RandomForestRegressor

Zeus_8_3_2_B_4_2.py

@@ -35,6 +35,23 @@ from collections import Counter
 
 logger = logging.getLogger(__name__)
 
+# Machine Learning
+from sklearn.ensemble import RandomForestClassifier,RandomForestRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
+from sklearn.metrics import accuracy_score
+import joblib
+import matplotlib.pyplot as plt
+from sklearn.metrics import (
+    classification_report,
+    confusion_matrix,
+    accuracy_score,
+    roc_auc_score,
+    roc_curve,
+)
+from sklearn.tree import export_text
+import inspect
+
 
 from tabulate import tabulate
 
@@ -58,6 +75,10 @@ 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"]
+
     levels = [1, 2, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
     # startup_candle_count = 12 * 24 * 5
 
@@ -1009,8 +1030,261 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         dataframe['stop_buying'] = latched
 
+        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]
+
+        # Sauvegarder la probabilité pour l’analyse
+        # dataframe['ml_prob'] = probs
+
+        # 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)
+
+        # Nettoyage
+        df = df.dropna()
+
+        # 4️⃣ Split train/test
+        X = df[self.model_indicators]
+        y = df['target']
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)
+
+        # 5️⃣ Entraînement du modèle
+        # train_model = RandomForestClassifier(n_estimators=200, random_state=42)
+        # 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,
+            random_state=42,
+            n_jobs=-1
+        )
+        train_model.fit(X_train, y_train)
+
+        # 6️⃣ Évaluer la précision (facultatif)
+        preds = train_model.predict(X_test)
+        # acc = accuracy_score(y_test, preds)
+        # 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)
+
+        print("R² :", r2_score(y_test, y_pred))
+        print("RMSE :", mean_squared_error(y_test, y_pred)) #, squared=False))
+        print("MAE :", mean_absolute_error(y_test, y_pred))
+
+        # self.analyze_model(train_model, X_train, X_test, y_train, y_test)
+
+    def inspect_model(self, model):
+        """
+        Affiche les informations d'un modèle ML déjà entraîné.
+        Compatible avec scikit-learn, xgboost, lightgbm, catboost...
+        """
+
+        print("===== 🔍 INFORMATIONS DU MODÈLE =====")
+
+        # Type de modèle
+        print(f"Type : {type(model).__name__}")
+        print(f"Module : {model.__class__.__module__}")
+
+        # Hyperparamètres
+        if hasattr(model, "get_params"):
+            params = model.get_params()
+            print(f"\n===== ⚙️ HYPERPARAMÈTRES ({len(params)}) =====")
+            for k, v in params.items():
+                print(f"{k}: {v}")
+
+        # Nombre d’estimateurs
+        if hasattr(model, "n_estimators"):
+            print(f"\nNombre d’estimateurs : {model.n_estimators}")
+
+        # Importance des features
+        if hasattr(model, "feature_importances_"):
+            print("\n===== 📊 IMPORTANCE DES FEATURES =====")
+
+            # Correction ici :
+            feature_names = getattr(model, "feature_names_in_", None)
+            if isinstance(feature_names, np.ndarray):
+                feature_names = feature_names.tolist()
+            elif feature_names is None:
+                feature_names = [f"feature_{i}" for i in range(len(model.feature_importances_))]
+
+            fi = pd.DataFrame({
+                "feature": feature_names,
+                "importance": model.feature_importances_
+            }).sort_values(by="importance", ascending=False)
+
+            print(fi)
+
+        # Coefficients (modèles linéaires)
+        if hasattr(model, "coef_"):
+            print("\n===== ➗ COEFFICIENTS =====")
+            coef = np.array(model.coef_)
+            if coef.ndim == 1:
+                for i, c in enumerate(coef):
+                    print(f"Feature {i}: {c:.6f}")
+            else:
+                print(coef)
+
+        # Intercept
+        if hasattr(model, "intercept_"):
+            print("\nIntercept :", model.intercept_)
+
+        # Classes connues
+        if hasattr(model, "classes_"):
+            print("\n===== 🎯 CLASSES =====")
+            print(model.classes_)
+
+        # Scores internes
+        for attr in ["best_score_", "best_iteration_", "best_ntree_limit", "score_"]:
+            if hasattr(model, attr):
+                print(f"\n{attr} = {getattr(model, attr)}")
+
+        # Méthodes disponibles
+        print("\n===== 🧩 MÉTHODES DISPONIBLES =====")
+        methods = [m for m, _ in inspect.getmembers(model, predicate=inspect.ismethod)]
+        print(", ".join(methods[:15]) + ("..." if len(methods) > 15 else ""))
+
+        print("\n===== ✅ FIN DE L’INSPECTION =====")
+
+    def analyze_model(self, model, X_train, X_test, y_train, y_test):
+        """
+        Analyse complète d'un modèle ML supervisé (classification binaire).
+        Affiche performances, importance des features, matrices, seuils, etc.
+        """
+        output_dir = "user_data/plots"
+        os.makedirs(output_dir, exist_ok=True)
+
+        # ---- Prédictions ----
+        preds = model.predict(X_test)
+        probs = model.predict_proba(X_test)[:, 1] if hasattr(model, "predict_proba") else preds
+
+        # ---- Performances globales ----
+        print("===== 📊 ÉVALUATION DU MODÈLE =====")
+        print("Colonnes du modèle :", model.feature_names_in_)
+        print("Colonnes X_test :", list(X_test.columns))
+        print(f"Accuracy: {accuracy_score(y_test, preds):.3f}")
+        print(f"ROC AUC : {roc_auc_score(y_test, probs):.3f}")
+
+        print("TN (True Negative)  / FP (False Positive)")
+        print("FN (False Negative) / TP (True Positive)")
+        print("\nRapport de classification :\n", classification_report(y_test, preds))
+
+        # | Élément             | Valeur | Signification                                               |
+        # | ------------------- | ------ | ----------------------------------------------------------- |
+        # | TN (True Negative)  | 983    | Modèle a correctement prédit 0 (pas d’achat)                |
+        # | FP (False Positive) | 43     | Modèle a prédit 1 alors que c’était 0 (faux signal d’achat) |
+        # | FN (False Negative) | 108    | Modèle a prédit 0 alors que c’était 1 (manqué un achat)     |
+        # | TP (True Positive)  | 19     | Modèle a correctement prédit 1 (bon signal d’achat)         |
+
+        # ---- Matrice de confusion ----
+        cm = confusion_matrix(y_test, preds)
+        print("Matrice de confusion :\n", cm)
+
+        plt.figure(figsize=(4, 4))
+        plt.imshow(cm, cmap="Blues")
+        plt.title("Matrice de confusion")
+        plt.xlabel("Prédit")
+        plt.ylabel("Réel")
+        for i in range(2):
+            for j in range(2):
+                plt.text(j, i, cm[i, j], ha="center", va="center", color="black")
+        # plt.show()
+        plt.savefig(os.path.join(output_dir, "Matrice de confusion.png"), bbox_inches="tight")
+        plt.close()
+
+        # ---- Importance des features ----
+        if hasattr(model, "feature_importances_"):
+            print("\n===== 🔍 IMPORTANCE DES FEATURES =====")
+            importance = pd.DataFrame({
+                "feature": X_train.columns,
+                "importance": model.feature_importances_
+            }).sort_values(by="importance", ascending=False)
+            print(importance)
+            importance.plot.bar(x="feature", y="importance", legend=False, figsize=(6, 3))
+            plt.title("Importance des features")
+            # plt.show()
+            plt.savefig(os.path.join(output_dir, "Importance des features.png"), bbox_inches="tight")
+            plt.close()
+
+        # ---- Arbre de décision (extrait) ----
+        if hasattr(model, "estimators_"):
+            print("\n===== 🌳 EXTRAIT D’UN ARBRE =====")
+            print(export_text(model.estimators_[0], feature_names=list(X_train.columns))[:800])
+
+        # ---- Précision selon le seuil ----
+        thresholds = np.linspace(0.1, 0.9, 9)
+        print("\n===== ⚙️ PERFORMANCE SELON SEUIL =====")
+        for t in thresholds:
+            preds_t = (probs > t).astype(int)
+            acc = accuracy_score(y_test, preds_t)
+            print(f"Seuil {t:.1f} → précision {acc:.3f}")
+
+        # ---- ROC Curve ----
+        fpr, tpr, _ = roc_curve(y_test, probs)
+        plt.figure(figsize=(5, 4))
+        plt.plot(fpr, tpr, label="ROC curve")
+        plt.plot([0, 1], [0, 1], linestyle="--", color="gray")
+        plt.xlabel("Taux de faux positifs")
+        plt.ylabel("Taux de vrais positifs")
+        plt.title("Courbe ROC")
+        plt.legend()
+        # plt.show()
+        plt.savefig(os.path.join(output_dir, "Courbe ROC.png"), bbox_inches="tight")
+        plt.close()
+
+        # ---- Interprétation SHAP (optionnelle) ----
+        try:
+            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, 10))
+            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===== ✅ FIN DE L’ANALYSE =====")
+
     def populateDataframe(self, dataframe, timeframe='5m'):
         heikinashi = qtpylib.heikinashi(dataframe)
         dataframe['haopen'] = heikinashi['open']
@@ -1130,6 +1404,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         # dataframe['atr'] = tr.rolling(window=self.DEFAULT_PARAMS['atr_period']).mean()
 
         dataframe['volume_sma_deriv'] = dataframe['volume'] * dataframe['sma5_deriv1'] / (dataframe['volume'].rolling(5).mean())
+        self.calculeDerivees(dataframe, 'volume', timeframe=timeframe, ema_period=12)
 
         self.setTrends(dataframe)
 
@@ -1253,13 +1528,13 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         eps_d1_series = eps_d1_series.fillna(global_eps_d1).replace(0, global_eps_d1)
         eps_d2_series = eps_d2_series.fillna(global_eps_d2).replace(0, global_eps_d2)
 
-        if verbose and self.dp.runmode.value in ('backtest'):
-            stats = dataframe[[d1_col, d2_col]].agg(['min', 'max']).T
-            stats['abs_max'] = dataframe[[d1_col, d2_col]].abs().max(axis=0)
-            print(f"---- Derivatives stats {timeframe}----")
-            print(stats)
-            print(f"rolling window = {window}, coef = {coef}, ema_period = {ema_period}")
-            print("---------------------------")
+        # if verbose and self.dp.runmode.value in ('backtest'):
+        #     stats = dataframe[[d1_col, d2_col]].agg(['min', 'max']).T
+        #     stats['abs_max'] = dataframe[[d1_col, d2_col]].abs().max(axis=0)
+        #     print(f"---- Derivatives stats {timeframe}----")
+        #     print(stats)
+        #     print(f"rolling window = {window}, coef = {coef}, ema_period = {ema_period}")
+        #     print("---------------------------")
 
         # mapping tendency
         def tag_by_derivatives(row):
@@ -2635,7 +2910,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
     def __init__(self, config: dict) -> None:
         super().__init__(config)
-        self.parameters = self.load_params_tree("user_data/strategies/params/")
+        # self.parameters = self.load_params_tree("user_data/strategies/params/")
 
     def setTrends(self, dataframe: DataFrame):
         SMOOTH_WIN=10