u4Dx4WWCf/o1ZMDEufNgQ8aq2MHoOm5XO6V7jpfjO2vFa2/BGBpGpNYW/7SJab/jd73vnW3A82wWxl4g0fs4uun/vC2Dz1FIXSWfvFLeLzv3ISCItJ8sbzND/nWBXaHnL79wmVDL5mNyDSuBHohvKMWhmL5BMLZv08+pqI143xMIzWRhtHKSgkKxGbyOVpxDgxjnt0H48zy0qRnfdb3HsfOzGFblLlWFQqFQKBTbz20j/L3xxhv84i/+Ik888QRHjx7l619fPzLk9ddf54d/+Ic5deoUzz33HH/8x3+8ap/PfvazPPPMM9x11138+I//OO+/v81F0bchTnaWkjNPqPP4tjyfboTxnO0RvLxoeMdFfWrJNGgCt6N1dzj+cgVfSApufbLXzk/juUXCbZW7JIPxA1t0/G18VaQZ6sBeI4rKcwoMv/VrTF74bOXHu/ybMb2CKLWdmB9eRgYt3IMDqx6zHz5N6ckHaz5XuS9rG+M+y5GyFUQFGQkjwyG0W52VUvorfvd4v4dCUQ0ZtLbR8bdQ/mxoBF5nm9/FOTwO+P28xvVRZNDC+tar6KOTSMsEo7LY4rW1IEo2gdffxd0/QP6HPl7uQVTcXhQ/+TTOPRu/pi09dA8yEMD62kvlhS/azByBF15HzPsdwkv9fhtZTNMo8smrjL33/8MtZWgZ+Fh5e6T9FIFwN3PXvlReuJSbPUMwfgAjECvvpxlBDKuV0i3Cn+vkKGZHCSW21o8ZjO2rWfhzSimc4jxWbP+WnlOxProRJtJ+F/n5C80eimKP4fV2+fe5jtPQ59HmFvzo8R3wOdxoStkxApHe9XdUKBQKhUKxLdw2wl8ul+Po0aP8k3/yT2raf3h4mM985jM8/PDD/Omf/ik/93M/xz/6R/+IF198sbzPl7/8ZX7t136Nv/k3/yZf+MIXOHbsGH/tr/01Zmd3RozeXqV4/V2QksC+e7fl+YxgK3Zhe0QSGQnvuKhPLZnCi0eRsUjZTbeTEbm8H79YBzfoUo9MtYimYPwAdn4K196cWOsUkxuK+gQwg53Y+cpRn1JKJs7/DsX0MMnRF8jMrF6IICNhZNC66VKRctv6LcoUS+hXruMcP+x3+20RGY8iTWNb4z5Fbkn4q+Lm6Wxb1aXo9ywVd8QErELRDLZN+JMSbW4Br72BXTpC4Pb3lHv+jHOXkaZB/geeRcaiGFdvIMPVe0rd3i7sU3eS/4GPUXr8fhXrq9g4QYvix59AlGysb7xC4KU3CT7/TYyhEQKvvF3uaPa/dzqaNkzXyTH63r9l+K1fxykl6bv7vyMQvtllKIRGx5EfIzt7hvTEa0jpkp37cFXEOvg9f3Z+ZdRnITkE0tuS4w/Aig5QrDHqs5i+DkBQCX/bghXbRzEzgpTbfL2q2NN47S2L1wvJhj7P7dLvJz2HUn6KQER1FSsUCoVCsVO4bYS/p556ir/7d/8uzz33XE37//7v/z4DAwP88i//MocPH+bTn/403/M938Nv//Zvl/f5z//5P/MTP/ET/OiP/ihHjhzhf/lf/heCwSCf//znG/RTKAAKE2cwtRhGx2qnUCMwQ524pRSe2/jJShmL7DjHn1hIIxNxX5TcDY6/fKEuMZ8AbmlJ+ItVfDwY92PZCulrK7ZL6THy7m+QnT27zvmTGIH4hsZkhjpw7SyuvfpvMX/jq2Sm3qLn5C8QaT/F1IX/uno/IfC62tGm59AmZwg+/02CX/jqhsawVYyLQwhP+n1+9UAIvNZEudNrOxD5RWdplRhBt7MNbWZ+haiqT06Xf/8KxW1JKIjIN/67VOTyiEKxoY4/8Hv+tLkFRCqDcWkI544DEA5SfO5xZCS0dnSjoWM/eDeykeKkYsPYhVluvPmrOMXGTgTXCxmLUvzYY+iz8+g3xig9ei+F5x5Hn5pFHxq52e/XxO+dhZFvk5s/T+/JX2D/Q/8z0Y57Vu0T7biHeM8jTF36AzLT7+A5OcJtJ1ftZ4a6Vzn+8snL6IEY5mIH4GaxovtwivM4pTRuKc3Iu7/B8Nv/B7NX/4zc/MUV+xZS19HNCEZQfZ9vB1ZsAM8trBt1r1BsBK8lDkI0PDFEn5j2P4P3eL9fKTcJ0lOOP4VCoVAodhDNL3vYobz77rs8+uijK7Y98cQT/Oqv/ioApVKJs2fP8pnPfKb8uKZpPPbYY7zzzjsbei5NE2ha47vq9gqF1FXCkQMY5tadQrUQjHYhhEDacxhWf0OfS8SjaPkChpB1cULViq5rK/53xWPpNO4dBxCRsD82jR1946IXCshoCMPY+hill0HTDALBCKKCg1CPdaObYezMdYyuu8rbs7Pnyc+fwwp3kui+a9Vx4EdySq9EINy6obGG4n0IIfCKk1ihw8ue80Nmh75Ax6Hvo7XvAaJth7n66v/M7NXP0Xfy51eepLsd460zGF/5NtIKIIolDOltT/+P5xG4cAXvyD6MWHU3TCXWep2Kzlb0kUm8OvzdaxpLsQghq+rnkOjtQHvLxUxnkIvigzE5g+xqwwipOL+9zFqv09sdEQmhFUsYumhoR6+2kEIIEF1tdfkuqMr+XoQmCL70BprtIO+603++eAT7h/2FZg19/i2gXqeVSc2+SzF9nWL6EsHIQ80eTm30dVL6sU/4UbFWAA3wDvZjvfU+sr118XunMV2X6yE9h9TYt2npe5TW/ofX3Lf3+E9x5ZUPmTz3nzECUaJth1a9ToOxHtJTr6PronxdVkxdJtJ6B+YW7wsiLfsRQlBMXWB26Cs4pSThxGGSY99i7vqX6LvrF0j0+K+JUvYGofj+LT+nojYiCf9v4+RHCMd7mj2cVajP012KoSHbEhjzSWjUd7XnoU/N4t5ztOnXA41+neYKEwghiMT70XfotY9i56M+TxW7AfU6VewmlPBXhZmZGTo6VsbidHR0kMlkKBQKJJNJXNelvX3lSs/29nauXt1YSXRbW2VRQVGZ6WgXnceepbV1jZX0dSQS3MeIrmHpmYY/p9ffjq1rtOggtunnW048vtIpJ0s2pUKRYH8nIhTE1gQtAQ2xlouhyZTsElp3D0Ydfn/ZcZtgOEFbW+WoT4CWziN4hRsrXhtzV95G1zW8wnDV10whnULXNdo6eohvYKxe/BDDhoEh5mhtvbu8ffrci8TbD3PnAz+NEBoQgft+lqG3fgv96EeJd97sKfTuPoIzOoF+7zFEIor9h39BQpdo2/Ca80ansPMFzIdObfr5bn2dArj7e3AuDhGOBBABc6vDXBdHungtUSJVfgYZtSgZOrFcFv1IP7JYojQxhX7/CaJ1/j27dp6ht/9f9t/zM5jBjUXHKhpHpdfp7Y7XmcDWBS2Wjog07vfjnM/iRUOEBjoafH0VwR7oxhubQrtzP+F9N+MLacJ3+GZQr9OVTJ69gK5rCHt0264z68ItY5XPPUrpd/4URifQH7yr7t871ZDSW7wG8Zm58RLSSXPgrk8Rjq83hgj6Q3+Di6/+n7T3n6at7WbaQvl1mt/P7BWHaKhEINSG59rY2Rt0n/yxLf+9ZMsBhq0wU+d/G8OMcNcz/5hwfAApPc5/51cpzLzGgeMfBeBafpT2wUd312tkVxNhJNqO5k7u6N+5+jzdfTgDXXgz88Qa9LryhiewPZfQsQPbcp9VC416nWbHZwlGWujo3pr7WqEA9Xmq2B2o16liN6CEvx3A3FxWOf42QOcn/jEA8/PbE4kppYknNeambkDoaIOfTMdyPZKj00i21/EXj4dIpfK47s1oQjE9R8D1yBsBkIKA65EcmUFuYrGtduka2o1xnI89uv7OWyCwkMEd1HDr8PpIL8zgidCar7VA4i4mLvwekyNXCUS68dwi09dfQw+0kpq7xuzMPJq+2uGVmx/HdT1yxcCGx6oF2pibGMJMPFjelpwZIt79AAsLN3sYjfgDCPOPGL/6Bq6xrIdGM+H7/Mkrsjks16MwOoOnNV4w0y9eR9d0csEQbPDnrvY6BRDBMAHHJXllFLkNHXrmbAoMk+waP4OZiFO6Oooz2I/+wUWMQonc4MCGf+71SE+/x9T1VzATdxPvvr+u51ZsnLVep7c7wpH+98jEPLKtcb8bY3gSEY+TXWh8PLXe3YExPEH+yEHkNl2X1AP1Ol2N5xaZnzgLUjA3cYH44O75e65GQz9+BOO9cxRaEnjb8NqUnsPQa/8bwfh+ek/8HAiNG2e/SLDlOEW3lWItYwgdo+OOv0S45Q7m57OrXqdFL4HrekyNXSPSZpFPXsUuFZDmvrrcFxjhAYrZcfru+bsrxhxsf5DxD3+XqfERhNDJZ2aQRu+23YsoQA/2MTdxmWjfzvudq8/T3YseiWCcuUx2Nt2QRBvj3QtooRA5a+P3PfWm0a/T+akhNKtHfS4qtoT6PFXsBtTrVLETqHUxnBL+qtDR0cHMzMoegZmZGaLRKMFgEE3T0HWd2dnZFfvMzs6ucgquh+dJPE9uecyKxmEEOylkp3GcBn+oWxYBBDKZxene/i8Q1/VW/Iz6bBIpwY5GQIIpwUtlcDvaNnRebXSCwAtvgJQ4TzzQuBhT2yZQKOEEQ7h1+FvZxTSaEV3z7x7pfgz96peYuvI8PSf+KqnJd3CdAl3HfpbxM/+B7MJ1QonDq44r5haQUoIe2/Drygj1kE+Plo9z7Ryl/AxGuH/VuQLh/hX7riLgv+a8hQxOb+Nfc/roFG5XO44rgc197t36OgUgFsUUGnJqHqej8Z07RjaP15pY82+ndbShjU/h2C7mmYvYBwZwLAvq/DmSnbuClJJCZpJwu7rw3ClUfJ3e5ggzgCnBzeTw4pW7U+uBOT2Pc3BwW37/zpGDuGYAp6Ot7u/t7UC9Tm+SmTmH59ok+j5CauI1bNtGiN0b5ejcdRTXsnC6Orbltbkw+hKFzBiF7Diu65DofYJ86jr99/ydDb3G4r1PA6w4Zul1qpltSAT59DhW/A7SM+dAmBih1dc/m6Hr2F9FaDp6IL7ifKH20yA+y/zoa1jRfqSUGOHt+YxR+JjhAZLjL+/o37n6PN19eC0JdNfDnV4oR/PXDdfFvDqMfeehLd331JtGvU4LmTHCrcfUe0BRF9TnqWI3oF6nit2ACqStwunTp3nttddWbHvllVc4ffo0AIFAgJMnT/Lqq6+WH/c8j1dffZV77713O4eq2AbMUAd2frrxT6RpyHAQkd4ZK+W0ZBoZDoFpQsBEBkxENr/+gcsQc0msb73mnwcQuUIjhuqfO+OPTdYpitS10+jm2pPTmmbSuv8TpCa/Syk3SXriNYKJw0Q7TiM0k0LqWsXjSrkJNN1C04MbHpcV6aOUHS//u5gd9bdHV3dQBqL95ccrIgReNIzIbMNrzvPQpmZxG+HI0zS81gTa7Hz9z10BkS8gQ2v/7dzONrRkGv3ydUQmh3PijoaMpZDy46W35TNKodgCcrFnTBSKjXuSQhGRzeO1tzbuOZYTtHCOHmpoZ6Fie8jNncUMdhDreRjplShlxpo9pK1hGDgnjmxLL7P0HOauf5lY1wP0nvzrpKfeZOyDf0cg0ke47cT6J6gRoRmYwXbs/CTF7Dhz175CrOt+hFaftaxmsBUjEF+1XTfCRDvuIT35OoX0dTQjjBlqfLqA4iZWbB9uKYlTTDZ7KIo9hNfmR+Rrc/W/f9DHphBFG+fQYN3PvdOQnoOdmyIQ6Wv2UBQKhUKhUCzjthH+stks586d49y5cwCMjIxw7tw5xsb8m/p/9a/+FX//7//98v4/+ZM/yfDwMP/yX/5Lrly5wmc/+1m+8pWv8PM///Plff7KX/kr/OEf/iFf+MIXuHLlCv/0n/5T8vk8P/IjP7KtP5ui8ZjBDuzCzPo71gEvGkFkGh9PVgtaMoWXuCl8yUgYLbuBsRWKBL/2EjIWpfjRhwEQuY0JhxtBWxSvZDRcl/M5pQx6oHq/3xKJvicxzBjTl/6Q7NyHxHseQWgGVnSQQmpo1f52fpq5G39BvPexTfVPBSK9OMU5XMf/XZYyfvRUILw6g9WK9OEU5nCd6n83GQ0jNvJ33STazDzCcfF6GzNZ5rW3oM0uNOTcK5DSF/7Cawt/XqfvjA288R5eV3v53/UdilcWl5Xwp9jxGAboOiLfOOFv6TPAq/fKfcWeRkpJdvYM4faTBGP7QGhVF+4oVpMcfwWnME/bwe/zxb8Tfx3PK9G273vq3rMZCHdTSF1n/IN/hxFso+vOn6rr+asR63mEYmaE9MTrBGP7VD/7NmNFBwAoZoabPJKNU8yMkps71+xhKCphmniJaEPuH/Srw3gtcWTr3u/fLuWnkNIlEO5t9lAUCoVCoVAs47aJ+jxz5gw/+7M/W/73r/3arwHwwz/8w/z6r/8609PTjI/fdNAMDg7y7//9v+fXfu3X+N3f/V16enr4Z//sn/Hkk0+W9/nkJz/J3Nwc/+bf/Bump6c5fvw4v/Vbv7XhqE/FzscMdWKPvYiUHkI0Vi+X0fCOcfyJZBq3t6v8bxkJbUiUNG6MIfIFCt//UaTp98dt1DG4EUQmB0KU3YVbpRbHH9x0/U1f+gOE0Il1+T1rwfgBsrNnVuwrpcfEud/FMON0HPrhTY1raTVlKTtOKHGIYmaEQKSv4op3K9K/bN/VkaMAMhpBm2/8CmptYhppGnhtLQ05v9fZhnHpGhSKELQa8hwAFEvgyXUdfzIeRVomomhTOn6kIUMpZUbx3ALBxGEl/Cl2PkIgQ1ZDHX/a3ALSNJDx9RdtKBRL2Pkp7Pw0kfZTaLqFFemjkB4iwZPrH3wbIqUE6SI0A+k5zF/3nXfW4vVJrPsBwu0n0I36LMRajhnuZmH4G2hGiH0P/AqasfHkhM0QaTuBbkYp5SaIdNyzLc+puIkZ6kAzQhQzw0TaTzV7ODUjpcfEh7+F5xQ4+NivNXs4igp4bQ1YOOg46DdGce4+dlskApSy/mJ6K6ocfwqFQqFQ7CRuG+Hv4Ycf5sKFC1Uf//Vf//WKx/zJn/zJmuf99Kc/zac//emtDk+xwzFDHUjPxi2lMKyWhj6XjEbQx3fABL7noSUzOMduChYyEkabnl3joJVo03P+SsdIGKREmkZDHX8ik8WLhutygyWlh2tn0M3aJo8TfU8yf/3PCSYOlY8Jxg+wMPJNXDuLbvrxo8nR75BfuMDA6b+36cmqQLgbECuEv0oxnwBmpAeERjEzuobwF0YMj1d8rJ7oE9N4XR0Nix1zB3pASvSxSdxD+xryHODHfALrC8xC4HW2o80lcQ9U/vtslXxqaFFsfoDpy3+E9Jy6RZ4pFI1ABq3ye+hWAq+9g3Hxpku6dP9dOCc3FpGrzczjtSZui4k2Rf3Izp5BaAbh1mOA//1dSF1v8qh2LnPXvsTctS8RjB9ED8SxC3P03f23VuzTCNEPWHSUCHpO/LXF66HtQWgGse4HWRj5lu8KVWwrQmhY0QGK6dodf05xAdfOlN2CzSAz/S7FjB+5v/x+QLFz8NpbMYfHQcqV1w7FEsEvfpPS4/fjbbCmQB8eRzguzsG9H/MJ/gJTPRCv+b5ZoVAoFArF9nDbRH0qFFthqcdjOxw1MhpG5PLoV4fRh1b+JxZSDX/+JUQ6C1Litdx0vHkbjITUpuduxhsuOvHWEv7E7AI4zmaHjMjk6hbz6Tl5kF5NUZ8Amh5g8P5/QPfRmwsBgvGDABTS1wCw8zPMXPk8if6nCLcd2/TYNN3CDHVQyo0hpUcxO0qgyqSGppkEQl2U1uj586IR34Gzhd/9unge2uQMboNiPsEX4ry2BPrIxIrtIpmGKkLDZljqqVzP8QdQeugeCs8+3jCxs5C8ghUd8J2d0sMu1C7MKxTNQIaCFR1/2sQ0xrkr2EcPUXrwbtyudozzV/yJuJpPLtHHJxsWJ6zYu+TmzhJK3IGm+25xK3aAYnYUz63NnSqlx8yVP8EuzDVymDuG7OwZrOgAupUgn7xEvPfRqguQ6k2891H2P/SPiXbcvS3Pt5xE35PogQTBRGNc/Iq1saKDG4r6nLr0Bwy/9S+x8825NpLSY+7aFzFDfnrKrWOfu/HVDQmZisbgtbcgHBeRyqzYrk/PoqUyBF59Bzzv5gO2g5hf+57cuDqM19F626QPFLNjKuZToVAoFIodiBL+FIoaMIPtgC/cNBqvrQWEwHrhdaxvr/5vu9CSaYAVNywyEkIUbbBrEIhsG20+idt1s9dMhkPVhUMpCX75W5jvbr4DQ8tkkdH6rKR1bf/mr5aozyXMUAd6ILbs351oRphC6jpSekye/x00M0Ln4R/d8vgCkT5K2XHs/DTSLa25mjkQ7aeYGav6+JJY2shuyXK/X09jo5DdgV5f+FsSC1yX4Je/TeC1d+r2HDcdf+sLfzIRQzawa6yQGiKYOLStixMUiq0ggxWiPqUk8Pq7eB2t2A/dg3P8CPZdR9FSmQ3Fb2nTc4iijTugJp8UteO5RXLzF4i0nyxvC8UPgvQopm/UdI5C6hpz179MZurNRg2zKdj5Ga69/k+xC/PlbZ5bpJi+Trz3cfpOfYbDT/wreo7//LaNSdPMpjm4rOgAh5/43zGDrU15/tsdKzpIKTeF56y/mMvzbHKzZ/HcApMX/osfT7vNZGfeo5gZofvYzyC0wAqRz7VzzFz+PHM3vrrt41KsZKmC4NbrDW1qDmnoaAspjAtX/Y2ui/W1Fwk9/3U/+r8C+o0x9OFxnCP7GzjqnUUpO65iPhUKhUKh2IEo4U+hqAFNt9ADCezCNgh/Ha3kPv1D5D79gyv+Kz16r9/DVrLXP0muQODVt8F1Nz0OkcmBrq2IM5SRRYGoBtefNu1PEnkd7cuOD6FV6fgTmRzCcTGGhlc4PEQ25/8sy1darjHmejn+3JIvfC4X8jaKEBrB2H4KqSGSYy+Sm79Az7Gfq0sfjRXppZgdK8cHrTUJZkX6KWVHq056LImlDRX+lvr92hs7WeYO9CCKJbRp33WhXx1GFIoYw+O1vXdqQOQLyIAJul6X820W185Syk0QjB/CCLYihE4pP9XUMSkU61Ep6tO4OIQ2l6T0yOlyzJbX24UMWuhXaxNewI/WklbgptNcoaiB7OwZpGcT6Thd3haI9CK0AIV0bXGf2Zn3AChmRhoxxKaRX7hEKTtW/vmAxcVMLqEW5XpTbC9+xKqs6X2Wn7+A5xboOPJj5OY+JDX+UuMHuAwpPWaHvki49Rjh1qNY0f4Vjr9C6iogyc5+gPQamLihWJ+g5ddZzM6v2KxNz+H1duHceRDznbNQKBJ48Q30mXnwJMb11Wkq2swcgRdex93Xh3OscsXCXkN6DnZuUjn+FAqFQqHYgSjhT6GoETPUsX1uGkMH01zxn7vYLaDNrB8jZVy5jnH+Kvr45kUAkcniRVb25W1I+JuZQ5oGcllUqO/4qyL8LcariEyuLNoAmB9cwDh/dd1IFRwHUSji1c3xtyj8bbGrIBg/SCF5mZnLnyfR9xHCbcfrMTwCkT6cwhyF5GX0QBwjEF9zX9fO4JYq/w5lOAhCoGWydRlbGc+DQhEKRfTxqYb2+5WfsqsdaZll15/54WVfbHQ99BvVXY8bQeQLNcV8Nhp/0sh/jQmhYYY6leNPseORIcv/XFiiWMJ8+wzO4X14nTcXiqBpuAcHMIZGqsd93rIgRB+ZwO3vUf1+ig2RmXoLK7aPQLirvE1oBlZskEJyaI0jl51jURgr1OgQ3C0sCSzZuTPlbfnkZTQjRCCi3B2K7SUQ6UUInUJq/fdlZuY9zGAHrYPPEe99nOlLf7Stcei+22+YtgPfD/huxcIyx19+4TJC6HhOjnzy8raNS1EZt6sNfXzZNbSUaDN+ZUXpvpPgSULPfwNjaITiRx7C7elctTBJpLNYX3sZ2Zqg+JGHbptrkVJ+Cild9Z2gUCgUCsUORAl/CkWNBEKd2xL1WQ2ZiCED5gpRrBr6qN9xdmvX2UbQKrjnZMR3/1Vz7a0Yw9QsXkfbLcJhyHd6VJjE1VKZxR7AIMbSjVTJRr98/ebja7DkVquf428p6nNrQmIwfgDXzqIZYTqObD3ic4mlVZXpqTfXjbxa6t0pZasIX5rm/23q6fhzHILPf4Pw7z1P+PeeRx+bami/XxkhcPt70Ecm0CZn0OYWKN13Ere7A+NqfXpURC5fU8xno8knr6IHYuWYTyX8KXYDMhhEOG45Mto8fwUcF/uBu1bt6xwcROTyaJOrv3v1G2OEfu95xOKCBZHLo80t4A70NPYHUOwpPLdIZvZ9Yp33r3osFD9Y7uhdi1JuilJ2jEj7KUq5CTy3cvzbbsQX/gS5+fN4nu+aLyQvE0ocRgh1G6nYXoRmEO28l/nhr63o35SeQ27uHFL6i0GklGRn3iPaeRohBJ1HfhzNCDI79Py2jFNKuej2O0q49U4ArNg+Srnx8udDPnmZSMc9GFZreeGAonm4A72+42+xx1sk04iSjdvZBqEg9ukTiEzO7yA+MIB7aBB9fPpmd72UWN96FWkaFD72mL+I9zZh6f4yEFGOP4VCoVAodhrqjk2hqBEzuI2Ov0oIgdfRir6e8Fey0SdmkFYAfXi8ulNivafL5Fb35WkaMhxc3/EnpR+PckvcmoyEQMpVMW8AIp3Bi0VwDg6iD42A52FcGkK4LtLQEan0uuMF6trxp5sRhNjajVswcRjDaqXn+M+hG6H1D6iRQKQHEDjFhbKwVw0z1InQTIrZ1ZE0S3ixSHkCvSpSol8bqSl21XzzA7RkmuKTD1J85lGKH3sM5/j2RN64/T1os/OYb5/FS8Tw+rv9G/SxScgX1z/BOuwYx1/yKsH4IcSiuK6EP8VuQIYsgHLPnzY6idffsyJWegmvqx0ZCVcU7fVrI4iSjfnmB/6/Fxe6uP3djRq6Yg+SnT2LdEtEu1YLf8H4Qez8NE4Vt3z5HDPvITST1n0fB+lVX2SzCylmR4l2nka6JQoLl5HSI5+8QjChYj4VzaHj8I/g2hnmr/9FedvUxd9n5N3/k+TYdwAopm/gFBeIdNwNgG6GifU84r/ft6Hr76bb71PlbVZ0oPz5ID2HQmqIUMsRIh13k51+rykdhIqbuP3+oqGlxbNLC229Dv9e1jl5B/kfeBbn5B3+v/f3gyb8e1YWI8tnFyh95CHYAfcI20Vu7hxTF3+PQKQXYwv1GAqFQqFQKBqDEv4UihoxQx04peSKFabbjdfZ7t+IrHFzqI9NgpTY9570e/OSawtm1RCZLF4F95yMhNd1holMzo/d7Fop/HnhpajQ1Y5BLZlGxqO4hwYRhSLa2BTmh5dxDgwiW+LrOv60TLbsGKwHrp1GN7d+A2MEYhx6/F/ULeJzCU23MIN+LJ4VWdvxJ4RGINJb7gOshIyE0db5u2pzC1jfeq18U1x1v9EJzHNXKD1wF+6R/bj7+3H39YFhrHlcvSjfvE/O4Jw4AkLg7Pd/R8b1rfcviVzzhT8pJYX0NYLxg+Vt5qIreWnFu0KxE5HBJeGvAI6LPj2H29NReWchcA4OrF5wICX66AReSxxjaARtYhp9ZAKvqx0Wz69Q1EJm6i2s6OCKmM8lQi3+BG9+/uLa55h5j3DrMf/zWGgU0/Vxlzcbp5jELaWIdT+MYbWSnT1DKTOK5+QJKeFP0STMUAetg88yd+Or2IU5Fka/Q3LsO1ixfcxc/jx2fobMzLtoRnjF6zTSehy3lKSUG2/o+KSUzF77IqGWm24/WEzfWPx8KKRvID2bUOII0Y57sAszlNZYnKfYBkIWXmdbeRGRPj2L1xKHgOk/LgSyveVmko0VwO3v8bvpiyXMt8/6keVd7ZXPv8fwOyyfZ+Td38CKDjJw7//Y7CEpFAqFQqGogBL+FIoaMRbj9Nbqh/CcAvPD32hYSbvX1YYoFNd0Zukj/mSoc8d+0LXNxX3aNqJYquie86LhdR1/S6sk3Y6VNz9LUaGVjhepjO/Oam/Fi0UIvPYOIpPDOXkELx4rdwAuoV+6hvnuhzePz+T889epQ853/G2t36/RBKJ+l8J6UZ8AVqR/TReCjEbWFXS12QUARHINEbZYwnrpTdzeLpzjTZoYXLx5lwET5/D+8ja3rxt9i3Gf2uQMWjbffOHPs/GcPGbwprhuhjqRno1TTDZxZArF2sig/94R+SLa9Cx4XrnDthLOoX2IYgl9dLK8TZuZQxRKlB69F6+zjcDr76KNTaqYT8WG8NwS2dn3K7r9AAyrhUC4m/xCdeHPtTPlyD5NtwiEeyhk9kbPXzHrL5SxogNE2k6SnTtDPun3kgXjB5o7OMVtTev+70Uzgkyc/S2mL/4eif6nGLz3f0IzI0ye/12yM+8Rab8Lod1ccBZMHEZoJrm5cw0dW3b2fYrpG7Qf/P4V25c+H4qZYf99pAewogOEWo+i6UEyM+83dFyK9XEHevwFtJ5XMbnmVpxDg2jTc1gvvwWOUzGyfK+Snf2A2aHnaT/4Kfrv+dtrds0rFAqFQqFoHkr4UyhqxAz6joS1ev6ycx8yfekPSI692JAxuItxI9pUlbhPKdFHJvzJT8PA7e1CH9n4yta1+vJkNIKWXjsSUpuZw4tFIHSL88IKgK7d7ENYwvPQ0llkPOp3tB0aREtn8bra8TrakPHoKsefcfkaxgcXwHUXx5z1n7NOOKU0+g6PLAmEexFCx4ysP9kdiPrCXzU3mBcN+xGsjlv1HNrsvP+/1dyXUhJ49W1wXEpPPtDUUvvSg3f7cTvmzUkf59Ag+uTM+lG1FRDZHIFvv0bwy9/Ga4njHhqs53A3jOf4P4Nm3HyPBpYWJ6i4T8VOJhgA/KhPP5baRLYmqu4u2xJ4bS0Y566Ut+kjE0jLxOtqp/TwPWhzSYTt4CjhT7EBsrNn8NwisSrCH0Co5U5yawh/2dkzID2ii5GCVnRwsRdv91PMjPrpAqEOwu0nKWXHSU1+Fyt+AE0PNHt4itsY3QjRcegHyScvY8UP0HXHX0IzgnQf+1ly8+cpZkaIdtyz4hhNDxBKHCE33zjhz+/2e55Qy52EW4+uetyKDlDIDFNYuEwofgihGWiaSbj9JNmZdxs2LkVtuAM9iJKNPjaJNp9aV/hzB/uQho5+fRT7nuMVI8v3Krm5c5ihTtoPfr/qe1UoFAqFYgejvqUVihoxrARCM9ecVLfzUwDMDv0Zrr1OX9pmCFp48SjaTGXhT8wtIPKFsuvBHehBn5yBkr2hp9EWHYUVhb941BcG3eoCkT41W/lmSQi8cGhV1KfI5EBKvLjvsHMO7QMhsBd7FLx41O+DWvo5pESbXUA4LtrEzOKYc8jI6vFuFj/qc2c7/lr6n6L7xF9B08x197UifXhusapwLRdF07VEsbLjr4rwp18dxhgaofTofXX9W2wGr7sDd3Blyby7rw80Df36xuOUrG++ij4xQ/GJByh86pmm/3yu47+H9GXCnxHqAIQS/hQ7G01DBgOQL6BNTOF1d669SEAI7BNH0EcnytHV+sgEbl83aBpeZzvOkf3ISBjZ1rI9P4NiT+DHfA4QCFfvhQy13EkpO1a15y8z/S7B+EEMqwUAKzZIMTO8JyKXS5kRApF+hNAItx4HoVFIXlExn4odQbz3cbqO/jR9d/1S2dkXaTtBou9JxKKYdivhtmPk5y82LJklv3DRd/sd+P6Kj1uxfWXH3/L3UbTjHgqpazjFhYaMS1EbXnsrMmhhvnsOpMRdR/jDNHD39/sd9Yv3rLcL+YULhFruXH9HhUKhUCgUTUUJfwpFjQihYQbb1xH+pjGDHUjPZfbaFxsyDq+zDX2qctyoPjKBNI1yv4A70AOeRB+f8t2A10fRLw2t7EqqgMjkQBMVVy4uiXMiVUXYdF202QW8zsodBzISWuX4WxKS5OK5ZUuc/E98EveAH2EpE/52LeVP+op0FmH7N+1LfXMina0YTbpZ/KjPne34M0MdxLsfqmlfK7YfoZnMXfsSskJH5JLIKxbdnPr1UcSi0OfvINHmkkjTqOj4E9kcgdfewTk02HQ3XFUCJm5XG/r4BoWxko02M0/p/lO4dxxoqpNxibLjz7wp/GmaiWG1lhcgKBQ7FRkMomVzi/1+1WM+l3APDfqTcecu+4LhzDzuwE1hv/TYfeR/4GM74r2p2B1IKcnNf0i04/Sa+y1NbOYXLq16LDn2Epnpt4kt+x4ORgeRbgk7N7lq/91GMTNSjhLXzTChxGEAQi1K+FM0HyE0WvqfXhUx2HXnT7H/of8Z3Vh9DxNuPY7nFiikhhoypuzseXQzSqi1siCy9Png2hmCy95H4bYTAOSTlxsyLkWNCIE70IM2PYc09DXTCJYoPXYfhU99DHR9Gwa4M3DtDMXMaEVXq0KhUCgUip2FEv4Uig1ghjpwilViNvGFPyu+n7YD30ty5NuUcpvo11sHr7MNbW6houPOGJnA6+su33zIWBQvEcM4fxXrKy9gffNVrJfeIvgnX0Nbo/tPZHJ40UjFSdQlcU5LV3Z9iVQWPA+vrfLNkgyvFv60VBp0bYWLarno6MWWxEb/OZecZ+7+PvThcXBcRKGIV8GhuFncUgY9sLMdfxvBCMToPvYzpCZeJTn67VWPy3AIhPAn46/cwPrmqwTe/KD8uFhIg+vi7uvzXYHLI0GlJPDiG2AYlB65dxt+ms3j9XSiTU5DBfGzGksO2/Uif7aTsvCnr5zYMkOdyvGn2PHIoIU+PAHu2v1+ZXQd59gh9EvXMBZ7Ot3+7hWPE7SqHKxQrMYpzODaWYKJg2vuZwZbMUNdq3r+kmMvM3n+v5Dof4qWgWfK262Yv/BlKe5Teg7F7MYj15uN9BxK2fEVHcKRtlOAIBQ/1LyBKRTrIDSjqovXiu1DNyPk5s835LlzCxcJtdxZNfqw/H4S2or3kW7G0PQgTmG+IeNS1M5Sao7X2VbbYiLD8KssbiOWFsKEWm4vl6NCoVAoFLsRJfwpFBvAsNqwC2sLf2aok5bBZzGsVqYvf67uY/A628CTZfGrTL6ANj1XvmFZYqmoXBRLFD7+JPkfeBYZChL82ksYZyr31ohMtmqUoQwFkaZRjly7lXJMaJW+PVkh6lNLZXxxr9oNlhVABq2y00ybnUeGQzhHDqClMr6jcY3n3CieW0R6pR3v+Nso8Z5HaB18lulLf0hu/pa/vaYhIyH0oWGsl95EhkPoE9Ow6KzU5vzJiCUXplgm/OqXr6OPT1N88oEdf/Pr9nQiijZiPlnzMdr0HDJgIhM75/Xg2r7wp5sr36eBsBL+FDsfGbIQubzf71dlkcit2EcPIzwP8+0zeB2tEAo2eJSKvUwhfR3w3fDrEW69k9z8hfK/U+OvMnn+d0n0f4SuO38KsezaRTejGFYbhfQNpJRMnPsdbnz3fy1/Zu8WSrkJpHSxov3lbS2DzzBw79/b8f3HCkU1hNAItRwlN+f3/LlOnvkbX8NzCls+t+eWyCevrimG6IEYhtWKFR1AM25+hwkhMIJt2GssLlVsD25ft19NUSW5RgG5+QuYwQ7MoPodKRQKhUKx01HCn0KxAYxgG04V4c/zbOziPIFQJ5pm0rr/e8jOfFCXm8kVz9OaAF1Dm14Z92leuIrUNZz9/Su223cdo/j0IxR+8Fm8/m5kewvFT3wEt7ezamSolslV7PcDQAhkLFLd8ZfJVo0JBZCRMFo2v8JxJVKZspOwGjIeven4m1vAa2/B7e0ETWCcv+LvUyfHn1ta7JHaQ46/JToO/wjBxBHGz/6HVZ1FXjSMPj6N291O4eNPgueVRVVtdgEZDeN2+K635XGf+tgkXmeb7zbd4Xid7X7P30TlrsNK6NNzvtCwg2IEPSePEDpCWym0mqFOSkr4U+xw5KI7z+vqqP19FQ7iHBxEOO6q/k6FYqMU0jcwrNZVMYGVCLUcXez5S5NPXvFFv97HF0W/1bdSSz1/CyPfJD35OlK6FFJXG/FjNIwlx2JgmfCn6ZaKdlPsesJtx8mnrpJbuMTwm7/K9OU/Ij399pbPm5m7jPScdd8jLf1P09L/9Krt5hr3mIptxApQePZx7BMq0rga+YWLhNR3gUKhUCgUuwIl/CkUG8AMtuHaGTy3uOoxpzAL0sMM+bFlwfhBQFLMjtZ3ELqO29Xux50tiWeui3HuCu6R/asdVyEL9+AAaMve7mJRmMtXFiVFJrume86Lx8oi3Opjc3iRcNXJXBkJ+R2DhdLNY1KZcndg9eeM+mKT9N2OXnsLmCZudyf6yMTNn2kT2IV50pNvUMr5Ipdr+z+bbu494U9oBr2nfgGkx9TF31vxmNfWgteaoPjRR5Gtcbx4FH3EjyjzxdZWCFm+43PZ31+bnsPt2iWrPg0dt7PNdzPWgpRo03M7buWv6+TQzPAKpwlAINyD5+QoZur8uaNQ1JNFt57bW0PM5zLsk3f4sZ/7+tffWaFYg2LqOlZsX037LvX8ZabeZOyD/4dg/CBdR3+6apxfMDpIPnmZ6ct/ROu+59ADcfILu6u7q5gZxQx2oBv1i1BXKHYC4dZjID1G3v7fEVoAPRDDrkM1Q2r6PLoZJhDpW3O/tgPfS6LviVXbDUsJfzsFb6BHpQpUwbWzfr+fivlUKBQKhWJXoIQ/hWIDGJbvdnKKqzsYluL1loQ/K9KHEDrF9HDdx2GfPoE2M49+5QYA+tAIolDEPl776kQZCiIKqwVMbAdRKPniXbVj41G0ZHXhby3nnYz44ly55891fYdhDcKfSKURubzf59fe6h8+2HPzvNrGPtLSU29y7bV/zNAr/4Dxs/+xLITdFP72ZpyVEYjTdedPk5l6i/TUm+Xt9kP3UPiBj5XFY3egxxdVF8VWt73FF1iXRFjwI2bTWbyOndN/tx4b6fkTmaz/euvaWT+f5+QrTsiG209hhjqZufL5JoxKoaiNsuOvln6/5ce1t5L79A/WHA+quH0pZscYfe/fMvLubzDy7m8wO/R8+TEpJcXMDYI1xHzCUs9fJ1OX/gAhBL2nPoPQjKr7W7FBpFsi3HKUjsM/QihxhHxytwl/IyvcfgrFXsEMdRFuO0HLwEcZfOCXsSIDlHKTWz5vevpDwmv0+62HivpU7Ab8vltZXhCjUCgUCoViZ6OEP4ViA5hBf/K/Us9fKT+N0AwMyxekhGYQiPRRzNyo+zi8nk6cAwME3vwAbBvzw0u4/d3IlvUjq5aQQaui8Ccyfg/N2o6/qC/cOc6qx7RMFhmtfuySK0/L5W4+n5TrOv5kPIoo2mhjvivPa2sBwB3wI9+8TcR8JkdfQGgBek99hvZDP0h+/jyunV0W9bk3hT+AaNf9RDvvY+rCf8NZ/HkRYoV46g70ILJ59OFxRMlGtrcAN0VY8GMwYbF7cpfg9nQgCiXEQmrdfbXFn8/dYcKm52TRjNUOV00z6TzyY2Rnz5CdPdOEkSkU6+P2dWOfOFL+HN8QG1zgobg9WRj5FoXUVTQjhHRLzF77Uvm73SnM4trZmh1/4LuEhNDoPfWLGNbawnO49TgtA8/Qe/KvI4ROqOUIhdQQnmdv6WfaToqZEazoQLOHoVDUHSEEA6f/B7ru/Ck0zSQQ6dmy8Oe5Npn5K4TbNh9/aAbbcEtpPLe0/s4KRZPILVz0+/1CHc0eikKhUCgUihpQsycKxQYwrBZAVIxisfPTmMGOFSs9/Z6XkYaMxX7wLkSphPXN19BmF7BPbCxyQwYtRMkG112xXctm/cfXcu0tinSV4j7XdfyFgiAEIus7/rRFAakWxx+AcW0EaQXKzkEZj+LFo8jYxmI5pZQU0jeIdt1PrOt+Er1PIKVHZvpdXDuNpgfRNHND59xNCCHoOvrTAExf/P2K+3g9nUhDx3zvHABumy9qL3f8adNzyKBVt37F7cDragdN1BT3qU3P+a+9RYfSTsF18mhVItgiHacJtRxl+vIfIb3V4rxC0WxkLIL98Okd1Zup2Dv43+XvEO99jL5Tn6H3rl8EIDP9LgCF9HWAmh1/4Pfj7nvgVwglDq27r2YE6brzJ8uLh0KJI0jPppiu/0KwRuAUk7ilpBL+FLcFgXAPdn4KKb1Nn6OQuorn2lvqwFxaOFopVUah2G4y0++ST67ups3PXyTUqtx+CoVCoVDsFpTwp1BsAN/Rl6gu/IVWxpZZ0UGKmdGGTL7LaAT7rqPoY5N48Shef/fGjl/sLrjV9SfSuXX78pZEOO1W4c92/FjENRx//rmDZeFPpDKg6+v28y0Jg/ropO8SWZowFoLic09Quv/Umsffip2fxnPy5Yk/w0oQarmDzPRbOHZmT/b73YoRiNN28PtJT7+F51Toe9R1vN4utJl5//US9l8zXjyGyBXAdhb779p21wS+YeB2tKHVIPzpU7M7MsbUs3NVu5eEEHTd8ROUshMkx17c5pEpFApFc8kvXMItpYh23g/433XhljtJT78FQDF9HcNqWde5txzdjGxaCLOiAwg9sGt6/gopf7I3GF9f5FQodjtmuBvpOdj5mU2fIzt3ASMQwdpCPO5Sqozq+VM0GyklUxc+y9z1L6/Y7vf7jaiYT4VCoVAodhFK+FMoNohhVe5gqCb8Sc+uS3dEJexTR/E627DvPblx4SXkO5hE/hbhL5P1YzPXOl/QQgbMVY4/kV2MCV3H/SUj4XLHn5bK+ELieuM3TV98khJvMXKyfL54dMMl7MXyiv+bUV+xrvvJzZ3Dzk2iB/a+8Ad+JBnSo5Aaqvi4O+B3KC7/nd90fKbRZuZwu9obPs564/V0ok/MrN3z57poc8kd1+8H4LnVHX/gu40TvY8ze+1LSOlW3U+hUCj2GpmptzCCbQTjB8vbol33k5s/j1tKU0jfwNqA22+rCM0gFD9Ul54/Oz9Dbv5iHUZVnXzyKobVihlsbejzKBQ7gUDYXzhZyk1s+hy5+QvEOo5uut8PQF9MlalUJ6FQbCd2fgqnlKSYur5ieyF1DZCEEoebMi6FQqFQKBQbRwl/CsUGMYNtq1ZjSulh52dWC38xf3V4MTPcoMEYFL7/GdxDgxs+VC5FF97q+FsnqtPfSayIe1x+LLBmxx+AFw6h3xgj+Pw30K8OrxvzWT4u4e/ntW99MqqQvoERbFvR4xftvBcpPbIz76Obe7ffbzmBcA+6Ga06IVnuUFzWxVWOXb0xjrCdXdXvt4Tb24koFBFzyar7aLML4Hk78udz7VzFjr/lJPo/gltKkW/wJLFCoVDsFKT0SE+/TazzfsSyBUXRznsByMy8SzF9fcWin+0glDhCIXllS3GCUkomzv1nJs79pzqObDWF1FWCNUSaKhR7AcNqRWgB7E0u0vTcIvnkVeIdx7c0Dk0zMQJxnAqLSxWK7SQ3fwEAp5TEKS6UtxfS19CMMGaoq0kjUygUCoVCsVGU8KdQbBAj2LZqNaZTXEB69irhTzfCmKFOiukGCX9bYEn4E/mVEY9aJruucAe++HOr40/LZMtRnmvhHD+Me2AAr60F98AA9okjtY05tiT8tdS0/1r4E38rV/wbVguhliNI6d4WUZ/gx0IGE4erRpDJaJjSw6dx7jxwc6MVQAZM9Kt+X5HXsftcAV53BzIaJvDO2ar7aNOzoGt4rbXHwW0XnlM96nMJK7YfM9hBeuqtbRqVQqHYCbh2bkvuld1MfuGyH/PZdf+K7X7c5x3MD38D185uq+MPINhyBNfObCkBIj9/gfzCJZzCfMP6W6XnUEhdJ6RiPhW3CUJoBMLdm35vzt/4KgCtfQ9seSyV7jEViu0mv3Cx3DlZWNZNW0hdIxg/sGJRjUKhUCgUip2NEv4Uig1iBNtwivMrVm3beb8rLHCL8AdLPX83Vm1vOrrux3VuxvEHlR1/6SwyEgJt7Y8Wr6eT0uP3l//zelb/3ioe15ZAWoGaHYLVkFJWjfpamixc7gTc64QSRyikrladSHROHCmLrsAKx6fXmgDT3KaR1hFdp/Tg3ejD42gjFSbIbQfj8nXcjjbQ9e0f3xpIKXGdHJq5tuNPCEG0634y02+ruE+F4jZi4sP/l5F3/jVyrSjjPUpm+i0Ma2XM5xLRrvspZccAtt/xFz8EQqOwyZ4/KSWz176IpgcBiV2Yre8AF/F7qUvK8ae4rQiEeza1WMIuzDF3/S9o2/csVqS2e5m1MCqkyigU24mUkvzCRWLdD6Gb0XI1hpSSQmqIYOxAcweoUCgUCoViQyjhT6HYIKbVhvRsXPum6OULfwIj1LFqfys2SDEzsiMn4GTQWun4cxxEoYhXi+MvFvWPte3yNpHN4cXWP3azOEcPUfih5zbeZ3gLdn4az8lVnPiLdd4HiNsm6hMg1HIEzy1SzIzUfMxS3OdOjMGsFXd/P25PB4E33gNvWfyalFgvvI5IZyk9fLpp46uGdIsgvTU7/paIdd2Pa2c2FPcppcQppbYyRIVC0SSyc2fJzn6AU1zAaZA4tFOR0iM99TaxrvsqOhKiHfcCAiOQwLBatnVsmhHEig6Qnvou05f/iBtv/iqz175c8/H5hQvkFy7ScfiHAb/rrxEUUlcRQsfaZmFUoWgmm3X8zVz5ApoRpP3gJ+syDjPYpqI+FU3Fzk/hFBcItx7Fiu0rO/6c4jxuKUUwfqC5A1QoFAqFQrEhlPCnUGwQI+gLHctXZNr5aQyrFU1b7XyyooO4dnZHruCUIWuF40+ks/72Whx/i317y+M+tUwOGVn/2E2j68jw2i6nWigu3sRUcvwZVgt9d/0S8d5Ht/w8uwUrtg+hmVV7/ioh474wupuFP4Sg9NBptGQG49wVf5uUBF5/F31kguLTjyDrECtbb1wnD7Bu1CdsLu4zNf4yQ6/8Mq6d3fQYFQrF9iOly/SlPyqLNvnU1SaPaHsppIZwS0minfdVfNywEoTbjhNsqS1evN6EW4+Tm79AevJNXDtLZurNmo+dHfoiVmwfif6PIISOXZhuyBjzyatYsX0Vr2cVir2KGe7GLSVxnVzNx+STV0lPvk7HoR9EX6dzuVYMy4/63ImLRRW3B/n5iyA0gonDBGP7y46/QuoagBL+FAqFQqHYZSjhT6HYIOai8GffIvwFwpUjXqzoIADFzM7r+SMYROQrCH81uPa8ReFnedynSNfWD9hsCunrGFYbRpU4z2jnaYxAfJtH1Tw0zSQYP1i1568SS44/dzcLf4Bsb8G58wCBN98n9AdfJPQHX8I4d4XSo/fiDfQ0e3gV8RYnpmpx/Plxn/eRmXmnprhP6TnMXvsi0nO21EW1m5i5+qekp95u9jAUii2THHuRUnac7mM/gxnqopC8vYS/3Nw5NCO0Zkxl36lfpOfYz23jqG7SfugHOPjYr3HwsV+ndfA5Stnxmrr6cvMXyS9cpP3gpxBCxwi2N9Txp2I+FbcbgYh/vWfXeN0jpWTm8uewooPEex+v2zjM4FKqTLpu51QoNkJu4SLB2D50I4QV2++nBxSTFNLXMKzWbXfLKxQKhUKh2BpK+FMoNohmRBB6YEUUSyk/hVmh3w98B5keiO1I4c93/N2M+hSpLOhaba46K4C0zJvCn+MuxoQ20PFXJ4rpGwTjKsZqOaHEEfLJyzWvMnb391F88kFky+4XSEsP3E3p/rtwjh7COXaI4tOP4BzduROfSyvSa11hHuu6H7eUJr9wad19U+Ov4hTmAT/uZ69Tyo4zd+1LTF/6fTzPXv8AhWKH4to5Zq/+GfHexwjG9hNKHKJQg+OvmBnj8nf+zqb6rXYauflzhFuPIUT12xvNCKIZwW0c1bLn1kzMYDtCCKxoP1K6Nf3ek6PfJhDpJdJ+NwBmqKMhwp9TSmHnp/0+QoXiNiIQ6gYoL3jy3GLZ4VSJQvIy+eRlOg7/0JqfNxulUqqMQrFdLPX7hVqOAje7cAvp6xRTQ8rtp1AoFArFLkQJfwrFBhFCYFo3y9ellNj56arCnz/BM0hhMSpjJyGDwVuiPjN+v1+NHXpeSwJt0u8QEpna3YLNREpJMXOjYszn7Uyo5Qju4qRfTRgG7pH9W+5b3BEETJxTd2KfPoF9+gTuwYFmj2hNNuL4A7BiBzCCbevGfUrPYe76V4h13Y8eiNf+WtjFzI98E92M4JRSpMZeavZwFIpNk556E9fJ0XHoBwEIxg9RTA/juaU1j0tOvI7n5MlMv7sNo2wcnlOgkLxKuPV4s4dSE4FoP8C63bpOKUVm+h0SfU+WewvNUGdDPp8LqSEA5fhT3HZoRhDDaqWU9YX4yXO/y/Bb/6IcrX4r88PfIBDuIdx2sq7jMK3VqTIKxXZh56dxivOEW+8EwAi2o5sRiunrFFLXlfCnUCgUCsUuRAl/CsUmMIJt5Zsyt5TEc/JVhT+ASMfdZGc+IJ+8sl1DrAkZtPyoz0WXl0hnNyTcOXfsRx+dQCTTiIwvRtTSD9hMnMIMrp0lqIS/FQTjhwCxoZ4/RXPwFieiNLO295oQgnjPo6QmXsUpVY+PSk28il2Yoe3A9xEIde35qE/XzpAaf5WWgY8R736Iuet/rlx/il1LMX2DQLi3HMMVTBxCSrfcz1MJKSXpSb9nLjt7ZjuG2TDyyUtI6RJu2x3Cn26EMYMdFDOja+6XGn8VhEa855HyNjPYgV2YqXsPWCF5BSOQwLB2d4S3QrEZAuFuSvlJ0lNvk556Ayld8gsXV+1n56fJTL9Dy+CzdXX7AWhmFKGtTJVRKLaL/MKFxX4/vwdXCIEV20968rt4boFg7GCTR6hQKBQKhWKjKOFPodgEZrCtfFOWHHsJoQUItx6run9L/9ME4weYPPc7666+305kyPJFv9LiZHcqg7cB4c89tA8ZtDDOXUbL5kCI2mJCtwEpJanxV1ZN5C85L62Yivpcjm6GsaL9NcVBKpqL6+QQmommmTUf0zr4MQQa88Nfq/i4U0wyd+0rRLvux4r2Y4a79rzjb2H0O4Ak0f8UbQc+iVNKbtr1l5s7t+XovdzcOUq5vR+vqmgMxcwwwdhg+d9WtB+hB8iv0fOXS96glJsi0nEPheSVqu6W3UBu7hyG1YYZ6mr2UGomEO1f0/EnpSQ59iLRzvvQzWh5uxnqxHPyeE62ruPJJ68STBwuOwsVituJQLiHYuo6Uxf/G5GOezCDHeTmzq3abykpIN7zcN3HIITw7zGV40+xzXieTXL0OwRjB1ZUCQRj+xYXAgosVZOhUCgUCsWuQwl/CsUmMCzf8ed5Nguj3ybe++iKSZlbEUKj+/jPYxdmmR16fhtHujYyaAEg8gWklBt2/KHrOEcPYly+jja3gIyEQNsZHyul7BgT536bzKKbYYlCcggj2IYR2P3ddPUm3Hac7Mx7O0qcVqzGs3M1x3wuoZtRWgY+SnLkW7jLXH9OMcn0pT9k6NVfwXVytB/8FABmqGtPd/xJzyE58i3iPQ9jBGIEwj3Euh7clOsvO3uGkXd/g+krn9/SmMY//E/MXfvils6huD2R0qOYGcWK3hT+hNAJxg6U4xsrMTf6OpoRpvPIjyGlW3GSe7eQmz9HuO34rhKtrOjAmsJffuECdn6KRN+TK7aboQ6Auvb8uU6eQmqIUMuRup1TodhNmOFu7MIMSJfuo3+ZcNsxcvPnV+zjOjlSYy+T6PsImm41ZBzLU2UUiu1ASsnUhf9GMTtG150/teKxpWqMQLgbfYP3HgqFQqFQKJrPzpihVyh2GWawDbeUIjX+Km4pRevgx9Y9xor00n7w+5kf/tqaK/C3ExkK+v8nX4RsHuG6yFh1AbMSzrHDCNfFuHQNbwfFfC7FFOZuienJLVwg3HJnM4a042npfxrXzpKeeL3ZQ1GsgefkVqzGrZXWwWcByq6/9OQbXHvtH5Mcf5m2/Z/g4KO/ihXpAyAQ6sK1s7h2fR0lzcR18qQmXic18RozV/8Ep5SkZfF3AtB+4JM4xQVS4y/XfM5Sborxs7+F0E3y8xeQ0tvU2JxSCreUpJC6tqnjFbc3pdwk0ithLXP8AYQShygkr1SMhJRSMjfyXWJdpwmEuwmEe8jNNS7u085PN8xF7BSTFDOjayYv7ESs6ABuKYlTSlV8PDn6IoFwD6GWO1Zsvyn81e/3mZ15H+nZRDvvrds5FYrdhBXxezc77/hLGFYL4dbjlLJjOMWF8j6psZeQnk3LwNMNG4dptamoT8W2khx7gdT4y3Qf/TTB+MoqjKVqjGBcxXwqFAqFQrEbUcKfQrEJjKDffzI79GdE2u8iEO6p6bjWfR8nEO5mYeSbjRxezZQdf4UCMum7gLwNCn8yHMI5MAiuh4xuwC3YYOy8L/wt7+dw7RzF9DChlqPNGtaOxgx1Eu08zfzI1+veHaSoH66T37DjD0APxGgZ+CgLI99m8vzvMn72PxJpv4uDj/4q7Qc/hb6sM9AM+52le8n1N/Hh/7v4339i/sZXiXbeWxY6AQKRXmLdDzJ37Ss1uf48t8j4md9EN6P0nvjruHZmTffOWpQWe75KuQlcJ7epcyhuX4rpGwArHH8AwcRhnFKy4iRyMTNCITNBvPsBACLtp8jOnm3YZ//42d9i5N3faEiP5pIrZ7f0+y1hRQeAm+//5bh2hszMOyT6nlzlYtSNMLoZqavjLzP1FsH4Qcxge93OqVDsJkKtd7Lvgf8vse6HF//tLyRY+nzxPJv54W8Q636o3KXaCJTjT7Gd5BcuM33xD2gZeIZ476OrHjeC7QQi/UTaTzVhdAqFQqFQKLaKEv4Uik1gWL7w57v9nl1n75sIoROKH945k+kBEzSByBWQyQwAMrZxQcE56UdD7SThz+/KEr7LYPEGOp+8DEjCrcrxV42WwWcpZcfJzZ1t9lAUVfCc3AqRbiO0Dj4HSFITr9N19NP0nPzr6Obq9+1ST9Z2dc7Z+emaBa/NOBGzcx+SnXmf3pO/wJGn/2+OPP1/03vqF1ft13bg+xZdf6+se87py5/Dzs/Qd9cvEW4/idADm45KXC4YFlM3NnUOxe1LMTOCGWxf9V5eWqFfqJAykJ58CyMQJrIoloXbTuIU5yllx+o+Pqe4QCE1hJ2fZmHkW3U/f27+PFa0f9dFeJuhToQWqLhgIDP9HlJ6xKr0iJmhTuxCfRx/rpMnO3eWWNf9dTmfQrEbEUIjGD9YFtqNQAwrOlj+Xk+NvYxTStJ24HsbOg5jMVWmEYskFIrlOMUFxs78e4KJQ3Qe+bGK+wghOPDwPyHW/eA2j06hUCgUCkU9UMKfQrEJjGAr4K/WDm0wWsoMd/uxXDvBUSUEMhiEQhGZTPvRn4ax4dN4HW2UHrgL5+BAAwa5OezcJOG2E8BN119+4QKG1YYR7Gjm0HY0ocQRgrH9zA9/vdlDUVTBczbe8beEHojRf8/fZt+D/5CW/o9U7cPSjRB6IN7wRQqF1BCj7/1bhl79h1x58e9x441/zvTlz1eMsHNKKaYvf46rr/wDxs/8+5qfQ0qX6Ut/RChxhGjXA2iaiaaZFX92a5nrT3pO1XO6To7U+Ku07v8EVrQfTTMJt9xJbn7zwl8wfgBND1JIV+9kUygqUUzfWOX2AzACccxQJ/nklRXbpZSk/v/s3Xd4W+X5N/Dv0Tjay3smdoad4WwygFAKDSMEKAVSKOuFsgu0rDbQQn+UllFGoYy20ABltqWMkLBp2ZAESEL2nt7b2uvonPcPRSKKty1btvz9XBcX8RnPeSQ/smzd577v+m/gyD8Cgir6nm9wlEFQifA2J7/cp7dpIyCoYMmdg5Z9b3Va2rIvFEWBr2UrjI7hle0HRAMNOnNBx4G/pm9hsI7pNJip1WclLePvuzKfDPwRHcqYMRG+1q2Q5TBa9r8DS87sHld56SutIVpxIZbJTTQQZDmMmk1PQBBUyK+4Mv67ABEREaUXBv6I+kCl0sKcMwuZY87o9IPzzojGHMiSH5Gwe4Bm1zuKXgfBH4TS5oZi7V2Zz0NJU8qh2IfO3fYhfwMMtjEQTYXwtx4M/LXugNFR1uvv2UgiCALsxQvga9mCYAflx1JFkSVUb1wKT8uuVE8l5aKlPnvf4y/GYB+fUOKyM6IhZ8Ay/mQ5jNrNS3Hgm3sQ9jcib+IlyC2/EKIpH67aL7B31e2o2/os/M7daKv+FLWbl2Lvyl/DWf0pDNZS+Nt2QpYCPbqWs+YLhLzVyB7/4x699qNZf61wdpH15677CooiwZZ/dHyb0TEB/rZdkCO9v0s/6KmCzlwEvXU0+/xRryiKgqCnsl1/vxhT1lQ4az5H0F0Z39ay/x2EfPXIGn1MfJtKpYXRUT4gff48Td/CYBuHnLJzAQho3rM8aWNHwm5IwRbobeOSNuZg0pmLEPQmvtfKkSB8LVthyp7e6XlaQ8eBP0WWULPpiS7fv9uqPkb91ufiGUWexrXRMp8GlvkkOpTRMRFSsA1Nu16FFGxDZskpA35Ng20stIZstFUOjbYQlJ4ad/wbQfd+FEy5athlyxMREVHPMfBH1EcFFVfCnDW11+dpjbkAgJCvPtlT6hPFoAMCAcDpgWIZOqU6+yMS9iESckFrzIXRUQZf23ZEJB8CnkoY7Czz2R1LzixodHY4az5N9VTi2mo+hatuNZz1G1I9lZSTwz6o+5jx1xtaQ/aAZPzJchi1m56Ep3Ed8iZejNFz74A1/0jYCo5G3qSfovSoe5A97mx4mzeics0f0bDjJYT9jcgYdRJKj7obOeUXQFEi8B3Sv7MzEcmH5r1vwJp3JPTWkh7NT2fKhyXnCLTsf7vDrD9FUeCs+RTmrKkJfX6MjolQ5FC77KruKLKEkK8WorkIOmspA3/UK1KwDZGwBzrLqA73Z405A6IpDzUb/4pI2ANv8yY073kDWWNOhTU7MUvOlDkFvradcNZ8lrSqBHIkCF/rNpizpkGtNSOz9FQ4az/vcz/Mw8WCX+LBLJnhRjQXIeStSfhZ42vdBkUOwZw1rdPztPpshIMt7X5GBT2V8DSsQVvV/zo8r/XAB2jY8RKcdV+iduPf4mvCzDKfRO0Y7OMgqDRoq/oQltzZEE35A35NQVDBUbwA7sY1CAeaB/x6NLJ4WnbhwNqH4az5FDll58dLghMREVF6YuCPaJBFS7gICA9S76zuxDP+nP3L+BtKYsEK0ZALg70cYX8j3PXfAIrMwF8PCCoNzNkz4G3aOCRK0kbCXrTsXQEACPr4IUikH6U+e0NrzO2w5GZ/KLKE2k1PwteyBQVTfgZr/lEQhMRfRVRqHRzFC1B65F0onvlLjD3mIYw64lZklp4GtdYMrSEHGl0G/K3bur2er3kzIiE3Msf8sFfzzChdBCnQAnfDmnb7gu59CHqqYCv4XsJ20VwItdYMby/7/IV89VBkCTpTEfSWEkjBVkjBtl6NQSNX0BMtB6czd1xqW6XWoaDiKsiRAGo2/AW1m5fClDUFWWNOa3esteBo2PLno37b86jf+gzkSLDf8/O1bIEih2E6GMSyF34fGtEGZ83n/R4bAKSDH4xrhmm2ms5UFA3+H3KThbdxPURjHsSDN4p1RGvIAhQ53sM4xt8WzYp3N6xp1yOstfK/aNz1H2SMXojCadfB17oNB76+G4ochiV7ZhIfFVF6UKl10FvHAhCQUbJo0K5rzTsSKrUebVUfx7d5GtehrXro3JBHw4ssh1H57WPY/NEdkAKtyK+4CraCo7s/kYiIiIY1Bv6IBplKpYVWn4mQf6hk/OkheLxQvP60yfiLZVNqjTkw2McDAFr3vwONzhHvnUFdM2VWIBxoQthXl+qpoHnfm1DkCAz2cQiN8MCfosiQ+1nqs6dEQzYiYQ8iYW/Sxmza8zp8LZtRMOVqmDInd3msSq2DwT4e6sMeqyAIMGZM7FGATQq2QaXWQ6vP6NU8daYCGOzlHQYnnNWfQaPPiPcQ/W5eKhgdE+Ft3tKra8Uyn3TmwnhWIrP+qKeC7kqotSZodJ2vca0hC/mTL4fftQca0YK8iT9tF3AHor+f5E64AHmTLoWncR2q1z/W43l0VhbY07QeoqkAojEHQPTGEoNtXNL6V4X9TVBrTYOSBT0QdOZCAN/9HFAUGZ7mDfFAaWdiv8uEA4nlPv3OXdDqsyBLfnibvsuQdzd8g8adLyNj9MnIHHMGTBmTUTD1Z5BCTuitJdFAIhG1kzH6RGSNPRO6Qcj2i1Fp9LAVHANnzaeQpQBcdatQs/FvaNjx0oCVYKf05mlYC0/jeoydfRVKj/w/WHJ4swcREdFIwMAfUQpojTkID5VSn3odBK8/+m9L+mT8qUUr1BoDNKIFoqkA4UAzDHb29+spg70MgkoLb8vmlM4j5K2Fs+pjZJQshMFagqB/ZAf+5EgAgAK1djAy/qIf1Ccz68/ftguWnNkwZVb0axyjYwJC3mpIQWeXx0khF9R97F1iKzwG/rbtCWWZI5IfroavYMuf32HgxJgxAQH3fkghT4+vE/RWQ6PPOBi8cUAt2hj4ox4LeiqhMxd3+95mzJiIouk3onD6jd3+/LDmzUVO2Xnwt22HFHJ1Owd/207sW3Ub/G07E7Yrigxv04Z2ZdF1liIEPVVQFLnbsbsTDjRBox+e2X4AoNaaIBpz0Vb5X0TCPgRcexEJubos8wkAGr0DEFQJff4URYHfuQuWvLnQW0vgqlsFIFputXHnf2DOno7MMT+KrxVTxmSMOuJW5E26dOAeINEwZ8qcgozRJw36de1Fx0GOBFG7ZSnqtjwDa/6RUGstaNn/zqDPhYY/Z81nMGZMQNaojn9/JSIiovTEd32iFBCNuUOnx59e992/remT8XdoiSyjo/zg/1nms6ei2VZl8DZvSuk8mva8AY3OAXvxAmj1GQj5modE+dFUkcPRIP2glPo0RAN/yfpZpSgKQr46iMa8fo9lzIj2JvN1U+4zEnJC08fAnzl7BtRaM5w1n8W3OWs+gyJLsOZ3XB7J6JgIKAo2ffhb7Fn5fzjwzd3tSvEdLuSpipdpFAQBemsJAu59fZozjTxBdyV0luIeHWt0lEGrd/Ts2IMZrd29xoBoCTogmlV2qIBzNyJhD0xZ0xO268yjIEcCCUGrvgr7m6DVD+9stbzJlyPsb0T1+j/DVbcKatECvW1Ml+cIghpafWbCjRlhfwMiITcMtnGw5M2Dr3kTIiE3Wg98gEjYjayxZ7cLEOvMRV2WFCWi1NDqM2DJngVv0wZY849C7oSLkDH6JLjqVia9DDult5C3Fv62HXAUHpPqqRAREdEgY+CPKAW0hhyE/Y1Judu9vxSDPvoPjRqI/XuYC/vqIRoODfxNACDAYC9P3aSGIVNmBfxtO+J9nhRFhrd5IxQlMijXV5QIfC2bYSs8BiqVFhp9BuRICJFwz7Op0o0s+QBgUMraqTUGqEVr0j5gioTdkCU/RFP/A38a0QqduRC+1q7LfUpBJ9Q6W5+uoVJpYc07Eq7aLyHLYfidu9G8+3XYC4/tNHiiNWQht+zHcORPP5iVWAdX7ZddXifoqYLO9F1/Nr21BAHXvhEd4KaeiYR9CAeaoDP3LPDXGxqdDaKpEL5uSuoqigJP03oIghruhrUJv9e4G9ZALVrjJWxjYoHKoKey3/MMB5qgHab9/WL0llEonH49wv4GOKs/gSlzao8yMrSG7ITn0N+2CxBU0NvGwJIzGwoUtFb+Dy0H3oW96AfxcqtENDxkjTsLOWXnIXfChRAEFWwFx0CtNaN539upnhoNI86az6HWmmHOnpHqqRAREdEgY+CPKAVEYy4UOQwp0JrqqQCGaMafYLMAaVAGU1EUhPwN0B5yB7spazpGz72DH3r1kilzMhRZgq91OwDAWfMpqtc/Gv96oIU8NZAjQeitYwEA2oPl3KRuMqjSWeRg4G8wevwB0Q+Ww/7k9JMJeaP9IrVJyi4xOibC17K1ywCZFHJBI/Yt8AcAtoL5iIQ9cFZ/gtqNf4PeWorscYu7PCdj9AkYPe0i5E34CczZM+CqW9XpHCMhN6RgWzzjD4gG/mTJl7TnndKTIkuo3/YcBEENg23cgFzDlNH9ayzkq0XY3wjH6JMRCTmjwSdEf1a5ar/ssCyuRrRCI9oQdPcv8KcoMqRAy7DP+AMAvWU0CqdfD9GYC2v+UT06x5I7B76WLQgd7MXrd+6CzlQYL3NuypiMlv1vQ6XWIaNk4UBOn4gGgFafAXvR9+M/Q1VqHTJGnQR33Spm/VGPyHIYrrqVsOYfCZVam+rpEBER0SBj4I8oBWJllUL+1Jf7jJX6FOyWFM8kOeJZRYcE+QRBgM6Un8JZDU9aQy60+iz4WjYj7G9C065XAWBAAtayHG5X9s3v2gNBUENvHR2djz4DALotnZjO5Hjgb+Az/gBANOQg5EtS4M9XBwgqaA3ZSRnPmDERUrAV4S5+jkZCzj73+AMA0ZQPg70MjTtfBgQB+RVXQlBpeny+NW8ewv4GBFx7O9wf9FQDAHTmwvg2vaUEADo9h0iRJdRueQrepvXIr7gSWsPABL4MjgmQgi1dBqG9jeujH0aPXgiNzgFP4xoAgKvmcyhyGPai73d4ns5S3O+MPynYCkWJDNjjH2x6y2iUzPs9jPbxPTrekjsbatGK1sr/AQACzl0w2L8LAlvz5gEAMkt/OChZ4kQ08GyF34NKa0Jr1YepngoNA57GdYiEPbAVsMwnERHRSMTAH1EKaPQZEAQ1wkOgz1888Gczp3gmyRHrR5asrKKRTBAEmDIr4G3aiPptz0GlNUGlMUAKOZN+rabdr2H/13+ALIfj2wLOPdCZi6BSR9eoWrRApdaO6MBfRIr2+FMPVsafMRdhf31Syk6GffXQ6jOhUiXnjmODbTwEQd1pKUJFlhAJe/qV8QcA9qLjoVLrkF9xFTS9LBtqcJRDo7PDXb+6w/1BbxUElRbaQ25UUGtNB8uYDk5mLQ0viqKgbus/4G38FvkVV8KcPX3ArmW0l0VfY4eU1HXXf42gpyr+tafpWxgzJkOlFmHOmQlPw1oosoTWqg9hyZ0Djc7e4dg68ygE3Qf6Nb+wvxkAoEmDjL++UKm0sBd+H67alQh6axHy1Sdkf5pzZqFgys9gK5ifwlkSUTKp1DoYbOMQ8tameio0DDirP4PBXpaU/tpEREQ0/DDwR5QCgqCG1pAdD1KllFoN2WGDkJ+cLJxUiwZThaRlFY10xswKhANN8LVuQ+6Ei6DVZyCS5MBfJOyDq+ZzyJIP/tYd8e0B1x7obWPiXwuCANGQiXCgOanXH05kyQeVWterrLP+0JnyEQl7k/I9D/nqkvrBg0qjh942ptM+f1LIDQC9DtYdzpIzE2OO+RMMh6zFnhIEFSy5c+Gu/xqKLLXbH+3vVwBBUCds70kZUxqZIiEn3PVfIbvs3AEN+gGHvMZatgEAfK3bUbv576j69mGEA62Qgk4EXHthzpoGALBkz4IUcqJx1yuQAi1wFC/odGydpRhSyAkp5Orz/KRANEs8VgZ6JLIVHgtAQf3WfwAA9IcE/gRBBXP29B71CySi4UOjd4zosvfUM+FAC/xt22HLPzrVUyEiIqIU4V+CRCkSzaQZGj2cwmedBHV5aaqnkRQhfwO0+oykZRWNdEZHOQS1CFvBfJgyJkEt2iAF25J6DWft51CUCDSiDZ6mbwEAkbAHIV899NbEYIvOmDmiM/5kyT9oZT4BQDRFS1AGvTX9Hivkq0/6HcdGx0T4WrdDUeR2+2LByv6U+ozpz88Ta95cRMIeeFs2t9sXcO2HzjK63fZoGdOuSyzSyBT0RsvDGh0TB+V60dfYNkQkP+q3Pgu9bSwEQY3aTU/A07gWEFQwZVYAAPS2MdDoHGir+hAGezl0luJOx9WZo/v60+cvHGiGWrRBpRb7PMZwpxEtsObNRcC1F1p9JrR6R6qnREQDTKtzHCx1zJuDqHOexrUQVBqYsqeleipERESUIgz8EaWIaMwdGhl/aSbsq2eZzyRSqXUYPfu3yCk7DwCg0dkhBZOX8acoEbRVfghLzmyYc4+At2kDFEWB37kHANplWYnGzBF9l3Mk7IVqkMp8AoDWkAVBJSJ0sBddX8lyGOFAU9Jfm8aMiZAlP4Lu/e32xTKJNEkI/PWHzlwEnbkYrrpVCdtlKYCQrxZ6a0m7c7orY0ojV8hTDUEtDlpfu+hrzIeaDY9DCrmQN/ESFEy5GkFPJRp3/QcG2zioxWiP4GiG2QwAgGNU59l+QPRni0qt71efv7C/aURn+8XYD2ZWHprtR0TpS6PPgBwJxvs+E3XE3bAGxoxJ7PFKREQ0gjHwR5QiWkMOwoHmDsu/Ud9Fs4oY+Esm0ZgTLy2pEW2QQm1JG9vTsBZSMFoSzpw5DVKwFUHPAQRce6EWLe16N4mGrBGf8TeYf8ALggo6c0G/M/7C/kZAkZOe8ae3jIZKrYe3gwBZJOgEIMSDEqlkzZsLb9MGyFIgvi3gPgAocoeBv+7KmNLIFfTWHCwPOzi/wsdeY/62Hcga+yOIxhzorSXIKTsPiizFy3zG2IuOh2PUiTBlTuly3OjPlqJ+Zvw1DVoAdCjTmQqQNfYs2IuOS/VUiGgQaHQZADCib4SjroUDrQg4d8OSPSvVUyEiIqIUYuCPKEVEYy6gyNEPxCkpFEWJZgCwv9+A0ejsiIRcfS4vpCgRNO97G666VQgHWtFa+V8YHROgsxTDYB8HlcYIb+N6BJx7oLeOgSAICefrjJmQgk7IcjgZD2fYiUg+qLSDe+euaCpAyNu/jL/wwexm0ZTcwJ+g0sDgKIe/gwCZFHJCLVra9c9LBVPWNChyGH7nzvi2gHsfBLUI0ZTf4TmxEosdlTGlkSvkrYmX4B0MgkoDU+YUGOzlCYElW8HRKJr5y3bBJtGYg+xxZ/coMKmzjEpCxh8DfwCQMfqkPvUhJaLhR6OLlvQNB1tTPBMaqjyNayEIapiyWOaTiIhoJGPgjyhFYllpIfZwShpZ8kGRQ9Do7KmeStpSizYosgRZ8vbp/IBrP5r3LEPdlqex98slCLj2xsuURT9groCnaR0Crr0dfogpGqNl3SJJ7jM4XAx2xh8A6EyFCHpr+hWACvnqoNIYoNYmP/vO6JgAv3M35EgwYXsk5IJGtCX9en2hNeRAo89IKN0ZdO2D3jyq08BkV2VMFUVOyB6kkUFRZAQ90Yy/wZQ3+VIUTf9Fu2Ce0T4+ng3eFzpzMUK++j6tZVkOQwq2QWtgqU8iGlk0OhsEQQ0pyIw/6pgnVuZzkG8WJCIioqGFgT+iFFGLNqjUeoS8dameypCmyBI8Tet7dKwUivaeGyof9qcjjS763Ep9DLyFvDUABJQedS/yK65CTtlPYMqsiO83Z01H0FMNORKA3to+8Kc7+CHvSC33GQm5oBrkwJ9oLoASCSHsb+rzGNESvHntMjiTwZgxEYoswe/clbA9mvGX2v5+MYIgHMzg+y7wF3Dtg95a2uk5XZUxbd6zHAe+uXtA5kpDV9jfBEUOQTQPXsYfEC3L2Z8AX2d0lmIACoJ9yCiOlrhTmPFHRCOOIKig1tkgBZjxR+2FA63wO3fBksMyn0RERCMdA39EKSIIAkRTHkI+Bv664m3ZjJoNj/eoJGq0pxeY8TeAYkHVWJC1t0LeGojGHGj1GbDkzIS96LiELBJj5uRoBpSg6rD3WSzjbyT2NYlIPoR89dCZiwf1urqDZQVD/ejzF/LWDVjvTdGYD41og69lW8J2KeSCZogE/oBogDLoqYYUdEIKuREONHW4xmM6K2Mqy2E4az5FyFeHSLhvmbc0PMVK7g52xt9AEU35EAR1n/r8hQPRGxHY44+IRiKtLgMSS31SB74r8zk91VMhIiKiFGPgjyiFRGN+vz5MHwmkg8G8nmQbxYJRamb8DRh1POOvb4G/oKcaYhcfWqs1BhgcE6A3F0Ol1rXbr1KLUIsWhEdgeaOAax8AZdD7OKlFG9RaU5+ycoBY7816aI3J7e8XIwgCjBmJ2XRA9EaAWIbqUGB0TAAA+Fq3IejeBwBdBv5i5xxextTbuB6RsAcAEPRUDchcaWgKemug1prS5j1OpdJCa8zr08+WsL8ZEFTxXldERCOJRucYsdUvqGuexrUs80lEREQAGPgjSinRVICQtxaKoqR6KkNWJOQC0LPSjlKwDSqNESq1ONDTGrFUKi3UWlM8u7K3gt7qeAZZZ3InXIi8yZd1ul+rzxiRGX8B5x6oNEZoByhzrjPR7ORChDx9u0khEvYgEvZCHKDAHwAYHRMRdB9AJOQGEA02SiHXkAqQaEQrdOYi+Fq3IeDaB7XWDE03ZQpNmRVQZAnOms/i25w1n0FvGwtBpU154C/oqUJb9acpncNIEvJUQzQVDkjJ3FTRmYv6tI6lQBM0OvuAlCAlIhrqNCP0d2HqWiTkht+5G+bs6ameChEREQ0BDPwRpZBoyoccCbA5exdiWXxSoLlHxw6lDJ90pRbtkEJtvT5PCrkRCbkgmrsuU6fVZ3RZFlKrzxiRdzkHXHtgsI1JKI06WHSmgl5l5QTc+3Hgm7vRVv0JQp7oeQNV6hMADIdk0wGAHPFDkUNDqtQnEA1Q+lq2IuDaC721pNsAjmjMhb3oeDTtXoaQrwEhXwN8rVthLzwWoim/y4BJJOyBosjJfggJ2qo+QsP2F+Bt3jig16GooLcGukHu7zfQdOYihDzVvV6rYX8z+/sR0Yil1UdLfQ70+zwNL97mjYCiwJQ1LdVTISIioiGAgT+iFIr16Ql5a1M8k6ErlsET7kngL9gGjWgf4BmRRmfrU6nPWH8qsZuMv+5EP+wYWYE/RZEPBosGt8xnjGguQNhXD0WWenS8s/ozBD01aNj+Eqo3PApAgNaYM2Dz0+odEI258LXtAPBdpvBQyvgDon3+pGALfK3boLeU9OicrLE/gkZnQ/225+Cs+QxqrQnm7JnRgEknwdiwvxF7vrgF7vqvkjj79oKeaG+2+m0vIBL2Dei1hjNf644evYd1RZbDCPvquyyVPBzpzIWQI4EelfM+VDjQxP5+RDRiaXQOKEok/ncSEQB4mtZDby0dcje+ERERUWow8EeUQhp9BgS1yD5/XehNqc+h1tMrXWlEWzwTszeC3hoIKg3EfgaAYuWNRlKJ3LC/AZGwF3pbaUqurzMVQlEiCPnq2u07/PugKBF4mtbBXvR9jJ77fzBnTYc5ewZUKu2AztFgL4f/YOBPCkZ/bgy1nwcG2zgIghqKLEHXTX+/GJVah9wJF8HftgOtlR/AkjcPKrV4sERiNRQl0u6cxl2vQpFD8LftTPIj+I6iRBD0VMNRfAJkyY/GXf8ZsGsNd3VblqJ5zxv9GiPsq4eiRLotlTzc6MxFANBpELsjnqYNCLj2pexGCCKiVNPoo/1NpWBrimdCQ4Ush+Ft2Qwzs/2IiIjoIAb+iFJIEFQHS+gx468z0sHAX09LfQ61DJ90pNHZ+tTjL+SpgWjMhyCo+3V9rT4TciQIWUpthpEUcmH35zch6K7s1zh+5x7IkVC3xwAC9NbUBP5iWUbBQ25SiIQ9aNq9DLs/ux6u2i/j2/2tOxEJuWHJmQWdqQD5FZejYMpVAz5Hg6MMIW8tpJALkYOBafUQu+NZpdFDbxsLAND3MPAHAEZHOexFxwGKDFvBMQAAnakIihxG2NeQcKyvdQc8jWuh0TkQcO1L1tTbCfnqochhmLKmIXv8Yrhqv4C3edOAXW+4kiNBSME2eFs296skWzBWMjfNMv7Uog1qrbnHff5CvnrUbXkKpqypsBXMH+DZERENTRpdBoCe3RhJI4OvZSuUSAimbAb+iIiIKEqT6gkQjXSiMZ8Zf12IhFzQ6rPifSw662+mKAokZvwNCrUu2uNPUZRue5QdKuitTkp/Kq0++mFHW/XHsOTMgtaQ26t5JEvYV49IyA1P03roLMV9GiPorkTlmnuhFm3IGH0ybAXHQKUW2x0XcO6BaMqHWmPs77T7RK01RQNJzj1QqfXwtWyBq/ZLKJCh1Weiac9yWHLnQFBp4G5cA60+E7oelrJMFqO9DADgb90BKeSEoBKhUusHdQ49Yc6eATkS6HUZpuxxi2HLnx8vES0efC0FPdUQTfkAoiVhG3e9DL21BNb8+WjY8RLkSBAqta7X81QUBa0H3oUlZ3aHJRWD7gMAohlbBvt4uOpWoWX/ezBlVvT6WuksVuIzEnIj6D7Qq4DvoULeGmh0Dqi1qfkZMFAEQYDOXNyjwJ8sBVCz8a/QiFbkTbokJf1OiYiGArXWDEGlZcYfxXmbNkBryIZozE/1VIiIiGiI4F/MRCkmmgoQ8taOqLKFPSVHgpAjAeitpVCUSJd95WTJD0UOscffINCINiiyFM+4U2QJIV895Eiw03MURUHIU52UbBWdqQAGexma967AvlW/xb5Vt3V57YESK3fqa93a5zF8bTsgqLQwZUxC467/YN/q33a4zgOuPTDYUlvWTmcuRFvVh6jZ8Bg8jetgKzwWpUfeg/yKKyEFW+Gs/RKKIsPTuA7mnJmDHozV6OwQjbnwt+2AFHJBI1pTEhDujqP4Bxg9+7ZenyeoNAkBZo1ogUa0JQRMXLUrEXQfQPb4c2CwlgKK3OeMVCnYhqbdr6N++wsdvj8FPZXQ6rOg1hohCAKMGZMQ8lbxvewwsd51gkrTr4zIZN04MRTpzIU9Cvw17noFUqAF+VOuTtlNEEREQ4EgCNDoHCOu5zV1TFFkeJvWw5w1fUj+7ktERESpwYw/ohQTTfmQIwFIwdZ4JhNFxRrW660lcDd8DSnYAu3BnhaHiwVh1Mz4G3AanR1ANDCg1prQcuC9eP8qtWiBVp8JjT4TWn0WrPlHQmcqgBRsgRwJJCXwp9LoUDzzZshSAO76r1G//XmEvHXQW0f3e+zeiJWhDTj3QJYCUGl6n13md+6C3jIaeZMuQUbJKahcez8adryI/Iqr43+4y1IAQW8N7EXHJ3X+vZU15kcwZ8+CwT4eWkN2fH4a0QJLzhFo2f82tIZMREIumLNnpWSOBnsZfG07oLeMHhE/C3SWYgS90YCJLIfRvPcNWHJmw2AbC0WWIKhEBNz7YLCP6/XYQfd+AICvZQu8zRthzpp62P7KhECkzlyESNgLKdjW6c/pkSjsb4wG9zOnwNuyGZmlp/Z6DFftSvhatsIx6sQBmGHqieYihCv/2+XPUTkSgrv+KzhGnRjPeiUiGsm0+gyW+iQA0d/ZpJATJvb3IyIiokMw448oxWIfYIXSsM9fONCC2k1/RyTs7dP5scCK7mBptHAXff4iwTYA0Ww0Glix5zgWbPU0rIUpswJ5k34Ke9Hx0JmKIId9cNWtRM36R6OBq4P9qZKZsaLS6GHOPQIAEPLVJW3cnooEnRBUWihKBH7nzl6frygK/G274kEZ0ZiLnLLz4Gn8Fp6Gb+LHBVx7AUWGPtUZf5Zi2AqOhmjMaXc3cUbpIkiBVtRvfQ4afUbKehEa7GUIeWsQ9FSNiJ8Foum7TClXzeeQQi5kjjkdwHcZgn3t8xdw74datMDomICmXa9AkaX4PkVREPRUQmc+JPBnir62Q96e9WobKcKBJmgNWTBlViDg2tur90M5EkTd1mdRt/UZWHKOQMbokwdwpqmjMxcBiGY1dsbXshlyJABLTmpuKiAiGmo0OgekAEt9EuCu/xpqrQkG+9hUT4WIiIiGEAb+iFJMo8+AoBbTss+fv20n3A1fo3Hny306P3Iw8Ccac6HSGCEdEvhr3PUqajb+Jf51LAjFHn8DL5ZJFQm2IexvRtBTCWvekbDmzUNmySLkTrwIRTNuwKhZSyCF3Wja/RpC3hqo1HpodMnNalVrDFCLNoR99UkdtyekkBM6czE0Ogd8Lb0v9xn2NyISckJv+y4by5IzE5ac2WjY8U9IIRcUWYK3eRNUGgNEY14yp59UOlMBLDmzIAVbYcmelbIyQ4aDff6Cnspe99AbjnTmIkiBFkhBJ1r2vwtr7hyIxtz4foO1NBo47oOg+wD0ltHIHv9jhPwNaKv+JL5PCrYgEvZCZxkV36bRZ0KlMSDoZuDvUGF/E7T6LBgzJgOKDF/Llh6f27DjX3DXf43ciRcjb9IlferVOByIpnxAUMVvEOmIu2ENRFNhvJ8lEdFIp9FnsMcfwdu8Ca2V/4O96HgIgjrV0yEiIqIhhIE/ohQTBBV0xvy0zPiTDpafcdWthKdpQ+/PD7kAQQW11gytPjOhnI23aT18LdugKHL02KATKo0hbT8YHUpUKi3UWhOkkBPe5g0QBDWMmZPbHac1ZCN77Floq/4YrrqVEM2FAxIQEo25Kcn4k4JOaHQ2GDMm9qnPX8C5CwBgsCXenZtddi4gCDjwzd3Y9en1aK384GDPjqH9lp1ReipUGiOsefNSNget3gGtIQcAoB4hgT8AaNz5MqSQExklpyTut5ZEA8xhT6/GVRQFAfd+6MyjoDMXwZY/Hy373oyPE8syPDTjTxAE6Ew969U2koT9jdAasqHVO6AzF/aqz1/AuRu2gvmw5R81gDNMPZVKC9GYh6Cn436UshyGt2kDs/2IiA6h0TkghZxQlEiqp0IpEvI1oHbzUpgyK5BRsijV0yEiIqIhZmh/ikg0QoimfATTMOMvHGyBzlwMU2YFGra/gEjY16vzIyEX1FozBEEVvav1YMafFHIj5KuDHAnEM72kUNuIKO03VKhFG6SgE56m9TA4yqHWGDs8zlZ47MHyi7XxUoDJJhrzEEpBxl8k5IRGtMHomIigpxpS0Nmr8/3OXRBNhVBrTQnbNaIFeRMvgd4yGlljTseoI36D3In/L5lTHxA6UwHGHvNQQt+3VDA6oll/IyH7VzTmQhDUcDd8DUvO7HZZoXpLCQD0utynFGxDJOSK983MHHM6FDmC5r1vAohmA6q15ni/zxiduajLco0jjaIo0Yw/QxYAwJhRAW/L5vgNK12R5TBC/oak9EUdDnTmIoQ6yfjztWyBHAnAzMAfEVGcVp8BKDKkg+0OaGSRI0HUbvor1Foz8iZdOuRvECQiIqLBx98OiIYA0VSAkK8WiqKkeipJJQVaoNFnIKf8QsiRIBp3/adX50fCbmhECwAkZPwFnLvjxwTcB6LHBp3tPoSmgaMRbQj56uBv3Q5zF43kBUGF3AkXQaXWx4MIySaaooG/nnyYnkxSyAm1zgajYwIAwNe6DQAQ9NbA27yx2/MP7e93OFNmBQqmXA3HqBOht44eNn/Mp6rE56EM9nIA0eB0uhNUmmhgSFAh87BsPyCadavWmnod+At6oj9XY6U8NaIVGSWL0Fb9MYLe2nh/v8O/36K5KHpThhzu2wNKM5GQC4ocglYfDfyZMisQCbkQdHec2XaosK8eUOR4H+B0pzNHs0U7+j3I07AGoqkAOpb5JCKK0+gcADCk+vwpigxX3SpEpN7d7Em917LvHYR8jSiYcjXU2o5vwCQiIqKRbXh8kkiU5kRTPmTJn3Z3bIYDLdDqM6DVO5Ax+mS461b1KjgjBZ1Qa6Pl+rQHM/4URYHfuQsaXQa0hmwE3fsPHts2Ij7oHyo0Ojt8LVuhKBGYsqZ2eaxozEHpUffAmn/0gMxFNOZCkUOD2udEkSVEQm5oRBs0OhtEUyF8rdvgrPkMB76+CzUb/9Zl6aVY1qrB1nHgj/rOlDEJBtu4hDKU6cySOweO4hM67H0mCAJ0lhIE3Pt6NWbQtf9gRt93PTntxcdDq89E065XEHRXJvT3i9GZiwBFTsuetYfrSWm1cKAJAOIZfwbbWAgqDQKuPd2eGyv/PVJ62unMxdEsfn9jwnZZDsPTtJ5lPomIDqPRR9+jw8GWbo4cPK7alajb8jQad/yr3b7BvkEvncmRIJw1n8BWeAx05oGpqEJERETDHwN/RENArASir2VLimfSsYjkR8C1v9fnScGW+AfHoqkQihLpVTnESMgVL9en0WdAjgQhS954ppTOMiqe8SeFmPE3mNQ6GwAFOnMxtPrM7o/XmgYsa01ryAWAQe3zJ4XcAL4rJ2nKmAhX3UrUb3seemspFDmMsK+h0/NjWasM/CWfWrSgeNavoNU7Uj2VQZEx+iRkjzur0/16awkCrn29yigPuPdDZxmdkNGnUmmRNfYseJs3IhxojvcXPFQ0O01AsJOSjYMt4D7Q6xK8PeF37sauT37RbYnusP9g4O9gxp+g0kBryOnRz6qgtwYa0dauFHC60tvGQqXWw1X7ZcJ2X8sWyJKfZT6JiA6j1hig0hgGNeNPCrbB79zT4e8UshRA055l0Bpy4KpbBU/TegDRgF/99hexf/X/pV11m1SJZlX64Sg6PtVTISIioiGMgT+iIUBryIIlZzaadr8CKeRK9XTacVZ/isq190GWAj0+JyL5IEv+aP8JfJfxIB3MgOjRGGE31IeU+gSAkLcOQfd+GGzjoLeMRtB9AIoiQwo6R0RPr6Ei1k/RnD09tRNBdG0JKs2g9vmLhKLBBLUYzUg158yCWmtB3qRLUVBxJQAg6Knq9Pxo1qojfrc20UDRW0sQCbkQ7kVgPOA+AL2lfWlec/YMGOzRHoodZfypNHpoDdkIdbH2B4uiKKhZ/yiqvn2oR+9dnsZ1aKv6qAfjymjc+W8ocgjepq5L+ob9jVCLVqg0+vg20ZiLkLf770XIWzti+vsB0Q+wrQVHw1nzCeRIEED0e9hW+T/ozEUjpuQpEVFvaEQrIuHB+9uxbuuzqFxzL6rWPQhf6/aEfS3734Us+VA0/QaYMqegYdsLiIS9aNj+EpzVnyDkq0+76japoCgy2ir/B3PWdGgN2ameDhEREQ1hDPwRDRHZZecCggoN21/q9C7KyMEso8EWDjRBkcPwtW3v/uCDpIP9+GKBDe3BzL9woLnnY4Rc8VKfmoOBP0/jWihKBAbbWOgtoyFHAgi690ORQ/FgFA28WF+R7sp8DgZBUEFryIn2xBok0sHAX2zNGWxjMXb+/bDmzYVatECjs3eZ9eRv2wWDbdyQ6IlH6c1gL4NWn4nqDY8h3IOsACnYhkjI2WFgTxAE5JSfD1vB9yAaczs8X2cu6jLoPVikYCukkBMhbw3qtj3baZaBLIfRsOOfqNn4VzTs/He3H0q661Yj4NoHrT4LvtatXR4bDjTFs/1iRGNuj25SCHlrRkyZzxhH0fGIhH1w1a0GAHib1sPXug1ZY3+U4pkREQ1Naq1l0P4+DPub4GvZAlvhsZAjAVStexCVax+Er3UHwv5mtFZ+AEfxCdAaMpFTfgFkOYT9X90JZ81nyChZFB1jEKtzpCtfy2aEfHVwFP8g1VMhIiKiIY6BP6IhQiNakFN2HjyNa+FpWNNuf/32F1Gz6YkUzOy7IJ63eVOPzwnHAn8HA34qjR5qrTm+vTtyJARZ8kNzMKNKrbVAUGnhqv8aKo0Borkw/sG0p2lD9BgG/gaNKWsKCqb8DDpz++BAKojGvB5l0SSLFHQCgiqe8Xc4nbkIQW/HwQ9ZDkezVu0s80kDT60xoGjGTVCUCKrWPdhh8M/fthPe5mj2WuBg39SOMv4AQGfKR+6ECzot3RsL/KW6nFfAtQ8AkFP2E3ga1qD1wHvtjgkHWlG19n44az5D1tizIAgquBu+6XRMORJE057XYc6ZBXvRcfC37YQshzs9Puxvime7x2iNeZCCLfGstg6vI4cR8jeMqIw/ANAasmHOnoG2yv9ClsNo3PUKjBmTYcyoSPXUiIiGJLVoHbRqMc6az6HS6JE97myMOuI3KJjyM8iSD1XrHsCBb+6CSmNExuiTAQBavQM548+FFHIid8JFyCw9FYJKg6CvdlDmms5aD/wXemsJ9GwXQERERN1g4I9oCLHkzII5ZxYadryEiOSLb5cjQXia1g1qD7NDxYJ1vuZNPf4wVwq2QBDUCeU3tYasLkt9KrIUHz9y8I/YWGBFEARo9RmIhJww2MZCEFRQa03Q6rPgPRj4Y4+/waNSaWHOnj5kMtaiWTSD9/qIhJzRYHQnwQ/RXISgu+PAX8hTDUWJQNdJYIUo2bSGLBTPuBmKIqH62z8hEvbE94UDraje8Biq1z+K+u0vIuDcDbXW3OcytDpzISJhT7wcbkTyQwq2RTMJw96kPJ6eCLj3QaNzwF50HDJGn4Km3a/DWfNFfH840IqqdQ9ACrlQPHMJMkafBFPmVLjqVnU6Zuv+9xAJe5A99iwYMyZCkcMItO3u9Piwv7FdGS7RmBfd10UP0LCvAVBk6EZYxh8AOIp/gJCvDrUb/4ZwoAnZ4xcPmfcZIqKhRiNaByXjT1EicNV+AWvuXKjUOgiCAHP2dIyafRsKplwN0VSAnLKfJJS2tuYfiXHHPAxbwdEQBDW0hlyEvAz89YencR18rVthL17A90YiIiLqFgN/RENM9rgfIyL54K77Kr7N27wZSiSESMjVZXbBQJGCLTDYyxEONPc4uCIFWqDRORICIxp9JsL+jkt9KoqCvStvg6vm8+j5BwN/mkMyqmLlPg+9w1FnHY2gpzK6nz3+RizRmAsp2NplFk0ydddTUmcughRs6TDQEXDvBwQVdOaigZwiUYJo8O8mRMJe1G5eCkWRoSgKGna8CEElInvcYrhqv0TL/nehs4zq8wdKsXXtbdqI+m0vYM9nN2LPF7/Cni9+hd2f3ThoAfqgay/01hIAQOaY02HLPxr1255F3dZnEfLVo2rdA4Aio3jGzdBbo0F4a95cBN0HEPTWtBsvEvai5cD7sBcvgNaQBdFUALVo7bTcpyyHIQXbOiz1CaDL5yF08PojLeMPiL6/660l8DZvhL3ge+ztR0TUBbVoGZSMP2/TRkghJ2wFxyRsjwYAZ6B45s2w5Mxsd15Cj1vT4FbnSCeKLKFx139Qs/GvMGdPhyW7/XNNREREdDgG/oiGGK3eAXPWVDhrPo1nv3ka1kAQ1ACAyCA3RY9IPsiSH9a8eRBUWvhaNvfovHCgpV3GiFaf0WmPv2hGSAvcjdEyp5Fw9O7VQ0spag+OZzgk8Kc/WO5TpTFApdb18FFRutEezKLpSe+sZJBCzi57SsaCHx31+Qu690NnKoBKLQ7Y/Ig6ojVkI3/y5fC1bkPznmVw16+Gt2kDcidcAMeoEzBq1i3QmYthzprW52to9JlQqfWo3/48PI1rkTnmdBROuw55ky8DoCDURaZbsiiKjIBrfzzwJwgq5E68CHkTL4a7/mvsW/VbKEoERTNuSijFacqcArXW1GHWn6tuFaBE4CheEB/T6JjQaeBPCjQDUNpl/Km1JqhFSzeBv1qoRRvUWlMvH/nwJwgCMkpOhWgqQGbpaameDhHRkKYWrYiE3VAUeUCv46z5DHprKXSW4j6PoTPmI8RSnz2mKDKCniq0Vv4PlWvvQ1vlh8ge/2PkV1wNQaVJ9fSIiIhoGOBvDERDkK3ge6he/wiC7n0QTYXwNm+AJXcOXHUrEQ60tvsgcSDF+vuJpjwY7GXwNm+Kf/DZ3XntehvpsyAFmqEocrsSibFm7/62HZAjQUSCLgAC1KI5foxGnwVBpYlnZwCIl0vsKghD6S+WRRP21ceDwQMpEnJCNBV2Ph9DTrSXibcKRkdZwr6A+wDLfFLKGDMmInvsmWjc9QoEtQhL7tx4oE9nKcboObf3a3xBUCFzzOlQ5AjshcfG7/ZXFBl1W55GJOjs92PoTshXDzkSgN5SmrDdmn8UdNYStB74AJkli9q9RwkqDcw5R8BdtxpZY86Iv08pigJnzWcwZU9PyEI3Oiagvv5rRMJeqLWm6HtXyA2tIQthf7Ss9eHXAADRkNvlTQpBb82ILPMZY86aCnPW1FRPg4hoyNOIVkCRIYe9UIuWAblGONAMb/Mm5E64sF/jiKZ8REKu+HsmdU6Ww6j85h4EPVUH//Ydi6KZv4TBNibVUyMiIqJhhIE/oiHImDEJGn0GnNWfwZhZATkShKN4AVx1KyEFWwZ1LrH+fhpdBkyZFWja/SrkSLDb7LpwsAUGe2LAQ6PPhKJEIAWd0OodCfuizd4FKLIEX+t2SGEX1KI5nukIAPbCY2Gwj0+4dizIwzKfI1s0i8Y6aGUEpaALRsekTvcLKg1EUwFCnsQ+f7IcRshTDVv+0QM9RaJO2YtPQMBdCX/bDuSUnZP08Tu6OSTal9UMKTTwgb+gay8AQGdtfxOAzlSAvIn/r9NzrXnz4Kz+BP62HTA6JgAAAs7dCHlrkDM+8bkyOiYCUOBr3Q6joxxV3z6EkKcaWePOgiCoIag0HfaeFU15CLgPdDqHkK/24NhERESdU2ujwT4p7B6wwJ+rbhVUah0sOUf0axwxXp2jDgbb2GRMLW25ar9A0FuDgqnXwOiYyCohRERE1Ccs9Uk0BAmCCrb8+XA1fAVX7RfQmYuhsxRDrTVBCrQO6lykYAsEQQ2NzgZT5uR4YK4r0eBeWwelPqM9+qQOyn2GvLUQTfnQ6rPga9mMSMgFtdaacIxaa4LRPv6wbWZo9VlQM+NvxBONuR32DpHlMBQlkrTrKIqCSKjrHn8AoDMVInhY4C/kqYaiRJjxRyklCALyJv0UJfN+D7XW3P0JSaIRbYgMQi+igGsfRGMe1Bpjr8/VW8dAa8hGa+X/4qXTnDWfQWvIhsFRnnCs1pAJrSEHnoY1qPr2IUiBFljzj0TjzpfRtGcZtPrMdtntQLQ0cdjXEC/nfShFlhD2NUAcwRl/RETUM7GWCNFKKQPD0/ANTFlTE/r19YXWmAtAYJ+/bshyGC373oUl5wiYs6Yx6EdERER9xsAf0RBlzT8aiizB27wR5oPN0jW6DIQHOeNPCrRAo3NAEFTQGnKjgbnmTV2fE3QCihzvyRcTC/x11Ocv7KuDaMqHKbMC3uZNkEKuhJJqXckpPw+OUSf28BFRuhKNuQj5E8vnKYqMA1/fjX0rb4Oz+jMostTv68hhDxQl0m2wWWcuQtBTk9B3JeA+AAiqeA9AolQRBGHQP0xSi1ZIgxH4c++D3lra/YEdEAQBWWPPhLd5I+q3PY9I2AN3wzewFczvMIhnzJgId8PXkAItKJpxI3InXISCKVcDQKfBO9GYCzkSgNRBz96QvwGKEoFoKujT/ImIaOSI/a0U642ebCFfHYKealhyZvV7LJVahFafyT5/3XDVfgkp2IqMkkWpngoRERENcyz1STREafUOmDKnwNu0Pv7HlkbviPfcGyzhQEs8c08QBBgzJsLXtrPLc2JzPDzjT6XRQ601dxj4C3rrYCuYD721BG3VH0ORwzDYy9sd1xFTZkWPjqP0prOUwFn75cH+WNEPzd31XyPkrYYpcwrqt7+Alv3voGDatfH9fRErVdhtxp+5CIocQtjfGO9BGHTvh86Uz7t3aUTS6Gxd9rZLBlkOI+iuhDXvyD6PYcmZBUWWULf1GQScuwBFhjXvqI6PzZ0Df9sO5E+6LB7QN2fPQKltHCAIHZ4TK3cW9tW1K3sd8tYAwIju8UdERD0jqHUQVNoBu6nG3bAGKrUexozJSRlPNOUj5GXgrzOKLKFl3zuw5M7m7wFERETUb8z4IxrCMktPQ0bJoviHhBqdA1JwkEt9BloSMvdEcyHCvrouSyfGshI1Oke7fVp9ZrtSn5GwD5GQE6IpH0bHBAgqTbRUaA8z/ogAwJp/JLT6TDTtegVANNuvZd9bMGVOQeG06zB6zm+hyGG4aj7v13Xigb9uMv7Eg0GAoKcyvi3g3s8ynzRiaUQrIsGB7fEXK6ert5b0axxr3lzkTbwYIX8jTFnTOg30G+3jUTL3d9BZihO2q0VLp2VUoyVA1R0GQYPuA1CL1kEtwUpERMOTIAjR99YBCvx5GtbAlDklaTesiaa8QevHPRw5me1HREREScTAH9EQpreMQtaYH8a/1uoyBj3wFw62QKP7LvCnMxZAUSII+xo6PUcKtEClMUKtMbTbp9Fntsv4i5V80RnzoVLrYLBF+/ipGfijXlCptMgaexa8zZvgbd4Ed8M3CPnqkFl6GgBAZy6EMXNytz0quxMLXKi7yfjTiBaoRRuCnmoA0UykkKcaegb+aIRSizZIIVeHve2SJeDaC0FQxwPv/WHNm4dRs5Ygp/z8JMzsO4JKA60hu92Hn4oiw1X3FcxZ05J6PSIiSl9q0YpIKPmlPkO+egQ9VTAnocxnjGjMR9jfBFkOJ23MdBFw7UPT7leZ7UdERERJw8Af0TCi0TsQCXshR4KDcj1FiUQz7w7N+Dv4h0iwizIth2cJHqqjjL9ok3cBWmMOgO9Kd2pES3+mTyOQOXsGDPYyNO76D1r2vglT5pSEzB+DvQxBTxUiYW+fryGFnFBpjFCptN0eqzMXwdeyFYosIeSpgaJEoLOM6vO1iYYzjWiFIocgR/wDdo2Aay90luIevT57Qm8tHZD3ItGY1y7jz9eyGVKwBbaCY5J+PSIiSk9q0QIpnPyMv2iZT11SWypEq9goCA9w2e/hJuDah6pvH4JoKkBu+QWpng4RERGlCQb+iIaRWObdYPX5k4JOQJETgnjREmSmLhuzh4Mt7fr7xWgM0Yw/RZHj20K+Omj1mVCpdQAAU9YUAAI0+qzkPBAaMQRBQPb4HyPkrTuY7Xdqwn6jvQyAAr9zV5+vIQWd3fb3i3GMWoCgez9qNy9FwLUXEFTxPmBEI436YHncgSpJJstheJrWJ60X0UDSGnPbffDprP4MOssolgMmIqIe02gHJuMvWuZzalL7UsduIGWfv+8E3PvjQb/CaT+HSqNP9ZSIiIgoTTDwRzSMaPXRnnnhQSr3GQswHhrEEwQBojEfIU9Nl+dpdZ1n/CmylPDBb8hXC9GUF/9aNOah9Mi7YLCP7+9DoBFIbxmFjNEnwZp/NPTW0oR9WkMWtPpM+PtR7jMScnXb3y/GlDEZ+VOugrdpPRp3vwLxYDlbopEoFjCXgv0P/IV8Dajf/iIUWYpv8zZtgCz5Yc2b1+/xB5pozEM40Aw5EgIAhAOt8DRvgK3gGAiCkOLZERHRcKEWLUm5oUaOhFC/9TlUfftnVH37MIKeyqSW+QQAtdYEtWhln79DNGz/J7T6LBRO+3mHbTKIiIiI+oqBP6JhRK2zAxA6zPiTgm3Y88Wv4GvZmrTrhYMHA386R8J20VTQdcZfoPOMP60+8+Ax35X7DHlrIRoTexloDVn88JP6LGvsmcib+P863Gewl8HXtqPPY0shZzxzqSfMWdOQX3ElIEcSyo4SjTSxvq2RkLPfY3mbN8JZ/Qlcdavj21x1q6C3lkI05vZ7/IEWzXpQ0Fb1IRRFgavuSwgqDSy5c1I9NSIiGkbUojUp/XOdNZ/CWfclVGodVBoDbAXzD1ZhSS7RmMeMv4P8zt0IuPYgc8zpDPoRERFR0mlSPQEi6jmVShv9466DjD93/VeQgm1o2PkvjJ7zWwiCut/XkwItUGmM7f4QEU35cNV9CUWJtLtORPJDlnydl/o8JPBnsI2FLIcRDjQnZPwRDSSDvQyuulWIhH1Qa429Pl8KOttlEnbHnD0do2bfBjX7VtIIplLrIahESEkI/IX9jQCAln1vwZo3F7Lkh695E7LGL+732INBby2Fo/gENO1+DQHXHgTcB2DNmcMP/oiIqFei/XPDUCJBCH0sEylHQmjZ/x6seUd2euNcsugto+CqWwkp5B7x/dxbK/8L0ZgLU2byA6xEREREzPgjGma0egfCHWT8uepWQ2cZhZC3Ds7qT5NyLSnQktDfL0ZnKoAiSwj7mzo4p/ngPDM7HFOtMUCtNUHyR48L++oBRW6X8Uc0UIyOckT7/O3s9bmKIkMKtUFzMHOpN3Tmwj6dR5QuBEGARmeDlISSZGF/I0RjLsKBJrjqVsPdsAYAYMmZ3e+xB0O0H+liFEy5Gr7WHZACLbAVHpPqaRER0TCj1kaDZ/15b3XWfIZI2I2M0QuTNa1OOUafDEBA445/Dvi1hrKwvxmexnWwF/0AgsCP5YiIiCj5+BsG0TCj0WW0y/gLeqoQ9FQis/Q0WPOPQvPeFYiEvf2+VjjYccnO7xqzt+/zF/I1AAC0hpxOx9XoM+OlPkPeuoNjMuOPBodGnwmNPgP+1t6X+wx5qqFEQtBZRg3AzIjSn1q0IhJMTsafMbMC5uwZaNn/Nlx1X8KYOXnYZQ+Ys2dg9OzbkDfpp9BZSlI9HSIiGmbUulgZ7b4F/mQ5jNb978KaNw+isfO/35JFI1qRU/YTuBu+id+0MxK1VX8ItcYAa/6RqZ4KERERpSkG/oiGGY3e0a7Hn6tuNdRaM0wZk5E15odQ5DBa9r3V72tJgRZode0Df2rRBrXWhGAH/RnC/gaoNAaoteZOx9XqM+F37kLY34iQrxZq0dLl8UTJJAgCjPYy+PvQ58/XuhWCSoTeNnYAZkaU/jSirdtSnxHJj4Yd/4K3eVOH+xVFRjjQBK0hG5klpyLsb0TAtQ/W3LkDMeUBpzVkwZo3j31tiYio1zQHM/4iYXefznfWfAYp7EbG6FOSOa0umXOOgDl7Bhp2vIRIqP28+9uvcKiTpQCcNZ/DWnAMVGpdqqdDREREaYo9/oiGGa3OASnYCkVRIAgCFEWGu/4rWHKOgKDSQKOzwzF6IVr2roCt8FiIxtw+XyscaIElt33gTxAEiMb8DjP+wv4GaA3ZXX6AaS/8Pmq3PIV9q34LldbEMp806Az2crjqv+p1nz9f6zYY7OOgUmkHcHZE6UsjWuF3NnS6P+iuRO3mJxHy1cPXshnGjEntSmBJwVYosgTRkA2dpRjm7BnwtW6DKWvaQE+fiIhoSFFpTYCg6lOpT1/LVrTsXQFr7txByfaLEQQBOWXnYf9Xd2DfV7+D0V4Og30cpGArfK07EHTvh9aYB6OjDOas6TBmTBy0uQ2G1qoPoURCsBcel+qpEBERURpjxh/RMKPRZUCOBCFLPgCAv3U7pGArLHnz4sc4ihdAo7Ojcdcrfb5ORPJDlnwdlvoEANFUgFAHGX8hXwNEQ9fBRmPGRJQeeReyxp0FADDYxvV5nkR9YXSUA4oMT2PPSwzJchi+th0wOtLrwweiwaTuosefu/5rHFhzLwSViLyJFyPkq+8w6y/sbwQAaA3ZAIDc8gtQPONmqNTiwE2ciIhoCBIEFdRaS69KfSqKjOa9K1D17cPQmUche/ziAZxhxzQ6G4pm3Axb/tEIB1vQuPNlOGu/hFafiayxP4LBWgpv00ZUffsQQr66QZ/fQAl6a9Cy703YixdAq3ekejpERESUxpjxRzTMaA7+gSAFWqDWmuCqWwWtIQd6a2n8GJVaRNbYM1G7+e/wtWzt012SsT6CHZX6BKJ9/lx1K6EockI2Rtjf2KNAnkqtg6N4ARzFC3o9N6L+0hqyYM6ZhZZ978CadyQEVfdvhwHnXiiRUNrddUw0mDSiFZGQG4oSgSCo49tDvgbUbX0W5qzpyJ10MQRBg7bqj9FW+T+Ys6YmjBH2NwKCChp9JgBEy0UPs95+REREyRJ7b+2phu0vwln7BTJLT0NGySntMusHi85cCJ35RwCiN9gJgiahakxE8mH3pzfA79wD0Tj8+8ErSgT1W5+FVp+FzDGnp3o6RERElOaY8Uc0zGgOBuLCwVb4WrbB3fBNh72BzDlHQG8bi8ZdL0NR5F5fJ5bNF/tg9XA6UwEUORzPvAAAORKEFGwd1FIxRH2VWbII4UATXHWre3S8r3Ur1FozdOaiAZ4ZUfpSizYASsIHlIoio37bc9CIVuROuBAqlRaCIMBevAC+1q0IeqoSxgj7G6HVOVhyl4iICNEbYHpa6lNRZHga1yJj1MnILD01ZUG/w8Xe+w+l1hghmvIQdO1LzaSSrPXAfxFw70fuxIv5OwwRERENuKHxWx4R9ZhGZ4MgqOGu/wrVGx6DwV4Gx+iT2h0nCAJyxp+DoKcaLfvfhbPmc9RteRo1G/+G1sr/Ieip6jIg6GlcB525sNMSJKIp2pfv0HKf35VfY+CPhj6duQjm7Jlo2f82FFnq9nhfy1YYHROGzAckRMORRrQCAKSQM77NWf0J/G07kDvxIqg0+vh2S/ZMaHQOtFb+N2GMkL8xXuaTiIhopItm/PUs8Bf21SMS9sLgKBvgWSWH3lKCgHtfqqfRb2F/I5r3Loej+AQYbGNSPR0iIiIaAfjpJdEwIwgqqHU2uOu/gsE+DgVTf9bpHYN6awmsefPQvGcZ6re/gJC3FpGwG027X8X+r+5E855lHZ4nR0LwNm2AOXtWp/NQizaoNEYEvTXxbfHAHzP+aJjILD0VYX9jt1l/EcmHgHsf+/sR9VM04w/xDyjD/iY07X4N9sLvw+iYkHCsoNLAXnQ83PVfQQp+FygMM/BHREQUpxYtiIR7VurT79oLQIDeWjKgc0oWvbUEQXclZDmc6qn0i7thDSAIyCw9NdVTISIiohGCPf6IhiG9ZTQUUwHyK67qtkxIdtlPYMmbC72lFGqtEUA0sFez4fF25dNifC2bIUcCsOR0HvgTBAE6cxGC7gPxbSFfA1RqPdRa9lqi4eHQrD9r/ryEnmOH8rfuABSZ/f2I+klzsBdfLJDXvHcFVBojssae2eHxtoL5aN67HO761XCMOhGKoiDsb4Ql54hBmzMREdFQptZae1zqM+DcA9GUD7XGOMCzSg69tRSKEkHIU5XQ03648TZvgtExESq1LtVTISIiohGCGX9Ew1B+xZUomHodVGqx22PVGgNMGZPjQT8AUKlFiKY8hAMtHZ7jblgL0VQYL+fZGaOjHP627fGSoWF/A7TGnHb9GYiGsozRCxH2N8LbvKnTY3ytW6E1ZENryBrEmRGlH0GlgVprhhRyIRL2wt3wDeyF308o8XkotdYEY8ZEeJo2AADksAey5GfGHxER0UEa0QJZ8vcoKy7g2j2sSk2K5kIIghqBYdznLyL5EXDuhilzcqqnQkRERCPIiAv8vfjiizj++OMxZcoULF68GBs2bOj02HA4jMceewwLFizAlClTcPrpp+PTTz9NOMbj8eCuu+7Ccccdh6lTp+Lcc8/tckyiZBAEVb+DaxpdBqRga7vtshyGt2l9l9l+MUbHBETCXgTdlQAOBv74YSwNM3rraOitJXBWf9puXzjQjPptL8BZ/SlMmVNSMDui9KMWbYiEnHDXfwUoMqz5R3V5vDlrGvzOXYiE3AjFe8nyvYaIiAgA1Af750ZCXZf7lKUAgp4a6K3DJ/CnUmmhsxQj4Nqb6qn0ma9lKxQlAlNGRaqnQkRERCPIiAr8vf3227jnnntwzTXX4PXXX8eECRNw6aWXorm5ucPjH374Yfz73//G7bffjrfffhvnnnsurr32WmzZsiV+zG233YYvv/wS9913H1asWIGjjz4al1xyCerr6wfrYRH1iUbvgCz5EZF8Cdt9LVsgRwIw9yDwp7eWQqXWw9e6FUC01KdoyB2Q+RINJFv+MfC2bEY48N37QWvlf7Fv5W3wNK5F5pgzkDX2RymcIVH60OhskIJOOGs+hSlrGjQ6W5fHmzKnAooCb/Om73rJMvBHREQEINrjD/iuf25nosEzBfphlPEHAHpLSZ8z/oKeatRuXopI2JvcSfWCr2UTRGMeK4cQERHRoBpRgb9nnnkGP/7xj3HWWWdh3Lhx+N3vfge9Xo9XX321w+PfeOMNXHXVVTj22GNRXFyM8847D8ceeyyefvppAEAgEMD777+PX/7yl5g9ezZGjx6N6667DqNHj8ZLL700mA+NqNe0OgcAQAokZv15GtZANBVA102ZTyBass1gHw9fy1bIkRCkYCs/jKVhyZI7GyqVCFftlwCAgHs/Gne9AlvBMSg98m5kjD6JPTmIkkQjWuFv246gpxq2gmO6P15ng95aCk/TeoT9jVCLFqg1hkGYKRER0dCnEe0AgMAhvdc74nfthUpjgGjMG4RZJY/eWoKQr67dDavdiYS9qNn4F7jrv0LLvrcGaHbtBd2ViEh+AICiKPA2b4Ypk9l+RERENLhGTOAvFAph8+bNOOqo78pJqVQqHHXUUVi3bl2H54TDYYhiYg81nU6HtWvXAgAkSUIkEoFOp+v0GKKhSqPPAICEcp9yJAxPD8t8xhgzJsLv3IWQtwYAIBpzkjtRokGg0uhhyZ0DZ83nkCMh1G99FjpTIbLH/7jT3mNE1Ddq0YZI2AutPhPGjIk9OsecNQ2+ls0IeWuh1fMGEyIiohiNzgZL7hw073kdUtDZ6XEB527oraUQhOH1MZDOWgoACLr29/gcRZFRu3kpZMkHe+H30Vb1EUK+uoGaYlzQW4v939yFmg2PQ5ElhLw1kIKtMLK/HxEREQ0yTaonMFhaW1sRiUSQmZmZsD0zMxN79uzp8Jz58+fjH//4B2bPno1Ro0Zh5cqV+OCDDxCJRAAAZrMZM2bMwF/+8heMGTMGWVlZePPNN/Htt99i1KhRPZ6bSiVApepfvzai3lIbMyCoVJDDrVCro3/8SYF6KJEALNkV0Gh69gehNXsymnb9B56GVRAEAQZLXo/PJeqN2DqN/T/ZMkZ9D67az1C78VGEfLUonXsbtIfd/EHUnYFep+lANNggCAIcRcdAq+3Zr6K2vBlo3rsM3uZvYcmZxfeZfuI6peGA65SGg6GyTvMn/gR7Vv4fGne+iKJp17TrB68oCoLuvXAUHz/s3kPV1nyoNXqEvPthzelZAK1h5zL427Zi1IzrD1ao2YTmPa+hePq1AzrX2j2vQKuzIuDajeY9r0Krd0ClFmHJnABVCtfIUFmnRF3hOqXhgOuUhpMRE/jri9/85je47bbbsHDhQgiCgOLiYpx55pkJpUHvu+8+/PrXv8b3vvc9qNVqTJo0CYsWLcLmzZt7fJ2MDFO7X8yJBoPBlAlR5YXVGi2ZplFaoFarkFs4BhrR1KMxFPt41Jjs8DZ+BVFnRFZuPtczDajYek02h2MyWnaNgbdtF0ZN/hHyR00YkOvQyDBQ6zQdCIEStO7TY/SkEyAaevpeMw71tjwEPA2wZxXB4ejZedQ1rlMaDrhOaThI/To1QT37Uuxc9QgU7wZkFB+VsDfgrgVkP3KKJsM+DN9DbdnjgWB1j97/A556tFW+h5Kp56Bg3OzoxpnnY9fqx6AK74UtZ2DKbrbVbYC/dQvGz/s5wkEX9q37B7Q6KzLyK5CZZR+Qa/ZW6tcpUfe4Tmk44Dql4WDEBP4cDgfUajWam5sTtjc3NyMrq+MmyxkZGfjLX/6CYDCItrY25OTk4IEHHkBxcXH8mFGjRuGFF16Az+eDx+NBTk4Orr/++oRjutPS4mXGH6WG2gpnSx2sLj+sVgNaG/cDahPcXgHw9rwBumgph6tuNXSWYrS19a73AlFPqdUqWK0GuFx+RCLygFzDVrwIivoLGHMXoLW1568BopjBWKfDnaIbj9Kj/ghvQAdvoOevM51tCrzODyApNr4++4nrlIYDrlMaDobSOhWMk2HKmold3zwNSZUH0Zgb3+es2YxIRIakyhuW76EqfRFaa1ehpcXT7U2mLQdWQ5YF6DKOjj9WwVgB0VKKXd88h9J5v036jaqKHMHeNc9BZx0HGCZBawDMudvhrPkCtlETUv6cD6V1StQZrlMaDrhOaSjo6Y3QIybwJ4oiJk+ejJUrV2LBggUAAFmWsXLlSlxwwQVdnqvT6ZCbm4twOIz3338fCxcubHeM0WiE0WiE0+nE559/jl/+8pc9npssK5BlpXcPiCgJVKIdIX9L/M0q4K6F1pALSerdm5fePgHO2lXQ6LN7fS5Rb0Ui8oCtM72jAnmOCkRkADLXMvXdQK7TtCDoe/38GDOnoXn/+1Dre/8+RR3jOqXhgOuUhoOhsk6zxp0L/9r7se/rB1A04yaIxhyE/U1o2vc+tMYCKIJhSMyzt3TW8QjvfQc+ZyV05qIuj3U3boLeNg4yRMiHPNaM0aeh6tuH4GnZDYNtTFLn11b1CQKeGoya/WtEItHPdrLG/wRaQz5M2XOHzHM+VNYpUVe4Tmk44Dql4WDEBP4A4JJLLsGSJUtQUVGBqVOn4tlnn4Xf78eZZ54JAPjVr36F3Nxc3HTTTQCA9evXo76+HhMnTkR9fT0effRRyLKMyy67LD7mZ599BkVRUFpaigMHDuC+++7DmDFj4mMSDWVaXQa8nqr41yFfHURzz/tTxhgd0ZKIoiEnaXMjIiI6lME+HqNn3w6dpedVFYiIiEYStWhB0YwbUbXuQVStexAZJaegaffrUGuMyJ9yZaqn12cGRzlUaj08TRu6DPzJchi+1u3IHHN6h2NodHa461YlPfDnrPkElpwjoLeMjm9TqbRwjDohqdchIiIi6qkRFfg75ZRT0NLSgkceeQSNjY2YOHEili5dGi/1WVtbC5Xqu+acwWAQDz/8MCorK2E0GnHsscfivvvug9VqjR/jdrvxpz/9CXV1dbDb7TjxxBNxww03QKvVDvrjI+otjd6BcKAFiqJAURSEfA0w58zp9ThafQYyRp8Cc87MAZglERFRFIN+REREXdPo7CiacROq1j2Ihu0vwpw9A7kT/h/UWmOqp9ZnKpUWxszJ8DZ9i8ySUzo9zt+6A4ocgimzfR8/QVDBkjsXrtovkD3+xxBUyfk4TI4EEfTWwl54fFLGIyIiIkqGERX4A4ALLrig09Kezz//fMLXc+bMwdtvv93leKeccgpOOaXzXzyJhjKNLgOKHEYk7EE4EIIcCUJrzOvTWFljz0ju5IiIiIiIiKjXNDo7imb+EgHnHpiypiW9p10qmLOmoW7L05CCbdDo7B0e423ZBI3OAdGY3+F+a95ctB54D96WzTBnTUvKvIKeKkCRobP2vnIOERER0UBRdX8IEaUrrT4DACAFWuF31wBAQhN4IiIiIiIiGn40ohXm7OlpEfQDEM3iE1TwNG3o9Bhf8yaYMis6fcw6cxF05mK46lYlbV5B9wEIKg10psKkjUlERETUXwz8EY1gGp0DABAOtiDgroEgqKE1ZKV4VkRERERERETfUWvNMNjGwdu0vsP9YX8jQr76Dst8HsqSNxfepg2IhH1QFAX+tp0I+5s7PV6Ww3DVrUZb9adoq/4UvpatCfsD7v3QmQqTVjqUiIiIKBn4mwnRCKYWLRAENcKBFshyK7TGbAiCOtXTIiIiIiIiIkpgzpqGpj3LIEsBqDT6hH3e5s0QBDUMjgldjmHNnYOm3a+hec8yBDyVCDh3w5w1HQVTf9bh8c27l6G18gNAUAGKAkFQYcz8B6DWmgAAQfd+6K1jk/MAiYiIiJKEGX9EI5ggqKDROSD5WxDw1ELXx/5+RERERERERAPJlDUNihyGr3Vru33elk3Q28ZCrTF0OYZGZ4fRMQFt1R8DSgTm7JnwO3dCUeR2x/qdu9Fa+V9kjT0LZcf9DaVH3QtFicDbvAkAIEeCCHprobOwvx8RERENLQz8EY1wGn0GwsFWBNw1EE0M/BEREREREdHQIxpzIJoK4Gn8NmG7Ikvwt26HKXNyj8bJLb8ARTNuRPGsW2EvOh6RsBchT3XCMXIkhPqtz0JvLYFj1AkAAK3eAb1lNLwH+wwGPVWAIkNvGd3/B0dERESURAz8EY1wWn0GQr56BH3NEI25qZ4OERERERERUYdMmVPgbdkMRVHi2wLu/ZAjQRi7KfMZozVkweiYAEEQoLeVQlBp4WvbnnBM894VCAeakTvxYgjCdx+dmbKmwduyCYosIeg+AEFQQzQXJOfBERERESUJA39EI5xG50DAdQAAIJryUzwbIiIiIiIioo6ZMiYhEnIh5K2Jb/O37oBKre9TyU2VSgu9dQz8bTvi20K+BrRWfoDM0lOhO+xvZHPWNMiSH762HQi490M0F0Kl0vb9ARERERENAAb+iEY4jd4BIHq3pI4Zf0RERERERDRE6W1joxl6h/T587Vth8E+DoKg7tOYRkcZ/G3f9flz1n4OlVoPe9EP2h0rmoug1WfC27QeQfd+lvkkIiKiIYmBP6IRTqPLAABodRaoRXOKZ0NERERERETUMZVahME2Dr6WaOBPkSUEnLthsJf3eUyDvTze50+RJbhqv4Q1bx5UarHdsYIgwJQ1DZ7GdQh6a6Fj4I+IiIiGIAb+iEY4rc4BANCbWeaTiIiIiIiIhjZjxgT423ZEg37uA5AjQRjs4/s83nd9/nbA07QekZALtoJjOj3enDUNUrANUGTo+1BelIiIiGigMfBHNMJp9NGMP70lL8UzISIiIiIiIuqa0TERciSIgGsv/G3boVLr+xWAi/b5K4W/bQecNZ9BbxsLnbmw0+MN9vFQaQwQBDXELo4jIiIiShUG/ohGOJXGCLVogdFWkuqpEBEREREREXVJZxkFtdYEX+s2+Fp3RPv7qTT9GtPoKIevZQt8LVtg7yLbDwAElQbmrGnQWUZDpdL267pEREREA6F/vxkR0bAnCAJK5/4WWbm5cDqDqZ4OERERERERUacEQQWDvRze5k0IeWuRUXJKv8c02MvQvHcFVBoDzDmzuj0+p+w8KHK439clIiIiGgjM+CMiaPV2qPp5hyQRERERERHRYDBmTETAtRdyJACDvazf4+ltYyCotLDmzYNKrev2eJVGD7Vo6fd1iYiIiAYCP+knIiIiIiIiIqJhw+iYCABQqXX96u8Xo1JpUTzzZmiNef0ei4iIiCjVGPgjIiIiIiIiIqJhQ2vIhlafCa0xt9/9/WL01tKkjENERESUagz8ERERERERERHRsCEIAnInXgK11pjqqRARERENOQz8ERERERERERHRsGJ09L+3HxEREVE6UqV6AkRERERERERERERERETUfwz8EREREREREREREREREaUBBv6IiIiIiIiIiIiIiIiI0gADf0RERERERERERERERERpgIE/IiIiIiIiIiIiIiIiojTAwB8RERERERERERERERFRGmDgj4iIiIiIiIiIiIiIiCgNMPBHRERERERERERERERElAYY+CMiIiIiIiIiIiIiIiJKAwz8EREREREREREREREREaUBBv6IiIiIiIiIiIiIiIiI0gADf0RERERERERERERERERpgIE/IiIiIiIiIiIiIiIiojTAwB8RERERERERERERERFRGmDgj4iIiIiIiIiIiIiIiCgNMPBHRERERERERERERERElAYY+CMiIiIiIiIiIiIiIiJKAwz8EREREREREREREREREaUBBv6IiIiIiIiIiIiIiIiI0gADf0RERERERERERERERERpgIE/IiIiIiIiIiIiIiIiojTAwB8RERERERERERERERFRGhAURVFSPQkiIiIiIiIiIiIiIiIi6h9m/BERERERERERERERERGlAQb+iIiIiIiIiIiIiIiIiNIAA39EREREREREREREREREaYCBPyIiIiIiIiIiIiIiIqI0wMAfERERERERERERERERURpg4I+IiIiIiIiIiIiIiIgoDTDwR0RERERERERERERERJQGGPgjIiIiIiIiIiIiIiIiSgMM/BERERERERERERERERGlAQb+iIagF198EccffzymTJmCxYsXY8OGDe2OWbduHS666CJMnz4dM2fOxPnnn49AINDluDU1Nbjiiiswbdo0HHnkkfjjH/8ISZLi+99//31ccsklmDdvHmbOnIlzzjkHn332WZdjBoNB3HLLLTjttNMwadIk/OxnP+vy+DVr1mDSpEn44Q9/2OVxNPSl2zpdvXo1ysvL2/3X2NjYw2eEhqJ0W6cAEAqF8NBDD+G4445DRUUFjj/+eLzyyis9eDZoqEq3dXrLLbd0+PN00aJFPXxGaChKt3UKAMuXL8fpp5+OadOmYf78+bj11lvR2trag2eDhqp0XKcvvvgiFi5ciKlTp+Kkk07CsmXLun8iaEgbTut09erVuPrqqzF//nxMnz4dP/zhD7F8+fJ2x73zzjs4+eSTMWXKFJx22mn45JNPevhs0FCVbut0586duO6663D88cejvLwc//jHP3r+ZNCQlW7r9OWXX8Z5552H2bNnY/bs2bj44os7fExEPcHAH9EQ8/bbb+Oee+7BNddcg9dffx0TJkzApZdeiubm5vgx69atw2WXXYb58+fjP//5D1555RWcf/75UKk6f0lHIhFceeWVCIfD+Ne//oV7770Xr7/+Oh555JH4MV9//TWOOuooPPnkk3jttdcwd+5cXH311diyZUuX4+p0Olx44YU48sgju3xsLpcLS5Ys6fY4GvrSeZ2+++67+Pzzz+P/ZWZm9uKZoaEkXdfpL37xC6xcuRJ33XUX3n33XTz44IMoLS3t5bNDQ0U6rtPf/OY3CT9HP/nkE9jtdpx88sl9eIZoKEjHdbpmzRosWbIEZ599Nt588008/PDD2LhxI26//fY+PEM0FKTjOn3ppZfw4IMP4rrrrsNbb72Fn//85/jd736HDz/8sA/PEA0Fw22drlu3DuXl5XjkkUewfPlynHnmmViyZAk++uij+DFr167FTTfdhLPPPhvLli3DD37wA1xzzTXYsWNHP58tSpV0XKd+vx9FRUW46aabkJ2d3c9niIaCdFynq1evxqJFi/Dcc8/hX//6F/Lz8/HTn/4U9fX1/Xy2aERSiGhIOfvss5Xf/e538a8jkYgyf/585YknnohvW7x4sfLQQw/1atyPP/5YmTBhgtLY2Bjf9tJLLykzZ85UgsFgp+edcsopyqOPPtqjayxZskS5+uqrO91//fXXKw899JDyyCOPKKeffnrPJ09DTjqu01WrVillZWWK0+ns1Zxp6ErHdfrJJ58os2bNUlpbW3s1Zxq60nGdHu6DDz5QysvLlaqqqh6NS0NPOq7TpUuXKj/4wQ8Stj333HPKMccc08PZ01CTjuv0nHPOUe69996Ebffcc49y7rnn9nD2NNQM53Uac/nllyu33HJL/Otf/OIXyhVXXJFwzOLFi5Xbb7+9V+PS0JGO6/RQxx13nPLMM8/0ajwaetJ9nSqKokiSpMyYMUN5/fXXezUukaIoCjP+iIaQUCiEzZs346ijjopvU6lUOOqoo7Bu3ToAQHNzM9avX4/MzEyce+65OOqoo3DBBRfgm2++6XLsb7/9FmVlZcjKyopvmz9/PjweD3bt2tXhObIsw+v1wm639/uxvfrqq6isrMS1117b77EotdJ5nQLAGWecgfnz5+OSSy7BmjVrkjImDb50XacffvghKioqsHTpUhxzzDE46aST8Mc//rHbUiU0NKXrOj3cK6+8gqOOOgqFhYVJHZcGR7qu0+nTp6Ourg6ffPIJFEVBU1MT3nvvPRx77LH9GpdSI13XaSgUgk6nS9im0+mwceNGhMPhfo1Ngy9d1qnb7U4459tvv22XtTp//nx8++23vRqXhoZ0XaeUXkbKOvX7/ZAkCTabrVfjEgEs9Uk0pLS2tiISibQrLZiZmYmmpiYAQGVlJQDgsccew+LFi7F06VJMmjQJF198Mfbt29fp2E1NTQlvWgDiX3fWw+ypp56Cz+fDwoUL+/qQAAD79u3Dgw8+iPvvvx8ajaZfY1Hqpes6zc7Oxu9+9zs88sgjeOSRR5CXl4eLLroImzdv7te4lBrpuk4rKyuxZs0a7Ny5E48//jh+/etf47333sPvfve7fo1LqZGu6/RQ9fX1+PTTT3H22WcnbUwaXOm6TmfNmoX7778f119/PSoqKnD00UfDbDbjt7/9bb/GpdRI13U6f/58vPLKK9i0aRMURcHGjRvxyiuvIBwOsx/lMJQO6/Ttt9/Gxo0bceaZZ3Z57UMfEw0v6bpOKb2MlHX6wAMPICcnJyHASdRT/ASeaJiRZRkAcM455+Css84CAEyaNAkrV67Eq6++iptuugmXXXZZPFOpoKAAb731Vq+vs2LFCjz++OP4y1/+0q8eZ5FIBDfddBOuu+469qAaQYbbOgWAMWPGYMyYMfGvZ86cicrKSvzjH//A/fff36+xaWgajutUURQIgoAHHngAFosFAHDLLbfg5z//Of7v//4Per2+X+PT0DMc1+mhli1bBovFggULFiRtTBp6huM63bVrF+666y5cc801mD9/PhobG3Hffffh//7v/3D33Xf3a2wamobjOv3Zz36GxsZGnHPOOVAUBZmZmTjjjDOwdOnSLvsT0fA1lNfpqlWr8Otf/xp/+MMfMH78+F5fk9IH1ykNB8N9nT755JN4++238dxzz7XL/ifqCQb+iIYQh8MBtVqd0IgWiKanx+4uiTUhHjt2bMIxY8eORU1NDQDgrrvuipd9i2XYZWVlYcOGDQnnxO6CObyx8VtvvYXbbrsNf/7zn/t9V4nX68WmTZuwdetW/P73vwcQffNVFAWTJk3CU0891Wkzexqa0nGddmbKlClYu3btgIxNAytd12l2djZyc3PjQb/YfBVFQV1dHUpKSvp9DRo86bpOYxRFwauvvoof/vCHEEUxaePS4ErXdfrEE09g5syZuOyyywAAEyZMgMFgwPnnn4/rr78eOTk5/b4GDZ50Xad6vR733HMP7rzzTjQ3NyM7Oxv//ve/YTKZkJGR0e/xaXAN53X61Vdf4eqrr8att96KM844I2FfVlZWu+y+Qx8TDS/puk4pvaT7On3qqafw5JNP4plnnsGECRN6NC7R4XiLGNEQIooiJk+ejJUrV8a3ybKMlStXYsaMGQCAoqIi5OTkYO/evQnn7tu3L947Jzc3F6NHj8bo0aPj26ZPn44dO3YkvCl++eWXMJvNGDduXHzbm2++iVtvvRUPPvggvv/97/f7MZnNZqxYsQLLli2L/3fuueeitLQUy5Ytw7Rp0/p9DRpc6bhOO7Nt27Z2v9jR8JCu63TmzJloaGiA1+uNb9u7dy9UKhXy8vKScg0aPOm6TmO++uor7N+/n2U+h7l0XaeBQKBdxpRarQYQDVrT8JKu6zRGq9UiLy8ParUab7/9No477jhm/A1Dw3Wdrl69GldeeSVuvvlmnHPOOe32T58+HatWrUrY9uWXX2L69Ok9Gp+GlnRdp5Re0nmd/v3vf8df/vIXLF26FFOmTOnRuEQdUohoSHnrrbeUiooK5bXXXlN27dql3H777coRRxyhNDY2xo955plnlJkzZyrvvPOOsm/fPuWhhx5SpkyZouzfv7/TcSVJUk499VTlpz/9qbJ161bl008/VebNm6c8+OCD8WOWL1+uTJo0SXnhhReUhoaG+H8ul6vLOe/cuVPZsmWLcuWVVyoXXHCBsmXLFmXLli2dHv/II48op59+ei+eFRpq0nGdPvPMM8oHH3yg7Nu3T9m+fbvyhz/8QZkwYYLy5Zdf9uOZolRKx3Xq8XiU733ve8p1112n7Ny5U/nqq6+UE088UfnNb37Tj2eKUikd12nMzTffrCxevLgPzwoNNem4Tl999VVl0qRJyosvvqgcOHBA+eabb5QzzzxTOfvss/vxTFEqpeM63bNnj7Js2TJl7969yvr165Xrr79emTNnjlJZWdmPZ4pSabit05UrVyrTpk1THnzwwYRzWltb48esWbNGmTRpkvLUU08pu3btUh555BFl8uTJyvbt2/v3ZFHKpOM6DQaD8Z+xRx99tHLvvfcqW7ZsUfbt29e/J4tSJh3X6RNPPKFMnjxZeffddxOO8Xg8/XuyaEQSFIW3MxINNS+88AKeeuopNDY2YuLEibjtttvaZcY9+eSTePHFF+F0OjFhwgTcfPPNOOKII7oct7q6GnfccQe++uorGAwG/OhHP8JNN90UT2e/8MIL8dVXX7U770c/+hHuvffeTsc9/vjjUV1d3W779u3bOzz+0UcfxX//+1+88cYbXc6XhrZ0W6d///vf8fLLL6O+vh4GgwFlZWW45pprMG/evG6fCxq60m2dAsDu3bvxhz/8AWvXroXdbsfChQtx/fXXs7/fMJaO69TtdmP+/Pn4zW9+gx//+MddzpOGh3Rcp88//zz+9a9/oaqqChaLBfPmzcMvf/lL5ObmdjlnGrrSbZ3u3r0bN910E/bu3QuNRoO5c+fi5ptvTuhLTcPPcFqnt9xyC15//fV22+fMmYPnn38+/vU777yDhx9+GNXV1SgpKcEvf/lLHHvssd0+FzR0pds6raqqwg9+8IMuj6HhJ93WaWe/F1x77bW47rrrupwz0eEY+CMiIiIiIiIiIiIiIiJKAywKT0RERERERERERERERJQGGPgjIiIiIiIiIiIiIiIiSgMM/BERERERERERERERERGlAQb+iIiIiIiIiIiIiIiIiNIAA39EREREREREREREREREaYCBPyIiIiIiIiIiIiIiIqI0wMAfERERERERERERERERURpg4I+IiIiIiIiIiIiIiIgoDTDwR0RERERERERERERERJQGGPgjIiIiIiIiIiIiIiIiSgMM/BERERERERERERERERGlAQb+iIiIiIiIiIiIiIiIiNIAA39EREREREREREREREREaYCBPyIiIiIiIiIiIiIiIqI0wMAfERERERERERERERERURpg4I+IiIiIiIiIiIiIiIgoDTDwR0RERERERERERERERJQGGPgjIiIiIhogt9xyC44//vhUT2NAPfrooygvL0/1NLp1xx134JJLLkn1NEaEf/7zn/j+97+PUCiU0nm8/fbbmDNnDrxeb0rnkc5uuOEG/OIXv+jVOV6vF0ceeSSWL18+QLOi3hoqr1kiIiIiSg5NqidARERERDQUvfbaa7j11lvjX4uiiIKCAhx99NH42c9+hqysrBTOrnOHz1utViMzMxNHH300brjhBuTm5vZ6TL/fj6VLl2LOnDmYO3duMqfbzqOPPorHHnss/rVer0dhYSFOOOEEXH755TCbzb0es7KyEq+88gqWLl3a4f7du3fjlFNOgSiK+OKLL2C1Wtsdc+GFF+Krr74CAAiCAKPRiOzsbEydOhVnnHEGjj766E6v35PxOxL7Xr7yyiuYMmVKu/1XXnkldu7ciQ8//DC+zev14qmnnsL777+Pqqoq6HQ65OXlYfbs2bj88svj3/+OnmeHw4EJEybghBNOwGmnnQZRFAEAq1evxkUXXdSjOW/fvh1nnnkmHnvsMfzrX//q9Lznn38eDz/8MFatWoW1a9cmHKfRaJCXl4dZs2bhuuuuQ3FxcY+ufahIJIJHH30UF1xwAUwmU3x7KBTCP//5T7z++us4cOAAVCoVcnNzMXPmTFx88cUYO3Zswjg7d+7EE088gdWrV6O1tRV2ux1z587FVVddhfHjxycc2933qyu33HIL3nvvPaxbt67D/TNmzMBJJ52Ee++9N76tqqoKjz/+OL7++mvU19fDarWipKQEc+fOxc9//vP4cb1Zu4evi87MmTMHzz//PC6//HKcddZZ2LZtGyZMmNCjx/rcc8/BZDJh0aJF8W0rV67E8uXLsXbtWtTV1SErKwvz5s3DL37xC+Tk5LQbY+3atbj//vuxZcsWmM1mLFy4EDfccEPC93rDhg1YtmwZVq9ejerqatjtdkybNg3XX389SktLE8a75ZZb8Prrr7e7TmlpKd59990ePa5QKIQ///nPeOONN+ByuVBeXo7rr7++w58NoVAITz/9NJYtW4bq6mpYLBZUVFTgzjvvRF5eXrfXcrlcuP/++/HBBx8gEAhgypQpuOWWWzB58uT4Ma2trXj11Vfx0UcfYffu3ZAkCWPGjMHFF1+MU045JWG8nrxmiYiIiGj4YOCPiIiIiKgLP//5z1FUVIRQKIQ1a9bgn//8Jz755BO8+eabMBgMXZ77+9//HoqiDNJMEx0672+//Ravv/461qxZgzfffBM6na5XY/n9fjz22GO49tpr2wX+rr76alxxxRXJnDqAaIae0WiEz+fDF198gb/97W9YvXo1/vnPf0IQhF6N9dxzz6GwsBDz5s3rcP/y5cuRnZ0Np9OJ9957D4sXL+7wuLy8PNx4440Aos/J/v378cEHH2D58uVYuHAh7r//fmi12j6P31/hcBgXXHAB9uzZgzPOOAMXXHABfD4fdu7ciTfffBMnnHBCu8Bv7HkOhUKor6/H559/jl//+td49tln8cQTTyA/Px9jx47Ffffdl3Den/70JxiNRlx11VXt5qHT6XDGGWfgH//4By688MIOv18ff/wxjj766ITn68ILL8SUKVMgSRK2bNmCf//73/jkk0+wfPnyXgesP/roI+zduxfnnHNOwvaf//zn+PTTT7Fo0SIsXrwYkiRhz549+PjjjzFjxoyEwN/777+PG2+8EXa7HWeddRaKiopQXV2NV155Be+99x4eeughnHDCCb2aV7Ls378fZ599NnQ6XXxuDQ0N2LJlC/7+978nBP6Anq/dE044AaNGjYqf5/P5cMcdd+CEE05IeKyxGx8mTZqEiooKPP300+3WSEfC4TCee+45XHzxxVCr1fHt999/P5xOJ04++WSUlJSgsrISL7zwAj7++GMsW7YM2dnZ8WO3bt0aD9LecsstqKurw9NPP419+/YlBPeXLl2KtWvX4uSTT0Z5eTkaGxvx4osv4swzz8S///1vlJWVJcxNFEX84Q9/SNhmsVi6fUwxseDtRRddhJKSErz++uu44oor8Oyzz+KII45IeA6uvPJKrFu3DosXL0Z5eTlcLhfWr18Pt9vdbeBPlmVcccUV2L59Oy699FI4HA689NJLuPDCC/Haa6+hpKQEAPDtt9/i4Ycfxve+9z1cffXV0Gg0eO+993DDDTdg165dCWukJ69ZIiIiIhpGFCIiIiIiaufVV19VysrKlA0bNiRsv+eee5SysjJlxYoVnZ7r9XoHenqd6mze999/v1JWVqa89dZbvR6zublZKSsrUx555JFkTbNTjzzyiFJWVqY0NzcnbL/22muVsrIyZe3atb0aLxQKKXPnzlUeeuihDvfLsqwcd9xxyj333KNcc801ygUXXNDhcRdccIGyaNGidtslSVLuuOMOpaysTLnvvvv6PH5HOvtexlxxxRXKcccdF//67bffVsrKypTly5e3OzYQCChutzv+dWfPs6IoyhtvvKFMmDBBWbx4cadzW7RoUZePZePGjUpZWZny5Zdfttvn8/mUKVOmKK+++qqiKIqyatUqpaysTHnnnXcSjnvuueeUsrIy5W9/+1un1+nMVVddpfzkJz9J2LZ+/XqlrKxM+etf/9rueEmSlJaWlvjX+/fvV6ZNm6acfPLJ7Z6j5uZm5eSTT1amT5+uHDhwIL69u+9XV5YsWaJMnz690/3Tp09XlixZEv/6jjvuUCZNmqRUVVW1O7apqSnh676uXUXp2Wv/qaeeUqZPn654PJ5Oj4l5//33lbKyMmX//v0J27/66islEom021ZWVqb86U9/Sth+2WWXKUcffXTCen755ZeVsrIy5bPPPotvW7NmjRIMBhPO3bt3r1JRUaHcdNNNCdu7e/67E1tbS5cujW8LBALKggULlHPOOSfh2CeffFKZPHmysn79+j5d66233mr3emlublaOOOII5cYbb4xvO3DgQLv1IcuyctFFFykVFRXt3qe6es0SERER0fDCHn9ERERERL0QyxqrqqoCEM3ymDFjBg4cOIDLL78cM2bMwM033xzfd3iPP1mW8eyzz+K0007DlClTMG/ePFx66aXYuHFjwnFvvPEGzjzzTEydOhVz5szBDTfcgNra2j7PO5ZxUllZGd8WK0135plnYtasWZg+fTrOO+88rFq1Kn5MVVUVjjzySADAY489hvLycpSXl+PRRx8F0HGPP0mS8Pjjj2PBggWoqKjA8ccfjz/96U/96h916PMeCARw8skn4+STT0YgEIgf09bWhvnz5+Pcc89FJBIBAKxZswatra046qijOhx3zZo1qK6uximnnIJTTjkF33zzDerq6no8L7Vajdtuuw3jxo3Diy++CLfbndTxeyP2vZ05c2a7fTqdrsdlUk8//XQsXrwY69evxxdffNGnuVRUVMBut+N///tfu30rV65EKBTC9773vS7HOPy1tmTJEkyZMgW7d+9OOO7SSy/F7NmzUV9fDwAIBoP47LPP2n3Pu3p+1Go1HA5H/OulS5fC7/fj97//PTIyMhKOzcjIwJ133gmfz4e///3vXT6GgXLgwAHk5uaisLCw3b7MzMwejdHd2u2po446Cj6fD19++WW3x/73v/9FYWFhQlYhAMyePRsqlardNrvdjj179sS3eTwefPnllzj99NMT1vMPf/hDGI1GvPPOO/FtM2fOjJerjSkpKcH48eMTxjxUJBKBx+Pp9nEc7t1334VarU7IMNXpdDj77LOxbt26+M9uWZbx3HPPYcGCBZg6dSokSYLf7+/Vtd577z1kZWXhxBNPjG/LyMjAwoUL8b///S/+c7a4uLjd+hAEAQsWLEAoFEp4LwC6fs0SERER0fDCwB8RERERUS8cOHAAAGC32+PbJEnCpZdeiszMTCxZsiThA9nD/eY3v8Hdd9+NvLw83Hzzzbjiiiug0+mwfv36+DF//etfsWTJEowePRq33HILLrroIqxcuRLnn38+XC5Xn+ZdXV0NAAn95TweD/7zn/9gzpw5uPnmm3HttdeipaUFl112GbZu3Qog+oHyHXfcAQA44YQTcN999+G+++7rssThbbfdhkceeQSTJk3CrbfeitmzZ+OJJ57ADTfc0Ke5A4nPu16vxx//+EccOHAADz30UPyYO++8E263G/fcc0+8jOC6desgCAImTZrU4bgrVqzAqFGjMHXqVBx//PHQ6/V48803ezU3tVqNRYsWwe/3Y82aNUkfv6cKCgoAAMuWLet3idnTTz8dAPD555/3eYxJkyZh7dq17bZ/8sknmDx5crd9Mg9/rf3mN79BRkYGlixZEg/s/utf/8Lnn3+O2267LV4OdNOmTQiHw+2+57HnZ8WKFZAkqctrf/TRRygsLEwo0Xio2bNno7CwEJ988kmX4wyUwsJC1NXVYeXKlf0ap6u121Pjxo2DXq/v8Ht9uHXr1iX0oeuK1+uF1+tNCMhu374dkiShoqIi4VhRFDFx4sT4z63OKIqCpqamhDFj/H4/Zs2ahVmzZmHOnDn43e9+B6/X26O5bt26FSUlJe2C61OnTo3vB4Bdu3ahoaEB5eXluP322zF9+nRMnz4dp512WsINF91da9KkSe0CpVOmTIHf78fevXu7PL+pqQkAOnwOOnvNEhEREdHwwh5/RERERERd8Hg8aGlpQSgUwtq1a/H4449Dr9fjuOOOix8TCoVw8skn46abbupyrFWrVuG1117DhRdeiNtuuy2+/ac//Wk8UFNdXY1HH30U119/fUL/tBNPPBE/+tGP8NJLL3XYV62rea9fvx6PPfYYRFFMmLfNZsOHH36YkBXz4x//GAsXLsTzzz+Pu+++G0ajESeddBLuuOMOlJeX44c//GGX1922bRtef/11LF68ON4v6/zzz0dGRgaefvpprFq1qtNee4dyOp0AEO/x99JLLyE88uCNAAEAAElEQVQrKyseiJk2bRouu+wy/P3vf8cJJ5yApqYmvPXWW/j1r3+N0tLS+Dh79uyBzWbrMNstHA7j3XffxbnnngsA0Ov1OP7447FixQpcdtll3c7xULF+YbFgVbLH74kFCxagtLQUjzzyCF599VXMnTsXs2bNwnHHHdfjLLCY2OM5PCuoN4qLizsMInz66ac488wz2233er1oaWmBJEnYunUr7rrrLgiCEA+kW61W3HXXXbj00kvx5JNP4tRTT8Uf//hHLFiwIGFdxrK5ioqKEsafPn065syZg5dffhkffvgh5s2bh5kzZ+K4446LBwUBwO12o6GhAT/4wQ+6fHzl5eX48MMP4fF4epxNmSwXXngh3njjDVx88cWYOHEiZs+ejblz5+Loo4/utvfo4Tpau72h0WiQl5eHXbt2dXmcJEk4cOBAt89rzLPPPotwOIyFCxfGtzU2NgIAcnJy2h2fnZ3dbfBy+fLlqK+vb9cDMTs7G5dddhkmTZoERVHw2Wef4aWXXsK2bdvw/PPPQ6Pp+qOTxsbGhD6Eh44LAA0NDQCivRkB4B//+AfsdjvuvPNOAMATTzyByy67DK+88gomTJjQ7bU6CkjHnpNYYLEjbW1t+M9//oMjjjiiw+ews9csEREREQ0vDPwREREREXXh4osvTvi6sLAQDzzwQDy7KOYnP/lJt2O9//77EAQB1157bbt9giAAAD744APIsoyFCxeipaUlvj8rKwujR4/G6tWrexT462je999/P/Ly8uLb1Gp1PDNOlmW4XC7IsoyKigps2bKl22t0JJYBdckllyRs/+lPf4qnn34an3zySY8CfyeffHLC1+PHj8e9996bENS49tpr8dFHH2HJkiXw+XyYM2cOLrroooTz2traYLPZOrzGp59+ira2Npx66qnxbaeeeiquuuoq7Ny5E+PHj+92njFGoxEAEjKEkjl+T+j1evznP//BX//6V7z77rt47bXX8Nprr0GlUuG8887DkiVL2pU+7M3j6S2r1YpAIAC/3x//vu3YsQM1NTU49thj2x3/61//OuHrjIwM3HvvvZgyZUp82/z583HOOefg8ccfx3vvvQedThcPnsS0tbUBQLvvuyAIeOqpp/DUU09h+fLlePPNN/Hmm2/izjvvxMKFC3HnnXfCarXGH7PJZOry8cX2e73eQQ/8jR8/HsuWLcNf/vIXfPzxx9i6dSuee+45GI1G3Hrrrfjxj3/c47GS8b222WxobW3t8hin0wlFURKyjjvz9ddf4/HHH8fChQvjpYYBxEv7drSOdTpdQunfw+3evRt33nknZsyYgR/96EcJ+w6/aWPRokUoKSnBQw89hPfeew+LFi3qcr6BQKDTOR0679hz7PV6sWzZMuTn5wOIlrU98cQTsXTpUjzwwAN9ulZsWzAY7PA8WZZx8803w+Vy4fbbb+/wmI5es0REREQ0/DDwR0RERETUhd/+9rcoLS2FWq1GVlYWSktL25VYi2W8dOfAgQPIyclJKBN6uH379kFRlE7LhXaXeXL4vN1uN1599VV8/fXXHX5Y/Prrr+Ppp5/G3r17EQ6H49sPz5bqqerqaqhUqnY9vLKzs2G1WuMlR7vz6KOPwmw2x5/bw8cDoh9033333Tj77LOh0+lw9913xwOoh+qs7OXy5ctRVFQEURTjmTijRo2CwWDAihUrcOONN/ZorkA0MxFIDBb1dPxYFlOMxWKBXq/v8bUPP/dXv/oVfvWrX6G6uhorV67E008/jRdeeAFms7nH5VY7ejy9FXveD/2efPzxx8jKykoI5sVcc801OOKII6BSqeBwODB27NgO1/uSJUvw4YcfYuvWrXjwwQc7zWbs6PsuiiKuvvpqXH311WhoaMDXX3+N5557Du+88w40Gg0eeOCBhIBeV3oaIIwJhULxTNaYjIyMePC9O4ev7dLSUtx///2IRCLYtWsXPv74YyxduhS33347ioqKOu1rebhkfa87eu11dmxXdu/ejWuvvRbjx4+PZw3HxF4XHfULDQaDnb5uGhsbceWVV8JiseDPf/5zj57ziy++GH/+85/x5ZdfYtGiRYhEIgk3YwDRgKcoitDr9Z3O6dB5x/4/c+bMeNAPiJahnTlzJtatWxd/fJ2tlc6uFdsWCzYe7ve//z0+++wz/PGPf+w0q7Cj1ywRERERDT8M/BERERERdWHq1KkdBikOJYpiu2BgX8myDEEQ8Pe//73DD6dj2TndOXTeCxYswHnnnYebbroJ7777bvwD/jfeeAO33HILFixYEO9RqFar8cQTT/SrxCPQ/w+OjzjiCGRkZHR7XKwHXTAYxP79+1FcXJyw3263d9gX0ePx4KOPPkIwGOwwyPrmm2/ihhtu6PHj2LFjBwBg9OjRvR5//vz5CfvuuecenHnmmfEP8DvL4PH7/Z1+yA9EszzPPvtsnHDCCViwYAFWrFjR48Bf7PF0FHDtKZfLBYPBkBCM+fTTT3HMMcd0+LyWlZX1KFi1detWNDc3J8zzULHAutPp7DIgn5OTg0WLFuHEE0/EqaeeinfffRf33nsvLBYLsrOzsX379i7nsX37duTm5vY422/dunXtMlL/97//xYPDoVCowwCaoigIBoOdZmuq1WqUl5ejvLwc06dPx0UXXYQVK1b0OPB3+NrtC5fL1e35NpsNgiB02ae0trYWl156KcxmM5588sl2z+3hpTMP1djY2GH5Srfbjcsvvxxutxsvvvhiu2ztzuj1etjt9ngArra2tl2Z0ueeew5z585FdnY26uvrO5wT8F0Zztj/O+pvmZmZGe8F2NVayc7ObnezAPDdc9LRc/DYY4/hpZdewk033YQzzjij08fc0WuWiIiIiIYfBv6IiIiIiAbJqFGj8Pnnn6Otra3TrL9Ro0ZBURQUFRUl9KrrD7VajRtvvBEXXXQRXnzxRVxxxRUAgPfeew/FxcV47LHHEoINjzzySML5vQniFRYWQpZl7N+/H2PHjo1vb2pqgsvlQmFhYT8fzXe2bduGxx9/HGeeeSa2bduG2267DStWrIDFYokfM2bMGKxYsQJutzth+/vvv49gMIg77rgDDocjYdy9e/fi4Ycfxpo1azrspXW4SCSCN998EwaDAbNmzer1+M8880zC/nHjxgFAvO/c3r17O5zHvn37elQu1Gazobi4GDt37uz22Jjly5cDAI455pgen3O4qqoqjBkzJv61y+XCunXrcP755/d5TJ/Ph1tvvRXjxo3DjBkzsHTpUixYsABTp06NHxO7ZlVVVae9zg6l1WpRXl6Offv2obW1FdnZ2TjuuOPw8ssv45tvvunwuf/mm29QXV2Nc845p8dznzBhQrvvdSyQVVhYGO+Bd3gAbf/+/YhEIj167VRUVADoODDWkY7Wbm9JkoTa2locf/zxXR6n0WgwatQoVFVVdbi/tbUVP/3pTxEKhfDSSy91GMAqKyuDRqPBpk2bcMopp8S3h0IhbN26NaEfIBANml911VXYt28fnnnmmfhrqyc8Hg9aW1vjNyBkZ2e3+/7FMucmTJiA1atXt+v3uH79egDAxIkT4/PXarUdBgkbGhri1+pqrUyYMAFr1qyBLMsJN5xs2LABBoOh3fvGiy++iEcffRT/7//9v/jP/s4c/polIiIiouEpObclExERERFRt0488UQoioLH/j979x1fd13vcfx1zslJcs7J3qtN2qRtugeFMsqeBSvIUlQQBeQKKMvLUBTEASIqeEWULQgCghRki2BZZZXuPdNm73X2un+c5LQhO01yctL38/HgIfmd7/n9vif5JcHzzufz+eMfuz3W2WLtlFNOwWQy8cc//rFbS7xgMNjvHK3eLFq0iDlz5vDXv/41XEHWWVG4/3XWrFnD6tWruzy3c9ZTX5U6nTpnt/31r3/tcrzzTeyeZrsNhdfr5eabbyYrK4sf//jH3HHHHdTX1/OrX/2qy7p58+YRDAZZv359l+MvvfQSEyZM4IILLuC0007r8s8ll1yC1WrlX//6V7/78Pv9/OIXv2DHjh1ceOGF4Tf9B3P+I488sss/nYHHzJkzSU9P5x//+Ee31n5vvfUWNTU1HHPMMeFjmzdv7taKEELtV3fs2DHgIPlf//oX//jHP5g/f36X+WqDtXHjRhYsWBD+uLM684sVjoNx9913U1VVxZ133slNN91Efn4+N910U5fPz6xZszCbzd2+5rt376aysrLbOTsDyeTk5HDwcskllxAfH8+tt97a7XuuubmZW2+9FYvFwqWXXjrgvScnJ3f7WndWbHZ+Hf/2t791e96TTz7ZZQ2Egsf9W/N26pyxOZCvdW/37mBt374dt9vN/Pnz+107b968bl8XCAW63/3ud6mpqeGBBx6gqKiox+cnJiZyxBFH8NJLL9He3h4+/uKLL+JwOLrMBvX7/VxzzTWsXr2ae++9t9f9ud3uLufq9Kc//YlgMBgOv+Pi4rp9/TrnSJ522mn4/X6eeeaZ8PM9Hg///Oc/mTt3britZ0JCAscccwyrVq1ix44d4bU7duxg1apV4SrNvu6V0047jfr6et58883w8xsbG3n99dc5/vjju1SGvvrqq/ziF79g6dKl3HzzzT2+/v198XtWRERERKKTKv5EREREREbJ4YcfzplnnskTTzxBWVkZRx99NIFAgJUrV7Jo0SK++c1vMnHiRK655hp++9vfUlFRwUknnYTNZqO8vJy33nqL888/n0suuWRI17/kkku4+uqr+ec//8kFF1zAcccdx5tvvsmVV17JcccdR3l5OU8//TQlJSXhuV8QanlXUlLCa6+9RlFRESkpKUyZMoWpU6d2u0ZpaSlf+cpXeOaZZ2htbeXQQw9l3bp1vPDCC5x00kkcfvjhQ/787e/+++9n06ZNPPbYYyQkJFBaWsqVV17JPffcw2mnnRYOGA855BBSUlJYsWJFOMSqqanh448/5sILL+zx3LGxsRx99NG8/vrr3HLLLZjNZiDUMvDFF18EwOVyUVZWxr///W/27NnDGWecwdVXX31A5+9p3Q033MBNN93EOeecw+mnn05KSgqbNm3i+eefZ9q0aV0qzj744AP+7//+jxNOOIG5c+ditVopLy/n+eefx+Px8P3vf7/bNd544w2sViter5eamhref/99Pv/8c0pLS7n33nsH8qXo0fr162lubu7SGnH58uUsWLCgS+XlYKxYsYKnnnqKq666ipkzZwKhtqgXXngh99xzDzfccAMQCmgWL17MihUrwl8TCAWjP/zhDzn66KNZuHAhycnJ1NTUsGzZMmpra/nRj34UDsOLioq48847+d///V+WLl3KueeeS0FBARUVFTz33HM0NTXxu9/9rsdWqM8//zzvvfdet+MXXXRRr+Ha9OnTOe+883j88ccpKysLB0Affvghy5cv57zzzusyl+3BBx9kw4YNnHzyyeGqxo0bN7Js2TJSUlL41re+1eX8A713h+LDDz/EYrEMqLXoiSeeyIsvvsiuXbu6hJM//OEPWbt2Leeccw47duzoEorZbDZOOumk8MfXXnstX/va17jwwgs5//zzqa6u5tFHH2Xx4sVdwtE777yTt99+m+OPP57m5ubw6+905plnAqF2nF/5ylc444wzwtVu77//PsuXL+foo4/u1t6zJ3PnzuW0007jd7/7HQ0NDRQWFvLCCy9QUVHBL3/5yy5rr7vuOlasWMG3vvWtcDvPxx9/nOTkZP7nf/6n32udeuqpzJs3j5tvvpnt27eTmprK3//+d/x+f5fv8bVr13LDDTeQkpISDkv3t2DBgi6tkXv6nhURERGR6KTgT0RERERkFN1xxx1MmzaN5557jrvuuovExERmzZrVpRrlu9/9LkVFRTz22GPcd999AOTk5HDUUUf1206vL6eccgoTJ07kkUce4fzzz+fss8+mvr6eZ555hvfff5+SkhJ+85vf8Prrr/PJJ590ee4vfvELfv7zn3PHHXfg9Xq56qqregz+OtcWFBTwwgsv8NZbb5GRkcHll1/OVVddNeS972/Dhg385S9/4Zvf/GaXIPG73/0u//nPf7jlllt45ZVXSEpKIjY2lqVLl/L6669z3XXXAaEqmEAgwPHHH9/rNY4//njeeOMN3n333fAb4dXV1eFwyWq1kpWVxbx587jttts46qijws8d6vl7ctZZZ5GWlsZDDz3EQw89hNvtJjs7mwsvvJArrriiyyyuU045BbvdzgcffMBHH31ES0sLSUlJzJkzh29/+9s9hq633XYbEArLUlNTmT59Or/61a9YunRprzPlBuL1118nLy8vfM1gMMh7773Hd77znSGdr729nR//+MfMmDGjSziycOFCLrroIh599FFOOeUU5s2bB8A555zD97//faqqqsLVVoceeig/+MEPeO+993j00UdpamrCZrMxffp0fvjDH3Lqqad2ueaSJUuYPHkyDzzwAM8991y4Re+iRYu4/PLLe73///73v/d4/Oyzz+6zqu72229n6tSpPP/88/zud78DQpV7t9xyS7f2qJdffjkvv/wyn376Kf/6179wuVxkZmZyxhlncMUVV3SbdTnQe3coXn/9dU4++eQBVQwef/zxpKam8tprr3HFFVeEj2/evBkIhabPP/98l+fk5+d3Cf5mzpzJo48+yt13380dd9yBzWbj3HPPDX9/f/Gc77zzDu+88063vXQGf0lJSRx33HF8+OGHLFu2DL/fT2FhIddddx3f+c53Bjy/9a677uKee+7hpZdeoqWlhWnTpvHnP/+ZQw89tMu6kpIS/va3v3H33Xdz//33YzAYOPzww7nhhhsGNH/QZDLxwAMPcNddd/HEE0/gdruZPXs2d9xxR5c2ndu3b8fr9dLY2MiPfvSjbue54447utwnX/yeFREREZHoZQh+sX+QiIiIiIjIOLJ3716WLFnCgw8+eECtK2VgPB4PJ5xwApdddlm48mzt2rWcd955vPLKK4OaszZUfr+f008/nSVLlnDNNdeM+PUOVps2beIrX/kKL7zwQniOXX/uu+8+/vnPf/Lmm2+GKywlsnr6nhURERGR6KUZfyIiIiIiMq5NmDCBc845hwceeCDSWzkoPP/888TExHDBBRd0OX7dddeNSugHoaqoq6++mqeeegq73T4q1zwYPfDAA5x66qkDDv0ALr74YhwOB6+88soI7kwGo7fvWRERERGJTqr4ExERERERERERERERERkHVPEnIiIiIiIiIiIiIiIiMg4o+BMREREREREREREREREZBxT8iYiIiIiIiIiIiIiIiIwDCv5ERERERERERERERERExgEFfyIiIiIiIiIiIiIiIiLjgII/ERERERERERERERERkXEgJtIbEKira4v0FuQgZzQaSEuz0dhoJxAIRno7Ij3SfSrRQPepRAPdpxINdJ9KNNB9KtFA96lEA92nEg10n8pYkJmZOKB1qvgTEYxGAwaDAaPREOmtiPRK96lEA92nEg10n0o00H0q0UD3qUQD3acSDXSfSjTQfSrRRMGfiIiIiIiIiIiIiIiIyDig4E9ERERERERERERERERkHFDwJyIiIiIiIiIiIiIiIjIOKPgTERERERERERERERERGQcU/ImIiIiIiIiIiIiIiIiMAwr+RERERERERERERERERMYBBX8iIiIiIiIiIiIiIiIi44CCPxEREREREREREREREZFxQMGfiIiIiIiIiIiIiIiIyDig4E9ERERERERERERERERkHFDwJyIiIiIiIiIiIiIiIjIOKPgTEdnPuecu5dlnn4r0NkREREREREREREREBi0m0huQ8W39+rVcccWlLFp0BL/5zb1dHquqquS8874c/thisZKdncP8+Ydw/vkXMGHCxEGdrye//OVttLe3cccdv+1y/PPPP+MHP/gfXnvtHRITE/H7/Tz11BO89tq/qK6uJi4ujoKCCXz5y19h6dKzwud67bWXATCZTCQlJVNcXMJJJ53K6acvxWg0hs/blz/84c8sWLAQgNdee5mXXnqB++9/mMrKCh544E+sWrWStrZWkpNTmDatlO997wcUFhaFn//BB+/x978/wZYtmwkE/EyaVMzZZ5/H6acv7fa5ffTRJ5kyZVq/nyeAE044gfPO+xrnnntBl+MPP/wX3ntvOY89FgrDmpqaePjhP/Phh+/T1NRIYmISJSVTuPjiS5kzZx4QCs+qq6sAiI2NIy0tjenTZ3LWWedwyCGHhs/76KMP9rmn99//rNuxL36OU1JSKC2dwfe+9wOKi0sG9FoBXn31X/zhD7/l9df/2+X4gw8+jsViGfB5RERERERERERERETGCgV/MqJefvlFzjnnq7z88ovU19eRkZHZbc099/yJSZMm43K52LlzO//4x9NcfPEF/PrXv2fhwsMGfb6hePTRB3nxxX9y7bU3UFo6HbvdzpYtG2ltbeuybtGiI/nRj35KIBCgsbGRjz/+kHvv/S3//e9/uPPO3zF79lxefPH18Pp77/0tdrudH/3op+FjSUnJ4X9/773lLF58DD6fj2uvvZKJEwv55S9/Q0ZGBrW1NXz00Ye0te3bw3PPPc0f/vA7vvGNb3H99TdhNpt5773l3H33HezcuYOrrrpmWD4ffbnllhvwer3ccsvPyMvLp7GxgZUrP6W1taXLuksv/R+WLj0Lr9dHdXUlb7zxGtdccwWXXvo/fOtbl3DBBRdy1lnnhNdfdtm3ugSt/Xnqqeex2WzU19fzpz/dy//+79U888wyzGbzAb2+1NTUA3q+iIiIiIiIiIiIiEikKPiTEeNwOPjPf/7Nww8/TmNjPa+++i8uuug73dYlJyeTnp4BQH5+AUcddQxXX/097rzz5zzzzDJMJtOgzjcU77//Ll/5yrmccMJJ4WNTpkztti421hzea2ZmFtOmlTJz5myuvvp7vPbayyxdelb4cYC4uDi8Xk+XY53cbjeffvoRl19+Jbt27aCiopx7772fnJxcAHJycsMVdAA1NdX88Y/3cN55F3D55VeGj19wwTcxm2O45567Of74k5g5c9YBfz5609bWxpo1q/i///sL8+cfEt7njBndr2m1WsOvOycnh3nzFpCRkcHDD/+F448/kYkTi7BareH1RqOxy3P6k5qaRmJiIunpGZx33gXcdNN1lJXtpqRkCgBPP/03Xn31X1RWVpCUlMyRRx7NFVf8AKvVyueff8avfvUzABYvDlVffvvbl3HJJZdz7rlLOf/8Czj//K8DUF1dzT333MXKlZ9iMBhZtOgIrr32f0lLSx/iZ1FEREREREREREREZGRoxl+UqmqvZFvT1lH7p6q9ctB7fPvtf1NYWMTEiUWccsrpvPLKSwSDwX6fZzQaOe+8C6iurmLLlk0HfL6BSEtL5/PPP6OpqWnQzz3kkEMpKZnK8uVvD+p5K1d+SkZGJoWFRaSkpGI0Gnnnnf/g9/t7XP/f//4Hn8/HBRdc2O2xM888B4vFyltvvTHo/Q+GxWLBYrHy3nv/xePxDPr55533NYLBIO+9t3zY9tTe3s5//vMmQJdqP6PRyDXX/C9PPPEsP/7xbXz++af86U9/AGD27Ln84AfXY7PZePHF13nxxdd7/LwGAgFuvvk6Wltb+b//e4Df//4+Kisr+OlPbx62/YuIiIiIiIiIiIiIDBdV/EWhFnczF7/+dQLDFHoNhNFg4Nmly0iOSxnwc1555UVOOWUJAIsWHYHd3s6qVSvD8+360jnTrqqqKlxNdiDn68/3v38tP/nJjZx55qlMmjSZWbPmsHjxsRxxxFEDen5hYSE7dmwf1DVDbT6PBULVg1df/UPuv/8PPProg5SWTmfBgoWcfPJp5OcXALB37x4SEhLIyOheEWc2m8nLy2fv3rJB7WGwYmJi+PGPb+XXv/4ly5b9k2nTpjFv3iGceOIp4Uq7viQlJZOamkZVVdUB7+Xss08HwOl0ArB48TFdZiF2VuwB5Obmcdll3+Puu+/ghz8MtUhNSEjAYDD0WWG4cuUn7Ny5g2effZHs7BwAbrnlZ1x44fls2rSB6dNnHvDrEBEREREREREREREZLgr+olByXAqPnfYU7d72UbtmgjlhUKHfnj272bhxA7/61d1AKDA64YSTeeWVFwcU1HVW8hkMhgGdr7q6mgsvPC/8/Asv/Pag2oBOmjSZxx9/hi1bNrFu3RpWr17FTTddx5IlX+Kmm34ygP0CGAZ8vWAwyIcfvsvtt98ZPnbOOeezZMkZfP75SjZsWMc777zF448/yq9//VsOPfTwAZ+7P2+++Rq/+c2vwh/fffcfOOSQQwb8/OOOO5EjjljM2rWr2LBhPR999CFPPfU4N954C6efvrTf5weDwfDX9UDcd9+DxMfHs2HDeh5//BF++MMfdXn8008/5m9/e4yyst3Y7Xb8fj8ejxuXy0V8fPyArrF7926ysrLDoR+E7pWEhER2796l4E9ERERERERERERExhQFf1EqNyEv0lvo08svv4jf7+ess5aEjwWDQcxmM9deeyMJCQl9Pr+sbBcAeXl5AzpfRkYGjz76VPixpKQkAGw2G9XV3avL2tvbMZlMWCyW8DGj0cj06TOZPn0m55//dd5441V+/vOfctFF3yEvL7/f/XbudSA2btyA3+9n1qw5XY5brTYWLz6GxYuP4bvfvYLrrruKv/71EQ499HAmTJhIe3s79fV1ZGRkdnme1+ulsrJ8QKHq4sXHdJnJl5kZOpfNZqO9vXuY3N7e3u3rFRcXx6GHHs6hhx7OxRdfyp13/pyHH/5Lv8FfS0szzc1N5OYe+P2bm5tPYmIiEycW0dTUyK233sx99z0IQFVVJTfeeC1nnXUOl112BUlJSaxdu5o77/w5Xq93wMGfiIiIiIiIiIiIiEg00Yw/GXY+n4/XX3+Vq666hkcffTL8z2OPPUVGRiZvvfV6n88PBAL84x9Pk5ubz5Qp0wZ0vpiYGAoKJoT/SUpKBmDChEJ27drZbR7d1q2byc3NIyam9+y7qGgyAC6Xs8/9rlz5KTt2bOfYY08YyKcHgPffX84RRyzGZDL1usZgMFBYWBRuZXnssScSExPD3//+t25rly17HqfTyUknndrvta1WW5fPVVxcKASbNGkSmzdv6rZ+69bNTJgwsc9zFhVN6vfzBPCPfzyN0WjkmGOO63ftYJx99vns3LmD5cvfAWDLlk0EAgGuuupaZs2azcSJhdTX13V5TkyMGb8/0Od5i4qKqK2toaamOnxs166dtLe3MWnS5GF9DSIiIiIiIiIiIiIiB0oVfzLsPvzwfdraWvnSl87qVil27LEn8PLLL3HWWeeGj7W0tNDQUI/L5WLXrh08++zf2bRpA7/5zb2YTCbeffe/gzrf/k45ZQmPPfYQv/jFrXz96xeRkJDA6tWf8+yzf+eKK74fXnfLLTcwe/ZcZs2aS3p6OpWVFfzlL/cxYcJEJk4sCq/zeLw0NNQTCARobGzk448/5IknHuPII4/mtNPOGPDn6P33l3Pppf8T/njbti08/PBfOPXU0ykqmozZbGb16pW88spLfOMb3wIgJyeHK674AX/84z3ExsZy2mlnEBMTw3vv/ZcHHvgTX/vaN5k5c1aX6+zZ033m36RJxT0GnhdffDHf+MY3+OtfH+bYY08gEPDz73+/wfr1a7n++huBUMXeT35yE2ec8WWKi6dgtVrZvHkTTz31RHheYSeHw0FDQz0+n4+qqkreeOM1Xn55GZdffiUFBRMG/LkaiPj4eJYuPYtHHvkLxxxzHPn5E/D5fDz33DMcddTRrFu3hhdf/GeX5+Tm5uJ0Ovjss08oKZlKfHx8t0rAhQsXMXlyMbff/hN+8IPr8ft9/Pa3v2bevAWUls4Y1tcgIiIiIiIiIiIiB689rWX4g34mJavgQA6Mgj8Zdi+//CILFx7WYzvP4447gaeeepzt27dhs9kAuOaaK4BQeJOTk8v8+Qu54YYfh8OhgZ6vpGRKt8cTExO5774H+fOf/8hNN12H3d5Ofv4Evv/9a/nSl84MrzvssCN46603eOKJx7Db20lLS+eQQw7lO9/5bpeQ7OOPP+TMM0/DZDKRmJhESckUrrnmhyxZ8iWMxoEV0FZUlFNRUc5hhx0RPpaZmU1OTh6PPvogVVVVGAwGcnNz+c53LuerX/16eN3553+dvLx8/v73v/Hcc0/j9weYNGky118fCuO+6NZbf9Tt2D//+QpZWdndji9YsIDf//7/eOihB3j66ScxGg1MnlzCvffez+TJJQBYLFZmzJjFM888RWVlOT6fj6ysbJYuPYuLLvp2l/M99NCfeeihP2M2m0lLS2fmzNnce+/9A2pHOhTnnHM+zzzzJG+//RYnnngy3//+tTz55F/5y1/+yNy5C7j88iv5xS9uDa+fPXsuZ511DrfeejMtLS18+9uXcckll3c5p8Fg4I47fsc999zFVVddhsFgZNGiI7j22v8dkdcgIiIiIiIiIiIiB6fHNjyM0+fgjqPvjvRWJMoZgsFgMNKbONjV1bVFegsyip5++m989tkn3H33HyK9lbCYGCOpqTaamuz4fH23vxSJFN2nEg10n0o00H0q0UD3qUQD3acSDXSfSjTQfSrRYDTu0x+997+0edr4vxP/PCLnl+iXmZk4oHWa8ScyyjIzs7nwwm/3v1BEREREREREREREDgoevxenzxnpbcg4oFafIqPsxBNPjvQWRERERERERERERGQM8QY8OLz2SG9DxgFV/ImIiIiIiIiIiIiIiESQx+9RxZ8MCwV/IiIiIiIiIiIiIiIiEeQNeHH47ASDwUhvRaKcgj8REREREREREREREZEI8gQ8BIJBPAFPpLciUU7Bn4iIiIiIiIiIiIiISAR5/aHAT3P+5EAp+BMREREREREREZGo4A/4WV37eaS3ISIy7DwBLwAunyvCO5Fop+BPREREREREREREosIn1R/xv8uvpc5RF+mtiIgMq3DFn08Vf3JgFPyJiIiIiIiIiIhIVKixVwPQ6GqI8E5ERIaXN9AZ/DkjvBOJdgr+REREREREREREJCrUO0OVfk3upgjvRERk+ASCAXwBPwBOr4I/OTAK/kRERERERERERCQqdAZ/zS4FfyIyfng75vuBWn3KgRszwd+TTz7JCSecwOzZsznvvPNYu3Ztn+tfe+01TjvtNGbPns3SpUtZvnx5l8eDwSD33nsvixcvZs6cOVx88cXs3r27x3N5PB7OPPNMpk2bxqZNm3pcU1ZWxvz581m4cGGX42+++SZnn302CxcuZN68eZx55pksW7ZswK9bREREREREREREBqbOWQ9Asyr+RGQc6ZzvB+BUq085QGMi+Hv11Ve54447uPLKK3nhhRcoLS3lkksuoaGh517dn3/+Oddffz3nnnsuy5Yt48QTT+TKK69k69at4TUPPvggTzzxBLfddhvPPvssFouFSy65BLfb3e18d911F1lZWb3uz+v1ct1113UL/QCSk5P53ve+xzPPPMNLL73E2WefzY9+9CPee++9IXwmREREREREREREpDfhVp+q+BORccQT2Bf8Obyq+JMDMyaCv0cffZTzzz+fc845h5KSEn72s58RHx/P888/3+P6xx9/nKOPPppLL72U4uJirrnmGmbMmMHf/vY3IFTt9/jjj/O9732Pk046idLSUu666y5qa2t56623upxr+fLlfPDBB9x444297u+ee+5h8uTJLFmypNtjixYt4uSTT6a4uJiJEyfyrW99i2nTprFy5coD+IyIiIiIiIiIiIjI/oLBYDj4a1HFn4iMI17/vlafqviTAxXx4M/j8bBhwwaOPPLI8DGj0ciRRx7JqlWrenzO6tWrOeKII7ocW7x4MatXrwagvLycurq6LudMTExk7ty5Xc5ZX1/PT37yE+666y7i4+N7vNaKFSt4/fXXufXWW/t9LcFgkBUrVrBr1y4OPfTQfteLiIiIiIiIiIjIwLR72/D4PcQYTTQp+BORcWT/ij+nzxHBnch4EBPpDTQ1NeH3+0lPT+9yPD09nZ07d/b4nPr6ejIyMrqtr68P9fiuq6sLH+ttTTAY5KabbuJrX/sas2fPpry8vMe93XzzzfzmN78hISGh19fQ1tbGMcccg8fjwWg0cuutt3LUUUf188r3MRoNGI2GAa8XGW4mk7HL/4qMRbpPJRroPpVooPtUooHuU4kGuk8lGoy3+7SxvR6DASanFNPiaSYmZny8roPdeLtPZXwa6fs0YPBh6IgIXH6nfr7JAYl48BcpTzzxBHa7ncsvv7zXNT/5yU/40pe+1G/1ns1mY9myZTgcDlasWMGdd97JhAkTWLRo0YD2kpZmw2BQ8CeRl5RkifQWRPql+1Sige5TiQa6TyUa6D6VaKD7VKLBeLlPPe12TCYjc/Jm8d6e90hNtUV6SzKMxst9KuPbSN2nFm8MJpORxLhEgjE+/XyTAxLx4C81NRWTyURDQ0OX4w0NDd2q+jplZGSEK/d6Wp+ZmRk+lpWV1WVNaWkpAB999BGrV69m9uzZXc5zzjnnsHTpUn7961/z0Ucf8fbbb/PII48AoSrBQCDAjBkzuP322zn33HOBUGvSwsJCAKZPn86OHTt44IEHBhz8NTbaVfEnEWUyGUlKstDa6sTvD0R6OyI90n0q0UD3qUQD3acSDXSfSjTQfSrRYLzdpztqyggGIDduAvXtDTQ0tmE0qCom2o23+1TGp5G+T+ubWvD7AySYEmloa6apyT7s15DoN9BAOOLBX2xsLDNnzmTFihWcdNJJAAQCAVasWME3v/nNHp8zb948PvroIy6++OLwsQ8//JB58+YBUFBQQGZmJitWrGD69OkAtLe3s2bNGi644AIAbrnlFq655prw82tra7nkkkv4/e9/z9y5cwF45pln8Pv94TX/+c9/ePDBB3n66afJzs7u9TUFAgE8Hk+vj3dfHyQQCA54vchI8fsD+Hz6DywZ23SfSjTQfSrRQPepRAPdpxINxuJ9+ln1J7yw7Tl+sfjX6jAkwNi8T4eitr2O5NgUUmPT8QcCNDtaSIpLjvS2ZJiMl/tUxreRuk9dXjfBICSak7F7HPpekAMS8eAP4Nvf/jY33ngjs2bNYs6cOfz1r3/F6XRy9tlnA3DDDTeQnZ3N9ddfD8BFF13EhRdeyCOPPMKxxx7Lq6++yvr167n99tsBMBgMXHTRRdx///0UFhZSUFDAvffeS1ZWVjhczMvL67IHq9UKwMSJE8nJyQGguLi4y5r169djNBqZOnVq+Nhf/vIXZs2axcSJE/F4PCxfvpyXXnqJ2267bfg/USIiIiIiIiIi/Xhh23N8Uv0x9c56Mq2Zkd6OyLCpc9aSYckkJT4VgEZXo4I/ERkXPP5QIVFSXDKNzoZ+Vov0bUwEf6effjqNjY384Q9/oK6ujunTp/PQQw+FW3dWVVVhNO4r21+wYAF3330399xzD7/73e8oKirivvvu6xLIXXbZZTidTn7605/S2trKIYccwkMPPURcXNyw7t3hcPCzn/2M6upq4uPjmTx5Mr/5zW84/fTTh/U6IiIiIiIiIiL9aXW3sLLmUwB2tuxQ8CfjSoOzngxLJqlxoeCvxd0c2Q2JiAwTb8ALQHJsMuVteyO8G4l2YyL4A/jmN7/Za2vPJ554otuxJUuWsGTJkl7PZzAYuPrqq7n66qsHdP2CggK2bNnS55qzzz47XIXY6dprr+Xaa68d0DVEREREREREREbSu+XLCRIkzhTHzubtLMo9PNJbEhk2dY465mTNC1f8NbmbIrwjEZHh4fG7AUiJS8Hpc0R4NxLtxkzwJyIiIiIiIiIiB+a/e99mbuZ8fAEfO1t2RHo7IsOqwVVPpiUTW4yNGGMMzS4FfyIyPngDPgASY5NweBX8yYEx9r9ERERERERERETGugZnA2vrVnH8xJOYlFLMjubt/T4nGAzyUdUKXD7XKOxQZOicPidtnjYyLBkYDAZS4lJU8Sci44bH7ybGaMJmTsDlcxIMBiO9JYliCv5ERERERERERKJURVs5vo4qgeXlb2MymlicfzSTk4upaN+Lu6N1WG9W1a7kJ+/fxIPr/jwa2xUZsgZnPQCZliwAUuJSNeNPRMYNb8CL2RiL1WwlSOiPHUSGSsGfiIiIiIiIiEgUanW38J03vsmlb3yLFZUf8M6e/7AwZxGJsUkUp5QQCAbZ3bKrz3O8uP0FYowxvLxjGVsbt4zSzkUGr95ZB0C6JQOA1PhUmtTqU0TGCY/fg9kUizXGCij4kwOj4E9EREREREREJAo1u5sJBIMYDUZ++sGP2Ny4ieMnnABAUdIkjAZDn+0+axw1fFT1Af8z90qKkiZx7+e/JRAMjNb2RQalM/jLsGQCoYq/ZrX6FJFxwhvwEms0YzV3Bn+a8ydDFxPpDYiIiIiIiIiIyOA5Ot4U/PHhP6Xe2cCKyvc5Mu9oAOJj4slLKGBHS+/B38s7XiQ+xsLJhadRkjKVa965kld2vsTS4rNGY/sig1LvrCcxNpH4mHggVPG3vmFdj2sdXgexplhijHrrU0Sig8fvxmyKxdJR8efwKviTodNvPxERERERERGRKOTw2gGwmRMoTpnCotzDuzxenFzCruYdPT7X4/fw6q6XOaVwCVazlZkZs1gy6QweXvcAJ0w8GZvZNuL7FxmMOmcdGR1tPgGS41Jo3q/VZ3nbXv657R9sqF/HrpadLMheyK+O/g1GgxqeicjY5w34iDXGYomxAKr4kwOj33wiIiIiIiIiIlHIHg7+eg7pJqcUs7NlB8FgsNtj75a/Q6u7hS+XnBU+ds7U87F77Wxt3Dwi+xU5EPWOunCbT4C0+DScPiduvxuA+1bfy7vly5maVsrFsy5lVe1KHl3/UKS2KyIyKB6/G/N+rT4dCv7kAKjiT0REREREREQkCnUGf9aY3oK/EuxeOzWOanJsuV0ee2nHMhZkH8KExInhYxMSJxJrimVnyw7mZx8ychsXGYJ6Zx3FKSXhj5PjUgBodjWRGp/G2ro1fGvmdzh/2gUAGA1GHl73ANPSSlmcf0wktiwiMmDegJfY/Vp9quJPDoQq/kREREREREREopDDZyfOFIfJaOrx8eLkUEiy8wvtPts8rWxq2MiJE0/uctxoMDIpeTI7mnufCygSKfXOrhV/qXFpADS7m9nUsAGP38Mh2QvDj3912tdZnH8Md33yK8rb9o76fkVEBsPj92A2xhJviscAOLzOSG9JopiCPxERERERERGRKGT32sMtwXqSYckgMTaRHS1dg7wNDRsAmJUxp9tzipNL2Nmi4E/GFl/AR5OrsUvw11nx1+RuYmXtZyTFJTMpuTj8uMFg4H8PvZkYYwxvlb052lsWERmUUMWfGYPBQHyMBYfPHn6srHU3Xr83gruTaKPgT0REREREREQkCjm8dmzmhF4fNxgMTE4u6Vbxt7FhPSlxKeTa8ro9Z3JKsd5glDGnwVlPELoEfyn7tfpcVbOSBVmHYDR0favTarYyNXVat/BbRGSsCc34iwVCP7ucvlDFn9vv5nv/vpTl5W9HcnsSZRT8iYiIiIiIiIhEIYfXgTWm94o/gGlp01hfv5ZAMBA+tqF+HTMzZmMwGLqtL06Zgi/gZ29b2bDvV2So9nTcjxOT9s2kNJvMJMYmUt62h61Nm5mf1fNcyuKUEnaqfa2IjHGdM/4ALDFWHN7QjL+q9kq8AS/1zvpIbk+ijII/EREREREREZEoZPfasZltfa45PO+ojhloG4FQy8TNjZuYmT6rx/WTO1olas6fjCV7WsuINcWSZc3ucjw5LoXl5e8QCAZZkN1b8DeFWkctbZ7W0diqiMiQhGb8mQGwxlhx+jqCP3slAK3ulojtTaKPgj8RERERERERkSjk8PXd6hNgZvoskuNSWFH5PhAK9Dx+DzMzZve43mq2kmvLVWtEGVP2tJYxIXFit1aeqXGpVNuryU3II8eW2+Nzi1NKAIXZIjK2fbHir7PVZ2V7BQCt+uMFGQQFfyIiIiIiIiIiUcjutWM1993q02gwcnjuEXzQEfytr1+L2WimJGVKr8+ZnNJ9LuBICAQD/KfsTbUvk36Vte6mKKmo2/GU+FQAFvTS5hNgQuJEYk2xCv5EZEwLVfx1BH9mS7jir7Kj4q/Fo4o/GTgFfyIiIiIiIiIiUcjutWON6bvVJ8BR+UdT3raXPa1lbGzYwLS00nBVQU+KU0rY0bKDYDA4nNvtot5Zz43vXs+dn/ySB9f+acSuI9EvGAyyp62MiT0Ff3EdwV/2wl6fbzQYmZQ8me3N20ZqiyIiByxU8Rdq9Wnbr+KvqrPiT60+ZRAU/ImIiIiIiIiIRCHHAGb8QSgUiTPF8WHl+2xoWNfrfL9OxckltLpbRqwS75Oqj7n8zW+zp3U3JxaezLvl/6XR1TAi15Lo1+xuos3TxsSkwm6PpcSlYADmZc7v8xzFySXsahn5KlYRkaHy+N37Kv5irNi9dgAq2zsq/hT8ySAo+BMRERERERERiUIOn6PfVp8AcaY4Dsk+lFd2vkSDs6HX+X6dJqcUA7BzBIKST6o+5tYPb6Y0bToPnPIoV827GpMhhld2/mvYryXjQ1nrbgAKe6j4O6nwFK5deANJccl9nqM4pYSy1t14/d4R2KGIyIHbf8af1Ryq+AsEA9Q4qsiwZNCmGX8yCAr+RERERERERESiTCAYwDHAVp8AR+UvptpeDcCM9Jl9rs225mAz29g5zDPR1tev42crbmFhziJ+dtSvSI5LISE2kZMKT+HlHS/iC/iG9XoyPpS17ibGaCLPlt/tsbyEfJZMOqPfcxSnTMEX8LOnbXe3x9bVr+XH793An9f8kX/vfl0zJ0UkIjwBD2ZjqNWnJcaKw2unzlGLL+BnWtp02jytBIKBCO9SooWCPxERERERERGRKOPyuQjCgFp9AizKPQKjwUB+QgHJcSl9rjUYDKE5f8MY/O1s2cEt799Iadp0bjn8NmKMMeHHvlzyFRpdjbxXvnzYrifjx562PeQnTMBkNA35HJOSJ2MAtvdwT7+1+w02NKznw4r3uevTO/j5ip8ewG5FRIbG699X8WeJseD0OansmO9XmjadINDuaYvgDiWaKPgTEREREREREYkynbN/bOaEAa1PjkvhsNwjWJR7xIDWT0ouZkfL8AV//9z6DxJjE7n9qDuIM8V1eWxycjFzMufx0o4Xhu16Mn6Utezusc3nYFjNVvISCnoMszc3buSYguN4/PSn+e6c77GtaSv+gP+AriciMhjBYBBvwBue8Wc123D73VS0V2A0GJiaOg2AFo/m/MnAKPgTEREREREREYkydm87wIBm/HW6/chf8b15Vw1o7dTUqVS07R22mUJ1zlpKUqb2WqF4VsnZrK9fx47mbcNyPRk/ylp3MTGp8IDP01MVq8PrYHfrLqZ3tL+dkjoVb8BLefveA76eiMhAeQOh+aOxps5WnxYAdrZsJ9OSRVp8OgCtbs35k4FR8CciIiIiIiIiEmUcPgcw8FafEGrhOVBzMucRBNbWrRns1nrU4Gwg3ZLR6+NH5i0m3ZLOKztfHpbryfjQ5mml2d3MxMThCf52Nm8nGAyGj21t2kwgGKQ0bXp4DcDO5h0HfD0RkYHyBDwA+yr+Oub37mjeTm5CPslxyQC0quJPBkjBn4iIiIiIiIhIlHF0tPq0DiL4G4wcWy45thxW167qcvyOj2/nbxv/OujzNboayOgj+DMZTZxStIS39/wbl8/V45pnNj/FHR/fPuhrS/Qqay0DoDC56IDPVZwyhXZvO7WOmvCxzY2bsMRYwq1EE2OTyLRkqvJUREaV1x8K/sIz/sydFX87yLPlkRibBECLW8GfDIyCPxERERERERGRKBOe8Rcz8FafgzU3cz5r6/YFfy3uZv679202Nqwf1Hk8fg9tnjbSO1qV9WZJ0RnYvXbeK/9vj49/VvMp75b/F6fPOajrS/Qqa92N0WCgIGHCAZ9raupUDMCq2s/DxzY2bKA0bTpGw763SItTpwzrfEsRkf54Az4AzMZQq09rR6tPl89FbkIeMcYYbGabKv5kwBT8iYiIiIiIiIhEmc5WnyNV8QcwL2s+O1t20uJuBuDDyg8IBIM0uhoGdZ7O9WmWvoO/3IQ85mct4NVdPbf7LGvdhS/gZ0P9ukFdX6LXntYycm354SqYA5Ean8a8rAX8u+wNAILBIJsbN1KaPqPLuuLkErY3qeJPREaPx+8G9lX8dbb6BMi15QGQFJtEqyr+ZIAU/ImIiIiIiIiIRBmH144lxtKlUmm4zcmcD+yb89dZidfgHFzwV++sByA9vvdWn52WTPoS6+vXsaejxWOnNk8rTa4mAFbvV7El49uett1MSJo4bOc7ufBU1tatptpeRa2jhiZXE9PTvhD8pZTQ7G4edMAtIjJU3oAX2Dfjz9JR8QeQl5APQFJcMq2e1tHfnEQlBX8iIiIiIiIiIlHG7rVjNY9cm0+ALGsWuQl5rK5bRbunjVW1KylOKabZ3Yyvoy3ZQDR0Bn/9VPwBHJV/NEmxSbz2haq/Pa17AChMKurSqlHGt7KW3RR1zN8bDkflH0N8TDxvlb3JpsaNAJSmTe+ypiRlCgA7mtXuU0RGhyc846+j1ed+1fy5Cfsq/jTjTwZKwZ+IiIiIiIiISJSxe+3YzAkjfp25mfNYU7uKj6o+xBfw8+XiswFodDUO+BwNrnrMRjMJ5sR+18aaYjmp8FTeLHsDr98bPt45621p8Zlsb95Ku6dt8C9Gokqbp5U6Zx1FSZOG7ZxWs5Wj84/l32VvsKlhIzm2HFLj07qsybblYImxKPgTkVHzxYo/s9GMyWAkKTaJhI7f9UlxybSp4k8GSMGfiIiIiIiIiEiUcfjsWGNGtuIPYF7mfMpad/OvHS8yPX0G09KmAQyqDWKDs54MSwYGg2FA60+ceDKt7hY2d1RkQajlY44tj0W5RxAIBllXv3ZwL0SiTuecvZLUqcN63lOKTqOyvYJ/l73O9LSZ3R43GowUp5Qo+BORUfPFGX8GgwGr2Rau9gNIjk2hxaOKPxkYBX8iIiIiIiIiIlFmNFp9wr45fxsbNrA4/5jwnL7O9p0D0eBqIN3S/3y/TiWpU7DEWFhfvy58bE9rGROTCsmx5ZJjy1G7z4PA9uZtxJnimJA4fDP+AOZkziPLmkWbp43S9Ok9rpms4E9ERpG3o312Z8UfhOb85dnywx8nxSbRqlafMkAK/kREREREREREooxjlFp9ZlozyU8oAOCYguNIikvGZDAOquKv0dkQDgwHwmgwMjNjFuv3q+ora91NYWIhAHMz57O6duWAzyfRaUfzNianFGM0DO/bl0aDkRMLTwHoseIPQnP+ytv24PQ5h/XaIiI92VfxZw4fOyJvMUfkHRX+OCkumVZPC8FgcNT3J9FHwZ+IiIiIiIiISJRx+Byj0uoT4PC8I5iVMZscWy5Gg5G0+HTqB1nxl2ZJH9Q1Z6XPYUPDegLBAA6vg1pHLYVJRQDMz1rArpZdNA1izqBEn23N2yhJGd42n53OKjmbc6eez5Re2ohOTi4mCOxu2QWAw+sgEAyMyF5kePkCPj6p+hi71x7prYgM2Bdn/AFcNf9qjp94YvjjpNgkAsEgdm/7qO9Poo+CPxERERERERGRKGMfpYo/gO/OuYLfHHtP+OM0S/rgWn0660mPH2TwlzEbu9fO7pad7G3bA8DEjuBvbtYCANbUrR7UOSV6uHwuytv2UJIyZUTOnxafzuVzryTGGNPj40XJkzAaDLy445/c+O51nLVsCW/sfm1E9iKD89quV1jbx/f+f/f+hx+/fwPnvXQmt37wYz6t/nj0NicyRB6/B9g3468nyXHJALSo3acMgII/EREREREREZEoE2r1aRuVaxkNxi4BSXp8xoBbfTp9TuxeO+mDrPibljadGKOJ9fXr2NO6GyA86y3DksGExIl9vvkv0W1nyw4CweCIBX/9iTPFMSl5Mv8p+zdev5dsWw5rNFcy4oLBIA+uvZ9Xd73c65r19evIS8jnO7Mvo7x9L3d8/PNR3KHI0HgDXkwGY5+tjZNikwBo9bSO1rYkivX8Zy0iIiIiIiIiIjJm2b12rObRafX5RemWDDY2rBvQ2kZnKCAczIw/gPiYeKakTmN9/TqyrFlkWbO6vN7CpCIq2ysGdU6JHjuat2EyGClKnhSxPfzsyF8RCAbITcjjj6vu5bPqTyK2FwmpaC+nzdMW/rnSk40N65mbOY9zp36VxNgk7v70Tjx+T5+VVCKR5vG7MfdzjybFpQDQ4lHFn/RPFX8iIiIiIiIiIlEkEAzg9DmxxoxOxd8XpcenU9/HG+/7a3CFWoKmWwYX/AHMSp/Nuvo1lLWVMTGpsMtjWdZsahw1gz6nRIdtTVspSp4U0bAm25ZDbkIeAKVppR2hkyptImlL4yYAGnuZ79nubWd3yy5mpM8CIDUuDYAmd9PobFBkiLwBL7HGfoK/joq/NrX6lAFQ8CciIiIiIiIiEkUcPgfAqLX6/KJ0SwYt7mZ8AV+/axs6K/6GEvxlzKHeWc+6ujVMTCzq8li2LZtaRw3BYHDQ55Wxb3vzNooj1OazJ6VpMwDY2rQlwjs5uG0KB389/+HB5oaNBIGZGaHgLy0+FPw1uxT8ydjm8Xswm8x9rok1xWKJsajiTwZEwZ+IiIiIiIiISBRxeEPBX6RafabFh+b19VZ1s79GVwPxMfFYYwa/18437+1ee48Vfx6/h2ZV8ow7voCPXS07mZIyNdJbCctLyMdmtrGlcXOkt3JQ29y4EbPRTJunDY/f0+3xjQ0bSIxNpCBhAgCpHcHfQGeSikTKQCr+IFT1pxl/MhAK/kREREREREREoojd2w6AzZwQkeunWwb+ZnqDs560+HQMBsOgr5MclxIO/AqTiro8lm3NAaDWUTvo88rYVta6C1/AR3Hq2Kn4MxqMTE2dxuaOijMZfR6/hx3N2zkk51Cg558/GxvWMzN9VvjnTUpcCgagSRV/MsaFKv76D/4SY5NoVatPGQAFfyIiIiIiIiIiUSRc8TeEKrrhkB4fatvZ4Kzvd22Dq56MIbT57DQrfTYAhV+o+Mu2ZgNQqzl/48725u0YgOLkkkhvpYtpadPZ2qSKv0jZ0bwdX8DHUXlHA/vaCHcKBANsatwYnu8HYDKaSIpLoWkA1ckikeQNeDEb+271CZAcl0yLgj8ZAAV/IiIiIiIiIiJRxO61A5Gr+EuKS8ZkMA4o+Kt3NoRbgw7FyUWncWrREhJjk7ocT4xNIj4mnhpH9ZDPLWPT9qZt5CUURKyVbW9K06bT4GygzlEX6a0clLY0biLGGMPCnMOA7hV/u1t34fA6wi2CO6XGpdLoVvAnY5vH7yZ2ABV/SbHJavUpA6LgT0REREREREQkiuxr9WmLyPWNBiNp8ek0DLDVZ7pl6MHfrIzZ/PDQm7odNxgMZFmzqbGr4m+82d68lSmpY2e+X6epqaUAbGlSu89I2Ny4kZKUKaTHpxNjjOkW/G2s34DRYAh/nTqlxqeq4k/GPF/Ai3kgM/7iksOtPqvtVTyy/kECwcBIb0+ikII/EREREREREZEo4vA5MADxMfER20O6JWNAFX+NroZwa9Dhlm3NVsXfOOML+NjSuJlpaaX9Lx5lmdZM0i3pbGlUu89I2NS4iWlp0zEYDKTFp3X7w4MNDesoSZna7ediWnwazZrxJ2Oc2+8h1tR/q8+k2CRaPS0Eg0F+8+md/H3T36hsrxiFHUq0UfAnIiIiIiIiIhJF7N52LGYrRkPk3tZJi0/vVnEDUNa6m8veuJjdLaG2e06fk/QDmPHXlyxrtmb8jTM7mrfjDXi7zGkbS6amlrKlURV/o63V3UJlewXT06YDoZ8/X/zDg40NG3q8b1Lj02hUxZ+Mcb6Al5gBzvhr9bTwnz1vsrZuNRD6uSnyRQr+RERERERERESiiMPrwBYTmTafndItGT0Gfx9XrWB36y5u+/AW9rbtASBjhIK/bGuOgr9xZmPDemKMMZSkTIn0VnpUmjadrU1b1FpvlG3uqLIsTZsBdP/50+xqorK9ghnpM7s9NyUulSbN+JMxzu13E2eM63ddUmwyvoCfP63+P44tOJ50Szo7WnoP/v6y5j4+rHh/OLcqUULBn4iIiIiIiIhIFHH4HFgjNN+vU3p8OvXO7sHfhvr1FCYV0exu4pcf3QaEqnNGQrYtmzZPGw6vY0TOL6NvY8MGpqZOI9bU/6yrSJiWVorda6esdXekt3JQ2dy4kcTYRPIS8oGOiuP9fv7sbNkB0ONsyHRLOg6vA7ffPTqbFRkCX8BHjCmm33XJccnh9f8z7yqKk0vY0bStx7Uraz7lua3P8vy2Z4d1rxIdFPyJiIiIiIiIiEQRu7cdW6SDP0sGLe5mvH5v+FgwGGRDw3qOyj+amxf9lGp7FTBywV+mNRtAVX/jyKaGDUxPnxHpbfRqetpMMiwZ3Pnxz2n3tEV6OweNHc3bKUmZgsFgADr+8GC/ir/drbswG83hYHB/KXGpADSp3aeMYQOt+EuNTwPg4lmXkGHJoDh1Ctubuwd/gWCAB9fej9loZn39Wv28Oggp+BMRERERERERiSIOryPiwV9nmNfkbgofq2gvp8XdzMz02SzKPZxLZl9OYVIRVrN1RPaQbc0BoNZROyLnl9FV76ynxlHDzPTZkd5Kr6xmK3ccfTd1zjp+8sHNuHyuSG/poFBlr6AgcUL443RLBq3ulvAfHpS17GZi0sQe556mdQQlTa6mbo+JjBW+gJcYU/8z/oqSJvHb4+7lrJJzAChJmUKjq7FbsP1W2RvsaN7BDYf9iEAwyGc1n47IvmXsUvAnIiIiIiIiIhJF7N52rBGf8Rd6M73BWR8+tr5+HQZgRkZoztZXS7/Og6c8NmJ7yLBkYDIYqXFUj9g1ZPRsatgAwPQe5rSNJUXJk/jF4l+zrWkrv/zoNoLBYKS3NK4Fg0Gq7FXk2HLDx774hwe7W3dRlDSpx+enhoM/VfzJ2OX2e4gz9t/i2GAwMCdzXjjkLk4uAUJVsZ1cPhePrn+IowuO5bgJJzApeRIfV60YmY3LmKXgT0REREREREQkiti99hGrohuo9PgMABr3a7e3oWEdRcmTSDAnhI91tuYbCUaDkUxrFrUK/saFjQ3rybJmkWHJiPRW+jUjfSY/WHAtH1WtUKvZEdbsbsLlc3Vp47n/Hx4Eg0HKWndT2EvwlxyXgtFgoFHBn4xhA634+6LchDwsMZYuwd+y7c/T7G7iklnfBWBR7hF8Wv0JgWBg2PYrY5+CPxERERERERGRKOLwRb7VZ1JcMiaDsUvosaF+/ai3acyyZlNjV/AyHmxs2MCM9FmR3saAdQZNrZ7WCO9kfKtsrwQgz5YXPtZZ8dfoaqDeWY/da6couefgz2gwkhSbTJNbwZ+MXQOd8fdFRoORycnF4eDPF/DxwrbnOLXodPITCwA4LPcIWtzNbGncPKx7lrFNwZ+IiIiIiIiISBSxe9uxRjj4MxqMzMtawL92vIgv4KPV3cLetj3MzBjd4CZU8afgL9p5/B62Nm1hxhhv87m/5LhkAFo9LRHeyfhWZa8AIGe/4C85LgWTwUijq4HdrTsBKEwq6vUc6ZZ0zfiTMc0X8BFjjBnSc4tTp7C9eRsAH1V9SKOrkaXFZ4Yfn5E2k8TYRLX7PMgo+BMRERERERERiRJev5cWdzOpcWmR3gqXzfkfytv28MrOl9jQsB6AWRlzRnUP2dYczfgbB7Y3b8MX8EVVxV9ibBIArW5V/I2kyvZKUuJSurQ3NhqMpManUe+sp6x1N7Gm2C4zAL8oJS5VM/5kTPMEPMSZBl/xB1CSMoXytj04fU5e2fkS09JKKU6ZEn7cZDSxMPswBX8HGQV/IiIiIiIiIiJRosZRTSAYJC8hr//FI6w4ZQqnFC3h8Y2P8UnVR6TFp5FtzRnVPWRbc2h0NeAL+Eb1uuNdk6sRr987atfb1LABs9HM5OTiUbvmgbLGWDEZjKr4G2GV9oou8/06pcWnhyr+WnZRmFSE0dD729yp8WkK/mTMCgaDeP0eYoyDn/EHUJxcQhD4qPJDVlZ/ypcmn9ltzaLcw9nevI0GZ0P3E8i4pOBPRERERERERCRKhOdd9fBGeCRcPOtS3D4XL+98iZkZszEYDKN6/SxrFoFgkHpn3ahedzxz+Vx85/UL+Z+3LmFD/fpRuebOlh1MTinGbBraG9+RYDAYSIxN0oy/EVbVXkluD3/okGZJp9HZQFnr7j7bfAKkxafR5FarTxmb/EE/QSDOFDuk5xclT8JoMPDg2vuxmK0cO+H4bmsOzVkEwKrazw5kqxJFFPyJiIiIiIiIiESJKnsFMUYTWdbsSG8FgAxLBudPuwCAmRFo05htC1UY1tjV7nO4fFL9Ee3edszGGK5950r+tPr/8Af8I3rNdk87ybHJI3qNkZAcl6JWnyOsyl5Jrq178Jcen06DK9TqsyhpUp/nUMWfjGUevwdgyBV/saZYJiYWUees46TCU7HEWLqtSYpLJiUuJfzHQzL+KfgTEREREREREYkSle2VZFtz+2xrN9rOm/Y1Tpt0OscUdK8yGGmdAWito2bUrz1eLd/7DiUpU/jTSQ9x2Zzv8cK25/iw8v0RvabD58Bqto3oNUZCUmwSbWr1OWKcPidNribyegr+LBmUte7G6XNSmNxP8BeXitPnxOlzjtRWRYbMGwgFf0Od8QdQnFoCwBmTl/a6JtuWo9+VB5Gx81+JIiIiIiIiIiLSp9C8q8jP99ufJcbC9QtvJNOaOerXjjPFkRKXQpW9atSvPR45fU4+qvqQYyccj9Fg5LxpXyPdks7Wps0jel2H144tCoM/tfocXsFgEJfPFf64yh6qTsrtZcafr6MStaifVp+p8WkANLvU7lPGHk/HPNWhVvwBnDjxZL4y5dw+56RmWbMV/B1Exkzw9+STT3LCCScwe/ZszjvvPNauXdvn+tdee43TTjuN2bNns3TpUpYvX97l8WAwyL333svixYuZM2cOF198Mbt37+7xXB6PhzPPPJNp06axadOmHteUlZUxf/58Fi5c2OX4s88+y9e//nUOPfRQDj30UC6++OJ+9y4iIiIiIiIiMhSheVdjY77fWFGQOIGK9r2R3sa48EnVR3j8Ho4pOC58bErqNLY2bRnR69q9dqwx1hG9xkhIikuixa2Kv+Gyrm4t57y0lDpHaGZndXso0O+t1SdAfEx8v62PO4O/RrX7lDFoX8Xf0Gb8QWiG3xXzvt/nmmxrNrWO2iFfQ6LLmAj+Xn31Ve644w6uvPJKXnjhBUpLS7nkkktoaGjocf3nn3/O9ddfz7nnnsuyZcs48cQTufLKK9m6dWt4zYMPPsgTTzzBbbfdxrPPPovFYuGSSy7B7XZ3O99dd91FVlZWr/vzer1cd9113UI/gI8//pgzzjiDxx9/nKeffprc3Fy+853vUFOj9FxEREREREREhk8gGKCyvaLHtncHs/yEAsrbyiO9jXFheXmozWfefuHy1NRpbGvaSjAYHLHrOnz2KG71qYq/4VLRXo7H7+GDineBUIVznCmOtI7gbn/plgwACpOK+m193Pn8JreCPxl7DnTG30B1VvwFgoERvY6MDWMi+Hv00Uc5//zzOeeccygpKeFnP/sZ8fHxPP/88z2uf/zxxzn66KO59NJLKS4u5pprrmHGjBn87W9/A0LVfo8//jjf+973OOmkkygtLeWuu+6itraWt956q8u5li9fzgcffMCNN97Y6/7uueceJk+ezJIlS7o99tvf/pZvfOMbTJ8+neLiYn7xi18QCARYsWLFAXxGRERERERERORg5g/4+ahqBevq1oSPNTgb8Aa8XUIZCVX8VbZXjGgwdTBw+px8XLWCYyd0ndU4JXUabZ42ahzVI3Zth9eB1RyFFX+xyWr1OYw6qyffLf8vEJppmmvLw2AwdFub1lHxV5TU93w/CLVkNRoMNKnVp4xB3kCo1eeBzPgbiGxrDt6AlxZ384heR8aGmEhvwOPxsGHDBi6//PLwMaPRyJFHHsmqVat6fM7q1au5+OKLuxxbvHhxONQrLy+nrq6OI488Mvx4YmIic+fOZdWqVZxxxhkA1NfX85Of/IT77ruP+Pj4Hq+1YsUKXn/9dV588UXefPPNfl+P0+nE5/ORnJzc79pORqMBo7H7LzCR0WIyGbv8r8hYpPtUooHuU4kGuk8lGug+lWgwnPepy+fin1ufI0iQOFMcdY46/r37DZrdTaRbMnj2y//EYDBQ66rCYIAJyQXExOj7o9PE5AnYfe3Y/a2kxKdGejtjymDu088qP8Yb8HBC0Yld7q8ZGaUYDLCjdSsFycMfOvsDfjwBN4lxCVF3X6daUnD47BiMQUxGU6S3E7U67892bxsGA2xoWEeLt5FaZxX5Sfk93heZCenEmmIpSSsZwH1jJDU+jRZvU9TdYzJ2jNR/nwYMPgwGiI+NHdH7MzcpB4MB6t21ZCZkjNh1ZGyIePDX1NSE3+8nPT29y/H09HR27tzZ43Pq6+vJyMjotr6+vh6Aurq68LHe1gSDQW666Sa+9rWvMXv2bMrLu7eEaGpq4uabb+Y3v/kNCQkJA3o9d999N1lZWV1Cx/6kpdl6/MsVkdGWlGSJ9BZE+qX7VKKB7lOJBrpPJRroPpVoMBz36Vs7V/DohgdJjk/G7XNjNVs5o3QJ2bZs7v34XuymJiYkT6C1toGYGBMzJ0wl9gBmAY03MwJTMZmMtBkbmZRaEOntjEkDuU8/WfkBs3JmMnPClC7HU1Nt5CblUO7aTWrq8LfjbHW3YjIZyUnLGJHzj6T8jGxMJiNGq49US1KktxP1XNgpSiuksq2SVU2fUOeuYfHExb3eF4+d/QiTUycTH9NzQcf+cpKzcNEedfeYjD3D/d+n8U4TJpORrLRUUpNG7v4stRZjMhlxGlv1fXAQiHjwFylPPPEEdru9S6XhF/3kJz/hS1/6EoceeuiAzvnAAw/w6quv8vjjjxMXN/DS3MZGuyr+JKJMJiNJSRZaW534/erzLGOT7lOJBrpPJRroPpVooPtUosFw3qefla0iPS6Tp5c+1+V4u6edewL3snzbB5xRvJSt1TtIMadhb/Vix3tA1xxPEgJp+P0BNlZsZUJscaS3M6YM5j7dUb+L6WkzaGqyd3usKKGYVeVraJrS/bEDVW2vw+8P4HcZe7z2WGZwx+L3B9hTU4UheWTb9I1nnfdpbVsDWXE5ZMZm869Nr7KnqZyUooxe74vcmEKcbX6c9H/fJBiTqGyuibp7TMaOkfrv0/rmFvz+APY2D03+kbs/g0EjZkMs22t2syBV3wfRaqChbcSDv9TUVEwmEw0NDV2ONzQ0dKvq65SRkRGu3OtpfWZmZvhYVlZWlzWlpaUAfPTRR6xevZrZs2d3Oc8555zD0qVL+fWvf81HH33E22+/zSOPPAKEqgQDgQAzZszg9ttv59xzzw0/7+GHH+aBBx7g0UcfDV9joAKBIIGA+tBL5Pn9AXw+vbEiY5vuU4kGuk8lGug+lWig+1SiwXDcp5sbtjAlZVq388QbrRQnT2FV9eecWngG5a0V5Nry9H3xBTHEkh6fQVnLHn1uejGQ+9TpdRFrjOtxXUnKVF7Y9hxer3/Yu1a1utoIBiHOYIm6r5/VlEAwCI3OZvJt0bX3sajF1UKmJYu5mfP53Wd3AZBtGZ6feQnmJGrs1VF3j8nYM9Tf+4FggH+XvcGJE08mxrgvlnF53ASDYAqaR/z+zLLkUNWm74ODQcSbGsfGxjJz5kxWrFgRPhYIBFixYgXz58/v8Tnz5s3jo48+6nLsww8/ZN68eQAUFBSQmZnZ5Zzt7e2sWbMmfM5bbrmFF198kWXLlrFs2TIeeOABAH7/+99z7bXXAvDMM8+EH1+2bBk/+MEPsNlsLFu2jJNPPjl87gcffJA//elPPPTQQ92CRBERERERERGR3gSCAbY2bWZq6rQeH5+bOY81dasJBoNUtoeCP+kuP2ECFW3dx7jIwLl9LmJNPVetTU0tpc3TRo2jetiv6/A6ALCarcN+7pGWHJcMQJu7NcI7GR9a3S0kxSZzZN5RGDsC5ryE4fmZZzMn4PQ5huVcIkOxuvZz7v70Tj6r+bTLcY/fA0CM0Tzie8iyZo3Iz3EZeyJe8Qfw7W9/mxtvvJFZs2YxZ84c/vrXv+J0Ojn77LMBuOGGG8jOzub6668H4KKLLuLCCy/kkUce4dhjj+XVV19l/fr13H777QAYDAYuuugi7r//fgoLCykoKODee+8lKyuLk046CYC8vK6/NKzW0H9cTJw4kZycHACKi7u2h1i/fj1Go5GpU6eGjz3wwAP84Q9/4Le//S35+fnh+YJWqxWbTb1yRURERERERKR3le0VOLwOpqb1HPzNyZzHP7Y+Q5W9kip7JUflHz3KO4wOBYkFbG7cGOltRDW33028qedZaVNTQ++FbWncTI4td1ivGw7+YqLvfbTE2NBcv1aPgr/h0OJuISk2ieS4FOZmzmdN3SqyrTnDcm6b2Ybdq/aGEjlr69YAsKNpG4fnHhE+7g2Egr+4Xv7wYjhlWbPZ3LhpxK8jkTcmgr/TTz+dxsZG/vCHP1BXV8f06dN56KGHwq07q6qqMBr3FScuWLCAu+++m3vuuYff/e53FBUVcd9993UJ5C677DKcTic//elPaW1t5ZBDDuGhhx4a1Oy9gXj66afxer384Ac/6HL8qquu4vvf//6wXktERERERERExpetTZsBeq34m5UxG6PBwPsV79LmaVPFXy/yEwr4z55/EwwGh70V5cHC5XcRF9Pz+2ap8WlkWDLY1rSFYyccP6zXtXvbgVAwE21ijDFYzVZa3M2R3krUCwaDtHpaSeoIU8+eej4ZlkzMpuGpgrLGWBX8SUStq18LwPbmbV2Oe/xejAYDJqNpxPeQZc3m3fL/jvh1JPLGRPAH8M1vfpNvfvObPT72xBNPdDu2ZMkSlixZ0uv5DAYDV199NVdfffWArl9QUMCWLVv6XHP22WeHqxA7vf322wM6v4iIiIiIiIjIF21p3EKuLTdcOfRFCbGJFKdM4fVdrwKQl5A/mtuLGgWJE3D5XDS4GsiwZER6O1EnEAzg8XuI66XiD2BK6jS2NvX93tlQ2L12DIAlxjLs5x4NSbFJtKni74DZvXYCQT9JHe1TD889oktV1IGymW04vHb9cYBEhNfvZVPDBqxmK9ubt3Z9LODBbIwdlX1kW7Np87Th8Dqisr2yDFzEZ/yJiIiIiIiIiBystjVtYWpqaZ9r5mbOY2/bHmD45l2NN52BaGW75vwNReeMqfg+Ws1NTZ3G9uZtBIPBYb22w2fHarZFbRiTFJusVp/DoMXVAhCu+Btu1hgr/mAAT0dbRZHRtLVpC96Al9OKzqDaXt3ljwU8fg+xptEJ/rKs2QDUOmpG5XoSOQr+REREREREREQiIBAMsK15K1NSp/a5bm7mfAASYxN7rQw82OXa8jAaDJS3KfgbCo/fDUBcTO8Vf6Vp02nztA37fCiH14E1JnorT5LikhT8DYMWdyj4S+6o+Btu1o5Wsg61+5QIWFe/BkuMhVMnhToYbm/a1+7TG/BiNg5PS9v+ZNlCMzNrHbWjcj2JHAV/IiIiIiIiIiIRsKe1DJfPxbS0viv+Ouf8ab5f72JNsWRZs6lo3xvprUQlV2fw10fF34LsheQl5PPslr8P67UdPkc4lIlGavU5PJpdzQAkxo5M8Nc5Q1Jz/iQS1tWtYUb6TIqSJhFniusy529H8/ZwJd5Iy4jPwGgwhCv+Pq/5jEvf+BYun2tUri+jR8GfiIiIiIiIiEgEbG3aDEBJPxV/CbGJTE0tpTCpaBR2Fb3yEwpU8TdEbn/oTd/4Pmb8GQ1Gzpv6NT6oeJfytuELWB1eeziUiUaJscnhajUZus7gb6RafdrCFX+OETm/SG8CwQAbGtYzO2MuRoOR4pSS8Jw/p8/Jp9Ufc1T+0aOyF5PRRIYlk1pnKPj7++YnKWvdzdq6NaNyfRk9Cv5ERERERERERCJgS9MW8hMKSDAn9Lv2Z0f9ku/Nu2oUdhW98hMnUKEZf0Pi9nVW/PUe/AGcUnQayXEp/GPL08N2bbvXjtUcxa0+Y5No9Sj4O1AtrhZiTXHE99Fu9kBYYzqCP58q/mR07WrZgd1rZ3bmHACKU6awvXk7AJ9Vf4Lb7+bo/GNHbT9Z1mxq7dXsatnJ6trPMQCf1XwyateX0aHgT0REREREREQkArY1bWFa2rQBrU2LT9d8v34UJBRQ2V5BIBiI9Faijquj4i/WFNvnulhTLOdMPZ83y16nwdkwLNd2+OzhUCYaJccl0+ZpJRgMRnorUa3F3TJi1X5AOFxWxZ+MtnV1a4kxxlCaNgOAKalT2dtahtPn5L3y5UxOnkx+YsGo7Sfbmk2No4YXt/+TtPg0Tio8lc+qFfyNNwr+RERERERERERGmS/gY3vTNqam9j3fTwYuP2EC3oCXOkdtpLcSdcKtPgdQbfWlyV/GbDTzwrZ/DMu1HV5HVFf8JcYm4Qv4cfqckd5KVGt2NZMcNzLz/QAsMR3Bnyr+ZJStrV9Dadr08B9WTEmZShDY0riJj6o+ZHHB6FX7AWRas9nTtoe3yt5kafFZHJF3FHvb9lBjrx7VfcjIUvAnIiIiIiIiIjLKah01eANeipInRXor40ZBR8VEWWtZhHcSffa1+ozrd21CbCJLi8/k5Z0v4fK5DvjaDp8jqiv+OqvU1O7zwLS4WkY0+Is1xWI2mrF7FfzJ6AkGg6yrW8OsjDnhY4VJRcQYTTy75e84fc5RbfMJkG3NodXdgj/o54zJS5mftQCjwcCnqvobVxT8iYiIiIiIiIiMsnpnHQCZlqwI72T8yLXlkRafxpq6zyO9lajT2eqzvxl/nU6ftBS7186Hle8d8LUdXjs2czQHf6GwqtXdGuGdRLdQq8+RC/4ArGabgr8haHI18tSmJ/AH/JHeStRx+900u5spTCoMHzObzBQmTeLT6k8oSJxAYVLRqO4py5oNwLETjic1Po2E2ERK02awsubTUd2HjCwFfyIiIiIiIiIio6zeWQ9AhiUzwjsZPwwGA4dkH6pZRUPg9ocq/gbS6hMgP7GA2RlzeH3Xqwd8bbvXHtWtPpPiVPE3HJpdzeHP5Uixmq04FPwN2orKD3l0/UO8vvvAv98PNp1Bc4I5scvxkpQpABxdcCwGg2FU9zQpeTKJsYmcO+Wr4WMLcw5jVe3KQYW7Xr93JLYnw0TBn4iIiIiIiIjIKKtz1GI1W6M68BiLFuYcys6WnTQ4GyK9laji8bsxG80YDQN/q/CUoiWsrv2cGkfNkK8bCAZw+pxR3uqzo+LPo4q/A9Hsag63TR0p1hgrDp9jRK8xHlXZKwB4bP1DOLz6/A2G3dsO0K2qeUrqVIBRb/MJkGnN5Pkv/4uS1CnhYwuzD8PutbOpceOAzrG3bQ9nLltCWevuEdqlHCgFfyIiIiIiIiIio6zB1aBqvxGwIGshBmBljar+BsPlcw242q/TMQXHERcTz793vz7k6zp9ToCoDsAtMRZijCbaFPwdkBb3yM74g1D4ooq/wator2BiUiF2r51/bH060tuJKp0Vf18M/k6ceDLXHvK/4cq/0fbFKsNpaaUkxiYOuGL+w4r38Qa8rK9fNxLbk2Gg4E9EREREREREZJTVOWrJsGREehvjTkp8KiWpUzWraJDcfjdxprhBPcdqtnJMwXG8ufs1AsHAkK7bWT0UzRV/BoOBxNgkWtzR1erT7Xdz58c/p7xtb6S3gtvnxu1zkzTCwZ/VbMOuir9Bq2yvYHbGHM6Zeh7Pbvk7dY66SG8pauyr+EvocjwhNpHTJ39p1Nt89sZoMHJI9qF8Wv3xgNZ3/o7d3rR1JLclB0DBn4iIiIiIiIjIKGtw1avib4QszDmMz2o+G3IYdTBy+13EmQZX8QdwatESquxVrK9fO6Tr9tYGL9okxSZHXavPnc07+M+et/jJBzfT3vF1iJTO+YgjHfyp4m/wgsEgVfZKcm15fLX0G8THWPjrhocjva2o0VvF31i0KPdwtjZt6TfYdflcrK9fR4wxhu3N20ZpdzJYCv5EREREREREREZZqOJPwd9IWJh9KK3uFrY36Q3JgXL53cTHDK7iD2B2xlxybbm8sfu1IV23c95aNLf6BEiOS6bNE10Vf1X2SgAaXQ38+uNfRDQo76yWHPkZf7ZwECMD0+JuxuF1kJeQT4I5gS8Xn8WHle9HeltRo/N+s0ZB8Hd43lHEGGN4v2J5n+vW1q3BG/ByatESdjRvxx/wj9IOZTAU/ImIiIiIiIiIjKJAMECjZvyNmOlpM7HEWPhMc/4GzO1zETvIVp8QanN5StES3i3/b7ht52D01gYv2iTGJkVdxV+1vYqkuGR+vOg2Pq5awV83PBKxvXQGf8lxKSN6HZvZGg6bZWAqOwLivIQ8ALKtObR52vD6vZHcVtSwe9uxxFgwGsZ+DJNgTmBh9qG8V9538Ley5lMyLBmcMPEkvAEve9v2jNIOZTDG/h0nIiIiIiIiIjKONLoaCQSDZCr4GxFmk5m5WfM1528QQjP+Bt/qE+DkotNw+1y8W/7fQT83POMvyiv+kmKTaHVHV/BX2V5BjjWHw3IXceHMb/PUpidocTdHZC+doeloVPwNJaA+mFW2lwOQa8sHIDU+DYDmCN0r0cbutUdFm89ORxccy/r6tTQ4G3pds7LmUw7JPpTJKSUAbG/WnL+xSMGfiIiIiIiIiMgoanDWA6jibwQdmn0YG+rX6U3+AXL5XcQPoeIPINuazbysBbw5hHaf4VafMeMg+Iu6Vp9V5CWEwpzDcg4H6He210hpdbdgMppGPCCxmq3hKlMZmMr2SlLiUsLhfGpcKgBNrsZIbitqhIK/6KloPiLvKIwGY6/tPuscdZS17mZhzmEkmBPITchjm9pqj0kK/kRERERERERERlGdsxaADEtGhHcyfs3JnIc/GGBL46ZIbyUquH2uIVf8AZw26XTW1a+loq18UM9zeO1R0wavLynxqTS5mggGg6N+7Wp7FT9fcStuv3uQz6skx5YLQFp8OhCqRo6EVk8ryXHJGAyGEb2OzWzDF/Dh8XtG9DrjSaW9IhwQw76KvyZ3U6S2FFXs3vaoqvhLjE1iQfZC3u2l3efKmk8xAAuyDgFgSspUVfyNUdH9W1VEREREREREJMo0OOuJMcaM+Dyrg9nEpEIsMRa2NG2O9FaigtvvJi5maBV/AEfmHY3VbOXNstcH9Ty71x71bT4B8hMKcPvdNLh6b483UtbXr+Xd8v+yvn7tgJ/j9Xupc9SSawvNbUvp+FnUGIH9Q2jGX0p8yohfxxoTCmAcXvuIX2u8qGqvJLdjvh/su1dU8Tcw0dbqE+DoguNYX7+mx6/xyppPmZpWSlJcMgAlKVPY3ryNQDAw2tuUfij4ExEREREREREZRXWOWjIsGSNe3XIwMxqMTEmdxpZGBX8D4fYfWMVffEw8x084kX/vfn1QbwA7fPZwGBPNOiuiKtr2jvq1O6v0VteuGvBzahzVBIHcjoo/s8lMUlxyxIK/Vk8LyR1BwkjqDJk7W8xK/yrbK8hPKAh/bDaZSYxNpFkVfwMSba0+AY7MOwqADyre73I8EAzwec1nHJJ9aPhYSeoUHF4H1faqUd2j9E/Bn4iIiIiIiIjIKKp31Wu+3ygoTStlc+PGSG8jKrh87iHP+Ot0StES6px1rKpdOeDnOLyOcVHxl2vLw2gwUN4+uFanw6HZFQpgVtd+PuDndL5Jv38lV1pcWkQqFiE04y85fhSCv3DFn4K/gXB4HTS7m8mz5XU5nhIXam0r/Wv3tkVdxV9yXApzM+ezorJr8Fdjr6bV08rM9NnhYyUpUwDY3qw5f2ONgj8RERERERERkVFU56hT8DcKStNmUO+sp8G5L8x4cfs/+fOaP0ZwV2OT2+8iLmboFX8A09NmkJ9QwHu9zIbqicPrwBoT/cFfrCmWLGs2lREI/hrdoYq/rU2bsQ+whWWVvQqTwUimJSt8LM2SFrH2ja3u1lGp+OsMYBw+tfociCp7BQB5+1X8AaTGp6rV5wA5orDVJ8D09JndwrxdLTsBmJxSHD6WGp9GhiWDbU2a8zfWKPgTERERERERERlFDc56MiwZkd7GuDctbToQCkQAgsEgz219hs+qP43ktsYkt99N3AFW/BkMBianFFPZXjHg5zh80dcGrzcFiRPYOwKtPp/c+DjL977T6+NNrkaKU4oJBIOsG+Ccv2p7JVm2HExGU/hYanwajc5xPuOvo7p0oAHpwa6yvRKAvISuFX+pcWk0qdXngERjq0+A4pQSGl2NXQLenS07SIpNIj0+vcva0Jw/BX9jjYI/EREREREREZFREgwGqXPWquJvFGRaMkmNT2VTR7vPbU1bqbZXY/e2R3hnY4/L5yL+AGb8dcqx5lDjqBnwervXPi5afUKoKuqLoeeulp3UOeoO6Lwv7XihzyrKJlcjM9Jnk2HJYHUvbVbfKnuDdXVrwh9XtleSY83psiY9Pj1cPTjaWj2j0+qzM4BxKPgbkIr2cqxmK0mxXb82Kar4G5BgMIjd2x6VFX+Tk0NVfTtbdoSP7WrZSVHy5G7ziUtSp7KtaRvBYHBU9yh9U/AnIiIiIiIiIjKCfAEfvoAPCM378fg9Cv5GgcFgYFradLY0bgLg3fJQ1VS7gr8uAsEA3oCX2AOs+APItuVQ66gmEAwMaH2o1Wf0vSnekwkJE6hoL+/y2n/24U/47Wd3DvmcDq+DRlcjDa76Xtc0uZpIi09jXtYCVteu6vZ4naOO3372ax7b8Ej4WJW9kryE/C7r0uLTaXQ2jPqb9/6AH7vXPioVf2ajmRijCYev7xl/dY46HljzpwHfx+NVVXslebb8bkFPWrwq/gbC7XfjDwaiMvjLS8gnPiae7U372n3ubNkRDgT3NyVlKi3uZmqdtaO5RemHgj8RERERERERkRH08xW3cnfHm/91zlD1j4K/0VGaOp2tjVsIBAO8W7Ecq9mKy+cKB7ESenMaID5mOIK/XHwBf5e5in1x+MZXxZ8v4KO2o+KxydVIRXs5K2s+o6qjZeJgdc5Yq3f2XDUYCAZo9bSQEpfKvKwF7GzeTqu7pcua57Y+gy/gZ339Gto8rQSDQarsleTYcrusS41Pw+134/Q5h7TXoWr1hPY7GjP+DAYD1hhbv60+X9rxT/6x9ZlBta0djyraK7oFxAApcam0uVvwB/wR2FX06LzPorHVp9FgZHJyMbs6Kv7cfjeV7eVMSp7cbW1peqit9uaGjaO6R+mbgj8RERERERERkRFU3r6X/+75Dw3OBuodocqdDKuCv9EwLa2Udm8775Uvp6q9khMmnASgdp/78XQEf3HD1OoToMZRPaD1oYq/8RH8FSQWAKH2iAAbGzYAEGeK45WdLw3pnJ0zAxt6qcRrdjcRCAZJt6QzL3M+QWBN3erw4y3uZl7Z+RKnTTqdQDDIx1UraPe24fA6yLV1ndvWOber0TW6c/4+qHgfgAnJE0blelaztc+Kv0AwwDt7/gPsm3HXk90tu0b9czXaquwV3eb7AaTGpxIEmt3No76naNL5eyYaK/4gNOdve/N2AMpadhMIBpmc0r3iLy0+nRxbDpsaN4z2FqUPCv5EREREREREREZQs6sJfzDA67teod5Zh4F9b7LLyJqWVgrAI+sfJDE2kaPyjwbot+LnYOIKB38HXvGXZc0GoMZe1e/aYDCIw2ePymqYnmRbc4gxmijvCOs21K8jw5LBkklf4o3dr+H1e4HQ615Xv3ZAbSQ7K868AS/t3rZuj3fOWUuJSyXblkOuLZfVdfvafb6w7XkALp19OVNTp7Gi8sNwmPXF4C81Pg0Y3eCvxd3MI+sf4NRJS5ic2r2SaCTYzH1X/G1s2BCeU9lZcdmT21f8lMc3PDrs+xsrPH4PdY5a8hIKuj3Wea80R2gmZLTovM8SovRn3OTkEva2leHxe9jZsgMDUJhU1OPa0rQZbFLF35ii4E9EREREREREZIT4A37aPK1YzVZe2fkStY4aUuJTiTHGRHprB4XE2CTyEwqobK/gyLzFpMSlAtDuUcVfJ7fPBUBczIFX/FnNVpLiksPBSV9cfheBYHDctPo0GU3k2PKo6AjrNjSsZ1bGHM6YvJRmdzMfVL5HMBjk4XV/4bp3vs+q2pX9nnNv2x7MRjOwr03w/ho7gr+0jiBmXtYCPqh4l/fKl9PqbuHF7f/kS8VfJjkuhaPyj+bT6o/Z21YGQG5C11afaZbQHyMMtE3rcHh43QMEg0G+O/d/Ru2aoVafvX//v73n32RaMslNyKPK3nPFn8fvoaJ9b7giczza01pGEMjvodVnasfP0c77T3q2r+IvOoO/4pQSAsEgu1t2satlJ7kJ+VhiLD2unZ4+g21NW8N/4CCRp+BPRERERERERGSEtHpaCAJfKTmXOmcd/y57nUxLVqS3dVAp7aj6O7rguHDLtZ6qpw5W4Rl/w9DqEyDbmk31ACr+HN5Qu0VrlLbB60lB4gTK2/bg8XvY2rSFGekzKUqexOyMOby84yX+vvlvPLPl7wBUtJX3e77K9gqmp88AoMFZ3+3xZlcTsK8C6+wp55Een8HtK37KV18+G5ffxTlTvgrA4XlH4vQ5eWP36ySYE0iMTepyLluMDbPRTNMoVXFtbNjAa7te4duzLg3vfzRYzdbwvfdFvoCP5Xvf4fiJJ5KfkN9rq8+K9nICwSCV7f1/DaPV8vK3SYxNZHrazG6PhSv+Ou4/6dm+GX/R+TOuKHkSBmBnyw52tezocb5fp+lpM/EGvOzsmAkokafgT0RERERERERkhDS5Q2+MHpZ7OFNTp1HrqCXdkhHhXR1c5mUdQrolnQVZh5AQG6q8UKvPfVz+joq/YWj1CaGWlwMJ/sLVMONkxh+EqqMq2ivY1rQVX8DHjPRZAJwxeSlr6lbx6PqH+NbM74SqUPtoI9mpvG0vszPmAj1X4jW6GrGZbcSaYoHQG/X3nfQAD57yGGdPOZfvzb2KzI55opOSJpNjy2F17efk2HK7nctgMJBuSadxFCr+2r3t3LvybkpSpvCl4jNH/Hr7s8bYeg3+VtZ8RqunlRMmnkSuLZ/qXir+9rSGqibrnfXh4Hw86ZxzeEzBcZhN5m6Px5pisZqtqvjrR+fvmWj94wZLjIX8xAlsb97GrpadTE7uPt+vU3FKCTHGGDY3qt3nWKHgT0RERERERERkhHRWRKTEpbC0+CwAMq2q+BtNpxYt4Yklz2A2mbHGdFb8qdVnp+Fs9QmQY8sZUKtPh6+z4m/8BH8FCROptleypm4VcaY4ilNKgFC16cSkQi6Y/k2+Mf0ichNyqWrvOxxt87TS6mmlKHkSyXEpPVb8Nbkbw+1r91eUPInL5nyPL5d8JXzMYDBweO5RAOQm5HV7DkBafPqIhzn1znquf+f71Dpq+eGhN2I0jO7b0zazDYev5+D/nT3/pjCpiMnJJeTacqmyVxEMBrut29PRLhWgqpeqwGjWOefwhIkn9bomNS6NZrcq/vpi97ZjibGM+j0+nIqTS1hZ8ynN7uY+K/5iTbFMSZ3KpoYNo7g76Uv03nUiIiIiIiIiImNci7sFgJS4VI6bcALJcSlMSJgQ4V0dXAwGQ7hqxWQ0YYmx0O5Rq89O+1p9Dk/FX44tl1pHDYFgoM91jnAbvOicf9WT/IR8AsEgb+95i2lp08OzPGNNsTx0yl/5zqzLMBgM5Nryeq0m61Te0Qq0IGECGZZ06nuY8dfsagrP9xuII/KOBCC3h4o/CAV/I9nqc2/bHq55+wpaPa38/vg/UpwyZcSu1ZveWn26fC4+qHyfEyaehMFgIC8hH5fP1WO4tbd1Tzg87W0OYDTrnHM4K2NOr2tS41PDFe3SM7vXHrVtPjsVp5RQ3jHLsq/gD0LtPjeq4m/MUPAnIiIiIiIiIjJCmt1NmI1mLDEW4mPiefS0J8KVfxIZCeYEtfrcj7uj1WfsMLb69AV8/VaORfv8q57kJ4ZC/bLW3czMmNXlMYPBEP73nD6qyTpVtIfebM9LyCc9PoN6V/eKv0ZX46Dm483JnEeOLYfStBk9Pp4anzairT7v/vROzKZY7j3hfoqSJ43YdfpiMyeE28zub339Wlw+F0flHw3sC0d7mvO3p2038zLnE2uKpWKczfnbf85hX5VqKXGpNKnVZ59CwV90/2HD5I6q5VhTLHkJ+X2uLU2bTlV7pWY/jhEK/kRERERERERERkiTu4nU+NTwm/6JsUmYjKYI7+rgZjMnqNXnfly+UMXfsM34s+UAUGOv7nNdZ6tPyzia8ZdhyQjP25uZPrvXdbm2PJw+Jy3u5l7XlLeVkxafhtVsJd2S0WMg1+RqIiW+e6vP3sQYY3ji9Gc4uuDYHh9PH+FWn5XtFZw48WSyItju2BpjDd97+1tbv4bkuBQmJhYCkGPrrOjrOosxEAywt20vhUlF5Cfk9xgMRrP95xz2JTU+TQFPP+ze9qj/w4bOdsVFSZP6bVk6PT30BwWbGzeN+L6kfwr+RERERERERERGSIurmeS4lEhvQ/aTEJtAu1etPjt5/G7MRvOwzaHKtnYEf46+Z9hV26swG83hdpjjgdFgJL+jKmZGes9VdQB54TaR+z5Ha+tWc+/K34arACvayynoqCBMt2T02Oqz0d1Ienz6sO0/NT6NFncz/oB/2M7ZyRfw0eJuJm0Y9zsUVrMVj9+DL+DrcnxD/TpmZ8wJ/5GG1WwlJS6lW7BX66jB4/cwIbGQXFs+leOs4u/tPW+G5xz2JTU+dcTnQUa78dDqMz0+neS4lH7bfELoZ39KXAobGzXnbyxQ8CciIiIiIiIiMkKa3c2kKPgbU9TqsyuX30V8TPywnc9qtpIUm0R1HxV/y7Y9z5Mb/8opRacN23XHigmJhRQmFZEYm9Trmn3VZPtCpf+U/ZuXd77EmrpVAJS37SU/oQCA9PgMmt1NXQI5f8BPm7uFlLiBV/z1J82SThBGZHZbk6uJIKEQM5KsHUGMc7+qP4/fw6aGjczO7DrTLi8hv9sMv7LWMgAKkwpDFX/jaMZfIBjgw8oPOH7CiV1a0/YkNS6NVk9Lv7M8D2bjodWnwWDgxsN+xNdKvzGgtdPTZ7KpQXP+xgIFfyIiIiIiIiIiI6TZ3TSsb8zLgbPFJmD3qNVnJ7ffPWxtPjtlWbN7bPUZDAZ5eN0D3Lf6D5wz9Xx+sOC6Yb3uWHDp7Mu5edEtfa6xmW0kxSVTvV/F35amUHu857c+SzAYpKK9PBz8ZVgyCASDXSqsmt3NBIG0Qcz4609aXOhcIzHnr9EVOmeGJcIVfx2tZfcP/7c0bcYb8DInY16Xtbm2XKq+UPG3t62MOFMcmdYs8hIKqLFXdasejFa1jhpcPhdT06b1uzY1PpVAMNhnu9qD3Xho9QlwaM6icPVxf+ZnLWB9/Rrqnd1nksroUvAnIiIiIiIiIjJCVPE39tjMCdgjMONvff06fv/Zb0b9uv1x+1zEmYav4g8gx5ZLdQ+tPlfVruTpzU9y6ezLuXzulcPWXnQsyU3IozhlSv/rbLlUtofmx7n9bna17GRG+kw+qlrBuvo1OH3O8JvtGZZMYF94BtDUEQKmDmfw1xHKNbqHv4VjQ0cQEOlWn50VWPv/DFhXtwar2crklOIua3N7qPjb01pGQeIEjAYjeQl5BILBfudZjkU19mrK2/Z2OVbR0ba0IKH/kKfzD1pGojp0vLD7or/V52CdXHQaZmMsL25/PtJbOeiNv9+uIiIiIiIiIiJjRItbM/7GmgRzAu0RCP7+u/dtXt31Mh6/Z9Sv3ReX3018zPBW/GXbsqmx13Q7vrdtDzFGE+dN+9qwXi8a5drywjP+tjdtIxAM8t05V5Aan8ofV90DsK/VZ0cgt/+cv8YRCP5S41IxMDIVfw2uBowGQ8R/HlrNoYo/h3dfq8919WuYmT6rWxCda8ul0dWIy+cKH9vTWkZhUiEQagUKUGmvGOltD7v71/yR33726y7HKtrKMRmMZFmz+31+Z6Vps0vBX2/GQ6vPwUowJ/ClyV/mXzte7PI9JqNPwZ+IiIiIiIiIyAjw+D3YvXZS49XqcyyxmW0RCf52tewAQu30xhK3fwQq/qy51Dpqus3/qnXUkGnJGpeVfoOVm5BHdUc12bamLcQYY5iaOo2zSs5hV8suDOwLlpLjUjAZjOGqOYCmjqq84awoNhlNJMelhKsJN9Sv5/Oaz4bl3A3OetLi0yP+tbfGhCqw7B0z/vwBPxvq1zM7Y263tbkdn//OlqzBYJA9bWVMTCwCQi1tY4ymcOVmNNnTWsaulp0Eg8HwsYr2CnJseZiMpn6fn9Lxe63JNfzVoeNBMBjEMU5afQ7WWVPOxeVz8tqulyO9lYOafsuKiIiIiIiIiIyAFncLAClxw1eRIwcuwZyIw+voFkqNpGAwyM7mUPC3/1y3scDtG/4Zf9m2HLwBL01fqAaqsdeQZc0Z1mtFq1xbHnWOWrx+L5ubNjE5uRizycwZk5cSa4oly5pNrCkWAKPBSFp8OvWu/YI/VyOJsYnhNcMlLT6NBlcDNY4abnn/Rv685o/Dct4GV33E23wC4SDG0THjb0fzdpw+J7Mzewj+bHkA4XafLe5m2jxtTOyo+DMajOTY8qj8whxAj9/DisoPeGbzU2Ny/l8gGKDKXonda+8yi62yvZyCxIIBncMSYyE+Jl6tPnvhCXjwBfwHZfCXZc3iuIkn8s9t/xiT9//BIibSGxARERERERERGY+aO94QTY5LjvBOZH8JsftmfCXGJo3KNWudteEqw5qxWPEXM7wVf9m2ULhXba8Kt6kEqHFUh0OTg12uLZcgoc/J1sYtzMtaAISq+86b+jXavG1d1mdYM7tW/LmawnPWhlOaJZ06Zy13fvxz2r3tuNvcBIKBA67Ua3Q2kG7JGKZdDl2cKQ6jwRBuQ7iufg1mo5mpqdO6rU2PT8dsNIcr+va0lQEwIXFieE2eLS/c6tPlc/HHVffwbvl/cfqc4ef8cOFNGAyGEX1dvWlyNfLarlf4Wuk3wl/DWkdNOJDZ07abTGtohmR5ezmH5iwa8LlT41JV8deLzhmSB1urz07nT/0a/yn7N++Wv8MJE0+O9HYOSqr4ExEREREREREZAZ3BX4pafY4pCebO4M8+atfc1VHtF2uKpcZRPWrXHQi33038MLf6zO6o6vvia61xVIcfO9h1VpNtb95GedsepqWVhh+7eNYlfH/+NV3Wp8dnfCH4axyRNsKpcWl8UrWCjQ3r+eaMb+ENeKn6QkXbUDS46kkfAxV/BoMBmzkhHMysq1/L9PQZPVZOGgwG8hLyqeyo+NvTugejwRCevQiQl1AQDgaf2Pgob+95i3OnfpUHT3mMmw77MW/ufp0nNj42rK/BH/Dj9rsHtPa1Xa/w6PqHKG/bGz62t21P+N93t+wKn7PaXkl+R3vTgUiNT+tW1Sshnb9fDsaKP4DJKSXMzZzPW2VvRnorBy0FfyIiIiIiIiIiI6DF3QwM7wwuOXA2876Kv9Gys2UHCeYEpqWWUjPWWn36h7/Vp81sIyk2qcvsM4/fQ5OrKVwNeLDLtGZhMhh5r3w5QWBaammf69Ms6V3aMja5m0akdWa6JZ1AMMg3pn+L0yctBfZVuh2IhjFS8QdgjbGyuXETf1lzHytrPmVWxpxe1+Ym5FHdXkmNvZqPqz4k15aP2WQOP56fkE9VeyU7m7fz/NZn+cb0i7ho5rcpSp7EiYWn8J1Zl/HExsd4bdcrw7b/P666h7Nf/BJ3fvILVtWs7LNt8cdVK4DQz6BOle0VxBhNTEqexJ7W0Ne2zlmLL+DvEmr2JzU+jYb92s/KPgd78AcwNXVql8BZRpeCPxERERERERGREdDkasISYxn2UEUOTOcbse2e7sFfIBjg6c1PDns14I7m7UxOKSbLlk2Nffy3+gSYmFQYDhUg1F4QINuaPezXikZGg5FsWy4fVX1IfEx8vy1QMy2ZNLoawh83uRpHpNXnotwj+dLkL/ON6ReRYcnAEmPp8nUcCn/AT4u7eUzM+ANIikvm/Yp3eXvPWxxbcDxnTzm317W5tjw+q/mEb776VVbWfMYZk5d2fTwhH2/Ayy8++hl5CQWcN+1rXR7/Wuk3OLVoCfev/r9hm3e2p62MTEsWmxs2ccO713HtO1f1ODu0xd3M5saNQNfgr7ytnFxbPpOTi9nduqvjWCigGUzwV5Q0iR3NOwgGgwfycsalg73VJ4SqYWsc1ZrzFyGa8SciIiIiIiIiMgJa3M2q9huDOlt9tvdQ8berZQcPr3uAHFsux004YdiuuaN5OwtzDsNmtrGmdtWwnXc4uHxu4kcgnC5MKgqHDrCv7adafe6Ta8ulsr2C2Rlz+p2hlx6fTpunLVyh2eRqJC0+bdj3NCtjNrMyZoc/npA48YAr/hpdjQRhzFT8/WjRT3H7XUxOLul39t6xBcfT6m7msNwjWJR7RLcKrs7WmHvb9vDb4+7t1jLUYDCwtPgs3tj9GpsaNzK7j+rCgWpwNnBE3pF8d84VrKpdye9X/obvvvltfrDgWk6ceEr4NX1a/TGBYJDJyZPZ1bIz/PyK9r3kJxZQmDSJj6s+IhgMUtFeTozRRNYggvnS9Bk8uelxahzV5NhyD/h1jSeq+IO8hDwCwSA19mryEwceKMvwUMWfiIiIiIiIiMgIaHY3a77fGGTrI/jrnHe1/yy1A+XyuahsL6c4pYQcWy4Nznq8fu+wnf9Auf0u4oZ5xh90BEate8JtCGvsNRiADEvmsF8rWnXO+ZuaNq3ftZ2hWYOzHl/AR6unldQRCP6+6IuVm0PRWak4Fmb8ARQkTqA4ZUq/oR/AzIxZ3LToJ5ww8aQeQ5xsaw4xxhhOKTqNOZnzejzHlNSpJMYm8nnNZwe6dSD0+UyLT8dgMLAgeyH3n/wwR+Yv5tef/IqXd74YXvdx1UeUpExhYc5h4TmjAOXt5eQn5FOYVES7t50GVwMV7RXk2PIwGU0D3kdpR3vaTQ0b+1l58NkX/B3MFX+hULxiv5bPMnoU/ImIiIiIiIiIjIBmdxPJqvgbc8wmM3GmONo9bd0e62x7V++sG7br7W7dRRCYnFxMtjWbIKF5WmPFSMz4g1Bg5A14qbGHKv1qHNWkWzK6zEc72HVWSU1Lnd7v2s7gr9ZRwys7XwIYleCvMKmIPW1lB9TOsTNIT7eMjeBvOJlNZu45/j6+P//aXtcYDUbmZs4fluDP4XXg9Dm7fC4TzAncdNgtnFJ0Go+tf5h2bzv+gJ/Pqj9hUe4RTE4upsZRQ7unDV/AR429ivyECRQmFQGwp3U3le3lFAyizSdASnwqObYcNjduOuDXNd7Yve1YYiz9VvKOZ1nWbGKMJqrsCv4i4eC980RERERERERERlCTq0mtPseohNiEHuf47W7dDUD9MFb87WjejtFgoCh5UrjNZWcYNha4fC7iR2DGX2HSJADKOj6nNY5qtfn8gs65fqVp/Qd/nZWSP/3gR/xx1b0cN+EE5mctGNH9AUxMLMThddCw33zBwWpwNWA0GMbtH0JMSyvt93vokOxD2dy48YDnh+6rnuzeNvXbsy7D7Xfz901PsLFhPe3edhblHsGklGIAdrXspNpeRSAYpCChgNyEPMxGM7tbd1HeVk7eIIM/gNK0GV1a+kqI3Ws/qNt8QsccU2sule2Vkd7KQUkz/kRERERERERERkCrp4WUOLX6HIts5l6Cv445WHWO4avI29myg/yECcSZ4si0ZgFQ46gZtvMfiEAwgDfgHZFWn5mWTCwxFspad3N43pHU2mvItg18ftjBYFHuEfzhhPvJTcjrd601xkpxSjHp8Rl8e9ZllKROGYUd7gsn97TuJmOIM/oanPWkxacf1NVPC7IPIRAMsrZuNUfkHTXk83QGf2k9VE9mWDI4f9oFPL35SWodtSTHpTAtrRR/wE+M0cTOlh1k+xwA5CdOwGgwMjFpIjubd1BtrwzPKxyM0rTpfFDxHr6AjxijooZOoeDv4G3z2Sk/IZ9KVfxFxMH701ZEREREREREZASp4m/sSjAn0O7t2urT6XNSba8mLT6NBtfwVfztat7B5ORQxU2sKZZ0Szo1jrFR8efyuQBGpNWnwWAIzflrC82Hq3FUk6WKvy6MBiPT02cMaK3BYODPJz/CL4++a9RCPwjNIYwxxrC3bc+Qz9E5k+5glpeQT44th5UH2O6zwdkR/PXy+Tx36ldJiE3gv3vf5tCcwzAajJhNZiYmFrKrZScVbXsxG83hELcwqYjPaj7BHwyQnzi0ij9vwMvO/WYISqjV58Fe8QeQm5BPpWb8RYSCPxERERERERGRYeb0OXH73Qr+xiib2Ua7p73LsT2toYBqYc5h1DvrCAQDB3ydYDDIzpYdTO5otQeQbc2h2l51wOceDh6/G2BEWn1CqFpsT2sZ/oCfemedWn1GIZPRRH5CAWUd3x9D0eCs77FC7WCzIGshq2pXHtA5Glz1xMfEY42x9vi41Wzl27MuA0IVpZ0mpRSzs2UHFe3lFCQWhKsvC5MmhcPE/CG0+ixJnYLJYGRLk+b87U+tPkPyE/Kpaq8clt+nMjgK/kREREREREREhlmLuxmAlHi1+hyLEsyJtHu7Bn+7W3cBcEj2QnwBf/hreCBqHTXYvXYmp5SEj2Vbc6gdI60+3R3BX6wpdkTOX5hURFnr7o4gNUiWVa0+o1FhUlE4GB+KBlcDGT3MpDvYLMheyJ7WMuocdUM+R6MzVD1pMBh6XXNq0RJ+csTPODr/2PCxycnF7GrZyd62veQnTAgfL0wqAiDGGDOk7884UxyTU0rY1KA5f/tTq8+Q3IR8vAHvsM7NlYFR8CciIiIiIiIiMsyaO4M/VfyNSQmxCdi/GPy17CTXlht+U3w43qh8Zde/MBoMTEmZGj6Wbcumxj5GWn36O1t9jkzFX2FSEU6fkw0N6wHIsaniLxpNTCpkT9vuIT+/wVlP+hDnA44n87MWYABW1Q693Wejq4H0ftqmGg1Gjik4DpPRFD42ObkYl8/Fhvp1XWb5dQZ/uba8Ic9gLE2bweZGVfztT60+QzrvtSq1+xx1Cv5ERERERERERIZZs6sJgOQ4VfyNRTazrVvwV9a6m8LkSWRaMwGodw69Kgfg85rPeHrT3/jWzEtI36/NYbY1lzpnLf6AP7xuZ/P2A7rWULl9Ha0+Ryj4m5hYCMAn1R8BqOIvSk1MLKTJ1USbp7XXNS6fi+V73yEYDHY57u+onj3YZ/wBJMUlU5I6lc8PYM5fg6txSCFqZ7thb8BLfuK+ir+8hHzMRnOXMHCwStNK2du2h3ZPW/+LDxLtCv6AUIW70WCgQsHfqFPwJyIiIjKCgsFgn/8HWURERManzoq/5NjkyG5EemQzJ2D32rsc29Wyk6KkSaTEpWIyGKlz1A75/E2uRu74+OfMy1rA10q/0eWxLGs2gWCQemcdre4WbvvwFv6++ck+z/fFtqTDxd1Z8RcTNyLnz03Iw2w0s7L6U5LjUkZslqCMrIlJEwHY07qn1zX/3fs2v/joNp7Z8lSX442uRoKgir8OM9JnsbVp65Cf3+CsH1KImhafHq5AL9hvlp/RYGRWxmymp88c8p5K02YAsLVpy5DPMd7YPe0kmBMjvY2IizXFkmnJosqu4G+0KfgTERERGUGfVn/CBS+fq79+FBEROcg0u5tIjE3EbDJHeivSgwRzInZvO4FgAAgFa/XOeoqSijAajGRYMql3Da3VZzAY5Nef/BKAmxbd0q19Xme7yxpHNc9texanz9lndeGG+vWc99KZNLoahrSfvox0q0+jwciExAk0u5vJVrVf1CpInIgB2NPW+5y/9fVrMRoMPLr+QVbVrAwf77xv+2tPebCYlDyZiva9ePyeIT2/0dVAWnzakK8NkJ9Y0OX4Xcf+nq9Pv3BI5wQoSJyAzWzTnL8OwWCQdm8bCbGa8QehqlJV/I2+MRH8Pfnkk5xwwgnMnj2b8847j7Vr1/a5/rXXXuO0005j9uzZLF26lOXLl3d5PBgMcu+997J48WLmzJnDxRdfzO7du3s8l8fj4cwzz2TatGls2tRzL+KysjLmz5/PwoULuxzftm0b3//+9znhhBOYNm0ajz322IBfs4iIiBwcKtr34va72dmyI9JbERERkVHU7G4iSdV+Y5bNbCMQDOL0OQEoa9kNQFHyJCBUndRbxV8gGAgHhj2pcVSzsuYzrpj3gx4rczrbXW5v3saybc8TZ4rrs7pwZ8t2fAEfu1p2Dui1DYbb39nqc2Qq/gAmdswQU5vP6BVniiPHlsue1t29rtnQsJ4lk77E3Mz5/PLj26lzhMLscPBnUfAHUJQ0iUAwyN4+QtTeuHwu7F77kD+XxSklWGIspMYNLTjsjdFgZHbGHD6vXdn/4oOA0+ckEAxiMyv4g1DwV6ngb9RFPPh79dVXueOOO7jyyit54YUXKC0t5ZJLLqGhoee/Yvr888+5/vrrOffcc1m2bBknnngiV155JVu37iuRfvDBB3niiSe47bbbePbZZ7FYLFxyySW43e5u57vrrrvIysrqdX9er5frrruuW+gH4HQ6KSgo4PrrryczM3MIr15ERETGuyZ3aL7PSLxRIyIiImNXraOWTGvv7zdIZHVWYnS20NzdugujwcCEjpl0/8/efYfHVVj5w//e6b0X9W6ruFewwfReAgsBQg0E2IQNeVM3JJuQur9NIX1DyoaQBFIoISSEgAFTDBib4m5ZVu9leu/lvn9MscYzKjMaaSTrfJ6HB/vWI/lqNHPPPefoxQZYA9kVf3E2jq+/8xU8+PaXpjy2PWgHANQqanOuF/FEUAlV+NPxxxBjY7ix+WZYA5Ypk4nj3jEAwPA0bRYLFYqmWn3OXwvOmuT3wSilxN9SVi2vwYh3JOc6Z9CBEc8w1urX4b/O+BoEHD6+tufLMPkmYA1YwWEYKJNtJpe71MMFA67+vPdNJVELnZd4/cqb8I3t/w2GYQrafzpby89Eu/XIvLUlXkpSbaSp1WdCubQCY97RrPmfZH6VPPH3u9/9DjfeeCOuv/56NDU14Zvf/CZEIhGeeeaZnNs/9thj2LFjB+655x40NjbiM5/5DNra2vDHP/4RQKLa77HHHsN9992Hiy66CC0tLfj+978Ps9mMXbt2ZRxr9+7d2LNnDx544IEp4/vJT36ChoYGXH755Vnr1q5diwceeABXXnklBALBHL4LhBBCCDldOZI3fnqdPSWOhBBCCCELacw7gkpZZanDIFNI3ZD1pRJ/rn5UyKog4Cbu7+gkupztN399+BfYN/bOtA91uZLzHVUi9ZTbGCRGuMNuXNVwDVaoVyLGxuFMPjB2qnHfOABg2Ds88xeWp2Cy4k/Amb/7WjXJZGqZpHzezkHmX5W8Zsrk83FbOwBglXYNVCI1vnXWd+AJu/Hvr9yF14d2QSPSZrW8Xa6kfCkMEgMG3LkTf3E2jhf6nkckFslaZ5tj4k8n1mGjMbu4pRi2lJ2BGBvHAdMH83L8pcQbSYz5oFafCRWySgSigfTvRrIwSvqKGw6H0d7eju3bt6eXcTgcbN++HQcPHsy5z6FDh7Bt27aMZWeffTYOHToEABgZGYHFYsk4plwux7p16zKOabVa8eCDD+L73/8+RKLcTzXt3bsXO3fuxNe//vVCv0RCCCGELHPOYOIGDrX6JIQQQpYPlmUx5h1DubSi1KGQKUj5UgCAL5xI/A26+1GnqE+v14n1sAYsGRUKL/b/C3/rfhrNmhbYg7YpK/Qcyfd/SoFqyvOXScvB5/BxY/PN0IsTlaGp1oinGvelKv7ybw04k3AsBCFXOC8VQCm1yVafZVJK/C1lVfJqTPjGEI1Hs9a1245CJ9al27k2qVfgV5c8im0VZ+Go9UjBiarTVa2ibsqKvz5nL368/yEcNB/IWmcPLN62qWXSctQq6vDe+L5Sh1JyqapHGbX6BID0Q1A0529h8Up5cofDgVgsBq0288VKq9Wiry/3k1NWqxU6nS5re6s10X7BYrGkl021Dcuy+NKXvoSPfOQjWLNmDUZGssvUHQ4HvvzlL+Ohhx6CTDa/P6QcDgMOZ/7eYBEyEy6Xk/F/QhYjuk7JUpDrOnWGHeByOIn2UVzQk66k5Oj1lCwFdJ2SpWC669QZdCAYC6BaWQUej67jxUglVoBhgEDchzgTRZ+rB9euuC7972WUGRGKBRFk/ZDz5eiwHcfPDvwIH2q6BlvKt+Jrb38FvpgbalH2rCxP1AWFUA6hgD/l+W9svQnn114Ao1wPkYAPhgHsIQt4vLasbc3+CYh4Iox4h/O+nmZ6PY2wYYh4wnm9Ths1DfjvHf+DMyrOBI9DPw9LVa2qBnHEYQ2ZUCWvzljXbj+GtYZ14PO56WUqngJf3f41nF9zPoQ80bTX2HL7vd+obsTu4Tdyfk9sITMYBjAHx7PWO8M2CLgCqMTKeU3WF2pb5Ta8PPDSafu5d7bXaSDmBcMkfs/QewCgWlWdvqbX8daWOpxlo6SJv1J5/PHH4fP58PGPf3zKbR588EFcddVV2LJly7zHo9FIF+WLNVl+FApxqUMgZEZ0nZKlYPJ16o25sbpsFdrN7fBxHahR1pQwMkJOotdTshTQdUqWglzX6bCpF1wuB62VK6BWS0sQFZmJVMEHl8sBI4zhX8PPIsgGcN3aa6BWJf69msK14HI5iPB9UKvLsOvIi6hRV+FrF30FXbYucLkchPk+qNXVWccOc/wwyPXT/tufrT4j/WcVK4FYIIKf487axx1yIxD3Y0fNDrw19BaEMgYSviTvr3eq11OukIVMLJ336/QqzWXzenwy/9YIWsDlcuBkLVijbkkvD8fC6HN340OtV+a8jq5Sz/7ffrn83l9T2Ya/dj+Z8+fZP+YGl8uBO27P+n4GGC/KFAZoNIuzkuzi5gvw1+4nYYmPokXXMvMOS9RM1yljjYHL5aDaWJZuH72cqSGFQa6HPWam90QLqKSJP7VaDS6XC5vNlrHcZrNlVfWl6HS6dOVeru31en16mcFgyNimpSXxgrNv3z4cOnQIa9asyTjO9ddfj6uvvhrf+973sG/fPrz22mt49NFHASSqBOPxONra2vCtb30LH/7wh+fwlWey231U8UdKisvlQKEQw+0OIBbL3aqEkFKj65QsBadepyzLwuyx4rzKC3Fk/CgODB6BvHrxtWUhywu9npKlgK5TshRMd52eGOtBLBaHNKaGw+ErUYRkJhyWh2OjHXi2+xl8qPE6KFhd+t+LH5EiFoujZ2IQKhjwWu8buKLhSnhcIfDDEsRicfRODMLIzU78jTkmIOXI8/q3Vwu0GLAMZ+3TZU9cS2s1G/FG/24cHTqBlZrmWR8313Xqj/jR4+hGq7YNdrcbnDiPrlMyIx4rAQ8CHB/rwmrFxvTyY5ajCIZDqBOvKPg6Wm6/93XccsRicRwcPIY27aqMdf2WocTri2Ug6/s5bB+DnKdatD+vNcImCBghXj7xGoyrsl8bl7rZXqfjdgs4LA8+dwQ+ZM9qXI4a5Cvw3tAHuKlxcV67S8lsk6clTfwJBAKsWrUKe/fuxUUXXQQAiMfj2Lt3L2677bac+6xfvx779u3DnXfemV72zjvvYP369QCAqqoq6PV67N27F62trQAAr9eLw4cP4+abbwYAfPWrX8VnPvOZ9P5msxl33303fvzjH2PdunUAgCeffBKxWCy9zauvvorf/OY3eOKJJ2A0Gov1LQAAxOMs4nF25g0JmWexWBzR6On/BossbXSdkqUgdZ16I16EY2HUyOqhFmrQZevGWeXnljo8QgDQ6ylZGug6JUtBrut02DUCpUAFASOia3gRk/AkePrEU5Dwpbil+faMfyslTw2wgMlrxlHTMTiDTmw1bkc0GoeCrwYDBiavJee/rz3ggEKgyuvfXivSw+Q1Z+0z4h4FywKb9FvBssCAcwANihV5f62Tr9OnO57C79t/C7lADjFPDLlAQdcpmZVKWSWGXEMZ18sh0yEIuSLUyhrmfB0tl9/7ldIagAV67X1YqWzNWGfymsCywKhnNOt7YfFZoBFqF/H3iIONhi3YN7oXNzffXupg5s1M16k76IGEJ1nE/04Lb51uA3579P/gCwUg5ApLHc6yUPJWn3fddRceeOABrF69GmvXrsUf/vAHBAIBXHfddQCAL37xizAajfj85z8PALjjjjtw++2349FHH8W5556LF154AceOHcO3vvUtAADDMLjjjjvwy1/+ErW1taiqqsJPf/pTGAyGdHKxoiJzuLZEkiiprqmpQVlZGQCgsbExY5tjx46Bw+Fg5cqV6WXhcBi9vb3pP5tMJnR0dEAikaC2trbY3ypCCCGELDHOoAMAoBap0aBqRJ+rt8QREUIIIWQhjPlGUSGrLHUYZAZSvgzOkBN3rrobMoE8Yx2fy4dKpIbFb8aIZwgqoQqt2sT8PQ7DgUakhTVgyXlcV8iJSllVXrHoJXqYfBNZy8e9YxDzxCiTlkMtUmPIPZTXcXNptx1Fq7YN6w0b8dbIbjSp8k8kkuWpWl6LYU/mNdhuO4ZWTRu4HO4Ue5FTCblCVMiqMODqz1pnDSQ63U34xrPW2YN21Cjq5ju8OTmjfBt+9MH34Ao5oRSqSh1OSXgjHsgFilKHsahsMG5C5HAEx63HsMG4qdThLAslT/xdccUVsNvt+NnPfgaLxYLW1lY88sgj6dad4+Pj4Ewa/Ltx40b84Ac/wE9+8hP86Ec/Ql1dHR5++OGMhNy9996LQCCAr33ta3C73di0aRMeeeQRCIXFzSabzWZce+216b8/+uijePTRR7F161Y8/vjjRT0XIYQQQpYeRyiR+FMJ1WhQNmL38OsljogQQgghC2HcO4ZyWcXMG5KSUglVEPGEuKLhqpzrdWI9bAErjlqP4IzybeAwJ+9PacU62ALWnPs5gg6ohOq8YtGL9Wi3Hs1aPuEbR4WsAgzDoFpeixHPcF7HPVWcjeOEvQPXrbgBt7V9FB9bfe+cjkeWl0pZFY5YDqX/zrIsjluP4Zqm60oX1BJVp6xHf44HQ81+E8qkZZjwTcATdmckkOxBG7SixT06YkvZGWABHDDtx/k1F5Y6nJLwhr2QCRbnHMZSqVc0QClU4aDlACX+FkjJE38AcNttt03Z2jNXAu3yyy/H5ZdfPuXxGIbBpz/9aXz605+e1fmrqqrQ2dk57TbXXXddugoxn/0IIYQQsnylKv40Ig0alI14qvMJeCNeyPj0IYAQQgg5nY15R7HJuKXUYZAZfGHLlyDiiTMSepPpxHocthzCqHcE96z5eNY6WzA78ceyLFwhJ1R5VrroxQZYAxbE2XhGPOO+MRgl5QCAank1OmzteR33VCOeYXjCnnT1IiH5qJZXwx60wxfxQcqXot/dB3fYjdW6NaUObcmpU9Tjhf5/ZiyLs3FYA1acV30BJnwTmPBNpBN/4VgYnrAHWvHiTvxpxVqohCqMekdKHUrJ0Gf+bAzDYINhIw6a9gP0wMmCyP3OhhBCCCGEzJkjaAeX4UAmkKNelWgj3u/qK3FUhBBCCJlP/ogfzpATldTqc9GrkldDJ9ZNuV4vMWDUOwI+h59VoaAV62D1Zyf+vBEPYmw87xZ3eokB0XgMzmTHiJQJ3wTKZanEXw2GPcOIs4XPjeqwHwcDoFnTOuO2hJyqSl4DABj1JJI6+8begZgnxmrd2lKGtSTVKevhCDrgDrnSy1whJ6LxKNbpNwBIJP5T7EEbAECzyCv+AMAgMcLsN5U6jJLxhr2QUuIvy3rDRnQ5TsAb8ZY6lGWBEn+EEEIIIfPEEXJAJVKDw3BQI68Fj8NFn7On1GERQgghZB6N+0YBAOWU+FvydKJEUnCTcTPEPHHWulwVf86QE0BixnNe5xLrAQAW/8m5gXE2DpN/HOXSRNvYanktIvHInG6od9jaUaOoo2oUUpBKeWJ25Yg3Medvz+hb2Fy2FQKuoJRhLUl1inoAwID75Jy/1M9/vbIBEr4E496TiT9bIJn4W+QVf0Ai8WfyZ88sXS68EQ+9xuaw0bAJcZbFUcvhUoeyLFDijxBCCCFknjiC9vR8Fx6Hh1pFHVX8EUIIIae5seSN2gopzfhb6vSSRDLuzIqzstbpxDp4wh6EYqGM5alW73lX/KUSfwFzepk1YEU0HkNZ8lqqUSSqrYY9Q3kde7IT9uPU5pMUTMaXQSVUYcQzAmvAii5HJ7bn+PkgM6uSV4PH4WLANSnxl/z5N0gMKJeWY8I3nl6XqvhLPZCwmCUq/swzb3ia8kVoxl8u5bIKlEnLcMC0v9ShLAuU+COEEEIImSeOoAMakSb993plI3qp4o8QQgg5rY15RyHhS/JO/JDFp0m1EmXSspyJDW2yRah1UoUecLLiL98Zf0qhCjwOL+N4E8k2f+XSRKtPg8QIPodfcOLPH/Gj39WHVs2qgvYnBABqFLUY9gxh79gecBgGZ5RvK3VISxKPw0O1vAZ9rt70MovfDB6HB6VQBaOkHBP+zMQfj8NLz/xbzAwSA8x+E1iWLXUoJZGYgUmJv1zWGzbioJkSfwuBEn+EEEIIWfKe6XoKpkU4Q8AZcqQr/gCgUlaV8dQmIYQQQk4/474xlEsrwDBMqUMhc1SnrMfjVzwJ9aQHuVJSrTlPbffpDDnBYZi8b84zDAO9WJ9R8TeefN9Ylkz8cRgOquRVGPEM53XslC7HCcRZlir+yJxUyhLX4N6xt7FWv2FJJKIWqxXqZnQ7utJ/twTM0Iv14DAclMvKMe49+dmx39W3ZH63GKVlCMfCcCUfhFhO4mw8UfHHl5c6lEVpg2ETBt0D2Nn/Av5n37dw90t3ZMy5JMVDiT9CCCGELGnesAe/Ovww/tb1dKlDyeII2jPmu0j5Uvij/hJGlNDn6sVLAy+WOgxCCCHktDTqHUUFzfc77aUSf9bAqYk/BxQCJThM/rfc9BJDxoy/ce8YtGJtxvy0KnkNhtyFVfx12I5DwpegVlFX0P6EAInE37BnCAfNB6jN5xytVDejz9WDcCwMIDHjTy8xAADKpRUw+ScQZ+NgWRbvju/F5rKtpQx31gxiIwAsy3afgWgAcZalVp9T2GDYCAbADz/4Hgbd/bik9jLIBJQknQ+U+COEEELIkjbhSwwNf2fsrYxWIv6IH68PvVqqsAAAjpAj4wlxCV+KcCyMWDxWwqiAf3T/Df974MeIs/GM5ZFYJGsZIYQQQvIz7h1FJSX+TnsSvgRinhjWQHarz8kPfuVDL9ZnHG/CP44ySXnGNtXyGgx7Bgs6/nF7O5rVLQUlJQlJqVHUIhQLIRqPYnvF2aUOZ0lboW5GNB5Lz/mzBizpeZ9l0gpE41HYAjb0uXpgDVhx5hJpq2qUJhJ/Jv9EiSNZeN6wB0BiHibJphZp8MPzfobfX/Yn/PqS3+Gmllvod9I8oe8qIYQQQpa08eTskwnfBPpcJ+fnPd31BP7n3W+V7CnDQDSAYDQI9aRWnxKeBADgj/pKElNKj7MboVgI5lPao372jfvxl44/ligqQgghZOkLx8KwBMwol1LibznQifWwBWwZy1xBJ5TCwhJ/ulNbfXrHUCbLTPzVKephD9rzbqHHsiw6bMfRqqX5fmRuKmVVAIBGVSOM0rISR7O0NaqawGEYdDk6ASRaferSib/E93bcN4p3x/dBzBNjjW5dyWLNh0KghIAryPq8uRx4I8nEH1WxTWmNfh0q5VWlDuO0R4k/QgghhCxpY95RiHliSPlSvD36FgAgGo/iX33PAQBGPIW1QporZ9ABAKdU/CUTf5HStfuMxqPod/UBQPrJUgAIRoPodnTiuO1YqUIjhBBClrxEWzYWFbKKUodCFoBWrMuq+HOEHFAJVQUdTy8xwBqwpDswTPjGUS7NvJZWqFcCSDzIlY9x3zhcISfatKsLio2QlHJpBXgcHrZX7Ch1KEuekCtEnaIe3Y5OxNl4ouIv2eozNdtzwjeOd8f3YqNxM/hcfinDnTWGYWCQGJdlq09fJPGQL1X8kVKjxB8hhBBClrQJ3zgqZBU4s3wb3kkm/vaMvgVH0AEGwIhnuCRx2YN2AIBKNLniTwoA8JWw4m/IM4hIPAIAGHCfTPwNuPsRZ1kMFdg6ihBCCCHAmDfRiaBCRk+yLwdasTYr8ecKOaEqsOJPLzYgGo/BFXLCH/HDHrSjXJpZ8Vchq4SYJ0aPI7/EX29y+xXqFQXFRkgKn8vHj8//OW5svrnUoZwWVqib0ek4AWfIgWg8BoMk0SZTyBVCI9Kg09GJDls7zlgibT5TjBLj8qz4C3sBAFK+tMSRkOWOEn+EEEIIWdLGfWMok1Zge+UO9Ln6MO4dwz97/4HVujWokFVhxDtSkricoWTF3+RWn4ug4i9106dWUZeR+Ot1JtqkmnwTCMVCJYmNEEIIWeomfGPgcbjQiXWlDoUsAL1YD1vAmrHMGXIWXPGXavHX7+rDV99+AAKuIKtCj8Nw0KBszLviz+Q3QcAVQC3UzLwxITNo0bRCxBOVOozTwkp1Mwbd/Rj1jgI4+ToAJKr+dg2+BBbA1rIzSxRhYQzLNfGXavXJp1afpLQo8UcIIYSQJW3cN45yaTk2G7eCz+HjLyf+iMOWg7i68VpUK2pK1urTEXSAwzBQTrrxk6r4K2Xir8fZg3JZBVq1bRh0DaSX97l6wWU4YFG69qhkcfNH/OnWY4QQQnKzBW3QiLTgMHS7ZTnQifWwBa3p349xNg53yJnx/i8feknihv839z6IAXc/vn/Oj1Elr87arlG9Iv/En88Eg8QIhmEKio0QMj9WaloQjcfw3vg+AIkHClLKZRXwR/xoUq2AVqwtVYgFMUiMMC3LxJ8XQq5wybRlJacveidKCCGEkCUrzsZhSs4+kfAl2FS2BS/2/wtKoQpnV56DKllVyVp9OkJ2KATKjBt/qXYfvoi3JDEBQI+zC02qFahT1GPIM5i+UdXr6MZG42YAwKCb2n2STOFYGB9/5S58a+/XwLJsqcMhhJBFyx5IJP7I8qAV6xCNx+AOuQAk2nyyANSiwlp9KoUqCLgCSHgS/Oi8/8UqXe55fCtUKzHqGc7rYTKz3wRjsoUgIWTxaFA2gstw8M7Y2+Bz+BkPDpRJEq1+l1qbTyDR6tMVci6abjLhWHhBzuMNeyET0Hw/UnqU+COEEELIkmXxmxFj4yiTVgAAzkoOmL+8/koIuAJUyWtg8k8s2Jv8yRxBBzSizFZKqXY4gWhgweMBAJZl0evsQZNqBWoVdQjHwpjwjSPOxtHn6sVa/XpoRBoMUeKPnOL14Vcx4ZvAntG38FTnX0odDiGELFqOoB1qEbVSXC5SLflswUS7T1cyAVjojD8Ow8F3z/kh/vfCX6NOWT/ldk2qFWCRaAk6WybfRHp2GCFk8RBwBahT1mPIPQidRJ9RlVsmXbqJv9TrzWJo9zniGca1f78CA67+mTeeI2/ES20+yaJAiT9CCCGELFnjvjEAQIUskfg7u3IHdlSdi2uargMAVMurEWdZjCXnJSwkR9AO1SlPe3MYDsQ8MfxR34LHAwATvnH4Ij40qVaiVpG4mTToHoDJN4FANIAGZRNqFHUYdA+UJD6yOLEsi6c7n8CZ5dvwkZZb8eix/8MRy6FSh0UIIYtSotUnJf6WC60oMcvRkpzzl5rxrBQqCz7mGt3aGWdE1irqwONw0ePsmvVxTf4JGCVlBcdFCJk/K9UtAAC92JCx/KzKs/Hxdf+BZk1LKcKak8WU+Ht3fC8i8UheD0sUyhv2pDv9EFJKlPgjhBBCyJI17hsHA6RvYsgEcnxt27fSN0tSM1FGvAvf7tMZcuR84l/Kl5Zsxl9qFkyjqgk6sQ5SvhQDrn70unrSy2sUtRimGX9kkvcn3sOgewA3NH8Ed666G2t06/HtvV+HI2gvdWiEELLoOIJ2avW5jGhEGnAYBla/BUCi4wOArIe/io3P5aNWUTfrOX+BSACukAtGKVX8EbIYrVA3Azg55zNFLlDgwytvWpJzY3ViPRgAZr8ZQKJC+ZvvPFiSbjz7Te8DSDwAMd8SFX/U6pOU3tJ71SCEEEIISRr3jUEvMUw5OFst1EDCl5Rkzp8j6IA6R5snCV8KX6Q0FX/dzi6ohCpoxVowDINaRR0G3f3odfZAKVRBI9KgRl6DUe8wYvFYSWIki89fu57ASnUz1ujWgcvh4itnfg3eiAdvjewudWiEELKoxNk4nCEHJf6WES6HC5VQnW716Q67wOPwIOXNf7VHk2olehyzS/yZfImKG2r1ScjitDKV+Dul4m8p43P50Ii1MPkSybYnTvwRb4++ieO2YwsaRzgWxuFkt5IJ3/i8n88XoRl/ZHGgxB8hhBBClqxx71h6vl8uDMOgSlZdmsRfyJ5zvouEJylZq89eRzdWqFem/16nqMdAMvHXoGwAwzCokdciGo9hzLfw7VHJ4tPr7MZB8wF8eOVN6XkjapEGlbJqDHpoFiQhhEzmCjkRZ1lq9bnMGCTGdOWdI+iASqjKmNE1X5pUKzDg7kc0Hp1x23FP4mY3Jf4IWZzqlQ2Q8CWokdeUOpSiMkrKYPabYA/a8ObIGwCA/aYPFjSGY9YjCMfCqJBVLkjFny/ihZRm/JFFgBJ/hBBCCFmyxryjKE8OPJ9KtXzhE3/hWBj+iD/njT8JX1LSVp+NqhXpv9cq6zDkHkKPowuNqiYAQI2iDgAw5KakDgGe7X4GRokR51Sdl7G8TlGPAVd/aYIihJBFyh60AQA0Yqr4W06ubPgQ9o29g3brMThDDiiFqgU5b6N6BaLxKIZmMZt53DsODsOBTqyfcVtCyMITcAX4/WV/woW1l5Q6lKIySIww+814oe95cBketpadkW67uVAOmD6ASqjCmeXbMeGb/8SfJ+yhij+yKFDijxBCCCFL1oRvHBWyymm3qZRXY3iBZ/xNN99FwpMiEF2YxJ875MInd/07Hj74MxwwfQB70I6mSYm/OkU9IvEILAFLOvGnEWkg5Usp8UfAsiz2m97HOVXngcvhZqyrUdTO6kYjIYQsJ7ZAYvZprhm/5PR1Sd1laFQ14VeHf55o9T7P8/1SGpVNYIBZzfmb8E5AK9aBx+HNf2CEkIKoRZolOctvOgaJAeO+UTzf9w9cVHsJzq0+Hz2OLrhCzgWL4QPT+9hUtgVl0jKYfBOIs/F5PR/N+COLxen1akIIIYSQZcMb8cIddqNshoq/Klk13CEXPGH3rI5r8VvwyJFfwRv2FBxbqsIwV2wSvmReZvy9PfpmVgXWgLsfXY5OvDK4Ew+8+XkAyEj81Srq039uSCb+0rP/PANFj5EsLWa/CdaAFWv067LW1Srq4Aw5Z/Wh3R1ywR1yzUOEhBCyuDhCycRfjlbf5PTFYTj4xLpP4oS9A+9PvLtgFX8SvgQVsir0OHtm3HbcMw6jpGwBoiKEkJNSFX+2gA0farwWGwybwQI4YNq/IOd3BO3odfZgs3ELyqTliMQj6Yd050OcjcMf8UNGrT7JIkCJP0IIIYQsSabkYO7yaWb8AUB1ck7C8CzaffY4uvGp1z6OJzv/ghf6ny84tk77CYh5YlTKqrLWSXjSorf6jLNxPPT+d/CPnr9lLLcFEi3HHrv8L/j69m/jnjUfz6iQ1Ig0kAvk4HF4qJHXppdXy2uo4o/gmPUIAKBNuyprXW0eLWEfev87eOj97xQ1NkIIWYwcQTvkAjkEXEGpQyELbL1hI7ZVnIVIPALVAiX+AGCFeiXen3gXgWhg2u0mvBMwSg0LFBUhhCSk5oqu0a1Fg6oJeoketYq6BWv3edCcSDBuMGyGMRnLRPI+wnQOmQ9gzJv/zHtfxAsA1OqTLAqU+COEEELIkjTmHQOAGWf8pRJdozMk/vaNvYPPvnE/NCItNpdtwUsDL4Jl2YJi67SfwAp1c85WLRK+BP5ocSv+ehzd8Ef8sAatGcvtQRuEXCHkAgXOrjwHN7XcAoZh0utT1X21itqM1k81iloMe4bmvQ0KWTwsfgv+0vHHjGv+qPUIahS1OSsXKmVV4DIcDMzQ7pNlWXTYO9BuO1bwzxMhhCwVtoANGhHN91uu/n3tfeAu8By9m1tuhTVgwfff+59p37eNe6nijxCy8FKfxa9pui69bJNxCw6YPliQzwb7TR+gXlkPrVgLgzTxGmjyTz/nj2VZfHvfN/BU51/yPl+qsw+1+iSLASX+CCGEELIkjfvGIOaJZ2ynJOFLoBPrpp3zF46F8e19X8d6/Qb88Lyf4cMrb8KQexDHbe0FxdZpP4EWTUvueHiSolf8HbYcBADYkxV+KfagDRqRJiPZd6qbW27HR1fdnbGsRlGHYDQIS8BS1DhJtmg8iuO29pInxd4ZewuPHvtNxjV/zHoUa3Rrc27P5/JRKa/G4AyJP1vQBlfICU/YU9BTs4QQspQ4gnaa77eMVcmr8YuLfoMr6q9esHM2qJrwX2d8DXtG38Tvjj2Sc5tYPAazzwyjlBJ/hJCFVauowy8vegTnVJ2XXrbJuAWWgAXDnqF5PXevsxvvje/DJuMWAIlknFwgh8k3feJvxDsMd8gFs9+U9zm94UTFn5QSf2QRoMQfIYQQQpakCd84yqXl0ya1UqrkNem5e7mY/SaEY2Fct+IGiHlibDBsgkFiwEsDL+Qdlz1gh9lvwgp1c871Er4U/qivqImeI9bDiXMHT0382WesPNhafga2VZyVsaw22fZzaIakDpm7F/ufx6df+w88uOdLsPhLl2hNfbB9ZXAngMRcvkH3AFbr1ky5T428dsZrpHfS3KFOR8fcAyWEkEXMEbJDS4m/Za1B1QQJX7Kg59xWcRbuXXsfnjjxJ7w+9GrWekvAgjgbh0FqXNC4CCEEAJrUKzI+s6/RrwWPw5u3dp/DniF8e+/X8YlX7oGEL8WVDR9KrzNIjDNW/LVbjwEATIUk/iIeAFTxRxYHSvwRQgghZEka942hbIb5finV8uppZ5Gl2oamWpFwGA4urbsCbwy/lnd1Xoclkdxo0bTmXC/lSxBnWYRiobyOO5U4G8cxyxHoxDrYg7aMNk/2oA0acf4tx4zSMoh4IvQ5e4sSI5naBxPvo1xWgS5HJ+59+aN4c+SNksRh9psBALuHX0c4Fka7LfGBd/UUFX9A4gneAXf/tMftdXZDypeiXFqOE/YTxQuYEEIWIVvARhV/pCQ+vPImtGlX4a3R3VnrzL7EzevUfCtCCCklMU+MVdo1OGD6oOjHfm/8Xdz3yj04YT+Oz29+AI9e+jiq5NXp9WXS8hln/KXmnJt8E3k/rOsJJxJ/coE8z8gJKT5K/BFCCCFkSRr3jqNshvl+KZuMWzDoHsDesT25j+UbBY/DhV5iSC+7tO5yBKOBvBMxxy3HIRfIp5yjIuFJAaBoc/76nL3wRrw4r/oCxFkWzpAjvc4eKKzlGIfhYKW6GZ0OStTMpzgbx2HLQVxceyl+e+ljaNW24ReHflaSWEz+CbRq2+CNePHu+F4csyaSydPNA6pV1MERdMATdk+5Ta+zB42qJrRo2tBpp4o/QsjpzR6kxB8pDYZhUC2vgSX5IM9kqeoWavVJCFks1hs24Jj1aFG74Lw88CIe3PMANhg34beXPo7L6q8Al8PN2KZMWoaJGVp9Hre1Qy1SIxQLwR125RVDasYftfokiwEl/gghhBCy5LAsC2vAAsOkRN10tlecjc1lW/DwwZ8iEA1krR/zjsEoKQeHOfnWyCgtwwbjprzbfR63HEezpmXKFqSp9k/FmvN32HIQfA4f2yt3AEhUG6TYQ3ZoZ2j1OZVmdQslauZZl6MTvogPGw2bIRcosKPyPNgDNkTj0QWPxew3YZNxC1aqm/Hy4E4csx7Fat3aaVvp1irrAACD01TTJhJ/K9CsaUG3o6skXxshhCyEQDSAQDRQ8O9dQubKIDHmnEll8pmgFCkh5olLEBUhhGSrVdTBG/FmPLQ6F8/3PoeH3v8uLqu7Et/Y9t8Q8UQ5tyuTlMPkn8jokjOZO+TCsGcI51dfBCDx+pkPb8QDMU+clXAkpBQo8UcIIYSQJccb8SAUC0En1s9qe4ZhcP/6z8AetOPPHY9lrR/zjaJClt029OLaS3HMehSukHNW52FZFseticTfVKT8RMVf6mnAuTpsOYRW7SqUJ9ue2gJWAEAkFoE75Jpxxt9UmjWtMPvNWXMDSfEcNO2HmCdOXy86sR4sANsCf88jsQjsARsMEiMuqbsM74/vQ5fjxLTz/QCgSlYNDsNgcIo5f/6IH2PekXTFXyQeQb+rbx6+AkIIKT1H0A4AVPFHSsYgMcIetCMcC2csN/knUC6bXZcMQghZCKn2myOe4Tkfi2VZPHHijzi/+kJ8ZtMXpk26GaVliMajsCd/Z58qNe7ggppk4m+GeYCn8oa96c/7hJQaJf4IIYQQsuRYAhYAmHXiDwAq5VW4ufU2PN35BAZcmXPJxr1jKE/O95usQdkIABjxjMzqHNaABTa/bdrEn5iXqvibe+IvzsZxzHoE6/TroRaqwWGYdNLIkXx6stDEX2pGYSfNZZs3B8z7sVa/DjwODwCglySu51xtuuaTNWABi8Tsn/OqLwAAROOxGRN/Aq4A5dLKKRN//a4+sACaVE1oUq8Ah2FwgqpICSGnqdRNxEJ/7xIyV6n3EamHwFLMPhPKZNTmkxCyeFTKqsAAGC5C4u+EvQMmvwlXNlw9bbcS4GTLY9MU7T7brUehEWmwUt0MAVeQs4p6Ot6IFzI+zfcjiwMl/gghhBCy5Fj9iRsa+lm2+ky5qfkWlEkr8Pv236aXsSyLcd8YKqTZFX8VsioAwKh3dh9IUkmy6RJ/kmTiL1fL0Xz1u3rhCXuwTr8eXA4XKqE6fbMnVXmgFRdWeWCQGKESqpZt4i8aj+LHHzw05VzIuQrFQmi3HsUGw6b0Mr24NIm/1JOsBokRSqEKW8rPhIQvQX0y8T2dWkUdBt39Odf1OLvA43BRI6+DkCtEvbKR2scSQk5bqQp5rZgSf6Q0DBIjAMASyHwfMeGjij9CyOIi4ApglJZhxDM052O9MfwaVEIV1ujXzbhtan65yT+ec/1xWztW6daAYRgYJWUw5Z3480AmoPl+ZHGgxB8hhBBClhxLwAwOw0CTZzstAVeA82suxFHrkfQgcVvQhnAsjIocFX8ingg6sQ4j3tlV/HXaT0Ar0U5biShJtv7wR+de8XfEchg8Dg8t2jYAgFasS994TP2/0JZjDMOgRdOKTsfyTNT835Ff4oX+5/Fc77Pzcvx261FE4hFsMJ5M/En5Moh4IliTFa0LJfUkayqR/om1n8RXzvhGxszLqdQq6zA0xYy/XmcPahV14HP5ABJVpMWs+IvEIvjEKx/DIfOBoh2TEEIK5QjaweNw6Ul/UjJ6ceL3+OQKFZZlYfKbUC6nxB8hZHGpltdgeJYP2E4lzsbx5sjrOKf6/Fl9dpHypZAL5JjIUfEXiUVwwt6BVdrVAACj1DhlZeBUnEEHFAJlXvsQMl8o8UcIIYSQJccasEAt0qRbJOajWd0Cd8iVrnIa944CAMqkuW+IVMqqMTrLVp+d9hNo07VN22JEwBWAx+EVZcbfEcthtGraIOQKAQBakTZd8WcL2MBhGKiE6oKPv1LTgk77iXSSFEDGn09XrwzsxLPdf0WTagWOWo4gEosU/RwHzfuhEqpQr2hIL2MYBnqxAdZTWnTNN5PfBJVQlb6OKuVV2Fp+xqz2rVPUwRqwwhvxZq3rdfagUbUi/fcWTRuG3APwR/xFiXvIM4BeZy+e6X66KMcjhJAUlmXxp+OP4bdH/2/W+9iCNqiFmhnbjBEyX0Q8ERRCJcyTOge4Qk6EYyFq9UkIWXSq5DUYds+t4u+4rR3WgBXnVZ0/632MkrKcCb1uZxci8QhW69ae3C7PGX+WgCXvrkSEzBdK/BFCCCFkybH4zXnN95ss1YYz1cJy3DcGACjP0eoTAKrkVbNq9cmybCLxp2+bcVsJXzrn5AfLsmi3HcWqSXPYtGJdOmlkD9qgEqpn9eTjVJrVrfCEPRhLJkffGtmNj+68Jd1G9HTUaT+BH+//AS6tuxyf2fQFhGIhdDqK3+70gGk/Nhg2Zd0g1ol1WS265pvZb0q3B8tXraIOADDoGshYHovH0O/qQ6OqKb2sWdMCFkC3o7PASDP1OnsAAO+N701XuBJCyFyxLItfH3kYv2//LZ7v/cesH3ixB2zQUJtPUmJ6sT6j4m842UavVllbqpAIISSnank1JnxjiMajBR9j9/Dr0Iq1GZ+JZ1ImLcdEjlaf7dajEHKF6c8vBokxrxl/LMvC4jenxzcQUmqU+COEEELIkmMJmNPtjPKlFmmgF+vRlUzmjHnHoBFpIOKJcm5fKavCiGdkxht/w54heMIerDasnjEGCU8851afE75xOIKOjA85GpE2o9VnoW0+U1pSSVLHCQSjQfzi0M8w7h3DM11Pzem4i9nv2x9BjaIG/9/Gz6FJtQISvgSHzQeLeg5P2I1uRyc2GjdnrdNLDAve6tPkmyg48VclrwGHYTDkyWz3OewZQiQeQdOkir9aRR1EPBE67MfnFG9Kr7MXGpEGXIaHVwZeKsoxCSHLG8uy+OXhn+OZrqexreIseCPeWVdhO4L2Of/eJWSuTr1RPeDuB4fhokZZU8KoCCEkW5WsGjE2nn4QN1/pNp9Vs2vzmZJo4Zmd0Ht3fB9aNG3prkIGiQGesGfWD+z6Il4EooGCP1cRUmyU+COEEELIkmMNWOfUQqN50qyxcd9ozvl+KVXyaoRioRlv/B2zHgWH4WCtce2M55fypfDNseKv3XYUANCmOVlhqBXr4Aw5EIvHYAvaoBXNrfJAIVSiXFaBTvsJPNP1FJwhB86vvhD/6H0WrpBzTsdejOJsHB224zin6nwIuAJwOVys1a3DIUtxE39HLIfBAlhv2JC1TifWw+Jf6Io/M4zSwj6gCrlClEkrMOjuz1je6+wGADQoG9PLOAwH6w0b8fbom4UHO0mfqxdt2tU4u/IcvDTw4rJoQ0sImT+D7gF8+a0v4Nnuv+L/2/hZ3LfufgBAv6tvVvvbg/Y5/94lZK4Sib+T7yMG3AOollen5+0SQshiUSVPPJAw4ilszt8x6xHYg3acm0ebTwAok5TD5J9AnI2nlx21HsFhy0Fc0/Rv6WVGSaJF8myr/szJri2U+COLBSX+CCGEELLkJFp96grev1nTgm5HF+JsHGPesSnbfAKJij8AGPNOP+fvmPUIGlSNkAqkM55fwpPOueLvuK0d1fIaKIQnh4drxTrEWRaOkKNoLcda1K14f+JdPNH5J1zbdD3+Y/2nwLLsaTlXbdgzBF/EhxZNa3rZesNGtFuPIhwLF+08hy2HUCYtyzlXUi82wB60IRaPFe1802FZdk6tPgGgRlGLIXdmxV+Psxtl0jLIBPKM5RdUX4RO+4lZz82cCsuy6HP2oEHViMvqr8CwZwjHbe1zOiYhZHmKs3H88tDP8fGX78KYdxTfPus7uLrxWhilZRDxROh39c7qOPagDRpK/JESM0gMMPtN6YdhBlx9qFc2zLAXIYQsPJ1YBzFPnPU5Yrb2mz6ASqhCq3bmURuTtWpXIRqPZnSxeaz9d2hQNuCsyh3pZQZpKvE3u4cyLf5E1xaa8UcWC0r8EUIIIWRJ8UV8CEQDBbf6BIBmdQsC0QCG3IMY942jXDZ14q9cWgEOw2BkFom/Nfp1szq/hC+Z84y/dutRrNJlthXViRLJUFvAWrSWY82aFgx7hiDgCHBr6x1QidT4UOO1+EfP3+AJu+d8/MXkhL0DDBIVoSnrDRsQiUfQUcSk0hHLQazVr8+5TifRI86ysC/QHEVnyIFIPJJ+orUQdYo6DLgyK/66HF1oUq3M2nZbxVkQ8UR4Y/i1gs8HJKp+3WE3GpVNWG/YCIPEgJcGXpjTMQkhy9NB8378rftp3Nr2UTxy6WM4s2I7gESVcp2iHgOnVDTnEmfjcIYc1OqTlJxebEAgGoAv4gXLshhw9aNOWV/qsAghJAvDMImxGt7Mir84G8eIZxj7xt5BIBqYcn+TfwJV8uq8Z9o3a1rw4ZU34tFjv0GvsxtHLIdwyHwAt6+6K+NYOpEOHIaByT8xq+Na/GZwGIaq/8miQYk/QgghhCxqB0wf4DdHfpn+e6oNok5S+NDsFZpmAMBhy0G4Qk5UTFPxx+fyYZSUYXSaFiTWgBXjvnGs0c3c5hNIVvzNIfHni/jQ7+rDKm3mEHONOHHD0RawwhEqTsuxlmQr0TtW3ZWu3rqh+SOIxCJ4tvuZOR9/IQy4+mdVQXfCdhw1ijpI+SerNuuVjZAL5EVr9+kJu9Hn7MW6KRJ/+mQlqyWwMO0+U0+wzqXir1ZRB0vAAl8kUcUaZ+PodnSiOTkjcjIRT4SzKnfg1aFX5tSasy9ZgdOgagSH4eDSuivwxvBrc06oE0KWnwnfBBgAN7fcBgFXkLGuTlk/q1afrpATcZaFhhJ/pMRSv8/NATMcITvcYTfqKfFHCFmkquU1Ga0+Hz74M1z79ytw187b8OCeL+O1oV1T7mvxm2EosLrurtX3olZRi++8+9/4/bHfolHViLMqdmRsw+VwoRcbYJ5l4s8cMEEj0oLL4RYUEyHFRok/QgghhCxqf2h/FH/tejJ9Q98aSLbQEBee+JPxZaiSV+P1oVcBAOXTzPgDgEp5VUbFnyNoz5j5125NzNtbo1+TtW8uEr5kTq0+O2ztYIGsij+VUA0Ow2DA3Y9oPFaUlmNt2lX43jk/xNWN16aXqUUaXNFwNf7Z+/eM2QiLUTgWxn277sGL/f+acdsO+/GMNp9AouJjrX49DpuLk/hLzfebquIvdcMudZ3Pt9TMCuOcEn+JG4rDniEAwKh3BIFoACvVzTm3P7/6Igx7htDn6slaN+wZwk/3/3DG66rP2QMpX5quVLy07nKEYkHsHJj535kQQiaz+M3QiLXgcXhZ6+qVDRhyD6YfHmFZFp945WO4/rkP4f5XP45vv/MN/OCdH+Dx9j8AALX6JCWXTvz5zelq/HoVtfokhCxOVfJqDCcTf132Tvy95xlcUnc5vrPjISiFKtiDtin3ncu4AgFXgC9tfRBj3lEctR7B7W13gWGYrO2M0jKYfNkz/liWzYotkYik+X5k8aDEHyGEEEIWrQFXP47b2hFnWfQ4uwAgnXDTigqf8QckWny0244BwLQVfwBQIavC6KTE3//b9y385+7PpG8EHrUeQbmsAtpZzh2U8ObW6vO4rR0KgQJVsuqM5RyGA41Ii25H4ntVjBuQDMNgo3FzVguVsyrOhjPknFULtFKyB22IxqM4Zj2csTzVQiYlGA2i39WbrnCcbL1+Azrs7QhGg3OOZ7r5fgAg48sh4ArSla3zzew3QcgVQi5QFHyMankNGACD7gEAQJf9BABghTq71ScAbDJuhkKgyPkE7+tDr+L5vucw5h2d9px9rl40KBvTH9CN0jKcX3MRnu58oqjzGAkhpz9rwDJl+/B6RQMi8QjGfInXpB5nN3qdvdhReQ7qFPWwB214b/Q97B5+A1qxFlXyqgWMnJBsWrEWXIYDi9+EAXc/+Bw+KmZ4wI0QQkqlWl4DV8gJT9iNv5z4I8plFbhv3f3YXLYVGpEazpAz535xNj7nRFudsh6f2/yfuLDmImyvODvnNnqJIWerzzdH3sBt/7opY/SFxT/1+wlCSoESf4QQQghZtF7ofx5KoQoinggdtuMAEi0QVUIV+Fz+nI7drE60IRTzxFAKVdNuWy2rxph3FHE2jlHPCA5bDmLEM4xXh14GkJjvt1o7u2o/AJDwpfBFvAXH3m47ijbd6pxPJSYSf50AEjd/5kubbjX4HD4OmvZnLPeGPdg7tgePH/89/t++b6aTQaWSShSnkrwpLw/sxN0v3Z6uUut2diHOsmjVtmYdY71hI6LxGNptR+ccz3Tz/YBEolUvNixYq0+T3wSjtCzntTRbIp4IRmkZBpOVBV2OLpRLy6dMJvI4PJxbfT5eH3o1q7Kv094BAOnk9VR6nT2oVzVmLLu55TbYAlbsGny50C+FELIMmf0m6KdoFZaajZZq97l3bA+kfCnu3/AZfGHLl/DjC/4XT93wFJ659h944qq/zekhCkKKgcNwoBPr0xV/NYravOdfEULIQqlR1AAA9o29gz2jb+LGlTenW2UqhWq4gs6c+9kCNsTYOAxzmFMOABfVXoovnfHglJ+FjJKydIeUyd4ffw+ReAR9zt70MkvADP0cxpEQUmz0258QQgghBftn7z/SM8KKLRwLY9fgS7i07jKsVDfjRDIhYPVbprxBl4+VycRfhaxixqRHhawK0XgUZr8JOwdegJQvxZayrfjj8T/AHXKh39WLNfp1sz63hC+ZdlD5dOJsHB2241ilXZ1zvVasgyn54UQ9j7OGhFwhVunW4JD5QEZsn379k/janv/C37ufwd6xPXi+97l5i2E2Ui1YJnwTGe1Z35vYhzjL4q+dTwJItE8VcoWoU2S3w6pV1IHH4WHYPTSnWGaa75eiE+th9Vun3aZYEi1y5v7zVKuoSyd5ux2d6Z+vqZxffSEsAUu6TS6QaJnTmUxap5LXuYRiIYx6h9GobMqK4azKc/BE559mNdOREEIAwDJNxZ9apIFKqEq3TNw7tgdbys7I2RaUkMXCIDGmK/7qaL4fIWQRq5AlKuV/feSXUIs0uKTusvQ6lVA1ZcVfKhlXjM8x0zFKymALWBGJRTKWH7EcAnBy7jjLsrD4zUW5T0FIsVDijxBCCFkAY95RuEOuUodRVM6gAz878CM81v7ovBz/7dHd8IQ9uLz+KrRoWnHCfrLiTzeH+X4pTeoV4DIclEtnbn9ULU+01BxyD+GVwZ24oOYi3LPm4xj3jeMnB36IOMtitS6Pij+eFKFYqKDkRL+rF4FoAG3aVTnX65LtRqV8KYRcYd7Hz8cGw0YcthxKfx3HrEcw5B7Ef5/9Pfz1Q8/hkrrLsXfsbbAsO+OxwrEwQrFQ0WO0B2zgJBO7x5NVf3E2joOm/dCJdXh5cCdsARtO2DuwUt2ccxg7wzDQirSwBeeWjJtpvl+KTqLLqPj7YOI9vDa0CyOe4Vl9L/Nh8k2k5+TNRa2iDkOeQcTZOLqdXVO2+UxZpVsDlVCF9ybeTS8z+01whZyQ8qXomqbib8DVjzjLolHVlLXu1tY7MO4dw+6R1wr/Ygghy0bqRt10Nw7rlQ3od/XB4regx9mNbRXbFzBCQvJnkBhg8psw4OpHnYISf4SQxUvME0Mv1sMVcuK6FTdAwBWk1ymFKrhmTPzN70w9o9QIFpnz161+K0Y8iTEgvc7EzHJXyIlIPEIz/siiQok/QkhO/ogfT5z4U1YLLkJIYb6190H878GflDqMoupOztx7bWhXRm/7Ynmh719Yq1+HKnk1WjRtsAassAasid75RXiSTsgVYlvF2Vhv2DDjtgaJETwOF3/v+StsARsuq7sSDaomnFt1Pt4a2Q2lUJU1b286Ur4UAOCP+vKO+7itHVyGg2ZNdktK4OTsw2LM95vJesNGBKIBdDoSM912Db4Mo8SILWVbwTAMtlecBZPfhH5X75THiLNx7Ox/Abf+6wZ89e0vFT1GW9AKg8SIMmkZ2q2JxN8Jewe8ES8+u+mL4HP4eLb7aZywHUfLFN9TIFFJaQtMPVx+Nmaa75eiFxvSHy79ET++/s5X8J13v427dt6G65+7Gsdt7XOKYzJzoDhD6GsVdZjwTaDL0YlgNIiV6uZpt+cwHGwwbMIB0wfpZanr6MLaS9Dj7Joyydnr7AGHYXJWMTSpV2BL2Vb8ueOPRU+SEkJOP96IB6FYaNr3FfXKRvS7+vDu+F5wGAZbys5YwAgJyZ9eYkSXoxOBaAB1yuxOBoQQsphUyash48twdeO1GcsTFX+OnPuY/BOQ8qXpz9XzJfWA5OQ5fwfGEx1vtpRtTVf8WZKf3WjGH1lMKPFHCMnpoHk/fnv0/zCQnGdBCJmbcd849o7tKbi942LU4+iGiCcCizheHthZ1GOn5uhdXn8lAKBF0wYgMf/LGrBAX4SKPwD4+vZv45qm62bcjsvholxaifcn3kOjqjFdzXT7qjvBYRis1q3Ja0aamCcGkEjq5GvUO4IyacWU1Xya5Fy/hUj8NatbIOaJcch8AOFYGG+OvIELai9Oz5JZq18PMU+MvWPv5Nx/zDuKT736Cfzwg++hSl6NQ+YD6eRcsdgCNmhEWqzSrk7P6Ntveh9SvhSbjJtxdeM1+HvP32AJWNA6RRUlkPh+zqXiLxKL4JB5/4zVfkDiSX1bwIo4G8fbo7sRjoXxy4sewf/s+D4i8QiOWg4XHMdkgWgA7pCrSK0+E0m41Hy9mSr+AGCjcTN6nF3pBwc67R3QiXU4o2wbfBEfxryj6W37nD3YN/YOwrEwel09qJRVT/kzcN2KGzDoHkg/gUsIIVNJVQxMd6OuTlmPMe8I3hh+Dat1a2mOH1n0DBIDwrEwAKCeKv4IIYvcbW0fxQNnfBUSviRjuVKogjvsylmQYPabYVyA6jq9xAAGyJhbf2D8AKrk1dhk3IIBVz9i8RgsyfEn8916lJB8UOKPEJKTJ+wBAAx55jbPiBCSSO74I36EYiHsmyIBshR1O7uwUt2MHZXn4Z+9/yhqhfBR6xEAwNmV5wIA9BI9tGItDpoPwBvxpttZLqRKeWL+wKV1V6STfLWKOnxqw+dww8qP5HUsSfLJRF8BFX+ukAsqoWrK9Scr/uZvvl8Kl8PFOv16HDQfwN6xPfBFfLi49tL0egFXgK1lZ+Kdsbdz7v/kiT/DEjDjR+f9DD8872eoltfgyc4/T3m+yTP6ZsseTCT+2rSr0ePoQigWwv6J97HBsAlcDhf/tuIGxNhEq9JUgjmXQiv+9o3vxVfe+iKue+4q9Lv6sa3irBn30Yn1iLFxOIIO7Bp8GWv165OVbGegQlqBCd943nHkYvIlnlwtRsVftbwGAPD68KuolFVBJpDPuM8Gw0bEWRaHkzMyOu2daNa0YmUyaZiqKmZZFv/z7rfx4J4v48PPfQivD+3K2eYzZb1hI6R86ZTXHSGEpFj8iSf0p2shXq9sAAvgsOXgrF7DCSk1ffL3upgnprZzhJBFb61+Pc4s35a1XCVUIc6y8CbvT05m8ZsW5PVNyBXijPJt+Fffc+luIgfGD2C9YQMalI2IxCMY8Q7D7DeBx+FBOc3ndEIWGiX+CCE5ucOJWWQjnuESR0LI0peqEhJwBdg98nqJoymebkcnmlQrcXXTtRj1jmS07JurCd84tGItRDxRelmzuhV7Rt8EUJoWGlWyKvA4PFxYc3HG8qsaP4RVutV5HUvCSzzNWEjFnyvknPYDhVacSPhpxPOf+AMSSZZ261G80P9PNGta0gmglG0V29Hl6EzfXJ3smO0ozqrYgTX6deAwHNzYfDP2ju3BkHswa9sO23Hc8vz16M+zEt0WsEEj1mKVbjVibBwHTB+gw96OTcYtABIzES+pvQxl0jLoJVPf+NWJdbAH80v8xdk4vvvut2ELWnFr6x34xUW/wdmV58y4X+r67rC345D5QEYy1Sgtx4S/OIm/vWPvQMAVoEk1c3XeTCR8CYwSI9wh14xtPlOM0jJUyCpxwLQ/ORuwE83qFqhEaujFenQ7OgEAfa4eDLoHcP+GT+Om5ltQIavEWZU7pjwuj8PDlrIzKPFHCJmRJWAGl+FAK566Sr5WUZf+85nlNN+PLH6pipNaRV1eHSkIIWQxST3s6sjR7tPsN6Ufcphv16+8Ef2ufhw074cz6ECfow9r9OvSDyL2OXthCZihF+vTnW8IWQzoaiSE5OSNeAEAw57sm6+EkPxYkwmPS2ovw3vj++CL5F/ltdh4wm5M+CawUr0Sq7Vr0KBswHO9fy/a8Sf84yiTZM5Ba9W2pSu+pnsyf75ct+JG/L+zvweFUDnnY0n5MgCFJv5mW/E3/60+AWCDcRMi8QgOmPZnJKhStpafCQ7DYN94ZrWrK+TEkHsQa/Rr08suqLkIWrEWT3X+Jes4rwzuBAvkXe1mD9qgFWlRr2yEmCfGnzseR5xlsblsS3qbT274NH5y/i+mPY5GpIEn7Em3zpqNYc8QfBEf/n3tf+AjLbfOqv0lgHRF65Mn/gweh48dVeem15VLKzDuLU7i75WBl7C94uystjqFqlHUAphdm8+Ujck5fyOeYfgjfjRrWpLHaEa3I1Hxt2vwZSiFKlzVcA1ubbsDP7/w1ziv+oJpj7u94mz0OntgSrbxI4Sc/mLxWN6V4Wa/GVqxbtobdWKeGOXSclTJq1Eln/08X0JKJVUFk2sWLiGELBWph11dIWfWOpPftGBtNdfpN6BR1Yi/dj2JI8mRC+sN66EQKqET69Dn6oXZb552XjAhpUCJP0JITp5QYt7OMFX8ETJn1uSg5+tW3oBIPIJ9Y3tKHNHc9Ti6AQBN6pVgGAZXN/4b3h1/B+Zkb/u5MvtMKJOWZSxr0bSm/1yKN9V6iR4bjZuLcqxUosVfQKtPd9g1bfJRIVRivWEjVmnXFBxfPuoU9VAKVeAynJzJGLlAgTW69dh7SvVVapbfKt3JxJ+AK8C/NX0Yrw69nFEhGI1HsXs4US1rD9pnHVskFoE77E7f1G3VtuGEvQMVskqUSU8mlgVcwbTVHkCi1Wfi/LOv+uuwHQcDoHnStTsbSqEKPA4PJ+wdOLvynIyh9WXSMpj8E3Nurdtt68aAqx8X1l4yp+NMlqqKSSXvZmOjcTNGvSN4O1nNm0oarlQ3o8fZjVg8hteHX8V51ReAy+HO+rhbys8Aj8M9LV5vCSGzs3PgBdy189acLcGmknhCf+b3FNetuAG3tt4+l/AIWTAyvgxGiXHB3gsSQsh8UIvUABIPvk7mjXjhi/gWrJUxwzC4fsWNeH/iPfyz5x+oVFSm70fUKxvQ5+yBhRJ/ZBGixB8hJKfUjL8Rz3C6jzUhpDDWgBVygRzV8hq0aVfhjdOg3WeXoxMinijd1vG8mkTC593xvUU5/rhvDIZTEn8r1M1gkEhsCbiCopynVMQ8MYDCKv6cIee0FX8choOHzv1x3u1HC8VhODi3+nycX3PRlC1It1eehYPmAxlf7zHrEejF+qyh7Fc1XgMhV5Qx6++DiffgDrvBYRg4g9mtXqZiDyWShKl5h6kbYJvLts76GCmpSsp8qkk6bO2oU9ZnJO5mg2EY6JNVrRedUkVZJi1HNB4taN7gZC/2vAilUInNxi0zbzxLK9TNEHAFaFStmPU+6w0bwAD4W/dfUSGrhFygSB/LE/bgpYEXYQvYslrszkTGl2Gtfj32jL6V136EkKWr29GJYDSIt5IPEszGbG/UXbvi+qzXY0IWs99d9idcWnd5qcMghJCCSfmyxOe/U1p9WpIPGxslZbl2mxfnVV8IjUiD/aYPsKl8U3p5o6oJfa7eWT9IRMhCosQfIYuAN+zBh569LF1Bsxi4wy6IeWIEooG8W+YQQjJZg9Z0675zq8/HBxPv5vU0+mLU4+xGo7Ip3RpLxpehRdNWlDl/kVgEtoA1q9WnlC9FjaIO+uT3cinjMByIeeK8K/5CsRCC0SCURWg3Wkyf2vAZPLD1K1OuP6tiB6LxKN4a3Z1edsx2FKsnVfulSPlSfKTlVvyz91kMuPoBAK8OvYJaRR1q5HXpZN5s2JK/v7TJtqepZOgmQ/6Vm6mKQFs+iT97O1o1q/I+F5CoalUJVdh0SpVpqlJxLnP+4mwcO3t24vyaC8Hj8Ao+zqnOq74Af7j8L3klOuUCBVaom+EKOdGsPlkpuEKdSB7+vv0RlMsqMip+Z2t7xdk4YjmUbl9OCDm9pWbA7hp8edb7WPzmBWsVRshC4nP5NN+PELKkcRgOFAIlnKe0+kx1GVqoij8g8Zp6TdN1AIANZRvSy+uVjbAGrPR+gixKlPgjZBGwBCwIRAMY842WOpQ0T9iTvslGc/4ImRur35KeSbej8jzE4jHsXeLt57odXWg6ZY7XJuMWHDIfmHMLQkvADBbIavUJABfXXortFTvmdPzFQsKX5F3xl/rQoxCoih/QPDJKy7DJuBn/6nsOABCMBtHt6MRqXe4WVNetuAHl0ko8fOhn8Ef8eGfsbVxYczHUInV+FX/JtpypNp0bDJvwxS1fxhnl2/L+GmR8OfgcPmzB2SX+/BE/Blz9aNUWlvi7tul6/Mf6/y+rvWXqyVZTnrMOJztsPgSzz4yLavOropsJh+GkH3LIx0Zj4qnZyS1C1SINdGIdHEEHLqq5pKCbl9sqzkaMjeP98Xfz3pcQsrSwLItB9wBqFLU4Yjk0q9bjcTYOS8BCT+gTQgghi5RKqM6R+JsAh2FmHNVQbFc3XoOL6y7FObXnpJc1KBsBACwA/QImIgmZDUr8EbIIeMKJeXqFtHybL96IByvUK8HjcDFCc/7IMvfP3r/PqZLNGrCkEw96iR4NqkYcNB8oVngLzhfxYdQ7gpWnJP42GjfDG/Giy9E5p+NPJBMak2ewpdzUcgvuWHXXnI6/WEh4Uvgi+VX8uZPzDaZr9blYXdV4DTpsx9Hr7EanvQPReGzKxJ+AK8B96z+FQ+YDeOj97yAcC+OCmougEWngyGPGnz1gA4/DhUKQqJDkMBxcXHdZXrPiUpjkh0v7LFtsdto7wAIFVaoBwI6qc3F+zYVZyyV8CZRCFcZnmfgz+SbwTNdT+Pwbn8Z/7/0G3hh+DS/0/RNViqqCk5LFtinZbrRF05axfIW6GQBwQc1FBR3XIDGgUdWEd06ZL0kIOf1YA1b4Ij7c3HIr+Bw+Xh/aNeM+rpAT0XiUZvIQQgghi5RKpIYr6MxYZvaboBcb0t2HFopcoMCXz/wqlKKT3Xeq5TXgc/gAAEPyYW9CFgtK/BGyCLiTiT/fImpF5Q67oRSqUCGrwpBnqNThEFJST3c9iZcHdxa8vzVwsuIPANbqN+Co9XAxQiuJXmeiLfGpFX8tmlaIeeI5t/uc8E2AAU77J/AlfAkC0Xwr/hLVbkqRah4iml9nlm+HRqTBv/r+iWPWo5DypahTNky5/RnlZ+LM8m14e/RNrNatgVFaBpVIDXseiT9r0Aq1UFO0VldakQ7WWVb8ddiPQ8KXoEZRW5RzT1YmLUsnyKfz0/0/xG0v3ITfHv0/iHkijHpH8P/2fROvDu7C5U2XL5oWYOv0G/CT8x9G2ymJyPOqz8eFtRejSl5d8LE3GTejw9Y+1xAJIYvcgDvR5nOVdg3OqtyBV4dmbveZqgo83d9vEEIIIUuVSqjKUfFngjFHd6BS4HK4qFXUAQA9SEQWneIN9SCEFMyTnPXlz/MG8HyJxqPwR/xQCJSoklVTq0+y7DmDDlj8loL2jcVjcIYcpyT+1uHZ7r/C5JtYNG9Y89Ht6AKfw0etvC5jOY/DwzrDBuw3vY9bWm8v+PgT/nFoxTrwufw5Rrq4SXj5t/p0JT/0KAWLa8bfbPA4PFxWfyWe7f4rGpSNWKVdPeNTmp9Ydz8OmPfjkrrLAQBqoSZruPt07AFbutq2GLRi3awr/jrsx9GiaZ2XJ1HLpRUzJv7eGtmN5/uew91r/h1XN16bnrs37h3DIet+XLvmKsQWx9sOMAyTnr842QU1F+OCmrm1I9WJ9bAH7WBZdtEkOgkhxTfoHoCQK4RRWoYLai7C1/b8F/qcPWhQNWVsd9C0Hw2qRiiFKlgCycSfhJ7QJ4QQQhYjhVCZnvueYvabF9V9lEZVE0a8w5Dx5aUOhZAMc74T4XK58NZbb+H555/HW2+9BZfLVYy4CFlWUq3b/Hm2fJsv3mQiUi6Qo0ZRQ60+ybIWioUQiAZgDRSW+LMH7YizbEbib41uLQDgiOVQMUJccN3OLjSqmnK2S9xs3ILjtmNzal1s9k3kbPN5upHyZXCH83vf5Aq5IOQKIeKJ5imq+XVFw9UIxYJotx3D6uTPwXQq5VV44qpncFndFQAAtUgNX8SHcCw8q/PZgzZoRMWb/aAV62AL5k78HbUcxm+P/h/ibBwsy6LDdhytmvlppTlTxZ8r5MTPDvwI2yrOwk3Nt6STfgBQLqvA1U3XQCFUzEtsi41apEEkHoEvujjeYxFC5seAqx81ilpwGA42G7dCIVBg12Bm1d+TJ/6ML775Ofz84E8BABa/GXwOHyqhuhQhE0IIIWQGaqE668FPs98EwyKap3dp3eW4qfkWesiQLDoFJ/5YlsX3v/997NixA/feey++8IUv4N5778WOHTvw0EMPFTNGQk57qRl/gWigKMez+C34xjtfRTAaLCyeyMnEX5W8Bma/uWixEbLUpNpKWPxmsCyb9/6phKFu0uBppVCFOkU9Di/BxN8JewfeHdubNYsrZaNxM6LxGI5ZjxZ8joklWgmZrwpZBUa9I3nt4wq7luR8vxSjxIgtZWcAwJTz/U4lFyjSH6JUQg0AwDHLqj970FbUoe9akRa2QO5Wn091PYEnTvwJPzvwI4z7xuAKOdGmza5iKwajpBzWgAXReDTn+p8f/ClibAyf3vj5Zf8BVCNKXjN5tIglhCw9A+5+1CnqAQB8Lh8X112Kp7uexA/e/y4cQTv+3PE4Hjn6a7Rq2/DmyOsY847C4jdDJ9Ev+9dJQgghZLFSClVwh12Is3EAiY5K1oAFBvHiSfyt0a/DbW0fLXUYhGQpuNXnr371K/zhD3/APffcg8svvxw6nQ5WqxUvvvgiHnnkESgUCnz84x8vZqyEnLZSrT59Rar4O2E/jj2jb+GE/TjWGzbmvb87lEhEygUK8JJDase8I2hUrShKfIQsJc5gIsEQiUfgCbuhEObXYvFk4i+zjdRaw3q8P/FucYJcIPtN7+Mb73wVDcpG3LHqzpzbVMmqoRfrsd/0PraWn1HQeSb84wW9di01dYp6mP1m+CN+SPiSWe3jCjrzvgYXmxubb4YtYEOzpjXvfVNJHGfQAeMsnvK0BYpd8aeFL+JDMBrMqLqMxCI4bD6INu0q/Kvvn+h3JWZNtWhainbuycpl5YizLMx+EypklRnr9o7twRvDr+FLW79S1KTnUqWelPirlteUOBpCyHyIs3EMuQexo/Lc9LJ719yHCmklft/+W7w2tAuReAQfXfUx3ND8Edz6rxvx164n4Ql7aL4fIYQQsoiphCrEWRbesAcKoRLWoBVxll1UFX+ELFYFJ/6efvpp3Hfffbj//vvTy3Q6HVpaWsDn8/Hkk09S4o+QWXInK/6K1eozlUg8Ye8o6Ob5yYo/BXTJ2UhD7iFK/JFlafIgaUvAXFDij8/hQ3HKTLZ1+vV4rudZWPyWJTFbZt/4Xnzzna9go2EzHtz2rSlbTTIMg43Gzdhveh/hWBgCriCv84RjYdgCtmXR6rM2WZkw5BlEyyyTYEu94g8A1urX45cXP1LQvuo8qrdi8RhcIWdRZ/ylkoj2oC0j4dZhb0cgGsAn138aH5jew++OPYJKWdW8JWnLJImfjwnfeFbi75+9f8cq7eo5z8Y7XaiTLfwcwdnPhiSELC1mvwmBaAB1yob0Mi6Hiw81/RvOq74Afz7xOMqlFbim6ToAwHUrPow/Hv8DjNIyNM/TAxqEEEIImbvUZ19HyAGFUAmz3wQAMEjowR1CZlJwq0+LxYKNG3MnFDZs2ACLJb9ZSH/6059wwQUXYM2aNbjhhhtw5MiRabd/8cUXcdlll2HNmjW4+uqrsXv37oz1LMvipz/9Kc4++2ysXbsWd955JwYGBnIeKxwO45prrkFzczM6OjpybjM4OIgNGzZg8+bNecdCyEzSib9o4TOxJku1Du20nyhof284VfEnh1yggEqowrBnqCixEbLUTO4nb5mixd90rAELdGJdVhup1Jy/o9ZDc4pvofyj5xmsVLfgm2f9z4zz5c6q3IFB9wCu+ftl+MQrd+PPHY/P+jypN/KzqeZa6qoVieqjU4eVT8cVWvoVf3OhEqrAIDE7cyb2oB0sUPQZfwCy2n1+YHofCoECTeoVuLnlNty1+h5cv/LGop33VAaJEQyA8VPm/HnDHhw078d51RdQ67okKV8GPodPrT4JOY0NuAcAAHXK+qx1CqESn1h3fzrpBwBXN14DHoeHEc8wVfwRQgghi5gymfhzJR/IHvEMgwGWxWgQQuaq4MRfZWUl3njjjZzrdu/ejcrKypzrcnnhhRfwne98B5/85Cfx7LPPoqWlBXfffTdsNlvO7Q8cOIDPf/7z+PCHP4y///3vuPDCC/HJT34SXV1d6W1+85vf4PHHH8c3vvENPPXUUxCLxbj77rsRCoWyjvf9738fBsPUb/gjkQg+97nP5Uz6zSYWQmbiLXLFnzfiBQB02nMnsmfiDrsh4Aog5AoBANXyGkr8kWXLGXRAzBODwzCw+vN7qAVIJAhObfMJJCqXahS1OGw+VIQo59+IZxirdWvA48zcLGBbxVn4+YW/xn3rPgWFQIHHj/9+yllkpzL5JwAA5bKKOcW7FIh5YpRJyzDonn3izxlyQilQzV9QixyXw4VcqMwa8J6LPZh4H6mdh8Sf9ZTE3wHTB9ho3AwOwwHDMLil9XZc3XhN0c57Kj6XD51Yj4lTEn/vju9FNB7DWZXnzNu5lxqGYaAWqWEPUeKPkNPVoKsfYp4Yhlkm8eQCBa5ouAoAVQwQQgghi5kqnfhzAQCOWY+gQdUIMU9cwqgIWRoKTvzdeeedeOyxx/CFL3wBu3btwsGDB7Fr1y584QtfwOOPP4677rpr1sf63e9+hxtvvBHXX389mpqa8M1vfhMikQjPPPNMzu0fe+wx7NixA/fccw8aGxvxmc98Bm1tbfjjH/8IIFHt99hjj+G+++7DRRddhJaWFnz/+9+H2WzGrl27Mo61e/du7NmzBw888MCU8f3kJz9BQ0MDLr/88rxjIWQ2il3x5022+rQELLAFcifQp+MJe6AQKNJ/p8QfWc6cIQc0Ii00Ii0sAXPe+1sC1ilbDa7VrcNhy6E5Rjj/wrEwTL4JVOUxH6tZ04IPNf0bbmv7KKLxKEY8w7Pab9w7Dg7D5EyWno7qFPUYTFYqzIY7tPRbfc6VWqieZcVfMvFXxDl3Up4UQq4wfWwgUWXfZT+BTcYtRTvPbJTLKrISf2+PvoUWTeuSaB+8kFRCNVX8EXIa63f3oU5Zn1el8/UrboJKqKJRBoQQQsgiJhPIwWGY9IOfx6xHsVq3rsRREbI0FDzj7yMf+QgikQh+8Ytf4PnnnwfDMGBZFhqNBl/5yldw0003zeo44XAY7e3tGfMAORwOtm/fjoMHD+bc59ChQ7jzzjszlp199tnppN7IyAgsFgu2b9+eXi+Xy7Fu3TocPHgQV155JQDAarXiwQcfxMMPPwyRKHfbsr1792Lnzp34xz/+gZdffjnvWGaDw2HA4VA7puXME/FAK9YiEPWDxys4H5/mi3pRrajCiGcEve5OGOVnT7s9l8vJ+L8v6oFCqEjHUq2sxhsjrxYlNkIKdep1ulBcESfUYjXibBz2kDXvnwNHyIoWbUvO/TaUbcS/+p+DK2Iv6hyyYhvxjQMMUKusyfvrb9I0gWGAQW8/mrSNM25vDZqglxggEuQ3G3CxyPc6rVfX47XB2b2+xtk4vBEPNBL1sn491ko0cIedM34PnGE7uBwOtFINOEzxvl86iQ7OsD19/sNjBwEGOKNy64L+u5TLyjHsGUqfMxgN4n3Tu7hz9cdmjKNUr6elopPo4JrFNUMWl+V2nZLCDXsG0aRekdfPeLnCiL/923NzbotM1ylZCug6JUsBXackNw6UQhXcERfcEQfGfaNYZ1xXsvf1dJ2SpaTgxB8A3H777bj11lvR19cHl8sFlUqF+vp6cDizv/gdDgdisRi02synsbVaLfr6+nLuY7VaodPpsra3WhNtl1LzBXMdM7UNy7L40pe+hI985CNYs2YNRkZGcsb25S9/GQ899BBkMllBscyGRiOlOSzLWCgaQgwR1GqqcdxyHCqVZM7XQ5gJoNXYghAbxFCgD1eqL53VfgpFolQ+yglBJ9dArZYCAKq0ZQizIciVwlm1+SNkPqWu04USZH0oVxrAYThwhx3pn4vZYFkW9rANdfqqnPudLzob33mPgy5fO66ouKKYYRfVIacFXC4Ha2paoBbP/usHADWkKFeUwRQemdX3zhmzoVZTndf3eTGa7XW6prINT3c9AaGMgYQvmXZbe8AODpdBla5syX9/5qJcaYTFb5nxexDkeGGQ66HVyIt6/gplGXysO33+9qOHsELXhJWV2bOl5lOjoQ4HrR+k43i9/z3EEMGVqy6FWjm762OhX09LpUJtRJeta1n/3Cxly+U6JYWJs3GM+odx7aoPlfRnnK5TshTQdUqWArpOyakMCh3CHD8Ggt3gcjnY0XQm1NLSvq+n65QsBXO+g8/hcNDU1FSMWBbU448/Dp/Pl1FpeKoHH3wQV111FbZsmd/WTXa7jyr+ljGL34xYLA41T4tQJAyzzQkBd26VLjavA2q1Dk2KZhwYOQRH0/SzA7lcDhQKMdzuAGKxOMxuK4RcCRyOxH6ciACxWBxDpnGoRZo5xUZIoU69ThfKhNuMFk0rhBwROhyd6Z+L2XCH3PCHAhDHFTn3YyBEo2IFXu16Hdt05xYz7KLqGO+GkBEBAQEcwfxnkVZL63B0/HjG9yAcC4PH4WVVYg3Yh1Elq8rr+7yY5HudajhGxGJxHBw8hjbtqmm3HXSNIRaLgxsWLdnvTzGIIceEq33G78GQbRRKnrro3ys5V4URxxgcDh9YlsVb/Xuwo+rcBf83UTJaWLxWjFmsEPPE+FfHTtTI6iCPa2eMpVSvp6UiZmWYcJuX9c/NUrTcrlNSmBHPCPyhAPS8ipL8jNN1SpYCuk7JUkDXKZmKlCPHuMOMd4LvwSAqAy8sgSNcmvf1dJ2SxWC2D7vllfj73e9+h6uvvho6nQ6/+93vpt2WYZisFpi5qNVqcLlc2GyZc8hsNltWJV2KTqfLqqibvL1er08vMxgMGdu0tLQAAPbt24dDhw5hzZo1Gce5/vrrcfXVV+N73/se9u3bh9deew2PPvoogETlRjweR1tbG771rW/hwx/+8IyxzEY8ziIeZ2e9PTm9OAIusCygF5eBZQFXwD3n5Jor5IaEK4VWpcffup9GJBKbVRVhLBZHNBqHK+hGnbIe0Wjil5iYIwXLAg6/C3Keak6xETJXDr8Tn3/tM/jatm+hXFYx/+cLOKDgqyDlS2H2m2f98wQAJq8ZLAuoBdr0z9OpNhvPwHO9zyIciRa1HWExDToHUSmrRizGAsj/91W9ogGvDr6S8T341K7/wHrDBty79r6Mbcc8o9io3zzl92upSL2ezqRSUgOwQJ+9HyuVrdNua/c7wLKAjKdY8t+fuVAKVHAEHTN+D6w+K1RCTdG/VyqBBl32LkSjcQx7hmDymbChBNesQVwOlgV+/N4PcWXDh/DO6B5cv+LGvOKY7XW61CkFatgD9rxev8nisVyuU5I/lmXxyKH/g4grRoO8qaTXCV2nZCmg65QsBXSdklMp+CrYAw6MeEbQpl29KK4Puk7JUpBX4u973/seNm3aBJ1Oh+9973vTbjvbxJ9AIMCqVauwd+9eXHTRRQCAeDyOvXv34rbbbsu5z/r167Fv376M47/zzjtYv349AKCqqgp6vR579+5Fa2viJprX68Xhw4dx8803AwC++tWv4jOf+Ux6f7PZjLvvvhs//vGPsW5dYkjok08+iVgslt7m1VdfxW9+8xs88cQTMBqNs4qFkJl4wx4AQJm0HADgj/jnnPjzhj2QCxRoVDXBE/ZgzDuKSnnVrPf3hD1QCBTpv8uTf/YkYyWklLodXehxduO47di8J/5YloUz5IBapIZCoEQwGoQv6oOMn9n+2RG045+9/8BtbR/NSN5ZA4nW07pp5vdtLT8Tf+p4DMdt7VitW5O13hN2IxaPQSVSF+mryt+odwTV8uqC969XNsIS+DO8YQ9kAjksfgu6HJ2wBiy4e83H09+zUCwER9ABo7SsWKEveiKeCGXScgy6+2fc1hlyAgCUQuU8R7W4qUUaeMIehGPhKSvkWZaFyT+BNu3qop9fK9bCHrTBG/HiB+9/F3KBHGv0a4t+npms0q7GXavvwT97/45XBl8CAJxduWPB41gK1CINovEovBFP+j0NIWTp+3vPM9g98joe3PZNKJb570ZCCCHkdKUQKtFha4clYMZVDdeUOhxCloy8En8nTpzI+ee5uuuuu/DAAw9g9erVWLt2Lf7whz8gEAjguuuuAwB88YtfhNFoxOc//3kAwB133IHbb78djz76KM4991y88MILOHbsGL71rW8BSCQd77jjDvzyl79EbW0tqqqq8NOf/hQGgyGdXKyoyLxZLJEk5urU1NSgrCxxw7GxsTFjm2PHjoHD4WDlypXpZTPFQshMXCEXAKAseaM7EA3M6XhxNp64uc6Xo1mdqHDtdHSgXFaBXx/+BawBCz6/+YFpZ0l5wu6MG2MnE3/uOcVGSDEMuQcBAGO+sXk/ly/iRTQeg1Kogk6cqCa3+i2QKTMTf68N7cLjx3+PLWVnoFXbll5uCySq2TWizJmzk7VoWqEQKPDexL6cib+fH/wJrAEbfnjeT4vxJRVkxDOMjcbNBe9fr2wAAPS7+rBGvw77Te8DAOxBOzpsx7FKl0jODLgSya/y5IMQy0Wtsh6D7oEZt3OFnOAwDKT83HOHlwu1MPFwjDPkhEFiyFrPsiweOfor9Lv6cceqjxX9/FqRFv6IH19449Mw+Sbw3XN+CDFv4Wc8MAyDW1pvx0dabsUHE+9j3DeKemXjzDsuQ2ph4sEJe9BOiT9CThPt1mP49eGHcd2KG3BO1XmlDocQQggh80QtVMPkNwFAznsmhJDcCp7x9/7776OtrQ3SHMM0/X4/2tvbZz0b74orroDdbsfPfvYzWCwWtLa24pFHHkm3yxwfHweHc7KCYuPGjfjBD36An/zkJ/jRj36Euro6PPzwwxkJuXvvvReBQABf+9rX4Ha7sWnTJjzyyCMQCoWFfsk5zSYWQqbjCXvAADBIElWk/ujc+lT7Iz6wAOQCORRCJcplFThqOYK3R9/CntE3IeAK8cU3P4v/d/b3oBSqsvaPs3F4I4nEYYpckPizN0IVf6T0hj1DAIBx7/wn/lIVVmqhGvpk4s8SMKNOWZ+x3THrUQDAQfP+jMSfJWCGSqgCn8uf8hwchoMtZVvx3vhefGz1vVnruxxdMPkmEI1HwePMeTRv3rxhD5whJ6pkhVf8VctrwONw0efqTSf+VqhXwhaw4s2RN9KJv3/1PQedWDcvVVqLWZ2iDq8N7ZpxO1fIBYVAuWhbwi4UdbL61RG050z8/anjMTzV+QQ+se6TOLvynKKfX5us4LX4zXjo3J+gSb2i6OfIB4fhYGv5GSWNYbFLdVJwBh2oVdQBACx+C0Q8ISUCCVlC9o29g0OWA5jwTeCo5TCaNa24d+0nSh0WIYQQQuZR6t6lQqhEtbymtMEQsoQUfAfxjjvuwJNPPom1a7NbG/X19eGOO+5AR0fHrI932223Tdna8/HHH89advnll+Pyyy+f8ngMw+DTn/40Pv3pT8/q/FVVVejs7Jx2m+uuuy5dhZhPLIRMxx12QSaQp1sH+iJzS/x5I14AJ5N1LepWPN/3HPgcPr6+7b+hlxjw5bf+E599/VP47jk/zLpp6o/4EGfZ9P4AIOAKIOAK4KaKP7IIDLsTib8J3/i8n+tka0UVNCItGJys4kthWRbHrEcAAPtNH+CW1tvT62wBa7pScDpby8/Eq0O7YPFboJec3D4cC2PMO4I4y2LA1V+SBMOIdwQAUDWHVp88Dg+1ijr0u/oQZ+PYb3ofVzVeA1/Eh7dG3sAn1n0S3ogHrw69gltabi9JgrOUahV1MPvN8Ef801ZjO0POnA9sLDeqZPWWI+TIWvfP3r/jD+2P4q7V9+D6lTfOy/kbVU04s2I77lp9Dxqowm5JSCX+7EF7etlnX/8knCEnLqu/EtevuGFBZsYSQgoXi8fw7X1fh1wgR62iDufVXLgs3zMQQgghy40q+Rl4tXYNzesmJA8FPzLOsuyU6wKBAEQiUaGHJmRZ8YTdkAnkkPAT1bP+OSb+UnP4UhV7m8u2QC6Q47vn/ADbK8/GCvVK/OT8n8Mb8eDx47+bcv/JiT8AUAgUNOOPLApDniFwGQ7GfKPzfi5nMrGgFqnB5/KhEqlhCZgzthn1jsAZcmJL2VYctx1DMBoEkPg9ecD0ARpVTTOeZ7NxKzgMgw9M72UsH/YMIp78fXvCPvuHaYppJFlhWSmb/ZzQXOqUDeh39aHb0QVP2IMtxq3YUXkOLAELOh0n8ELf84izcVzRcFUxwl5SahWJCtKZ2n26KPEH4GTFnzOYmfhzhZz4zZFf4cqGqzMS8MUmFyjw7bO+Q0m/JUTCk4DP4cMRSiT+7EEbTH4TNhg34fXhV3Hnzltw2HywxFESQqYz7BlCOBbGl7c+iO+d8yN8asNnoBVP3UqdEEIIIaeHdOKP2nwSkpe8Ho87dOgQDh48+aH4n//8J/bv35+xTSgUwquvvoqGhobiREjIac4T9kDOl0PAEYDLcOCP+ud4vERVnkKYaF11ce1luLDmEnA53PQ2VfJqbDRswohnOGv/VFWfQqDMWC4XyCnxR0rOH/HD4jdjrW4DDlsOIhQLQcgtbgvnyZzBxEy1VCs4vdgAq9+Ssc0x61EwAG5t/Sjen3gPx6xHsLlsK9ptxzDuG8fnNz8w43kUQiVaNG14b3wfLq+/Mr2839UHIDHzrtPRgavwoeJ9cbM04hmBRqSZthJtNuoVDXhn9G18MPEexDwxWrWrwICBUqjC7uHX8NbIbpxXfUG6Mmc5qZbXgEEi8Te5VeypXCFn+kPPcsbj8KAQKOCYVL0FAH858UcAwF2r7ylFWGQRYxgGWrEWjmSyuMfRAwD4j3Wfgkasxcdf/hheG9qFdYYNpQyTEDKNXmc3AMzqgSpCCCGEnD4qZFWQ8WXYUkbjDQjJR16Jv7fffhs///nPASQ+QOdqwcnj8dDY2Iivf/3rxYmQkNOcO+yGUqgEwzCQ8KXwR2ZO/L0ysBMqkTrnL72TFX+J1qEMw4DLcLO2K5NW4JAl++n2VOLw1Io/uUABL7X6JCU25EpUn20tPwOHLQcx7h3LmrdXTM6QI2Ommk6sz6r4O2Y9ggZVI9q0q6ARaXDQvB+by7Zi1+BLMEgMWKNfN6tznVG+DU+c+BMisUh6JuCAqx9GiREbDJvQYW8v7hc3S6PeEVQVoY9+g6oRgWgAOwf+hXWGDenWXGdX7sDfe/6GaDyKB5uun/N5liIRT4RyWSX6XL3TbucOu+bUcvV0ohZpMlp9mv1mPNfzd3yk5VaqiiQ5qYTqdLK4x9kFKV+KMmk5GIbBGeXb8ObI62BZltoHEbJI9Ti7USYtg+yUzyiEEEIIOb1pxVr87Zrn6X06IXnKq9Xn/fffjxMnTuDEiRNgWRZPPfVU+u+p/44dO4Z//OMf2Lhx43zFTMhpxRN2p5NsUr4UvujMrT6f7noCz/c+N8XxPGCAdOvQqZTLKmAL2BCOhbP2B5CucEqR8eU044+U3KBzEACwtexMAPM/588RcqTbCgKATqKHNZBd8bdKtxYMw2CDcRMOmPYjHAtj9/DruLD2knTScCabjFsQiAZwwnGypWe/qw91yno0a1ox6B6Y1YMBxTbsGUJ1EZJNDcrEE/oTvglsNm5JLz+78hxE41G0aVehWdMy5/MsVa2aVhy3HZt2G5rxd9LkJA4A/On4HyDmS/DhlTeVMCqymKlFmkmJv240qprSNw+2lG2FNWBFv7uvlCESQqbR4+xBo2rhZx0TQgghpPQo6UdI/gqe8XfixAmsXbu2mLEQsiwlEn+JtppinnhWN/adISdM/twJD2/EA5lAPmOyoVxaDiA7ceIJu8FlOBDzxBnLqdUnWQwGnANQizSoUdSCz+Fj3DeWXhdn41mJ7LlyBZ1QCU8m/vRiPawBa/rvjqAdo94RrNYmes1vMGxCr7MbLw/shDfixUU1l8z6XCvUKyHjy3DIfCC9bMDdjzpFPVo0LYizLHqcXUX4qmaPZVmMekfmPN8PADQiDRTCxGvdZuPW9PL1ho1o0bTiltY75nyOpWyVbg16HF3pGZGnYlkWrpCLWn0mqUVq2JNJnBHPMHYO/Au3tNw255a05PSlEWnS10yPsxtNqpXpdWv16yHkCvH++LulCo8QMg2WZdHr7EYTJf4IIYQQQgiZlYITfymhUAg9PT1ob2/P+o8QMjN32A1FsrpOwpPCH5m+4o9lWbhDLoz7xsGybNZ6T9g9qxY45dJKAMB4VuLPA7lAkfU0jUKgoMQfKbkB5wBqFDXgMByUScsx5j2Z+PtHz99wz0t35Py5KJQj5MhK/HnCHgSiAQCJaj8AWK1LPAizwbAJLIDfHv01VqqbUaOonfW5OAwHa/Xr04k/X8QHs9+MOmU9ahX1EHKF6LSfKNJXNjvWgBXBaLAo7SUZhkG9ogFl0jJUyCrTy3kcHv73wl/hjPIz53yOpWyVbg1ibByd9o6c6/1RP6LxaNb81eVKLdLAGXJg2DOEr7z9APRiA65uvLbUYZFFTCVUwxlywBvxYtw7hhXqkwkEAVeADYaNeG+CEn+ELEbmgBmesIcSf4QQQgghhMxSXjP+JguHw/jGN76B5557DrFYLOc2HR25b14RQk5yh062+pTwJfBHp6/480Y8iLFx+CN+eCOerJac3rAXcv7MiT+tWAseh4eJSRVTQCIReeoxAUAmkMMTocQfKa0B1wBWyBPtICtkFRnX796xPRj3jcPkn0BZsqJ1rlwhJxpVTem/6yUGAIAtYEWVvBrHrEdRJi2DXqIHABgkBlTKqjDqHcEldZflfb6Nxk341eGfIxgNYsDVDwCoVzaAy+Fipbp5wRN/o95hACjaXLnb2z6KQDRAbTpyqFPUQ8qXot12DOsMG7LWu0JOAIBSSIk/AFAL1ZjwjeP/e/U+aMRafHfHDyDgCkodFlnENMlWn72ObgDIahm4pewM/OLQz+CL+CCdoV06IWRhTfVzSwghhBBCCMmt4Iq/hx9+GHv27MF3v/tdsCyLBx98EN/5znewbds2VFZW4le/+lUx4yTktBSMBhGJR9IVf1K+bMaKP1fIlf7zuDe73acn4oFMIJvx3ByGA6OkLKNiCsicOTiZXKCAN+wuajUVIfmIs3EMOgdRragBAJRJKzCWTPxFYhG0J+ejFTM5lqj4U6X/rhMnEnwWvxkAcNR6GKt0azL22WjcDC7DwXnVF+R9vnX6DYjGYzhmPYIBdz84DINqeaJqsFnTgk5H8R6oMfkm8B+77sVB0/4ptxnxjIDDMCiTFCeRus6wAWdWbC/KsU43HIaDNu0qHLMeybnemUz8UavPBI1Yi3AsjJWaZvz0/IdRLqsodUhkkVOLNIixcRww7wefw0eNPLMie2v5mYixcRw0T/2aSAgpjR5nNxRCJXRiXalDIYQQQgghZEkouOJv586duP/++3H55ZfjC1/4AtauXYvVq1fj2muvxQMPfdaU5QAAlpZJREFUPIDXXnsN5557bjFjJeS0k2qdmZrxJ+FJMmaW5eIIOdJ/NvknsFLTnLHeG/bMuhVchawi63yeiCediJxMLpAjzrLwR/30JDwpCZPPhHAsnE78Vcgq8ELfPxFn4zjh6EA4FgaPw0WnvQPnVp8/5/PF4jF4Qq6MVp+pxN+vDv8cZdIK9Dq7cUX91Rn73dxyG7aWnwllAQmaWkUd1CI1DpkPIBQLo0JWla5iata04q9dT8ERtEMt0hT+hSFRPfalt76AEc8w3h57CxuMm3JuN+DuR4WsCnwuf07nI7OzWrcWT3X+BXE2njWnNVXxp6DEHwDgnKrzwAGD82suAo9T8NtZsoykXjc/mHgvXUk9WZm0HFXyarw//i7OqtiBN4Zfw67Bl+CJeOANeyHhS7C94mzsqDoX1fKaUnwJhCxb3c4uNKmaqGMAIYQQQgghs1Rwxd/ExATq6+vB5XIhFArhdrvT6z70oQ9h586dRQmQkNOZJ5yo3ktV2In5Yvgj07f6TN385TBMziShO+yeVcUfkKiYOrXVpzfsyTkjMJUM9ITdWesIWQhDnkEASFdplEsrEIlHYAvYcMR8CBK+BGeUb0enozgVf+6wCywAtehk4k/AFeCu1fegWl6LSDyMjcbN2FZxVsZ+eokeZ5ZvK+icDMNgg2EjDpoPYMDdjzpFfXpdi6YVANDp6Czo2CmBaABffftL8IQ9WKffgBO241Nu2+/qRaOyacr1pLhW69bAF/FhwNWXte6w5SAUQiXUkxLRy5mYJ8bFdZdR0o/MWupnp8vRiRXqlTm32Vp2JvaO78FnX78f//PutxCMhVAjr8WWsjNglJThLyf+iI/tvB2PHvvNQoZOyLLX5+yh+X6EEEIIIYTkoeC7JXq9Pp3sq6qqwrvvvovt2xPtuwYGBooSHCGnO3cyiZZKqkl4Uvij07f6dAad6fZ/E76JrPW+iDfnjL5cyqXleGVwZ0b7Tk/Yg5XqlqxtZcm5gZ6wp2jz0wjJx7B7CAKuAEapEfFYIvEHABO+MRy2HMRa3Tq0atrwx44/5KyYylequvbUyr1bWm+f03FnssGwGW8MvwYxT4LrVtyQXm6UlEEpVKHT3lFwYhEAfvj+9zDoHsAPzv0pTtiP45eH/xfhWDhrPhrLsuhxduMjzbcWfC6Sn5XqFnAZDtptx9AwabZkNB7Fq4Ov4MKai7OqlAghszO5UrpJlTvxd0b5mfhb99NQClT43jk/xEbj5oz1oVgIP97/EF4f2oWPrb53XuMlhCS4Qy6Y/eYpf24JIYQQQggh2QpO/G3duhUffPABLrjgAtxwww34/ve/j76+PvD5fLzyyiu4+uqrZz4IIctcqtWnQpia8SedseLPHXZBLlCiXJZdrZc6pow/u4q/clkFgtEgnCEHNJBNOn6Oij8hVfyR0hpyD6JWVQsOw0Ec8XQCetA9iGPWo/jYmnuxQrUSwWgQg+4B1Csb5nQ+ZzCR+FvoCqv1hg2Isyx8ER/qlCcr/hiGQaWsMj1fsBBj3lHsHnkdn9v8RazUNIMFi2g8hm5HF1bpVmdsa/JPwB/xZySgyPwS8URYoW7GMesRXN14bXr5BxPvwRly4tK6y0oXHCFLnIQvgZArRCgWQpM6d+XQBsMm/O+Fv8IK1cqcSXYhV4gzy7fj1cFXitJ2mRAysx5nNwCgkd6PEEIIIYQQMmsFl0N89rOfxbXXXgsAuPPOO/HFL34RFosF/f39+OhHP4r/+q//KlaMhJy23GE3GADSZKJOwpciEA0gzsYBAAdMH+Ab73w1Yx9H0AGVUIVyaUVWxV+cjcMX8eVV8QcA495EApFlWXjCnpyJv1T7z1SykpCFNuwZQp2yLv13EU8EjUiD14dfRSQewTr9BqxQN4MB0GkvrN1nt6MLf2h/FHE2DmeyrW4hs/rmokxanv7ZnNzqE0i0BZ7Lz+C/+p6DjC/DBTUXAQAalI3gc/jodHRkbdvr7AFAN9oW2irdarRbj2Use2ngRTSqGtFIbc4ImRO1SA0Ow0z5YAjDMGjRtE5bWdumTTwkcdzWPi8xEkIy9Ti7IeQKUSWvLnUohBBCCCGELBkFJ/70ej1WrjzZbuPOO+/EE088gd/85jdgGAbnn39+UQIk5HTmCbshE8jTLQklPAmAxAwuAHh/4l3sGX0LwWgwvY8r5IRSqESZtAwTvvGMNp3eZEIgV+Iul7Jkq8Qx7zgAIBgLIhqP5txfypOCwzCU+CMlM+QeQp2qLmNZhawSRyyHIOPL0KhqgoQvQY2iDp327ETWbLw69DL+ePwP+MH734UjaIeAK4CYJy5C9PlZb9gIHoeHSllVxnKZQF5w1W04FsZLAztxSd3lEHKFAAA+l48V6pU4Ycud+FMIldCKtAWdjxRmtW4tTH4TzMnKTnfIhX3je3BxLVX7ETJXGpEWNfK69GtgIQwSA3RiHY7bjs28MSFkznqd3WhUNc25hTshhBBCCCHLSd6tPg8dOoRnn30W4+PjqK6uxu233466ujpYrVY8/PDD+Nvf/oZoNIorrrhiPuIl5LTiCbszqvMk/ETizx/xQ8qXYtQ7CgCwBMyoltcAAJwhB1RCNcqlFYjEI7AH7dCKtcnj5Zf4k/KlUAiVGE+2DB1Lnk+Vo7UhwzCQ8QtPOhAyF/agDY6gHY2axozlZdJyHLMexWr92vQNoWZNCzodhVX8jXnHoBFpsGvwJbw7sQ9qoRoMw8w5/nzd2Hwz1urXZVWdKARK9Di6p9xv2DOE3xz5Fb645cvpKt2UPaNvwRVy4oqGqzKWN2tasW9sT9axep09aFQ2luTrX85WJauJ/tzxGO5b/ym8PvwqWJZNV2kSQgq3veLsoiQP2rSrqeKPkAUy7htPfw4ihBBCCCGEzE5en3x3796NW265BU899RTa29vx5JNP4qabbsLu3btx5ZVX4sknn8Qll1yC559/Hg899NB8xUzIouKP+PHAm59Lt8vMhzvszkjSSXhSAIAv4gVwsgXn5Jle7rALSqEKRmkZAGDCN55e503uN9vEH5Bo95k6z0v9O6EQKrFatzbntnKBAt4IVfyRhZdq3bnakDmHrjxZtbpevyG9rFndgj5nD8KxcN7nGfOO4uyqc/G5zQ/AHXIteJvPlCp5NS6qvTRruXyGir+3RnZj79ge/PLwz7PWPd/3D6zVr0Otoi5jeYumFeO+cbiSrU1T+lw9aKLWkgtOLdLgE+s+iZcGXsQnXrkbz3Y/g63l22iWGCFFcFPLLbih+SNzPk6bdhU67ScQiUWKEBUhZDqukAuqEr0fI4QQQgghZKnKK/H361//Gq2trXjjjTewZ88evPvuu9i+fTs++clPQiKR4KmnnsJDDz2E+vr6mQ9GyGnivYl9OGDajw57/k9+u0NuKHJV/EX9iLNxjPkSFXjmSYm/1Iy/MkliBpjJfzLxl0oIyPj5JP4qMOYdRSQWwSsDO3FRzSUQcAU5t5UL5HBTxR8pgRP2DqiEahilxozlFbJk4s8wKfGnaUWMjaPH2Q1P2I0fvP9d7B5+fcZzxNk4xryjqJBW4LL6K/DA1v/Ch5r+rbhfyBwpBAp4wp6MFr+TddjaIeVL8fLATrw7vi+9fNA9gCOWw7iq4ZqsfVo1bQCAE5PmInrDHkz4JtCgaszansy/61feiF9d/FvIBXKMekdwad3lpQ6JEDJJq3YVIvEI+ly9pQ6FkNOeO+SCQqAsdRiEEEIIIYQsKXkl/np7e3HffffBaEzceJVKpfjP//xPRKNRfP7zn8fq1atnOAIhp5994+8ASCTk8uWNeE5J/CUq/vwRH2wBW7piyew3AQBYlk1W/Ckh4UugECgw4ZtI759q9Xlqi7/plMsqMO4bx5uDb8IVcuHy+iun3DZRbUQVf2Thddo70Kpty2o7ub1iBz676T/RoGxKL2tQNoLH4eHlgRfxyV3/jpcGXsTrw6/OeA5bwIZIPIIKWSUA4KLaSxddwkUukCMSjyAUC2WtY1kWx23t+LcVH8aWsq348f7vwxv24IS9Az878CMohSqcVbkja78yaTkUQiVO2I+nl6VuZjdSxV/J1Crq8JPzH8ZPL/gFtlecXepwCCGTNKlWgMfh0Zw/QuZZNB6FN+ItWQcGQgghhBBClqq8En8ulwsGgyFjWSoJWFtbW7yoCFkiYvEY3ktW1TiC9ox1XfZO/CpHu73JXCEXZJMSf1JeouIvEA1gPFntJ+VL04k/X8SLaDyW/vBbJi1Pz+cDEolEDsNAkjzObJRLK2D1W/D08afRpluNOuXUFbsztRkkZD6wLItO+wk0a1qy1kn4ElzRcFVGQpDP5aNR1YR/9f0TEr4E2yrOwohneMbzpH7mUom/xUjGT7xe5ErAj3pH4A670aZdhc9u+iIC0QA+9tLt+NSrn4AlYMHnN38xZzUvwzBoUbdkJv6cveBxeDRTp8Q4DAdt2lU0Z5GQRUbAFWClupnm/BEyz1KfO5RCqvgjhBBCCCEkH3Ofbp/E5XKLdShClozjtmPwhD0Q88Swn5L42zf+Dp7penra+S+e8KmtPlMz/nwY846BAbBGtzad+HMmZ3CphWoAicTfqRV/MoEir5vE5dJysGDxwdgHuGKaaj8AkAkU8M5Q8ceyLF4b2lXQfDVCchn1jsAb8aJF2zrrfa5p/Df824oP4yfn/wIbDZsw6h1BnI1Pu0/qZy41N3AxUghTiT9X1rpU5Umrpg16iR6f2/RFNKqa8PXt38bvL/sTtlWcNeVxW7Rt6LSfSLcQ7XF2o05RDx6HNw9fBSGELH1t2lVU8UfIPHOFEu93qNUnIYQQQggh+cn7jt5HP/rRnEmFW2+9NWM5wzDYv3//3KIjZJHbN/4OVEIVWrRtcIYyW33aAtbE/4NWlEnLc+7vCXsgn9SWk8NwIOQK4Y/64Ag6oBPrUSGrwrvjewGc/PB7suKvDF2OzLlc8jzm+wEnkxwSvgTn1Vww7bYKgWLGGX8HzfvxnXe/jW+f9R2cWbE9r1gIyaXT3gEAaNHMPvF3cd1luBiXAQAq5VWIxqMw+01T/iwCiQSjTqyfcsblYpB6UCDXz+FxWztqFXXpVr/nVp+Pc6vPn9VxWzSt8CTbgrZq29Dr7EGjqmnmHQkhZJlq067GX7uegjVghU6sK3U4hJyW3OHUZx9K/BFCCCGEEJKPvBJ/999//3zFQciStHfsHZxRvg18Dh8dk9rkAYmEHwBYA7kTf+FYGJF4JCPxByQScP6IH+PeMVTIqmCQGGD2m8CybDq5mPrwWyatgNlvQiweA5fDhSfigUwgy+tr0EsM4DI8XNxwMSR8CaLRqauiZHzZjDP+Xh16BQCyKiAJKVSnoxMVssp0tVu+Uu0qhz1D0yb+xryjqJBVFXSOhZJ6vcj1c3jcdgxt2lUFHXeNbh3qFPX40lufx5fP+BoG3QO4pO6yOcVKCCGns9bk622HrR07qs4tcTSEnJ5OPvRIiT9CCCGEEELyQYk/Qgo06hnBsGcId6/5d/Q6e7Jm/FkDqcSfJef+vogXACDlZybqJDwp/BEfRr0jWKluhkFiRCQegTvsgjPkBIPMir84y8ISMKNMWg5P2JPROnQ2OAwH3zz72zizYTMQmn5bhUCBUCyEcCycsyoqGA3irZHdAJBVAUlIoTrtHWjJMd9vtgwSI3gcHkY9I9hSdsaU2415R7FCvbLg8ywEKV8GBtkVf76IDwOufly34saCjiviifCTCx7G/+z7Jh58+0sAQBV/hBAyDZ1YB41Ig15nDyX+CJknrpALHIbJ+rxECCGEEEIImV7RZvwRstzsG38HfA4fG42boRap4Qw5MmaI2WZI/HnTiT9pxnIpXwp/1I8x3yjKZRUwSIwAALPfDFfICblAAQ6T+NFNtemc8I0DSM74y7PVJwBsrzwbWol2xu3kyaSiN5K76m/v2B4EogHIBXI4gpT4I3MXjUfR7ehCcx5tPk/FYTiolFVh2Ds85TYsy2LMN4oKWWXB51kIHIYDmUAOzymJvxP242CBgiv+gMRrz7fP/i6uW3EDVEIVGijxRwgh01IKlen3c4SQ4nOHXZBN+uxDCCGEEEIImR16B01IgfaOvYP1hg0Q88RQizSIsyzcyXY00XgUrpATwDSJv3DiRpHs1Io/vhTjvjH4I35UJlt9AoDZb4Ir5IJKpE5va5AYwQAY9Y4CAHwRT3q+13xIJf7codxz/nYNvoQ27So0KJuo4o8URb+rD5F4BM3qwhN/AFApq8KoZ+rEnzvsSv/MLXZygSIr8Xfc1g65QI4qefWcjs1hOLhv/f148upns16bCCGEZBLzJAhE/aUOg5DTlivkglJAbT4JIYQQQgjJFyX+CCkAy7LosLdjvWEjAEAt1AAAHMlklyPoAAuAwzCwBWw5j5GqmpOekqgT88TocXQDACpkFVAKVeBz+DD7TXCG7FAl23wCgIArwFr9Bvyl43F4wu5ExV+eM/7ykTq2J0fFnyNoxwem93BR7SVQi9Rzqviz+C34xCt3Y2SaRA1ZHk7YO8BlOGhSr5jTcarkVRj1jky5PpU8r5BVzOk8C0EukGfN+DtuO4ZWTVvRnoinJ+sJIWRmqbnMhJD54Qo7ab4fIYQQQgghBaA7e4QUwB/1IxwLQyfWAwA0omTiLznnzxZMtPmskdcVUPEnSc/vKpdWgsNwoJcYYPGb4Qw5oTjlqdf/3PIl+CI+/OiDhwqa8ZeP1LG94ezE3xvDr4HDcHBu1flQCdVzqvj7U8cf0OvswbBnqOBjkNNDp70DdcoGCLnCOR2nSl4Dk28C4Vg45/qxZFKwXLq4W30CiZ/DyTP+4mwcHbbjaNOuLmFUhBCy/Eh4UvijvlKHQchpyx1yZX32IYQQQgghhMyMEn+EFMCZrGZTCxNtN1PtN9OJv+R8v2ZNy5SJP1/EBw7DQMwTZyxPDa9XCVWQ8CUAEi09UzP+Jlf8AYBRWobPbf4i3h59E4FooKAZf7OVOvapbQYBYNfgy9hSdiYUQmWy4s9e0DlGPMN4sf95AEAwGiw8WHJa6HF2oVndMufjVMmqwAIYS1b2nWrMO5bxM7eYyU+Z8TfmHYUv4kOLdm7tUAkhhORHzBNTxR8h88gVclHFHyGEEEIIIQWgxB8hBUi19FQnK/3EPDFEPFF6uS1gBZfhoFHVBGvACpZls47hjXgg5cuyWupJeInEw+RZY3qxHuaAKfnhV5V1rB1V5+LqxmsAJJIC84XP5UPME2e1GXSHXOhydOKcqnMBACqhGu6wG7F4LO9z/P7Yb6ERaQEAoVho7kGTJW3CN4HyIrTfTM2+G/Hmbh875htFhWzxV/sBgFygzEj8jfvGAGBJzCckhJDTiZQvQyAaKHUYhJy23GFK/BFCCCGEEFIISvwRUoBUNZs6WemX+LMmvdwatEIj0kIvMSAaj8IVcmYdwxfxQsqXZi1PLZuc7EhU/JngDDkzzjnZx9d9Ere33Ym1+nUFf12zIePLshJ/7bZjAIA1usS5UzE6c3zd0+myd2L3yOv46Kq7wefwEYpRxd9y5o144Yv4YJSUzflYKqEaEr5kyrmR496xJZP4UwgUGT+DJp8JHIZJtx4mhBCyMMQ8MXwRb6nDIOS05Q67qdUnIYQQQgghBaDEHyEFcIac4DAM5JPm6amFatiTiT97wAatWAe92AAAsCZbf07mDXvTbT0ny1XxZ5AYYQvYEI1Hp/zwK+QKccequzJimg8KoQKeSGbi75j1CHRiHQwSI4BEkgUAnCH7pG2O4scfPJSz+hEAWJbFb4/9GtXyGlxceylEPBG1+lzmzH4TAKSvq7lgGAZVsuopE3+j3qVU8SfPmPFnDpigFenA4/BKGBUhhCw/Er6EKv4ImSeRWAT+iJ8q/gghhBBCCCkAJf4IKYAjaIdSqMpo06kWaeBMtfoMWqEV66AV6wAg55w/X8SXcx5fasbY5CSEQWJI//nUGX8LTcZXwBPKnPF31HoEa3TrwDAMgJMVf47kLEQA2Dv2Nl7ofx6D7oH/n737Do+ruvb//5mmmZE0qiPJkiVb7nKRbWxMMQYHTEIJBkJPgrkQIIRALnx/JBeSAAm5ySUhEFNCcpOYEggplEAgGG66Q7Epbrj3Jll11KWZ0ZTz+0PSGKFi9dGM3q/n8YN1Zp991hy2bWnWWWt3O+/ao+9oQ8V6XV/8ZVnMFtktdvmo+BvTKpvbEn85SYOv+JOkAleBSptKuhxvDjSr3l+n8TGU+GsNtUZa4VY2lw/ZPQIA9F2iNUneYEuPDzUBGLiOh5xSovyzDwAAABCLSPwBA1Dnr1VG+/5+HTI+1uqzxutRhiNTGY4MmU2m7iv+Ao1KTuiu4q+t1efHE39ZH0v8pTrShuItDJgrwaXGwLHEny/o057aXZrjLo4cO1bxdyzxV9ZcJkl6p/StLnP6gj79bNOjWjTuJC3OWyJJslsc8lPxN6ZVtJTLarZ0+bM2UONd3Vf8lTW17ZGXFyN75Lnaq3472n1WtFQoZwiqIgEA/ZNoS1TYMHhQCRgG9a11ktpanAMAAADoHxJ/wADU+mqV+omnT9M+1uqz2ueR2+mW2WRWhiNTVd7KLnO0Vfx1TfxNS5+hU/IWqzBlUuRYR8tQqa2laDRlJ+boQP1+hcIhSdLu2p0KhkOakzU3MsZhdchpdXaq+OtIrrxztGvi77c7n1WNr0a3zL8tUjXosNrla69owthU2VKhLGd2p8rawchPLlCdv05NH9sfr7G1Qf+7+XHZzDblx0zir61SuKG1XpJU0VyubCr+AGDEdbRnbwm0RDkSIP40+Nu+z0lljz8AAACg30j8AQNQ569VepeKv0zV++vkD/nV4K9XhiNTkuR2ZsnTbcVf93v8ZSVm6b9Pu18OqyNyLNGWGPmwP9ob3H+m8Bx5vB69e/RtSdKWqo+UZEvqlKiUpDRHeqeKv/LmMk1Jm6I9tbtV0b53mySVNB7RC7t+ryuLvqDxrmOJF7vFIf8QPUG/r26PvvDny9TY2nD8wRg1KporlJ04dAmtfFeBpLb9/KS2tfe1v9+s/fV79cMzHlRyQtfWu6NRx5PvTa2NCoaD8viqle2k4g8ARlpHe3ZvkMQfMNTqOxJ/7PEHAAAA9BuJP2AAan21XSrv0h3pChuGDtYfkCRlOo8l/rrb46+ptftWnz3JTsyWK8Eli9kyiMgHb0raNM3OnKM/7X1ZkrTV85FmZ87pUpWVbk+PVPw1tjaoKdCk5VM+J6vZonfb232GjbAe3fATuZ1uXVX0xU7nO6wO+Yao1efOmp2q8lZph2fHkMyHkVHRUq6cpKFLaI1vr+j7/rrv6No3vqib/volmUwmPXbW/2pu1vwhu85wO1bx16Bqb5XChjGk9wkA0Dcd7dmbA81RjgSIPw2t9TKbTN0+KAkAAACgdyT+gAGo9ddE9rHr0PH1ntrdkqRMpzvy3+73+GvqttVnT7ISc7q0F42WC6d+TpurNmp/3V5tq96q4qx5Xcak2Y9V/JU1te3vNzVtmk7IXqi32xN/r+59WRsrN+j2hV+X3WLvdP5QVvxVtJRLkvbU7hqS+TAyKlsqlDOEFX+JtkRdN+cGnZhzkhbnnaYVs67Vo2f9rFOlaSxItrUl/hpbG1XZXj07lPcJANA3VPwBw6fB36CUhNTINgAAAAAA+s4a7QCAWNMaalVLoEXpjs6Jv44Kv311eyRJbkdb4i+rm4q/1lCrWkOt/Ur8nTzuVE1wTRhM6EPm9PFL9XP7Y3pkw0/kDXo1213cZUy6I127anZKkspb2hJ/uUm5Wpx3uh7b+BNtqdqsX370c1009RItzFnU5XyHxRHZM3GwOpIju2p3Dsl8GH6toVbV+GqUnTi0lWxfmLliSOeLBovZomRbshr89bKa2iqAh/o+AQCOz2l1SpK8QW+UIwHiT31r/ah56BEAAACINVT8Af1U217FlmbvvMdfR8Xf3ro9spqtcrXvw+V2utUcaO70oVBzoEmS+rWn2AVTLtSNc28eVOxDxWax6YIpF2m7Z5usZqtmpBd1GdO54u9o+z6FKTpt/BIZhqFvvf1fGpeUqxuKb+r2Gnarfcgq/iqb2xJ/VPzFjqqWSklSDgmtbrkSXGoKNKqipUKp9rROe4ICAEZGRwvCju/rAAydBn8d+/sBAAAAA0TiD+inuvZ96zIcnRN/DqtDTqtT++r2yu10R9rSuJ1ZkiTPx9p9duwFk2RLGomQh8X5k5bLbDKpKGOmEiwJXV5Pd7Ql/gzDUFnzUeUm5cpkMindkaFZmXMUCLXqrpPu7jFh4bA4h2yPv4qWco1Pzle1t1oer2dI5sTw6mjPSiVb91wJKWpsbWzbB5F7BABRYTPbZDGZqfgDhkF9a71SEkj8AQAAAANB4g/op0jF3ydafUpSuiNDgXBAGY7MyLGOxF9HBZPUtr+fpH61+hxtshKz9B+zr9eFUz7X7evpjgwFwyE1BRrbE3/jI6/desLt+t5pP9T0jBk9zu8Yooq/UDikam+VTs9fKomqv1hR2f7nJSsxO8qRjE6uBJfq/fWqaC4nOQoAUWIymZRoS4o80AVg6NT766n4AwAAAAaIxB/QTx0Vf2nd7DmR3t7uM9PpjhxzJ7ZX/PmOVfw1tTZKOtYiKlZ9YeYKnTlhWbevddyfWl+typrLlJuUG3ltavo0nZR7cq9z2y0O+YYg8VftrVLYMFTsnqcUe6p2k/iLCRUt5cpwZHRbTYpjFX+VLZXKSSLxBwDRkmhNVEuwJdphAHGnwV+vFBJ/AAAAwICQ+AP6qdZXI1eCS1aztctr6e3tPzMdxxJ/dotdrgSXqj/W6rOj4i8pIbYTf73puBceb7Uqm8uVm5zXr/PtFrv8If+g46hsadvfLzsxWzPSZ2h37c4BzWMYho42lQ46HvRNRXO5cpLGRTuMUcuVkKKG1jpVtlQoJ5H7BADRkmhLVEuAxB8w1Opb65VKq08AAABgQEj8Af1U66+NJLU+KZL4c2Z2Op7pcKvKWxX5uqm1SSa1PSUerzoq/nbX7lLICGvcxyr++sJhdcg/BHv8dewVl5M0TtPSZ2hXzU4ZhtHvedaVvavr3vyian01g44Jx1fZUklCqxeuBJdKGksUCAe4TwAQRU5rorxU/OE4Gvz1JIj7oTXUKl/QR6tPAAAAYIC6liwB6FWdr7bbNp+SlN6+75/7Y60+pbb98KpbjiX+mgNNSrIly2yK39x7sq2tKnJnzQ5JUm5S/yr+HBaHQkZYgVBANottwHFUtlQqJSFFTqtTM9KL9Fv/s6r2ViurvQVrX22s3KCwYajG5+kx8YuhU9lSrhm97AE51qUkpCgQDkgSrT4BIIqSbEkkdNCrWl+Nvvj6FQqEA0pJSNGElIn6/pIfKcmWFO3QRq16f70kKSUhLbqBAAAAADEqfrMOwDCp9dcq3d5DxV/78QxH54o/tzNLVd7KyNdNgSYlx3GbT0kymUxKs6dpR802mSRlJ/YvOWG3OiRJ/kHu81fRXB659rT0tkTSngHs87e1+iNJxz6IwPAJG2FVean4601KQkrk9/39swUAGDpOa6Jags3RDgOj2Afl7ykYDuj/LfyGzp54jrZWb9GhhoPRDmtUa2itkyQq/gAAAIABIvEH9FOtr0Zp7ZV9n9TR4jPzExV/ecnjdbSpNNJisq3iL/6f8k2zp8vj9cjtzFKCJaFf59otbYk/3yD3+atoObZXnNvpVrojXbva9/nra8vPlkCL9tXtkUTibyR4vB4FwyESWr1wtSf+nFankm2uKEcDAGMXe/zheN4rW6fpGUU6f/IFuqroC5JE6/jj6Ph+m8QfAAAAMDC0+gT6qc5fp4weWj2ekL1QX53/NRW4JnQ6npc8Xs2BZjW2NijFnqqm1iYljYEP6ztan+Ym96/NpyQ5LHZJGvQ+fxUtFTpp3CmS2qoQZ6QX6cPy9xUIteqfR/4uh9WpO0/6tooyZkpqSwYebDig8cn5kWTlds9WhduThA2tJP6GW2VLhSQpmxaWPepI/OUkjpPJZIpyNAAwdiVak+QNeqMdBkapYDio9RUf6JJpl0uSUu1pMptMqvF5ohzZ6Has1SeJPwAAAGAgSPwB/RAKh9Tgr1NqD3v8OawOfW7aZV2Oj08eL0kqbSpVij1VzYEmJdviu9WnpMheeP3d30+SHFanJMkXGviHaYZhqLKlQtmJ2ZFjRRmz9PS2J1TeUq6l+Wdqd+1O3faPm/Ufs69XTmKOXtrzgvbU7tbnZ16tL825UZK0tXqLUuypspjMVPyNgIqWckmi1WcvXAltDw6wvx8ARFeiLVHNgaZoh4FRartnq5oDzTo591RJktlkVrojQzVU/PWqobVeVrNVzvafBwAAAAD0D4k/oB/qW+tkSD1W/PUkN6kt8Xe0qUQzM2epKdCkfFfBMEQ4uqS1J0gHkvizt1f8+YIDb/VZ569Va6g10upTkj437TLNzZ6vmRmzZDVbFQwH9cz2p/T01lUyJC3MOVEn556iN/b/WStmXiubxaYt1R9pTmaxyppLVU/F37CrbKlQsi15TLTDHaiOxF82yVEAiCqn1UnFH3r0XtlapdnTNC19euRYuj2DVp/HUe+vV6o9la4GAAAAwACR+AP6oeOH9DR793v89STRlqg0e5pKm0olSc2B5jGxL1ek4i85t9/nOqxte/z5QwNv9VnZUimpc+VYoi1Rxe65ka+tZqu+NOdGnVVwtkwmkyamFOpA/X59+S/X6d2jb2tx3hLt8GzTl4pvVHOgWQ1DVPFnGIbCRlgWs2VI5osnFc3lVLIdx7FWn9wnAIimtlafLTIMgyQFunivbJ1Oyj1FZpM5cizTmSkPrT57VeevVSptPgEAAIABMx9/CIAOdf46Scf2ruuP8cn5OtrclvhrCjSOiWqmjoq/cYOo+POHBl7xd6xl5PGTI4WpkzQxpVCSNCl1sua4i/Xn/a9qT91uBcIBzXHPVZo9TfXta2Cw3jy4Wjf85T+GZK54YhiGPqx4X9PTi6IdyqhmNVv1tRNu15kTzo52KAAwpiXaEhU2DPkG8aAS4lNFc7kONRyM7DXdId2RoRovib/e7K/bpwnt35cDAAAA6D8Sf0A/DLTiT5LyksfraHvFX1Nrk5IT4n+Pvylp05STmKMJrgn9Ptduaav48wUH/kFaRXO57BZ7pDqqPy6YfKE2VW7Qmwdel91i19S0aUqxp6phiFp97qrZodLGIwob4SGZL15s82xVeXO5zp74mWiHMupdOPVznfavBACMvMT2B7laAi1RjgSjzfvl62Q2mbRw3KJOxzMcmaqh4q9HYSOs/fX7NDVtWrRDAQAAAGIWiT+gH2p9tXJanZE2lP0xPjlfpU2lCoQC8of8SrbFf+JvUupk/eazzys5of9tTY+1+hxMxV+FcpLGDaj11un5n1JKQoreOPC6ZmbOltVsVao9VfVD1OqztKlUhqSWQPOQzBeLwkZYFc3lnY79/dBflOXM0pyPtWMFAGC0SrQ6JUneIIk/dPZe2VrNcc/t8j1/hiNDdf5aHv7qwdGmUnmDXk1NJ/EHAAAADBSJP6Af6vy1ShtAm0+preKvwV8faT85Fvb4GwyzySyb2TbIPf4qBrwHWoIlQZ8pPFeSInsCpia0Jf4MwxhwTB2ONpVIkhpbGwc9V6z6sPwDrVh9pTZXbpQkBUIBrSn5p5ZN/HSnvXAAABitOlq3N4/hB3nQve2ebTohe2GX4xmOTAXDITWN4e8Be7OndrckUfEHAAAADAKfrAL9UOurVfoA2nxKbYk/6dgPs2Oh1edgOayOQbX6rGwpV/YAE3+SdMHki2S32HXiuJMkSSn2VAXCAXmD3gHPKUmtoVZVtVRKGtuJvwP1+2RI+sn6H8sf8uuD8vfU2NqoZRNo8wkAiA1Oa6IkKv7QWVOgSY2tjRqfnN/ltQxHpiTJQ7vPbu2t2y23063U9r3CAQAAAPQfiT+gH+r8tQPa30+S8pLzJEm7a3dKOvaEOHpmt9jlG1TFX6VyEscN+Pzxrny9fNHrmpU5W5KUak+VpEHv83e0vc2nJDUGGgY1Vywraz6qDEeGKlsq9Oy2p/S3w3/RlLQpKkydFO3QAADok8T2xB97/OHjKttbmeckdf0+NMORIenY3uHobG/dHk1Nnx7tMAAAAICYRuIP6Ican0fpA2z16UpIUUpCinbX7pJEq8++sFsc8g+w4q8l0KLG1kblJA284k+SbBZb5PepCWmSNOh9/sqaj0Z+P5Yr/o42lWpW5hytmHWtXtj9e609+g7VfgCAmJLY/iBXS7Brq88NFR/qlr99mb3cxqCKlgpJ0rhuEn/p7Ym/Gir+ujAMQ3vr9mpaGok/AAAAYDBI/AH94PF6lOl0D/j8vOTxkVafSbT6PC6H1S5fyD+gczv2UsweRMXfJ6W0V/z1N/H3yT1cSptKZLfYZTaZxnTir6z5qPKS83TFjM+rMGWSQuGgzpxwdrTDAgCgz2xmm6xmS7dtwF/b9yftrt2len/dyAeGqCpvLpPNbFO6PaPLaw6rQ4m2RHm8JP4+qdpbrQZ/Pfv7AQAAAIM0KhJ/zz33nM466ywVFxfr8ssv10cffdTr+DfeeEPnnnuuiouLtXz5cq1Zs6bT64Zh6JFHHtGSJUs0d+5cXXvttTp48GC3c7W2tuqiiy7SjBkztGPHjsjx/fv3a8WKFVq8eLGKi4u1bNkyrVy5UoFAIDImEAjopz/9qc4++2wVFxfrwgsv1L///e+B3wiMaqFwSPX+OmU6Bp74G588Xt6gVyYdaw2FntktDvkH2Oqz40nrwbT6/KSUhBRJUkNrXZ/P2V+3V5e9eqG2VG2OHDvaVKrxyeOVbHOpsXVstvoMhAKqbKlQXnK+rGarvrv4B/r2Kd+VexCJdQAARprJZJLTmqjmQOeKv+ZAs94rWyuJlo5jUXlzuXKSxslkMnX7ero9Q7V+1sUn7a1re0BySjqJPwAAAGAwop74W716te6//37dcsstevnll1VUVKTrr79eHk/3T0Bu2LBBd9xxhy677DK98sorWrZsmW655Rbt3r07MuZXv/qVnn32WX33u9/V888/L6fTqeuvv15+f9fKoQceeEDZ2dldjttsNl188cV68skn9eabb+pb3/qWXnjhBT322GORMQ8//LD+8Ic/6J577tHq1at11VVX6dZbb9X27duH4M5gtKnx1ciQBlnxly+prS2U2RT1P36jnsPqkG+ArT4rm8tlMZmV6cwc0njsFnu/Kv6e3/U7hYyw1ld8GDlW2lSivOR8uRJSulQDxqOwEdY1b3xe/y75V+RYRUu5woahvKS2vS9zk/O0tODMKEUIAMDAJdmS1BLsvMff2qNvKxBue2CwhsTfmFPRUt5tm88Omc5M1XhZF5+0t26PXAkuZTu7/nwOAAAAoO+innl46qmndMUVV+jSSy/V1KlTdd9998nhcOill17qdvwzzzyj008/XTfccIOmTJmi22+/XbNmzdJvfvMbSW3Vfs8884xuvvlmnX322SoqKtIDDzygyspK/e1vf+s015o1a/TOO+/ozjvv7HKdgoICXXrppSoqKtL48eO1bNkyLV++XB9+eOzD+z/96U/6yle+oqVLl6qgoEBf+MIXtHTpUj355JNDeIcwWnTsw5HpGHgiKS+5LcmRbKPNZ18MruKvXFmJ2UOeYE21p6q+tW+Jv4rmcv3zyN9lM9u0zbM1cvxoU6nykvOUnJCshjFQ8Xegfp/Kmo5qffkHkWNHm9r2OcxLHh+tsAAAGBJOq1Mtgc6Jv38d+UekXSF7uY095c1lvXadyHBkkhDuxp7a3ZqaNq3HSkkAAAAAfRPVxF9ra6u2bdumxYsXR46ZzWYtXrxYGzdu7PacTZs26dRTT+10bMmSJdq0aZMkqaSkRFVVVZ3mdLlcmjdvXqc5q6urdc899+iBBx6Qw+E4bqyHDh3SW2+9pUWLFkWOBQIBJSQkdBpnt9u1YcOG486H2BNJ/A2igqyj4i+Z/f36xGFxyBcc2B5/lS2VQ7q/X4dUe5oa+ljx98c9LyrJlqyrir6oHZ5tCoaDnVpcpiSkqCnQNOQxjjbbPdskSbtrd0WOHW0qkdVsVVYiT3QDAGJbojVJ3o9V/DW2Nmh9xQc6p/A8JduSSfCMQZUtFRqXlNvj6xmOTFp9dmNf3R729wMAAACGgDWaF6+trVUoFFJmZudESmZmpvbv39/tOdXV1XK73V3GV1dXS5Kqqqoix3oaYxiG7rrrLl111VUqLi5WSUlJjzFeddVV2rZtm1pbW3XllVfqtttui7y2ZMkSPf3001q0aJEmTJigtWvX6q9//atCoVAf70Abs9kks5mnGke72laPLGazMpMyBlxFNjGtQCZTW+LPao16wW2ExWLu9N/RwpngUMDrH9C9qvRWKN+VP+T3Od2RpsZAw3HnbfA36I2Dr+my6VfolLyT9ZsdT+tg4z4l2ZJkyNCE1AJt9aSoqqVqVK2F4bC9ZqtMJulgwwEZppBsFpvKvWXKTc5Vgq3v/wyN1nUKfBzrFLGAdTq0ku1J8oW8kX/P1x1+R2EjrDMLz9Rr+19RfWtd3P9bPxxidZ02B5rVFGhUniuvx//v7qRM1fo8rIuPafDXq8pbqemZM2LqvsTqOsXYwjpFLGCdIhawThFLopr4i5Znn31Wzc3Nuummm447duXKlWpubtbOnTv1wAMP6IknntCNN94oSfr2t7+tu+++W+edd55MJpMKCgp0ySWX9NimtCcZGUm0M4kBXlOjclzZysxwDXiONCNRqc4UuV0ZSk9PGsLohkZKijPaIXSS4UrV/sbgce/V+6XvK92RrmmZx54Qrmmt0hlZpw35fc5JzVJFc8Vx53154x9kMkvXnXSNXAkuORMcOuDdrQkJE2SxmDWnYIY+9KxVScvhUbkWhtLu+h2alztXWyu3qkblKkovUk2gUlPckwb03kfbOgW6wzpFLGCdDo2M5DQ1+Bsi/6a9U75Gi/JP1NS8icpNzVGLGuL+3/rhFGvr1FNTJovFrOm5PX+fM8GdJ2+4RUkpNiVYErodM9bsKd0mi8WsRZPmKz0t9v68xNo6xdjEOkUsYJ0iFrBOEQuimvhLT0+XxWKRx9N53wuPx9Olqq+D2+2OVO51Nz4rKytyLDs7u9OYoqIiSdK6deu0adMmFRcXd5rn0ksv1fLly/WjH/0ociw3t61Fy9SpUxUKhXTvvffqS1/6kiwWizIyMvSzn/1Mfr9fdXV1ys7O1oMPPqiCgoJ+3YeammYq/mLAEc9RpVjTVFvbPKh5xidNUKLJNeh5hpLFYlZKilMNDV6FQuFohxMRbjWpoaXpuPfqnr99RzMyinTfku9LklpDraporJRL6UN+nxOMRFU2VHc7b43Xo3dK39YH5e/r/bL39JnCc2Ty2dXka9W01Blad/ADtWT5ZTassvidsobs8jTVjqq1MNQ83modqSvRF2Zcoy3lW/XBwY3KsRRoX/UBnZh7Ur/e+2hdp8DHsU4RC1inQ8sUsqq2uV61tc2q89Vq3ZH3dNvC/0+1tc1KMqfoaF15XP9bP1xidZ3uOrpPoVBYzlBKj//fE4JJCoXC2nv0sHKTe24JOpb8e+87cpidcoUzY+rPS6yuU4wtrFPEAtYpYgHrFKNBXx8qjWriLyEhQbNnz9batWt19tlnS5LC4bDWrl2rq6++uttz5s+fr3Xr1unaa6+NHHv33Xc1f/58SVJ+fr6ysrK0du1azZw5U5LU1NSkzZs36/Of/7wk6e6779btt98eOb+yslLXX3+9Vq5cqXnz5vUYr2EYCgaDCofDslgskeN2u105OTkKBAL6y1/+ovPOO69f9yEcNhQOG/06ByOvqrlKafYMBYOD+4v97pPvk92SMOh5hkMoFB5VcdlMdnmD3l5jqvZWq6K5QjbzsXta1lguw5Dcjuwhfz8ua4rqfHVd5l1f8YG+v/a7agk2a1bmHH1+xtW6eNqlkXGzMov1xv4/K82eodykPIVDUqIlWQ3+hlF1z4faRxVbZBjS3MwFKkieqJ3VO/WZCeerrKlMuc68Ab330bZOge6wThELWKdDw2FOVHNri4LBsNaWrFUoHNYp405TMBhWWkKG9tTs4T4PQqyt06MNZbKabHJZ03qMO9WWJsOQqpqrleXIGeEIR5+wEdab+9/Q0vyzFA5JYcXO/+8OsbZOMTaxThELWKeIBaxTxIKot/q87rrrdOedd2rOnDmaO3eufv3rX8vr9eqSSy6RJP3Xf/2XcnJydMcdd0iSrrnmGq1YsUJPPvmkli5dqtWrV2vr1q363ve+J0kymUy65ppr9POf/1wTJ05Ufn6+HnnkEWVnZ0eSi3l5eZ1iSExMlCRNmDBB48aNkyS9+uqrslqtmjFjhhISErRlyxY99NBDOu+882Sz2SRJmzdvVkVFhWbOnKmKigo99thjCofDuuGGG4b/xmHEeXwezcyYNeh53M7uq1nRld1ilz/k73XMDs82SdLRphK1hlqVYElQZUuFJCk7ceg/SEmxp6qhtV6GYchkMskwDP1p7x/1882PaWHOIt150reVak/rct6czLn63Y7f6IPy91SYMkmS5EpwyR/yR+KOR9s8W5SdmK2sxCxNTZ+mPbW7Ve2tViAcUG7y+GiHBwDAoCXaEtUcaJIkfVjxvqamTVO6I0OSlO5IV62vJprhYYSVt5QpOzGn1z3BMxxt+9GzNtpsqPhQ1d5qnVN4frRDAQAAAOJC1BN/559/vmpqavToo4+qqqpKM2fO1KpVqyKtO8vKymQ2H/uhacGCBXrwwQf18MMP6yc/+YkKCwv1+OOPa/r06ZExN954o7xer+699141NDRo4cKFWrVqlex2e5/jslqtWrVqlQ4cOCCpLVl49dVXd6o09Pv9evjhh3XkyBElJiZq6dKleuCBB5SSkjLIu4LRyOOtVoYzM9phjCkOq1P+oK/XMTs822SSFDYMHWk8pClp01QxjIm/VHuqwoah5kCTkhNcen7X77Rqyy906fTL9eW5X+3xQ55Z7tkySSprOqrT8pZIklwJbX9XNAUalWGJz7W13bNNszPb2ipPT5+hNUf+qcMNByVJecl5vZwJAEBscFqd8ga9ChthfVjxoc6fdEHktUxHppoCTX16yKeptVFJtmT2/o5xFc0VGpc0rtcxKfZUWUxm1fg8vY4bK/7v4BuakDJRRRkzox0KAAAAEBeinviTpKuvvrrH1p7PPvtsl2PnnXder+00TSaTbrvtNt122219un5+fr527drV6dj555+v88/v/YnDk046SatXr+7TNRDbQuGQ6v11ynRQrTeSHBa7QkZYgVBANout2zE7arZr4bhF+rD8Ax2o368padNU2VKhdEf6sFTRpSakSpLq/HVKTnDpr4f+T8smnK2vzLu11/OSbcmanDZF++r2KS85v+1YgkuS1NjaGHnyO574Q37tqd2lZRM+LUmalj5DgXBAa8veldlkUk5i7x+KAQAQCxKtSfIGW7Sndrca/PVaNO6kyGsdlX+1vhrl9JIMagm06Mo/X6LPTb1UN8z9yrDHjOFT3lymGRlFvY4xm8xKd2SoZgxW/DUHmvXYxpW6aMolmpk5S42tDXqn9C39x+wvkfQGAAAAhkjP/UcARNT4amRIyqRN54iyWx2SJH+o+6q/YDioXTU7tTBnkXISc3Swvq1Ct6KlfNiSSin2tsRfvb9eNT6PDjUc1Em5p/Tp3NnuuZKk8e0tLl22Y4m/eLS7dpeC4ZBmZc6RJE1JmyqTpH8f+aeynNlx294UADC2JNoSFTYMvV36bzmtTs3MmB15LaM98Xe8BE9581G1hlr1h12/02v7/jSs8cayipYKPbHlF/qw/H0FQoFoh9Otvn4fmu7IGJOtPn++6TH9/dBfdddbd2hXzU7968g/FDKC+nThOdEODQAAAIgbJP6APuhow5MZh1VZo5nd0pb48/Wwz9++ur0KhAOamTFbhamTdKB+v6S2FkvDlfhLbU/8NbTWa2PFeknSCdkL+3TuXPc8SVK+a4KkY60+G1sbhjrMUWF79VY5rA5NTp0iqa0VWkHKRNX565TH/n4AgDiRaEuSJP275F+an72gU5eCj1f89aa8uVySdGbBMv1040qtK1s7TNHGtrdL1uj3O3+rb771DV3+2kV6dMNKeYPeaIcV0RxoVmNrY6/VnR0yHBnyjLFWn2uPvqP/O/iGbp5/qwpTJumuf9+hl3a/oJPGnRKX3S8AAACAaCHxB/RBJPHHHn8jytlR8dfDPn87a7bLarZoWvp0FaZM0sGGtoq/ypZy5SQN/f5+kpSScKzib0Plek1KnRT5UO94Ts9fqp8u+4WyE7MlSa72Vp9NcVrxt82zVUUZs2QxWyLHpqW37cdK4g8AEC8SrU5J0tGm0k5tPiUp1Z4ms8l03Iq/suajspltuvOkb+uU3NP0/bXfUUV7MhDHVHurND45X//76VW6eOql+uuhN3XL374cefgr2ipa2v6f9eUBtAxHpmq8YyfxV+er1U8+fECn5C3W56Zeph+c/oDyksertKlEnynseRsPAAAAAP1H4g/og2pvtcwmk1LtadEOZUw5VvHX/ZPc2z1bNTVtuhIsCSpMnaTKlko1tTaqylup7MThSfxZzVYl25JV76/Txor1OiH7xD6fazaZO+35kmBJUIIlQQ1xWPFnGIa2e7Zpdnubzw7T0toSf7lJedEICwCAIZfUXvEnSSfmdE78mU1mpdnTIw+R9aS8uVzjknJlMVt050nfVoIlQX/c8+KwxBvLqr3VynS6NSVtmq6dc70eX/ZLWc0W3fr3m/RO6VvRDi+SrB2XlHvcsW17/I2dxN9jGx9WWIb+v4XfkMlkUrItWT8840H9v4Xf0OK8JdEODwAAAIgrJP6APqjxeZThyJTZxB+ZkWS32CVJvmD3rT53eLZrZmbbPjqTUidLkjZUrlcwHBq2xJ/Uts/fds82VXmrdEJO39p89jhXQoqaAk1DFNnoUdpUonp/XWR/vw7T02dIouIPABA/nNZESW3/tuUmd32wpS8JnvKWMo1rbw+ZaEvUZydfqDcO/FnNgeahD7jdO6Vv6b2ydcM2/3DweKuV9bE9tyekTNRjy36h2Zlz9My2J6MYWZuK5nLZzDalO9KPOzbTkalaX43CRngEIouupkCT3i5do2tmXdupU4YrIUXnT76gU3cIAAAAAINHFgPoA4+3mn0nosDR0eoz1LXVZ62vRmXNZZqZMUuSVOCaKLPJpPfa98TJGcbEX6o9VR+UvyeLyRzZt2+gXAmuuKz42+7ZKpOkWZmzOh0vypilz8+8Wgty+l4pCQDAaJbYnvg78RNtPjtkODKO2+qzvKlM4z5WDX/R1EsUCLfqzQOvH/f65c1lagm09CPiNr/Z/mv9dscz/T4vmqq9Vcr8WOJPantQ7PzJy7W/fn/U26OWt5QpOzGnTw8L5ibnKWSEVdlSMQKRRde26q0KG0aXilgAAAAAw4PEH9AHHm+1Mtjfb8R1VPz5Q10r/nbW7JAkzXK3VZQlWBI0PrlA77c/uZ6ddPy9VQYqNSFVgXBARRmzlGhLHNRcroQUNcVh4m9b9VZNTJmk5PZ9DDvYLDZ9ac6NndqiAQAQy1wJKZqVOVvLJny629cz2iu7emIYhspbypT7sfaQbqdbSwvO0it7X+q1IiwYDuqWv9+kP+55oV8xh42wDjce0v76fTFTcWYYhqq91XI7s7q8duK4k2QxmSMPgEVLW8vWvn0Pmp9cIEkqaTwynCGNCh9VbZTb6abjAwAAADBCSPwBfeDxeeR2uI8/EEMqssdfsGvF37bqLcpwZCjbmR05Vpg6SXX+OiXZkpRsSx62uFLsqZI0JFVryTaXGlsbBz3PaLPNs1Wz3XOOPxAAgBhnMVv0yFk/06z29uOflOHsPfFX76+TL+jrsi/cpdOuUHlzea97131UtUkN/nqVN5f1K+bKlgq1hlrlC/p0tKm0X+dGS2NrgwLhQLeJv2Rbsoqz5mtd2btRiKxN2Ahra/UWTUmb2qfxOUnjZDVbVNpUMsyRRd+myo2amzVPJpMp2qEAAAAAYwKJP6APPN7qLm2FMPyOtfrsXPEXNsJaU/JPLcxZ1OkDhMKUSZKGt82n1FbxJ2nQ+/tJba0+4y3x19TaqEMNB3v8ABQAgLEkw97W6tMwjG5fL29pa0+Zm9R5f8Bp6dM1N2ueXtz9hx7nfufo25LUa2KxOwcbDkZ+v69ub7/OjZZqb5Uk9fg9+Sm5p2pj5YYBtT0dCts921Tvr9PivNP7NN5sMisvOV8ljfGd+GsONGtv3W7NzToh2qEAAAAAYwaJP+A4QuGQ6v117PEXBWaTWTazrcsefxsr16u8uVznT17e6fik1MmShrfNpyRlJ+Yo2ZasovSZg54rHhN/2z3bJUmzM4ujHAkAANGX7shQIBxQc6Cp29fLmo5KUrctIi+Zdrm2e7Zpb+2eLq8ZhqF326sBPT5Pj9d/r2ydrn79CgVCgcixQ/UH5LA6lOnM1N66rnOPRtXetvfYXcWfJJ2ce6qC4aA2VW4YybAi1h59W2n2NM38xP7GvclLHq/Spvhu9dmxv9+8rPnRDgUAAAAYM0j8AcdR46uRoZ6fLsbwclgdXVp9rt7/Z01MKdTszM6tJAtT2yr+soe54u/8ycv1i888JZvFNui5XAkpagzEV+Jvm2eLUu1p7OMCAICkDEeGpLbvKbtT0VKuZFtyl31xJemU3MVyO9368/4/dXltd+0uVXurNTdrfq8Vf1urN6uipaJTgu9w4yFNcE3U1LRp2hczib8qmXTsfn5SvqtA+a4CrS17Z2QDU1sS9p3St3VK3mKZTX3/EbsguSDu9/jbXLVBGY4MjU/Oj3YoAAAAwJhB4g84jpr2J6gzqfiLCrvFLt/HKv5qfTV6p/TfOn/yBV32CclLGq9EW6IKkguGNaYES4KyE7OPP7APXAkuNbU29Nj+KxZt92zTrMzZ7OMCAIDaKv6knttxljeXKaeHbgUWs0XnTbpAfz/8VzUHmju99nbpv+VKcGlp/qdU569V2Ah3O8fhhsOSpO2erR87dkgTUwo1OW1qTLX6THOky2q29jjmlNxT9V7Z2h7vxXA53HhIpU0lWpy3pF/njXcVqKKlvFM1Zrz5qGqz5mWdwPeFAAAAwAgi8QccRyTx5yTxFw12i0P+j1X8/eXgmzKbLPr0xHO6jLWYLfrfs5/o0gJ0NEu2uRQ2DLUEo7MfzVALhUPaWbO9SzUmAABjVUe7+J4q/sqaj3bZ3+/jzpt0gVpDfv390F86HX+n9C2dkrtYWYnZChuG6v113Z5/pLEt8bejvRW3YRg61HBQE1MKNS1tump8Nf3eIzAaqr1VPbb57HBK7mLV+mq1p3b3CEXVZu3Rd2S32LUg58R+nZefnK+wYais+egwRRZdLYEW7a7dqbm0+QQAAABGFIk/4DiqvdUym0xKtadFO5QxyWG1yxfyS2r7oGr1gT/rjIJPyZWQ0u343OQ8JVgSRjLEQXG1t/VqbG2IciRDY3/9PvmCPs12s78fAACS5LQ6ZbfYIw+TfVJ5c3m3+/t1yErM0ql5S/Tavj9FOgQcbjikI42HtWT8GZGKwu7mD4aDOtpUIleCK1LxV+2tljfo1cSUQk1JmypJMbHPX7W36rit92e7i+VKcOkvh94coajavHv0bZ047iTZLfZ+nTfe1dalorSpZDjCirqt1Vva9vfLnh/tUAAAAIAxhcQfcBw1Po8yHJn92q8DQ8dhccrf3upzc9VGHW0q1WcnxU5F3/F0JDCbWpuiHMnQ2O7ZKqvZqunpM6IdCgAAo4LJZFKGMzNSVberZqcO1O+XJIWNsCpbyjUuKbfXOS6YfKEONhzQNs9WhY2w/nb4L7Jb7Fo4blGkotDj7Vq1V9Z8VCEjrDMnnK0qb5WqWqp0qOGAJGliSqHGJeXKaXXGRLtPj7daWcep+LOarbpixuf1532v6GD9gX7N/1HVJu2q2dnvuGp8Hu30bNfivNP6fW6mI1MOqyNu9/nbUr1J6Y505Q9zG34AAAAAnZHJAI6j1lcTeZIaI89utcvX3urzndK3lZOYoznuuVGOauh0VPw1tNZHOZKhsbV6i6anz4ipqksAAIZbhj1DNb4a/ePwX3XbP27Wd9+9W2EjrCpvlYLhkMb10upTkhbknKjc5Dz96P3v67JXL9TvdvxGSwvOlN1iV1p7V4paf9fE35H2/f3OmXieJGlHzTYdajioBEuCcpLGyWwya0qM7PNX7a0+bqtPSbpk2uUal5Snn216tM97KDcHmvXdd+/WLz/6Wb/jWnv0XZlMJp2ce2q/zzWZTBqfPD5uK/4+qtqsue757O8HAAAAjDASf8BxeIMtcloTox3GmGW3OCIVf1uqN2l+9oK4+vCgI/HXFIifir9ZmbOjHQYAAKNKuiND75Wt1f3vfV/zsk/Q0aZSvVP6liqayySp11afkmQ2mXXt7C8pJzFXF0+9VD/51KP6fwu/IUlKsCTIleDqdp++w42H5LQ6NS19unISc7SteqsONxxSgasg0s1i8ihK/L1Xtk4VzeVdjreGWtXQ2nDcVp9S2/24ef7XtLFyg94u/XefrvvynhfV2NqoXTU7FQqHuh1jGIYONxzqEtfzu36nhTmLBrwtQH7yBB2Jw4q/QCigPbW7NTNzVrRDAQAAAMYcEn/AcXiDPjmtjmiHMWY5LA75gn41tjZof90+zc2aF+2QhlSSLVkmSQ3+2N/jr6qlSpUtlZqVOSfaoQAAMKpkODPV0NqgK2ZcpR+e/pCK3XP14u4/qCyS+Ou91acknTXh03rwUw/rmtnXqThrnqxm67H5HZnyeLvu8VfSeEQFrgkymUyamTk7UvE3MaUwMmZa2nSVNB6WN+gd/BsdBMMw9IN139Wz25/u8prHWy1Jcvch8SdJp+SeqpPGnaxfbH5c/va9onvS1NqoF3f/QTMzZ8kf8mt//b5ux22p3qzr/+8avbbvT5Fjf9zzgsqbj+rLc7/ap7i6k+car6NxWPG3v36fAuGAijJI/AEAAAAjjcQfcBz+kE8OizPaYYxZdqtd/pBPW6o+kiFpbtb8aIc0pMwms5ITXGoKNEY7lEHb5tkiSZrtJvEHAMDHXTbtCn1n8X/rxrk3y2Qy6fIZV2m7Z5v+fugvSneky26xD2r+DEdGtxV/RxoPqyBlgiRpVuZs7andrYMNBzQxZVJkzJS0qTKkyL6D0VLZUiFv0Kv3y9d1adFZ7a2SJGU6+pb4k6Sb539NHl+1Xt7zYq/jXtz9vALhgL518r2ymi3a7tna7bit1W3f5/xs0yPaWr1F1d5qPbfjGV009VIVpk7q9py+KEguULW3Wi2BlgHPMRrtrNkuq9miaenTox0KAAAAMOaQ+AOOwxf0yW4d3IcxGDiHxSl/yKfNVZuUk5jTpyfiY01ygkuNrbFf8bfds025SbnKcGRGOxQAAEaV3OQ8LRl/RuTrk3NPVYFrgjZWblDucfb364sMZ6ZqfJ0r/gzD0JHGw5rgmihJmpU5R8FwUM2BZk1ImRgZNzGlUBaTWfvq9gw6jsE40HBAklTrq+3SerS6o+Iv8fh7/HXIdxXovEnL9fyu3/XYUr3BX68/7nlBF065WOOScjU1bXqPib8dNds1N2u+ZmbM1vfW3qNH1j8ou8Wha2Zd2+eYujPeVSBJKmsuHdQ8o82Omu2anDqVfZ8BAACAKCDxBxyHP+STw0rFX7Q4rHb5gj5trtqo4jhr89nBZXOpsTUOKv6qt2gW1X4AAByX2WTWZdOvlHT8/f36IsOeoZpPVPzV+WvV2NqoAldbxd+UtGNJmI+3+kywJGhcUp5KGqPbbvJg/X45rU45rU69X76u02vV3io5rA4lWZP6NecXZq6QL+jTH3e/0OW1nTU7dN/ae2TI0JUzviCpLTm63bOty1jDMLTDs11zs+bpnlPvk8Vk0bqytbq++MtKbt+veaDyk/MlKer3f6jt9OxQEfv7AQAAAFFB4g84Dm/QJ8cg2y9h4OwWh+r8ddpft1fzsk6IdjjDwpUQ+4k/X9CnfXV7NDuzONqhAAAQE86e+BllObM0KXXKoOdKd2R0qfg70nhYkiKJP6vZqhnpRbKaLcpLGt9prNuZ1eX8kXaw4YAKUydpQc6Jeq9sbafXqr3VynS4ZTKZ+jWn2+nWRVM/pxd3/0EN/vq269Qf0D3vfFNf+/tXVOev072nfk9pjnRJbe1Qy5vLu9yL8uYy1fvrNDNjttIdGfr+kh/q8zOv1jmF5w3iHbdJsafKleBSaQzv83e0qVR/3vdq5OvG1gaVNpVoZsbMKEYFAAAAjF3W4w8BxjZ/yCe7xRHtMMYsu8Uub9ArSZoXZ/v7dXAlpHR5Sj/W7K7dqZAR1uzM2dEOBQCAmJBgSdCqc54Z9P5+kpTpzJQ36JU36JWzvVPF4YbDMptMyks+luQ7OfdUWc1WWcyWTue7E92qaC4fdByDcbD+gKalT9fMjNl6ZMODavDXK11tFX4eb7Xczr63+fy4K2d8Qa/vf02/3v6UEsw2vbznRWUnjdNdJ31bZ044W2bTsWdhZ2W2dS7Y7tnWqTXrjpq2KsCijCJJ0pS0aZqSNm1A8XQn31UQSdTGmpZAi7799p0qaTyiyWlTNCtztnbW7JAkFWVQ8QcAAABEAxV/wHH4gj45rCT+oqWjzarb6Y7L/f0kKcPRdV+eWLOteqsSbYkqTJ0c7VAAAIgZibbELkm4gUi3Z0iSaj/2INGRxsPKTRrfaY+1K4u+oAeWruxyvtvhVrW3atBxDFTYCOtwwyEVpkzSonEnK2wY+qD8g8jr1d4quRPdA5o7zZGuz027TK/ufVmv7fuT/mP29Vr1mV9r2cTPdEr6SVJWYpbcTre2V3fe52+7Z7vGJ+crxZ46oBiOJy95vA43HBqWuYeTYRhauf7H7YlZt17e86KktjaqrgSXxre3MQUAAAAwskj8Acfh+9iT0xh5HW1W52Wf0O/2TrEiKzFLVS2VMgwj2qEM2DbPFs3MmNXlAzQAADD80h1tiT/Pxx4kOtJ4SAUpE/p0fqbTLY/XE7XvRY42lSoQDmhS6mRlJWZpStqUTu0+Pb5quR0DS/xJbVV/1825QU+c+6w+P/PqTsnQT5qdWdxln78dnm0qyhy+tpULshdqd+0uHW0qHbZrDIdX972sfx35h76+6C5dPuMqvVXyL1W1VGmnZ7uKMmbG7ffuAAAAwGjHJ7RAL4LhoEJGWA5afUaNvb3acq57fnQDGUZZzmz5Q341BWJzn7+wEdZ2z7ZIeywAADCyMp2ZkrpW/E1w9S3x53ZmKRAOqLG1odvXDcNQS6Bl8IH24FDDQUnSxJRJkqRF407RB2XvKWyEZRiGqgfR6lNqq6z8wswVyknMOe7YmZmztLt2lwKhgCTJH/JrX90ezcoYvnbmSwvOUrItWav3vzZs1xhKYSOs1/a9ov/d/FNdMu1ynZH/KZ1TeL4SLHa9uu9l7azZQZtPAAAAIIpI/AG98LXvLWen1WfUpNnTZDaZdEL2gmiHMmyyErMlSZUtFVGOZGBKGo+osbVRs90k/gAAiIZkm0tWszWS+POH/KpoLleBa2Kfzs90tlXT9dTu863SNbp69RWRZNhQO1h/QK4ElzLaKxdPyj1F9f56rdqwSjtrdigYDkZiHG6zMucoEA5ob90eSdLe2j0KGWHNHMZ9jO0Wu86eeI7ePPjGsN3joVLSeERf/9dtenTDSp1TeL5uKL5JkpRkS9J5ky7QH/e8oIbWBhJ/AAAAQBSR+AN64Q36JEkOC60+o6XYPU9PnfuccpPzoh3KsMlydiT+KqMcycBs82yV2WTiAx4AAKLEZDIpw5ERafV5uOGQDEkFfaz4O5b4637P4V01O9TY2qiKlvIhifeTDjYcUGHKpEhryFkZs3V24Wf09Kandctf2xJLg6n464+padNkM9v0j8N/k2EY2lGzTQmWBE0a5n2MPzt5uer9dXq79N/Dep3BaGxt0Ff/dqOqfdX68dKVun3h12Wz2CKvXzz1EgVCrZKkooyiaIUJAAAAjHnWaAcAjGb+UHviz2qPciRjl8lkUl7y+GiHMawynZmymMyqitHE3+bKDZqcOlVJtqRohwIAwJiV4chUTXvibu3Rd5RoS9S09Ol9OjfTkSmT2vbS605JY4kk6WjTUeW7CoYk3o87WH9AxVlzI19bzBZ965R7ZE826a87/qm9Nfv6/F4Gy2ax6ZrZ1+mJLb+U1FYFOSO9SFbz8P7oXJg6ScXuuXp9/2s6c8KyYb3WQH1Y/oG8Qa9WLX1G2e0dKz4uNzlPp+Yt0ZHGw0qxp0YhQgAAAAASiT+gVx2JPzt7/GEYmU1muZ1ZqvLGXuLPMAytr/hQ5xSeF+1QAAAY09IdGarxeWQYhtaU/FOL85YowZLQp3OtZqtS7Wk9tvosbWpL/JU1lw5ZvB2C4aBKmg7rgikXdnkt0ZaoMwo+pcW5Zwz5dXtzVdEXlWRL1mMbfiJD0hUzrhqR63528nL98P0fqKTxyLAkWAdrfcUHmphS2G3Sr8PXF92p5kDzCEYFAAAA4JNo9Qn0ItLqkz3+MMyyErNjsuLvQMN+1fnrtCDnxGiHAgDAmJbhyFCNr0YHGw7ocMMhLS04q1/nu51Z8ni7VvyFjXAk8Xe06eiQxPpxJY1HFAyHhr2VZn8tn3KR7j71Ptktdp2Yc9KIXPP0/E8pJSFFbxz484hcrz/aHvb6QCeOW9TrOFdCisYl5Y5QVAAAAAC6Q8Uf0Atf0CtJcljZ4w/DK8uZraoenrIfzTZWrJfNbNMc99zjDwYAAMMmw5GpWl+N/nXkH0q2JWthdv8eynE73d0m/ipbKhQMB2W32HW0PQE4lA41HJQkTUwpHPK5B+uM/E/ptLzTZTFbRuR6CZYELcg5UTtrdozI9frjUMNBVXurtTCn98QfAAAAgOij4g/ohT/klyQ5LOzxh+GVlZilypaKaIfRbxsqPtQcd3GfW4kBAIDhkeHIVJ2/Vv868g+dNv502Sy2fp2f6XR3+xDS4YbDkqQTchbqaHPPFX/eoFdhI9ynaxmGIW/QK8MwdLDhgNLsaUq1p/Ur3pEyUkm/DgWuCTrccGhEr9kXH1a8L5vZprlZ86MdCgAAAIDjIPEH9IKKP4yU7MQcVXur+vyB2WgQCAX0UfVm2nwCADAKZDozFTYMHW0q1Rn5Z/b7/J5afZY2HZHNbNP8rBNU1nS0x+9Vbvnbl/X7nc/16VpPb3tCF758ri54+TN6ftfvVDjK2nxGU4Frgur8dWpqbYx2KJ2sr/hAc7Pmyc4DkQAAAMCoR+IP6IU36JNJUoKZaiYMryxntoLhkOr8tdEOpc921GyTL+ij5RMAAKNAuiNDkuRKcGlBzsJ+n+92ZqnOX6dAKNDpeElTicYn52u8q0CBcEDV3SQHG1sbdKTxsN4pfatP11pz5J9aNO4k3VB8ky6dfoVWzPqPfscbrwpcEyRJRxqPRDmSY/whvzZXbtKJ40Zmr0MAAAAAg8Mef0Av/CGfHFanTCZTtENBnMtOzJEkVbZUKsORGeVo+mZDxXq5ElyakjY12qEAADDmdST+low/Q1Zz/3/M6/j+o8Zfo5z270skqaTxsPJdBRqfPF6SVNZUquzE7E7nHqjfL0naU7tLDf56pdhTe7xOSeMRlTaV6KZ5X9Wpeaf1O854N96VL6ntvs/MnBXlaNpsqdqsQDigE3NI/AEAAACxgIo/oBf+kJ92NhgRWYlZkqSqlsooR9J3GyvX64TshTKb+KcEAIBoczvcWphzopZPuXhg5ye6JUnVLZ33+SttLFG+K185ieNkklTaVNrl3AP1+2U2mWRI2lC5vtfrvFe2tq11aPaCAcUZ75xWp9xOt440jZ6Kvw8r3pfb6dbElMJohwIAAACgD/i0FuiFN+iVw+qIdhgYA1ISUmUz21TZUhHtUPqkKdCknTXbdUJ2/1uJAQCAoWcxW/TDMx7StPTpAzo/y9n2EJLHd6yVpz/kV2VLhcYnFyjBkqDsxByVNXef+CtMmaSJKYX6sPz9Xq/zXtlazc8+QU720O5RgWuCjjQcjnYYkiTDMPRh+QdamLOILigAAABAjCDxB/TCF/TJYeFDCQw/k8mk7MQcVXljo+Jve/U2hQ1DJ/C0PgAAcSHZ5pLNbFO191jFX2lTiQxJ+a4CSVJu8vgeK/4mpU7WwpxFWl/xgQzD6PYazYFmfVS1SSfnnjos7yFeFKRM1JHG0ZH42+7ZpkMNB3VG/pnRDgUAAABAH5H4A3rhD/lkt9LqEyMjKzFbVZ9orzVaVbSUyWIyKzc5L9qhAACAIWAymeR2uuXxHqv4K20skSTlJ7ftO5eXlKejn0j8hY1wW8Vf6mSdOG6Rqr3VOtRwsNtrrK/4QCEjTOLvOAqSC3S0qVRhIxztUPTK3pc0PjlfJ45bFO1QAAAAAPQRiT+gF1T8YSRlObNipuKvsqVSmU43+/sBABBH3M6sTom/ksYjSrYlK9WeJknKSx6vsuajnSr6Klsq5A16NTl1qord82Qz27S+4oNu53+vbK0mphRqXFLusL6PWJfvKlAgHFBFc3lU46hqqdJbJf/SRVM/x/d8AAAAQAzhu3egF96QV072+MMIaav4i43EX5W3UlnO7GiHAQAAhlCm062qjyf+mo4o31UQ2dstL3m8WgItqvfXRcYcqN8vSZqUOlkOq0Nz3MXdJv7CRljvla3TKVT7HVdBykRJ0uEot/v88/4/KcFi12cKz4tqHAAAAAD6h8Qf0At/0CeHlYo/jIzsxBzV+DwKhUPRDuW4qluqlZVI4g8AgHiS6czsUvE33pUf+TqvvcX30eajkWMH6vcr2ZYst9MtSTpx3EnaXLVJraHWTnPvqtmpen8dbT77IMuZpQRLgkqimPhrDbXq9f2v6dOF5yrJlhS1OAAAAAD0H4k/oBf+kF92C3v8YWRkObMVNgxV+6qPPzjK2ir+sqIdBgAAGEJuZ5Y8vupIK8+SphIVJE+IvJ6bNF6SdLSpJHLsQP1+TUqdHKkKPDFnkVpDrdpa/VGnud8rWytXgkuzMucM99uIeWaTWQWuAh2JYuJvzZF/qN5fp4unXhK1GAAAAAAMDIk/oBe+oFd2Wn1ihGS3V9BVtlREOZLeGYahqpZKuRNJ/AEAEE8yHW75gj41B5vV2NqgBn99p4q/RFui0uxpOtrUueKvMHVy5OtJqVOU5czSO0ff7jT3urJ3tWjcSbKYLcP/RuJAfvIEHWk8ErXr/+XQ/+mE7AUqcE04/mAAAAAAowqJP6AX3pBPTguJP4yMjtaZo32fv4bWegXCAfb4AwAgznQ81LO1eot+/MEPJUmFKZM6jclLHq+jzaWS2tpBHmk8pMmpUyKvm0wmLR5/ut4tfStSOVjVUqV9dXtp89kP+a4CHWk8FJVrG4ahPbW7ND97QVSuDwAAAGBwSPwBvfAHfVT8YcQk2ZKUaEsc9Ym/am+VJLHHHwAAccbtaNun756379Ke2l361sn3qjC1a+LvQN0+hcIhHWk8pLBhaNLHKv4kacn401Xtrdbu2l2SpPfL18lsMmnRuJNH5o3EgQkpE1Trq1VzoHnEr13eXKbmQLOmpk0f8WsDAAAAGDwSf0AvfCGfHFT8YQRlO3NU6R3dib+qlrbEn5s9/gAAiCtZidmamzVfn595tZ4451mdOWFZlzGfKlimgw0H9L2192pnzU5J6pIcLHbPU0pCit4u/bektjafszOL5UpIGf43ESfy2/dWLIlCu8+9dXskSVPTp474tQEAAAAMHok/oAeGYcgX9MphdUY7FIwh+a4CHWmITlunvqryVspiMivDkRHtUAAAwBCymq166FOP6EtzblSiLbHbMSfnnqL7TrtfH1a8r8c3PqKcxBwl2ZI6jbGYLTo17zS9U/qWWkOt2lixXiflnjISbyFu5LsKJCkq7T731O1Wmj1NGY7MEb82AAAAgMEj8Qf0IBgOKmwYcljs0Q4FY8iElIk6HKX9XPqqylulTKdbZhP/hAAAMBadknuqHjhjpRxWh6alz+h2zGnjT9eRxsN6bd8r8of8OiV38QhHGdsSbYkalzROW6u3jPi199ft1bR02nwCAAAAsYpPbYEe+EJeSaLiDyNqYkqhPF6Pmlob+3VetbdaR5tKhymqzipbKmjzCQDAGDfbPUdPnvOs/t/Cr3f7+sKcRXJYHfr1tieVk5ijiSmFIxtgHDhzwtn655G/yxv0juh199bt0ZS0aSN6TQAAAABDh8Qf0ANf0C9JsrPHH0bQxJSJkqRD/Wz3+eiGn+iH739/OELqorqlWtmJOSNyLQAAMHqlOdKVYk/t9rUES4JOGneKvEGvTs5bLJPJNMLRxb7zJ12glkCL/l3yr8gxj9ej/938UzUHmjuNrWguH5LqwFpfjTxej6aS+AMAAABiFok/oAcdFX9OK4k/jJwC10SZTSYdajjY53NC4ZA2V23U4YZDMgxj+IJrV+2tktvpHvbrAACA2Hba+NMlSSePOzXKkcSmcUm5WpCzUKv3vxY59tOND+ul3S/oxd1/iBwLG2Hdt/Ye3fGvr2nt0XcGdc29dXskicQfAAAAEMNI/AE98FPxhyhIsCQoN2l8vxJ/u2p3qiXQouZAs+r8tcMXnCTDMGj1CQAA+uSM/E/pvxZ9UyeOWxTtUGLW+ZOWa7tnmw7WH9C6o+/q7dJ/a1r6dL20+3nV++skSWuO/FN7andrenqR/nvtd7SteuuAr7evbq+cVqdyk/OG6B0AAAAAGGkk/oAeeCN7/JH4w8iakDJRhxsPdvtaKBzSds+2Tsc2VW6I/L6kqWQ4Q1Nja4MC4QCtPgEAwHFZzVZ9uvBcmU382DlQi/OWKNWepj/ueUGPbVyphTkn6n+WPCBJen7X79QaatWTW3+pU/IW66FPPaqZmbN0zzt36dW9L+s323+tRzes1L72Kr6+2Fe3V1PSpvL/DAAAAIhhfDcP9MAX7Ej8OaMcCcaaiSmFOlR/sNvXXtj9e932j69qZ82OyLENFet1QvYCSVJp4/Am/qq8lZJExR8AAMAIsFlsOqfwXL1x4HXV+mr1nwv+P6U50vW5aZfplb1/1LPbn1JlS4Wun/NlJVgSdN9p/6PcpDw9tvFh/WnvH/WXg2/o5T0v9fl6e2p3awptPgEAAICYRuIP6IE/1Nbq02GxRzkSjDUTUyaqylul5kBzp+MN/nr9fudzkqTX9r0iqW2dbvds1cm5pyo7MVulTUeGNbaqlipJUlZi9rBeBwAAAG3Om3SBLCazrp71H8pLHi9Junz6lUowJ+j3O3+rcwrPV2HqJElSsi1Zjy37X71+yV/1woV/0vmTl2tz1cY+Xacl0KKjTSXs7wcAAADEOBJ/QA+o+EO0TExp++DmSOPhTsd/t/M3ChthXTLtcv3z8N/V4K/XtuotCoQDOiFnocYn56tk2Cv+qmQ2mZThyBjW6wAAAKBNvqtAv/nsC/p80dWRY8kJLn1h5gol2ZK0YtZ1ncabTWYlWBIkSfOzTlB5c7nKm8uOe5399ftkSJqWTuIPAAAAiGUk/oAe+IJ+mU0m2cy2aIeCMabANUEmSQfrD0SOVTSX65W9f9TlM67SVUVfkCFD/3fwDW2s3KA0e5ompUxWvqtg+Cv+vJXKdLjZ9wUAAGAEuZ1umUymTscun3GVfnfBS8pK7LkFe3HWXJkkbao8ftXfvro9spotmuAqHGS0AAAAAKKJT26BHvhCXtktji4/YAPDzWF1aFxSrg43HIwce3rbE0q2JevSaVco3ZGhpfmf0mv7/6QNFR9qfvYCmUymSMVf2AgPW2xVLZW0+QQAABglnMfpTuJKSNGUtGnaVLXhuHMdajik/OQJsll48BEAAACIZaMi8ffcc8/prLPOUnFxsS6//HJ99NFHvY5/4403dO6556q4uFjLly/XmjVrOr1uGIYeeeQRLVmyRHPnztW1116rgwcPdjtXa2urLrroIs2YMUM7duyIHN+/f79WrFihxYsXq7i4WMuWLdPKlSsVCAQ6nf/000/rnHPO0dy5c7V06VL9z//8j/x+/8BuBEYVf9AvO/v7IUomphTqUHvib2fNDv390F+0Yta1SrQlSpKWT/mcypqOanftLs3PXiBJyndNUCAcUJW3atjiqmqpUpaTxB8AAECsmJ99gjZXbpRhGL2Oq/F5eq0eBAAAABAbop74W716te6//37dcsstevnll1VUVKTrr79eHo+n2/EbNmzQHXfcocsuu0yvvPKKli1bpltuuUW7d++OjPnVr36lZ599Vt/97nf1/PPPy+l06vrrr+82IffAAw8oO7vrh9g2m00XX3yxnnzySb355pv61re+pRdeeEGPPfZYZMxrr72mhx56SLfeeqtWr16tH/zgB1q9erV+8pOfDMGdQbT5Qt7jPkELDJcJKRN1uPGQWkOtevCDH2pq+nR9dvKFkddnZc7WlLQpkqQF2QslSeOTx0uSShuHr91ntbeKD4QAAABiyLzsBar2Vqu0qfe9oGt8HmU4MkcoKgAAAADDJeqJv6eeekpXXHGFLr30Uk2dOlX33XefHA6HXnrppW7HP/PMMzr99NN1ww03aMqUKbr99ts1a9Ys/eY3v5HUVu33zDPP6Oabb9bZZ5+toqIiPfDAA6qsrNTf/va3TnOtWbNG77zzju68884u1ykoKNCll16qoqIijR8/XsuWLdPy5cv14YcfRsZs3LhRCxYs0PLly5Wfn68lS5boggsuOG7FImKDN+iTw+qIdhgYowpTJqm8uVxPbPmlSpuO6OuL7pLFbIm8bjKZtGLWdTpt/OnKTc6TJI1LypXFZD7uhzoDZRiGqryVcjtJ/AEAAMSKYvdcmU2m4+7zV+urUbojY4SiAgAAADBcrNG8eGtrq7Zt26abbropcsxsNmvx4sXauLH7H0o2bdqka6+9ttOxJUuWRJJ6JSUlqqqq0uLFiyOvu1wuzZs3Txs3btRnP/tZSVJ1dbXuuecePf7443I4jp/cOXTokN566y19+tOfjhw74YQT9Oqrr+qjjz7S3LlzdeTIEa1Zs0YXXXRRn+9B23s2yWxmH7nRJmD45bQ5ZbVGPT8+7CwWc6f/IvompU+SySS9vPcFXTPnOk3PnNZlzNKJS7V04tLI11YlKM+Vp6PNpcOybhv89QqEWzXOlROVPxesU8QC1iliAesUsYB1OnRSrS4VZc7UFs8mXTzj4m7HGIahGl+N3EmZY+Lnn6HCOkUsYJ0iFrBOEQtYp4glUU381dbWKhQKKTOzczuRzMxM7d+/v9tzqqur5Xa7u4yvrq6WJFVVVUWO9TTGMAzddddduuqqq1RcXKySkp6rY6666ipt27ZNra2tuvLKK3XbbbdFXlu+fLlqa2v1hS98QYZhKBgM6qqrrtJXvvKVPt6BNhkZSTKZSPyNOtaQUhNdSk9PinYkIyYlhdamo8W85FmyWMyamjFVty7+imwWW5/Om+KerKrWsmFZt9Weo7JYzJqcMyGqfy5Yp4gFrFPEAtYpYgHrdGgsLjxFr+56VWlpid3+7NnU2qSwKaiJ7vFj6uefocI6RSxgnSIWsE4RC1iniAVRTfxFy7PPPqvm5uZOlYY9WblypZqbm7Vz50498MADeuKJJ3TjjTdKkt577z394he/0He+8x3NnTtXhw8f1g9+8AM9/vjjuuWWW/ocT01NMxV/o1BdU6NMsqq2tjnaoQw7i8WslBSnGhq8CoXC0Q4H7W6ae4sWjTtJTQ2tklr7dI7bNk7vlb07LOv2QEWJQqGwrK2JUflzwTpFLGCdIhawThELWKdDa3rybFU1PalNh7apMHVSl9cPNxxWKBRWQjBpTPz8M1RYp4gFrFPEAtYpYgHrFKNBXx/Si2riLz09XRaLRR6Pp9Nxj8fTpaqvg9vtjlTudTc+Kysrciw7O7vTmKKiIknSunXrtGnTJhUXF3ea59JLL9Xy5cv1ox/9KHIsNzdXkjR16lSFQiHde++9+tKXviSLxaJHHnlEF154oS6//HJJ0owZM9TS0qJ7771XN998s8zmvpX9hsOGwmGjT2MxcrwBr1LtaQoGx85f5KFQeEy939Hu4imXSVK//p/kJeWrtLFUvtZWWc1D+1d8VXOVDENKtaVHdZ2wThELWKeIBaxTxALW6dAoSp8ts8zaULZB+UkTu7xe1VQtw5BSbGPr55+hwjpFLGCdIhawThELWKeIBVFtSJuQkKDZs2dr7dq1kWPhcFhr167VCSec0O058+fP17p16zode/fddzV//nxJUn5+vrKysjrN2dTUpM2bN0fmvPvuu/WnP/1Jr7zyil555RX98pe/lNRW3ff//t//6zHejnae4XDbH2yfz9cluWexWCJjEdt8IZ/sFnu0wwD6JT85X2HDUHlz2ZDPXeOrkSvBpQRLwpDPDQAAgOHjtDo1PaNIH1Vt7vb1Gl+NJCnDkdnt6wAAAABiR9RbfV533XW68847NWfOHM2dO1e//vWv5fV6dckll0iS/uu//ks5OTm64447JEnXXHONVqxYoSeffFJLly7V6tWrtXXrVn3ve9+TJJlMJl1zzTX6+c9/rokTJyo/P1+PPPKIsrOzdfbZZ0uS8vLyOsWQmJgoSZowYYLGjRsnSXr11VdltVo1Y8YMJSQkaMuWLXrooYd03nnnyWZr22vrzDPP1FNPPaVZs2ZFWn0+8sgjOvPMMyMJQMQuX9Arh5WezYgt410FkqSSphLlt/9+qNT4apTuyBjSOQEAADAy5rrn6S+H3pRhGF32+avxeWS32OXk5x8AAAAg5kU98Xf++eerpqZGjz76qKqqqjRz5kytWrUq0rqzrKysU1XdggUL9OCDD+rhhx/WT37yExUWFurxxx/X9OnTI2NuvPFGeb1e3XvvvWpoaNDChQu1atUq2e19r96yWq1atWqVDhw4IKktWXj11Vfr2muvjYy5+eabZTKZ9PDDD6uiokIZGRk688wze60aROzwBX1yWh3RDgPoF7fTrQRLgkobj0i5pw7p3DVeD0+BAwAAxKi5WSfoD7t+p9JuHhCr8XmU4czskhAEAAAAEHuinviTpKuvvlpXX311t689++yzXY6dd955Ou+883qcz2Qy6bbbbtNtt93Wp+vn5+dr165dnY6df/75Ov/883s9z2q16tZbb9Wtt97ap+sgtrS1+iTxh9hiNplVmDJJr+x9SRNTCnXiuJOGbO4an0fZidnHHwgAAIBRZ7Z7jswmkzZXbeom8VejDDudHQAAAIB4ENU9/oDRzBf0yUHFH2LQXSffrezEHH3zrW/oe2vvVWNrw5DMS6tPAACA2JVkS9LUtOnaUrWpy2u1fJ8HAAAAxA0Sf0A3DMOQP+STw8IeF4g9Ba4JenDpI7rrpG/rw/L39Yedvx2SeWt8tPoEAACIZXOz5mlz1SYZhtHpeK2vRhlOvs8DAAAA4gGJP6AbgXBAYcOQw9r3fSGB0cRkMmnZxM/ojPxP6e3St7p8uNNfLYEWeYNeZfKBEAAAQMyamzVf1d5qlTeXdTru8dUokwe8AAAAgLhA4g/ohj/kkyT2+EPMW5K/VKVNJTrUcHBQ89T6aiRJ6ez9AgAAELPmuItlkvRR9ebIsWA4qAZ/Ha0+AQAAgDhB4g/ohjfYlvhjjz/EugXZC+W0OvV26b8HNU+NzyNJtIACAACIYa6EFE1Om6K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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Price Statistics:\n", + " ADA-USDT: Mean=$0.68, Std=$0.01\n", + " SOL-USDT: Mean=$153.54, Std=$1.05\n", + " Price Ratio: Mean=0.00, Std=0.00\n", + " Correlation: 0.9240\n", + "Running RollingFit analysis...\n", + "\n", + "=== SLIDING FIT ANALYSIS ===\n", + "Processing first 200 iterations for demonstration...\n", + "***ADA-USDT & SOL-USDT*** STARTING....\n", + "OPEN_TRADES: 2025-06-02 15:31:00 open_scaled_disequilibrium=2.892080636255072\n", + "OPEN TRADES:\n", + " time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n", + "0 2025-06-02 15:31:00 ADA-USDT BUY OPEN 0.6736 -2.892081 2.892081 -2.892081 ADA-USDT & SOL-USDT OPEN\n", + "1 2025-06-02 15:31:00 SOL-USDT SELL OPEN 153.2400 -2.892081 2.892081 -2.892081 ADA-USDT & SOL-USDT OPEN\n", + "CLOSE TRADES:\n", + " time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n", + "0 2025-06-02 15:41:00 ADA-USDT SELL CLOSE 0.6734 0.014633 0.014633 0.014633 ADA-USDT & SOL-USDT CLOSE\n", + "1 2025-06-02 15:41:00 SOL-USDT BUY CLOSE 153.1000 0.014633 0.014633 0.014633 ADA-USDT & SOL-USDT CLOSE\n", + "OPEN_TRADES: 2025-06-02 16:44:00 open_scaled_disequilibrium=2.364778510607668\n", + "OPEN TRADES:\n", + " time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n", + "0 2025-06-02 16:44:00 ADA-USDT BUY OPEN 0.6712 -2.364779 2.364779 -2.364779 ADA-USDT & SOL-USDT OPEN\n", + "1 2025-06-02 16:44:00 SOL-USDT SELL OPEN 152.5100 -2.364779 2.364779 -2.364779 ADA-USDT & SOL-USDT OPEN\n", + "CLOSE TRADES:\n", + " time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n", + "0 2025-06-02 17:01:00 ADA-USDT SELL CLOSE 0.6744 -0.45725 0.45725 -0.45725 ADA-USDT & SOL-USDT CLOSE\n", + "1 2025-06-02 17:01:00 SOL-USDT BUY CLOSE 153.0700 -0.45725 0.45725 -0.45725 ADA-USDT & SOL-USDT CLOSE\n", + "OPEN_TRADES: 2025-06-02 17:06:00 open_scaled_disequilibrium=2.191024540541887\n", + "OPEN TRADES:\n", + " time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n", + "0 2025-06-02 17:06:00 ADA-USDT BUY OPEN 0.674 -2.191025 2.191025 -2.191025 ADA-USDT & SOL-USDT OPEN\n", + "1 2025-06-02 17:06:00 SOL-USDT SELL OPEN 153.030 -2.191025 2.191025 -2.191025 ADA-USDT & SOL-USDT OPEN\n", + "CLOSE TRADES:\n", + " time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n", + "0 2025-06-02 17:17:00 ADA-USDT SELL CLOSE 0.6743 -0.152501 0.152501 -0.152501 ADA-USDT & SOL-USDT CLOSE\n", + "1 2025-06-02 17:17:00 SOL-USDT BUY CLOSE 153.0900 -0.152501 0.152501 -0.152501 ADA-USDT & SOL-USDT CLOSE\n", + "OPEN_TRADES: 2025-06-02 17:24:00 open_scaled_disequilibrium=2.748538160528875\n", + "OPEN TRADES:\n", + " time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n", + "0 2025-06-02 17:24:00 ADA-USDT BUY OPEN 0.6759 -2.748538 2.748538 -2.748538 ADA-USDT & SOL-USDT OPEN\n", + "1 2025-06-02 17:24:00 SOL-USDT SELL OPEN 153.7000 -2.748538 2.748538 -2.748538 ADA-USDT & SOL-USDT OPEN\n", + "CLOSE TRADES:\n", + " time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n", + "0 2025-06-02 17:35:00 ADA-USDT SELL CLOSE 0.6715 -0.413061 0.413061 -0.413061 ADA-USDT & SOL-USDT CLOSE\n", + "1 2025-06-02 17:35:00 SOL-USDT BUY CLOSE 152.9900 -0.413061 0.413061 -0.413061 ADA-USDT & SOL-USDT CLOSE\n", + "OPEN_TRADES: 2025-06-02 18:02:00 open_scaled_disequilibrium=2.0472288892294728\n", + "OPEN TRADES:\n", + " time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n", + "0 2025-06-02 18:02:00 ADA-USDT SELL OPEN 0.6743 2.047229 2.047229 2.047229 ADA-USDT & SOL-USDT OPEN\n", + "1 2025-06-02 18:02:00 SOL-USDT BUY OPEN 153.6400 2.047229 2.047229 2.047229 ADA-USDT & SOL-USDT OPEN\n", + "CLOSE TRADES:\n", + " time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n", + "0 2025-06-02 18:06:00 ADA-USDT BUY CLOSE 0.6747 -0.089168 0.089168 -0.089168 ADA-USDT & SOL-USDT CLOSE\n", + "1 2025-06-02 18:06:00 SOL-USDT SELL CLOSE 153.8400 -0.089168 0.089168 -0.089168 ADA-USDT & SOL-USDT CLOSE\n", + "OPEN_TRADES: 2025-06-02 19:35:00 open_scaled_disequilibrium=2.016877535891162\n", + "OPEN TRADES:\n", + " time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n", + "0 2025-06-02 19:35:00 ADA-USDT BUY OPEN 0.6721 -2.016878 2.016878 -2.016878 ADA-USDT & SOL-USDT OPEN\n", + "1 2025-06-02 19:35:00 SOL-USDT SELL OPEN 152.1300 -2.016878 2.016878 -2.016878 ADA-USDT & SOL-USDT OPEN\n", + "ADA-USDT & SOL-USDT: *** Position is NOT CLOSED. ***\n", + "CLOSE_POSITION TRADES:\n", + " time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n", + "0 2025-06-02 22:29:00 ADA-USDT SELL CLOSE 0.6908 0.0 0.0 0.0 ADA-USDT & SOL-USDT CLOSE_POSITION\n", + "1 2025-06-02 22:29:00 SOL-USDT BUY CLOSE 156.7000 0.0 0.0 0.0 ADA-USDT & SOL-USDT CLOSE_POSITION\n", + "***ADA-USDT & SOL-USDT*** FINISHED *** Num Trades:24\n", + "Generated 24 trading signals\n", + "\n", + "Strategy execution completed!\n", + "\n", + "================================================================================\n", + "BACKTEST RESULTS\n", + "================================================================================\n" + ] + }, + { + "data": { + "text/html": [ + " \n", + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== SLIDING FIT INTERACTIVE VISUALIZATION ===\n", + "Note: Rolling Fit strategy visualization with interactive plotly charts\n", + "Using consistent timeline with 540 timestamps\n", + "Timeline range: 2025-06-02 13:30:00 to 2025-06-02 22:30:00\n", + "\n", + "Symbol_A trades:\n", + " time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n", + "0 2025-06-02 15:31:00 ADA-USDT BUY OPEN 0.6736 -2.892081 2.892081 -2.892081 ADA-USDT & SOL-USDT OPEN\n", + "2 2025-06-02 15:41:00 ADA-USDT SELL CLOSE 0.6734 0.014633 0.014633 0.014633 ADA-USDT & SOL-USDT CLOSE\n", + "4 2025-06-02 16:44:00 ADA-USDT BUY OPEN 0.6712 -2.364779 2.364779 -2.364779 ADA-USDT & SOL-USDT OPEN\n", + "6 2025-06-02 17:01:00 ADA-USDT SELL CLOSE 0.6744 -0.457250 0.457250 -0.457250 ADA-USDT & SOL-USDT CLOSE\n", + "8 2025-06-02 17:06:00 ADA-USDT BUY OPEN 0.6740 -2.191025 2.191025 -2.191025 ADA-USDT & SOL-USDT OPEN\n", + "10 2025-06-02 17:17:00 ADA-USDT SELL CLOSE 0.6743 -0.152501 0.152501 -0.152501 ADA-USDT & SOL-USDT CLOSE\n", + "12 2025-06-02 17:24:00 ADA-USDT BUY OPEN 0.6759 -2.748538 2.748538 -2.748538 ADA-USDT & SOL-USDT OPEN\n", + "14 2025-06-02 17:35:00 ADA-USDT SELL CLOSE 0.6715 -0.413061 0.413061 -0.413061 ADA-USDT & SOL-USDT CLOSE\n", + "16 2025-06-02 18:02:00 ADA-USDT SELL OPEN 0.6743 2.047229 2.047229 2.047229 ADA-USDT & SOL-USDT OPEN\n", + "18 2025-06-02 18:06:00 ADA-USDT BUY CLOSE 0.6747 -0.089168 0.089168 -0.089168 ADA-USDT & SOL-USDT CLOSE\n", + "20 2025-06-02 19:35:00 ADA-USDT BUY OPEN 0.6721 -2.016878 2.016878 -2.016878 ADA-USDT & SOL-USDT OPEN\n", + "22 2025-06-02 22:29:00 ADA-USDT SELL CLOSE 0.6908 0.000000 0.000000 0.000000 ADA-USDT & SOL-USDT CLOSE_POSITION\n", + "\n", + "Symbol_B trades:\n", + " time symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n", + "1 2025-06-02 15:31:00 SOL-USDT SELL OPEN 153.24 -2.892081 2.892081 -2.892081 ADA-USDT & SOL-USDT OPEN\n", + "3 2025-06-02 15:41:00 SOL-USDT BUY CLOSE 153.10 0.014633 0.014633 0.014633 ADA-USDT & SOL-USDT CLOSE\n", + "5 2025-06-02 16:44:00 SOL-USDT SELL OPEN 152.51 -2.364779 2.364779 -2.364779 ADA-USDT & SOL-USDT OPEN\n", + "7 2025-06-02 17:01:00 SOL-USDT BUY CLOSE 153.07 -0.457250 0.457250 -0.457250 ADA-USDT & SOL-USDT CLOSE\n", + "9 2025-06-02 17:06:00 SOL-USDT SELL OPEN 153.03 -2.191025 2.191025 -2.191025 ADA-USDT & SOL-USDT OPEN\n", + "11 2025-06-02 17:17:00 SOL-USDT BUY CLOSE 153.09 -0.152501 0.152501 -0.152501 ADA-USDT & SOL-USDT CLOSE\n", + "13 2025-06-02 17:24:00 SOL-USDT SELL OPEN 153.70 -2.748538 2.748538 -2.748538 ADA-USDT & SOL-USDT OPEN\n", + "15 2025-06-02 17:35:00 SOL-USDT BUY CLOSE 152.99 -0.413061 0.413061 -0.413061 ADA-USDT & SOL-USDT CLOSE\n", + "17 2025-06-02 18:02:00 SOL-USDT BUY OPEN 153.64 2.047229 2.047229 2.047229 ADA-USDT & SOL-USDT OPEN\n", + "19 2025-06-02 18:06:00 SOL-USDT SELL CLOSE 153.84 -0.089168 0.089168 -0.089168 ADA-USDT & SOL-USDT CLOSE\n", + "21 2025-06-02 19:35:00 SOL-USDT SELL OPEN 152.13 -2.016878 2.016878 -2.016878 ADA-USDT & SOL-USDT OPEN\n", + "23 2025-06-02 22:29:00 SOL-USDT BUY CLOSE 156.70 0.000000 0.000000 0.000000 ADA-USDT & SOL-USDT CLOSE_POSITION\n" + ] + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "green", + "width": 2 + }, + "name": "Absolute Scaled Dis-equilibrium", + "opacity": 0.8, + "type": "scatter", + "x": [ + "2025-06-02T13:30:00.000000000", + "2025-06-02T13:31:00.000000000", + "2025-06-02T13:32:00.000000000", + "2025-06-02T13:33:00.000000000", + "2025-06-02T13:34:00.000000000", + "2025-06-02T13:35:00.000000000", + "2025-06-02T13:36:00.000000000", + "2025-06-02T13:37:00.000000000", + "2025-06-02T13:38:00.000000000", + "2025-06-02T13:39:00.000000000", + "2025-06-02T13:40:00.000000000", + "2025-06-02T13:41:00.000000000", + "2025-06-02T13:42:00.000000000", + "2025-06-02T13:43:00.000000000", + "2025-06-02T13:44:00.000000000", + "2025-06-02T13:45:00.000000000", + "2025-06-02T13:46:00.000000000", + 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BUY ADA-USDT @ $0.67 at 2025-06-02 15:31:00\n", + " 2. SELL SOL-USDT @ $153.24 at 2025-06-02 15:31:00\n", + " 3. SELL ADA-USDT @ $0.67 at 2025-06-02 15:41:00\n", + " 4. BUY SOL-USDT @ $153.10 at 2025-06-02 15:41:00\n", + " 5. BUY ADA-USDT @ $0.67 at 2025-06-02 16:44:00\n", + " 6. SELL SOL-USDT @ $152.51 at 2025-06-02 16:44:00\n", + " ... and 18 more signals\n", + "\n", + "================================================================================\n", + "\n", + "Detailed Trading Signals:\n", + "Time Action Symbol Price Scaled Dis-eq Status \n", + "------------------------------------------------------------------------------------------\n", + "2025-06-02 15:31:00 OPEN ADA-USDT $0.67 2.892 OPEN \n", + "2025-06-02 15:31:00 OPEN SOL-USDT $153.24 2.892 OPEN \n", + "2025-06-02 15:41:00 CLOSE ADA-USDT $0.67 0.015 CLOSE \n", + "2025-06-02 15:41:00 CLOSE SOL-USDT $153.10 0.015 CLOSE \n", + "2025-06-02 16:44:00 OPEN ADA-USDT $0.67 2.365 OPEN \n", + "2025-06-02 16:44:00 OPEN SOL-USDT $152.51 2.365 OPEN \n", + "2025-06-02 17:01:00 CLOSE ADA-USDT $0.67 0.457 CLOSE \n", + "2025-06-02 17:01:00 CLOSE SOL-USDT $153.07 0.457 CLOSE \n", + "2025-06-02 17:06:00 OPEN ADA-USDT $0.67 2.191 OPEN \n", + "2025-06-02 17:06:00 OPEN SOL-USDT $153.03 2.191 OPEN \n", + "... and 14 more trading signals\n", + "\n", + " -------------- Suggested Trades \n", + " symbol side action price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium pair status\n", + "time \n", + "2025-06-02 15:31:00 ADA-USDT BUY OPEN 0.6736 -2.892081 2.892081 -2.892081 ADA-USDT & SOL-USDT OPEN\n", + "2025-06-02 15:31:00 SOL-USDT SELL OPEN 153.2400 -2.892081 2.892081 -2.892081 ADA-USDT & SOL-USDT OPEN\n", + "2025-06-02 15:41:00 ADA-USDT SELL CLOSE 0.6734 0.014633 0.014633 0.014633 ADA-USDT & SOL-USDT CLOSE\n", + "2025-06-02 15:41:00 SOL-USDT BUY CLOSE 153.1000 0.014633 0.014633 0.014633 ADA-USDT & SOL-USDT CLOSE\n", + "2025-06-02 16:44:00 ADA-USDT BUY OPEN 0.6712 -2.364779 2.364779 -2.364779 ADA-USDT & SOL-USDT OPEN\n", + "2025-06-02 16:44:00 SOL-USDT SELL OPEN 152.5100 -2.364779 2.364779 -2.364779 ADA-USDT & SOL-USDT OPEN\n", + "2025-06-02 17:01:00 ADA-USDT SELL CLOSE 0.6744 -0.457250 0.457250 -0.457250 ADA-USDT & SOL-USDT CLOSE\n", + "2025-06-02 17:01:00 SOL-USDT BUY CLOSE 153.0700 -0.457250 0.457250 -0.457250 ADA-USDT & SOL-USDT CLOSE\n", + "2025-06-02 17:06:00 ADA-USDT BUY OPEN 0.6740 -2.191025 2.191025 -2.191025 ADA-USDT & SOL-USDT OPEN\n", + "2025-06-02 17:06:00 SOL-USDT SELL OPEN 153.0300 -2.191025 2.191025 -2.191025 ADA-USDT & SOL-USDT OPEN\n", + "2025-06-02 17:17:00 ADA-USDT SELL CLOSE 0.6743 -0.152501 0.152501 -0.152501 ADA-USDT & SOL-USDT CLOSE\n", + "2025-06-02 17:17:00 SOL-USDT BUY CLOSE 153.0900 -0.152501 0.152501 -0.152501 ADA-USDT & SOL-USDT CLOSE\n", + "2025-06-02 17:24:00 ADA-USDT BUY OPEN 0.6759 -2.748538 2.748538 -2.748538 ADA-USDT & SOL-USDT OPEN\n", + "2025-06-02 17:24:00 SOL-USDT SELL OPEN 153.7000 -2.748538 2.748538 -2.748538 ADA-USDT & SOL-USDT OPEN\n", + "2025-06-02 17:35:00 ADA-USDT SELL CLOSE 0.6715 -0.413061 0.413061 -0.413061 ADA-USDT & SOL-USDT CLOSE\n", + "2025-06-02 17:35:00 SOL-USDT BUY CLOSE 152.9900 -0.413061 0.413061 -0.413061 ADA-USDT & SOL-USDT CLOSE\n", + "2025-06-02 18:02:00 ADA-USDT SELL OPEN 0.6743 2.047229 2.047229 2.047229 ADA-USDT & SOL-USDT OPEN\n", + "2025-06-02 18:02:00 SOL-USDT BUY OPEN 153.6400 2.047229 2.047229 2.047229 ADA-USDT & SOL-USDT OPEN\n", + "2025-06-02 18:06:00 ADA-USDT BUY CLOSE 0.6747 -0.089168 0.089168 -0.089168 ADA-USDT & SOL-USDT CLOSE\n", + "2025-06-02 18:06:00 SOL-USDT SELL CLOSE 153.8400 -0.089168 0.089168 -0.089168 ADA-USDT & SOL-USDT CLOSE\n", + "2025-06-02 19:35:00 ADA-USDT BUY OPEN 0.6721 -2.016878 2.016878 -2.016878 ADA-USDT & SOL-USDT OPEN\n", + "2025-06-02 19:35:00 SOL-USDT SELL OPEN 152.1300 -2.016878 2.016878 -2.016878 ADA-USDT & SOL-USDT OPEN\n", + "2025-06-02 22:29:00 ADA-USDT SELL CLOSE 0.6908 0.000000 0.000000 0.000000 ADA-USDT & SOL-USDT CLOSE_POSITION\n", + "2025-06-02 22:29:00 SOL-USDT BUY CLOSE 156.7000 0.000000 0.000000 0.000000 ADA-USDT & SOL-USDT CLOSE_POSITION\n", + "\n", + "================================================================================\n", + "\n", + "====== Returns By Day and Pair ======\n", + "\n", + "--- 20250602-ADA-USDT & SOL-USDT ---\n", + "ADA-USDT & SOL-USDT:\n", + " 15:31:00-15:41:00 ADA-USDT: BUY @ $0.67, SELL @ $0.67, Return: -0.03% | Open Dis-eq: 2.89,\n", + " 15:31:00-15:41:00 SOL-USDT: SELL @ $153.24, BUY @ $153.10, Return: 0.09% | Open Dis-eq: 2.89,\n", + " 16:44:00-17:01:00 ADA-USDT: BUY @ $0.67, SELL @ $0.67, Return: 0.48% | Open Dis-eq: 2.36,\n", + " 16:44:00-17:01:00 SOL-USDT: SELL @ $152.51, BUY @ $153.07, Return: -0.37% | Open Dis-eq: 2.36,\n", + " 17:06:00-17:17:00 ADA-USDT: BUY @ $0.67, SELL @ $0.67, Return: 0.04% | Open Dis-eq: 2.19,\n", + " 17:06:00-17:17:00 SOL-USDT: SELL @ $153.03, BUY @ $153.09, Return: -0.04% | Open Dis-eq: 2.19,\n", + " 17:24:00-17:35:00 ADA-USDT: BUY @ $0.68, SELL @ $0.67, Return: -0.65% | Open Dis-eq: 2.75,\n", + " 17:24:00-17:35:00 SOL-USDT: SELL @ $153.70, BUY @ $152.99, Return: 0.46% | Open Dis-eq: 2.75,\n", + " 18:02:00-18:06:00 ADA-USDT: SELL @ $0.67, BUY @ $0.67, Return: -0.06% | Open Dis-eq: 2.05,\n", + " 18:02:00-18:06:00 SOL-USDT: BUY @ $153.64, SELL @ $153.84, Return: 0.13% | Open Dis-eq: 2.05,\n", + " 19:35:00-22:29:00 ADA-USDT: BUY @ $0.67, SELL @ $0.69, Return: 2.78% | Open Dis-eq: 2.02,\n", + " 19:35:00-22:29:00 SOL-USDT: SELL @ $152.13, BUY @ $156.70, Return: -3.00% | Open Dis-eq: 2.02,\n", + " Pair Total Return: -0.16%\n", + " Day Total Return: -0.16%\n", + "\n", + "====== GRAND TOTALS ACROSS ALL PAIRS ======\n", + "Total Realized PnL: -0.16%\n", + "\n", + "====== NO OUTSTANDING POSITIONS ======\n" + ] + } + ], + "source": [ + "setup()\n", + "load_config_from_file()\n", + "print_config()\n", + "prepare_market_data()\n", + "print_strategy_specifics()\n", + "visualize_prices()\n", + "run_analysis()\n", + "visualization()\n", + "summary() \n", + "performance_results()\n", + "print_summary()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3.12-venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/research/pt_backtest.py b/research/pt_backtest.py index baf7a76..cc87b19 100644 --- a/research/pt_backtest.py +++ b/research/pt_backtest.py @@ -3,104 +3,100 @@ import glob import importlib import os from datetime import date, datetime -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Optional, Tuple import pandas as pd +from research.research_tools import create_pairs from tools.config import expand_filename, load_config -from tools.data_loader import get_available_instruments_from_db, load_market_data from pt_trading.results import ( BacktestResult, create_result_database, store_config_in_database, - store_results_in_database, ) from pt_trading.fit_method import PairsTradingFitMethod from pt_trading.trading_pair import TradingPair +DayT = str +DataFileNameT = str -def resolve_datafiles(config: Dict, cli_datafiles: Optional[str] = None) -> List[str]: - """ - Resolve the list of data files to process. - CLI datafiles take priority over config datafiles. - Supports wildcards in config but not in CLI. - """ - if cli_datafiles: - # CLI override - comma-separated list, no wildcards - datafiles = [f.strip() for f in cli_datafiles.split(",")] - # Make paths absolute relative to data directory - data_dir = config.get("data_directory", "./data") - resolved_files = [] - for df in datafiles: - if not os.path.isabs(df): - df = os.path.join(data_dir, df) - resolved_files.append(df) - return resolved_files - - # Use config datafiles with wildcard support - config_datafiles = config.get("datafiles", []) - data_dir = config.get("data_directory", "./data") - resolved_files = [] - - for pattern in config_datafiles: +def resolve_datafiles( + config: Dict, date_pattern: str, instruments: List[Dict[str, str]] +) -> List[Tuple[DayT, DataFileNameT]]: + resolved_files: List[Tuple[DayT, DataFileNameT]] = [] + for inst in instruments: + pattern = date_pattern + inst_type = inst["instrument_type"] + data_dir = config["market_data_loading"][inst_type]["data_directory"] if "*" in pattern or "?" in pattern: # Handle wildcards if not os.path.isabs(pattern): - pattern = os.path.join(data_dir, pattern) + pattern = os.path.join(data_dir, f"{pattern}.mktdata.ohlcv.db") matched_files = glob.glob(pattern) - resolved_files.extend(matched_files) + for matched_file in matched_files: + import re + match = re.search(r"(\d{8})\.mktdata\.ohlcv\.db$", matched_file) + assert match is not None + day = match.group(1) + resolved_files.append((day, matched_file)) else: # Handle explicit file path if not os.path.isabs(pattern): - pattern = os.path.join(data_dir, pattern) - resolved_files.append(pattern) - + pattern = os.path.join(data_dir, f"{pattern}.mktdata.ohlcv.db") + resolved_files.append((date_pattern, pattern)) return sorted(list(set(resolved_files))) # Remove duplicates and sort +def get_instruments(args: argparse.Namespace, config: Dict) -> List[Dict[str, str]]: + + instruments = [ + { + "symbol": inst.split(":")[0], + "instrument_type": inst.split(":")[1], + "exchange_id": inst.split(":")[2], + "instrument_id_pfx": config["market_data_loading"][inst.split(":")[1]][ + "instrument_id_pfx" + ], + "db_table_name": config["market_data_loading"][inst.split(":")[1]][ + "db_table_name" + ], + } + for inst in args.instruments.split(",") + ] + return instruments + + def run_backtest( config: Dict, - datafile: str, - price_column: str, + datafiles: List[str], fit_method: PairsTradingFitMethod, - instruments: List[str], + instruments: List[Dict[str, str]], ) -> BacktestResult: """ Run backtest for all pairs using the specified instruments. """ bt_result: BacktestResult = BacktestResult(config=config) + # if len(datafiles) < 2: + # print(f"WARNING: insufficient data files: {datafiles}") + # return bt_result - def _create_pairs(config: Dict, instruments: List[str]) -> List[TradingPair]: - nonlocal datafile - all_indexes = range(len(instruments)) - unique_index_pairs = [(i, j) for i in all_indexes for j in all_indexes if i < j] - pairs = [] - - # Update config to use the specified instruments - config_copy = config.copy() - config_copy["instruments"] = instruments - - market_data_df = load_market_data(datafile, config=config_copy) - - for a_index, b_index in unique_index_pairs: - pair = TradingPair( - config=config_copy, - market_data=market_data_df, - symbol_a=instruments[a_index], - symbol_b=instruments[b_index], - price_column=price_column, - ) - pairs.append(pair) - return pairs + if not all([os.path.exists(datafile) for datafile in datafiles]): + print(f"WARNING: data file {datafiles} does not exist") + return bt_result pairs_trades = [] - for pair in _create_pairs(config, instruments): - single_pair_trades = fit_method.run_pair( - pair=pair, bt_result=bt_result - ) + + pairs = create_pairs( + datafiles=datafiles, + fit_method=fit_method, + config=config, + instruments=instruments, + ) + for pair in pairs: + single_pair_trades = fit_method.run_pair(pair=pair, bt_result=bt_result) if single_pair_trades is not None and len(single_pair_trades) > 0: pairs_trades.append(single_pair_trades) - print(f"pairs_trades: {pairs_trades}") + print(f"pairs_trades:\n{pairs_trades}") # Check if result_list has any data before concatenating if len(pairs_trades) == 0: print("No trading signals found for any pairs") @@ -109,23 +105,22 @@ def run_backtest( bt_result.collect_single_day_results(pairs_trades) return bt_result - def main() -> None: parser = argparse.ArgumentParser(description="Run pairs trading backtest.") parser.add_argument( "--config", type=str, required=True, help="Path to the configuration file." ) parser.add_argument( - "--datafiles", + "--date_pattern", type=str, - required=False, - help="Comma-separated list of data files (overrides config). No wildcards supported.", + required=True, + help="Date YYYYMMDD, allows * and ? wildcards", ) parser.add_argument( "--instruments", type=str, - required=False, - help="Comma-separated list of instrument symbols (e.g., COIN,GBTC). If not provided, auto-detects from database.", + required=True, + help="Comma-separated list of instrument symbols (e.g., COIN:EQUITY,GBTC:CRYPTO)", ) parser.add_argument( "--result_db", @@ -139,19 +134,13 @@ def main() -> None: config: Dict = load_config(args.config) # Dynamically instantiate fit method class - fit_method_class_name = config.get("fit_method_class", None) - assert fit_method_class_name is not None - module_name, class_name = fit_method_class_name.rsplit(".", 1) - module = importlib.import_module(module_name) - fit_method = getattr(module, class_name)() + fit_method = PairsTradingFitMethod.create(config) # Resolve data files (CLI takes priority over config) - datafiles = resolve_datafiles(config, args.datafiles) - - if not datafiles: - print("No data files found to process.") - return + instruments = get_instruments(args, config) + datafiles = resolve_datafiles(config, args.date_pattern, instruments) + days = list(set([day for day, _ in datafiles])) print(f"Found {len(datafiles)} data files to process:") for df in datafiles: print(f" - {df}") @@ -163,51 +152,26 @@ def main() -> None: # Initialize a dictionary to store all trade results all_results: Dict[str, Dict[str, Any]] = {} - - # Store configuration in database for reference - if args.result_db.upper() != "NONE": - # Get list of all instruments for storage - all_instruments = [] - for datafile in datafiles: - if args.instruments: - file_instruments = [ - inst.strip() for inst in args.instruments.split(",") - ] - else: - file_instruments = get_available_instruments_from_db(datafile, config) - all_instruments.extend(file_instruments) - - # Remove duplicates while preserving order - unique_instruments = list(dict.fromkeys(all_instruments)) - - store_config_in_database( - db_path=args.result_db, - config_file_path=args.config, - config=config, - fit_method_class=fit_method_class_name, - datafiles=datafiles, - instruments=unique_instruments, - ) - + is_config_stored = False # Process each data file - price_column = config["price_column"] - for datafile in datafiles: - print(f"\n====== Processing {os.path.basename(datafile)} ======") - - # Determine instruments to use - if args.instruments: - # Use CLI-specified instruments - instruments = [inst.strip() for inst in args.instruments.split(",")] - print(f"Using CLI-specified instruments: {instruments}") - else: - # Auto-detect instruments from database - instruments = get_available_instruments_from_db(datafile, config) - print(f"Auto-detected instruments: {instruments}") - - if not instruments: - print(f"No instruments found for {datafile}, skipping...") + for day in sorted(days): + md_datafiles = [datafile for md_day, datafile in datafiles if md_day == day] + if not all([os.path.exists(datafile) for datafile in md_datafiles]): + print(f"WARNING: insufficient data files: {md_datafiles}") continue + print(f"\n====== Processing {day} ======") + + if not is_config_stored: + store_config_in_database( + db_path=args.result_db, + config_file_path=args.config, + config=config, + fit_method_class=config["fit_method_class"], + datafiles=datafiles, + instruments=instruments, + ) + is_config_stored = True # Process data for this file try: @@ -215,14 +179,17 @@ def main() -> None: bt_results = run_backtest( config=config, - datafile=datafile, - price_column=price_column, + datafiles=md_datafiles, fit_method=fit_method, instruments=instruments, ) + + if bt_results.trades is None or len(bt_results.trades) == 0: + print(f"No trades found for {day}") + continue - # Store results with file name as key - filename = os.path.basename(datafile) + # Store results with day name as key + filename = os.path.basename(day) all_results[filename] = { "trades": bt_results.trades.copy(), "outstanding_positions": bt_results.outstanding_positions.copy(), @@ -230,12 +197,20 @@ def main() -> None: # Store results in database if args.result_db.upper() != "NONE": - store_results_in_database(args.result_db, datafile, bt_results) + bt_results.calculate_returns( + { + filename: { + "trades": bt_results.trades.copy(), + "outstanding_positions": bt_results.outstanding_positions.copy(), + } + } + ) + bt_results.store_results_in_database(db_path=args.result_db, day=day) print(f"Successfully processed {filename}") except Exception as err: - print(f"Error processing {datafile}: {str(err)}") + print(f"Error processing {day}: {str(err)}") import traceback traceback.print_exc() diff --git a/research/research_tools.py b/research/research_tools.py new file mode 100644 index 0000000..a85aef3 --- /dev/null +++ b/research/research_tools.py @@ -0,0 +1,94 @@ +import glob +import os +from typing import Dict, List, Optional + +import pandas as pd +from pt_trading.fit_method import PairsTradingFitMethod + + +def resolve_datafiles(config: Dict, cli_datafiles: Optional[str] = None) -> List[str]: + """ + Resolve the list of data files to process. + CLI datafiles take priority over config datafiles. + Supports wildcards in config but not in CLI. + """ + if cli_datafiles: + # CLI override - comma-separated list, no wildcards + datafiles = [f.strip() for f in cli_datafiles.split(",")] + # Make paths absolute relative to data directory + data_dir = config.get("data_directory", "./data") + resolved_files = [] + for df in datafiles: + if not os.path.isabs(df): + df = os.path.join(data_dir, df) + resolved_files.append(df) + return resolved_files + + # Use config datafiles with wildcard support + config_datafiles = config.get("datafiles", []) + data_dir = config.get("data_directory", "./data") + resolved_files = [] + + for pattern in config_datafiles: + if "*" in pattern or "?" in pattern: + # Handle wildcards + if not os.path.isabs(pattern): + pattern = os.path.join(data_dir, pattern) + matched_files = glob.glob(pattern) + resolved_files.extend(matched_files) + else: + # Handle explicit file path + if not os.path.isabs(pattern): + pattern = os.path.join(data_dir, pattern) + resolved_files.append(pattern) + + return sorted(list(set(resolved_files))) # Remove duplicates and sort + + +def create_pairs( + datafiles: List[str], + fit_method: PairsTradingFitMethod, + config: Dict, + instruments: List[Dict[str, str]], +) -> List: + from pt_trading.trading_pair import TradingPair + from tools.data_loader import load_market_data + + all_indexes = range(len(instruments)) + unique_index_pairs = [(i, j) for i in all_indexes for j in all_indexes if i < j] + pairs = [] + + # Update config to use the specified instruments + config_copy = config.copy() + config_copy["instruments"] = instruments + + market_data_df = pd.DataFrame() + extra_minutes = 0 + if "execution_price" in config_copy: + extra_minutes = config_copy["execution_price"]["shift"] + + for datafile in datafiles: + md_df = load_market_data( + datafile=datafile, + instruments=instruments, + db_table_name=config_copy["market_data_loading"][instruments[0]["instrument_type"]]["db_table_name"], + trading_hours=config_copy["trading_hours"], + extra_minutes=extra_minutes, + ) + market_data_df = pd.concat([market_data_df, md_df]) + + if len(set(market_data_df["symbol"])) != 2: # both symbols must be present for a pair + print(f"WARNING: insufficient data in files: {datafiles}") + return [] + + for a_index, b_index in unique_index_pairs: + symbol_a=instruments[a_index]["symbol"] + symbol_b=instruments[b_index]["symbol"] + pair = fit_method.create_trading_pair( + config=config_copy, + market_data=market_data_df, + symbol_a=symbol_a, + symbol_b=symbol_b, + ) + pairs.append(pair) + return pairs diff --git a/researchresults/equity/20250714_003409.equity_results.db b/researchresults/equity/20250714_003409.equity_results.db deleted file mode 100644 index 7c1c179..0000000 Binary files a/researchresults/equity/20250714_003409.equity_results.db and /dev/null differ diff --git a/scripts/load_crypto_1min.sh b/scripts/load_crypto_1min.sh index 44ef3a2..fd4f058 100755 --- a/scripts/load_crypto_1min.sh +++ b/scripts/load_crypto_1min.sh @@ -16,7 +16,12 @@ cd $(realpath $(dirname $0))/.. mkdir -p ./data/crypto pushd ./data/crypto -Cmd="rsync -ahvv cvtt@hs01.cvtt.vpn:/works/cvtt/md_archive/crypto/sim/*.gz ./" +Files=$1 +if [ -z "$Files" ]; then + Files="*.gz" +fi + +Cmd="rsync -ahvv cvtt@hs01.cvtt.vpn:/works/cvtt/md_archive/crypto/sim/${Files} ./" echo $Cmd eval $Cmd # ------------------------------------- diff --git a/scripts/load_equity_1min.sh b/scripts/load_equity_1min.sh index 23270cb..33d315f 100755 --- a/scripts/load_equity_1min.sh +++ b/scripts/load_equity_1min.sh @@ -26,8 +26,12 @@ for srcfname in $(ls *.db.gz); do tgtfile=${dt}.mktdata.ohlcv.db echo "${srcfname} -> ${tgtfile}" - gunzip -c $srcfname > temp.db - rm -f ${tgtfile} && sqlite3 temp.db ".dump md_1min_bars" | sqlite3 ${tgtfile} && rm ${srcfname} + Cmd="gunzip -c $srcfname > temp.db && rm $srcfname" + echo ${Cmd} + eval ${Cmd} + Cmd="rm -f ${tgtfile} && sqlite3 temp.db '.dump md_1min_bars' | sqlite3 ${tgtfile}" + echo ${Cmd} + eval ${Cmd} done rm temp.db popd diff --git a/strategy/pair_strategy.py b/strategy/pair_strategy.py index 7407115..a7f7604 100644 --- a/strategy/pair_strategy.py +++ b/strategy/pair_strategy.py @@ -20,12 +20,9 @@ from pt_trading.fit_methods import PairsTradingFitMethod from pt_trading.trading_pair import TradingPair - - def run_strategy( config: Dict, datafile: str, - price_column: str, fit_method: PairsTradingFitMethod, instruments: List[str], ) -> BacktestResult: @@ -44,14 +41,20 @@ def run_strategy( config_copy = config.copy() config_copy["instruments"] = instruments - market_data_df = load_market_data(datafile, config=config_copy) + market_data_df = load_market_data( + datafile=datafile, + exchange_id=config_copy["exchange_id"], + instruments=config_copy["instruments"], + instrument_id_pfx=config_copy["instrument_id_pfx"], + db_table_name=config_copy["db_table_name"], + trading_hours=config_copy["trading_hours"], + ) for a_index, b_index in unique_index_pairs: - pair = TradingPair( + pair = fit_method.create_trading_pair( market_data=market_data_df, symbol_a=instruments[a_index], symbol_b=instruments[b_index], - price_column=price_column, ) pairs.append(pair) return pairs @@ -156,7 +159,6 @@ def main() -> None: ) # Process each data file - price_column = config["price_column"] for datafile in datafiles: print(f"\n====== Processing {os.path.basename(datafile)} ======") @@ -182,7 +184,6 @@ def main() -> None: bt_results = run_strategy( config=config, datafile=datafile, - price_column=price_column, fit_method=fit_method, instruments=instruments, )