60 lines
2 KiB
Python
60 lines
2 KiB
Python
"""Mean reversion trading strategy.
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Buy when RSI < 30 (oversold), sell when RSI > 70 (overbought).
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Signal strength is proportional to RSI extremity.
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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 MeanReversionStrategy(BaseStrategy):
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"""Contrarian strategy based on RSI extremes."""
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name: str = "mean_reversion"
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def __init__(
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self,
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oversold_threshold: float = 30.0,
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overbought_threshold: float = 70.0,
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) -> None:
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self.oversold_threshold = oversold_threshold
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self.overbought_threshold = overbought_threshold
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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 when RSI indicates oversold/overbought conditions."""
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if market.rsi is None:
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return None
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rsi = market.rsi
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if rsi < self.oversold_threshold:
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direction = SignalDirection.LONG
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# Strength proportional to how oversold: RSI 0 -> strength 1.0, RSI 30 -> strength 0.0
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strength = (self.oversold_threshold - rsi) / self.oversold_threshold
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elif rsi > self.overbought_threshold:
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direction = SignalDirection.SHORT
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# Strength proportional to how overbought: RSI 100 -> strength 1.0, RSI 70 -> strength 0.0
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strength = (rsi - self.overbought_threshold) / (100.0 - self.overbought_threshold)
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else:
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return None
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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=None,
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timestamp=datetime.now(timezone.utc),
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)
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