2569718930@qq.com
866 lines
42 KiB
Python
866 lines
42 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.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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risk_manager = RiskManager(config_data.get("config", {}))
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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:
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ts = json.loads(ts)
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except:
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ts = []
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active_tid = m.get("active_token_id")
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# 智能识别买入/买否 Token
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if active_tid and isinstance(ts, list):
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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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if len(ts) == 2:
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# 传统的二选一，直接获取 No Token 的 Ask
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no_tid = ts[1] if ts[0] == active_tid else ts[0]
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price_requests.append({"token_id": no_tid, "side": "ask"})
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else:
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# 多选一，需要用 1 - Bid(Yes) 来模拟 Buy No
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price_requests.append({"token_id": active_tid, "side": "bid"})
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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() # Initialize seen_condition_ids here
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for i, m in enumerate(all_weather_markets):
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# Use condition_id + active_token_id as unique key to support multi-bracket markets
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unique_market_key = f"{m.get('condition_id')}_{m.get('active_token_id')}"
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if unique_market_key in seen_condition_ids:
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continue
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seen_condition_ids.add(unique_market_key)
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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:
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ts = json.loads(ts)
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except:
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ts = []
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active_tid = m.get("active_token_id")
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if active_tid and isinstance(ts, list):
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m["buy_yes_live"] = token_price_map.get(f"{active_tid}:ask")
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if len(ts) == 2:
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no_tid = ts[1] if ts[0] == active_tid else ts[0]
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m["buy_no_live"] = token_price_map.get(f"{no_tid}:ask")
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else:
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# 1 - Bid(Yes) = Ask(No)
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bid_val = token_price_map.get(f"{active_tid}:bid")
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if bid_val:
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m["buy_no_live"] = 1.0 - bid_val
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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，再尝试从问题文本或 Slug 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', '')} {m.get('slug', '')}"
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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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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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logger.info(
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f"☁️ {city} 当前气温: {consensus['average_temp']}{temp_symbol} (unit={temp_unit}) | 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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city_total_vol = 0
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city_pred_high = None
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city_target_date = None
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city_strategy_tips = []
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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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# 累计城市总成交量
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vol_raw = market.get("volume", 0)
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if isinstance(vol_raw, str):
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try:
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vol_raw = float(
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vol_raw.replace("$", "").replace(",", "")
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)
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except:
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vol_raw = 0
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city_total_vol += vol_raw
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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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# 使用 token_price_map 获取实时数据
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active_tid = market.get("active_token_id")
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ts = market.get("tokens", [])
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if isinstance(ts, str):
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ts = json.loads(ts)
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buy_yes_price = None
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buy_no_price = None
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bid_yes_price = None
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if len(ts) == 2:
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# 传统二选一市场 (Yes/No Token 独立)
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buy_yes_price = token_price_map.get(f"{ts[0]}:ask")
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buy_no_price = token_price_map.get(f"{ts[1]}:ask")
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bid_yes_price = token_price_map.get(f"{ts[0]}:bid")
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elif active_tid:
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# 多选一市场 (单 Token 对应一个档位)
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buy_yes_price = token_price_map.get(f"{active_tid}:ask")
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bid_yes_price = token_price_map.get(f"{active_tid}:bid")
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if bid_yes_price is not None:
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buy_no_price = 1.0 - bid_yes_price
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# 兜底概率计算
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current_prob = (
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(buy_yes_price + bid_yes_price) / 2
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if (buy_yes_price and bid_yes_price)
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else (buy_yes_price or 0.5)
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)
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if buy_no_price is None:
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buy_no_price = 1.0 - current_prob
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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_prob = prev_data.get("price", current_prob)
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prob_change = (current_prob - prev_prob) * 100
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trend_str = (
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f"▲{abs(prob_change):.0f}%"
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if prob_change > 0.5
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else (
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f"▼{abs(prob_change):.0f}%"
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if prob_change < -0.5
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else ""
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)
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)
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# 更新历史缓存
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price_history[market_id] = {
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"price": current_prob,
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"timestamp": datetime.now().isoformat(),
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}
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# --- 预警收集 (自动推送逻辑) ---
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# 严格触发条件: 价格必须处于 85-95¢ 区间 (真正的高概率信号)
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yes_in_range = buy_yes_price and 0.85 <= buy_yes_price <= 0.95
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no_in_range = buy_no_price and 0.85 <= buy_no_price <= 0.95
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# 50¢ 保护：价格接近 50% 说明市场无明确方向，跳过
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is_undecided = 0.45 <= current_prob <= 0.55
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if (yes_in_range or no_in_range) and not is_undecided:
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alert_key = f"alert_{market_id}_{int(current_prob * 100)}"
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if alert_key not in pushed_signals:
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# 获取温度符号（在此处定义以便后续使用）
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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 = (
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"°F" if temp_unit == "fahrenheit" else "°C"
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)
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# 预测偏差分析
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if ref_temp:
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city_pred_high = ref_temp # 记录到城市概览
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temp_match = re.search(
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r"(\d+)(?:-(\d+))?°[FC]", question
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)
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if temp_match:
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low_b = int(temp_match.group(1))
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high_b = (
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int(temp_match.group(2))
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if temp_match.group(2)
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else low_b
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)
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diff = ref_temp - ((low_b + high_b) / 2)
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# 偏差信息将在后面构建 msg 时统一添加
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# 生成策略建议：仅保留模型一致提示
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if abs(diff) < 2 and current_prob > 0.7:
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city_strategy_tips.append(
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f"预测温度{ref_temp}{temp_symbol}落在{question}区间，市场与模型一致"
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)
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# 模拟下单 - 使用 Ask 价格（实际可成交价格）
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if buy_yes_price and buy_yes_price > 0.5:
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trigger_side = "Buy Yes"
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trigger_price = int(buy_yes_price * 100)
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else:
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trigger_side = "Buy No"
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trigger_price = (
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int(buy_no_price * 100)
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if buy_no_price
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else int((1 - current_prob) * 100)
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)
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# 构建预测文本
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forecast_text = (
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f"{ref_temp}{temp_symbol}" if ref_temp else "N/A"
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)
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# 构建简约版消息
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side_display = (
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"Buy No" if trigger_side == "Buy No" else "Buy Yes"
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)
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msg = f"⚡ {question} ({target_date}): {side_display} {trigger_price}¢ | 预测:{forecast_text}"
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success = paper_trader.open_position(
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market_id=market_id,
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city=city,
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option=question,
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price=trigger_price,
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side="YES" if trigger_side == "Buy Yes" else "NO",
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amount_usd=5.0,
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target_date=target_date,
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predicted_temp=ref_temp,
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)
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# 添加模拟交易标签
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if success:
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msg += " [🛒 $5.0 💡试探]"
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city_alerts.append(
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{
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"market": target_date or "今日",
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"msg": msg,
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"bought": success,
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"amount": 5.0,
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"confidence": "💡试探",
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}
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)
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pushed_signals[alert_key] = time.time()
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if target_date:
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city_target_date = target_date
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# C. 准备缓存数据
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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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price_change_pct = (
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((current_price - prev_price) / prev_price * 100)
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if prev_price > 0
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else 0
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)
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# 更新价格历史缓存
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price_history[market_id] = {
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"price": current_price,
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"timestamp": datetime.now().isoformat(),
|
|
}
|
|
|
|
cache_entry = {
|
|
"city": city,
|
|
"full_title": event_title,
|
|
"option": question,
|
|
"prediction": f"{ref_temp}{temp_symbol}",
|
|
"price": int(current_price * 100),
|
|
"buy_yes": int(buy_yes_price * 100) if buy_yes_price else 0,
|
|
"buy_no": int(buy_no_price * 100) if buy_no_price else 0,
|
|
"url": f"https://polymarket.com/event/{market.get('slug')}",
|
|
"local_time": city_local_time,
|
|
"target_date": target_date,
|
|
"score": 0,
|
|
"rationale": "ACTIVE",
|
|
"trend": round(price_change_pct, 1),
|
|
}
|
|
|
|
# --- 最终过滤器 (拦截垃圾信号) ---
|
|
|
|
# 1. 过滤已锁定价格 (>= 98.5c)
|
|
if (buy_yes_price and buy_yes_price >= 0.985) or (
|
|
buy_no_price and buy_no_price >= 0.985
|
|
):
|
|
cache_entry["rationale"] = "ENDED"
|
|
all_markets_cache[market_id] = cache_entry
|
|
continue
|
|
|
|
# 2. 过滤已过期日期 (动态获取当前日期)
|
|
current_today = datetime.now().strftime("%Y-%m-%d")
|
|
if target_date and target_date < current_today:
|
|
cache_entry["rationale"] = "EXPIRED"
|
|
all_markets_cache[market_id] = cache_entry
|
|
continue
|
|
|
|
# 3. 评分计算
|
|
try:
|
|
signal = decision_engine.calculate_signal(
|
|
model_prediction=predictor.predict_ensemble([ref_temp]),
|
|
market_data={
|
|
"orderbook": {},
|
|
"price_history": [current_price],
|
|
"transactions": [],
|
|
},
|
|
weather_consensus={"average_temp": ref_temp},
|
|
whale_activity=None,
|
|
)
|
|
cache_entry["score"] = signal.get("final_score", 0)
|
|
cache_entry["rationale"] = signal.get(
|
|
"recommendation", "ACTIVE"
|
|
)
|
|
except Exception as e:
|
|
logger.error(f"计算信号失败 [{market_id}]: {e}")
|
|
cache_entry["score"] = 0
|
|
cache_entry["rationale"] = "ERROR"
|
|
|
|
all_markets_cache[market_id] = cache_entry
|
|
|
|
# --- 预警收集 (自动推送逻辑) ---
|
|
if (buy_yes_price and 0.85 <= buy_yes_price <= 0.95) or (
|
|
buy_no_price and 0.85 <= buy_no_price <= 0.95
|
|
):
|
|
alert_key = f"alert_{market_id}_range_85_95"
|
|
if alert_key not in pushed_signals:
|
|
# --- 基础参数识别 ---
|
|
is_categorical = len(ts) > 2 and active_tid
|
|
if is_categorical:
|
|
# 语义转换逻辑保持一致
|
|
if buy_no_price and buy_no_price >= 0.85:
|
|
trigger_side = "Buy No" # 直接统一为 Buy No
|
|
trigger_price = int(buy_no_price * 100)
|
|
else:
|
|
trigger_side = "Buy Yes"
|
|
trigger_price = int(buy_yes_price * 100)
|
|
else:
|
|
trigger_side = (
|
|
"Buy Yes" if buy_yes_price >= 0.85 else "Buy No"
|
|
)
|
|
trigger_price = (
|
|
int(buy_yes_price * 100)
|
|
if trigger_side == "Buy Yes"
|
|
else int(buy_no_price * 100)
|
|
)
|
|
|
|
# --- 智能动态仓位计算 ---
|
|
# 1. 获取 Open-Meteo 对目标日期的最高温预测
|
|
predicted_high = None
|
|
weather_supports = False
|
|
daily_data = weather_data.get("open-meteo", {}).get(
|
|
"daily", {}
|
|
)
|
|
if daily_data and target_date:
|
|
dates = daily_data.get("time", [])
|
|
max_temps = daily_data.get("temperature_2m_max", [])
|
|
for idx, d_str in enumerate(dates):
|
|
if target_date == d_str and idx < len(
|
|
max_temps
|
|
):
|
|
predicted_high = max_temps[idx]
|
|
break
|
|
|
|
# 2. 判断天气预测是否支持当前方向
|
|
if predicted_high is not None:
|
|
# 解析选项的温度范围 (例如 "40-41°F" 或 "32°F or below")
|
|
temp_match = re.search(
|
|
r"(\d+)(?:-(\d+))?°[FC]", question
|
|
)
|
|
if temp_match:
|
|
low_bound = int(temp_match.group(1))
|
|
high_bound = (
|
|
int(temp_match.group(2))
|
|
if temp_match.group(2)
|
|
else low_bound
|
|
)
|
|
|
|
# 如果买 NO，天气预测应该在这个区间之外
|
|
if trigger_side == "Buy No":
|
|
weather_supports = (
|
|
predicted_high < low_bound - 2
|
|
) or (predicted_high > high_bound + 2)
|
|
else: # 买 YES
|
|
weather_supports = (
|
|
low_bound - 2
|
|
<= predicted_high
|
|
<= high_bound + 2
|
|
)
|
|
|
|
# 3. 获取成交量信息
|
|
market_volume = market.get("volume", 0)
|
|
if isinstance(market_volume, str):
|
|
try:
|
|
market_volume = float(
|
|
market_volume.replace("$", "").replace(
|
|
",", ""
|
|
)
|
|
)
|
|
except:
|
|
market_volume = 0
|
|
high_volume = market_volume >= 5000 # $5000+ 算高成交量
|
|
|
|
# --- Pro 级仓位决策系统 ---
|
|
# 1. 计算离结算剩余小时数 (假设气温市场在目标日期晚上 23:59 结算)
|
|
hours_to_settle = 24.0
|
|
if target_date:
|
|
try:
|
|
settle_dt = datetime.strptime(
|
|
f"{target_date} 23:59:59",
|
|
"%Y-%m-%d %H:%M:%S",
|
|
)
|
|
now_utc = datetime.utcnow()
|
|
diff = settle_dt - now_utc
|
|
hours_to_settle = diff.total_seconds() / 3600.0
|
|
except:
|
|
pass
|
|
|
|
# 2. 计算相对成交量比例
|
|
total_daily_vol = sum(
|
|
[
|
|
float(
|
|
str(m.get("volume", 0))
|
|
.replace("$", "")
|
|
.replace(",", "")
|
|
)
|
|
for m in city_markets
|
|
if (
|
|
weather.extract_date_from_title(
|
|
m.get("event_title", "")
|
|
)
|
|
or weather.extract_date_from_title(
|
|
m.get("question", "")
|
|
)
|
|
)
|
|
== target_date
|
|
]
|
|
)
|
|
market_vol = float(
|
|
str(market.get("volume", 0))
|
|
.replace("$", "")
|
|
.replace(",", "")
|
|
)
|
|
is_rel_high_vol = (
|
|
(market_vol / total_daily_vol > 0.3)
|
|
if total_daily_vol > 0
|
|
else False
|
|
)
|
|
|
|
# 3. 基础意向仓位 (基于置信度)
|
|
base_pos = 3.0 # 默认探路
|
|
confidence_tag = "💡试探"
|
|
if (
|
|
trigger_price >= 90
|
|
and weather_supports
|
|
and high_volume
|
|
):
|
|
base_pos, confidence_tag = 10.0, "🔥高置信"
|
|
elif trigger_price >= 90 and weather_supports:
|
|
base_pos, confidence_tag = 7.0, "⭐中置信"
|
|
elif trigger_price >= 92:
|
|
base_pos, confidence_tag = 5.0, "📌价格锁定"
|
|
|
|
# 4. 仓位决策
|
|
amount_usd, risk_reason = (
|
|
risk_manager.calculate_position_size(
|
|
base_confidence_usd=base_pos,
|
|
hours_to_settle=hours_to_settle,
|
|
is_high_relative_volume=is_rel_high_vol,
|
|
)
|
|
)
|
|
|
|
logger.info(
|
|
f"【Pro仓位】{city} {question} | "
|
|
f"基础:{base_pos}$ -> 最终:{amount_usd}$ | 原因:{risk_reason} | "
|
|
f"剩:{hours_to_settle:.1f}h"
|
|
)
|
|
|
|
# --- 模拟交易触发逻辑 ---
|
|
if amount_usd > 0:
|
|
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,
|
|
)
|
|
if success:
|
|
risk_manager.record_trade(amount_usd)
|
|
else:
|
|
# 如果被风控拦截（金额为0），则不进行任何推送，避免刷屏
|
|
success = False
|
|
logger.info(
|
|
f"Skipping alert for {question}: {risk_reason}"
|
|
)
|
|
continue
|
|
|
|
# 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"{predicted_high}{temp_symbol}"
|
|
if predicted_high
|
|
else "N/A"
|
|
)
|
|
|
|
# 构建简约版消息: ⚡ {question} ({date}): {side} {price}¢ | 预测:{forecast} [🛒 ${amount} {tag}]
|
|
side_display = trigger_side
|
|
msg = (
|
|
f"⚡ {question} ({target_date}): {side_display} {trigger_price}¢ | "
|
|
f"预测:{forecast_text} [🛒 ${amount_usd} {confidence_tag}]"
|
|
)
|
|
|
|
city_alerts.append(
|
|
{
|
|
"type": "price",
|
|
"market": f"{target_date or '今日'}",
|
|
"msg": msg,
|
|
"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
|
|
|
|
# E. 统一发送城市汇总通知 (使用新 Pro 模板)
|
|
if city_alerts:
|
|
# 去重策略建议
|
|
unique_tips = list(dict.fromkeys(city_strategy_tips))
|
|
# 获取 METAR 数据（仅当天结算的市场才显示）
|
|
today_str = datetime.now().strftime("%Y-%m-%d")
|
|
# 检查是否有当天结算的市场
|
|
has_today_market = any(
|
|
a.get("market") == today_str or a.get("market") == "今日"
|
|
for a in city_alerts
|
|
)
|
|
metar_data = (
|
|
weather_data.get("metar") if has_today_market else None
|
|
)
|
|
# notifier.send_combined_alert(
|
|
# city=city,
|
|
# alerts=city_alerts,
|
|
# local_time=city_local_time,
|
|
# forecast_temp=f"{city_pred_high}{temp_symbol}"
|
|
# if city_pred_high
|
|
# else "N/A",
|
|
# total_volume=city_total_vol,
|
|
# brackets_count=len(city_markets),
|
|
# strategy_tips=unique_tips,
|
|
# metar_data=metar_data,
|
|
# )
|
|
|
|
except Exception as e:
|
|
logger.error(f"分析城市 {city} 时出错: {e}")
|
|
# --- 每处理完一个城市，立即更新 JSON 文件 ---
|
|
try:
|
|
# --- 周期性结算：保存高价值信号 ---
|
|
active_signals = []
|
|
for mid, entry in all_markets_cache.items():
|
|
# Relaxed filtering: Let the bot decide, but mark ENDED
|
|
rationale = entry.get("rationale")
|
|
if rationale == "ERROR":
|
|
continue
|
|
|
|
target_dt = entry.get("target_date")
|
|
# Only filter out truly ancient history
|
|
if target_dt and target_dt < "2026-02-01":
|
|
continue
|
|
|
|
active_signals.append(entry)
|
|
|
|
# 按分数排序
|
|
active_signals.sort(key=lambda x: x.get("score", 0), reverse=True)
|
|
|
|
with open("data/active_signals.json", "w", encoding="utf-8") as f:
|
|
json.dump(active_signals, f, ensure_ascii=False, indent=4)
|
|
|
|
logger.info(
|
|
f"已更新活跃信号库，包含 {len(active_signals)} 个有效信号。"
|
|
)
|
|
|
|
# 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()
|