feat: signal generator — weighted ensemble with market data

shared/strategies/mean_reversion.py

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+"""Mean reversion trading strategy.
+
+Buy when RSI < 30 (oversold), sell when RSI > 70 (overbought).
+Signal strength is proportional to RSI extremity.
+"""
+
+from __future__ import annotations
+
+from datetime import datetime, timezone
+
+from shared.schemas.trading import MarketSnapshot, SentimentContext, SignalDirection, TradeSignal
+from shared.strategies.base import BaseStrategy
+
+
+class MeanReversionStrategy(BaseStrategy):
+    """Contrarian strategy based on RSI extremes."""
+
+    name: str = "mean_reversion"
+
+    def __init__(
+        self,
+        oversold_threshold: float = 30.0,
+        overbought_threshold: float = 70.0,
+    ) -> None:
+        self.oversold_threshold = oversold_threshold
+        self.overbought_threshold = overbought_threshold
+
+    async def evaluate(
+        self,
+        ticker: str,
+        market: MarketSnapshot,
+        sentiment: SentimentContext | None = None,
+    ) -> TradeSignal | None:
+        """Generate a signal when RSI indicates oversold/overbought conditions."""
+        if market.rsi is None:
+            return None
+
+        rsi = market.rsi
+
+        if rsi < self.oversold_threshold:
+            direction = SignalDirection.LONG
+            # Strength proportional to how oversold: RSI 0 -> strength 1.0, RSI 30 -> strength 0.0
+            strength = (self.oversold_threshold - rsi) / self.oversold_threshold
+        elif rsi > self.overbought_threshold:
+            direction = SignalDirection.SHORT
+            # Strength proportional to how overbought: RSI 100 -> strength 1.0, RSI 70 -> strength 0.0
+            strength = (rsi - self.overbought_threshold) / (100.0 - self.overbought_threshold)
+        else:
+            return None
+
+        strength = min(max(strength, 0.0), 1.0)
+
+        return TradeSignal(
+            ticker=ticker,
+            direction=direction,
+            strength=round(strength, 4),
+            strategy_sources=[self.name],
+            sentiment_context=None,
+            timestamp=datetime.now(timezone.utc),
+        )