TIANHE
556 lines
22 KiB
Python
556 lines
22 KiB
Python
"""
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Polymarket预测市场分析器
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分析预测市场，生成AI预测和交易机会推荐
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"""
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import json
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import re
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from typing import Dict, List, Any, Optional
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from datetime import datetime
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from app.utils.logger import get_logger
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from app.utils.db import get_db_connection
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from app.services.llm import LLMService
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from app.services.market_data_collector import get_market_data_collector
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from app.data_sources.polymarket import PolymarketDataSource
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logger = get_logger(__name__)
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class PolymarketAnalyzer:
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"""预测市场AI分析器"""
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def __init__(self):
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self.llm_service = LLMService()
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self.data_collector = get_market_data_collector()
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self.polymarket_source = PolymarketDataSource()
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def analyze_market(self, market_id: str, user_id: int = None, use_cache: bool = True) -> Dict:
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"""
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分析单个预测市场
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Args:
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market_id: 市场ID
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user_id: 用户ID（可选，用于用户特定分析）
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use_cache: 是否使用缓存的分析结果（默认True）
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Returns:
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分析结果字典
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"""
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try:
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# 1. 获取市场数据
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market = self.polymarket_source.get_market_details(market_id)
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if not market:
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return {
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"error": "Market not found",
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"market_id": market_id
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}
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# 2. 如果使用缓存，检查是否有缓存的分析结果（30分钟有效）
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if use_cache:
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cached_analysis = self._get_cached_analysis(market_id, user_id)
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if cached_analysis:
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cache_minutes = 30 # 缓存30分钟
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if self._is_analysis_fresh(cached_analysis, max_age_minutes=cache_minutes):
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logger.debug(f"Using cached analysis for market {market_id}")
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return cached_analysis
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# 3. 收集相关数据
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related_news = self._get_related_news(market['question'])
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related_assets = self._identify_related_assets(market['question'])
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asset_data = self._get_asset_data(related_assets)
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# 4. AI分析
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ai_result = self._ai_predict_probability(
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question=market['question'],
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current_market_prob=market['current_probability'],
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related_news=related_news,
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asset_data=asset_data
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)
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# 5. 计算机会评分
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opportunity_score = self._calculate_opportunity_score(
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ai_prob=ai_result['predicted_probability'],
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market_prob=market['current_probability'],
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confidence=ai_result['confidence']
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)
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# 6. 生成推荐
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recommendation = self._generate_recommendation(
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divergence=ai_result['predicted_probability'] - market['current_probability'],
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confidence=ai_result['confidence']
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)
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# 7. 构建分析结果
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analysis_result = {
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"market_id": market_id,
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"ai_predicted_probability": ai_result['predicted_probability'],
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"market_probability": market['current_probability'],
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"divergence": ai_result['predicted_probability'] - market['current_probability'],
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"recommendation": recommendation,
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"confidence_score": ai_result['confidence'],
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"reasoning": ai_result['reasoning'],
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"key_factors": ai_result.get('key_factors', []),
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"risk_factors": ai_result.get('risk_factors', []),
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"related_assets": related_assets,
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"risk_level": self._assess_risk(market, ai_result),
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"opportunity_score": opportunity_score
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}
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# 8. 保存到数据库
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self._save_analysis_to_db(analysis_result, user_id)
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return analysis_result
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except Exception as e:
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logger.error(f"Failed to analyze market {market_id}: {e}", exc_info=True)
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return {
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"error": str(e),
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"market_id": market_id
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}
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def generate_asset_trading_opportunities(self, market_id: str) -> List[Dict]:
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"""
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基于预测市场生成相关资产的交易机会
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Args:
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market_id: 预测市场ID
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Returns:
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资产交易机会列表
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"""
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try:
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# 1. 分析预测市场
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market_analysis = self.analyze_market(market_id)
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if market_analysis.get('error'):
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return []
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# 2. 识别相关资产
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related_assets = market_analysis.get('related_assets', [])
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if not related_assets:
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return []
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# 3. 对每个资产进行技术分析
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opportunities = []
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for asset in related_assets:
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try:
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# 推断市场类型
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market_type = self._infer_market(asset)
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# 获取资产数据
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asset_data = self.data_collector.collect_all(
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market=market_type,
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symbol=asset,
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timeframe="1D",
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include_polymarket=False # 避免循环
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)
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# 技术分析
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technical_analysis = self._analyze_technical(asset_data)
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# 结合预测市场信号
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if market_analysis['recommendation'] == "YES":
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# 预测事件发生概率高 → 相关资产可能上涨
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signal = "BUY" if technical_analysis.get('trend') == "bullish" else "HOLD"
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elif market_analysis['recommendation'] == "NO":
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# 预测事件发生概率低 → 相关资产可能下跌
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signal = "SELL" if technical_analysis.get('trend') == "bearish" else "HOLD"
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else:
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signal = "HOLD"
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# 计算综合置信度
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confidence = (
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market_analysis['confidence_score'] * 0.6 +
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technical_analysis.get('confidence', 50) * 0.4
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)
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if signal != "HOLD" and confidence > 60:
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opportunities.append({
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"asset": asset,
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"market": market_type,
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"signal": signal,
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"confidence": round(confidence, 2),
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"reasoning": f"预测市场分析：{market_analysis['reasoning'][:200]}。技术面：{technical_analysis.get('summary', '')[:200]}",
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"related_prediction": {
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"market_id": market_id,
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"question": market_analysis.get('question', ''),
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"ai_probability": market_analysis['ai_predicted_probability'],
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"market_probability": market_analysis['market_probability']
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},
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"entry_suggestion": technical_analysis.get('entry_suggestion', {})
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})
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except Exception as e:
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logger.debug(f"Failed to analyze asset {asset} for market {market_id}: {e}")
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continue
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# 保存机会到数据库
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if opportunities:
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self._save_opportunities_to_db(market_id, opportunities)
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return opportunities
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except Exception as e:
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logger.error(f"Failed to generate asset opportunities for {market_id}: {e}")
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return []
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def _ai_predict_probability(self, question: str, current_market_prob: float,
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related_news: List, asset_data: Dict) -> Dict:
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"""使用AI预测事件概率"""
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try:
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# 构建prompt
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news_text = "\n".join([f"- {n.get('title', '')[:100]}" for n in related_news[:5]])
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asset_text = ""
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if asset_data:
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price_data = asset_data.get('price', {})
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indicators = asset_data.get('indicators', {})
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if price_data:
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asset_text = f"""
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相关资产数据：
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- 当前价格: {price_data.get('current_price', 'N/A')}
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- 24h涨跌幅: {price_data.get('change_24h', 0):.2f}%
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- RSI: {indicators.get('rsi', {}).get('value', 'N/A')}
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- MACD: {indicators.get('macd', {}).get('signal', 'N/A')}
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"""
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prompt = f"""分析以下预测市场事件，评估其发生的概率：
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问题：{question}
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当前市场概率：{current_market_prob}%
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相关新闻：
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{news_text if news_text else "暂无相关新闻"}
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{asset_text}
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请基于以下维度分析：
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1. 历史类似事件的成功率
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2. 当前新闻和趋势
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3. 相关资产价格走势和技术指标
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4. 宏观环境因素（VIX、DXY、利率等）
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5. 市场情绪指标
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输出JSON格式：
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{{
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"predicted_probability": 72.5, // 你预测的概率（0-100）
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"confidence": 75.0, // 置信度（0-100）
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"reasoning": "详细分析...",
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"key_factors": ["因素1", "因素2"],
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"risk_factors": ["风险1", "风险2"]
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}}"""
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# 调用LLM
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messages = [
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{
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"role": "system",
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"content": "你是一个专业的市场分析师，擅长分析预测市场事件。请基于提供的数据，客观评估事件发生的概率。"
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},
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{
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"role": "user",
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"content": prompt
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}
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]
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result = self.llm_service.call_llm_api(
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messages=messages,
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use_json_mode=True,
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temperature=0.3
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)
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# 解析结果
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if isinstance(result, str):
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result = json.loads(result)
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# 验证和规范化
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predicted_prob = float(result.get('predicted_probability', current_market_prob))
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predicted_prob = max(0, min(100, predicted_prob)) # 限制在0-100
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confidence = float(result.get('confidence', 70))
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confidence = max(0, min(100, confidence))
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return {
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'predicted_probability': round(predicted_prob, 2),
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'confidence': round(confidence, 2),
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'reasoning': result.get('reasoning', ''),
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'key_factors': result.get('key_factors', []),
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'risk_factors': result.get('risk_factors', [])
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}
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except Exception as e:
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logger.error(f"AI prediction failed: {e}", exc_info=True)
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# 返回默认值
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return {
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'predicted_probability': current_market_prob,
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'confidence': 50.0,
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'reasoning': f'分析失败: {str(e)}',
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'key_factors': [],
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'risk_factors': []
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}
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def _calculate_opportunity_score(self, ai_prob: float, market_prob: float,
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confidence: float) -> float:
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"""
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计算机会评分（0-100）
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逻辑：
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- AI与市场差异越大，机会越好
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- 置信度越高，机会越好
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"""
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divergence = abs(ai_prob - market_prob)
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# 差异越大，机会越好（最大40分）
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divergence_score = min(divergence * 2, 40)
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# 置信度越高，机会越好（最大60分）
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confidence_score = confidence * 0.6
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return round(divergence_score + confidence_score, 2)
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def _generate_recommendation(self, divergence: float, confidence: float) -> str:
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"""
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生成推荐：YES/NO/HOLD
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逻辑：
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- AI概率 > 市场概率 + 5% 且置信度 > 60 → YES
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- AI概率 < 市场概率 - 5% 且置信度 > 60 → NO
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- 其他 → HOLD
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"""
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if divergence > 5 and confidence > 60:
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return "YES"
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elif divergence < -5 and confidence > 60:
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return "NO"
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else:
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return "HOLD"
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def _assess_risk(self, market: Dict, ai_result: Dict) -> str:
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"""评估风险等级"""
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confidence = ai_result.get('confidence', 50)
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divergence = abs(ai_result.get('predicted_probability', 50) - market.get('current_probability', 50))
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if confidence < 50 or divergence > 30:
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return "high"
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elif confidence < 70 or divergence > 15:
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return "medium"
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else:
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return "low"
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def _get_related_news(self, question: str) -> List[Dict]:
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"""获取相关问题相关的新闻"""
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# 提取关键词
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keywords = self._extract_keywords(question)
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# 这里可以调用新闻API，暂时返回空列表
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# 实际实现时可以调用现有的新闻服务
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return []
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def _identify_related_assets(self, question: str) -> List[str]:
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"""识别问题中提到的相关资产"""
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assets = []
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# 加密货币关键词映射
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crypto_keywords = {
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'BTC': ['BTC', 'Bitcoin', 'bitcoin', 'btc'],
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'ETH': ['ETH', 'Ethereum', 'ethereum', 'eth'],
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'SOL': ['SOL', 'Solana', 'solana', 'sol'],
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'BNB': ['BNB', 'Binance', 'binance', 'bnb'],
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'XRP': ['XRP', 'Ripple', 'ripple', 'xrp'],
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'ADA': ['ADA', 'Cardano', 'cardano', 'ada'],
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'DOGE': ['DOGE', 'Dogecoin', 'dogecoin', 'doge'],
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'AVAX': ['AVAX', 'Avalanche', 'avalanche', 'avax'],
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'DOT': ['DOT', 'Polkadot', 'polkadot', 'dot'],
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'MATIC': ['MATIC', 'Polygon', 'polygon', 'matic']
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}
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question_upper = question.upper()
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for symbol, keywords in crypto_keywords.items():
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if any(kw in question_upper for kw in keywords):
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assets.append(f"{symbol}/USDT")
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# 去重
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return list(set(assets))
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def _get_asset_data(self, assets: List[str]) -> Optional[Dict]:
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"""获取资产数据（取第一个资产）"""
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if not assets:
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return None
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try:
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asset = assets[0]
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market_type = self._infer_market(asset)
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return self.data_collector.collect_all(
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market=market_type,
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symbol=asset,
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timeframe="1D"
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)
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except Exception as e:
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logger.debug(f"Failed to get asset data for {assets}: {e}")
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return None
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def _analyze_technical(self, asset_data: Dict) -> Dict:
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"""简单的技术分析"""
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if not asset_data:
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return {
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'trend': 'neutral',
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'confidence': 50,
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'summary': '数据不足',
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'entry_suggestion': {}
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}
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indicators = asset_data.get('indicators', {})
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price_data = asset_data.get('price', {})
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# 简单的趋势判断
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rsi = indicators.get('rsi', {}).get('value', 50)
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macd_signal = indicators.get('macd', {}).get('signal', 'neutral')
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trend = 'neutral'
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if rsi > 60 and macd_signal == 'bullish':
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trend = 'bullish'
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elif rsi < 40 and macd_signal == 'bearish':
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trend = 'bearish'
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confidence = 60 if abs(rsi - 50) > 15 else 50
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return {
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'trend': trend,
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'confidence': confidence,
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'summary': f'RSI: {rsi:.1f}, MACD: {macd_signal}',
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'entry_suggestion': {}
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}
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def _infer_market(self, symbol: str) -> str:
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"""推断市场类型"""
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if '/' in symbol:
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return "Crypto"
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elif len(symbol) <= 5 and symbol.isupper():
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return "USStock"
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else:
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return "Crypto" # 默认
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def _extract_keywords(self, text: str) -> List[str]:
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"""提取关键词"""
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# 简单的关键词提取
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words = re.findall(r'\b[A-Z][a-z]+\b|\b[A-Z]{2,}\b', text)
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return [w.lower() for w in words if len(w) > 2]
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def _get_cached_analysis(self, market_id: str, user_id: int = None) -> Optional[Dict]:
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"""获取缓存的分析结果"""
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try:
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with get_db_connection() as db:
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cur = db.cursor()
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query = """
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SELECT ai_predicted_probability, market_probability, divergence,
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recommendation, confidence_score, opportunity_score,
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reasoning, key_factors, related_assets, created_at
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FROM qd_polymarket_ai_analysis
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WHERE market_id = %s
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"""
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params = [market_id]
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if user_id:
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query += " AND user_id = %s"
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params.append(user_id)
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else:
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query += " AND user_id IS NULL"
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query += " ORDER BY created_at DESC LIMIT 1"
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cur.execute(query, params)
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row = cur.fetchone()
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cur.close()
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if row:
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# RealDictCursor返回字典，使用键访问
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key_factors_raw = row.get('key_factors')
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key_factors = []
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if key_factors_raw:
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try:
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if isinstance(key_factors_raw, str):
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key_factors = json.loads(key_factors_raw)
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else:
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key_factors = key_factors_raw if isinstance(key_factors_raw, list) else []
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except:
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key_factors = []
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return {
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"market_id": market_id,
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"ai_predicted_probability": float(row.get('ai_predicted_probability') or 0),
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"market_probability": float(row.get('market_probability') or 0),
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"divergence": float(row.get('divergence') or 0),
|
||
"recommendation": row.get('recommendation') or 'HOLD',
|
||
"confidence_score": float(row.get('confidence_score') or 0),
|
||
"opportunity_score": float(row.get('opportunity_score') or 0),
|
||
"reasoning": row.get('reasoning') or '',
|
||
"key_factors": key_factors,
|
||
"related_assets": row.get('related_assets') if row.get('related_assets') else [],
|
||
"created_at": row.get('created_at')
|
||
}
|
||
except Exception as e:
|
||
logger.debug(f"Failed to get cached analysis: {e}")
|
||
|
||
return None
|
||
|
||
|
||
def _is_analysis_fresh(self, analysis: Dict, max_age_minutes: int = 30) -> bool:
|
||
"""检查分析结果是否新鲜"""
|
||
created_at = analysis.get('created_at')
|
||
if not created_at:
|
||
return False
|
||
|
||
if isinstance(created_at, str):
|
||
created_at = datetime.fromisoformat(created_at.replace('Z', '+00:00'))
|
||
|
||
age = (datetime.now() - created_at.replace(tzinfo=None)).total_seconds() / 60
|
||
return age < max_age_minutes
|
||
|
||
def _save_analysis_to_db(self, analysis: Dict, user_id: int = None):
|
||
"""保存分析结果到数据库"""
|
||
try:
|
||
with get_db_connection() as db:
|
||
cur = db.cursor()
|
||
cur.execute("""
|
||
INSERT INTO qd_polymarket_ai_analysis
|
||
(market_id, user_id, ai_predicted_probability, market_probability,
|
||
divergence, recommendation, confidence_score, opportunity_score,
|
||
reasoning, key_factors, related_assets, created_at)
|
||
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, NOW())
|
||
""", (
|
||
analysis['market_id'],
|
||
user_id,
|
||
analysis['ai_predicted_probability'],
|
||
analysis['market_probability'],
|
||
analysis['divergence'],
|
||
analysis['recommendation'],
|
||
analysis['confidence_score'],
|
||
analysis['opportunity_score'],
|
||
analysis['reasoning'],
|
||
json.dumps(analysis.get('key_factors', [])),
|
||
analysis.get('related_assets', [])
|
||
))
|
||
db.commit()
|
||
cur.close()
|
||
except Exception as e:
|
||
logger.error(f"Failed to save analysis to DB: {e}")
|
||
|
||
def _save_opportunities_to_db(self, market_id: str, opportunities: List[Dict]):
|
||
"""保存交易机会到数据库"""
|
||
try:
|
||
with get_db_connection() as db:
|
||
cur = db.cursor()
|
||
for opp in opportunities:
|
||
cur.execute("""
|
||
INSERT INTO qd_polymarket_asset_opportunities
|
||
(market_id, asset_symbol, asset_market, signal, confidence,
|
||
reasoning, entry_suggestion, created_at)
|
||
VALUES (%s, %s, %s, %s, %s, %s, %s, NOW())
|
||
""", (
|
||
market_id,
|
||
opp['asset'],
|
||
opp['market'],
|
||
opp['signal'],
|
||
opp['confidence'],
|
||
opp['reasoning'],
|
||
json.dumps(opp.get('entry_suggestion', {}))
|
||
))
|
||
db.commit()
|
||
cur.close()
|
||
except Exception as e:
|
||
logger.error(f"Failed to save opportunities to DB: {e}")
|