added close position and trade session

configuration/crypto.cfg

@@ -21,6 +21,11 @@
     "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": true,
+    "trading_hours": {
+        "begin_session": "15:30:00",
+        "end_session": "20:00:00",
+        "timezone": "UTC"
+    }
     
 }

configuration/equity.cfg

@@ -21,6 +21,7 @@
     "funding_per_pair": 2000.0,
     "fit_method_class": "pt_trading.sliding_fit.SlidingFit",
     # "fit_method_class": "pt_trading.static_fit.StaticFit",
-    "exclude_instruments": ["CAN"]
+    "exclude_instruments": ["CAN"],
+    "close_outstanding_positions": false
     
 }

configuration/equity_single.cfg

@@ -21,6 +21,7 @@
     "funding_per_pair": 2000.0,
     "fit_method_class": "pt_trading.sliding_fit.SlidingFit",
     # "fit_method_class": "pt_trading.static_fit.StaticFit",
-    "exclude_instruments": ["CAN"]
+    "exclude_instruments": ["CAN"],
+    "close_outstanding_positions": false
     
 }

lib/pt_trading/fit_method.py

@@ -1,8 +1,8 @@
 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  # type: ignore[import]
 from pt_trading.results import BacktestResult
 from pt_trading.trading_pair import TradingPair
 
@@ -22,7 +22,7 @@ class PairsTradingFitMethod(ABC):
 
     @abstractmethod
     def run_pair(
-        self, config: Dict, pair: TradingPair, bt_result: BacktestResult
+        self, pair: TradingPair, bt_result: BacktestResult
     ) -> Optional[pd.DataFrame]: ...
 
     @abstractmethod
@@ -33,3 +33,4 @@ class PairState(Enum):
     INITIAL = 1
     OPEN = 2
     CLOSED = 3
+    CLOSED_POSITIONS = 4

lib/pt_trading/sliding_fit.py

@@ -16,12 +16,15 @@ NanoPerMin = 1e9
 class SlidingFit(PairsTradingFitMethod):
     def __init__(self) -> None:
         super().__init__()
-        self.curr_training_start_idx_ = 0
 
     def run_pair(
-        self, config: Dict, pair: TradingPair, bt_result: BacktestResult
+        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
@@ -39,22 +42,23 @@ class SlidingFit(PairsTradingFitMethod):
         training_minutes = config["training_minutes"]
         curr_predicted_row_idx = 0
         while True:
-            print(self.curr_training_start_idx_, end="\r")
+            print(curr_training_start_idx, end="\r")
             pair.get_datasets(
                 training_minutes=training_minutes,
-                training_start_index=self.curr_training_start_idx_,
+                training_start_index=curr_training_start_idx,
                 testing_size=1,
             )
 
             if len(pair.training_df_) < training_minutes:
                 print(
-                    f"{pair}: current offset={self.curr_training_start_idx_}"
+                    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
@@ -64,30 +68,33 @@ class SlidingFit(PairsTradingFitMethod):
                 if not is_cointegrated:
                     if pair.user_data_["state"] == PairState.OPEN:
                         print(
-                            f"{pair}  {self.curr_training_start_idx_} LOST COINTEGRATION. Consider closing positions..."
+                            f"{pair}  {curr_training_start_idx} LOST COINTEGRATION. Consider closing positions..."
                         )
                     else:
                         print(
-                            f"{pair} {self.curr_training_start_idx_} IS NOT COINTEGRATED. Moving on"
+                            f"{pair} {curr_training_start_idx} IS NOT COINTEGRATED. Moving on"
                         )
                 else:
                     print("*" * 80)
                     print(
-                        f"Pair {pair} ({self.curr_training_start_idx_}) IS COINTEGRATED"
+                        f"Pair {pair} ({curr_training_start_idx}) IS COINTEGRATED"
                     )
                     print("*" * 80)
             if not is_cointegrated:
-                self.curr_training_start_idx_ += 1
+                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
 
-            self.curr_training_start_idx_ += 1
+            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)
@@ -105,7 +112,7 @@ class SlidingFit(PairsTradingFitMethod):
         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]:
+            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
                 )
@@ -130,7 +137,18 @@ class SlidingFit(PairsTradingFitMethod):
                 f"{pair}:  *** Position is NOT CLOSED. ***"
             )
             # outstanding positions
-            # last_row_index = self.curr_training_start_idx_ + training_minutes
+            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,
@@ -284,5 +302,61 @@ class SlidingFit(PairsTradingFitMethod):
             "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:
-        self.curr_training_start_idx_ = 0
+        curr_training_start_idx = 0

lib/pt_trading/static_fit.py

@@ -14,8 +14,9 @@ NanoPerMin = 1e9
 class StaticFit(PairsTradingFitMethod):
 
     def run_pair(
-        self, config: Dict, pair: TradingPair, bt_result: BacktestResult
+        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()

lib/pt_trading/trading_pair.py

@@ -23,11 +23,17 @@ class TradingPair:
     predicted_df_: Optional[pd.DataFrame]
 
     def __init__(
-        self, 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, price_column: str
     ):
         self.symbol_a_ = symbol_a
         self.symbol_b_ = symbol_b
         self.price_column_ = price_column
+        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.market_data_ = pd.DataFrame(
             self._transform_dataframe(market_data)[["tstamp"] + self.colnames()]
         )
@@ -36,9 +42,19 @@ class TradingPair:
         self.market_data_['tstamp'] = pd.to_datetime(self.market_data_['tstamp'])
         self.market_data_ = self.market_data_.sort_values('tstamp')
 
+    def get_begin_index(self) -> int:
+        if "trading_hours" not in self.config_:
+            return 0
+        start_time = pd.to_datetime(self.config_["trading_hours"]["begin_session"]).time()
+        mask = self.market_data_['tstamp'].dt.time >= start_time
+        return int(self.market_data_.index[mask].min())
 
-        self.user_data_ = {}
+    def get_end_index(self) -> int:
-        self.predicted_df_ = None
+        if "trading_hours" not in self.config_:
+            return 0
+        end_time = pd.to_datetime(self.config_["trading_hours"]["end_session"]).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

research/pt_backtest.py

@@ -84,6 +84,7 @@ def run_backtest(
 
         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],
@@ -95,7 +96,7 @@ def run_backtest(
     pairs_trades = []
     for pair in _create_pairs(config, instruments):
         single_pair_trades = fit_method.run_pair(
-            pair=pair, config=config, bt_result=bt_result
+            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)