Update

polymarket/strategies/examples/simple_probability.py

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