notebook progress

lib/pt_trading/fit_method.py

@@ -18,6 +18,7 @@ class PairsTradingFitMethod(ABC):
         "price",
         "disequilibrium",
         "scaled_disequilibrium",
+        "signed_scaled_disequilibrium",
         "pair",
     ]
 

lib/pt_trading/rolling_window_fit.py

@@ -177,6 +177,7 @@ class RollingFit(PairsTradingFitMethod):
         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}"]
 
@@ -214,6 +215,7 @@ class RollingFit(PairsTradingFitMethod):
                 open_px_a,
                 open_disequilibrium,
                 open_scaled_disequilibrium,
+                signed_scaled_disequilibrium,
                 pair,
             ),
             (
@@ -224,6 +226,7 @@ class RollingFit(PairsTradingFitMethod):
                 open_px_b,
                 open_disequilibrium,
                 open_scaled_disequilibrium,
+                signed_scaled_disequilibrium,
                 pair,
             ),
         ]
@@ -241,6 +244,7 @@ class RollingFit(PairsTradingFitMethod):
                 "price": "float64",
                 "disequilibrium": "float64",
                 "scaled_disequilibrium": "float64",
+                "signed_scaled_disequilibrium": "float64",
                 "pair": "object",
             }
         )
@@ -254,6 +258,7 @@ class RollingFit(PairsTradingFitMethod):
         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}"]
 
@@ -269,6 +274,7 @@ class RollingFit(PairsTradingFitMethod):
                 close_px_a,
                 close_disequilibrium,
                 close_scaled_disequilibrium,
+                signed_scaled_disequilibrium,
                 pair,
             ),
             (
@@ -279,6 +285,7 @@ class RollingFit(PairsTradingFitMethod):
                 close_px_b,
                 close_disequilibrium,
                 close_scaled_disequilibrium,
+                signed_scaled_disequilibrium,
                 pair,
             ),
         ]
@@ -297,6 +304,7 @@ class RollingFit(PairsTradingFitMethod):
                 "price": "float64",
                 "disequilibrium": "float64",
                 "scaled_disequilibrium": "float64",
+                "signed_scaled_disequilibrium": "float64",
                 "pair": "object",
             }
         )

lib/pt_trading/vecm_rolling_fit.py

@@ -75,9 +75,12 @@ class VECMTradingPair(TradingPair):
             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["disequilibrium"] - self.training_mu_)
-            / self.training_std_
+            abs(predicted_df["signed_scaled_disequilibrium"])
         )
         
         predicted_df = predicted_df.reset_index(drop=True)

lib/pt_trading/z-score_rolling_fit.py

@@ -45,6 +45,7 @@ class ZScoreTradingPair(TradingPair):
         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)