fixes

configuration/crypto_zscore.cfg

@@ -27,7 +27,7 @@
     # "close_outstanding_positions": false,
     "trading_hours": {
         "begin_session": "9:30:00",
-        "end_session": "19:00:00",
+        "end_session": "22:30:00",
         "timezone": "America/New_York"
     }
 }

lib/pt_trading/results.py

@@ -570,9 +570,12 @@ class BacktestResult:
                     symbol_trades =  [trade for trade in trades if trade["symbol"] == symbol]
 
                     # Calculate returns for all trade combinations
-                    for i in range(len(symbol_trades) - 1):
+                    for idx in range(0, len(symbol_trades), 2):
-                        trade1 = trades[i]
+                        trade1 = trades[idx]
-                        trade2 = trades[i + 1]
+                        trade2 = trades[idx + 1]
+                        
+                        assert trade1["timestamp"] < trade2["timestamp"], f"Trade 1: {trade1['timestamp']} is not less than Trade 2: {trade2['timestamp']}"
+                        assert trade1["action"] == "OPEN" and trade2["action"] == "CLOSE", f"Trade 1: {trade1['action']} and Trade 2: {trade2['action']} are the same"
                         
                         # Calculate return based on action combination
                         trade_return = 0
@@ -589,6 +592,8 @@ class BacktestResult:
                         pair_trades.append(
                             (
                                 symbol,
+                                trade1["timestamp"],
+                                trade2["timestamp"],
                                 trade1["side"],
                                 trade1["price"],
                                 trade2["side"],
@@ -596,7 +601,7 @@ class BacktestResult:
                                 trade_return,
                                 trade1["scaled_disequilibrium"],
                                 trade2["scaled_disequilibrium"],
-                                i + 1,  # Trade sequence number
+                                f"{idx + 1}",  # Trade sequence number
                             )
                         )
                     
@@ -607,6 +612,8 @@ class BacktestResult:
                     print(f"  {pair}:")
                     for (
                         symbol,
+                        trade1["timestamp"],
+                        trade2["timestamp"],
                         trade1["side"],
                         trade1["price"],
                         trade2["side"],
@@ -625,7 +632,7 @@ class BacktestResult:
                             f" Close Dis-eq: {trade2["scaled_disequilibrium"]:.2f}"
 
                         print(
-                            f"    {symbol} (Trade #{trade_num}):"
+                            f" {trade2['timestamp'].time()}  {symbol} (Trade #{trade_num}):"
                             f" {trade1["side"]} @ ${trade1["price"]:.2f},"
                             f" {trade2["side"]} @ ${trade2["price"]:.2f},"
                             f" Return: {trade_return:.2f}%{disequil_info}"
@@ -633,6 +640,7 @@ class BacktestResult:
                     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:
                 print(f"  Day Total Return: {day_return:.2f}%")

lib/pt_trading/rolling_window_fit.py

@@ -10,15 +10,17 @@ 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__()
 
@@ -33,7 +35,8 @@ class RollingFit(PairsTradingFitMethod):
 
         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({
+        pair.user_data_["trades"] = pd.DataFrame(columns=self.TRADES_COLUMNS).astype(
+            {
                 "time": "datetime64[ns]",
                 "symbol": "string",
                 "side": "string",
@@ -41,8 +44,9 @@ class RollingFit(PairsTradingFitMethod):
                 "price": "float64",
                 "disequilibrium": "float64",
                 "scaled_disequilibrium": "float64",
-            "pair": "object"
+                "pair": "object",
-        })
+            }
+        )
 
         training_minutes = config["training_minutes"]
         curr_predicted_row_idx = 0
@@ -66,7 +70,9 @@ class RollingFit(PairsTradingFitMethod):
                 # ================================ PREDICTION ================================
                 self.pair_predict_result_ = pair.predict()
             except Exception as e:
-                raise RuntimeError(f"{pair}: TrainingPrediction failed: {str(e)}") from e
+                raise RuntimeError(
+                    f"{pair}: TrainingPrediction failed: {str(e)}"
+                ) from e
 
                 # break
 
@@ -93,7 +99,13 @@ class RollingFit(PairsTradingFitMethod):
             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]:
+            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
@@ -121,7 +133,9 @@ class RollingFit(PairsTradingFitMethod):
                         pair, row=pred_row, close_threshold=close_threshold
                     )
                     if close_trades is not None:
-                        close_trades["status"] = pair.user_data_["stop_close_state"].name
+                        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"]
@@ -129,15 +143,11 @@ class RollingFit(PairsTradingFitMethod):
 
         # Outstanding positions
         if pair.user_data_["state"] == PairState.OPEN:
-            print(
+            print(f"{pair}:  *** Position is NOT CLOSED. ***")
-                f"{pair}:  *** Position is NOT CLOSED. ***"
-            )
             # outstanding positions
             if config["close_outstanding_positions"]:
                 close_position_trades = self._get_close_trades(
-                    pair=pair,
+                    pair=pair, row=pred_row, close_threshold=close_threshold
-                    row=pred_row,
-                    close_threshold=close_threshold
                 )
                 if close_position_trades is not None:
                     close_position_trades["status"] = PairState.CLOSE_POSITION.name
@@ -223,15 +233,17 @@ class RollingFit(PairsTradingFitMethod):
             columns=self.TRADES_COLUMNS,
         )
         # Ensure consistent dtypes
-        return df.astype({
+        return df.astype(
+            {
                 "time": "datetime64[ns]",
                 "action": "string",
                 "symbol": "string",
                 "price": "float64",
                 "disequilibrium": "float64",
                 "scaled_disequilibrium": "float64",
-            "pair": "object"
+                "pair": "object",
-        })
+            }
+        )
 
     def _get_close_trades(
         self, pair: TradingPair, row: pd.Series, close_threshold: float
@@ -277,16 +289,17 @@ class RollingFit(PairsTradingFitMethod):
             columns=self.TRADES_COLUMNS,
         )
         # Ensure consistent dtypes
-        return df.astype({
+        return df.astype(
+            {
                 "time": "datetime64[ns]",
                 "action": "string",
                 "symbol": "string",
                 "price": "float64",
                 "disequilibrium": "float64",
                 "scaled_disequilibrium": "float64",
-            "pair": "object"
+                "pair": "object",
-        })
+            }
+        )
 
     def reset(self) -> None:
         curr_training_start_idx = 0
-