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if suppress_warnings:
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suppress_warnings_method()
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print(f"Starting analysis for {symbol}...")
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print(f"Fetching stock data for {symbol}...")
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data = fetch_stock_data(symbol, start_date, end_date)
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print(f"Adding technical indicators...")
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data = add_technical_indicators(data)
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data.dropna(inplace=True)
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if quick_test:
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print("Quick test mode: Using only the last 100 data points.")
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data = data.tail(100)
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print("Preparing data for model training...")
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features = [
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"Close",
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"Volume",
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"SMA_20",
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"SMA_50",
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"RSI",
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"MACD",
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"BB_upper",
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"BB_middle",
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"BB_lower",
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"Volatility",
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"Price_Change",
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"Volume_Change",
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"High_Low_Range",
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]
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X, y, scaler = prepare_data(data[features])
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print("Augmenting data...")
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X_aug, y_aug = augment_data(X, y)
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X = np.concatenate((X, X_aug), axis=0)
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y = np.concatenate((y, y_aug), axis=0)
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print("Splitting data into training and testing sets...")
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X_train, X_test, y_train, y_test = time_based_train_test_split(X, y, test_size=0.2)
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print("\nStarting model training and hyperparameter tuning...")
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models = []
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if "LSTM" in models_to_run:
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models.append(("LSTM", create_lstm_model((X.shape[1], X.shape[2]))))
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if "GRU" in models_to_run:
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models.append(("GRU", create_gru_model((X.shape[1], X.shape[2]))))
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if "Random Forest" in models_to_run:
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models.append(("Random Forest", tune_random_forest(X, y, quick_test)))
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if "XGBoost" in models_to_run:
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models.append(("XGBoost", tune_xgboost(X, y, quick_test)))
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results = {}
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oof_predictions = {}
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model_stats = []
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with tqdm(total=len(models), desc="Training Models", position=0) as pbar:
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for name, model in models:
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print(f"\nTraining and evaluating {name} model...")
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cv_score, cv_std, overall_score, oof_pred = train_and_evaluate_model(
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model, X, y, n_splits=3 if quick_test else 5, model_name=name
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)
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print(f" {name} model results:")
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print(f" Cross-validation R² score: {cv_score:.4f} (±{cv_std:.4f})")
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print(f" Overall out-of-fold R² score: {overall_score:.4f}")
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print(f"Retraining {name} model on full dataset...")
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if isinstance(model, (RandomForestRegressor, XGBRegressor)):
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model.fit(X.reshape(X.shape[0], -1), y)
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train_score = model.score(X.reshape(X.shape[0], -1), y)
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else:
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with tqdm(total=100, desc="Epochs", leave=False) as epoch_pbar:
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class EpochProgressCallback(Callback):
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def on_epoch_end(self, epoch, logs=None):
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epoch_pbar.update(1)
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history = model.fit(
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X,
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y,
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epochs=100,
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batch_size=32,
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verbose=0,
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callbacks=[EpochProgressCallback()],
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)
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train_score = (
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1 - history.history["loss"][-1]
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) # Use final training loss as a proxy for R²
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results[name] = model
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oof_predictions[name] = oof_pred
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overfitting_score = train_score - overall_score
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model_stats.append(
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{
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"Model": name,
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"CV R² Score": cv_score,
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"CV R² Std": cv_std,
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"OOF R² Score": overall_score,
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"Train R² Score": train_score,
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