RandomForestClassifier

Zeus_8_3_2_B_4_2.py

@@ -48,10 +48,13 @@ from sklearn.metrics import (
     accuracy_score,
     roc_auc_score,
     roc_curve,
+    precision_score, recall_score, precision_recall_curve,
+    f1_score
 )
 from sklearn.tree import export_text
 import inspect
-
+from sklearn.feature_selection import mutual_info_classif
+from sklearn.inspection import permutation_importance
 
 from tabulate import tabulate
 
@@ -77,7 +80,19 @@ def normalize(df):
 class Zeus_8_3_2_B_4_2(IStrategy):
     # Machine Learning
     model = joblib.load('rf_model.pkl')
-    model_indicators = ['rsi_deriv1', "max_rsi_12", "mid_smooth_5_deriv1", "volume_deriv1"]
+    model_indicators = [
+        'rsi', 'rsi_deriv1', "max_rsi_12",
+        "bb_percent",
+        'vol_24',
+        'percent3',
+        'sma5_dist', 'sma5_deriv1',
+        'sma24_dist', 'sma24_deriv1',
+        'sma60_dist', 'sma60_deriv1',
+        'down_count', 'up_count',
+        'down_pct', 'slope_norm',
+        'min_max_60',
+        'rsi_slope', 'adx_change', 'volatility_ratio'
+    ]
 
     levels = [1, 2, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
     # startup_candle_count = 12 * 24 * 5
@@ -286,12 +301,12 @@ class Zeus_8_3_2_B_4_2(IStrategy):
     pct_inc = DecimalParameter(0.0001, 0.003, default=0.0022, decimals=4, space='buy', optimize=True, load=True)
 
     indic_5m_slope_sup_buy = CategoricalParameter(indicators, default="sma60", space='buy')
-    indic_deriv_5m_slop_sup_buy = CategoricalParameter(indicators, default="sma12", space='buy', optimize=True, load=True)
+    # indic_deriv_5m_slop_sup_buy = CategoricalParameter(indicators, default="sma12", space='buy', optimize=True, load=True)
-    deriv_5m_slope_sup_buy = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='buy', optimize=True, load=True)
+    # deriv_5m_slope_sup_buy = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='buy', optimize=True, load=True)
 
     indic_5m_slope_inf_buy = CategoricalParameter(indicators, default="sma60", space='buy')
-    indic_deriv_5m_slop_sup_buy = CategoricalParameter(indicators, default="sma12", space='buy', optimize=True, load=True)
+    # indic_deriv_5m_slop_inf_buy = CategoricalParameter(indicators, default="sma12", space='buy', optimize=True, load=True)
-    deriv_5m_slope_sup_buy = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='buy', optimize=True, load=True)
+    # deriv_5m_slope_inf_buy = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='buy', optimize=True, load=True)
 
 
     # indic_deriv1_5m = DecimalParameter(-2, 2, default=0, decimals=2, space='buy', optimize=True, load=True)
@@ -392,7 +407,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         condition = True #(last_candle[f"{indic_5m}_deriv1"] >= indic_deriv1_5m) and (last_candle[f"{indic_5m}_deriv2"] >= indic_deriv2_5m)
 
         # allow_to_buy = True #(not self.stop_all) #& (not self.all_down)
-        # and val > self.buy_val.value #not last_candle['tendency'] in ('B-', 'B--')  # (rate <= float(limit)) | (entry_tag == 'force_entry')
+        # and val > self.buy_val.value #not last_candle['tendency'] in (-1, -2)  # (rate <= float(limit)) | (entry_tag == 'force_entry')
         allow_to_buy = (condition and not self.pairs[pair]['stop']) | (entry_tag == 'force_entry')
 
         # if allow_to_buy:
@@ -562,7 +577,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         # if (last_candle['mid_smooth_deriv1'] >= 0):
         #     return None
-        # if (last_candle['tendency'] in ('H++', 'H+')) and (last_candle['rsi'] < 80):
+        # if (last_candle['tendency'] in (2, 1)) and (last_candle['rsi'] < 80):
         #     return None
         #
         # if (last_candle['sma24_deriv1'] < 0 and before_last_candle['sma24_deriv1'] >= 0) and (current_profit > expected_profit):
@@ -573,7 +588,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         # trend = last_candle['trend_class_1d']
         # if (trend == "B-" or trend == "B--") and self.pairs[pair]['has_gain'] == 0: # and (last_candle[f"{indic_5m_sell}_deriv1"] <= indic_deriv1_5m_sell and last_candle[f"{indic_5m_sell}_deriv2"] <= indic_deriv2_5m_sell):
         #
-        #     if (last_candle['max_rsi_12_1h'] > 75) and last_candle['trend_class_1h'] == 'H+' and profit > max(5, expected_profit) and (last_candle['hapercent'] < 0):
+        #     if (last_candle['max_rsi_12_1h'] > 75) and last_candle['trend_class_1h'] == 1 and profit > max(5, expected_profit) and (last_candle['hapercent'] < 0):
         #         self.pairs[pair]['stop'] = True
         #         self.log_trade(
         #             last_candle=last_candle,
@@ -854,11 +869,11 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
             # On considère les petites valeurs comme zéro
             if abs(d1) < eps:
-                return 'P'  # Palier / neutre
+                return 0  # Palier / neutre
             if d1 > d1_lim_sup:
-                return 'H++' if d2 > eps else 'H+'  # Acceleration Hausse / Ralentissement Hausse
+                return 2 if d2 > eps else 1  # Acceleration Hausse / Ralentissement Hausse
             if d1 < d1_lim_inf:
-                return 'B--' if d2 < -eps else 'B-'  # Acceleration Baisse / Ralentissement Baisse
+                return -2 if d2 < -eps else -1  # Acceleration Baisse / Ralentissement Baisse
             if abs(d1) < eps:
                 return 'DH' if d2 > eps else 'DB'  # Depart Hausse / Depart Baisse
             return 'Mid'
@@ -874,13 +889,13 @@ class Zeus_8_3_2_B_4_2(IStrategy):
     #         d1_lim_inf = -0.01
     #         d1_lim_sup = 0.01
     #         if d1 >= d1_lim_inf and d1 <= d1_lim_sup:  # and d2 >= d2_lim_inf and d2 <= d2_lim_sup:
-    #             return 'P'  # Palier
+    #             return 0  # Palier
     #         if d1 == 0.0:
     #             return 'DH' if d2 > 0 else 'DB'  # Depart Hausse / Départ Baisse
     #         if d1 > d1_lim_sup:
-    #             return 'H++' if d2 > 0 else 'H+'  # Acceleration Hausse / Ralentissement Hausse
+    #             return 2 if d2 > 0 else 1  # Acceleration Hausse / Ralentissement Hausse
     #         if d1 < d1_lim_inf:
-    #             return 'B--' if d2 < 0 else 'B-'  # Accéleration Baisse / Ralentissement Baisse
+    #             return -2 if d2 < 0 else -1  # Accéleration Baisse / Ralentissement Baisse
     #         return 'Mid'
     #
     #     dataframe[f"tendency{suffixe}"] = dataframe.apply(tag_by_derivatives, axis=1)
@@ -898,6 +913,19 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         ################### INFORMATIVE 1h
         informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h")
         informative = self.populateDataframe(informative, timeframe='1h')
+        # informative['target_value'] = informative['sma5'].shift(-6).rolling(5).max() - informative['sma5'] * 1.005
+        # if self.dp.runmode.value in ('backtest'):
+        #     self.trainModel(informative, metadata)
+        #
+        # # Préparer les features pour la prédiction
+        # features = informative[self.model_indicators].fillna(0)
+        #
+        # # Prédiction : probabilité que le prix monte
+        # probs = self.model.predict_proba(features)[:, 1]
+        #
+        # # Sauvegarder la probabilité pour l’analyse
+        # informative['ml_prob'] = probs
+
         dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
 
         ################### INFORMATIVE 1d
@@ -953,9 +981,6 @@ class Zeus_8_3_2_B_4_2(IStrategy):
                                             dataframe['mid_smooth_5h']
         dataframe["mid_smooth_5h_deriv2"] = 100 * dataframe["mid_smooth_5h_deriv1"].diff().rolling(window=60).mean()
 
-        # Compter les baisses / hausses consécutives
-        # self.calculateDownAndUp(dataframe, limit=0.0001)
-
         # ===============================
         # Lissage des valeurs Journalières
         horizon_d = 12 * 5 * 24
@@ -1030,29 +1055,36 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         dataframe['stop_buying'] = latched
 
-        self.trainModel(dataframe, metadata)
+        if self.dp.runmode.value in ('backtest'):
+            self.trainModel(dataframe, metadata)
 
         # Préparer les features pour la prédiction
         features = dataframe[self.model_indicators].fillna(0)
 
         # Prédiction : probabilité que le prix monte
-        # probs = self.model.predict_proba(features)[:, 1]
+        probs = self.model.predict_proba(features)[:, 1]
 
         # Sauvegarder la probabilité pour l’analyse
-        # dataframe['ml_prob'] = probs
+        dataframe['ml_prob'] = probs
 
-        # self.inspect_model(self.model)
+        self.inspect_model(self.model)
 
         return dataframe
 
     def trainModel(self, dataframe: DataFrame, metadata: dict):
         df = dataframe.copy()
         # 3️⃣ Créer la cible : 1 si le prix monte dans les prochaines bougies
-        df['target'] = (1000 * (df['sma24'].shift(-24) - df['sma24'])) #.astype(int)
+        # df['target'] = (df['sma24'].shift(-24) > df['sma24']).astype(int)
+        df['target'] = (df['sma5'].shift(-12).rolling(12).max() > df['sma5'] * 1.00025).astype(int)
+        df['target'] = df['target'].fillna(0).astype(int)
 
         # Nettoyage
         df = df.dropna()
 
+        X = df[self.model_indicators]
+        y = df['target']  # ta colonne cible binaire ou numérique
+        print(self.feature_auc_scores(X, y))
+
         # 4️⃣ Split train/test
         X = df[self.model_indicators]
         y = df['target']
@@ -1060,39 +1092,50 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         # 5️⃣ Entraînement du modèle
         # train_model = RandomForestClassifier(n_estimators=200, random_state=42)
-        # train_model = RandomForestClassifier(
+        train_model = RandomForestClassifier(
-        #     n_estimators=300,
-        #     max_depth=12,
-        #     min_samples_split=4,
-        #     min_samples_leaf=2,
-        #     max_features='sqrt',
-        #     random_state=42,
-        #     n_jobs=-1
-        # )
-        train_model = RandomForestRegressor(
             n_estimators=300,
-            max_depth=None,
+            max_depth=12,
-            random_state=42,
+            # min_samples_split=4,
-            n_jobs=-1
+            # min_samples_leaf=2,
+            # max_features='sqrt',
+            # random_state=42,
+            # n_jobs=-1,
+            class_weight='balanced'
         )
         train_model.fit(X_train, y_train)
 
         # 6️⃣ Évaluer la précision (facultatif)
         preds = train_model.predict(X_test)
-        # acc = accuracy_score(y_test, preds)
+        acc = accuracy_score(y_test, preds)
-        # print(f"Accuracy: {acc:.3f}")
+        print(f"Accuracy: {acc:.3f}")
 
         # 7️⃣ Sauvegarde du modèle
         joblib.dump(train_model, 'rf_model.pkl')
         print("✅ Modèle sauvegardé sous rf_model.pkl")
 
-        y_pred = train_model.predict(X_test)
+        # X = dataframe des features (après shift/rolling/indicators)
+        # y = target binaire ou décimale
+        # model = ton modèle entraîné (RandomForestClassifier ou Regressor)
 
-        print("R² :", r2_score(y_test, y_pred))
+        # # --- 1️⃣ Mutual Information (MI) ---
-        print("RMSE :", mean_squared_error(y_test, y_pred)) #, squared=False))
+        # mi_scores = mutual_info_classif(X.fillna(0), y)
-        print("MAE :", mean_absolute_error(y_test, y_pred))
+        # mi_series = pd.Series(mi_scores, index=X.columns, name='MI')
+        #
+        # # --- 2️⃣ Permutation Importance (PI) ---
+        # pi_result = permutation_importance(train_model, X, y, n_repeats=10, random_state=42, n_jobs=-1)
+        # pi_series = pd.Series(pi_result.importances_mean, index=X.columns, name='PI')
+        #
+        # # --- 3️⃣ Combinaison dans un seul dataframe ---
+        # importance_df = pd.concat([mi_series, pi_series], axis=1)
+        # importance_df = importance_df.sort_values(by='PI', ascending=False)  # tri par importance modèle
+        # print(importance_df)
+        #
+        # importance_df.plot(kind='bar', figsize=(10, 5))
+        # plt.title("Mutual Info vs Permutation Importance")
+        # plt.ylabel("Score")
+        # plt.show()
 
-        # self.analyze_model(train_model, X_train, X_test, y_train, y_test)
+        self.analyze_model(train_model, X_train, X_test, y_train, y_test)
 
     def inspect_model(self, model):
         """
@@ -1252,39 +1295,138 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         plt.savefig(os.path.join(output_dir, "Courbe ROC.png"), bbox_inches="tight")
         plt.close()
 
-        # ---- Interprétation SHAP (optionnelle) ----
+        # # ---- Interprétation SHAP (optionnelle) ----
-        try:
+        # try:
-            import shap
+        #     import shap
+        #
+        #     print("\n===== 💡 ANALYSE SHAP =====")
+        #     explainer = shap.TreeExplainer(model)
+        #     shap_values = explainer.shap_values(X_test)
+        #     # shap.summary_plot(shap_values[1], X_test)
+        #     # Vérifie le type de sortie de shap_values
+        #     if isinstance(shap_values, list):
+        #         # Cas des modèles de classification (plusieurs classes)
+        #         shap_values_to_plot = shap_values[0] if len(shap_values) == 1 else shap_values[1]
+        #     else:
+        #         shap_values_to_plot = shap_values
+        #
+        #     # Ajustement des dimensions au besoin
+        #     if shap_values_to_plot.shape[1] != X_test.shape[1]:
+        #         print(f"⚠️ Mismatch dimensions SHAP ({shap_values_to_plot.shape[1]}) vs X_test ({X_test.shape[1]})")
+        #         min_dim = min(shap_values_to_plot.shape[1], X_test.shape[1])
+        #         shap_values_to_plot = shap_values_to_plot[:, :min_dim]
+        #         X_to_plot = X_test.iloc[:, :min_dim]
+        #     else:
+        #         X_to_plot = X_test
+        #
+        #     plt.figure(figsize=(12, 4))
+        #     shap.summary_plot(shap_values_to_plot, X_to_plot, show=False)
+        #     plt.savefig(os.path.join(output_dir, "shap_summary.png"), bbox_inches="tight")
+        #     plt.close()
+        # except ImportError:
+        #     print("\n(SHAP non installé — `pip install shap` pour activer l’analyse SHAP.)")
 
-            print("\n===== 💡 ANALYSE SHAP =====")
+        y_proba = model.predict_proba(X_test)[:, 1]
-            explainer = shap.TreeExplainer(model)
-            shap_values = explainer.shap_values(X_test)
-            # shap.summary_plot(shap_values[1], X_test)
-            # Vérifie le type de sortie de shap_values
-            if isinstance(shap_values, list):
-                # Cas des modèles de classification (plusieurs classes)
-                shap_values_to_plot = shap_values[0] if len(shap_values) == 1 else shap_values[1]
-            else:
-                shap_values_to_plot = shap_values
 
-            # Ajustement des dimensions au besoin
+        # Trace ou enregistre le graphique
-            if shap_values_to_plot.shape[1] != X_test.shape[1]:
+        self.plot_threshold_analysis(y_test, y_proba, step=0.05,
-                print(f"⚠️ Mismatch dimensions SHAP ({shap_values_to_plot.shape[1]}) vs X_test ({X_test.shape[1]})")
+                                save_path="/home/souti/freqtrade/user_data/plots/threshold_analysis.png")
-                min_dim = min(shap_values_to_plot.shape[1], X_test.shape[1])
-                shap_values_to_plot = shap_values_to_plot[:, :min_dim]
-                X_to_plot = X_test.iloc[:, :min_dim]
-            else:
-                X_to_plot = X_test
 
-            plt.figure(figsize=(12, 10))
+        # y_test : vraies classes (0 / 1)
-            shap.summary_plot(shap_values_to_plot, X_to_plot, show=False)
+        # y_proba : probabilités de la classe 1 prédites par ton modèle
-            plt.savefig(os.path.join(output_dir, "shap_summary.png"), bbox_inches="tight")
+        # Exemple : y_proba = model.predict_proba(X_test)[:, 1]
-            plt.close()
+
-        except ImportError:
+        seuils = np.arange(0.0, 1.01, 0.05)
-            print("\n(SHAP non installé — `pip install shap` pour activer l’analyse SHAP.)")
+        precisions, recalls, f1s = [], [], []
+
+        for seuil in seuils:
+            y_pred = (y_proba >= seuil).astype(int)
+            precisions.append(precision_score(y_test, y_pred))
+            recalls.append(recall_score(y_test, y_pred))
+            f1s.append(f1_score(y_test, y_pred))
+
+        plt.figure(figsize=(10, 6))
+        plt.plot(seuils, precisions, label='Précision', marker='o')
+        plt.plot(seuils, recalls, label='Rappel', marker='o')
+        plt.plot(seuils, f1s, label='F1-score', marker='o')
+
+        # Ajoute un point pour le meilleur F1
+        best_idx = np.argmax(f1s)
+        plt.scatter(seuils[best_idx], f1s[best_idx], color='red', s=80, label=f'Max F1 ({seuils[best_idx]:.2f})')
+
+        plt.title("Performance du modèle selon le seuil de probabilité")
+        plt.xlabel("Seuil de probabilité (classe 1)")
+        plt.ylabel("Score")
+        plt.grid(True, alpha=0.3)
+        plt.legend()
+        plt.savefig("/home/souti/freqtrade/user_data/plots/seuil_de_probabilite.png", bbox_inches='tight')
+        # plt.show()
+
+        print(f"✅ Meilleur F1 : {f1s[best_idx]:.3f} au seuil {seuils[best_idx]:.2f}")
 
         print("\n===== ✅ FIN DE L’ANALYSE =====")
 
+    def plot_threshold_analysis(self, y_true, y_proba, step=0.05, save_path=None):
+        """
+        Affiche la précision, le rappel et le F1-score selon le seuil de décision.
+        y_true : labels réels (0 ou 1)
+        y_proba : probabilités prédites (P(hausse))
+        step : pas entre les seuils testés
+        save_path : si renseigné, enregistre l'image au lieu d'afficher
+        """
+
+        # Le graphique généré affichera trois courbes :
+        #
+        # 🔵 Precision — la fiabilité de tes signaux haussiers.
+        #
+        # 🟢 Recall — la proportion de hausses que ton modèle détecte.
+        #
+        # 🟣 F1-score — le compromis optimal entre les deux.
+
+        thresholds = np.arange(0, 1.01, step)
+        precisions, recalls, f1s = [], [], []
+
+        for thr in thresholds:
+            preds = (y_proba >= thr).astype(int)
+            precisions.append(precision_score(y_true, preds))
+            recalls.append(recall_score(y_true, preds))
+            f1s.append(f1_score(y_true, preds))
+
+        plt.figure(figsize=(10, 6))
+        plt.plot(thresholds, precisions, label="Precision", linewidth=2)
+        plt.plot(thresholds, recalls, label="Recall", linewidth=2)
+        plt.plot(thresholds, f1s, label="F1-score", linewidth=2, linestyle="--")
+        plt.axvline(0.5, color='gray', linestyle=':', label="Seuil 0.5")
+        plt.title("📊 Performance selon le seuil de probabilité", fontsize=14)
+        plt.xlabel("Seuil de décision (threshold)")
+        plt.ylabel("Score")
+        plt.legend()
+        plt.grid(True, alpha=0.3)
+
+        if save_path:
+            plt.savefig(save_path, bbox_inches='tight')
+            print(f"✅ Graphique enregistré : {save_path}")
+        else:
+            plt.show()
+
+    # # =============================
+    # # Exemple d’utilisation :
+    # # =============================
+    # if __name__ == "__main__":
+    #     # Exemple : chargement d’un modèle et test
+    #     import joblib
+    #
+    #     model = joblib.load("/media/Home/home/souti/freqtrade/user_data/strategies/tools/sklearn/model.pkl")
+    #     data = np.load("/media/Home/home/souti/freqtrade/user_data/strategies/tools/sklearn/test_data.npz")
+    #     X_test, y_test = data["X"], data["y"]
+    #
+    #     y_proba = model.predict_proba(X_test)[:, 1]
+    #
+    #     # Trace ou enregistre le graphique
+    #     plot_threshold_analysis(y_test, y_proba, step=0.05,
+    #                             save_path="/media/Home/home/souti/freqtrade/user_data/strategies/tools/sklearn/threshold_analysis.png")
+
+
     def populateDataframe(self, dataframe, timeframe='5m'):
         heikinashi = qtpylib.heikinashi(dataframe)
         dataframe['haopen'] = heikinashi['open']
@@ -1292,10 +1434,10 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         dataframe['hapercent'] = (dataframe['haclose'] - dataframe['haopen']) / dataframe['haclose']
         dataframe['mid'] = dataframe['haopen'] + (dataframe['haclose'] - dataframe['haopen']) / 2
 
-        dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"]
+        dataframe["percent"] = dataframe['close'].pct_change()
-        dataframe["percent3"] = (dataframe["close"] - dataframe["open"].shift(3)) / dataframe["open"].shift(3)
+        dataframe["percent3"] = dataframe['close'].pct_change(3)
-        dataframe["percent12"] = (dataframe["close"] - dataframe["open"].shift(12)) / dataframe["open"].shift(12)
+        dataframe["percent12"] = dataframe['close'].pct_change(12)
-        dataframe["percent24"] = (dataframe["close"] - dataframe["open"].shift(24)) / dataframe["open"].shift(24)
+        dataframe["percent24"] = dataframe['close'].pct_change(24)
 
         if self.dp.runmode.value in ('backtest'):
             dataframe['futur_percent'] = 100 * (dataframe['close'].shift(-1) - dataframe['close']) / dataframe['close']
@@ -1323,6 +1465,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         dataframe['max12'] = talib.MAX(dataframe['close'], timeperiod=12)
         dataframe['max60'] = talib.MAX(dataframe['close'], timeperiod=60)
         dataframe['min60'] = talib.MIN(dataframe['close'], timeperiod=60)
+        dataframe['min_max_60'] = ((dataframe['max60'] - dataframe['close']) / dataframe['min60'])
         # dataframe['min36'] = talib.MIN(dataframe['close'], timeperiod=36)
         # dataframe['max36'] = talib.MAX(dataframe['close'], timeperiod=36)
         # dataframe['pct36'] = 100 * (dataframe['max36'] - dataframe['min36']) / dataframe['min36']
@@ -1332,6 +1475,13 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         dataframe['bb_lowerband'] = bollinger['lower']
         dataframe['bb_middleband'] = bollinger['mid']
         dataframe['bb_upperband'] = bollinger['upper']
+        dataframe["bb_percent"] = (
+            (dataframe["close"] - dataframe["bb_lowerband"]) /
+            (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
+        )
+        dataframe["bb_width"] = (
+            (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
+        )
 
         # Calcul MACD
         macd, macdsignal, macdhist = talib.MACD(
@@ -1403,6 +1553,46 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         # tr = DataFrame({'hl': high_low, 'hc': high_close, 'lc': low_close}).max(axis=1)
         # dataframe['atr'] = tr.rolling(window=self.DEFAULT_PARAMS['atr_period']).mean()
 
+        ###########################
+        # df = ton DataFrame OHLCV avec colonnes: open, high, low, close, volume
+        # Assure-toi qu'il est trié par date croissante
+
+        # --- Volatilité normalisée ---
+        dataframe['atr'] = ta.volatility.AverageTrueRange(
+            high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], window=14
+        ).average_true_range()
+        dataframe['atr_norm'] = dataframe['atr'] / dataframe['close']
+
+        # --- Force de tendance ---
+        dataframe['adx'] = ta.trend.ADXIndicator(
+            high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], window=14
+        ).adx()
+
+        # --- Volume directionnel (On Balance Volume) ---
+        dataframe['obv'] = ta.volume.OnBalanceVolumeIndicator(
+            close=dataframe['close'], volume=dataframe['volume']
+        ).on_balance_volume()
+
+        # --- Volatilité récente (écart-type des rendements) ---
+        dataframe['ret'] = dataframe['close'].pct_change()
+        dataframe['vol_24'] = dataframe['ret'].rolling(24).std()
+
+        # Compter les baisses / hausses consécutives
+        self.calculateDownAndUp(dataframe, limit=0.0001)
+
+        # df : ton dataframe OHLCV + indicateurs existants
+        # Assurez-vous que les colonnes suivantes existent :
+        # 'max_rsi_12', 'roc_24', 'bb_percent_1h'
+
+        # --- Filtrage des NaN initiaux ---
+        # dataframe = dataframe.dropna()
+
+        dataframe['rsi_slope'] = dataframe['rsi'].diff(3) / 3  # vitesse moyenne du RSI
+        dataframe['adx_change'] = dataframe['adx'] - dataframe['adx'].shift(12)  # évolution de la tendance
+        dataframe['volatility_ratio'] = dataframe['atr_norm'] / dataframe['bb_width']
+
+        ###########################
+
         dataframe['volume_sma_deriv'] = dataframe['volume'] * dataframe['sma5_deriv1'] / (dataframe['volume'].rolling(5).mean())
         self.calculeDerivees(dataframe, 'volume', timeframe=timeframe, ema_period=12)
 
@@ -1410,6 +1600,15 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         return dataframe
 
+    def feature_auc_scores(self, X, y):
+        aucs = {}
+        for col in X.columns:
+            try:
+                aucs[col] = roc_auc_score(y, X[col].fillna(method='ffill').fillna(0))
+            except Exception:
+                aucs[col] = np.nan
+        return pd.Series(aucs).sort_values(ascending=False)
+
     def macd_tendance_int(self, dataframe: pd.DataFrame,
                           macd_col='macd',
                           signal_col='macdsignal',
@@ -1462,8 +1661,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         return tendance
 
     def calculateDownAndUp(self, dataframe, limit=0.0001):
-        dataframe['down'] = dataframe['mid_smooth_1h_deriv1'] < limit  # dataframe['hapercent'] <= limit
+        dataframe['down'] = dataframe['hapercent'] <= limit
-        dataframe['up'] = dataframe['mid_smooth_1h_deriv1'] > limit  # dataframe['hapercent'] >= limit
+        dataframe['up'] = dataframe['hapercent'] >= limit
         dataframe['down_count'] = - dataframe['down'].astype(int) * (
                 dataframe['down'].groupby((dataframe['down'] != dataframe['down'].shift()).cumsum()).cumcount() + 1)
         dataframe['up_count'] = dataframe['up'].astype(int) * (
@@ -1502,6 +1701,12 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         factor1 = 100 * (ema_period / 5)
         factor2 = 10 * (ema_period / 5)
 
+        dataframe[f"{name}{suffixe}_inv"] = (dataframe[f"{name}{suffixe}"].shift(2) >= dataframe[f"{name}{suffixe}"].shift(1)) \
+                                 & (dataframe[f"{name}{suffixe}"].shift(1) <= dataframe[f"{name}{suffixe}"])
+        # --- Distance à la moyenne mobile ---
+        dataframe[f"{name}{suffixe}_dist"] = (dataframe['close'] - dataframe[f"{name}{suffixe}"]) / dataframe[f"{name}{suffixe}"]
+
+
         # dérivée relative simple
         dataframe[d1_col] = (dataframe[name] - dataframe[name].shift(1)) / dataframe[name].shift(1)
         # lissage EMA
@@ -2730,7 +2935,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         # return last_candle['slope_norm_1d'] < last_candle['slope_norm_1h']
 
-        if self.pairs[pair]['stop'] and last_candle['max_rsi_12_1h'] <= 60 and last_candle['trend_class_1h'] == 'B-':
+        if self.pairs[pair]['stop'] and last_candle['max_rsi_12_1h'] <= 60 and last_candle['trend_class_1h'] == -1:
             dispo = round(self.wallets.get_available_stake_amount())
             self.pairs[pair]['stop'] = False
             self.log_trade(
@@ -2948,15 +3153,15 @@ class Zeus_8_3_2_B_4_2(IStrategy):
                 q1, q2, q3, q4 = q
                 v = series.iloc[i]
                 if v <= q1:
-                    trend_class.append('B--')
+                    trend_class.append(-2)
                 elif v <= q2:
-                    trend_class.append('B-')
+                    trend_class.append(-1)
                 elif v <= q3:
-                    trend_class.append('P')
+                    trend_class.append(0)
                 elif v <= q4:
-                    trend_class.append('H+')
+                    trend_class.append(1)
                 else:
-                    trend_class.append('H++')
+                    trend_class.append(2)
             return trend_class
 
         dataframe['slope_norm'] = df['slope_norm']

tools/sklearn/RandomForestRegressor.py

@@ -1,3 +1,4 @@
+
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.metrics import r2_score, mean_absolute_error
 import pandas as pd