calcul model sur bouton

src/app.py

@@ -15,6 +15,10 @@ from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import Dense
 from tensorflow.keras.utils import plot_model
 
+from keras.models import Sequential
+from keras.layers import Dense
+from ann_visualizer.visualize import ann_viz
+
 app = Flask(__name__)
 FREQTRADE_USERDATA_DIR = '/mnt/external'
 
@@ -128,43 +132,6 @@ def read_feather(filename):
         # dataframe['min200'] = talib.MIN(dataframe['close'], timeperiod=200)
         # dataframe['max200'] = talib.MAX(dataframe['close'], timeperiod=200)
 
-        # Choisir les colonnes techniques comme variables d'entrée (X)
-        feature_cols = ['rsi', 'sma20', 'sma5_1h', 'volume']
-        df = dataframe
-        # Variable cible
-        df['target'] = df['futur_price_1h']
-
-        # Supprimer les lignes avec des NaN
-        df.dropna(subset=feature_cols + ['target'], inplace=True)
-
-        X = df[feature_cols].values
-        y = df['target'].values
-
-        # Normalisation
-        scaler = StandardScaler()
-        X = scaler.fit_transform(X)
-
-        # Split
-        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
-
-        # Modèle
-        model = Sequential([
-            Dense(64, input_dim=X.shape[1], activation='relu'),
-            Dense(32, activation='relu'),
-            Dense(1)  # Prédiction continue
-        ])
-
-        model.compile(optimizer='adam', loss='mse', metrics=['mae'])
-
-        # Entraînement
-        model.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_test, y_test))
-
-        loss, mae = model.evaluate(X_test, y_test)
-        print(f"Erreur moyenne absolue : {mae:.4f}")
-
-        model.summary()
-
-        plot_model(model, show_shapes=True, show_layer_names=True, to_file=FREQTRADE_USERDATA_DIR + "/reports/model.png")
         return dataframe.to_json(orient="records")
     except Exception as e:
         print(e)
@@ -229,8 +196,57 @@ def get_chart_data():
 
     return df.to_json(orient="records") #jsonify(chart_data)
 
-@app.route('/model')
-def show_model():
+
+@app.route('/generate_model')
+def generate_model():
+    filename = request.args.get('filename', '')
+    path = os.path.join(FREQTRADE_USERDATA_DIR + "/data/binance/", filename)
+    print(path)
+    # indicators = request.args.get('indicators', '').split(',')
+    df = pd.read_feather(path)
+
+    # Choisir les colonnes techniques comme variables d'entrée (X)
+    feature_cols = ['close', 'rsi', 'sma5', 'sma10', 'sma20', 'sma5_1h', 'volume', 'sma5_1h']
+
+    # Variable cible 2 heures
+    df['target'] = (df['close'].shift(-24) - df['close']) / df['close']
+
+    # Supprimer les lignes avec des NaN
+    df.dropna(subset=feature_cols + ['target'], inplace=True)
+
+    X = df[feature_cols].values
+    y = df['target'].values
+
+    # Normalisation
+    scaler = StandardScaler()
+    X = scaler.fit_transform(X)
+
+    # Split
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+    # Modèle
+    model = Sequential([
+        Dense(64, input_dim=X.shape[1], activation='relu'),
+        Dense(32, activation='relu'),
+        Dense(1)  # Prédiction continue
+    ])
+
+    model.compile(optimizer='adam', loss='mse', metrics=['mae'])
+
+    # Entraînement
+    model.fit(X_train, y_train, epochs=100, batch_size=64, validation_data=(X_test, y_test))
+
+    loss, mae = model.evaluate(X_test, y_test)
+    print(f"Erreur moyenne absolue : {mae:.4f}")
+
+    model.summary()
+
+    plot_model(model, show_shapes=True, show_layer_names=True, to_file=FREQTRADE_USERDATA_DIR + "/reports/model.png")
+
+    model.save(FREQTRADE_USERDATA_DIR + "/reports/model.h5")
+
+    # ann_viz(model, title="Mon réseau", filename=FREQTRADE_USERDATA_DIR + "/reports/network.gv", view=True)
+
     # Créer un exemple de modèle si non encore généré
     model_path = FREQTRADE_USERDATA_DIR + "/reports/model.png"
     if not os.path.exists(model_path):
@@ -242,6 +258,7 @@ def show_model():
         plot_model(model, to_file=model_path, show_shapes=True, show_layer_names=True)
     return render_template('model.html', model_image=model_path)
 
+
 # Route pour servir les fichiers statiques (optionnelle si bien configuré)
 @app.route('/static/<path:filename>')
 def static_files(filename):