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"Overfitting Score": overfitting_score,
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}
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)
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pbar.update(1)
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# Create a DataFrame with model statistics
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stats_df = pd.DataFrame(model_stats)
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stats_df = stats_df.sort_values("OOF R² Score", ascending=False).reset_index(
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drop=True
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)
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# Add overfitting indicator
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stats_df["Overfit"] = stats_df["Overfitting Score"].apply(
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lambda x: "Yes" if x > 0.05 else "No"
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)
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# Print the table
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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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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(
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[model for _, model in models], X, ensemble_weights
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)
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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 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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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(
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ensemble_predictions.reshape(-1, 1)
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)
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future_predictions = close_price_scaler.inverse_transform(
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future_predictions.reshape(-1, 1)
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)
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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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print("Plotting results...")
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fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(20, 24))
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# Price prediction plot
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plot_data = data.iloc[-len(ensemble_predictions) :]
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future_dates = pd.date_range(
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start=plot_data.index[-1] + pd.Timedelta(days=1), periods=future_days
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)
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ax1.plot(plot_data.index, plot_data["Close"], label="Actual Price", color="blue")
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ax1.plot(
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plot_data.index,
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ensemble_predictions,
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label="Predicted Price",
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color="red",
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linestyle="--",
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)
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ax1.plot(
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future_dates,
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future_predictions,
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label="Future Predictions",
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color="green",
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linestyle="--",
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)
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# Add price indications for every day (initially invisible)
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annotations = []
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for i, (date, price) in enumerate(zip(plot_data.index, ensemble_predictions)):
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ann = ax1.annotate(
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f"${price[0]:.2f}",
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(date, price[0]),
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xytext=(0, 10),
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textcoords="offset points",
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ha="center",
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va="bottom",
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fontsize=8,
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alpha=0.7,
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visible=False,
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)
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annotations.append(ann)
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for i, (date, price) in enumerate(zip(future_dates, future_predictions)):
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ann = ax1.annotate(
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f"${price[0]:.2f}",
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(date, price[0]),
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xytext=(0, -10),
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textcoords="offset points",
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ha="center",
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va="top",
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