feat: Implement initial PolyWeather web map API with comprehensive weather data collection, analysis, and Polymarket integration.
This commit is contained in:
2569718930@qq.com
@@ -0,0 +1,292 @@
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"""
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Polymarket Weather Market Client
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Fetches real-time odds from Polymarket's Gamma API for weather contracts.
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Used by the web dashboard only (not the Telegram bot).
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"""
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import re
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import time
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import logging
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import requests
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from typing import Dict, List, Optional, Any
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from datetime import datetime, timedelta
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logger = logging.getLogger(__name__)
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GAMMA_API = "https://gamma-api.polymarket.com"
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# Map our city names → Polymarket contract keywords
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CITY_KEYWORDS = {
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"ankara": ["ankara", "Ankara"],
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"london": ["london", "London"],
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"paris": ["paris", "Paris"],
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"seoul": ["seoul", "Seoul"],
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"toronto": ["toronto", "Toronto"],
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"buenos aires": ["buenos aires", "Buenos Aires"],
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"wellington": ["wellington", "Wellington"],
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"new york": ["new york", "New York", "NYC"],
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"chicago": ["chicago", "Chicago"],
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"dallas": ["dallas", "Dallas"],
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"miami": ["miami", "Miami"],
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"atlanta": ["atlanta", "Atlanta"],
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"seattle": ["seattle", "Seattle"],
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}
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# In-memory cache: {city: {date: data, ...}}
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_market_cache: Dict[str, Any] = {}
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_cache_ts: float = 0
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CACHE_TTL = 300 # 5 minutes
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def _parse_threshold_from_question(question: str) -> Optional[dict]:
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"""
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Parse a Polymarket weather question to extract city, threshold, and date.
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Examples:
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"Will the high temperature in Ankara exceed 8°C on March 5?"
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"Highest temperature in London on March 4?"
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"Will the high in New York City exceed 45°F on March 5, 2026?"
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"""
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# Pattern 1: "exceed X°F/°C"
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m = re.search(
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r"exceed\s+([\d.]+)\s*°\s*([FC])", question, re.IGNORECASE
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)
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if m:
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value = float(m.group(1))
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unit = m.group(2).upper()
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return {"threshold": value, "unit": unit, "type": "exceed"}
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# Pattern 2: "Highest temperature in City on Date?" (multi-outcome)
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m = re.search(r"[Hh]ighest\s+temperature", question)
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if m:
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return {"type": "range"}
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return None
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def _match_city(question: str) -> Optional[str]:
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"""Match a Polymarket question to one of our tracked cities."""
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q_lower = question.lower()
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for city, keywords in CITY_KEYWORDS.items():
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for kw in keywords:
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if kw.lower() in q_lower:
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return city
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return None
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def _parse_date_from_question(question: str) -> Optional[str]:
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"""Extract date from question, return as YYYY-MM-DD."""
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# "on March 5, 2026" or "on March 5"
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m = re.search(
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r"on\s+(\w+)\s+(\d{1,2})(?:,?\s*(\d{4}))?", question, re.IGNORECASE
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)
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if m:
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month_str, day_str, year_str = m.group(1), m.group(2), m.group(3)
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month_map = {
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"january": 1, "february": 2, "march": 3, "april": 4,
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"may": 5, "june": 6, "july": 7, "august": 8,
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"september": 9, "october": 10, "november": 11, "december": 12,
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}
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month = month_map.get(month_str.lower())
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if month:
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year = int(year_str) if year_str else datetime.now().year
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return f"{year}-{month:02d}-{int(day_str):02d}"
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return None
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def fetch_weather_markets(
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proxy: Optional[str] = None, timeout: int = 15
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) -> List[Dict]:
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"""
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Fetch all active weather markets from Polymarket.
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Returns a list of dicts, each representing a market with:
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- question, city, date, odds, volume, etc.
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"""
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global _market_cache, _cache_ts
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if time.time() - _cache_ts < CACHE_TTL and _market_cache:
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return _market_cache.get("_all", [])
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try:
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session = requests.Session()
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if proxy:
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if not proxy.startswith("http"):
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proxy = f"http://{proxy}"
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session.proxies = {"http": proxy, "https": proxy}
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# Fetch weather-tagged events
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resp = session.get(
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f"{GAMMA_API}/events",
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params={
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"tag": "weather",
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"active": "true",
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"closed": "false",
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"limit": 50,
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},
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timeout=timeout,
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headers={"Accept": "application/json"},
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)
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resp.raise_for_status()
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events = resp.json()
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all_markets = []
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for event in events:
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markets = event.get("markets", [])
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event_title = event.get("title", "")
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for mkt in markets:
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question = mkt.get("question", event_title)
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city = _match_city(question)
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if not city:
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continue
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date_str = _parse_date_from_question(question)
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parsed = _parse_threshold_from_question(question)
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# Extract outcome prices
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outcome_prices = mkt.get("outcomePrices", "")
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outcomes = mkt.get("outcomes", "")
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yes_price = None
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no_price = None
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try:
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if isinstance(outcome_prices, str) and outcome_prices:
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import json
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prices = json.loads(outcome_prices)
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if len(prices) >= 2:
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yes_price = float(prices[0])
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no_price = float(prices[1])
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elif isinstance(outcome_prices, list) and len(outcome_prices) >= 2:
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yes_price = float(outcome_prices[0])
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no_price = float(outcome_prices[1])
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except Exception:
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pass
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market_info = {
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"id": mkt.get("id"),
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"question": question,
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"city": city,
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"date": date_str,
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"threshold": parsed.get("threshold") if parsed else None,
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"threshold_unit": parsed.get("unit") if parsed else None,
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"contract_type": parsed.get("type", "unknown") if parsed else "unknown",
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"yes_price": yes_price, # 0.00-1.00 = market probability
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"no_price": no_price,
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"volume": mkt.get("volume"),
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"liquidity": mkt.get("liquidityNum"),
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"slug": mkt.get("slug", ""),
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"url": f"https://polymarket.com/event/{event.get('slug', '')}",
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}
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all_markets.append(market_info)
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# Organize by city
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_market_cache = {"_all": all_markets}
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for m in all_markets:
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c = m["city"]
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if c not in _market_cache:
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_market_cache[c] = []
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_market_cache[c].append(m)
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_cache_ts = time.time()
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logger.info(f"📊 Polymarket: 获取 {len(all_markets)} 个天气合约")
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return all_markets
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except Exception as e:
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logger.warning(f"Polymarket API 请求失败: {e}")
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return _market_cache.get("_all", [])
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def get_city_markets(city: str, target_date: Optional[str] = None) -> List[Dict]:
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"""
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Get Polymarket contracts for a specific city.
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Args:
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city: City name (lowercase)
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target_date: Optional date filter (YYYY-MM-DD)
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Returns:
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List of market dicts for this city, sorted by volume desc.
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"""
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# Ensure markets are fetched
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if not _market_cache or time.time() - _cache_ts >= CACHE_TTL:
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fetch_weather_markets()
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markets = _market_cache.get(city, [])
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if target_date:
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markets = [m for m in markets if m.get("date") == target_date]
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# Sort by volume (descending)
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markets.sort(key=lambda m: float(m.get("volume") or 0), reverse=True)
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return markets
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def compute_divergence(
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city_markets: List[Dict],
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prob_distribution: List[Dict],
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temp_symbol: str = "°C",
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use_fahrenheit: bool = False,
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) -> List[Dict]:
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"""
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Compare our probability engine output with Polymarket odds.
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Args:
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city_markets: Markets from get_city_markets()
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prob_distribution: Our engine's [{value, probability}, ...]
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temp_symbol: "°C" or "°F"
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use_fahrenheit: Whether our data is in Fahrenheit
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Returns:
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List of divergence signals:
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[{threshold, our_prob, market_prob, divergence, signal}, ...]
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"""
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signals = []
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for mkt in city_markets:
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if mkt.get("contract_type") != "exceed" or mkt.get("yes_price") is None:
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continue
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threshold = mkt.get("threshold")
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mkt_unit = mkt.get("threshold_unit", "F")
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if threshold is None:
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continue
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# Convert threshold to match our unit
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if mkt_unit == "F" and not use_fahrenheit:
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threshold_c = (threshold - 32) * 5 / 9
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elif mkt_unit == "C" and use_fahrenheit:
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threshold_c = threshold # keep as-is, our data is F
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else:
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threshold_c = threshold
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# Calculate our probability of exceeding this threshold
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# Sum probabilities for all values >= threshold (rounded)
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threshold_wu = round(threshold_c)
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our_exceed_prob = 0.0
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for p in prob_distribution:
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if p.get("value", 0) >= threshold_wu:
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our_exceed_prob += p.get("probability", 0)
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market_prob = mkt["yes_price"]
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divergence = our_exceed_prob - market_prob
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signal = "neutral"
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if abs(divergence) > 0.10:
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signal = "underpriced" if divergence > 0 else "overpriced"
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elif abs(divergence) > 0.05:
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signal = "slight_under" if divergence > 0 else "slight_over"
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signals.append({
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"question": mkt["question"],
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"threshold": threshold,
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"threshold_unit": mkt_unit,
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"our_prob": round(our_exceed_prob, 3),
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"market_prob": round(market_prob, 3),
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"divergence": round(divergence, 3),
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"signal": signal,
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"volume": mkt.get("volume"),
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"url": mkt.get("url", ""),
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})
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return signals
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+30
@@ -26,6 +26,11 @@ from src.utils.config_loader import load_config
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from src.data_collection.weather_sources import WeatherDataCollector
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from src.data_collection.city_risk_profiles import CITY_RISK_PROFILES
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from src.analysis.deb_algorithm import calculate_dynamic_weights, get_deb_accuracy
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from src.data_collection.polymarket_client import (
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fetch_weather_markets,
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get_city_markets,
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compute_divergence,
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)
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# ──────────────────────────────────────────────────────────
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# Setup
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@@ -453,6 +458,31 @@ def _analyze(city: str) -> Dict[str, Any]:
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"updated_at": datetime.now(timezone.utc).isoformat(),
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}
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# ── 16. Polymarket odds (web-only, non-blocking) ──
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try:
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proxy = _config.get("proxy")
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fetch_weather_markets(proxy=proxy, timeout=10)
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city_mkts = get_city_markets(city, target_date=local_date_str)
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if city_mkts:
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result["polymarket"] = {
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"markets": [
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{
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"question": m["question"],
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"yes_price": m["yes_price"],
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"volume": m.get("volume"),
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"threshold": m.get("threshold"),
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"threshold_unit": m.get("threshold_unit"),
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"url": m.get("url", ""),
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}
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for m in city_mkts[:5]
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],
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"divergence": compute_divergence(
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city_mkts, probabilities, sym, use_fahrenheit=info.get("f", False)
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),
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}
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except Exception as e:
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logger.debug(f"Polymarket data skipped for {city}: {e}")
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_cache[city] = {"t": _time.time(), "d": result}
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return result
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@@ -326,6 +326,8 @@ function renderPanel(data) {
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renderForecast(data);
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// AI
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renderAI(data);
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// Market Odds
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renderMarket(data);
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// Risk
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renderRisk(data);
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}
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@@ -824,6 +826,75 @@ function renderAI(data) {
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container.innerHTML = text;
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}
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function renderMarket(data) {
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const container = document.getElementById("marketOdds");
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if (
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!data.polymarket ||
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!data.polymarket.markets ||
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data.polymarket.markets.length === 0
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) {
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container.innerHTML = '<div class="market-no-data">暂无相关天气合约</div>';
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return;
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}
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const { markets, divergence } = data.polymarket;
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let html = "";
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markets.forEach((mkt) => {
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// Find matching divergence signal
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const divSignal = divergence.find((d) => d.question === mkt.question);
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let probText =
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mkt.yes_price != null ? `${Math.round(mkt.yes_price * 100)}¢` : "N/A";
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let noText =
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mkt.yes_price != null
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? `${Math.round((1 - mkt.yes_price) * 100)}¢`
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: "N/A";
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html += `
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<div class="market-card">
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<div class="market-question">
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<a href="${mkt.url}" target="_blank" style="color:var(--text-primary);text-decoration:none;">${mkt.question} 🔗</a>
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</div>
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<div class="market-prices">
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<span class="market-price yes">${probText}</span><span class="market-price-label">Yes</span>
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<span style="margin: 0 4px; color:var(--text-muted)">|</span>
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<span class="market-price no">${noText}</span><span class="market-price-label">No</span>
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</div>
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<div class="market-volume">
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交易量: $${mkt.volume ? Math.round(mkt.volume).toLocaleString() : 0}
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</div>
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`;
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if (divSignal) {
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let divClass = divSignal.signal;
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let divIcon = "⚪";
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let divText = "市场定价合理";
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let diffPct = (Math.abs(divSignal.divergence) * 100).toFixed(1);
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if (divClass === "underpriced" || divClass === "slight_under") {
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divIcon = "🟢";
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divText = `低估 (偏离 +${diffPct}%)`;
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} else if (divClass === "overpriced" || divClass === "slight_over") {
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divIcon = "🔴";
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divText = `高估 (偏离 -${diffPct}%)`;
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}
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html += `
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<div class="market-divergence ${divClass.replace("slight_", "")}">
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<div><b>${divIcon} 你的模型:</b> ${Math.round(divSignal.our_prob * 100)}%</div>
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<div style="margin-top:2px;"><b>🆚 市场定价:</b> ${Math.round(divSignal.market_prob * 100)}%</div>
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<div style="margin-top:6px;font-weight:600;">💡 信号: ${divText}</div>
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</div>
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`;
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}
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html += `</div>`;
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});
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container.innerHTML = html;
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}
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function renderRisk(data) {
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const container = document.getElementById("riskInfo");
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const risk = data.risk || {};
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@@ -131,6 +131,14 @@
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</div>
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</section>
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<!-- ── Polymarket Odds ── -->
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<section class="market-section">
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<h3>📈 市场赔率 <span style="font-size:11px;color:var(--text-muted);font-weight:400;">Polymarket</span></h3>
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<div id="marketOdds" class="market-odds">
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<div style="color:var(--text-muted);font-size:13px;">点击城市后加载...</div>
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</div>
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</section>
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<!-- ── Risk Profile ── -->
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<section class="risk-section">
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<h3>⚠️ 数据偏差风险</h3>
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@@ -804,6 +804,95 @@ body {
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font-style: italic;
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}
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/* ── Market Odds Section ── */
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.market-odds {
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display: flex;
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flex-direction: column;
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gap: 10px;
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}
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.market-card {
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background: rgba(255, 255, 255, 0.03);
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border: 1px solid var(--border-subtle);
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border-radius: 10px;
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padding: 12px 14px;
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transition: var(--transition);
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}
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.market-card:hover {
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border-color: var(--border-glass);
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background: rgba(255, 255, 255, 0.05);
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}
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.market-question {
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font-size: 12px;
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color: var(--text-secondary);
|
||||
margin-bottom: 8px;
|
||||
line-height: 1.4;
|
||||
}
|
||||
|
||||
.market-prices {
|
||||
display: flex;
|
||||
gap: 8px;
|
||||
align-items: center;
|
||||
margin-bottom: 6px;
|
||||
}
|
||||
|
||||
.market-price {
|
||||
font-size: 20px;
|
||||
font-weight: 700;
|
||||
font-family: "Inter", sans-serif;
|
||||
}
|
||||
|
||||
.market-price.yes {
|
||||
color: var(--accent-green);
|
||||
}
|
||||
.market-price.no {
|
||||
color: #f87171;
|
||||
}
|
||||
.market-price-label {
|
||||
font-size: 11px;
|
||||
color: var(--text-muted);
|
||||
margin-left: 2px;
|
||||
}
|
||||
|
||||
.market-volume {
|
||||
font-size: 11px;
|
||||
color: var(--text-muted);
|
||||
}
|
||||
|
||||
.market-divergence {
|
||||
margin-top: 10px;
|
||||
padding: 10px 12px;
|
||||
border-radius: 8px;
|
||||
font-size: 12px;
|
||||
line-height: 1.6;
|
||||
}
|
||||
|
||||
.market-divergence.underpriced {
|
||||
background: rgba(52, 211, 153, 0.08);
|
||||
border: 1px solid rgba(52, 211, 153, 0.2);
|
||||
color: var(--accent-green);
|
||||
}
|
||||
|
||||
.market-divergence.overpriced {
|
||||
background: rgba(248, 113, 113, 0.08);
|
||||
border: 1px solid rgba(248, 113, 113, 0.2);
|
||||
color: #f87171;
|
||||
}
|
||||
|
||||
.market-divergence.neutral {
|
||||
background: rgba(255, 255, 255, 0.03);
|
||||
border: 1px solid var(--border-subtle);
|
||||
color: var(--text-muted);
|
||||
}
|
||||
|
||||
.market-no-data {
|
||||
font-size: 13px;
|
||||
color: var(--text-muted);
|
||||
padding: 8px 0;
|
||||
}
|
||||
|
||||
/* ── Risk Section ── */
|
||||
.risk-info {
|
||||
font-size: 12px;
|
||||
|
||||
Reference in New Issue
Block a user