AI Signal Aggregator — Meta-Strategy Signal Combiner

import pandas as pd, numpy as np
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.calibration import CalibratedClassifierCV

class AISignalAggregator:

    @staticmethod
    def weighted_vote(signals: dict, weights: dict = None) -> dict:
        """Combine signals from multiple strategies using weighted voting."""
        default_weights = {
            "trend_following": 1.2, "mean_reversion": 0.8, "breakout": 1.0,
            "price_action": 1.3, "divergence": 0.9, "momentum": 0.8,
            "institutional": 1.5, "news": 0.7, "sentiment_contrarian": 0.6,
            "fibonacci": 0.7, "harmonic": 0.6, "elliott_wave": 0.5,
            "wyckoff": 1.2, "supply_demand": 1.1, "volume_profile": 1.0,
            "market_structure": 1.3, "session_breakout": 0.9, "mtf_confluence": 1.4,
        }
        weights = weights or default_weights
        total_score = 0
        total_weight = 0
        details = []

        for strategy, signal in signals.items():
            w = weights.get(strategy, 1.0)
            # Normalize signal to -1 (sell) to +1 (buy)
            if isinstance(signal, str):
                s = signal.upper()
                score = 1.0 if "BUY" in s or "BULL" in s or "LONG" in s else -1.0 if "SELL" in s or "BEAR" in s or "SHORT" in s else 0
            elif isinstance(signal, (int, float)):
                score = np.clip(signal, -1, 1)
            elif isinstance(signal, dict):
                score = signal.get("score", signal.get("signal_score", 0))
            else:
                continue

            total_score += score * w
            total_weight += abs(w)
            details.append({"strategy": strategy, "signal_score": round(score, 2), "weight": w, "contribution": round(score * w, 3)})

        normalized = total_score / max(total_weight, 1e-10)
        agreement = sum(1 for d in details if np.sign(d["signal_score"]) == np.sign(normalized)) / max(len(details), 1)

        return {
            "composite_score": round(normalized, 4),
            "direction": "STRONG BUY" if normalized > 0.5 else "BUY" if normalized > 0.2 else "STRONG SELL" if normalized < -0.5 else "SELL" if normalized < -0.2 else "NEUTRAL",
            "confidence": round(min(abs(normalized) * agreement * 1.5, 0.95), 3),
            "agreement_pct": round(agreement * 100, 1),
            "n_strategies": len(details),
            "bullish_count": sum(1 for d in details if d["signal_score"] > 0),
            "bearish_count": sum(1 for d in details if d["signal_score"] < 0),
            "neutral_count": sum(1 for d in details if d["signal_score"] == 0),
            "top_contributors": sorted(details, key=lambda d: abs(d["contribution"]), reverse=True)[:5],
            "conflicts": [d["strategy"] for d in details if np.sign(d["signal_score"]) != np.sign(normalized) and d["signal_score"] != 0],
            "trade_decision": AISignalAggregator._make_decision(normalized, agreement, len(details)),
        }

    @staticmethod
    def _make_decision(score: float, agreement: float, n_strategies: int) -> str:
        if n_strategies < 3:
            return "INSUFFICIENT DATA — need at least 3 strategy signals"
        if abs(score) > 0.4 and agreement > 0.7:
            return f"HIGH CONVICTION {'BUY' if score > 0 else 'SELL'} — full position size"
        if abs(score) > 0.25 and agreement > 0.5:
            return f"MODERATE {'BUY' if score > 0 else 'SELL'} — reduced position size"
        if abs(score) > 0.15:
            return f"LOW CONVICTION {'BUY' if score > 0 else 'SELL'} — test position only"
        return "NO TRADE — insufficient consensus across strategies"

    @staticmethod
    def train_meta_model(historical_signals: pd.DataFrame, outcomes: pd.Series) -> dict:
        """Train an ML meta-model to learn optimal signal weights from history."""
        X = historical_signals.dropna()
        y = (outcomes.reindex(X.index) > 0).astype(int)
        common = X.index.intersection(y.index)
        X, y = X.loc[common], y.loc[common]

        # Time-series split
        split = int(len(X) * 0.7)
        X_train, X_test = X.iloc[:split], X.iloc[split:]
        y_train, y_test = y.iloc[:split], y.iloc[split:]

        model = CalibratedClassifierCV(GradientBoostingClassifier(n_estimators=100, max_depth=3), cv=3)
        model.fit(X_train, y_train)
        accuracy = model.score(X_test, y_test)

        # Extract learned weights (feature importance)
        base_model = model.calibrated_classifiers_[0].estimator
        importances = dict(zip(X.columns, base_model.feature_importances_))
        top = sorted(importances.items(), key=lambda x: x[1], reverse=True)

        return {
            "oos_accuracy": round(accuracy, 4),
            "learned_weights": {k: round(v, 4) for k, v in top[:10]},
            "most_predictive": top[0][0],
            "least_predictive": top[-1][0],
            "WARNING": "Meta-model overfits easily. Re-train monthly with walk-forward.",
        }