TIANHE
@@ -0,0 +1,442 @@
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"""
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Polymarket预测市场API路由
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提供预测市场数据和分析接口（只读，不涉及交易）
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"""
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from flask import Blueprint, jsonify, request, g
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from app.utils.auth import login_required
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from app.utils.logger import get_logger
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from app.data_sources.polymarket import PolymarketDataSource
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from app.utils.db import get_db_connection
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import json
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from datetime import datetime, timedelta
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logger = get_logger(__name__)
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polymarket_bp = Blueprint('polymarket', __name__)
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# 初始化服务
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polymarket_source = PolymarketDataSource()
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@polymarket_bp.route("/markets", methods=["GET"])
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@login_required
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def get_markets():
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"""
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获取预测市场列表
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Query params:
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category: crypto/politics/economics/sports (optional)
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sort_by: volume_24h/ai_score/probability_change (default: volume_24h)
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limit: 数量 (default: 20)
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"""
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try:
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category = request.args.get("category")
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sort_by = request.args.get("sort_by", "volume_24h")
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limit = int(request.args.get("limit", 20))
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# 获取市场列表
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markets = polymarket_source.get_trending_markets(category, limit * 2)
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logger.info(f"Fetched {len(markets)} markets from PolymarketDataSource (category={category}, limit={limit})")
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if not markets:
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logger.warning(f"No markets returned from PolymarketDataSource. This may indicate API issues or empty cache.")
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return jsonify({
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"code": 1,
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"msg": "success",
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"data": [],
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"warning": "No markets available. API may be unavailable or cache is empty."
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})
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# 从数据库读取缓存的AI分析结果（由后台任务批量分析生成）
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# 只读取30分钟内的分析结果
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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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market_ids = [m.get('market_id') for m in markets if m.get('market_id')]
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if market_ids:
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# 查询30分钟内的分析结果
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from datetime import datetime, timedelta
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cache_cutoff = datetime.now() - timedelta(minutes=30)
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placeholders = ','.join(['%s'] * len(market_ids))
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cur.execute(f"""
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SELECT market_id, ai_predicted_probability, market_probability, divergence,
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recommendation, confidence_score, opportunity_score, reasoning, key_factors
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FROM qd_polymarket_ai_analysis
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WHERE market_id IN ({placeholders})
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AND user_id IS NULL
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AND created_at > %s
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ORDER BY opportunity_score DESC
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""", market_ids + [cache_cutoff])
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rows = cur.fetchall()
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cur.close()
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# 构建分析结果映射
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analysis_map = {}
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for row in rows:
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market_id = row.get('market_id')
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if market_id:
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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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analysis_map[market_id] = {
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'predicted_probability': float(row.get('ai_predicted_probability') or 0),
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'recommendation': row.get('recommendation') or 'HOLD',
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'confidence_score': float(row.get('confidence_score') or 0),
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'opportunity_score': float(row.get('opportunity_score') or 0),
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'divergence': float(row.get('divergence') or 0),
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'reasoning': row.get('reasoning') or '',
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'key_factors': key_factors
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}
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# 为每个市场添加分析结果
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for market in markets:
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market_id = market.get('market_id')
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if market_id and market_id in analysis_map:
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market['ai_analysis'] = analysis_map[market_id]
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else:
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market['ai_analysis'] = None
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else:
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# 如果没有市场ID，设置分析为None
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for market in markets:
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market['ai_analysis'] = None
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except Exception as e:
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logger.warning(f"Failed to load cached analysis: {e}")
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# 出错时，所有市场都没有分析结果
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for market in markets:
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market['ai_analysis'] = None
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# 筛选：优先返回有交易机会的市场，但也包含其他活跃市场
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# 重点：不是简单复制数据，而是找到交易机会，但也要保证有足够的数据展示
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opportunity_markets = []
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other_markets = []
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for market in markets:
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ai_analysis = market.get('ai_analysis')
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volume = market.get('volume_24h', 0) or 0
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prob = market.get('current_probability', 50.0) or 50.0
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if ai_analysis:
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opportunity_score = ai_analysis.get('opportunity_score', 0) or 0
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divergence = abs(ai_analysis.get('divergence', 0) or 0)
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confidence = ai_analysis.get('confidence_score', 0) or 0
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# 筛选条件：机会评分>60 或 (差异>15% 且 置信度>70) 或 (差异>10% 且 置信度>60)
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if opportunity_score > 60 or (divergence > 15 and confidence > 70) or (divergence > 10 and confidence > 60):
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opportunity_markets.append(market)
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elif volume > 5000: # 交易量较大的也作为备选
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other_markets.append(market)
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else:
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# 如果没有AI分析，但交易量大且概率不是50%（说明有明确的市场共识），也包含
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if volume > 5000 and abs(prob - 50.0) > 5: # 降低阈值，包含更多市场
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other_markets.append(market)
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# 合并结果：优先显示机会市场，然后补充其他活跃市场
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if opportunity_markets:
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# 如果有机会市场，优先显示它们，然后补充其他市场直到达到limit
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result_markets = opportunity_markets[:limit]
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remaining = limit - len(result_markets)
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if remaining > 0 and other_markets:
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result_markets.extend(other_markets[:remaining])
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opportunity_markets = result_markets
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elif other_markets:
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# 如果没有机会市场，至少返回高交易量的市场
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opportunity_markets = other_markets[:limit]
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else:
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# 如果都没有，返回原始市场列表的前几个
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opportunity_markets = markets[:min(limit, len(markets))]
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# 排序和筛选
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if sort_by == "ai_score":
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# 按AI机会评分排序（高概率/高回报比优先）
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opportunity_markets.sort(
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key=lambda x: (
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x.get('ai_analysis', {}).get('opportunity_score', 0) or 0,
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abs(x.get('ai_analysis', {}).get('divergence', 0) or 0), # 差异越大越好
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x.get('ai_analysis', {}).get('confidence_score', 0) or 0 # 置信度越高越好
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),
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reverse=True
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)
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elif sort_by == "high_probability":
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# 高概率机会：AI预测概率 > 市场概率 + 10%
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opportunity_markets.sort(
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key=lambda x: (
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x.get('ai_analysis', {}).get('ai_predicted_probability', 0) or 0,
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x.get('ai_analysis', {}).get('confidence_score', 0) or 0
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),
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reverse=True
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)
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elif sort_by == "high_return":
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# 高回报比机会：AI与市场差异大且置信度高
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opportunity_markets.sort(
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key=lambda x: (
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abs(x.get('ai_analysis', {}).get('divergence', 0) or 0) *
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(x.get('ai_analysis', {}).get('confidence_score', 0) or 0) / 100,
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x.get('ai_analysis', {}).get('opportunity_score', 0) or 0
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),
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reverse=True
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)
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elif sort_by == "probability_change":
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# 需要历史数据，暂时按volume排序
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opportunity_markets.sort(key=lambda x: x.get('volume_24h', 0), reverse=True)
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else:
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opportunity_markets.sort(key=lambda x: x.get('volume_24h', 0), reverse=True)
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return jsonify({
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"code": 1,
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"msg": "success",
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"data": opportunity_markets[:limit],
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"total_opportunities": len(opportunity_markets),
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"total_markets": len(markets)
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})
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except Exception as e:
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logger.error(f"get_markets failed: {e}", exc_info=True)
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return jsonify({
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"code": 0,
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"msg": str(e),
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"data": None
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}), 500
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@polymarket_bp.route("/markets/<market_id>", methods=["GET"])
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@login_required
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def get_market_detail(market_id: str):
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"""
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获取单个市场详情和AI分析
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支持通过market ID或slug查询
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"""
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try:
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# 确保market_id是字符串
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market_id = str(market_id).strip()
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# 获取市场数据
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market = polymarket_source.get_market_details(market_id)
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if not market:
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return jsonify({
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"code": 0,
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"msg": "Market not found",
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"data": None
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}), 404
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# 从数据库读取缓存的AI分析结果（30分钟内）
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analysis = None
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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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cache_cutoff = datetime.now() - timedelta(minutes=30)
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cur.execute("""
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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 AND user_id IS NULL AND created_at > %s
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ORDER BY created_at DESC LIMIT 1
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""", (market_id, cache_cutoff))
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row = cur.fetchone()
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cur.close()
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if row:
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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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analysis = {
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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),
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"recommendation": row.get('recommendation') or 'HOLD',
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"confidence_score": float(row.get('confidence_score') or 0),
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"opportunity_score": float(row.get('opportunity_score') or 0),
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"reasoning": row.get('reasoning') or '',
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"key_factors": key_factors,
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"related_assets": row.get('related_assets') if row.get('related_assets') else []
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}
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except Exception as e:
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logger.warning(f"Failed to load cached analysis for market {market_id}: {e}")
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# 资产交易机会（暂时返回空，可以后续实现）
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asset_opportunities = []
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return jsonify({
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"code": 1,
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"msg": "success",
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"data": {
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"market": market,
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"ai_analysis": analysis,
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"asset_opportunities": asset_opportunities
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}
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})
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except Exception as e:
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logger.error(f"get_market_detail failed: {e}", exc_info=True)
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return jsonify({
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"code": 0,
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"msg": str(e),
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"data": None
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}), 500
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@polymarket_bp.route("/markets/<market_id>/opportunities", methods=["GET"])
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@login_required
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def get_market_opportunities(market_id: str):
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"""获取基于该预测市场的资产交易机会（暂时返回空，后续可扩展）"""
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try:
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# 暂时返回空列表，可以后续实现基于预测市场的资产推荐
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opportunities = []
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return jsonify({
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"code": 1,
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"msg": "success",
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"data": opportunities
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})
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except Exception as e:
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logger.error(f"get_market_opportunities failed: {e}", exc_info=True)
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return jsonify({
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"code": 0,
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"msg": str(e),
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"data": None
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}), 500
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@polymarket_bp.route("/recommendations", methods=["GET"])
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@login_required
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def get_recommendations():
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"""
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获取AI推荐的高价值预测市场
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Query params:
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limit: 数量 (default: 10)
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"""
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try:
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limit = int(request.args.get("limit", 10))
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# 获取所有活跃市场
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all_markets = polymarket_source.get_trending_markets(limit=100)
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# 从数据库读取缓存的AI分析结果（30分钟内，按机会评分排序）
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recommendations = []
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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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cache_cutoff = datetime.now() - timedelta(minutes=30)
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market_ids = [m.get('market_id') for m in all_markets if m.get('market_id')]
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if market_ids:
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placeholders = ','.join(['%s'] * len(market_ids))
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cur.execute(f"""
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SELECT a.market_id, a.opportunity_score, a.recommendation,
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a.confidence_score, a.reasoning, a.key_factors,
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m.question, m.current_probability, m.volume_24h, m.category
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FROM qd_polymarket_ai_analysis a
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JOIN qd_polymarket_markets m ON a.market_id = m.market_id
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WHERE a.market_id IN ({placeholders})
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AND a.user_id IS NULL
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AND a.created_at > %s
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AND a.opportunity_score > 70
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ORDER BY a.opportunity_score DESC
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LIMIT %s
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""", market_ids + [cache_cutoff, limit])
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rows = cur.fetchall()
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cur.close()
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for row in rows:
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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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recommendations.append({
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"market_id": row.get('market_id'),
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"question": row.get('question'),
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"current_probability": float(row.get('current_probability') or 0),
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"volume_24h": float(row.get('volume_24h') or 0),
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"category": row.get('category'),
|
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"ai_analysis": {
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"opportunity_score": float(row.get('opportunity_score') or 0),
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"recommendation": row.get('recommendation') or 'HOLD',
|
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"confidence_score": float(row.get('confidence_score') or 0),
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"reasoning": row.get('reasoning') or '',
|
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"key_factors": key_factors
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}
|
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})
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except Exception as e:
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logger.warning(f"Failed to load recommendations: {e}")
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||||
# 如果没有缓存结果，返回空列表（等待后台任务分析）
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||||
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return jsonify({
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||||
"code": 1,
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"msg": "success",
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"data": recommendations[:limit]
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})
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||||
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except Exception as e:
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logger.error(f"get_recommendations failed: {e}", exc_info=True)
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return jsonify({
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"code": 0,
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"msg": str(e),
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"data": None
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||||
}), 500
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@polymarket_bp.route("/search", methods=["GET"])
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@login_required
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||||
def search_markets():
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"""搜索预测市场"""
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try:
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keyword = request.args.get("q", "").strip()
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if not keyword:
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||||
return jsonify({
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||||
"code": 0,
|
||||
"msg": "Keyword required",
|
||||
"data": None
|
||||
}), 400
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|
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limit = int(request.args.get("limit", 20))
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||||
|
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markets = polymarket_source.search_markets(keyword, limit)
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||||
return jsonify({
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"code": 1,
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||||
"msg": "success",
|
||||
"data": markets
|
||||
})
|
||||
|
||||
except Exception as e:
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logger.error(f"search_markets failed: {e}", exc_info=True)
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||||
return jsonify({
|
||||
"code": 0,
|
||||
"msg": str(e),
|
||||
"data": None
|
||||
}), 500
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Reference in New Issue
Block a user