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https://github.com/tcsenpai/goldigger.git
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fixed a plotting problem
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tcsenpai
parent
65ce172f3f
commit
aec7171ed9
75
goldigger.py
75
goldigger.py
@ -120,15 +120,11 @@ def create_gru_model(input_shape):
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# Train and evaluate a model using time series cross-validation
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def train_and_evaluate_model(model, X, y, n_splits=5, model_name="Model"):
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predictions, true_values = walk_forward_validation(X, y, model, n_splits)
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score = r2_score(true_values, predictions)
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return score, 0, score, predictions # Return the same score for consistency
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def walk_forward_validation(X, y, model, n_splits=5):
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tscv = TimeSeriesSplit(n_splits=n_splits)
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all_predictions = []
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all_true_values = []
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with tqdm(total=n_splits, desc=f"Cross-validation for {model_name}", leave=False) as pbar:
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for train_index, test_index in tscv.split(X):
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X_train, X_test = X[train_index], X[test_index]
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y_train, y_test = y[train_index], y[test_index]
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@ -146,8 +142,10 @@ def walk_forward_validation(X, y, model, n_splits=5):
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all_predictions.extend(predictions)
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all_true_values.extend(y_test)
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pbar.update(1)
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return np.array(all_predictions), np.array(all_true_values)
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score = r2_score(all_true_values, all_predictions)
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return score, 0, score, np.array(all_predictions)
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# Make predictions using an ensemble of models
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def ensemble_predict(models, X):
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@ -317,41 +315,45 @@ def augment_data(X, y, noise_level=0.01):
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return X_aug, y_aug
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# Main function to analyze stock data and make predictions
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def analyze_and_predict_stock(symbol, start_date, end_date, future_days=30, suppress_warnings=False, quick_test=False):
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def analyze_and_predict_stock(symbol, start_date, end_date, future_days=30, suppress_warnings=False, quick_test=False, models_to_run=['LSTM', 'GRU', 'Random Forest', 'XGBoost']):
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# Suppress warnings if flag is set
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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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# Fetch and prepare stock data
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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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# Use only the last 100 data points for quick testing
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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 = ['Close', 'Volume', 'SMA_20', 'SMA_50', 'RSI', 'MACD', 'BB_upper', 'BB_middle', 'BB_lower', 'Volatility', 'Price_Change', 'Volume_Change', 'High_Low_Range']
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X, y, scaler = prepare_data(data[features])
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# Augment data
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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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# Split data into training and testing sets
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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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# Train and evaluate models
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print("\nStarting model training and hyperparameter tuning...")
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models = [
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("LSTM", create_lstm_model((X.shape[1], X.shape[2]))),
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("GRU", create_gru_model((X.shape[1], X.shape[2]))),
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("Random Forest", tune_random_forest(X, y, quick_test)),
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("XGBoost", tune_xgboost(X, y, quick_test))
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]
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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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@ -364,18 +366,17 @@ def analyze_and_predict_stock(symbol, start_date, end_date, future_days=30, supp
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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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# Retrain on full dataset
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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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history = model.fit(X, y, epochs=100, batch_size=32, verbose=0)
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history = model.fit(X, y, epochs=100, batch_size=32, verbose=1)
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train_score = 1 - history.history['loss'][-1] # 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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# Calculate overfitting score (difference between train and cv scores)
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overfitting_score = train_score - overall_score
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model_stats.append({
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@ -400,38 +401,31 @@ def analyze_and_predict_stock(symbol, start_date, end_date, future_days=30, supp
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print("\nModel Performance Summary:")
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print(tabulate(stats_df, headers='keys', tablefmt='pretty', floatfmt='.4f'))
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# Use out-of-fold predictions for ensemble
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print("\nCalculating ensemble weights...")
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ensemble_weights = calculate_ensemble_weights(models, X_test, y_test)
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print(f"Ensemble weights: {ensemble_weights}")
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print("Making ensemble predictions...")
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ensemble_predictions = weighted_ensemble_predict([model for _, model in models], X, ensemble_weights)
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# Predict future data
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print(f"Predicting future data for the next {future_days} days...")
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future_predictions = []
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for model in results.values():
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for name, model in models:
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print(f" Making future predictions with {name} model...")
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future_pred = predict_future(model, X[-1], scaler, future_days)
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future_predictions.append(future_pred)
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future_predictions = np.mean(future_predictions, axis=0)
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# Inverse transform the predictions (only for 'Close' price)
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print("Inverse transforming predictions...")
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close_price_scaler = MinMaxScaler(feature_range=(0, 1))
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close_price_scaler.fit(data['Close'].values.reshape(-1, 1))
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ensemble_predictions = close_price_scaler.inverse_transform(ensemble_predictions.reshape(-1, 1))
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future_predictions = close_price_scaler.inverse_transform(future_predictions.reshape(-1, 1))
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# Calculate returns and risk metrics
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actual_returns = data['Close'].pct_change().dropna()
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predicted_returns = pd.Series(ensemble_predictions.flatten()).pct_change().dropna()
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# Ensure ensemble_predictions matches the length of the actual data
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ensemble_predictions = ensemble_predictions[-len(data):]
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sharpe_ratio, max_drawdown = calculate_risk_metrics(actual_returns)
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print(f"Sharpe Ratio: {sharpe_ratio:.4f}")
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print(f"Max Drawdown: {max_drawdown:.4f}")
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# Implement trading strategy
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strategy_returns = implement_trading_strategy(data['Close'].values[-len(ensemble_predictions):], ensemble_predictions.flatten())
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strategy_sharpe_ratio = np.mean(strategy_returns) / np.std(strategy_returns) * np.sqrt(252)
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print(f"Trading Strategy Sharpe Ratio: {strategy_sharpe_ratio:.4f}")
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# Plot results
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print("Plotting results...")
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plt.figure(figsize=(20, 24)) # Increased figure height
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# Price prediction plot
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@ -462,6 +456,11 @@ def analyze_and_predict_stock(symbol, start_date, end_date, future_days=30, supp
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# Lower the title and add more space between plot and table
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plt.title('Model Performance Summary', pad=60)
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# Implement trading strategy
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strategy_returns = implement_trading_strategy(plot_data['Close'].values, ensemble_predictions.flatten())
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strategy_sharpe_ratio = np.mean(strategy_returns) / np.std(strategy_returns) * np.sqrt(252)
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print(f"Trading Strategy Sharpe Ratio: {strategy_sharpe_ratio:.4f}")
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# Calculate cumulative returns of the trading strategy
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cumulative_returns = (1 + strategy_returns).cumprod() - 1
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