Initial commit: Polymarket Whale Watcher
- 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>
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SII-leiyu
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"""LLM analyzer service - analyzes whale trades using AI."""
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import json
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import logging
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import os
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import re
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from datetime import datetime
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from typing import Optional
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from google import genai
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from google.genai import types
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from src.config import get_settings
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from src.models.trade import WhaleTrade
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from src.models.decision import LLMDecision, TradeRecommendation, TradeAction, TraderCredibility
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from src.services.anomaly_detector import AnomalyDetector
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from src.prompts.whale_analyzer import WhaleAnalyzerPrompts
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logger = logging.getLogger(__name__)
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class LLMAnalyzer:
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"""
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Analyzes whale trades using LLM (Google Gemini models).
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Combines trade context with superforecaster methodology to generate
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comprehensive analysis reports with trading recommendations.
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"""
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def __init__(self):
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self.settings = get_settings()
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# Configure Gemini API using new client SDK
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os.environ["GOOGLE_API_KEY"] = self.settings.gemini_api_key
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self.client = genai.Client()
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self.anomaly_detector = AnomalyDetector()
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self.prompts = WhaleAnalyzerPrompts()
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def _extract_json_from_response(self, response: str) -> Optional[dict]:
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"""
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Extract JSON from LLM response.
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Args:
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response: The LLM response text
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Returns:
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Parsed JSON dict or None
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"""
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# Try to find JSON in code blocks
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json_pattern = r"```(?:json)?\s*([\s\S]*?)```"
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matches = re.findall(json_pattern, response)
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for match in matches:
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try:
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return json.loads(match.strip())
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except json.JSONDecodeError:
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continue
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# Try to find raw JSON
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try:
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# Find JSON-like content
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start = response.find("{")
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end = response.rfind("}") + 1
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if start >= 0 and end > start:
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return json.loads(response[start:end])
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except json.JSONDecodeError:
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pass
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return None
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def _parse_recommendation(self, json_data: dict) -> TradeRecommendation:
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"""
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Parse JSON data into TradeRecommendation.
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Args:
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json_data: Parsed JSON from LLM
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Returns:
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TradeRecommendation object
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"""
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action_str = json_data.get("action", "HOLD").upper()
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try:
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action = TradeAction(action_str)
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except ValueError:
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action = TradeAction.HOLD
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confidence = float(json_data.get("confidence", 0.0))
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# Clamp confidence to valid range
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confidence = max(0.0, min(1.0, confidence))
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suggested_price = json_data.get("suggested_price")
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if suggested_price is not None:
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suggested_price = float(suggested_price)
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suggested_size = float(json_data.get("suggested_size_percent", 0.1))
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suggested_size = max(0.0, min(1.0, suggested_size))
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# Parse insider trading assessment fields
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insider_likelihood = float(json_data.get("insider_trading_likelihood", 0.0))
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insider_likelihood = max(0.0, min(1.0, insider_likelihood))
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credibility_str = json_data.get("trader_credibility", "UNKNOWN").upper()
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try:
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trader_credibility = TraderCredibility(credibility_str)
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except ValueError:
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trader_credibility = TraderCredibility.UNKNOWN
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insider_evidence = str(json_data.get("insider_evidence", ""))
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return TradeRecommendation(
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action=action,
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outcome=str(json_data.get("outcome", "")),
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confidence=confidence,
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suggested_price=suggested_price,
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suggested_size_percent=suggested_size,
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reasoning=str(json_data.get("reasoning", "")),
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insider_trading_likelihood=insider_likelihood,
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trader_credibility=trader_credibility,
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insider_evidence=insider_evidence,
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)
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async def analyze_whale_trade(self, whale_trade: WhaleTrade) -> LLMDecision:
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"""
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Analyze a whale trade using LLM.
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Args:
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whale_trade: The whale trade to analyze
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Returns:
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LLMDecision with analysis and recommendation
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"""
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# Format trade context for LLM
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trade_context = self.anomaly_detector.format_for_llm(whale_trade)
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# Build prompt (Gemini uses single prompt with system instruction)
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system_prompt = self.prompts.system_prompt()
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user_prompt = self.prompts.analyze_whale_trade(trade_context)
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full_prompt = f"{system_prompt}\n\n---\n\n{user_prompt}"
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try:
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# Call Gemini API with Google Search tool enabled
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response = self.client.models.generate_content(
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model=self.settings.llm_model,
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contents=full_prompt,
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config=types.GenerateContentConfig(
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tools=[types.Tool(google_search=types.GoogleSearch())],
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),
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)
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analysis_text = response.text
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logger.debug(f"LLM response: {analysis_text[:500]}...")
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# Extract JSON from response
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json_data = self._extract_json_from_response(analysis_text)
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if json_data:
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recommendation = self._parse_recommendation(json_data)
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else:
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# Default to HOLD if we can't parse the response
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logger.warning("Could not parse LLM response as JSON, defaulting to HOLD")
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recommendation = TradeRecommendation(
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action=TradeAction.HOLD,
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outcome="",
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confidence=0.0,
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reasoning="Failed to parse LLM response",
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)
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return LLMDecision(
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whale_trade_id=whale_trade.id,
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market_id=whale_trade.market_id,
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analysis=analysis_text,
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recommendation=recommendation,
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)
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except Exception as e:
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logger.error(f"Error calling LLM: {e}")
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# Return a safe default decision
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return LLMDecision(
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whale_trade_id=whale_trade.id,
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market_id=whale_trade.market_id,
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analysis=f"Error during analysis: {str(e)}",
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recommendation=TradeRecommendation(
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action=TradeAction.HOLD,
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outcome="",
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confidence=0.0,
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reasoning=f"Analysis failed: {str(e)}",
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),
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)
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def format_full_report(self, whale_trade: WhaleTrade, decision: LLMDecision) -> str:
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"""
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Format a complete analysis report with trade info, analysis, and decision.
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Args:
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whale_trade: The whale trade
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decision: The LLM decision
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Returns:
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Formatted report string
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"""
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trade = whale_trade.trade
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rec = decision.recommendation
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# Format outcome prices
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prices_str = ""
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if whale_trade.market_outcomes and whale_trade.market_outcome_prices:
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prices_str = " | ".join([
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f"{o}: {p:.1%}"
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for o, p in zip(whale_trade.market_outcomes, whale_trade.market_outcome_prices)
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])
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# Action emoji and color indicator
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action_indicator = {
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TradeAction.BUY: "🟢 BUY",
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TradeAction.SELL: "🔴 SELL",
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TradeAction.HOLD: "⚪ HOLD",
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}
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# Insider trading likelihood indicator
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insider_likelihood = rec.insider_trading_likelihood
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if insider_likelihood >= 0.7:
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insider_indicator = f"🔴 高度可疑 ({insider_likelihood:.0%})"
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elif insider_likelihood >= 0.4:
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insider_indicator = f"🟡 中等可能 ({insider_likelihood:.0%})"
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else:
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insider_indicator = f"🟢 普通交易 ({insider_likelihood:.0%})"
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# Trader credibility indicator
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credibility_indicators = {
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TraderCredibility.HIGH: "🏆 高可信度 (前100名)",
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TraderCredibility.MEDIUM: "⭐ 中等可信度 (100-500名)",
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TraderCredibility.LOW: "📉 低可信度 (500名+)",
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TraderCredibility.UNKNOWN: "❓ 未知 (未上榜)",
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}
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credibility_str = credibility_indicators.get(rec.trader_credibility, "❓ 未知")
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# Trader ranking info
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trader_ranking_str = ""
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if whale_trade.trader_ranking:
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tr = whale_trade.trader_ranking
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rank_str = f"#{tr.rank}" if tr.rank else "未上榜"
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pnl_str = f"${tr.pnl:,.2f}" if tr.pnl else "N/A"
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trader_ranking_str = f"| **交易者排名** | {rank_str} (PnL: {pnl_str}) |"
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report = f"""
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{'='*70}
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# 🐋 鲸鱼交易分析报告
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{'='*70}
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**生成时间**: {datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S')} UTC
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## 交易摘要
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| 项目 | 详情 |
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|------|------|
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| **市场** | {whale_trade.market_question} |
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| **交易金额** | ${trade.usdc_size:,.2f} USDC |
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| **交易方向** | {trade.side} |
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| **交易价格** | {trade.price:.4f} ({trade.price:.1%}) |
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| **交易结果** | {trade.outcome} |
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| **当前赔率** | {prices_str} |
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| **交易时间** | {datetime.fromtimestamp(trade.timestamp).strftime('%Y-%m-%d %H:%M:%S') if trade.timestamp else 'N/A'} |
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{trader_ranking_str}
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{'='*70}
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{decision.analysis}
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{'='*70}
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## 🔍 内幕交易评估
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{'='*70}
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| 项目 | 评估 |
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|------|------|
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| **内幕交易可能性** | {insider_indicator} |
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| **交易者可信度** | {credibility_str} |
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**关键证据**: {rec.insider_evidence or '无明确证据'}
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{'='*70}
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## 📊 决策摘要
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{'='*70}
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| 项目 | 建议 |
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|------|------|
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| **操作建议** | {action_indicator.get(rec.action, '⚪ HOLD')} |
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| **目标结果** | {rec.outcome or 'N/A'} |
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| **信心程度** | {rec.confidence:.1%} |
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| **建议仓位** | {rec.suggested_size_percent:.1%} |
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| **建议价格** | {f'{rec.suggested_price:.4f}' if rec.suggested_price else 'Market'} |
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**决策理由**: {rec.reasoning}
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{'='*70}
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⚠️ 免责声明：本报告由AI生成，仅供参考，不构成投资建议。
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预测市场具有高风险，请基于自身判断谨慎决策。
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{'='*70}
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"""
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return report
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