adaptive_agent.py

class AdaptiveAgent:
    def run(self, df):
        returns = df['Close'].pct_change().dropna()
        volatility = returns.std()
        mean_return = returns.mean()

        score = mean_return / volatility if volatility else 0.0

        if score > 0.05:
            signal = "High Confidence"
        elif score < -0.05:
            signal = "Risky"
        else:
            signal = "Uncertain"

        return {
            "signal": signal,
            "score": round(score, 4)
        }