SII-leiyuandClaude
0254e2c01a
- Add trade monitoring and whale detection system - Add LLM-powered trade analysis - Add market data fetching from Polymarket API - Include example environment configuration - Add automated report generation 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
60 lines
1.7 KiB
Python
60 lines
1.7 KiB
Python
"""LLM decision models."""
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from datetime import datetime
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from typing import Optional
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from enum import Enum
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from pydantic import BaseModel, Field
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class TradeAction(str, Enum):
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"""Recommended trade action."""
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BUY = "BUY"
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SELL = "SELL"
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HOLD = "HOLD" # Do not trade
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class TraderCredibility(str, Enum):
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"""Trader credibility level based on leaderboard ranking."""
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HIGH = "HIGH" # Top 100
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MEDIUM = "MEDIUM" # 100-500
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LOW = "LOW" # 500+
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UNKNOWN = "UNKNOWN" # Not on leaderboard
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class TradeRecommendation(BaseModel):
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"""Trade recommendation from LLM."""
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action: TradeAction
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outcome: str # Which outcome to trade
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confidence: float = Field(ge=0.0, le=1.0) # 0-1 confidence score
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suggested_price: Optional[float] = None
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suggested_size_percent: float = Field(default=0.1, ge=0.0, le=1.0) # % of balance
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reasoning: str = ""
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# Insider trading assessment fields
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insider_trading_likelihood: float = Field(default=0.0, ge=0.0, le=1.0) # 0-1 likelihood
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trader_credibility: TraderCredibility = TraderCredibility.UNKNOWN
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insider_evidence: str = "" # Evidence supporting insider trading assessment
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class LLMDecision(BaseModel):
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"""Complete LLM decision for a whale trade."""
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whale_trade_id: str
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market_id: str
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analysis: str # Full LLM analysis text
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recommendation: TradeRecommendation
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created_at: datetime = Field(default_factory=datetime.utcnow)
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executed: bool = False
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execution_result: Optional[str] = None
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@property
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def should_trade(self) -> bool:
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"""Check if we should execute this trade."""
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return (
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self.recommendation.action != TradeAction.HOLD
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and self.recommendation.confidence >= 0.6
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)
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