feat: trading strategies — momentum, mean reversion, news-driven

shared/strategies/mean_reversion.py

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+"""Mean reversion strategy — buy oversold, sell overbought using RSI."""
+
+from datetime import datetime, timezone
+
+from shared.schemas.trading import MarketSnapshot, SentimentContext, SignalDirection, TradeSignal
+from shared.strategies.base import BaseStrategy
+
+
+class MeanReversionStrategy(BaseStrategy):
+    """Trade on the assumption that extreme RSI readings will revert to the mean.
+
+    **Buy signal** (LONG):
+        RSI < 30 (oversold).
+
+    **Sell signal** (SHORT):
+        RSI > 70 (overbought).
+
+    Signal strength is proportional to how far the RSI is from its
+    threshold, clamped to [0, 1].
+
+    * Buy strength  = ``(30 - rsi) / 30``
+    * Sell strength  = ``(rsi - 70) / 30``
+    """
+
+    name: str = "mean_reversion"
+
+    async def evaluate(
+        self,
+        ticker: str,
+        market: MarketSnapshot,
+        sentiment: SentimentContext | None = None,
+    ) -> TradeSignal | None:
+        if market.rsi is None:
+            return None
+
+        rsi = market.rsi
+
+        if rsi < 30:
+            direction = SignalDirection.LONG
+            raw_strength = (30 - rsi) / 30
+        elif rsi > 70:
+            direction = SignalDirection.SHORT
+            raw_strength = (rsi - 70) / 30
+        else:
+            # RSI in neutral territory — no opinion.
+            return None
+
+        strength = max(0.0, min(1.0, raw_strength))
+
+        return TradeSignal(
+            ticker=ticker,
+            direction=direction,
+            strength=strength,
+            strategy_sources=[self.name],
+            timestamp=datetime.now(tz=timezone.utc),
+        )