refactor(qav3): fine tune default values

Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>

diff --git a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
index f368eaeb0a25e2dc5ee1c3cb94fdc07b05514b74..8a50f79226736cb799a9cc18c58f2ac74edff460 100644 (file)
--- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py
@@ -272,7 +272,7 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
     ) -> tuple[float, float]:
         predictions_smoothing = self.freqai_info.get("predictions_smoothing", "mean")
         if predictions_smoothing == "quantile":
-            return quantile_min_max_pred(
+            return self.quantile_min_max_pred(
                 pred_df, fit_live_predictions_candles, label_period_candles
             )
         elif predictions_smoothing == "mean":
@@ -490,6 +490,29 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel):
         except ValueError:
             return False
 
+    def quantile_min_max_pred(
+        self,
+        pred_df: pd.DataFrame,
+        fit_live_predictions_candles: int,
+        label_period_candles: int,
+    ) -> tuple[float, float]:
+        pred_df_sorted = (
+            pred_df.select_dtypes(exclude=["object"])
+            .copy()
+            .apply(lambda col: col.sort_values(ascending=False, ignore_index=True))
+        )
+
+        label_period_frequency: int = int(
+            fit_live_predictions_candles / (label_period_candles * 2)
+        )
+        min_pred = pred_df_sorted.iloc[-label_period_frequency:].quantile(
+            self.freqai_info.get("min_quantile", 0.25)
+        )
+        max_pred = pred_df_sorted.iloc[:label_period_frequency].quantile(
+            self.freqai_info.get("max_quantile", 0.75)
+        )
+        return min_pred[EXTREMA_COLUMN], max_pred[EXTREMA_COLUMN]
+
 
 def mean_min_max_pred(
     pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int
@@ -508,23 +531,6 @@ def mean_min_max_pred(
     return min_pred[EXTREMA_COLUMN], max_pred[EXTREMA_COLUMN]
 
 
-def quantile_min_max_pred(
-    pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int
-) -> tuple[float, float]:
-    pred_df_sorted = (
-        pred_df.select_dtypes(exclude=["object"])
-        .copy()
-        .apply(lambda col: col.sort_values(ascending=False, ignore_index=True))
-    )
-
-    label_period_frequency: int = int(
-        fit_live_predictions_candles / (label_period_candles * 2)
-    )
-    min_pred = pred_df_sorted.iloc[-label_period_frequency:].quantile(0.25)
-    max_pred = pred_df_sorted.iloc[:label_period_frequency].quantile(0.75)
-    return min_pred[EXTREMA_COLUMN], max_pred[EXTREMA_COLUMN]
-
-
 def median_min_max_pred(
     pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int
 ) -> tuple[float, float]:
@@ -555,7 +561,7 @@ def period_objective(
     candles_step,
     model_training_parameters,
 ) -> float:
-    min_train_window: int = 600
+    min_train_window: int = fit_live_predictions_candles * 2
     max_train_window: int = (
         len(X) if len(X) > min_train_window else (min_train_window + len(X))
     )

diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
index 0326a78251b6575c5e7a3066eb6d2be3d263a9f1..a1438bdc0c105349953f34324377a65fd8988d60 100644 (file)
--- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
+++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py
@@ -273,7 +273,7 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel):
     ) -> tuple[float, float]:
         predictions_smoothing = self.freqai_info.get("predictions_smoothing", "mean")
         if predictions_smoothing == "quantile":
-            return quantile_min_max_pred(
+            return self.quantile_min_max_pred(
                 pred_df, fit_live_predictions_candles, label_period_candles
             )
         elif predictions_smoothing == "mean":
@@ -491,6 +491,29 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel):
         except ValueError:
             return False
 
+    def quantile_min_max_pred(
+        self,
+        pred_df: pd.DataFrame,
+        fit_live_predictions_candles: int,
+        label_period_candles: int,
+    ) -> tuple[float, float]:
+        pred_df_sorted = (
+            pred_df.select_dtypes(exclude=["object"])
+            .copy()
+            .apply(lambda col: col.sort_values(ascending=False, ignore_index=True))
+        )
+
+        label_period_frequency: int = int(
+            fit_live_predictions_candles / (label_period_candles * 2)
+        )
+        min_pred = pred_df_sorted.iloc[-label_period_frequency:].quantile(
+            self.freqai_info.get("min_quantile", 0.25)
+        )
+        max_pred = pred_df_sorted.iloc[:label_period_frequency].quantile(
+            self.freqai_info.get("max_quantile", 0.75)
+        )
+        return min_pred[EXTREMA_COLUMN], max_pred[EXTREMA_COLUMN]
+
 
 def mean_min_max_pred(
     pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int
@@ -509,23 +532,6 @@ def mean_min_max_pred(
     return min_pred[EXTREMA_COLUMN], max_pred[EXTREMA_COLUMN]
 
 
-def quantile_min_max_pred(
-    pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int
-) -> tuple[float, float]:
-    pred_df_sorted = (
-        pred_df.select_dtypes(exclude=["object"])
-        .copy()
-        .apply(lambda col: col.sort_values(ascending=False, ignore_index=True))
-    )
-
-    label_period_frequency: int = int(
-        fit_live_predictions_candles / (label_period_candles * 2)
-    )
-    min_pred = pred_df_sorted.iloc[-label_period_frequency:].quantile(0.25)
-    max_pred = pred_df_sorted.iloc[:label_period_frequency].quantile(0.75)
-    return min_pred[EXTREMA_COLUMN], max_pred[EXTREMA_COLUMN]
-
-
 def median_min_max_pred(
     pred_df: pd.DataFrame, fit_live_predictions_candles: int, label_period_candles: int
 ) -> tuple[float, float]:
@@ -556,7 +562,7 @@ def period_objective(
     candles_step,
     model_training_parameters,
 ) -> float:
-    min_train_window: int = 600
+    min_train_window: int = fit_live_predictions_candles * 2
     max_train_window: int = (
         len(X) if len(X) > min_train_window else (min_train_window + len(X))
     )

diff --git a/quickadapter/user_data/strategies/QuickAdapterV3.py b/quickadapter/user_data/strategies/QuickAdapterV3.py
index bbf89ecded7ba05f50118dd16cb9ad7d101219ed..0160834136709386b83f2cad4dd2318af02d1238 100644 (file)
--- a/quickadapter/user_data/strategies/QuickAdapterV3.py
+++ b/quickadapter/user_data/strategies/QuickAdapterV3.py
@@ -103,9 +103,9 @@ class QuickAdapterV3(IStrategy):
             },
             {
                 "method": "StoplossGuard",
-                "lookback_period_candles": fit_live_predictions_candles,
+                "lookback_period_candles": int(fit_live_predictions_candles / 2),
                 "trade_limit": 1,
-                "stop_duration_candles": fit_live_predictions_candles,
+                "stop_duration_candles": int(fit_live_predictions_candles / 2),
                 "only_per_pair": True,
             },
         ]