feat: implement PolyWeather dashboard with map UI, data collection, analysis, and comprehensive documentation.
This commit is contained in:
2569718930@qq.com
@@ -394,6 +394,7 @@ class PolymarketReadOnlyLayer:
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"liquidity": None,
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"volume": None,
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"sparkline": fallback_sparkline or [],
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"top_buckets": [],
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"recent_trades": [],
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"websocket": {},
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}
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@@ -485,6 +486,13 @@ class PolymarketReadOnlyLayer:
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signal_label, confidence = self._derive_signal(edge_percent, liquidity)
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top_buckets = self._build_top_temperature_buckets(
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city_key=city_key,
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target_date=date_str,
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primary_market=market,
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limit=4,
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)
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yes_payload = {
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"outcome": yes_token.get("outcome") or "Yes",
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"token_id": yes_token.get("token_id"),
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@@ -551,6 +559,7 @@ class PolymarketReadOnlyLayer:
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"liquidity": liquidity,
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"volume": volume,
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"sparkline": sparkline_values,
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"top_buckets": top_buckets,
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"websocket": {
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"market_url": market_url,
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"asset_ids": [
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@@ -1174,3 +1183,232 @@ class PolymarketReadOnlyLayer:
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if slug:
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return f"https://polymarket.com/market/{slug}"
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return None
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def _build_top_temperature_buckets(
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self,
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city_key: str,
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target_date: str,
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primary_market: Dict[str, Any],
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limit: int = 4,
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) -> List[Dict[str, Any]]:
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candidate_markets = self._collect_related_temperature_markets(
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city_key=city_key,
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target_date=target_date,
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primary_market=primary_market,
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)
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if not candidate_markets:
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return []
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ranked: List[
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Tuple[
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float,
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float,
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Dict[str, Any],
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Dict[str, Any],
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Dict[str, Any],
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Dict[str, Any],
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Dict[str, Any],
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]
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] = []
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for market in candidate_markets:
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tokens = self._extract_market_tokens(market)
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yes_token, no_token = self._resolve_yes_no_tokens(tokens)
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if not yes_token or not no_token:
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continue
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yes_token_id = str(yes_token.get("token_id") or "").strip()
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no_token_id = str(no_token.get("token_id") or "").strip()
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yes_prices = self._get_token_market_data(yes_token_id) if yes_token_id else {}
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no_prices = self._get_token_market_data(no_token_id) if no_token_id else {}
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yes_midpoint = _extract_price(yes_prices.get("midpoint"))
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yes_implied = _extract_price(yes_token.get("implied_probability"))
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no_implied = _extract_price(no_token.get("implied_probability"))
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market_prob = (
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yes_midpoint
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if yes_midpoint is not None
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else (
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yes_implied
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if yes_implied is not None
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else (1.0 - no_implied if no_implied is not None else None)
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)
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)
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if market_prob is None:
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continue
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market_prob = max(0.0, min(1.0, float(market_prob)))
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volume = (
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_extract_price(
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market.get("volumeNum")
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or market.get("volume")
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or market.get("volume24hr")
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)
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or 0.0
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)
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ranked.append(
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(
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market_prob,
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volume,
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market,
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yes_token,
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no_token,
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yes_prices,
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no_prices,
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)
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)
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if not ranked:
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return []
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ranked.sort(key=lambda item: (item[0], item[1]), reverse=True)
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top_rows: List[Dict[str, Any]] = []
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max_items = max(1, int(limit or 4))
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primary_slug = str(primary_market.get("slug") or "").strip().lower()
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for market_prob, _volume, market, yes_token, no_token, yes_prices, no_prices in ranked[
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:max_items
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]:
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yes_buy = _extract_price(yes_prices.get("buy"))
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yes_sell = _extract_price(yes_prices.get("sell"))
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yes_midpoint = _extract_price(yes_prices.get("midpoint")) or market_prob
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no_buy = _extract_price(no_prices.get("buy"))
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no_sell = _extract_price(no_prices.get("sell"))
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if no_buy is None and yes_buy is not None:
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no_buy = max(0.0, min(1.0, 1.0 - yes_buy))
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if no_sell is None and yes_sell is not None:
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no_sell = max(0.0, min(1.0, 1.0 - yes_sell))
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bucket_temp = self._extract_market_bucket_temp(market)
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market_slug = str(market.get("slug") or "").strip()
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top_rows.append(
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{
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"label": self._extract_market_bucket_label(market, bucket_temp),
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"value": bucket_temp,
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"temp": bucket_temp,
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"probability": market_prob,
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"market_price": yes_midpoint,
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"yes_buy": yes_buy,
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"yes_sell": yes_sell,
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"no_buy": no_buy,
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"no_sell": no_sell,
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"slug": market_slug or None,
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"question": market.get("question") or market.get("title"),
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"is_primary": bool(
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primary_slug
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and market_slug
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and primary_slug == market_slug.strip().lower()
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),
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}
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)
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return top_rows
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def _collect_related_temperature_markets(
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self,
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city_key: str,
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target_date: str,
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primary_market: Dict[str, Any],
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) -> List[Dict[str, Any]]:
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related: List[Dict[str, Any]] = []
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canonical_event_slug = self._build_weather_event_slug(city_key, target_date)
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if canonical_event_slug:
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related.extend(self._load_event_markets(canonical_event_slug))
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event_slug = self._extract_event_slug(primary_market)
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if event_slug and event_slug != canonical_event_slug:
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related.extend(self._load_event_markets(event_slug))
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if not related:
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for market in self._load_markets(active_only=True):
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if self._score_market(city_key, target_date, market) <= 0:
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continue
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if self._extract_market_bucket_temp(market) is None:
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continue
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related.append(market)
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related.append(primary_market)
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unique: List[Dict[str, Any]] = []
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seen = set()
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for market in related:
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if not isinstance(market, dict):
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continue
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dedupe_key = str(
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market.get("id")
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or market.get("slug")
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or market.get("conditionId")
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or ""
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).strip()
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if not dedupe_key:
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continue
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if dedupe_key in seen:
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continue
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seen.add(dedupe_key)
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unique.append(market)
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return unique
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def _extract_event_slug(self, market: Dict[str, Any]) -> Optional[str]:
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event_slug = str(market.get("eventSlug") or "").strip().lower()
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if event_slug:
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return event_slug
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slug = str(market.get("slug") or "").strip().lower()
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if not slug:
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return None
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trimmed = re.sub(
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r"-(?:m)?\d+(?:-\d+)?c(?:-or-(?:higher|lower|above|below))?$",
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"",
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slug,
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)
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trimmed = trimmed.strip("-")
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return trimmed or None
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def _load_event_markets(self, event_slug: str) -> List[Dict[str, Any]]:
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normalized_slug = str(event_slug or "").strip().lower()
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if not normalized_slug:
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return []
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try:
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resp = self._session.get(
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f"{self.gamma_url}/events",
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params={"slug": normalized_slug, "limit": 5},
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timeout=self.http_timeout,
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)
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resp.raise_for_status()
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payload = resp.json()
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except Exception:
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return []
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events = payload if isinstance(payload, list) else []
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out: List[Dict[str, Any]] = []
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for event in events:
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if not isinstance(event, dict):
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continue
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event_item_slug = str(event.get("slug") or "").strip().lower()
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if event_item_slug and event_item_slug != normalized_slug:
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continue
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for market in event.get("markets") or []:
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if not isinstance(market, dict):
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continue
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market["eventSlug"] = market.get("eventSlug") or event_item_slug
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market["eventTitle"] = market.get("eventTitle") or event.get("title")
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out.append(market)
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return out
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def _extract_market_bucket_label(
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self,
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market: Dict[str, Any],
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bucket_temp: Optional[float],
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) -> str:
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question = str(market.get("question") or market.get("title") or "").strip()
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text = question.lower()
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if bucket_temp is not None:
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if "or higher" in text or "or above" in text or "and above" in text:
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return f"{bucket_temp:g}C+"
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if "or lower" in text or "or below" in text or "and below" in text:
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return f"<={bucket_temp:g}C"
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return f"{bucket_temp:g}C"
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return question or str(market.get("slug") or "")
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