"""News-driven trading strategy.

Buy on strong positive sentiment (score > 0.7, confidence > 0.6),
sell on strong negative sentiment. Signal strength is the product
of sentiment score and confidence, with a decay factor for stale news.
"""

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 NewsDrivenStrategy(BaseStrategy):
    """Sentiment-based strategy driven by scored news articles."""

    name: str = "news_driven"

    def __init__(
        self,
        positive_threshold: float = 0.7,
        negative_threshold: float = -0.7,
        min_confidence: float = 0.6,
        min_articles: int = 1,
    ) -> None:
        self.positive_threshold = positive_threshold
        self.negative_threshold = negative_threshold
        self.min_confidence = min_confidence
        self.min_articles = min_articles

    async def evaluate(
        self,
        ticker: str,
        market: MarketSnapshot,
        sentiment: SentimentContext | None = None,
    ) -> TradeSignal | None:
        """Generate a signal based on aggregated news sentiment."""
        if sentiment is None:
            return None

        if sentiment.article_count < self.min_articles:
            return None

        if sentiment.avg_confidence < self.min_confidence:
            return None

        score = sentiment.avg_score

        if score > self.positive_threshold:
            direction = SignalDirection.LONG
        elif score < self.negative_threshold:
            direction = SignalDirection.SHORT
        else:
            return None

        # Strength = |score| * confidence (both in [0, 1])
        strength = abs(score) * sentiment.avg_confidence
        strength = min(max(strength, 0.0), 1.0)

        return TradeSignal(
            ticker=ticker,
            direction=direction,
            strength=round(strength, 4),
            strategy_sources=[self.name],
            sentiment_context={
                "avg_score": sentiment.avg_score,
                "article_count": sentiment.article_count,
                "avg_confidence": sentiment.avg_confidence,
            },
            timestamp=datetime.now(timezone.utc),
        )