timezone converting added

src/pt_backtest_slide.py

@@ -16,8 +16,44 @@ UNSET_INT: int = sys.maxsize
 
 # ------------------------ Configuration ------------------------
 # Default configuration
-CONFIG: Dict = {
+CRYPTO_CONFIG: Dict = {
-    "exchange_id": "ALPACA",
+    # --- Data retrieval
+    "data_directory": "./data/crypto",
+    "datafiles": [
+        "20250519.mktdata.ohlcv.db",
+    ],
+    "db_table_name": "bnbspot_ohlcv_1min",
+ 
+    # ----- Instruments
+    "exchange_id": "BNBSPOT",
+    "instrument_id_pfx": "PAIR-",
+
+    "instruments": [
+        "BTC-USDT",
+        "ETH-USDT",
+        "LTC-USDT",
+    ],    
+    
+    "trading_hours": {
+        "begin_session": "00:00:00", 
+        "end_session": "23:59:00", 
+        "timezone": "UTC"
+    },
+
+    # ----- Model Settings
+    "price_column": "close",
+    "min_required_points": 30,
+    "zero_threshold": 1e-10,
+    "equilibrium_threshold_open": 5.0,
+    "equilibrium_threshold_close": 1.0,
+    "training_minutes": 120,
+
+    # ----- Validation
+    "funding_per_pair": 2000.0,  # USD
+}
+# ========================== EQUITIES
+EQT_CONFIG: Dict = {
+    # --- Data retrieval
     "data_directory": "./data/equity",
     "datafiles": [
         "20250508.alpaca_sim_md.db",
@@ -30,6 +66,12 @@ CONFIG: Dict = {
         # "20250519.alpaca_sim_md.db",
         # "20250520.alpaca_sim_md.db"
     ],
+    "db_table_name": "md_1min_bars",
+
+     # ----- Instruments
+   "exchange_id": "ALPACA",
+    "instrument_id_pfx": "STOCK-",
+
     "instruments": [
         "COIN",
         "GBTC",
@@ -37,7 +79,14 @@ CONFIG: Dict = {
         "MSTR",
         "PYPL",
     ],
-    "trading_hours": {"begin_session": "14:30:00", "end_session": "21:00:00"},
+    
+    "trading_hours": {
+        "begin_session": "9:30:00", 
+        "end_session": "16:00:00", 
+        "timezone": "America/New_York"
+    },
+    
+    # ----- Model Settings
     "price_column": "close",
     "min_required_points": 30,
     "zero_threshold": 1e-10,
@@ -45,33 +94,18 @@ CONFIG: Dict = {
     "equilibrium_threshold_close": 1.0,
     "training_minutes": 120,
 
+    # ----- Validation
     "funding_per_pair": 2000.0,
 }
 
-# ====== later ===================
+# ==========================================================================
-# # Try to load configuration from file, fall back to defaults if not found
+CONFIG = EQT_CONFIG
-# CONFIG_FILE = "config.json"
-# try:
-#     with open(CONFIG_FILE, "r") as f:
-#         user_config = json.load(f)
-#         CONFIG.update(user_config)
-#     print(f"Loaded configuration from {CONFIG_FILE}")
-# except (FileNotFoundError, json.JSONDecodeError) as e:
-#     print(f"Using default configuration. Error loading {CONFIG_FILE}: {str(e)}")
-#     # Create a default config file if it doesn't exist
-#     try:
-#         with open(CONFIG_FILE, "w") as f:
-#             json.dump(CONFIG, f, indent=4)
-#         print(f"Created default configuration file: {CONFIG_FILE}")
-#     except Exception as e:
-#         print(f"Warning: Could not create default config file: {str(e)}")
-# ------------------------ Settings ------------------------
-
 TRADES = {}
 TOTAL_UNREALIZED_PNL = 0.0  # Global variable to track total unrealized PnL
 TOTAL_REALIZED_PNL = 0.0    # Global variable to track total realized PnL
 OUTSTANDING_POSITIONS = []  # Global list to track outstanding positions with share quantities
-class Pair:
+
+class TradingPair:
     symbol_a_: str
     symbol_b_: str
     price_column_: str
@@ -81,17 +115,35 @@ class Pair:
         self.symbol_b_ = symbol_b
         self.price_column_ = price_column
 
-
     def colnames(self) -> List[str]:
         return [f"{self.price_column_}_{self.symbol_a_}", f"{self.price_column_}_{self.symbol_b_}"]
 
     def __repr__(self) ->str:
         return f"{self.symbol_a_} & {self.symbol_b_}"
 
+def convert_time_to_UTC(value: str, timezone: str):
+
+    from zoneinfo import ZoneInfo
+    from datetime import datetime
+
+    # Parse it to naive datetime object
+    local_dt = datetime.strptime(value, '%Y-%m-%d %H:%M:%S')
+
+    zinfo = ZoneInfo(timezone)
+    result = local_dt.replace(tzinfo=zinfo)
+    
+    result = result.astimezone(ZoneInfo('UTC'))
+    result = result.strftime('%Y-%m-%d %H:%M:%S')
+
+    return result
+
+
+    pass
+    
 def load_market_data(datafile: str) -> pd.DataFrame:
     from tools.data_loader import load_sqlite_to_dataframe
 
-    instrument_ids = ["\"" + "STOCK-" + instrument + "\"" for instrument in CONFIG["instruments"]]
+    instrument_ids = ["\"" + CONFIG["instrument_id_pfx"] + instrument + "\"" for instrument in CONFIG["instruments"]]
     exchange_id = CONFIG["exchange_id"]
 
     query = "select tstamp"
@@ -105,7 +157,7 @@ def load_market_data(datafile: str) -> pd.DataFrame:
     query += ", num_trades"
     query += ", vwap"
 
-    query += " from md_1min_bars"
+    query += f" from {CONFIG['db_table_name']}"
     query += f" where exchange_id ='{exchange_id}'"
     query += f" and instrument_id in ({','.join(instrument_ids)})"
 
@@ -113,8 +165,12 @@ def load_market_data(datafile: str) -> pd.DataFrame:
 
     # Trading Hours
     date_str = df["tstamp"][0][0:10]
-    start_time = f"{date_str} {CONFIG['trading_hours']['begin_session']}"
+    trading_hours = CONFIG['trading_hours']
-    end_time = f"{date_str} {CONFIG['trading_hours']['end_session']}"
+    start_time = f"{date_str} {trading_hours['begin_session']}"
+    end_time = f"{date_str} {trading_hours['end_session']}"
+    
+    start_time = convert_time_to_UTC(start_time, trading_hours["timezone"])
+    end_time = convert_time_to_UTC(end_time, trading_hours["timezone"])
 
     # Perform boolean selection
     df = df[(df["tstamp"] >= start_time) & (df["tstamp"] <= end_time)]
@@ -149,7 +205,7 @@ def transform_dataframe(df: pd.DataFrame, price_column: str):
 
     return result_df
 
-def get_datasets(df: pd.DataFrame, training_minutes: int, pair: Pair) -> Tuple[pd.DataFrame, pd.DataFrame]:
+def get_datasets(df: pd.DataFrame, training_minutes: int, pair: TradingPair) -> Tuple[pd.DataFrame, pd.DataFrame]:
     # Training dataset
     colname_a, colname_b = pair.colnames()
     df = df[["tstamp", colname_a, colname_b]]
@@ -164,7 +220,7 @@ def get_datasets(df: pd.DataFrame, training_minutes: int, pair: Pair) -> Tuple[p
 
     return (training_df, testing_df)
 
-def fit_VECM(training_pair_df, pair: Pair):
+def fit_VECM(training_pair_df, pair: TradingPair):
     vecm_model = VECM(training_pair_df[pair.colnames()].reset_index(drop=True), coint_rank=1)
     vecm_fit = vecm_model.fit()
 
@@ -174,7 +230,7 @@ def fit_VECM(training_pair_df, pair: Pair):
 
     return vecm_fit
 
-def create_trading_signals(vecm_fit, testing_pair_df, pair: Pair) -> pd.DataFrame:
+def create_trading_signals(vecm_fit, testing_pair_df, pair: TradingPair) -> pd.DataFrame:
     result_columns = [
         "time",
         "action",
@@ -401,7 +457,7 @@ def create_trading_signals(vecm_fit, testing_pair_df, pair: Pair) -> pd.DataFram
         columns=result_columns,
     )
 
-def run_single_pair(market_data: pd.DataFrame, price_column:str, pair: Pair) -> Optional[pd.DataFrame]:
+def run_single_pair(market_data: pd.DataFrame, price_column:str, pair: TradingPair) -> Optional[pd.DataFrame]:
     colname_a = f"{price_column}_{pair.symbol_a_}"
     colname_b = f"{price_column}_{pair.symbol_b_}"
     training_pair_df, testing_pair_df = get_datasets(df=market_data, training_minutes=CONFIG["training_minutes"], pair=pair)
@@ -447,16 +503,15 @@ def run_single_pair(market_data: pd.DataFrame, price_column:str, pair: Pair) ->
 
     return pair_trades
 
-def add_trade(pair, symbol, action, price):
+def add_trade(pair_nm, symbol, action, price):
-    # Ensure we always use clean names without STOCK- prefix
+    pair_nm = str(pair_nm)
-    pair = str(pair).replace("STOCK-", "")
-    symbol = symbol.replace("STOCK-", "")
 
-    if pair not in TRADES:
+
-        TRADES[pair] = {symbol: []}
+    if pair_nm not in TRADES:
-    if symbol not in TRADES[pair]:
+        TRADES[pair_nm] = {symbol: []}
-        TRADES[pair][symbol] = []
+    if symbol not in TRADES[pair_nm]:
-    TRADES[pair][symbol].append((action, price))
+        TRADES[pair_nm][symbol] = []
+    TRADES[pair_nm][symbol].append((action, price))
 
 def collect_single_day_results(result):
     if result is None:
@@ -469,7 +524,7 @@ def collect_single_day_results(result):
         action = row.action
         symbol = row.symbol
         price = row.price
-        add_trade(pair=row.pair, action=action, symbol=symbol, price=price)
+        add_trade(pair_nm=row.pair, action=action, symbol=symbol, price=price)
 
 def print_single_day_results(result):
     for pair, symbols in TRADES.items():
@@ -574,10 +629,8 @@ def run_pairs(summaries_df: pd.DataFrame, price_column: str) -> None:
         colname_b = stock_price_columns[b_index]
 
         symbol_a = colname_a[len(f"{price_column}-") :]
-        symbol_b = colname_b[len(f"{price_column}-") :].replace(
+        symbol_b = colname_b[len(f"{price_column}-") :] 
-            "STOCK-", ""
+        pair = TradingPair(symbol_a, symbol_b, price_column)
-        )
-        pair = Pair(symbol_a, symbol_b, price_column)
 
         single_pair_trades = run_single_pair(market_data=result_df, price_column=price_column, pair=pair)
         if len(single_pair_trades) > 0: