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

shared/strategies/news_driven.py

@@ -0,0 +1,60 @@
+"""News-driven strategy — trade on aggregated news sentiment."""
+
+from datetime import datetime, timezone
+
+from shared.schemas.trading import MarketSnapshot, SentimentContext, SignalDirection, TradeSignal
+from shared.strategies.base import BaseStrategy
+
+
+class NewsDrivenStrategy(BaseStrategy):
+    """Generate signals from aggregated news sentiment for a ticker.
+
+    **Buy signal** (LONG):
+        ``avg_score > 0.3`` AND ``avg_confidence > 0.5`` AND
+        ``article_count >= 2``.
+
+    **Sell signal** (SHORT):
+        ``avg_score < -0.3`` AND ``avg_confidence > 0.5`` AND
+        ``article_count >= 2``.
+
+    Signal strength = ``abs(avg_score) * avg_confidence``, clamped to
+    [0, 1].
+    """
+
+    name: str = "news_driven"
+
+    async def evaluate(
+        self,
+        ticker: str,
+        market: MarketSnapshot,
+        sentiment: SentimentContext | None = None,
+    ) -> TradeSignal | None:
+        if sentiment is None:
+            return None
+
+        # Require at least 2 articles for statistical confidence.
+        if sentiment.article_count < 2:
+            return None
+
+        # Require minimum confidence.
+        if sentiment.avg_confidence <= 0.5:
+            return None
+
+        if sentiment.avg_score > 0.3:
+            direction = SignalDirection.LONG
+        elif sentiment.avg_score < -0.3:
+            direction = SignalDirection.SHORT
+        else:
+            # Sentiment is neutral — no opinion.
+            return None
+
+        raw_strength = abs(sentiment.avg_score) * sentiment.avg_confidence
+        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),
+        )