progress

src/pt_backtest.py

@@ -8,6 +8,7 @@ import numpy as np
 # ============= statsmodels ===================
 from statsmodels.tsa.vector_ar.vecm import VECM
 
+from backtest_configs import CRYPTO_CONFIG
 from tools.data_loader import load_market_data, transform_dataframe
 from tools.trading_pair import TradingPair
 from results import BacktestResult
@@ -16,90 +17,11 @@ NanoPerMin = 1e9
 UNSET_FLOAT: float = sys.float_info.max
 UNSET_INT: int = sys.maxsize
 
-# ------------------------ Configuration ------------------------
-# Default configuration
-CRYPTO_CONFIG: Dict = {
-    "security_type": "CRYPTO",
-    # --- Data retrieval
-    "data_directory": "./data/crypto",
-    "datafiles": [
-        "20250519.mktdata.ohlcv.db",
-        # "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,
 
-    "disequilibrium_open_trshld": 2,
-    "disequilibrium_close_trshld": 0.5,
+# # ==========================================================================
 
-    "training_minutes": 120,
-    # ----- Validation
-    "funding_per_pair": 2000.0,  # USD
-}
-# ========================== EQUITIES
-EQT_CONFIG: Dict = {
-    # --- Data retrieval
-    "security_type": "EQUITY",
-    "data_directory": "./data/equity",
-    "datafiles": [
-        "20250508.alpaca_sim_md.db",
-        # "20250509.alpaca_sim_md.db",
-        # "20250512.alpaca_sim_md.db",
-        # "20250513.alpaca_sim_md.db",
-        # "20250514.alpaca_sim_md.db",
-        # "20250515.alpaca_sim_md.db",
-        # "20250516.alpaca_sim_md.db",
-        # "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",
-        "HOOD",
-        "MSTR",
-        "PYPL",
-    ],
-    "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,
-    "disequilibrium_open_trshld": 2.0,
-    "disequilibrium_close_trshld": 0.5,
-    "training_minutes": 120,
-    # ----- Validation
-    "funding_per_pair": 2000.0,
-}
-
-
-# ==========================================================================
-
-# CONFIG = CRYPTO_CONFIG
-CONFIG = EQT_CONFIG
+CONFIG = CRYPTO_CONFIG
+# CONFIG = EQT_CONFIG
 
 BacktestResults = BacktestResult(config=CONFIG)
 
@@ -146,8 +68,8 @@ def create_trading_signals(pair: TradingPair) -> pd.DataFrame:
     open_row_index = None
     initial_abs_term = None
 
-    open_threshold = CONFIG["disequilibrium_open_trshld"]
-    close_threshold = CONFIG["disequilibrium_close_trshld"]
+    open_threshold = CONFIG["dis-equilibrium_open_trshld"]
+    close_threshold = CONFIG["dis-equilibrium_close_trshld"]
     for row_idx in range(len(pair_result_df)):
         curr_disequilibrium = pair_result_df["scaled_disequilibrium"][row_idx]
 
@@ -299,7 +221,10 @@ def run_single_pair(
         market_data=market_data, training_minutes=CONFIG["training_minutes"]
     )
     try:
-        pair.train_pair()
+        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
@@ -387,8 +312,6 @@ if __name__ == "__main__":
 
     # BacktestResults.print_results_summary(all_results)
     BacktestResults.calculate_returns(all_results)
-
     # Print grand totals
     BacktestResults.print_grand_totals()
-
     BacktestResults.print_outstanding_positions()