Enhancements & Best Practices

Add ensemble models, portfolio optimization, and risk management features. Includes important disclaimers and a comprehensive FAQ on stock prediction models.

Enhancement 1: Ensemble Models

# Combine multiple model predictions
class EnsemblePredictor:
    def __init__(self, models):
        self.models = models

    def predict(self, df):
        predictions = [m.predict(df) for m in self.models]
        min_len = min(len(p) for p in predictions)
        predictions = [p[:min_len] for p in predictions]
        return np.mean(predictions, axis=0)

Enhancement 2: Risk Management

# Position sizing with Kelly Criterion
def kelly_fraction(win_rate, avg_win, avg_loss):
    if avg_loss == 0: return 0
    b = avg_win / abs(avg_loss)
    p = win_rate
    return max(0, (b * p - (1 - p)) / b)

# Stop-loss implementation
def apply_stop_loss(signals, prices, stop_loss_pct=0.05):
    entry_price = None
    for i, signal in enumerate(signals):
        if signal == 1:
            entry_price = prices[i]
        elif entry_price and prices[i] < entry_price * (1 - stop_loss_pct):
            signals[i] = -1  # Force sell
            entry_price = None
    return signals

Important Disclaimers

Frequently Asked Questions

What You Built