73 lines
2.3 KiB
Python
73 lines
2.3 KiB
Python
"""News-driven trading strategy.
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Buy on strong positive sentiment (score > 0.7, confidence > 0.6),
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sell on strong negative sentiment. Signal strength is the product
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of sentiment score and confidence, with a decay factor for stale news.
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"""
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from __future__ import annotations
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from datetime import datetime, timezone
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from shared.schemas.trading import MarketSnapshot, SentimentContext, SignalDirection, TradeSignal
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from shared.strategies.base import BaseStrategy
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class NewsDrivenStrategy(BaseStrategy):
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"""Sentiment-based strategy driven by scored news articles."""
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name: str = "news_driven"
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def __init__(
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self,
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positive_threshold: float = 0.7,
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negative_threshold: float = -0.7,
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min_confidence: float = 0.6,
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min_articles: int = 1,
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) -> None:
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self.positive_threshold = positive_threshold
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self.negative_threshold = negative_threshold
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self.min_confidence = min_confidence
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self.min_articles = min_articles
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async def evaluate(
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self,
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ticker: str,
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market: MarketSnapshot,
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sentiment: SentimentContext | None = None,
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) -> TradeSignal | None:
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"""Generate a signal based on aggregated news sentiment."""
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if sentiment is None:
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return None
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if sentiment.article_count < self.min_articles:
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return None
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if sentiment.avg_confidence < self.min_confidence:
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return None
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score = sentiment.avg_score
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if score > self.positive_threshold:
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direction = SignalDirection.LONG
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elif score < self.negative_threshold:
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direction = SignalDirection.SHORT
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else:
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return None
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# Strength = |score| * confidence (both in [0, 1])
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strength = abs(score) * sentiment.avg_confidence
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strength = min(max(strength, 0.0), 1.0)
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return TradeSignal(
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ticker=ticker,
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direction=direction,
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strength=round(strength, 4),
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strategy_sources=[self.name],
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sentiment_context={
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"avg_score": sentiment.avg_score,
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"article_count": sentiment.article_count,
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"avg_confidence": sentiment.avg_confidence,
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},
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timestamp=datetime.now(timezone.utc),
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)
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