105 lines
3.7 KiB
Python
105 lines
3.7 KiB
Python
"""
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Simple Probability Strategy Example
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Trades when market probability deviates significantly from fair value.
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"""
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from typing import Dict, Optional
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from ..base_strategy import BaseStrategy, MarketSignal
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class SimpleProbabilityStrategy(BaseStrategy):
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"""
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Simple strategy that buys when probability is too low,
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sells when probability is too high.
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"""
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def __init__(self,
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name: str = "SimpleProbability",
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initial_balance: float = 1000.0,
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threshold: float = 0.15,
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min_confidence: float = 0.7):
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"""
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Initialize strategy.
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Args:
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name: Strategy name
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initial_balance: Starting balance
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threshold: Probability deviation threshold (0.15 = 15%)
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min_confidence: Minimum confidence to trade
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"""
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super().__init__(name, initial_balance)
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self.threshold = threshold
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self.min_confidence = min_confidence
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def analyze_market(self, market_data: Dict) -> Optional[MarketSignal]:
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"""
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Analyze market and generate signal.
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Strategy logic:
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- If Yes probability < 0.5 - threshold: Buy (undervalued)
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- If Yes probability > 0.5 + threshold: Sell (overvalued)
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"""
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market = market_data.get('market', {})
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prices = market_data.get('prices', {})
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if not prices:
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return None
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yes_price = prices.get('Yes', 0.5)
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no_price = prices.get('No', 0.5)
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# Calculate deviation from fair value (0.5)
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deviation = abs(yes_price - 0.5)
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if deviation < self.threshold:
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return None # Not enough deviation
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# Get token_id from market
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market_obj = market_data.get('market', {})
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token_ids = market_obj.get('clobTokenIds', [])
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if not token_ids:
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return None
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token_id = token_ids[0]
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# Determine action
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if yes_price < (0.5 - self.threshold):
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# Yes is undervalued, buy
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confidence = min(1.0, deviation / self.threshold)
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if confidence >= self.min_confidence:
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return MarketSignal(
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action='BUY',
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token_id=token_id,
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size=0.2, # 20% of balance
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confidence=confidence,
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reason=f"Yes probability {yes_price:.2%} is undervalued (deviation: {deviation:.2%})",
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metadata={'yes_price': yes_price, 'deviation': deviation}
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)
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elif yes_price > (0.5 + self.threshold):
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# Yes is overvalued, sell (close position if we have one)
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confidence = min(1.0, deviation / self.threshold)
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if confidence >= self.min_confidence:
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# Check if we have a position to close
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if token_id in self.positions:
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return MarketSignal(
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action='SELL',
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token_id=token_id,
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size=1.0, # Close entire position
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confidence=confidence,
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reason=f"Yes probability {yes_price:.2%} is overvalued (deviation: {deviation:.2%})",
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metadata={'yes_price': yes_price, 'deviation': deviation}
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)
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return None
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def get_parameters(self) -> Dict:
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"""Return strategy parameters"""
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return {
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'threshold': self.threshold,
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'min_confidence': self.min_confidence,
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'max_position_size': self.max_position_size,
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'max_total_exposure': self.max_total_exposure
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}
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