.

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

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").
+- `stat_model_price`: 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.
 - `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).

configuration/crypto.cfg

@@ -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"
-    }
-    
-}

configuration/equity_single.cfg

@@ -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
-    
-}

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",
+    }
+}

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",
+    }
+}

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

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],
+    datafiles: List[Tuple[str, str]],
-    instruments: List[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)
+        datafiles_str = ", ".join([f"{datafile}" for _, datafile in datafiles])
-        instruments_str = ", ".join(instruments)
+        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(
+def convert_timestamp(timestamp: Any) -> Optional[datetime]:
-    db_path: str, datafile: str, bt_result: "BacktestResult"
+    """Convert pandas Timestamp to Python datetime object for SQLite compatibility."""
-) -> None:
+    if timestamp is None:
-    """
+        return None
-    Store backtest results in the SQLite database.
+    if isinstance(timestamp, pd.Timestamp):
-    """
+        return timestamp.to_pydatetime()
-    if db_path.upper() == "NONE":
+    elif isinstance(timestamp, datetime):
-        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()
         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
+                symbol_b = trades[idx + 1]["symbol"]
-            for pair, symbols in data["trades"].items():
+                trade_b_1 = trades[idx + 1]
-                pair_return = 0
+                trade_b_2 = trades[idx + 3]
-                pair_trades = []
 
-                # Calculate individual symbol returns in the pair
+                symbol_return = 0
-                for symbol, trades in symbols.items():
+                assert (
-                    if len(trades) == 0:
+                    trade_a_1["timestamp"] < trade_a_2["timestamp"]
-                        continue
+                ), f"Trade 1: {trade_a_1['timestamp']} is not less than Trade 2: {trade_a_2['timestamp']}"
-                    
+                assert (
-                    symbol_return = 0
+                    trade_a_1["action"] == "OPEN" and trade_a_2["action"] == "CLOSE"
-                    symbol_trades = []
+                ), f"Trade 1: {trade_a_1['action']} and Trade 2: {trade_a_2['action']} are the same"
-                    
-                    # 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
 
-                # Print pair returns with disequilibrium information
+                # Calculate return based on action combination
-                if pair_trades:
+                trade_return = 0
-                    print(f"  {pair}:")
+                symbol_a_return = _symbol_return(trade_a_1["side"], trade_a_1["price"], trade_a_2["side"], trade_a_2["price"])
-                    for (
+                symbol_b_return = _symbol_return(trade_b_1["side"], trade_b_1["price"], trade_b_2["side"], trade_b_2["price"])
-                        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}"
 
-                        print(
+                pair_return = symbol_a_return + symbol_b_return
-                            f"    {symbol} (Trade #{trade_num}): {action1} @ ${price1:.2f}, {action2} @ ${price2:.2f}, Return: {trade_return:.2f}%{disequil_info}"
+
-                        )
+                self.pairs_trades_[pair].append(
-                    print(f"    Pair Total Return: {pair_return:.2f}%")
+                    {
-                    day_return += pair_return
+                        "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()
+

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

lib/pt_trading/sliding_fit.py

@@ -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

lib/pt_trading/static_fit.py

@@ -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
-
-

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.stat_model_price_ = config["stat_model_price"]
-        self.set_market_data(market_data)
         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_["tstamp"] = pd.to_datetime(self.market_data_["tstamp"])
-        self.market_data_ = self.market_data_.sort_values('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()
+        start_time = (
-        mask = self.market_data_['tstamp'].dt.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()
+        end_time = (
-        mask = self.market_data_['tstamp'].dt.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.stat_model_price_}_{self.symbol_a_}",
-            f"{self.price_column_}_{self.symbol_b_}",
+            f"{self.stat_model_price_}_{self.symbol_b_}",
         ]
 
-    def fit_VECM(self) -> None:
+    def exec_prices_colnames(self) -> List[str]:
-        assert self.training_df_ is not None
+        return [
-        vecm_df = self.training_df_[self.colnames()].reset_index(drop=True)
+            f"exec_price_{self.symbol_a_}",
-        vecm_model = VECM(vecm_df, coint_rank=1)
+            f"exec_price_{self.symbol_b_}",
-        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 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], 
+                    [existing_trades, trades], ignore_index=True, copy=False
-                    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()
+        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
         )
 
+    def cointegration_check(self) -> Optional[pd.DataFrame]:
+        print(f"***{self}*** STARTING....")
+        config = self.config_
 
-        predicted_df = pd.merge(
+        curr_training_start_idx = 0
-            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"] = (
+        COINTEGRATION_DATA_COLUMNS = {
-            predicted_df[self.colnames()] @ self.vecm_fit_.beta
+            "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"] = (
+        training_minutes = config["training_minutes"]
-            abs(predicted_df["disequilibrium"] - self.training_mu_)
+        while True:
-            / self.training_std_
+            print(curr_training_start_idx, end="\r")
-        )
+            self.get_datasets(
-        
+                training_minutes=training_minutes,
-        # print("*** PREDICTED DF")
+                training_start_index=curr_training_start_idx,
-        # print(predicted_df)
+                testing_size=1,
-        # print("*" * 80) 
+            )
-        # print("*** SELF.PREDICTED_DF")
-        # print(self.predicted_df_)
-        # print("*" * 80) 
 
-        predicted_df = predicted_df.reset_index(drop=True)
+            if len(self.training_df_) < training_minutes:
-        if self.predicted_df_ is None:
+                print(
-            self.predicted_df_ = predicted_df
+                    f"{self}: current offset={curr_training_start_idx}"
-        else:
+                    f" * Training data length={len(self.training_df_)} < {training_minutes}"
-            self.predicted_df_ = pd.concat([self.predicted_df_, predicted_df], ignore_index=True)
+                    " * Not enough training data. Completing the job."
-        # Reset index to ensure proper indexing 
+                )
-        self.predicted_df_ = self.predicted_df_.reset_index(drop=True)
+                break
-        return self.predicted_df_
+            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]: ...

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,
+        )

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,
+        )

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:
+def load_market_data(
-    from tools.data_loader import load_sqlite_to_dataframe
+    datafile: str,
+    instruments: List[Dict[str, str]],
+    db_table_name: str,
+    trading_hours: Dict = {},
+    extra_minutes: int = 0,
+) -> pd.DataFrame:
 
-    instrument_ids = [
+    insts = [
-        '"' + config["instrument_id_pfx"] + instrument + '"'
+        '"' + instrument["instrument_id_pfx"] + instrument["symbol"] + '"'
-        for instrument in config["instruments"]
+        for instrument in instruments
     ]
-    security_type = config["security_type"]
+    instrument_ids = list(set(insts))
-    exchange_id = config["exchange_id"]
+    exchange_ids = list(
+        set(['"' + instrument["exchange_id"] + '"' for instrument in instruments])
+    )
 
     query = "select"
-    if security_type == "CRYPTO":
+    query += " tstamp"
-        query += " strftime('%Y-%m-%d %H:%M:%S', tstamp_ns/1000000000, 'unixepoch') as tstamp"
+    query += ", tstamp_ns as time_ns"
-        query += ", tstamp as time_ns"
-    else:
-        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" from {db_table_name}"
-    query += f" where exchange_id ='{exchange_id}'"
+    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]
+    if len(df) > 0 and len(trading_hours) > 0:
-    trading_hours = config["trading_hours"]
+        date_str = df["tstamp"][0][0:10]
 
-    start_time = convert_time_to_UTC(
+        start_time = convert_time_to_UTC(
-        f"{date_str} {trading_hours['begin_session']}", trading_hours["timezone"]
+            f"{date_str} {trading_hours['begin_session']}", trading_hours["timezone"]
-    )
+        )
-    end_time = convert_time_to_UTC(
+        end_time = convert_time_to_UTC(
-        f"{date_str} {trading_hours['end_session']}", trading_hours["timezone"]
+            f"{date_str} {trading_hours['end_session']}", trading_hours["timezone"], extra_minutes=extra_minutes # to get execution price
-    )
+        )
 
-    # Perform boolean selection
+        # Perform boolean selection
-    df = df[(df["tstamp"] >= start_time) & (df["tstamp"] <= end_time)]
+        df = df[(df["tstamp"] >= start_time) & (df["tstamp"] <= end_time)]
-    df["tstamp"] = pd.to_datetime(df["tstamp"])
+        df["tstamp"] = pd.to_datetime(df["tstamp"])
 
     return cast(pd.DataFrame, df)
 
 
-def get_available_instruments_from_db(datafile: str, config: Dict) -> List[str]:
+# 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.
+#     Auto-detect available instruments from the database by querying distinct instrument_id values.
-    Returns instruments without the configured prefix.
+#     Returns instruments without the configured prefix.
-    """
+#     """
-    try:
+#     try:
-        conn = sqlite3.connect(datafile)
+#         conn = sqlite3.connect(datafile)
 
-        # Build exclusion list with full instrument_ids
+#         # Build exclusion list with full instrument_ids
-        exclude_instruments = config.get("exclude_instruments", [])
+#         exclude_instruments = config.get("exclude_instruments", [])
-        prefix = config.get("instrument_id_pfx", "")
+#         prefix = config.get("instrument_id_pfx", "")
-        exclude_instrument_ids = [f"{prefix}{inst}" for inst in exclude_instruments]
+#         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()
 
-        # Remove the configured prefix to get instrument symbols
+#         # Query to get distinct instrument_ids
-        instruments = []
+#         query = f"""
-        for instrument_id in instrument_ids:
+#         SELECT DISTINCT instrument_id
-            if instrument_id.startswith(prefix):
+#         FROM {config['db_table_name']}
-                symbol = instrument_id[len(prefix) :]
+#         WHERE exchange_id = ?
-                instruments.append(symbol)
+#         """
-            else:
-                instruments.append(instrument_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:
+#         # Remove the configured prefix to get instrument symbols
-        print(f"Error auto-detecting instruments from {datafile}: {str(e)}")
+#         instruments = []
-        return []
+#         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__":

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

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()

research/notebooks/__DEPRECATED__/pt_static.ipynb

@@ -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
-}

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]:
+def resolve_datafiles(
-    """
+    config: Dict, date_pattern: str, instruments: List[Dict[str, str]]
-    Resolve the list of data files to process.
+) -> List[Tuple[DayT, DataFileNameT]]:
-    CLI datafiles take priority over config datafiles.
+    resolved_files: List[Tuple[DayT, DataFileNameT]] = []
-    Supports wildcards in config but not in CLI.
+    for inst in instruments:
-    """
+        pattern = date_pattern
-    if cli_datafiles:
+        inst_type = inst["instrument_type"]
-        # CLI override - comma-separated list, no wildcards
+        data_dir = config["market_data_loading"][inst_type]["data_directory"]
-        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)
+                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)
+                pattern = os.path.join(data_dir, f"{pattern}.mktdata.ohlcv.db")
-            resolved_files.append(pattern)
+            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,
+    datafiles: List[str],
-    price_column: 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]:
+    if not all([os.path.exists(datafile) for datafile in datafiles]):
-        nonlocal datafile
+        print(f"WARNING: data file {datafiles} does not exist")
-        all_indexes = range(len(instruments))
+        return bt_result
-        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
 
     pairs_trades = []
-    for pair in _create_pairs(config, instruments):
+
-        single_pair_trades = fit_method.run_pair(
+    pairs = create_pairs(
-            pair=pair, bt_result=bt_result
+        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,
+        required=True,
-        help="Comma-separated list of data files (overrides config). No wildcards supported.",
+        help="Date YYYYMMDD, allows * and ? wildcards",
     )
     parser.add_argument(
         "--instruments",
         type=str,
-        required=False,
+        required=True,
-        help="Comma-separated list of instrument symbols (e.g., COIN,GBTC). If not provided, auto-detects from database.",
+        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)
+    fit_method = PairsTradingFitMethod.create(config)
-    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)()
 
     # Resolve data files (CLI takes priority over config)
-    datafiles = resolve_datafiles(config, args.datafiles)
+    instruments = get_instruments(args, config)
-
+    datafiles = resolve_datafiles(config, args.date_pattern, instruments)
-    if not datafiles:
-        print("No data files found to process.")
-        return
 
+    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]] = {}
-
+    is_config_stored = False
-    # 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
-    price_column = config["price_column"]
 
-    for datafile in datafiles:
+    for day in sorted(days):
-        print(f"\n====== Processing {os.path.basename(datafile)} ======")
+        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]):
-        # Determine instruments to use
+            print(f"WARNING: insufficient data files: {md_datafiles}")
-        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...")
             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,
+                datafiles=md_datafiles,
-                price_column=price_column,
                 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
+            # Store results with day name as key
-            filename = os.path.basename(datafile)
+            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()

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

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
 # -------------------------------------

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
+    Cmd="gunzip -c $srcfname > temp.db && rm $srcfname"
-    rm -f ${tgtfile} && sqlite3 temp.db ".dump md_1min_bars" | sqlite3  ${tgtfile} && 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

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,
             )