2569718930@qq.com
578 lines
28 KiB
Python
578 lines
28 KiB
Python
import sys
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import time
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import os
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import json
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import re
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from datetime import datetime, timedelta
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from loguru import logger
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from src.utils.config_loader import load_config
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from src.utils.logger import setup_logger
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from src.data_collection.polymarket_api import PolymarketClient
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from src.data_collection.weather_sources import WeatherDataCollector
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from src.data_collection.onchain_tracker import OnchainTracker
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from src.models.statistical_model import TemperaturePredictor
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from src.analysis.volume_analyzer import VolumeAnalyzer
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from src.analysis.orderbook_analyzer import OrderbookAnalyzer
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from src.analysis.technical_indicators import TechnicalIndicators
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from src.analysis.whale_tracker import WhaleTracker
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from src.strategy.decision_engine import DecisionEngine
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from src.strategy.risk_manager import RiskManager
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from src.trading.paper_trader import PaperTrader
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from src.utils.notifier import TelegramNotifier
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def main():
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# 1. 初始化配置与日志
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config_data = load_config()
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setup_logger(config_data.get("app", {}).get("log_level", "INFO"))
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logger.info("🌟 PolyWeather 监控引擎启动中...")
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# 2. 初始化核心组件
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polymarket = PolymarketClient(config_data["polymarket"])
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weather = WeatherDataCollector(config_data["weather"])
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onchain = OnchainTracker(config_data["polymarket"], polymarket)
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notifier = TelegramNotifier(config_data["telegram"])
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# 3. 初始化分析与交易组件
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predictor = TemperaturePredictor()
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decision_engine = DecisionEngine(config_data.get("config", {}))
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whale_tracker = WhaleTracker(config_data.get("config", {}), onchain)
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paper_trader = PaperTrader()
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# 发送启动通知
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notifier._send_message(
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"🚀 <b>Polymarket 天气监控系统启动成功</b>\n正在扫描 12 个核心城市的最高温市场..."
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)
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# 信号记忆（持久化到文件）
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pushed_signals = {}
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SIGNALS_FILE = "data/pushed_signals.json"
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if os.path.exists(SIGNALS_FILE):
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try:
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with open(SIGNALS_FILE, "r", encoding="utf-8") as f:
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pushed_signals = json.load(f)
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logger.info(f"已加载历史推送记录，共 {len(pushed_signals)} 条")
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except:
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pushed_signals = {}
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# 确保data目录存在
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if not os.path.exists("data"):
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os.makedirs("data")
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location_cache = {}
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# 价格历史追踪（用于计算趋势）
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PRICE_HISTORY_FILE = "data/price_history.json"
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price_history = {}
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if os.path.exists(PRICE_HISTORY_FILE):
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try:
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with open(PRICE_HISTORY_FILE, "r", encoding="utf-8") as f:
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price_history = json.load(f)
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except:
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price_history = {}
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try:
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while True:
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logger.info("--- 开启新一轮全量动态监控 (自动搜寻所有天气市场) ---")
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cached_signals = {}
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all_markets_cache = {}
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# 1. 直接从 Polymarket 获取所有天气合约
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all_weather_markets = polymarket.get_weather_markets()
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# 1.5 尝试通过slug获取可能遗漏的市场（如部分结算的市场）
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special_slugs = []
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for slug in special_slugs:
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event = polymarket.get_event_by_slug(slug)
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if event:
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title = event.get("title", "")
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logger.info(f"通过slug找到特殊事件: {title}")
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# 提取城市名
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city = weather.extract_city_from_question(title)
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if not city:
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city = "Unknown"
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# 将该事件的所有市场添加到列表
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for m in event.get("markets", []):
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# 检查是否已存在
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c_id = m.get("conditionId")
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if not any(
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existing.get("condition_id") == c_id
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for existing in all_weather_markets
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):
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all_weather_markets.append(
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{
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"condition_id": c_id,
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"question": m.get("groupItemTitle")
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or m.get("question"),
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"active_token_id": m.get("activeTokenId"),
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"tokens": m.get("clobTokenIds"),
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"prices": m.get("outcomePrices"),
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"event_title": title,
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"slug": slug,
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"city": city, # 提前标记城市
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}
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)
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logger.debug(f"添加特殊市场: {m.get('groupItemTitle')}")
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if not all_weather_markets:
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logger.warning("当前 Polymarket 似乎没有任何活跃的天气市场，等待中...")
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time.sleep(300)
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continue
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# 2. 批量同步盘口价格 (优化：为每个档位获取其对应的真实 Token 价格)
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token_price_map = {}
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price_requests = []
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for m in all_weather_markets:
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ts = m.get("tokens", [])
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if isinstance(ts, str):
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try: ts = json.loads(ts)
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except: ts = []
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active_tid = m.get("active_token_id")
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# 如果是多选一市场（比如 Dallas 76-77°F）
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if len(ts) > 2 and active_tid:
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# 获取该档位的买入价 (Ask)
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price_requests.append({"token_id": active_tid, "side": "ask"})
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# 获取该档位的买入“否”价所需的 Bid 价
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price_requests.append({"token_id": active_tid, "side": "bid"})
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# 如果是传统的 Yes/No 二选一市场
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elif len(ts) == 2:
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price_requests.append({"token_id": ts[0], "side": "ask"}) # Buy Yes
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price_requests.append({"token_id": ts[1], "side": "ask"}) # Buy No
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if price_requests:
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logger.info(f"正在同步 {len(price_requests)} 个档位的真实盘口价格...")
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token_price_map = polymarket.get_multiple_prices(price_requests)
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logger.info(f"价格同步完成，成功获取 {len(token_price_map)} 个实时报价")
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# 3. 按城市分组（按condition_id去重）
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markets_by_city = {}
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seen_condition_ids = set()
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for i, m in enumerate(all_weather_markets):
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c_id = m.get("condition_id")
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if c_id in seen_condition_ids:
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continue # 跳过重复
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seen_condition_ids.add(c_id)
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# 注入实时批量价格
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ts = m.get("tokens", [])
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if isinstance(ts, str):
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try: ts = json.loads(ts)
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except: ts = []
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active_tid = m.get("active_token_id")
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# 多选一市场逻辑
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if len(ts) > 2 and active_tid:
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m["buy_yes_live"] = token_price_map.get(active_tid) # 该档位的 Ask
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# 买入“否”的价格 = 1 - 该档位的 Bid (别人愿意买 Yes 的最高价)
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bid_price = token_price_map.get(active_tid) # 这里逻辑稍后在下文 fallback 处理中增强
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# 我们这里只是注入，真正复杂的 fallback 逻辑在循环内部
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# 二选一市场逻辑
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elif len(ts) == 2:
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m["buy_yes_live"] = token_price_map.get(ts[0])
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m["buy_no_live"] = token_price_map.get(ts[1])
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# 优先使用发现阶段已经识别出的城市名
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city = m.get("city")
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# 如果发现阶段没识别出，再尝试从问题文本提取
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if not city or city == "Unknown":
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full_context = f"{m.get('event_title', '')} {m.get('question', '')}"
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city = weather.extract_city_from_question(full_context)
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if i < 5:
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logger.debug(
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f"分析合约 {i}: City='{city}' | Title='{m.get('event_title')}"
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)
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if not city:
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continue
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if city not in markets_by_city:
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markets_by_city[city] = []
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markets_by_city[city].append(m)
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logger.info(
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f"动态发现 {len(markets_by_city)} 个受监控城市，共 {len(all_weather_markets)} 个合约"
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)
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# 3. 逐个城市分析
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for city, city_markets in markets_by_city.items():
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try:
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# 获取/缓存坐标
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if city not in location_cache:
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coords = weather.get_coordinates(city)
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if not coords:
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continue
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location_cache[city] = coords
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logger.info(
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f"📍 城市定位成功: {city} -> ({coords['lat']}, {coords['lon']})"
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)
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loc = location_cache[city]
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# A. 获取实时天气共识
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weather_data = weather.fetch_all_sources(
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city, lat=loc["lat"], lon=loc["lon"]
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)
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consensus = weather.check_consensus(weather_data)
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if not consensus.get("consensus"):
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continue
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logger.info(
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f"☁️ {city} 当前气温: {consensus['average_temp']}°C | 监控合约: {len(city_markets)}"
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)
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# --- 本城市汇总预警缓存 ---
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city_alerts = []
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city_local_time = None
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# B. 遍历该城市所有合约
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for market in city_markets:
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market_id = market.get("condition_id")
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question = market.get("question", "未知市场")
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event_title = market.get("event_title", "")
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# 识别该合约的目标日期
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target_date = weather.extract_date_from_title(
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event_title
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) or weather.extract_date_from_title(question)
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ref_temp = consensus["average_temp"]
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if target_date:
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daily_data = weather_data.get("open-meteo", {}).get(
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"daily", {}
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)
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if daily_data:
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dates = daily_data.get("time", [])
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max_temps = daily_data.get("temperature_2m_max", [])
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for idx, d_str in enumerate(dates):
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if target_date == d_str:
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ref_temp = max_temps[idx]
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break
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# --- 价格获取逻辑 (增强版) ---
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buy_yes_price = market.get("buy_yes_live")
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buy_no_price = market.get("buy_no_live")
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ts = market.get("tokens", [])
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if isinstance(ts, str): ts = json.loads(ts)
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active_tid = market.get("active_token_id")
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# 特殊逻辑：针对多选一型市场（Dallas/Chicago 等温度段）
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if len(ts) > 2 and active_tid:
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# 1. 尝试从批量映射中重新获取该档位的精确买入价(Ask)
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buy_yes_price = polymarket.get_price(active_tid, side="ask") if buy_yes_price is None else buy_yes_price
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# 2. 计算“否”的价格（在多选一里是 1 - 最高买入意愿价Bid）
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bid_price = polymarket.get_price(active_tid, side="bid")
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if bid_price:
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buy_no_price = 1.0 - bid_price
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if i < 3: # 仅对前几个档位打印调试
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logger.debug(f"Categorical价格匹配 [{market.get('city')}-{market.get('question')}]: Yes={buy_yes_price}, No={buy_no_price} (from Bid={bid_price})")
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# 通用回退逻辑 (二选一)
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if buy_yes_price is None or buy_no_price is None:
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if ts and len(ts) >= 2:
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prices = polymarket.get_buy_prices(ts[0], ts[1])
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if prices:
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buy_yes_price = prices.get("buy_yes")
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buy_no_price = prices.get("buy_no")
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# 最后的回退：使用原始 Gamma 概率
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if buy_yes_price is None or buy_no_price is None:
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gamma_prices = market.get("prices", [])
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if isinstance(gamma_prices, str): gamma_prices = json.loads(gamma_prices)
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prob = float(gamma_prices[0]) if gamma_prices else 0.5
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if buy_yes_price is None: buy_yes_price = prob
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if buy_no_price is None: buy_no_price = 1.0 - prob
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# C. 准备缓存
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temp_unit = weather_data.get("open-meteo", {}).get(
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"unit", "celsius"
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)
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temp_symbol = "°F" if temp_unit == "fahrenheit" else "°C"
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city_local_time = (
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weather_data.get("open-meteo", {})
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.get("current", {})
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.get("local_time")
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)
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current_price = buy_yes_price if buy_yes_price else 0.5
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# 计算价格趋势
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prev_data = price_history.get(market_id, {})
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prev_price = prev_data.get("price", current_price)
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if prev_price > 0:
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price_change_pct = ((current_price - prev_price) / prev_price) * 100
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else:
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price_change_pct = 0
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# 更新价格历史
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price_history[market_id] = {
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"price": current_price,
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"timestamp": datetime.now().isoformat()
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}
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cache_entry = {
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"city": city,
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"full_title": event_title,
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"option": question,
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"prediction": f"{ref_temp}{temp_symbol}",
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"price": int(current_price * 100),
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"buy_yes": int(buy_yes_price * 100),
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"buy_no": int(buy_no_price * 100),
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"url": f"https://polymarket.com/event/{market.get('slug')}",
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"local_time": city_local_time,
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"target_date": target_date,
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"score": 0,
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"rationale": "ACTIVE",
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"trend": round(price_change_pct, 1),
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}
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if buy_yes_price <= 0.01 or buy_yes_price >= 0.99:
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cache_entry["rationale"] = "ENDED"
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all_markets_cache[market_id] = cache_entry
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continue
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# D. 评分
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signal = decision_engine.calculate_signal(
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model_prediction=predictor.predict_ensemble([ref_temp]),
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market_data={
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"orderbook": {},
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"price_history": [current_price],
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"transactions": [],
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},
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weather_consensus={"average_temp": ref_temp},
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whale_activity=None,
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)
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cache_entry["score"] = signal["final_score"]
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cache_entry["rationale"] = signal.get("recommendation", "N/A")
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all_markets_cache[market_id] = cache_entry
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# --- 预警收集 (仅监控价格) ---
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if (0.85 <= buy_yes_price <= 0.95) or (
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0.85 <= buy_no_price <= 0.95
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):
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alert_key = f"alert_{market_id}_range_85_95"
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if alert_key not in pushed_signals:
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trigger_side = (
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"Buy Yes" if buy_yes_price >= 0.85 else "Buy No"
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)
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trigger_price = (
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int(buy_yes_price * 100)
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if trigger_side == "Buy Yes"
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else int(buy_no_price * 100)
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)
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# --- 智能动态仓位计算 ---
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# 1. 获取 Open-Meteo 对目标日期的最高温预测
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predicted_high = None
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weather_supports = False
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daily_data = weather_data.get("open-meteo", {}).get("daily", {})
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if daily_data and target_date:
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dates = daily_data.get("time", [])
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max_temps = daily_data.get("temperature_2m_max", [])
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for idx, d_str in enumerate(dates):
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if target_date == d_str and idx < len(max_temps):
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predicted_high = max_temps[idx]
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break
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# 2. 判断天气预测是否支持当前方向
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if predicted_high is not None:
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# 解析选项的温度范围 (例如 "40-41°F" 或 "32°F or below")
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temp_match = re.search(r'(\d+)(?:-(\d+))?°[FC]', question)
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if temp_match:
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low_bound = int(temp_match.group(1))
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high_bound = int(temp_match.group(2)) if temp_match.group(2) else low_bound
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# 如果买 NO，天气预测应该在这个区间之外
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if trigger_side == "Buy No":
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weather_supports = (predicted_high < low_bound - 2) or (predicted_high > high_bound + 2)
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else: # 买 YES
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weather_supports = (low_bound - 2 <= predicted_high <= high_bound + 2)
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# 3. 获取成交量信息
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market_volume = market.get("volume", 0)
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if isinstance(market_volume, str):
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try:
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market_volume = float(market_volume.replace("$", "").replace(",", ""))
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except:
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market_volume = 0
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high_volume = market_volume >= 5000 # $5000+ 算高成交量
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# 4. 动态仓位决策
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# 条件: 价格锁定程度 + 天气支持 + 成交量
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if trigger_price >= 90 and weather_supports and high_volume:
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# 三重确认：重注
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amount_usd = 10.0
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confidence_tag = "🔥高置信"
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elif trigger_price >= 90 and weather_supports:
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# 双重确认：中等仓位
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amount_usd = 7.0
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confidence_tag = "⭐中置信"
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elif trigger_price >= 92:
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# 价格接近锁定，即使其他条件不满足也小额参与
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amount_usd = 5.0
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confidence_tag = "📌价格锁定"
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else:
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# 普通信号：最小仓位
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amount_usd = 3.0
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confidence_tag = "💡试探"
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logger.info(
|
||
f"【仓位决策】{city} {question} | "
|
||
f"价格:{trigger_price}¢ | 预测:{predicted_high} | 天气支持:{weather_supports} | "
|
||
f"高量:{high_volume} | 仓位:${amount_usd} ({confidence_tag})"
|
||
)
|
||
|
||
# --- 模拟交易触发逻辑 ---
|
||
side = "YES" if trigger_side == "Buy Yes" else "NO"
|
||
success = paper_trader.open_position(
|
||
market_id=market_id,
|
||
city=city,
|
||
option=question,
|
||
price=trigger_price,
|
||
side=side,
|
||
amount_usd=amount_usd,
|
||
target_date=target_date,
|
||
predicted_temp=predicted_high,
|
||
)
|
||
|
||
# 构建预测温度显示文本
|
||
temp_unit = weather_data.get("open-meteo", {}).get("unit", "celsius")
|
||
temp_symbol = "°F" if temp_unit == "fahrenheit" else "°C"
|
||
forecast_text = f"预测:{predicted_high}{temp_symbol}" if predicted_high else "预测:N/A"
|
||
|
||
city_alerts.append(
|
||
{
|
||
"type": "price",
|
||
"market": f"{question} ({target_date or '今日'})",
|
||
"msg": f"{trigger_side} {trigger_price}¢ | {forecast_text}",
|
||
"bought": success,
|
||
"amount": amount_usd,
|
||
"confidence": confidence_tag,
|
||
}
|
||
)
|
||
pushed_signals[alert_key] = time.time()
|
||
|
||
# 3. 信号暂存
|
||
cached_signals[market_id] = cache_entry
|
||
|
||
# --- 循环结束后统一推送本城市汇总 ---
|
||
if city_alerts:
|
||
notifier.send_combined_alert(
|
||
city, city_alerts, local_time=city_local_time
|
||
)
|
||
|
||
except Exception as e:
|
||
logger.error(f"分析城市 {city} 时出错: {e}")
|
||
continue
|
||
|
||
# --- 每处理完一个城市，立即更新 JSON 文件 ---
|
||
try:
|
||
# 1. 更新活跃信号缓存 (合并旧数据避免扫描中途变空)
|
||
final_signals = {}
|
||
if os.path.exists("data/active_signals.json"):
|
||
try:
|
||
with open(
|
||
"data/active_signals.json", "r", encoding="utf-8"
|
||
) as f:
|
||
final_signals = json.load(f)
|
||
except:
|
||
pass
|
||
|
||
final_signals.update(all_markets_cache)
|
||
|
||
with open("data/active_signals.json", "w", encoding="utf-8") as f:
|
||
json.dump(final_signals, f, ensure_ascii=False, indent=2)
|
||
|
||
# 2. 更新全量市场缓存
|
||
try:
|
||
with open("data/all_markets.json", "r", encoding="utf-8") as f:
|
||
existing_markets = json.load(f)
|
||
except:
|
||
existing_markets = {}
|
||
|
||
existing_markets.update(all_markets_cache)
|
||
|
||
# 清理过期日期
|
||
today_str = datetime.now().strftime("%Y-%m-%d")
|
||
cleaned_markets = {}
|
||
for k, v in existing_markets.items():
|
||
t_date = v.get("target_date")
|
||
if not t_date or t_date >= today_str:
|
||
cleaned_markets[k] = v
|
||
|
||
with open("data/all_markets.json", "w", encoding="utf-8") as f:
|
||
json.dump(cleaned_markets, f, ensure_ascii=False, indent=2)
|
||
|
||
# 3. 保存推送记录
|
||
with open("data/pushed_signals.json", "w", encoding="utf-8") as f:
|
||
json.dump(pushed_signals, f, ensure_ascii=False)
|
||
|
||
# 3.5 保存价格历史（用于趋势计算）
|
||
with open(PRICE_HISTORY_FILE, "w", encoding="utf-8") as f:
|
||
json.dump(price_history, f, ensure_ascii=False)
|
||
|
||
# --- 4. 更新模拟仓位盈亏 ---
|
||
price_snapshot = {}
|
||
for mid, entry in all_markets_cache.items():
|
||
price_snapshot[mid] = {"price": entry["price"]}
|
||
paper_trader.update_pnl(price_snapshot)
|
||
|
||
# --- 5. 每日收益总结推送 (北京时间 23:55 - 00:05 之间发送) ---
|
||
now_bj = datetime.utcnow() + timedelta(hours=8)
|
||
if now_bj.hour == 23 and now_bj.minute >= 50:
|
||
summary_key = f"daily_pnl_{now_bj.strftime('%Y%m%d')}"
|
||
if summary_key not in pushed_signals:
|
||
# 构造总结消息
|
||
total_cost = 0
|
||
total_pnl = 0
|
||
data = paper_trader._load_data()
|
||
pos_list = data.get("positions", {})
|
||
|
||
if pos_list:
|
||
report = [
|
||
f"📊 <b>每日模拟仓结算总结 ({now_bj.strftime('%Y-%m-%d')})</b>\n"
|
||
+ "═" * 15
|
||
]
|
||
for p in pos_list.values():
|
||
if p["status"] == "OPEN":
|
||
total_cost += p["cost_usd"]
|
||
total_pnl += p.get("pnl_usd", 0)
|
||
|
||
report.append(
|
||
f"💳 可用余额: <b>${data.get('balance', 0):.2f}</b>"
|
||
)
|
||
report.append(
|
||
f"💰 今日累计投入: <b>${total_cost:.2f}</b>"
|
||
)
|
||
report.append(
|
||
f"📈 累计浮动盈亏: <b>{total_pnl:+.2f}$</b>"
|
||
)
|
||
notifier._send_message("\n".join(report))
|
||
pushed_signals[summary_key] = time.time()
|
||
|
||
except Exception as e:
|
||
logger.error(f"即时保存数据失败: {e}")
|
||
|
||
logger.info("本轮扫描结束。等待 5 分钟...")
|
||
time.sleep(300)
|
||
|
||
except KeyboardInterrupt:
|
||
logger.info("收到关机指令，正在退出...")
|
||
except Exception as e:
|
||
logger.exception(f"系统运行出错: {e}")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|