2569718930@qq.com
3078 lines
117 KiB
Python
3078 lines
117 KiB
Python
"""
|
||
Polymarket read-only market layer.
|
||
|
||
P0 scope:
|
||
- Market discovery from Gamma REST
|
||
- Price / midpoint / spread / orderbook read from CLOB REST
|
||
- No signing, no order placement
|
||
"""
|
||
|
||
from __future__ import annotations
|
||
|
||
import json
|
||
import math
|
||
import os
|
||
import re
|
||
import threading
|
||
import time
|
||
import unicodedata
|
||
from datetime import datetime, timezone
|
||
from typing import Any, Dict, List, Optional, Tuple
|
||
|
||
import httpx
|
||
from loguru import logger
|
||
|
||
from src.data_collection.city_registry import ALIASES, CITY_REGISTRY
|
||
|
||
|
||
def _safe_float(value: Any) -> Optional[float]:
|
||
if value is None:
|
||
return None
|
||
try:
|
||
if isinstance(value, str):
|
||
value = value.strip()
|
||
if not value:
|
||
return None
|
||
numeric = float(value)
|
||
if math.isnan(numeric) or math.isinf(numeric):
|
||
return None
|
||
return numeric
|
||
except Exception:
|
||
return None
|
||
|
||
|
||
def _safe_int(value: Any, default: int) -> int:
|
||
try:
|
||
return int(value)
|
||
except Exception:
|
||
return default
|
||
|
||
|
||
def _safe_bool(value: Any) -> Optional[bool]:
|
||
if value is None:
|
||
return None
|
||
if isinstance(value, bool):
|
||
return value
|
||
if isinstance(value, (int, float)):
|
||
return bool(value)
|
||
if isinstance(value, str):
|
||
normalized = value.strip().lower()
|
||
if normalized in {"1", "true", "yes", "on"}:
|
||
return True
|
||
if normalized in {"0", "false", "no", "off"}:
|
||
return False
|
||
return bool(value)
|
||
|
||
|
||
def _normalize_text(value: Any) -> str:
|
||
text = str(value or "").strip().lower()
|
||
if not text:
|
||
return ""
|
||
text = unicodedata.normalize("NFKD", text)
|
||
text = "".join(ch for ch in text if not unicodedata.combining(ch))
|
||
text = text.replace("_", " ").replace("-", " ")
|
||
return " ".join(text.split())
|
||
|
||
|
||
def _normalize_city_key(city: Any) -> str:
|
||
raw = _normalize_text(city)
|
||
if not raw:
|
||
return ""
|
||
return ALIASES.get(raw, raw)
|
||
|
||
|
||
MARKET_CITY_ALIASES: Dict[str, str] = {
|
||
# Lau Fau Shan has its own HKO observation / settlement layer, but
|
||
# Polymarket lists this temperature market under nearby Shenzhen.
|
||
"lau fau shan": "shenzhen",
|
||
}
|
||
|
||
|
||
def _resolve_market_city_key(city_key: str) -> str:
|
||
return MARKET_CITY_ALIASES.get(city_key, city_key)
|
||
|
||
|
||
def _contains_token(haystack: str, token: str) -> bool:
|
||
token = _normalize_text(token)
|
||
if not token:
|
||
return False
|
||
pattern = r"\b" + re.escape(token) + r"\b"
|
||
try:
|
||
return re.search(pattern, haystack) is not None
|
||
except re.error:
|
||
return False
|
||
|
||
|
||
def _json_or_list(value: Any) -> List[Any]:
|
||
if value is None:
|
||
return []
|
||
if isinstance(value, list):
|
||
return value
|
||
if isinstance(value, tuple):
|
||
return list(value)
|
||
if isinstance(value, str):
|
||
text = value.strip()
|
||
if not text:
|
||
return []
|
||
try:
|
||
parsed = json.loads(text)
|
||
if isinstance(parsed, list):
|
||
return parsed
|
||
except Exception:
|
||
return []
|
||
return []
|
||
|
||
|
||
def _to_plain_dict(value: Any) -> Dict[str, Any]:
|
||
if isinstance(value, dict):
|
||
return value
|
||
if value is None:
|
||
return {}
|
||
if hasattr(value, "dict") and callable(value.dict):
|
||
try:
|
||
data = value.dict()
|
||
if isinstance(data, dict):
|
||
return data
|
||
except Exception:
|
||
pass
|
||
if hasattr(value, "__dict__"):
|
||
try:
|
||
data = dict(vars(value))
|
||
if isinstance(data, dict):
|
||
return data
|
||
except Exception:
|
||
pass
|
||
return {}
|
||
|
||
|
||
def _extract_price(value: Any) -> Optional[float]:
|
||
if value is None:
|
||
return None
|
||
direct = _safe_float(value)
|
||
if direct is not None:
|
||
return direct
|
||
if isinstance(value, dict):
|
||
for key in (
|
||
"price",
|
||
"mid",
|
||
"midpoint",
|
||
"value",
|
||
"last_trade_price",
|
||
"lastPrice",
|
||
):
|
||
numeric = _safe_float(value.get(key))
|
||
if numeric is not None:
|
||
return numeric
|
||
plain = _to_plain_dict(value)
|
||
if plain:
|
||
for key in (
|
||
"price",
|
||
"mid",
|
||
"midpoint",
|
||
"value",
|
||
"last_trade_price",
|
||
"lastPrice",
|
||
):
|
||
numeric = _safe_float(plain.get(key))
|
||
if numeric is not None:
|
||
return numeric
|
||
return None
|
||
|
||
|
||
def _clamp_probability(value: Optional[float]) -> Optional[float]:
|
||
if value is None:
|
||
return None
|
||
if value < 0.0:
|
||
return 0.0
|
||
if value > 1.0:
|
||
return 1.0
|
||
return value
|
||
|
||
|
||
def _clamp_float(value: Optional[float], lower: float, upper: float) -> Optional[float]:
|
||
if value is None:
|
||
return None
|
||
return max(lower, min(upper, float(value)))
|
||
|
||
|
||
def _parse_hhmm_to_minutes(value: Any) -> Optional[int]:
|
||
text = str(value or "").strip()
|
||
if not text or ":" not in text:
|
||
return None
|
||
try:
|
||
hh, mm = text.split(":", 1)
|
||
hour = int(hh)
|
||
minute = int(mm[:2])
|
||
except Exception:
|
||
return None
|
||
if hour < 0 or hour > 23 or minute < 0 or minute > 59:
|
||
return None
|
||
return hour * 60 + minute
|
||
|
||
|
||
def _extract_iso_date(value: Any) -> Optional[str]:
|
||
if not value:
|
||
return None
|
||
text = str(value).strip()
|
||
if not text:
|
||
return None
|
||
if len(text) >= 10 and text[4] == "-" and text[7] == "-":
|
||
return text[:10]
|
||
# Common API formats from Gamma/CLOB
|
||
candidates = (
|
||
text,
|
||
text.replace("Z", "+00:00"),
|
||
text.split(".")[0] + "Z" if "." in text and "T" in text else text,
|
||
)
|
||
for candidate in candidates:
|
||
try:
|
||
dt = datetime.fromisoformat(candidate.replace("Z", "+00:00"))
|
||
return dt.date().isoformat()
|
||
except Exception:
|
||
continue
|
||
return None
|
||
|
||
|
||
def _parse_iso_datetime_utc(value: Any) -> Optional[datetime]:
|
||
if not value:
|
||
return None
|
||
text = str(value).strip()
|
||
if not text:
|
||
return None
|
||
# Prefer timestamps that include a time component; plain dates are ambiguous.
|
||
if "T" not in text:
|
||
return None
|
||
try:
|
||
dt = datetime.fromisoformat(text.replace("Z", "+00:00"))
|
||
except Exception:
|
||
return None
|
||
if dt.tzinfo is None:
|
||
return dt.replace(tzinfo=timezone.utc)
|
||
return dt.astimezone(timezone.utc)
|
||
|
||
|
||
def _build_city_token_index() -> Dict[str, List[str]]:
|
||
result: Dict[str, List[str]] = {}
|
||
for key, info in CITY_REGISTRY.items():
|
||
normalized_key = _normalize_text(key)
|
||
tokens = {normalized_key, normalized_key.replace(" ", "")}
|
||
|
||
display_name = _normalize_text(info.get("name"))
|
||
if display_name:
|
||
tokens.add(display_name)
|
||
tokens.add(display_name.replace(" ", ""))
|
||
|
||
for alias, target in ALIASES.items():
|
||
if target != key:
|
||
continue
|
||
norm_alias = _normalize_text(alias)
|
||
if not norm_alias:
|
||
continue
|
||
# Ignore very short aliases to reduce false-positive matching.
|
||
if len(norm_alias) < 3 and norm_alias not in {"nyc"}:
|
||
continue
|
||
tokens.add(norm_alias)
|
||
|
||
if key == "new york":
|
||
tokens.update({"central park", "new yorks central park"})
|
||
if key == "sao paulo":
|
||
tokens.update({"sao paulo", "sao-paulo", "sao paulo"})
|
||
|
||
result[key] = sorted(tokens, key=len, reverse=True)
|
||
return result
|
||
|
||
|
||
CITY_TOKEN_INDEX = _build_city_token_index()
|
||
|
||
WEATHER_KEYWORDS = (
|
||
"temperature",
|
||
"temp",
|
||
"high",
|
||
"low",
|
||
"hotter",
|
||
"colder",
|
||
"above",
|
||
"below",
|
||
)
|
||
|
||
MONTH_TO_NUM = {
|
||
"jan": 1,
|
||
"january": 1,
|
||
"feb": 2,
|
||
"february": 2,
|
||
"mar": 3,
|
||
"march": 3,
|
||
"apr": 4,
|
||
"april": 4,
|
||
"may": 5,
|
||
"jun": 6,
|
||
"june": 6,
|
||
"jul": 7,
|
||
"july": 7,
|
||
"aug": 8,
|
||
"august": 8,
|
||
"sep": 9,
|
||
"sept": 9,
|
||
"september": 9,
|
||
"oct": 10,
|
||
"october": 10,
|
||
"nov": 11,
|
||
"november": 11,
|
||
"dec": 12,
|
||
"december": 12,
|
||
}
|
||
|
||
|
||
def _parse_target_date(value: str) -> Optional[datetime]:
|
||
try:
|
||
return datetime.fromisoformat(value)
|
||
except Exception:
|
||
return None
|
||
|
||
|
||
def _extract_dates_from_text(
|
||
text: str,
|
||
default_year: Optional[int],
|
||
) -> List[str]:
|
||
dates: List[str] = []
|
||
|
||
for year, month, day in re.findall(r"\b(20\d{2})[-/](\d{1,2})[-/](\d{1,2})\b", text):
|
||
try:
|
||
parsed = datetime(int(year), int(month), int(day)).date().isoformat()
|
||
dates.append(parsed)
|
||
except Exception:
|
||
continue
|
||
|
||
month_pattern = "|".join(sorted(MONTH_TO_NUM.keys(), key=len, reverse=True))
|
||
for month_name, day_raw, year_raw in re.findall(
|
||
rf"\b({month_pattern})\s+(\d{{1,2}})(?:st|nd|rd|th)?(?:\s*(20\d{{2}}))?\b",
|
||
text,
|
||
):
|
||
year = int(year_raw) if year_raw else default_year
|
||
if not year:
|
||
continue
|
||
try:
|
||
parsed = datetime(year, MONTH_TO_NUM[month_name], int(day_raw)).date().isoformat()
|
||
dates.append(parsed)
|
||
except Exception:
|
||
continue
|
||
|
||
for day_raw, month_name, year_raw in re.findall(
|
||
rf"\b(\d{{1,2}})(?:st|nd|rd|th)?\s+({month_pattern})(?:\s*(20\d{{2}}))?\b",
|
||
text,
|
||
):
|
||
year = int(year_raw) if year_raw else default_year
|
||
if not year:
|
||
continue
|
||
try:
|
||
parsed = datetime(year, MONTH_TO_NUM[month_name], int(day_raw)).date().isoformat()
|
||
dates.append(parsed)
|
||
except Exception:
|
||
continue
|
||
|
||
# Deduplicate while preserving order
|
||
unique: List[str] = []
|
||
seen = set()
|
||
for value in dates:
|
||
if value in seen:
|
||
continue
|
||
seen.add(value)
|
||
unique.append(value)
|
||
return unique
|
||
|
||
|
||
class PolymarketReadOnlyLayer:
|
||
def __init__(self) -> None:
|
||
self.enabled = (
|
||
str(os.getenv("POLYMARKET_MARKET_SCAN_ENABLED", "true")).strip().lower()
|
||
not in {"0", "false", "no", "off"}
|
||
)
|
||
self.gamma_url = (
|
||
str(os.getenv("POLYMARKET_GAMMA_URL", "https://gamma-api.polymarket.com"))
|
||
.strip()
|
||
.rstrip("/")
|
||
)
|
||
self.clob_url = (
|
||
str(os.getenv("POLYMARKET_CLOB_URL", "https://clob.polymarket.com"))
|
||
.strip()
|
||
.rstrip("/")
|
||
)
|
||
self.http_timeout = _safe_float(os.getenv("POLYMARKET_HTTP_TIMEOUT_SEC")) or 8.0
|
||
self.market_cache_ttl = _safe_int(
|
||
os.getenv("POLYMARKET_MARKET_CACHE_TTL_SEC", "60"),
|
||
60,
|
||
)
|
||
self.price_cache_ttl = _safe_int(
|
||
os.getenv("POLYMARKET_PRICE_CACHE_TTL_SEC", "30"),
|
||
30,
|
||
)
|
||
self.discovery_pages = _safe_int(
|
||
os.getenv("POLYMARKET_DISCOVERY_PAGES", "6"),
|
||
6,
|
||
)
|
||
self.discovery_limit = _safe_int(
|
||
os.getenv("POLYMARKET_DISCOVERY_LIMIT", "200"),
|
||
200,
|
||
)
|
||
self.min_liquidity_for_signal = (
|
||
_safe_float(os.getenv("POLYMARKET_SIGNAL_MIN_LIQUIDITY")) or 500.0
|
||
)
|
||
self.edge_threshold = _safe_float(os.getenv("POLYMARKET_SIGNAL_EDGE_PCT")) or 2.0
|
||
|
||
self._session = httpx.Client(
|
||
timeout=self.http_timeout,
|
||
follow_redirects=True,
|
||
)
|
||
self._markets_cache: Dict[str, Dict[str, Any]] = {}
|
||
self._active_markets_cache: Dict[str, Any] = {"data": [], "t": 0.0}
|
||
self._broad_markets_cache: Dict[str, Any] = {"data": [], "t": 0.0}
|
||
self._price_cache: Dict[str, Dict[str, Any]] = {}
|
||
self._lock = threading.Lock()
|
||
|
||
def _market_scan_debug_enabled(self) -> bool:
|
||
return (
|
||
str(os.getenv("POLYMARKET_MARKET_SCAN_DEBUG", "false")).strip().lower()
|
||
in {"1", "true", "yes", "on"}
|
||
)
|
||
|
||
def _debug_market_scan(self, message: str, **payload: Any) -> None:
|
||
if not self._market_scan_debug_enabled():
|
||
return
|
||
try:
|
||
details = json.dumps(payload, ensure_ascii=False, default=str)
|
||
except Exception:
|
||
details = str(payload)
|
||
logger.info(f"POLYMARKET_MARKET_SCAN_DEBUG {message} {details}")
|
||
|
||
def build_market_scan(
|
||
self,
|
||
city: Any,
|
||
target_date: Any,
|
||
temperature_bucket: Optional[Dict[str, Any]] = None,
|
||
model_probability: Optional[float] = None,
|
||
probability_distribution: Optional[List[Dict[str, Any]]] = None,
|
||
temp_symbol: Optional[str] = None,
|
||
fallback_sparkline: Optional[List[float]] = None,
|
||
forced_market_slug: Optional[str] = None,
|
||
include_related_buckets: bool = True,
|
||
scan_filters: Optional[Dict[str, Any]] = None,
|
||
scan_context: Optional[Dict[str, Any]] = None,
|
||
) -> Dict[str, Any]:
|
||
date_str = _extract_iso_date(target_date) or str(target_date or "")
|
||
city_key = _normalize_city_key(city)
|
||
market_city_key = _resolve_market_city_key(city_key)
|
||
requested_slug = str(forced_market_slug or "").strip().lower() or None
|
||
|
||
scan: Dict[str, Any] = {
|
||
"available": False,
|
||
"reason": None,
|
||
"city_key": city_key or None,
|
||
"market_city_key": market_city_key or None,
|
||
"primary_market": None,
|
||
"selected_date": date_str or None,
|
||
"selected_condition_id": None,
|
||
"selected_slug": requested_slug,
|
||
"temperature_bucket": temperature_bucket,
|
||
"model_probability": model_probability,
|
||
"market_price": None,
|
||
"midpoint": None,
|
||
"spread": None,
|
||
"edge_percent": None,
|
||
"signal_label": "MONITOR",
|
||
"confidence": "low",
|
||
"yes_token": None,
|
||
"no_token": None,
|
||
"yes_buy": None,
|
||
"yes_sell": None,
|
||
"yes_midpoint": None,
|
||
"yes_spread": None,
|
||
"no_buy": None,
|
||
"no_sell": None,
|
||
"no_midpoint": None,
|
||
"no_spread": None,
|
||
"last_trade_price": None,
|
||
"liquidity": None,
|
||
"volume": None,
|
||
"quote_source": None,
|
||
"quote_age_ms": None,
|
||
"price_analysis": None,
|
||
"sparkline": fallback_sparkline or [],
|
||
"top_buckets": [],
|
||
"all_buckets": [],
|
||
"recent_trades": [],
|
||
"scan_scope": "full" if include_related_buckets else "lite",
|
||
"distribution_bias": None,
|
||
"window_phase": None,
|
||
"window_score": None,
|
||
"primary_signal": None,
|
||
"signal_status": "no_market",
|
||
"candidate_count": 0,
|
||
"scan_rows": [],
|
||
"resolved_market_type": "maxtemp",
|
||
"websocket": {
|
||
"enabled": False,
|
||
"status": "disabled_rest_only",
|
||
},
|
||
}
|
||
|
||
if not self.enabled:
|
||
scan["reason"] = "Market scan disabled by POLYMARKET_MARKET_SCAN_ENABLED."
|
||
self._debug_market_scan("disabled", city=city_key, date=date_str)
|
||
return scan
|
||
|
||
if not city_key or city_key not in CITY_REGISTRY:
|
||
scan["reason"] = "City is not supported by the Polymarket market layer."
|
||
self._debug_market_scan("unsupported_city", city=city, normalized=city_key)
|
||
return scan
|
||
|
||
if not market_city_key or market_city_key not in CITY_REGISTRY:
|
||
scan["reason"] = "Mapped market city is not supported by the Polymarket market layer."
|
||
self._debug_market_scan(
|
||
"unsupported_market_city",
|
||
city=city_key,
|
||
market_city=market_city_key,
|
||
)
|
||
return scan
|
||
|
||
if not date_str:
|
||
scan["reason"] = "Missing target date for market discovery."
|
||
self._debug_market_scan("missing_date", city=city_key, market_city=market_city_key)
|
||
return scan
|
||
|
||
try:
|
||
preferred_temp = None
|
||
if isinstance(temperature_bucket, dict):
|
||
preferred_temp = _safe_float(temperature_bucket.get("temp"))
|
||
market, reason = self._find_primary_market(
|
||
market_city_key,
|
||
date_str,
|
||
forced_market_slug=requested_slug,
|
||
preferred_temp=preferred_temp,
|
||
)
|
||
except Exception as exc:
|
||
logger.warning(
|
||
f"Polymarket market discovery failed ({city_key}->{market_city_key}): {exc}"
|
||
)
|
||
scan["reason"] = "Market discovery failed."
|
||
self._debug_market_scan(
|
||
"discovery_exception",
|
||
city=city_key,
|
||
market_city=market_city_key,
|
||
error=str(exc),
|
||
)
|
||
return scan
|
||
|
||
if not market:
|
||
scan["reason"] = reason or "No active Polymarket market matched city/date."
|
||
self._debug_market_scan(
|
||
"no_market",
|
||
city=city_key,
|
||
market_city=market_city_key,
|
||
date=date_str,
|
||
forced_slug=requested_slug,
|
||
reason=scan["reason"],
|
||
)
|
||
return scan
|
||
|
||
market_date = self._extract_market_date(market)
|
||
condition_id = str(
|
||
market.get("conditionId")
|
||
or market.get("condition_id")
|
||
or market.get("conditionID")
|
||
or ""
|
||
).strip() or None
|
||
market_slug = str(market.get("slug") or "").strip() or None
|
||
liquidity = _extract_price(
|
||
market.get("liquidityNum")
|
||
or market.get("liquidity")
|
||
or market.get("liquidityClob")
|
||
)
|
||
volume = _extract_price(
|
||
market.get("volumeNum")
|
||
or market.get("volume")
|
||
or market.get("volume24hr")
|
||
)
|
||
trade_state = self._market_trade_state(market)
|
||
primary_market_payload = {
|
||
"id": market.get("id"),
|
||
"question": market.get("question") or market.get("title"),
|
||
"slug": market_slug,
|
||
"condition_id": condition_id,
|
||
"end_date": market_date,
|
||
"active": trade_state.get("active"),
|
||
"closed": trade_state.get("closed"),
|
||
"accepting_orders": trade_state.get("accepting_orders"),
|
||
"ended_at_utc": trade_state.get("ended_at_utc"),
|
||
"tradable": trade_state.get("tradable"),
|
||
"tradable_reason": trade_state.get("reason"),
|
||
"liquidity": liquidity,
|
||
"volume": volume,
|
||
}
|
||
if not trade_state.get("tradable"):
|
||
scan["reason"] = (
|
||
"Matched market is not tradable."
|
||
+ (
|
||
f" reason={trade_state.get('reason')}"
|
||
if trade_state.get("reason")
|
||
else ""
|
||
)
|
||
)
|
||
scan["primary_market"] = primary_market_payload
|
||
scan["selected_condition_id"] = condition_id
|
||
scan["selected_slug"] = market_slug
|
||
scan["liquidity"] = liquidity
|
||
scan["volume"] = volume
|
||
self._debug_market_scan(
|
||
"not_tradable",
|
||
city=city_key,
|
||
market_city=market_city_key,
|
||
date=date_str,
|
||
slug=market_slug,
|
||
trade_state=trade_state,
|
||
)
|
||
return scan
|
||
|
||
tokens = self._extract_market_tokens(market)
|
||
yes_token, no_token = self._resolve_yes_no_tokens(tokens)
|
||
if not yes_token or not no_token:
|
||
scan["reason"] = "Matched market has no resolvable YES/NO token pair."
|
||
scan["primary_market"] = primary_market_payload
|
||
scan["selected_condition_id"] = condition_id
|
||
scan["selected_slug"] = market_slug
|
||
scan["liquidity"] = liquidity
|
||
scan["volume"] = volume
|
||
self._debug_market_scan(
|
||
"missing_tokens",
|
||
city=city_key,
|
||
market_city=market_city_key,
|
||
date=date_str,
|
||
slug=market_slug,
|
||
token_count=len(tokens),
|
||
)
|
||
return scan
|
||
|
||
yes_prices = self._get_token_market_data(str(yes_token.get("token_id")))
|
||
no_prices = self._get_token_market_data(str(no_token.get("token_id")))
|
||
|
||
if liquidity is None:
|
||
liquidity = _extract_price(yes_prices.get("book_liquidity"))
|
||
last_trade_price = _extract_price(yes_prices.get("last_trade_price"))
|
||
market_price = (
|
||
_extract_price(yes_prices.get("midpoint"))
|
||
or _extract_price(yes_prices.get("buy"))
|
||
or _extract_price(yes_token.get("implied_probability"))
|
||
)
|
||
distribution_model_probability = self._aggregate_distribution_probability_for_market(
|
||
market=market,
|
||
probability_distribution=probability_distribution,
|
||
temp_symbol=temp_symbol,
|
||
)
|
||
if distribution_model_probability is not None:
|
||
model_probability = distribution_model_probability
|
||
|
||
edge_percent = None
|
||
if model_probability is not None and market_price is not None:
|
||
edge_percent = (model_probability - market_price) * 100.0
|
||
|
||
signal_label, confidence = self._derive_signal(edge_percent, liquidity)
|
||
|
||
top_buckets: List[Dict[str, Any]] = []
|
||
all_buckets: List[Dict[str, Any]] = []
|
||
if include_related_buckets:
|
||
top_bucket_limit = max(
|
||
1,
|
||
_safe_int(os.getenv("POLYMARKET_TOP_BUCKET_LIMIT", "4"), 4),
|
||
)
|
||
all_bucket_limit = max(
|
||
top_bucket_limit,
|
||
_safe_int(os.getenv("POLYMARKET_ALL_BUCKET_LIMIT", "8"), 8),
|
||
)
|
||
all_buckets = self._build_top_temperature_buckets(
|
||
city_key=market_city_key,
|
||
target_date=date_str,
|
||
primary_market=market,
|
||
probability_distribution=probability_distribution,
|
||
temp_symbol=temp_symbol,
|
||
limit=all_bucket_limit,
|
||
)
|
||
top_buckets = list(all_buckets[:top_bucket_limit])
|
||
|
||
yes_payload = {
|
||
"outcome": yes_token.get("outcome") or "Yes",
|
||
"token_id": yes_token.get("token_id"),
|
||
"implied_probability": _extract_price(yes_token.get("implied_probability")),
|
||
"buy_price": _extract_price(yes_prices.get("buy")),
|
||
"sell_price": _extract_price(yes_prices.get("sell")),
|
||
"midpoint": _extract_price(yes_prices.get("midpoint")),
|
||
"last_trade_price": _extract_price(yes_prices.get("last_trade_price")),
|
||
"quote_source": yes_prices.get("quote_source"),
|
||
"quote_age_ms": _safe_int(yes_prices.get("quote_age_ms"), 0),
|
||
"book": yes_prices.get("book"),
|
||
}
|
||
no_payload = {
|
||
"outcome": no_token.get("outcome") or "No",
|
||
"token_id": no_token.get("token_id"),
|
||
"implied_probability": _extract_price(no_token.get("implied_probability")),
|
||
"buy_price": _extract_price(no_prices.get("buy")),
|
||
"sell_price": _extract_price(no_prices.get("sell")),
|
||
"midpoint": _extract_price(no_prices.get("midpoint")),
|
||
"last_trade_price": _extract_price(no_prices.get("last_trade_price")),
|
||
"quote_source": no_prices.get("quote_source"),
|
||
"quote_age_ms": _safe_int(no_prices.get("quote_age_ms"), 0),
|
||
"book": no_prices.get("book"),
|
||
}
|
||
yes_midpoint = _extract_price(yes_payload.get("midpoint"))
|
||
no_midpoint = _extract_price(no_payload.get("midpoint"))
|
||
yes_buy = _extract_price(yes_payload.get("buy_price"))
|
||
yes_sell = _extract_price(yes_payload.get("sell_price"))
|
||
no_buy = _extract_price(no_payload.get("buy_price"))
|
||
no_sell = _extract_price(no_payload.get("sell_price"))
|
||
yes_spread = (
|
||
max(0.0, float(yes_buy) - float(yes_sell))
|
||
if yes_buy is not None and yes_sell is not None
|
||
else None
|
||
)
|
||
no_spread = (
|
||
max(0.0, float(no_buy) - float(no_sell))
|
||
if no_buy is not None and no_sell is not None
|
||
else None
|
||
)
|
||
price_analysis = self._build_price_analysis(
|
||
model_probability=model_probability,
|
||
yes_buy=yes_buy,
|
||
yes_sell=yes_sell,
|
||
no_buy=no_buy,
|
||
no_sell=no_sell,
|
||
)
|
||
distribution_scan = self._build_distribution_scan_pack(
|
||
city_key=market_city_key,
|
||
target_date=date_str,
|
||
primary_market=market,
|
||
probability_distribution=probability_distribution,
|
||
temp_symbol=temp_symbol,
|
||
scan_context=scan_context,
|
||
scan_filters=scan_filters,
|
||
)
|
||
primary_signal = distribution_scan.get("primary_signal")
|
||
if isinstance(primary_signal, dict):
|
||
signal_label = str(primary_signal.get("action") or signal_label or "").strip() or signal_label
|
||
signal_score = _safe_float(primary_signal.get("final_score"))
|
||
if signal_score is not None and signal_score >= 85.0:
|
||
confidence = "high"
|
||
elif signal_score is not None and signal_score >= 70.0:
|
||
confidence = "medium"
|
||
elif signal_score is not None:
|
||
confidence = "low"
|
||
signal_edge = _safe_float(primary_signal.get("edge_percent"))
|
||
if signal_edge is not None:
|
||
edge_percent = signal_edge
|
||
|
||
sparkline_values: List[float] = []
|
||
for candidate in (
|
||
_extract_price(yes_payload.get("sell_price")),
|
||
_extract_price(yes_payload.get("buy_price")),
|
||
market_price,
|
||
model_probability,
|
||
):
|
||
if candidate is None:
|
||
continue
|
||
sparkline_values.append(round(candidate * 100.0, 2))
|
||
if not sparkline_values:
|
||
sparkline_values = fallback_sparkline or []
|
||
|
||
market_url = self._build_market_url(market)
|
||
scan.update(
|
||
{
|
||
"available": True,
|
||
"reason": None,
|
||
"primary_market": primary_market_payload,
|
||
"selected_condition_id": condition_id,
|
||
"selected_slug": market_slug,
|
||
"model_probability": model_probability,
|
||
"market_price": market_price,
|
||
"midpoint": yes_midpoint if yes_midpoint is not None else market_price,
|
||
"spread": yes_spread,
|
||
"edge_percent": edge_percent,
|
||
"signal_label": signal_label,
|
||
"confidence": confidence,
|
||
"yes_token": yes_payload,
|
||
"no_token": no_payload,
|
||
"yes_buy": yes_buy,
|
||
"yes_sell": yes_sell,
|
||
"yes_midpoint": yes_midpoint,
|
||
"yes_spread": yes_spread,
|
||
"no_buy": no_buy,
|
||
"no_sell": no_sell,
|
||
"no_midpoint": no_midpoint,
|
||
"no_spread": no_spread,
|
||
"last_trade_price": last_trade_price,
|
||
"liquidity": liquidity,
|
||
"volume": volume,
|
||
"quote_source": yes_prices.get("quote_source"),
|
||
"quote_age_ms": _safe_int(yes_prices.get("quote_age_ms"), 0),
|
||
"price_analysis": price_analysis,
|
||
"sparkline": sparkline_values,
|
||
"top_buckets": top_buckets,
|
||
"all_buckets": all_buckets,
|
||
"distribution_bias": distribution_scan.get("distribution_bias"),
|
||
"window_phase": distribution_scan.get("window_phase"),
|
||
"window_score": distribution_scan.get("window_score"),
|
||
"primary_signal": primary_signal,
|
||
"signal_status": distribution_scan.get("signal_status"),
|
||
"candidate_count": distribution_scan.get("candidate_count"),
|
||
"scan_rows": distribution_scan.get("rows") or [],
|
||
"resolved_market_type": distribution_scan.get("resolved_market_type") or "maxtemp",
|
||
"websocket": {
|
||
"enabled": False,
|
||
"status": "disabled_rest_only",
|
||
"market_url": market_url,
|
||
"asset_ids": [
|
||
token
|
||
for token in [
|
||
yes_payload.get("token_id"),
|
||
no_payload.get("token_id"),
|
||
]
|
||
if token
|
||
],
|
||
"condition_ids": [condition_id] if condition_id else [],
|
||
},
|
||
}
|
||
)
|
||
self._debug_market_scan(
|
||
"scan_ready",
|
||
city=city_key,
|
||
market_city=market_city_key,
|
||
date=date_str,
|
||
selected_slug=market_slug,
|
||
market_price=scan.get("market_price"),
|
||
yes_buy=scan.get("yes_buy"),
|
||
yes_sell=scan.get("yes_sell"),
|
||
no_buy=scan.get("no_buy"),
|
||
no_sell=scan.get("no_sell"),
|
||
price_analysis_available=bool(price_analysis and price_analysis.get("available")),
|
||
all_buckets_count=len(all_buckets),
|
||
top_buckets=[
|
||
{
|
||
"temp": row.get("temp"),
|
||
"yes_buy": row.get("yes_buy"),
|
||
"market_price": row.get("market_price"),
|
||
"quote_source": row.get("quote_source"),
|
||
"slug": row.get("slug"),
|
||
}
|
||
for row in all_buckets[:6]
|
||
if isinstance(row, dict)
|
||
],
|
||
websocket=scan.get("websocket"),
|
||
)
|
||
return scan
|
||
|
||
def _hydrate_bucket_prices(self, buckets: List[Dict[str, Any]]) -> None:
|
||
for bucket in buckets:
|
||
if not isinstance(bucket, dict):
|
||
continue
|
||
yes_token_id = str(bucket.get("yes_token_id") or "").strip()
|
||
no_token_id = str(bucket.get("no_token_id") or "").strip()
|
||
if not yes_token_id:
|
||
continue
|
||
|
||
yes_prices = self._get_token_market_data(yes_token_id)
|
||
no_prices = self._get_token_market_data(no_token_id) if no_token_id else {}
|
||
yes_buy = _extract_price(yes_prices.get("buy"))
|
||
yes_sell = _extract_price(yes_prices.get("sell"))
|
||
no_buy = _extract_price(no_prices.get("buy"))
|
||
no_sell = _extract_price(no_prices.get("sell"))
|
||
yes_midpoint = _extract_price(yes_prices.get("midpoint"))
|
||
|
||
if yes_buy is not None:
|
||
bucket["yes_buy"] = yes_buy
|
||
if yes_sell is not None:
|
||
bucket["yes_sell"] = yes_sell
|
||
if no_buy is not None:
|
||
bucket["no_buy"] = no_buy
|
||
if no_sell is not None:
|
||
bucket["no_sell"] = no_sell
|
||
reference_price = yes_midpoint
|
||
if reference_price is None and yes_buy is not None and yes_sell is not None:
|
||
reference_price = (yes_buy + yes_sell) / 2.0
|
||
if reference_price is None:
|
||
reference_price = yes_buy if yes_buy is not None else yes_sell
|
||
if reference_price is not None:
|
||
reference_price = max(0.0, min(1.0, float(reference_price)))
|
||
bucket["market_price"] = reference_price
|
||
bucket["probability"] = reference_price
|
||
if yes_prices.get("quote_source"):
|
||
bucket["quote_source"] = yes_prices.get("quote_source")
|
||
if yes_prices.get("quote_age_ms") is not None:
|
||
bucket["quote_age_ms"] = _safe_int(yes_prices.get("quote_age_ms"), 0)
|
||
|
||
def _build_price_analysis(
|
||
self,
|
||
*,
|
||
model_probability: Optional[float],
|
||
yes_buy: Optional[float],
|
||
yes_sell: Optional[float],
|
||
no_buy: Optional[float],
|
||
no_sell: Optional[float],
|
||
) -> Dict[str, Any]:
|
||
"""Build read-only market price diagnostics.
|
||
|
||
Polymarket CLOB naming is from the user's perspective:
|
||
BUY is the executable ask to buy that outcome, SELL is the executable bid.
|
||
Kelly here is a sizing reference only; no order execution is performed.
|
||
"""
|
||
p_yes = _clamp_probability(_safe_float(model_probability))
|
||
p_no = _clamp_probability(1.0 - p_yes if p_yes is not None else None)
|
||
yes_ask = _clamp_probability(_safe_float(yes_buy))
|
||
no_ask = _clamp_probability(_safe_float(no_buy))
|
||
yes_bid = _clamp_probability(_safe_float(yes_sell))
|
||
no_bid = _clamp_probability(_safe_float(no_sell))
|
||
|
||
yes = self._build_side_price_analysis("yes", p_yes, yes_ask, yes_bid)
|
||
no = self._build_side_price_analysis("no", p_no, no_ask, no_bid)
|
||
|
||
ask_sum = None
|
||
lock_edge = None
|
||
lock_available = False
|
||
if yes_ask is not None and no_ask is not None:
|
||
ask_sum = yes_ask + no_ask
|
||
lock_edge = 1.0 - ask_sum
|
||
lock_available = lock_edge > 0
|
||
|
||
bid_sum = None
|
||
sell_side_edge = None
|
||
if yes_bid is not None and no_bid is not None:
|
||
bid_sum = yes_bid + no_bid
|
||
sell_side_edge = bid_sum - 1.0
|
||
|
||
best_side = None
|
||
side_rows = [
|
||
row
|
||
for row in [yes, no]
|
||
if isinstance(row.get("edge"), (int, float))
|
||
and isinstance(row.get("kelly_fraction"), (int, float))
|
||
and row.get("kelly_fraction") > 0
|
||
]
|
||
if side_rows:
|
||
best_side = max(
|
||
side_rows,
|
||
key=lambda row: (
|
||
float(row.get("edge") or 0.0),
|
||
float(row.get("kelly_fraction") or 0.0),
|
||
),
|
||
).get("side")
|
||
|
||
return {
|
||
"available": any(
|
||
value is not None
|
||
for value in (yes_ask, no_ask, yes_bid, no_bid, p_yes)
|
||
),
|
||
"source": "polymarket_clob_orderbook",
|
||
"model_probability": p_yes,
|
||
"yes": yes,
|
||
"no": no,
|
||
"best_side": best_side,
|
||
"lock": {
|
||
"available": lock_available,
|
||
"ask_sum": ask_sum,
|
||
"edge": lock_edge,
|
||
},
|
||
"sell_side": {
|
||
"bid_sum": bid_sum,
|
||
"edge": sell_side_edge,
|
||
},
|
||
}
|
||
|
||
def _build_side_price_analysis(
|
||
self,
|
||
side: str,
|
||
probability: Optional[float],
|
||
ask: Optional[float],
|
||
bid: Optional[float],
|
||
) -> Dict[str, Any]:
|
||
edge = None
|
||
kelly_fraction = None
|
||
if probability is not None and ask is not None:
|
||
edge = probability - ask
|
||
if 0.0 < ask < 1.0:
|
||
kelly_fraction = edge / (1.0 - ask)
|
||
|
||
return {
|
||
"side": side,
|
||
"model_probability": probability,
|
||
"ask": ask,
|
||
"bid": bid,
|
||
"edge": edge,
|
||
"edge_percent": edge * 100.0 if edge is not None else None,
|
||
"kelly_fraction": kelly_fraction,
|
||
"quarter_kelly": (
|
||
max(0.0, kelly_fraction) / 4.0
|
||
if kelly_fraction is not None
|
||
else None
|
||
),
|
||
}
|
||
|
||
def _market_trade_state(self, market: Dict[str, Any]) -> Dict[str, Any]:
|
||
active = _safe_bool(market.get("active"))
|
||
closed_raw = _safe_bool(market.get("closed"))
|
||
closed = bool(closed_raw) if closed_raw is not None else False
|
||
accepting_orders = _safe_bool(
|
||
market.get("acceptingOrders", market.get("accepting_orders"))
|
||
)
|
||
|
||
ended_at = None
|
||
for key in ("endDate", "resolutionDate", "closedTime", "gameStartTime"):
|
||
parsed = _parse_iso_datetime_utc(market.get(key))
|
||
if parsed is not None:
|
||
ended_at = parsed
|
||
break
|
||
|
||
tradable = True
|
||
reason = None
|
||
if closed:
|
||
tradable = False
|
||
reason = "closed"
|
||
elif active is False:
|
||
tradable = False
|
||
reason = "inactive"
|
||
elif accepting_orders is False:
|
||
tradable = False
|
||
reason = "not_accepting_orders"
|
||
|
||
return {
|
||
"active": active,
|
||
"closed": closed,
|
||
"accepting_orders": accepting_orders,
|
||
"ended_at_utc": ended_at.isoformat() if ended_at is not None else None,
|
||
"tradable": tradable,
|
||
"reason": reason,
|
||
}
|
||
|
||
def _derive_signal(
|
||
self,
|
||
edge_percent: Optional[float],
|
||
liquidity: Optional[float],
|
||
) -> Tuple[str, str]:
|
||
if edge_percent is None:
|
||
return "MONITOR", "low"
|
||
if liquidity is not None and liquidity < self.min_liquidity_for_signal:
|
||
return "MONITOR", "low"
|
||
|
||
absolute_edge = abs(edge_percent)
|
||
if absolute_edge >= 8:
|
||
confidence = "high"
|
||
elif absolute_edge >= 4:
|
||
confidence = "medium"
|
||
else:
|
||
confidence = "low"
|
||
|
||
if edge_percent >= self.edge_threshold:
|
||
return "BUY YES", confidence
|
||
if edge_percent <= -self.edge_threshold:
|
||
return "BUY NO", confidence
|
||
return "MONITOR", confidence
|
||
|
||
def _find_primary_market(
|
||
self,
|
||
city_key: str,
|
||
target_date: str,
|
||
forced_market_slug: Optional[str] = None,
|
||
preferred_temp: Optional[float] = None,
|
||
) -> Tuple[Optional[Dict[str, Any]], Optional[str]]:
|
||
if forced_market_slug:
|
||
return self._find_market_by_slug(
|
||
forced_market_slug,
|
||
preferred_temp=preferred_temp,
|
||
)
|
||
|
||
preferred_temp_key = (
|
||
f"{float(preferred_temp):.2f}"
|
||
if preferred_temp is not None
|
||
else "none"
|
||
)
|
||
cache_key = f"{city_key}|{target_date}|{preferred_temp_key}"
|
||
now = time.time()
|
||
|
||
with self._lock:
|
||
cached = self._markets_cache.get(cache_key)
|
||
if cached and now - cached.get("t", 0) < self.market_cache_ttl:
|
||
return cached.get("market"), cached.get("reason")
|
||
|
||
markets = self._load_markets(active_only=True)
|
||
if not markets:
|
||
return None, "No active markets returned by Gamma API."
|
||
|
||
scored: List[Tuple[float, Dict[str, Any]]] = []
|
||
for market in markets:
|
||
score = self._score_market(
|
||
city_key,
|
||
target_date,
|
||
market,
|
||
preferred_temp=preferred_temp,
|
||
)
|
||
if score <= 0:
|
||
continue
|
||
scored.append((score, market))
|
||
|
||
# Fallback to broader active universe when strict filters miss.
|
||
if not scored:
|
||
broader = self._load_markets(active_only=False)
|
||
for market in broader:
|
||
score = self._score_market(
|
||
city_key,
|
||
target_date,
|
||
market,
|
||
preferred_temp=preferred_temp,
|
||
)
|
||
if score <= 0:
|
||
continue
|
||
scored.append((score, market))
|
||
|
||
# Deterministic weather event fallback:
|
||
# If Gamma /markets discovery misses, resolve by canonical weather event slug.
|
||
if not scored:
|
||
event_slug = self._build_weather_event_slug(city_key, target_date)
|
||
if event_slug:
|
||
fallback_market, _ = self._find_market_by_slug(
|
||
event_slug,
|
||
preferred_temp=preferred_temp,
|
||
)
|
||
if fallback_market:
|
||
with self._lock:
|
||
self._markets_cache[cache_key] = {
|
||
"market": fallback_market,
|
||
"reason": None,
|
||
"t": now,
|
||
}
|
||
return fallback_market, None
|
||
|
||
scored.sort(
|
||
key=lambda item: (
|
||
item[0],
|
||
_extract_price(
|
||
item[1].get("volumeNum")
|
||
or item[1].get("volume")
|
||
or item[1].get("volume24hr")
|
||
)
|
||
or 0.0,
|
||
),
|
||
reverse=True,
|
||
)
|
||
|
||
market = scored[0][1] if scored else None
|
||
reason = None if market else "No market matched city/date with weather filters."
|
||
|
||
with self._lock:
|
||
self._markets_cache[cache_key] = {"market": market, "reason": reason, "t": now}
|
||
|
||
return market, reason
|
||
|
||
def _find_market_by_slug(
|
||
self,
|
||
market_slug: str,
|
||
preferred_temp: Optional[float] = None,
|
||
) -> Tuple[Optional[Dict[str, Any]], Optional[str]]:
|
||
normalized_slug = str(market_slug or "").strip().lower()
|
||
if not normalized_slug:
|
||
return None, "market_slug is empty."
|
||
|
||
# 0) Event slug path (Polymarket weather pages are often event slugs).
|
||
try:
|
||
resp = self._session.get(
|
||
f"{self.gamma_url}/events",
|
||
params={"slug": normalized_slug, "limit": 5},
|
||
timeout=self.http_timeout,
|
||
)
|
||
resp.raise_for_status()
|
||
payload = resp.json()
|
||
events = payload if isinstance(payload, list) else []
|
||
for event in events:
|
||
if not isinstance(event, dict):
|
||
continue
|
||
event_slug = str(event.get("slug") or "").strip().lower()
|
||
markets = event.get("markets") if isinstance(event.get("markets"), list) else []
|
||
market_candidates = [m for m in markets if isinstance(m, dict)]
|
||
# Try exact market slug match first.
|
||
for market in market_candidates:
|
||
item_slug = str(market.get("slug") or "").strip().lower()
|
||
if item_slug == normalized_slug:
|
||
market["eventSlug"] = market.get("eventSlug") or event_slug
|
||
market["eventTitle"] = market.get("eventTitle") or event.get("title")
|
||
return market, None
|
||
# If input is event slug, pick the most liquid active/ready market.
|
||
if event_slug == normalized_slug and market_candidates:
|
||
def _event_market_rank(m: Dict[str, Any]) -> Tuple[float, bool, bool, float]:
|
||
market_temp = self._extract_market_bucket_temp(m)
|
||
temp_score = 0.0
|
||
if preferred_temp is not None and market_temp is not None:
|
||
temp_score = max(0.0, 100.0 - abs(market_temp - preferred_temp) * 10.0)
|
||
liquidity_score = (
|
||
_extract_price(
|
||
m.get("volumeNum")
|
||
or m.get("volume")
|
||
or m.get("liquidityNum")
|
||
or m.get("liquidity")
|
||
)
|
||
or 0.0
|
||
)
|
||
return (
|
||
temp_score,
|
||
bool(m.get("active", False)),
|
||
not bool(m.get("closed", False)),
|
||
liquidity_score,
|
||
)
|
||
|
||
market_candidates.sort(
|
||
key=_event_market_rank,
|
||
reverse=True,
|
||
)
|
||
best = market_candidates[0]
|
||
best["eventSlug"] = best.get("eventSlug") or event_slug
|
||
best["eventTitle"] = best.get("eventTitle") or event.get("title")
|
||
return best, None
|
||
except Exception:
|
||
pass
|
||
|
||
# 1) Direct Gamma query by slug (fast-path for debug and deterministic checks).
|
||
query_params = [
|
||
{"slug": normalized_slug, "limit": 20, "offset": 0, "archived": "false"},
|
||
{"search": normalized_slug, "limit": 50, "offset": 0, "archived": "false"},
|
||
]
|
||
for params in query_params:
|
||
try:
|
||
resp = self._session.get(
|
||
f"{self.gamma_url}/markets",
|
||
params=params,
|
||
timeout=self.http_timeout,
|
||
)
|
||
resp.raise_for_status()
|
||
payload = resp.json()
|
||
if isinstance(payload, dict):
|
||
candidates = payload.get("markets")
|
||
if not isinstance(candidates, list):
|
||
candidates = []
|
||
elif isinstance(payload, list):
|
||
candidates = payload
|
||
else:
|
||
candidates = []
|
||
for item in candidates:
|
||
if not isinstance(item, dict):
|
||
continue
|
||
item_slug = str(item.get("slug") or "").strip().lower()
|
||
if item_slug == normalized_slug:
|
||
return item, None
|
||
except Exception:
|
||
continue
|
||
|
||
# 2) Fallback to cached discovery lists.
|
||
for active_only in (True, False):
|
||
for item in self._load_markets(active_only=active_only):
|
||
item_slug = str(item.get("slug") or "").strip().lower()
|
||
if item_slug == normalized_slug:
|
||
return item, None
|
||
|
||
return None, f"Specified market_slug not found: {normalized_slug}"
|
||
|
||
def _score_market(
|
||
self,
|
||
city_key: str,
|
||
target_date: str,
|
||
market: Dict[str, Any],
|
||
preferred_temp: Optional[float] = None,
|
||
) -> float:
|
||
city_tokens = CITY_TOKEN_INDEX.get(city_key, [city_key])
|
||
text_parts = [
|
||
market.get("question"),
|
||
market.get("title"),
|
||
market.get("slug"),
|
||
market.get("eventSlug"),
|
||
market.get("description"),
|
||
]
|
||
haystack = _normalize_text(" ".join(str(part or "") for part in text_parts))
|
||
if not haystack:
|
||
return 0.0
|
||
|
||
city_hit = any(_contains_token(haystack, token) for token in city_tokens)
|
||
if not city_hit:
|
||
return 0.0
|
||
|
||
if not self._is_temperature_market(market):
|
||
return 0.0
|
||
|
||
score = 40.0
|
||
score += 18.0
|
||
|
||
d_target = _parse_target_date(target_date)
|
||
text_dates = _extract_dates_from_text(haystack, d_target.year if d_target else None)
|
||
if d_target and text_dates:
|
||
diffs: List[int] = []
|
||
for date_str in text_dates:
|
||
try:
|
||
diffs.append(abs((datetime.fromisoformat(date_str).date() - d_target.date()).days))
|
||
except Exception:
|
||
continue
|
||
if diffs:
|
||
best = min(diffs)
|
||
if best == 0:
|
||
score += 45.0
|
||
elif best == 1:
|
||
score += 20.0
|
||
elif best == 2:
|
||
score += 10.0
|
||
else:
|
||
score -= 6.0
|
||
else:
|
||
market_date = self._extract_market_date(market)
|
||
if market_date and d_target:
|
||
try:
|
||
d_market = datetime.fromisoformat(market_date).date()
|
||
diff = abs((d_market - d_target.date()).days)
|
||
if diff == 0:
|
||
score += 18.0
|
||
elif diff == 1:
|
||
score += 8.0
|
||
elif diff == 2:
|
||
score += 3.0
|
||
else:
|
||
score -= 2.0
|
||
except Exception:
|
||
pass
|
||
|
||
if bool(market.get("active", False)):
|
||
score += 5.0
|
||
if not bool(market.get("closed", False)):
|
||
score += 5.0
|
||
if bool(market.get("enableOrderBook", market.get("enable_order_book", False))):
|
||
score += 4.0
|
||
|
||
volume = (
|
||
_extract_price(
|
||
market.get("volumeNum")
|
||
or market.get("volume")
|
||
or market.get("volume24hr")
|
||
)
|
||
or 0.0
|
||
)
|
||
score += min(volume / 50000.0, 8.0)
|
||
|
||
if preferred_temp is not None:
|
||
market_temp = self._extract_market_bucket_temp(market)
|
||
bucket_range = self._extract_market_bucket_range(market)
|
||
direction = self._extract_market_bucket_direction(market)
|
||
if market_temp is not None:
|
||
diff = abs(float(market_temp) - float(preferred_temp))
|
||
score += max(-40.0, 60.0 - diff * 15.0)
|
||
|
||
if bucket_range is not None:
|
||
lower, upper, _unit = bucket_range
|
||
contains_preferred = False
|
||
if upper is not None:
|
||
contains_preferred = lower <= float(preferred_temp) <= upper
|
||
elif direction == "above":
|
||
contains_preferred = float(preferred_temp) >= lower
|
||
elif direction == "below":
|
||
contains_preferred = float(preferred_temp) <= lower
|
||
else:
|
||
contains_preferred = abs(float(preferred_temp) - lower) <= 0.51
|
||
|
||
if contains_preferred:
|
||
if upper is not None or direction == "exact":
|
||
score += 28.0
|
||
else:
|
||
score += 18.0
|
||
return score
|
||
|
||
def _is_temperature_market(self, market: Dict[str, Any]) -> bool:
|
||
text_parts = [
|
||
market.get("question"),
|
||
market.get("title"),
|
||
market.get("slug"),
|
||
market.get("eventSlug"),
|
||
market.get("description"),
|
||
]
|
||
raw_text = " ".join(str(part or "") for part in text_parts)
|
||
if not raw_text:
|
||
return False
|
||
|
||
# Hard signal: contains explicit Celsius bucket text like "10C" / "10°C"
|
||
if re.search(r"(-?\d+(?:\.\d+)?)\s*[°º]?\s*c\b", raw_text, re.IGNORECASE):
|
||
return True
|
||
|
||
text = _normalize_text(raw_text)
|
||
if not text:
|
||
return False
|
||
|
||
# Weather temperature event patterns.
|
||
if "highest temperature" in text:
|
||
return True
|
||
if "temperature in" in text:
|
||
return True
|
||
if "high temperature" in text:
|
||
return True
|
||
|
||
# Conservative fallback: must explicitly mention temperature and boundary wording.
|
||
if "temperature" in text and any(
|
||
key in text for key in ("or higher", "or above", "or lower", "or below", "and above", "and below")
|
||
):
|
||
return True
|
||
|
||
return False
|
||
|
||
def _extract_market_date(self, market: Dict[str, Any]) -> Optional[str]:
|
||
for key in (
|
||
"endDate",
|
||
"endDateIso",
|
||
"endDateISO",
|
||
"resolutionDate",
|
||
"gameStartTime",
|
||
"closedTime",
|
||
):
|
||
date_str = _extract_iso_date(market.get(key))
|
||
if date_str:
|
||
return date_str
|
||
return None
|
||
|
||
def _extract_market_bucket_temp(self, market: Dict[str, Any]) -> Optional[float]:
|
||
parsed = self._extract_market_bucket_range(market)
|
||
if parsed:
|
||
lower, upper, _unit = parsed
|
||
if upper is not None:
|
||
return (lower + upper) / 2.0
|
||
return lower
|
||
return None
|
||
|
||
def _extract_market_bucket_range(
|
||
self,
|
||
market: Dict[str, Any],
|
||
) -> Optional[Tuple[float, Optional[float], str]]:
|
||
slug = str(market.get("slug") or "").strip().lower()
|
||
slug_range = re.search(
|
||
r"-(\d+(?:\.\d+)?)-(\d+(?:\.\d+)?)([cf])(?:$|-or-higher|-or-lower|orhigher|orlower)",
|
||
slug,
|
||
re.IGNORECASE,
|
||
)
|
||
if slug_range:
|
||
lower = _safe_float(slug_range.group(1))
|
||
upper = _safe_float(slug_range.group(2))
|
||
unit = slug_range.group(3).upper()
|
||
if lower is not None and upper is not None and abs(upper - lower) <= 20:
|
||
return min(lower, upper), max(lower, upper), unit
|
||
|
||
text = " ".join(
|
||
str(part or "")
|
||
for part in (
|
||
market.get("question"),
|
||
market.get("title"),
|
||
)
|
||
)
|
||
# Match range buckets such as "80-81°F" and "80 to 81F".
|
||
range_match = re.search(
|
||
r"(-?\d+(?:\.\d+)?)\s*(?:-|–|—|\bto\b)\s*(-?\d+(?:\.\d+)?)\s*°?\s*([cf])\b",
|
||
text,
|
||
re.IGNORECASE,
|
||
)
|
||
if range_match:
|
||
lower = _safe_float(range_match.group(1))
|
||
upper = _safe_float(range_match.group(2))
|
||
unit = range_match.group(3).upper()
|
||
if lower is not None and upper is not None and abs(upper - lower) <= 20:
|
||
return min(lower, upper), max(lower, upper), unit
|
||
|
||
slug_exact = re.search(
|
||
r"-(\d+(?:\.\d+)?)([cf])(?:$|-or-higher|-or-lower|orhigher|orlower)",
|
||
slug,
|
||
re.IGNORECASE,
|
||
)
|
||
if slug_exact:
|
||
value = _safe_float(slug_exact.group(1))
|
||
unit = slug_exact.group(2).upper()
|
||
if value is not None:
|
||
return value, None, unit
|
||
|
||
# Match "... 9°C ..." / "... 9F ..." / "... -2 C ..."
|
||
match = re.search(r"(-?\d+(?:\.\d+)?)\s*°?\s*([cf])\b", text, re.IGNORECASE)
|
||
if match:
|
||
value = _safe_float(match.group(1))
|
||
unit = match.group(2).upper()
|
||
if value is not None:
|
||
return value, None, unit
|
||
return None
|
||
|
||
def _build_weather_event_slug(self, city_key: str, target_date: str) -> Optional[str]:
|
||
try:
|
||
dt = datetime.fromisoformat(str(target_date))
|
||
except Exception:
|
||
return None
|
||
city_slug = str(city_key or "").strip().lower().replace(" ", "-")
|
||
if not city_slug:
|
||
return None
|
||
month_name = dt.strftime("%B").lower()
|
||
return f"highest-temperature-in-{city_slug}-on-{month_name}-{dt.day}-{dt.year}"
|
||
|
||
def _is_fahrenheit_symbol(self, symbol: Optional[str]) -> bool:
|
||
return "F" in str(symbol or "").upper()
|
||
|
||
def _convert_temp_to_market_unit(
|
||
self,
|
||
value: Optional[float],
|
||
source_symbol: Optional[str],
|
||
market_unit: Optional[str],
|
||
) -> Optional[float]:
|
||
numeric = _safe_float(value)
|
||
if numeric is None:
|
||
return None
|
||
normalized_unit = str(market_unit or "").upper()
|
||
source_is_f = self._is_fahrenheit_symbol(source_symbol)
|
||
if normalized_unit == "F":
|
||
return numeric if source_is_f else (numeric * 9.0 / 5.0) + 32.0
|
||
return ((numeric - 32.0) * 5.0 / 9.0) if source_is_f else numeric
|
||
|
||
def _market_bucket_contains_distribution_temp(
|
||
self,
|
||
market: Dict[str, Any],
|
||
distribution_temp: Optional[float],
|
||
temp_symbol: Optional[str],
|
||
) -> bool:
|
||
compare_temp = self._convert_temp_to_market_unit(
|
||
distribution_temp,
|
||
source_symbol=temp_symbol,
|
||
market_unit=(self._extract_market_bucket_range(market) or (None, None, "C"))[2],
|
||
)
|
||
if compare_temp is None:
|
||
return False
|
||
|
||
bucket_range = self._extract_market_bucket_range(market)
|
||
lower = bucket_range[0] if bucket_range else None
|
||
upper = bucket_range[1] if bucket_range else None
|
||
unit = bucket_range[2] if bucket_range else "C"
|
||
direction = self._extract_market_bucket_direction(market)
|
||
|
||
if lower is not None and upper is not None:
|
||
return compare_temp >= lower - 0.01 and compare_temp <= upper + 0.01
|
||
if lower is not None and direction == "above":
|
||
return compare_temp >= lower - 0.01
|
||
if lower is not None and direction == "below":
|
||
return compare_temp <= lower + 0.01
|
||
|
||
reference = self._extract_market_bucket_temp(market)
|
||
if reference is None:
|
||
return False
|
||
tolerance = 0.56 if str(unit or "").upper() == "F" else 0.26
|
||
return abs(compare_temp - reference) <= tolerance
|
||
|
||
def _aggregate_distribution_probability_for_market(
|
||
self,
|
||
market: Dict[str, Any],
|
||
probability_distribution: Optional[List[Dict[str, Any]]],
|
||
temp_symbol: Optional[str],
|
||
) -> Optional[float]:
|
||
if not isinstance(probability_distribution, list) or not probability_distribution:
|
||
return None
|
||
|
||
total = 0.0
|
||
matched = 0
|
||
for row in probability_distribution:
|
||
if not isinstance(row, dict):
|
||
continue
|
||
distribution_temp = _safe_float(row.get("value"))
|
||
if distribution_temp is None:
|
||
continue
|
||
if not self._market_bucket_contains_distribution_temp(
|
||
market,
|
||
distribution_temp,
|
||
temp_symbol,
|
||
):
|
||
continue
|
||
raw_probability = _safe_float(row.get("probability"))
|
||
if raw_probability is None:
|
||
continue
|
||
probability = raw_probability / 100.0 if raw_probability > 1.0 else raw_probability
|
||
probability = max(0.0, min(1.0, probability))
|
||
total += probability
|
||
matched += 1
|
||
if matched <= 0:
|
||
return None
|
||
return max(0.0, min(1.0, total))
|
||
|
||
def _load_markets(self, active_only: bool = True) -> List[Dict[str, Any]]:
|
||
now = time.time()
|
||
with self._lock:
|
||
cached = self._active_markets_cache if active_only else self._broad_markets_cache
|
||
if now - float(cached.get("t", 0)) < self.market_cache_ttl:
|
||
data = cached.get("data")
|
||
if isinstance(data, list):
|
||
return data
|
||
|
||
all_markets: List[Dict[str, Any]] = []
|
||
offset = 0
|
||
for _ in range(max(self.discovery_pages, 1)):
|
||
params = {"archived": "false", "limit": self.discovery_limit, "offset": offset}
|
||
if active_only:
|
||
params.update({"active": "true", "closed": "false"})
|
||
else:
|
||
params.update({"active": "true"})
|
||
url = f"{self.gamma_url}/markets"
|
||
try:
|
||
resp = self._session.get(url, params=params, timeout=self.http_timeout)
|
||
resp.raise_for_status()
|
||
payload = resp.json()
|
||
except Exception as exc:
|
||
logger.warning(f"Gamma markets fetch failed (offset={offset}): {exc}")
|
||
break
|
||
|
||
if isinstance(payload, dict):
|
||
batch = payload.get("markets")
|
||
if not isinstance(batch, list):
|
||
# Gamma can also return object arrays directly.
|
||
batch = []
|
||
elif isinstance(payload, list):
|
||
batch = payload
|
||
else:
|
||
batch = []
|
||
|
||
if not batch:
|
||
break
|
||
|
||
all_markets.extend(item for item in batch if isinstance(item, dict))
|
||
if len(batch) < self.discovery_limit:
|
||
break
|
||
offset += self.discovery_limit
|
||
|
||
with self._lock:
|
||
if active_only:
|
||
self._active_markets_cache = {"data": all_markets, "t": now}
|
||
else:
|
||
self._broad_markets_cache = {"data": all_markets, "t": now}
|
||
|
||
return all_markets
|
||
|
||
def _extract_market_tokens(self, market: Dict[str, Any]) -> List[Dict[str, Any]]:
|
||
result: List[Dict[str, Any]] = []
|
||
|
||
direct_tokens = market.get("tokens")
|
||
if isinstance(direct_tokens, list):
|
||
for token in direct_tokens:
|
||
token_obj = _to_plain_dict(token)
|
||
if not token_obj:
|
||
continue
|
||
token_id = str(
|
||
token_obj.get("token_id")
|
||
or token_obj.get("tokenId")
|
||
or token_obj.get("id")
|
||
or token_obj.get("clobTokenId")
|
||
or ""
|
||
).strip()
|
||
if not token_id:
|
||
continue
|
||
result.append(
|
||
{
|
||
"outcome": token_obj.get("outcome") or token_obj.get("name"),
|
||
"token_id": token_id,
|
||
"implied_probability": _extract_price(
|
||
token_obj.get("price")
|
||
or token_obj.get("probability")
|
||
or token_obj.get("lastPrice")
|
||
),
|
||
}
|
||
)
|
||
if result:
|
||
return result
|
||
|
||
outcomes = _json_or_list(market.get("outcomes"))
|
||
prices = _json_or_list(market.get("outcomePrices"))
|
||
token_ids = _json_or_list(market.get("clobTokenIds"))
|
||
if not token_ids:
|
||
token_ids = _json_or_list(market.get("tokenIds"))
|
||
|
||
for index, outcome in enumerate(outcomes):
|
||
token_id = str(token_ids[index]).strip() if index < len(token_ids) else ""
|
||
if not token_id:
|
||
continue
|
||
implied_probability = (
|
||
_extract_price(prices[index]) if index < len(prices) else None
|
||
)
|
||
result.append(
|
||
{
|
||
"outcome": str(outcome),
|
||
"token_id": token_id,
|
||
"implied_probability": implied_probability,
|
||
}
|
||
)
|
||
return result
|
||
|
||
def _resolve_yes_no_tokens(
|
||
self,
|
||
tokens: List[Dict[str, Any]],
|
||
) -> Tuple[Optional[Dict[str, Any]], Optional[Dict[str, Any]]]:
|
||
if not tokens:
|
||
return None, None
|
||
|
||
yes_token = None
|
||
no_token = None
|
||
for token in tokens:
|
||
label = _normalize_text(token.get("outcome"))
|
||
if label in {"yes", "true", "above", "over"}:
|
||
yes_token = token
|
||
elif label in {"no", "false", "below", "under"}:
|
||
no_token = token
|
||
|
||
if yes_token and no_token:
|
||
return yes_token, no_token
|
||
|
||
if len(tokens) == 2:
|
||
# Fallback for markets with unnamed binary outcomes.
|
||
return tokens[0], tokens[1]
|
||
|
||
return None, None
|
||
|
||
def _get_token_market_data(self, token_id: str) -> Dict[str, Any]:
|
||
token_id = str(token_id or "").strip()
|
||
if not token_id:
|
||
return {}
|
||
|
||
now = time.time()
|
||
with self._lock:
|
||
cached = self._price_cache.get(token_id)
|
||
if cached and now - cached.get("t", 0) < self.price_cache_ttl:
|
||
return cached.get("data", {})
|
||
|
||
data = self._fetch_token_market_data(token_id)
|
||
|
||
with self._lock:
|
||
self._price_cache[token_id] = {"data": data, "t": now}
|
||
return data
|
||
|
||
def _fetch_token_market_data(self, token_id: str) -> Dict[str, Any]:
|
||
# REST-only path: CLOB public endpoints.
|
||
bid = _extract_price(self._clob_get("/price", {"token_id": token_id, "side": "BUY"}))
|
||
ask = _extract_price(
|
||
self._clob_get("/price", {"token_id": token_id, "side": "SELL"})
|
||
)
|
||
midpoint = _extract_price(self._clob_get("/midpoint", {"token_id": token_id}))
|
||
last_trade = _extract_price(
|
||
self._clob_get("/last-trade-price", {"token_id": token_id})
|
||
)
|
||
orderbook_raw = self._clob_get("/book", {"token_id": token_id})
|
||
book, book_liquidity = self._normalize_orderbook(orderbook_raw)
|
||
buy, sell = self._resolve_trade_prices(buy=ask, sell=bid, book=book)
|
||
return {
|
||
"buy": buy,
|
||
"sell": sell,
|
||
"midpoint": midpoint,
|
||
"last_trade_price": last_trade,
|
||
"quote_source": "polymarket_clob_rest",
|
||
"quote_age_ms": 0,
|
||
"book": book,
|
||
"book_liquidity": book_liquidity,
|
||
}
|
||
|
||
def _clob_get(self, path: str, params: Dict[str, Any]) -> Any:
|
||
url = f"{self.clob_url}{path}"
|
||
try:
|
||
resp = self._session.get(url, params=params, timeout=self.http_timeout)
|
||
resp.raise_for_status()
|
||
return resp.json()
|
||
except Exception:
|
||
return None
|
||
|
||
def _resolve_trade_prices(
|
||
self,
|
||
buy: Optional[float],
|
||
sell: Optional[float],
|
||
book: Optional[Dict[str, Any]],
|
||
) -> Tuple[Optional[float], Optional[float]]:
|
||
payload = book if isinstance(book, dict) else {}
|
||
best_bid = _extract_price(payload.get("best_bid"))
|
||
best_ask = _extract_price(payload.get("best_ask"))
|
||
resolved_buy = best_ask if best_ask is not None else buy
|
||
resolved_sell = best_bid if best_bid is not None else sell
|
||
return resolved_buy, resolved_sell
|
||
|
||
def _normalize_orderbook(self, orderbook_raw: Any) -> Tuple[Optional[Dict[str, Any]], Optional[float]]:
|
||
payload = _to_plain_dict(orderbook_raw)
|
||
if not payload and isinstance(orderbook_raw, dict):
|
||
payload = orderbook_raw
|
||
if not payload:
|
||
return None, None
|
||
|
||
bids_raw = payload.get("bids") or []
|
||
asks_raw = payload.get("asks") or []
|
||
|
||
bid_levels: List[List[float]] = []
|
||
ask_levels: List[List[float]] = []
|
||
book_liquidity = 0.0
|
||
|
||
def _parse_side(items: Any, sink: List[List[float]]) -> None:
|
||
nonlocal book_liquidity
|
||
if not isinstance(items, list):
|
||
return
|
||
for item in items:
|
||
item_dict = _to_plain_dict(item)
|
||
if item_dict:
|
||
price = _extract_price(item_dict.get("price"))
|
||
size = _extract_price(item_dict.get("size") or item_dict.get("quantity"))
|
||
elif isinstance(item, (list, tuple)) and len(item) >= 2:
|
||
price = _extract_price(item[0])
|
||
size = _extract_price(item[1])
|
||
else:
|
||
continue
|
||
if price is None or size is None:
|
||
continue
|
||
sink.append([price, size])
|
||
book_liquidity += max(0.0, price * size)
|
||
|
||
_parse_side(bids_raw, bid_levels)
|
||
_parse_side(asks_raw, ask_levels)
|
||
|
||
bid_levels.sort(key=lambda level: level[0], reverse=True)
|
||
ask_levels.sort(key=lambda level: level[0])
|
||
best_bid = bid_levels[0][0] if bid_levels else None
|
||
best_ask = ask_levels[0][0] if ask_levels else None
|
||
normalized = {
|
||
"best_bid": best_bid,
|
||
"best_ask": best_ask,
|
||
"bid_levels": bid_levels[:10],
|
||
"ask_levels": ask_levels[:10],
|
||
}
|
||
return normalized, (book_liquidity if book_liquidity > 0 else None)
|
||
|
||
def _build_market_url(self, market: Dict[str, Any]) -> Optional[str]:
|
||
slug = str(market.get("slug") or "").strip()
|
||
event_slug = str(market.get("eventSlug") or "").strip()
|
||
if event_slug:
|
||
return f"https://polymarket.com/event/{event_slug}"
|
||
if slug:
|
||
return f"https://polymarket.com/market/{slug}"
|
||
return None
|
||
|
||
def _build_top_temperature_buckets(
|
||
self,
|
||
city_key: str,
|
||
target_date: str,
|
||
primary_market: Dict[str, Any],
|
||
probability_distribution: Optional[List[Dict[str, Any]]] = None,
|
||
temp_symbol: Optional[str] = None,
|
||
limit: int = 4,
|
||
) -> List[Dict[str, Any]]:
|
||
candidate_markets = self._collect_related_temperature_markets(
|
||
city_key=city_key,
|
||
target_date=target_date,
|
||
primary_market=primary_market,
|
||
)
|
||
if not candidate_markets:
|
||
return []
|
||
|
||
ranked: List[
|
||
Tuple[
|
||
float,
|
||
float,
|
||
float,
|
||
float,
|
||
Dict[str, Any],
|
||
Dict[str, Any],
|
||
Dict[str, Any],
|
||
Dict[str, Any],
|
||
Dict[str, Any],
|
||
Optional[Tuple[float, Optional[float], str]],
|
||
]
|
||
] = []
|
||
for market in candidate_markets:
|
||
if not self._market_trade_state(market).get("tradable"):
|
||
continue
|
||
bucket_temp = self._extract_market_bucket_temp(market)
|
||
bucket_range = self._extract_market_bucket_range(market)
|
||
if bucket_temp is None:
|
||
continue
|
||
|
||
tokens = self._extract_market_tokens(market)
|
||
yes_token, no_token = self._resolve_yes_no_tokens(tokens)
|
||
if not yes_token or not no_token:
|
||
continue
|
||
|
||
yes_token_id = str(yes_token.get("token_id") or "").strip()
|
||
no_token_id = str(no_token.get("token_id") or "").strip()
|
||
yes_prices = self._get_token_market_data(yes_token_id) if yes_token_id else {}
|
||
no_prices = self._get_token_market_data(no_token_id) if no_token_id else {}
|
||
|
||
yes_midpoint = _extract_price(yes_prices.get("midpoint"))
|
||
yes_implied = _extract_price(yes_token.get("implied_probability"))
|
||
no_implied = _extract_price(no_token.get("implied_probability"))
|
||
market_prob = (
|
||
yes_midpoint
|
||
if yes_midpoint is not None
|
||
else (
|
||
yes_implied
|
||
if yes_implied is not None
|
||
else (1.0 - no_implied if no_implied is not None else None)
|
||
)
|
||
)
|
||
if market_prob is None:
|
||
continue
|
||
|
||
market_prob = max(0.0, min(1.0, float(market_prob)))
|
||
model_prob = self._aggregate_distribution_probability_for_market(
|
||
market=market,
|
||
probability_distribution=probability_distribution,
|
||
temp_symbol=temp_symbol,
|
||
)
|
||
volume = (
|
||
_extract_price(
|
||
market.get("volumeNum")
|
||
or market.get("volume")
|
||
or market.get("volume24hr")
|
||
)
|
||
or 0.0
|
||
)
|
||
ranked.append(
|
||
(
|
||
model_prob if model_prob is not None else market_prob,
|
||
volume,
|
||
bucket_temp,
|
||
market_prob,
|
||
market,
|
||
yes_token,
|
||
no_token,
|
||
yes_prices,
|
||
no_prices,
|
||
bucket_range,
|
||
)
|
||
)
|
||
|
||
if not ranked:
|
||
return []
|
||
|
||
ranked.sort(key=lambda item: (item[0], item[1]), reverse=True)
|
||
top_rows: List[Dict[str, Any]] = []
|
||
max_items = max(1, int(limit or 4))
|
||
primary_slug = str(primary_market.get("slug") or "").strip().lower()
|
||
primary_direction = self._extract_market_bucket_direction(primary_market)
|
||
seen_temp_keys: set = set()
|
||
|
||
def _append_rows(enforce_primary_direction: bool) -> None:
|
||
for (
|
||
model_prob,
|
||
_volume,
|
||
bucket_temp,
|
||
market_prob,
|
||
market,
|
||
yes_token,
|
||
no_token,
|
||
yes_prices,
|
||
no_prices,
|
||
bucket_range,
|
||
) in ranked:
|
||
row_direction = self._extract_market_bucket_direction(market)
|
||
if (
|
||
enforce_primary_direction
|
||
and primary_direction in {"above", "below"}
|
||
and row_direction != primary_direction
|
||
):
|
||
continue
|
||
|
||
temp_key = f"{round(float(bucket_temp), 2):.2f}"
|
||
if temp_key in seen_temp_keys:
|
||
continue
|
||
|
||
yes_buy = _extract_price(yes_prices.get("buy"))
|
||
yes_sell = _extract_price(yes_prices.get("sell"))
|
||
yes_midpoint = _extract_price(yes_prices.get("midpoint")) or market_prob
|
||
no_buy = _extract_price(no_prices.get("buy"))
|
||
no_sell = _extract_price(no_prices.get("sell"))
|
||
|
||
if no_buy is None and yes_buy is not None:
|
||
no_buy = max(0.0, min(1.0, 1.0 - yes_buy))
|
||
if no_sell is None and yes_sell is not None:
|
||
no_sell = max(0.0, min(1.0, 1.0 - yes_sell))
|
||
|
||
market_slug = str(market.get("slug") or "").strip()
|
||
row_yes_token_id = str(yes_token.get("token_id") or "").strip()
|
||
row_no_token_id = str(no_token.get("token_id") or "").strip()
|
||
top_rows.append(
|
||
{
|
||
"label": self._extract_market_bucket_label(market, bucket_temp),
|
||
"value": bucket_temp,
|
||
"temp": bucket_temp,
|
||
"lower": bucket_range[0] if bucket_range else None,
|
||
"upper": bucket_range[1] if bucket_range else None,
|
||
"unit": bucket_range[2] if bucket_range else None,
|
||
"probability": model_prob,
|
||
"model_probability": model_prob,
|
||
"market_price": yes_midpoint,
|
||
"edge_percent": (
|
||
(model_prob - yes_midpoint) * 100.0
|
||
if model_prob is not None and yes_midpoint is not None
|
||
else None
|
||
),
|
||
"yes_buy": yes_buy,
|
||
"yes_sell": yes_sell,
|
||
"no_buy": no_buy,
|
||
"no_sell": no_sell,
|
||
"yes_token_id": row_yes_token_id or None,
|
||
"no_token_id": row_no_token_id or None,
|
||
"quote_source": yes_prices.get("quote_source"),
|
||
"quote_age_ms": _safe_int(yes_prices.get("quote_age_ms"), 0),
|
||
"slug": market_slug or None,
|
||
"question": market.get("question") or market.get("title"),
|
||
"is_primary": bool(
|
||
primary_slug
|
||
and market_slug
|
||
and primary_slug == market_slug.strip().lower()
|
||
),
|
||
}
|
||
)
|
||
seen_temp_keys.add(temp_key)
|
||
if len(top_rows) >= max_items:
|
||
break
|
||
|
||
if primary_direction in {"above", "below"}:
|
||
_append_rows(enforce_primary_direction=True)
|
||
if len(top_rows) < max_items:
|
||
_append_rows(enforce_primary_direction=False)
|
||
|
||
return top_rows
|
||
|
||
def _collect_related_temperature_markets(
|
||
self,
|
||
city_key: str,
|
||
target_date: str,
|
||
primary_market: Dict[str, Any],
|
||
) -> List[Dict[str, Any]]:
|
||
related: List[Dict[str, Any]] = []
|
||
canonical_event_slug = self._build_weather_event_slug(city_key, target_date)
|
||
if canonical_event_slug:
|
||
related.extend(self._load_event_markets(canonical_event_slug))
|
||
|
||
event_slug = self._extract_event_slug(primary_market)
|
||
if event_slug and event_slug != canonical_event_slug:
|
||
related.extend(self._load_event_markets(event_slug))
|
||
|
||
if not related:
|
||
for market in self._load_markets(active_only=True):
|
||
if self._score_market(city_key, target_date, market) <= 0:
|
||
continue
|
||
if self._extract_market_bucket_temp(market) is None:
|
||
continue
|
||
related.append(market)
|
||
|
||
related.append(primary_market)
|
||
|
||
unique: List[Dict[str, Any]] = []
|
||
seen = set()
|
||
for market in related:
|
||
if not isinstance(market, dict):
|
||
continue
|
||
dedupe_key = str(
|
||
market.get("id")
|
||
or market.get("slug")
|
||
or market.get("conditionId")
|
||
or ""
|
||
).strip()
|
||
if not dedupe_key:
|
||
continue
|
||
if dedupe_key in seen:
|
||
continue
|
||
seen.add(dedupe_key)
|
||
unique.append(market)
|
||
return unique
|
||
|
||
def _extract_event_slug(self, market: Dict[str, Any]) -> Optional[str]:
|
||
event_slug = str(market.get("eventSlug") or "").strip().lower()
|
||
if event_slug:
|
||
return event_slug
|
||
|
||
slug = str(market.get("slug") or "").strip().lower()
|
||
if not slug:
|
||
return None
|
||
|
||
trimmed = re.sub(
|
||
r"-(?:m)?\d+(?:-\d+)?c(?:-or-(?:higher|lower|above|below))?$",
|
||
"",
|
||
slug,
|
||
)
|
||
trimmed = trimmed.strip("-")
|
||
return trimmed or None
|
||
|
||
def _load_event_markets(self, event_slug: str) -> List[Dict[str, Any]]:
|
||
normalized_slug = str(event_slug or "").strip().lower()
|
||
if not normalized_slug:
|
||
return []
|
||
|
||
try:
|
||
resp = self._session.get(
|
||
f"{self.gamma_url}/events",
|
||
params={"slug": normalized_slug, "limit": 5},
|
||
timeout=self.http_timeout,
|
||
)
|
||
resp.raise_for_status()
|
||
payload = resp.json()
|
||
except Exception:
|
||
return []
|
||
|
||
events = payload if isinstance(payload, list) else []
|
||
out: List[Dict[str, Any]] = []
|
||
for event in events:
|
||
if not isinstance(event, dict):
|
||
continue
|
||
event_item_slug = str(event.get("slug") or "").strip().lower()
|
||
if event_item_slug and event_item_slug != normalized_slug:
|
||
continue
|
||
for market in event.get("markets") or []:
|
||
if not isinstance(market, dict):
|
||
continue
|
||
market["eventSlug"] = market.get("eventSlug") or event_item_slug
|
||
market["eventTitle"] = market.get("eventTitle") or event.get("title")
|
||
out.append(market)
|
||
return out
|
||
|
||
def _extract_market_bucket_label(
|
||
self,
|
||
market: Dict[str, Any],
|
||
bucket_temp: Optional[float],
|
||
) -> str:
|
||
question = str(market.get("question") or market.get("title") or "").strip()
|
||
direction = self._extract_market_bucket_direction(market)
|
||
bucket_range = self._extract_market_bucket_range(market)
|
||
unit = bucket_range[2] if bucket_range else "C"
|
||
if bucket_range and bucket_range[1] is not None:
|
||
return f"{bucket_range[0]:g}-{bucket_range[1]:g}{unit}"
|
||
if bucket_temp is not None:
|
||
if direction == "above":
|
||
return f"{bucket_temp:g}{unit}+"
|
||
if direction == "below":
|
||
return f"<={bucket_temp:g}{unit}"
|
||
return f"{bucket_temp:g}{unit}"
|
||
return question or str(market.get("slug") or "")
|
||
|
||
def _extract_market_bucket_direction(self, market: Dict[str, Any]) -> str:
|
||
text = " ".join(
|
||
str(part or "")
|
||
for part in (
|
||
market.get("question"),
|
||
market.get("title"),
|
||
market.get("slug"),
|
||
)
|
||
).lower()
|
||
if not text:
|
||
return "exact"
|
||
|
||
if any(
|
||
token in text
|
||
for token in (
|
||
"or higher",
|
||
"or above",
|
||
"and above",
|
||
"forhigher",
|
||
"forabove",
|
||
"or-higher",
|
||
"or-above",
|
||
)
|
||
):
|
||
return "above"
|
||
if any(
|
||
token in text
|
||
for token in (
|
||
"or lower",
|
||
"or below",
|
||
"and below",
|
||
"forlower",
|
||
"forbelow",
|
||
"or-lower",
|
||
"or-below",
|
||
)
|
||
):
|
||
return "below"
|
||
return "exact"
|
||
|
||
def _clob_post(self, path: str, payload: Any) -> Any:
|
||
url = f"{self.clob_url}{path}"
|
||
try:
|
||
resp = self._session.post(url, json=payload, timeout=self.http_timeout)
|
||
resp.raise_for_status()
|
||
return resp.json()
|
||
except Exception:
|
||
return None
|
||
|
||
def _batch_chunks(self, values: List[str], size: int = 200) -> List[List[str]]:
|
||
if not values:
|
||
return []
|
||
chunk_size = max(1, min(int(size or 200), 500))
|
||
return [values[index : index + chunk_size] for index in range(0, len(values), chunk_size)]
|
||
|
||
def _extract_payload_token_id(self, payload: Any) -> Optional[str]:
|
||
item = _to_plain_dict(payload)
|
||
if not item and isinstance(payload, dict):
|
||
item = payload
|
||
if not item:
|
||
return None
|
||
token_id = str(
|
||
item.get("asset_id")
|
||
or item.get("assetId")
|
||
or item.get("token_id")
|
||
or item.get("tokenId")
|
||
or item.get("id")
|
||
or ""
|
||
).strip()
|
||
return token_id or None
|
||
|
||
def _extract_batch_scalar_map(self, payload: Any) -> Dict[str, float]:
|
||
if not payload:
|
||
return {}
|
||
data = payload
|
||
if isinstance(data, dict):
|
||
for key in ("data", "midpoints", "spreads", "items", "results"):
|
||
nested = data.get(key)
|
||
if isinstance(nested, (dict, list)):
|
||
data = nested
|
||
break
|
||
result: Dict[str, float] = {}
|
||
if isinstance(data, dict):
|
||
for key, value in data.items():
|
||
numeric = _extract_price(value)
|
||
if numeric is None:
|
||
continue
|
||
result[str(key).strip()] = numeric
|
||
return result
|
||
if isinstance(data, list):
|
||
for item in data:
|
||
token_id = self._extract_payload_token_id(item)
|
||
if not token_id:
|
||
continue
|
||
item_dict = _to_plain_dict(item)
|
||
numeric = _extract_price(
|
||
item_dict.get("midpoint")
|
||
or item_dict.get("mid_price")
|
||
or item_dict.get("spread")
|
||
or item_dict.get("price")
|
||
or item_dict.get("last_trade_price")
|
||
or item_dict.get("value")
|
||
)
|
||
if numeric is None:
|
||
continue
|
||
result[token_id] = numeric
|
||
return result
|
||
|
||
def _extract_batch_price_map(self, payload: Any, side: str) -> Dict[str, float]:
|
||
if not payload:
|
||
return {}
|
||
data = payload.get("data") if isinstance(payload, dict) and isinstance(payload.get("data"), dict) else payload
|
||
result: Dict[str, float] = {}
|
||
if not isinstance(data, dict):
|
||
return result
|
||
for token_id, side_map in data.items():
|
||
token_key = str(token_id).strip()
|
||
if not token_key:
|
||
continue
|
||
item = _to_plain_dict(side_map)
|
||
if not item and isinstance(side_map, dict):
|
||
item = side_map
|
||
numeric = _extract_price(item.get(side) if item else side_map)
|
||
if numeric is None:
|
||
continue
|
||
result[token_key] = numeric
|
||
return result
|
||
|
||
def _extract_batch_book_map(self, payload: Any) -> Dict[str, Dict[str, Any]]:
|
||
if not payload:
|
||
return {}
|
||
data = payload
|
||
if isinstance(data, dict):
|
||
for key in ("data", "books", "items", "results"):
|
||
nested = data.get(key)
|
||
if isinstance(nested, (dict, list)):
|
||
data = nested
|
||
break
|
||
result: Dict[str, Dict[str, Any]] = {}
|
||
if isinstance(data, dict):
|
||
for token_id, book in data.items():
|
||
token_key = str(token_id).strip()
|
||
book_dict = _to_plain_dict(book)
|
||
if not token_key or not book_dict:
|
||
continue
|
||
result[token_key] = book_dict
|
||
return result
|
||
if isinstance(data, list):
|
||
for item in data:
|
||
token_id = self._extract_payload_token_id(item)
|
||
book_dict = _to_plain_dict(item)
|
||
if not token_id or not book_dict:
|
||
continue
|
||
result[token_id] = book_dict
|
||
return result
|
||
|
||
def _batch_get_token_market_data(
|
||
self,
|
||
token_ids: List[str],
|
||
*,
|
||
include_books: bool = False,
|
||
) -> Dict[str, Dict[str, Any]]:
|
||
unique_tokens = []
|
||
seen = set()
|
||
for token_id in token_ids:
|
||
normalized = str(token_id or "").strip()
|
||
if not normalized or normalized in seen:
|
||
continue
|
||
seen.add(normalized)
|
||
unique_tokens.append(normalized)
|
||
|
||
if not unique_tokens:
|
||
return {}
|
||
|
||
now = time.time()
|
||
results: Dict[str, Dict[str, Any]] = {}
|
||
missing: List[str] = []
|
||
with self._lock:
|
||
for token_id in unique_tokens:
|
||
cached = self._price_cache.get(token_id)
|
||
if not cached or now - cached.get("t", 0) >= self.price_cache_ttl:
|
||
missing.append(token_id)
|
||
continue
|
||
cached_data = cached.get("data", {}) or {}
|
||
if include_books and not cached_data.get("book") and cached_data.get("book_liquidity") is None:
|
||
missing.append(token_id)
|
||
continue
|
||
results[token_id] = dict(cached_data)
|
||
|
||
if not missing:
|
||
return results
|
||
|
||
ask_map: Dict[str, float] = {}
|
||
bid_map: Dict[str, float] = {}
|
||
midpoint_map: Dict[str, float] = {}
|
||
spread_map: Dict[str, float] = {}
|
||
last_trade_map: Dict[str, float] = {}
|
||
book_map: Dict[str, Dict[str, Any]] = {}
|
||
|
||
for chunk in self._batch_chunks(missing):
|
||
sell_payload = self._clob_post(
|
||
"/prices",
|
||
[{"token_id": token_id, "side": "SELL"} for token_id in chunk],
|
||
)
|
||
buy_payload = self._clob_post(
|
||
"/prices",
|
||
[{"token_id": token_id, "side": "BUY"} for token_id in chunk],
|
||
)
|
||
midpoint_payload = self._clob_post(
|
||
"/midpoints",
|
||
[{"token_id": token_id} for token_id in chunk],
|
||
)
|
||
spread_payload = self._clob_post(
|
||
"/spreads",
|
||
[{"token_id": token_id} for token_id in chunk],
|
||
)
|
||
last_trade_payload = self._clob_post(
|
||
"/last-trade-prices",
|
||
[{"token_id": token_id} for token_id in chunk],
|
||
)
|
||
books_payload = (
|
||
self._clob_post(
|
||
"/books",
|
||
[{"token_id": token_id} for token_id in chunk],
|
||
)
|
||
if include_books
|
||
else None
|
||
)
|
||
ask_map.update(self._extract_batch_price_map(sell_payload, "SELL"))
|
||
bid_map.update(self._extract_batch_price_map(buy_payload, "BUY"))
|
||
midpoint_map.update(self._extract_batch_scalar_map(midpoint_payload))
|
||
spread_map.update(self._extract_batch_scalar_map(spread_payload))
|
||
last_trade_map.update(self._extract_batch_scalar_map(last_trade_payload))
|
||
if include_books:
|
||
book_map.update(self._extract_batch_book_map(books_payload))
|
||
|
||
fetched: Dict[str, Dict[str, Any]] = {}
|
||
unresolved: List[str] = []
|
||
for token_id in missing:
|
||
raw_book = book_map.get(token_id)
|
||
book, book_liquidity = self._normalize_orderbook(raw_book)
|
||
ask = _extract_price(ask_map.get(token_id))
|
||
bid = _extract_price(bid_map.get(token_id))
|
||
midpoint = _extract_price(midpoint_map.get(token_id))
|
||
spread = _extract_price(spread_map.get(token_id))
|
||
last_trade = _extract_price(last_trade_map.get(token_id))
|
||
|
||
buy, sell = self._resolve_trade_prices(buy=ask, sell=bid, book=book)
|
||
if midpoint is None and buy is not None and sell is not None:
|
||
midpoint = (buy + sell) / 2.0
|
||
if midpoint is None:
|
||
midpoint = _extract_price(raw_book.get("last_trade_price") if isinstance(raw_book, dict) else None)
|
||
if spread is None and buy is not None and sell is not None:
|
||
spread = max(0.0, float(buy) - float(sell))
|
||
if spread is not None and midpoint is not None:
|
||
midpoint = _clamp_probability(midpoint)
|
||
if buy is None and midpoint is not None and spread is not None:
|
||
buy = _clamp_probability(midpoint + spread / 2.0)
|
||
if sell is None and midpoint is not None and spread is not None:
|
||
sell = _clamp_probability(midpoint - spread / 2.0)
|
||
|
||
if (
|
||
buy is None
|
||
and sell is None
|
||
and midpoint is None
|
||
and last_trade is None
|
||
and book is None
|
||
):
|
||
unresolved.append(token_id)
|
||
continue
|
||
|
||
fetched[token_id] = {
|
||
"buy": buy,
|
||
"sell": sell,
|
||
"midpoint": midpoint,
|
||
"spread": spread,
|
||
"last_trade_price": last_trade,
|
||
"quote_source": "polymarket_clob_rest_batch",
|
||
"quote_age_ms": 0,
|
||
"book": book,
|
||
"book_liquidity": book_liquidity,
|
||
}
|
||
|
||
for token_id in unresolved:
|
||
fetched[token_id] = self._fetch_token_market_data(token_id)
|
||
|
||
with self._lock:
|
||
for token_id, data in fetched.items():
|
||
self._price_cache[token_id] = {"data": data, "t": now}
|
||
|
||
results.update(fetched)
|
||
return results
|
||
|
||
def _normalize_scan_filters(self, scan_filters: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
|
||
raw = scan_filters if isinstance(scan_filters, dict) else {}
|
||
min_price = _clamp_float(_safe_float(raw.get("min_price")), 0.0, 1.0)
|
||
max_price = _clamp_float(_safe_float(raw.get("max_price")), 0.0, 1.0)
|
||
if min_price is None:
|
||
min_price = 0.05
|
||
if max_price is None:
|
||
max_price = 0.95
|
||
if min_price > max_price:
|
||
min_price, max_price = max_price, min_price
|
||
|
||
high_liquidity_only = bool(_safe_bool(raw.get("high_liquidity_only")))
|
||
min_liquidity = _safe_float(raw.get("min_liquidity"))
|
||
if min_liquidity is None:
|
||
min_liquidity = 5000.0 if high_liquidity_only else float(self.min_liquidity_for_signal or 500.0)
|
||
if high_liquidity_only:
|
||
min_liquidity = max(min_liquidity, 5000.0)
|
||
|
||
return {
|
||
"scan_mode": str(raw.get("scan_mode") or "tradable").strip().lower() or "tradable",
|
||
"min_price": float(min_price),
|
||
"max_price": float(max_price),
|
||
"min_edge_pct": max(0.0, _safe_float(raw.get("min_edge_pct")) or float(self.edge_threshold or 2.0)),
|
||
"min_liquidity": max(0.0, float(min_liquidity)),
|
||
"high_liquidity_only": high_liquidity_only,
|
||
"market_type": str(raw.get("market_type") or "maxtemp").strip().lower() or "maxtemp",
|
||
"time_range": str(raw.get("time_range") or "today").strip().lower() or "today",
|
||
"limit": max(1, _safe_int(raw.get("limit"), 60)),
|
||
"max_spread": max(0.0, _safe_float(raw.get("max_spread")) or 0.03),
|
||
}
|
||
|
||
def _build_window_meta(
|
||
self,
|
||
target_date: str,
|
||
scan_context: Optional[Dict[str, Any]] = None,
|
||
) -> Dict[str, Any]:
|
||
context = scan_context if isinstance(scan_context, dict) else {}
|
||
local_date = _extract_iso_date(context.get("local_date")) or _extract_iso_date(target_date)
|
||
local_time = context.get("local_time")
|
||
peak = context.get("peak") if isinstance(context.get("peak"), dict) else {}
|
||
first_h = int(_safe_float(peak.get("first_h")) or 13)
|
||
last_h = int(_safe_float(peak.get("last_h")) or 15)
|
||
target_iso = _extract_iso_date(target_date)
|
||
if not local_date or not target_iso:
|
||
return {
|
||
"phase": "today_default",
|
||
"score": 0.65,
|
||
"remaining_minutes": None,
|
||
"same_day": True,
|
||
}
|
||
|
||
try:
|
||
diff_days = (
|
||
datetime.fromisoformat(target_iso).date()
|
||
- datetime.fromisoformat(local_date).date()
|
||
).days
|
||
except Exception:
|
||
diff_days = 0
|
||
|
||
if diff_days >= 2:
|
||
return {
|
||
"phase": "week_ahead",
|
||
"score": 0.45,
|
||
"remaining_minutes": None,
|
||
"same_day": False,
|
||
}
|
||
if diff_days == 1:
|
||
return {
|
||
"phase": "tomorrow",
|
||
"score": 0.60,
|
||
"remaining_minutes": None,
|
||
"same_day": False,
|
||
}
|
||
if diff_days < 0:
|
||
return {
|
||
"phase": "past",
|
||
"score": 0.0,
|
||
"remaining_minutes": None,
|
||
"same_day": False,
|
||
}
|
||
|
||
now_minutes = _parse_hhmm_to_minutes(local_time)
|
||
if now_minutes is None:
|
||
return {
|
||
"phase": "today_default",
|
||
"score": 0.65,
|
||
"remaining_minutes": None,
|
||
"same_day": True,
|
||
}
|
||
|
||
first_minutes = max(0, first_h * 60)
|
||
last_minutes = min(23 * 60 + 59, last_h * 60)
|
||
if now_minutes > last_minutes + 120:
|
||
return {
|
||
"phase": "post_peak",
|
||
"score": 0.50,
|
||
"remaining_minutes": 0,
|
||
"same_day": True,
|
||
}
|
||
if first_minutes <= now_minutes <= last_minutes + 120:
|
||
return {
|
||
"phase": "active_peak",
|
||
"score": 1.00,
|
||
"remaining_minutes": max(0, last_minutes + 120 - now_minutes),
|
||
"same_day": True,
|
||
}
|
||
if first_minutes - 180 <= now_minutes < first_minutes:
|
||
return {
|
||
"phase": "setup_today",
|
||
"score": 0.85,
|
||
"remaining_minutes": max(0, last_minutes + 120 - now_minutes),
|
||
"same_day": True,
|
||
}
|
||
return {
|
||
"phase": "early_today",
|
||
"score": 0.70,
|
||
"remaining_minutes": max(0, first_minutes - now_minutes),
|
||
"same_day": True,
|
||
}
|
||
|
||
def _resolve_market_target_threshold(
|
||
self,
|
||
market_direction: str,
|
||
bucket_range: Optional[Tuple[float, Optional[float], str]],
|
||
bucket_temp: Optional[float],
|
||
) -> Optional[float]:
|
||
if not bucket_range:
|
||
return bucket_temp
|
||
lower, upper, _unit = bucket_range
|
||
if market_direction in {"above", "below"}:
|
||
return lower
|
||
if upper is not None:
|
||
return (lower + upper) / 2.0
|
||
return lower
|
||
|
||
def _resolve_temperature_direction(
|
||
self,
|
||
*,
|
||
side: str,
|
||
market_direction: str,
|
||
target_threshold: Optional[float],
|
||
current_reference: Optional[float],
|
||
) -> str:
|
||
if market_direction == "above":
|
||
return "hotter" if side == "yes" else "colder"
|
||
if market_direction == "below":
|
||
return "colder" if side == "yes" else "hotter"
|
||
hotter_bias = True
|
||
if target_threshold is not None and current_reference is not None:
|
||
hotter_bias = target_threshold >= current_reference
|
||
if side == "yes":
|
||
return "hotter" if hotter_bias else "colder"
|
||
return "colder" if hotter_bias else "hotter"
|
||
|
||
def _is_trend_aligned(
|
||
self,
|
||
*,
|
||
temperature_direction: str,
|
||
trend_info: Optional[Dict[str, Any]],
|
||
network_lead_signal: Optional[Dict[str, Any]],
|
||
) -> bool:
|
||
trend = trend_info if isinstance(trend_info, dict) else {}
|
||
network = network_lead_signal if isinstance(network_lead_signal, dict) else {}
|
||
trend_direction = _normalize_text(trend.get("direction"))
|
||
if temperature_direction == "hotter" and trend_direction == "rising":
|
||
return True
|
||
if temperature_direction == "colder" and trend_direction in {"falling", "stagnant"}:
|
||
return True
|
||
lead_delta = _safe_float(network.get("delta"))
|
||
if lead_delta is None:
|
||
return False
|
||
if temperature_direction == "hotter":
|
||
return lead_delta > 0
|
||
return lead_delta < 0
|
||
|
||
def _build_distribution_scan_pack(
|
||
self,
|
||
*,
|
||
city_key: str,
|
||
target_date: str,
|
||
primary_market: Dict[str, Any],
|
||
probability_distribution: Optional[List[Dict[str, Any]]] = None,
|
||
temp_symbol: Optional[str] = None,
|
||
scan_context: Optional[Dict[str, Any]] = None,
|
||
scan_filters: Optional[Dict[str, Any]] = None,
|
||
) -> Dict[str, Any]:
|
||
filters = self._normalize_scan_filters(scan_filters)
|
||
window_meta = self._build_window_meta(target_date, scan_context)
|
||
rows: List[Dict[str, Any]] = []
|
||
related_markets = self._collect_related_temperature_markets(
|
||
city_key=city_key,
|
||
target_date=target_date,
|
||
primary_market=primary_market,
|
||
)
|
||
if not related_markets:
|
||
return {
|
||
"rows": [],
|
||
"distribution_bias": {
|
||
"available": False,
|
||
"value": None,
|
||
"direction": "balanced",
|
||
"score": 0.0,
|
||
"valid_markets": 0,
|
||
},
|
||
"primary_signal": None,
|
||
"signal_status": "no_market",
|
||
"candidate_count": 0,
|
||
"window_phase": window_meta.get("phase"),
|
||
"window_score": window_meta.get("score"),
|
||
"resolved_market_type": "maxtemp",
|
||
}
|
||
|
||
market_entries: List[Dict[str, Any]] = []
|
||
token_ids: List[str] = []
|
||
for market in related_markets:
|
||
tokens = self._extract_market_tokens(market)
|
||
yes_token, no_token = self._resolve_yes_no_tokens(tokens)
|
||
if not yes_token or not no_token:
|
||
continue
|
||
yes_token_id = str(yes_token.get("token_id") or "").strip()
|
||
no_token_id = str(no_token.get("token_id") or "").strip()
|
||
if not yes_token_id or not no_token_id:
|
||
continue
|
||
bucket_range = self._extract_market_bucket_range(market)
|
||
bucket_temp = self._extract_market_bucket_temp(market)
|
||
raw_direction = self._extract_market_bucket_direction(market)
|
||
market_direction = "range" if bucket_range and bucket_range[1] is not None else raw_direction
|
||
model_event_probability = self._aggregate_distribution_probability_for_market(
|
||
market=market,
|
||
probability_distribution=probability_distribution,
|
||
temp_symbol=temp_symbol,
|
||
)
|
||
token_ids.extend([yes_token_id, no_token_id])
|
||
market_entries.append(
|
||
{
|
||
"market": market,
|
||
"yes_token": yes_token,
|
||
"no_token": no_token,
|
||
"yes_token_id": yes_token_id,
|
||
"no_token_id": no_token_id,
|
||
"bucket_range": bucket_range,
|
||
"bucket_temp": bucket_temp,
|
||
"market_direction": market_direction,
|
||
"target_threshold": self._resolve_market_target_threshold(
|
||
market_direction,
|
||
bucket_range,
|
||
bucket_temp,
|
||
),
|
||
"target_label": self._extract_market_bucket_label(market, bucket_temp),
|
||
"model_event_probability": model_event_probability,
|
||
"market_liquidity": _extract_price(
|
||
market.get("liquidityNum")
|
||
or market.get("liquidity")
|
||
or market.get("liquidityClob")
|
||
),
|
||
"volume": _extract_price(
|
||
market.get("volumeNum")
|
||
or market.get("volume")
|
||
or market.get("volume24hr")
|
||
),
|
||
"trade_state": self._market_trade_state(market),
|
||
"enable_order_book": bool(
|
||
market.get("enableOrderBook", market.get("enable_order_book", False))
|
||
),
|
||
}
|
||
)
|
||
|
||
broad_quotes = self._batch_get_token_market_data(token_ids, include_books=False)
|
||
bias_inputs: List[Tuple[float, float]] = []
|
||
for entry in market_entries:
|
||
yes_quote = broad_quotes.get(entry["yes_token_id"], {})
|
||
no_quote = broad_quotes.get(entry["no_token_id"], {})
|
||
market_event_probability = (
|
||
_extract_price(yes_quote.get("midpoint"))
|
||
or _extract_price(yes_quote.get("buy"))
|
||
or _extract_price(yes_quote.get("sell"))
|
||
or _extract_price(entry["yes_token"].get("implied_probability"))
|
||
)
|
||
if market_event_probability is not None:
|
||
market_event_probability = _clamp_probability(market_event_probability)
|
||
yes_ask = _extract_price(yes_quote.get("buy"))
|
||
yes_bid = _extract_price(yes_quote.get("sell"))
|
||
no_ask = _extract_price(no_quote.get("buy"))
|
||
no_bid = _extract_price(no_quote.get("sell"))
|
||
if no_ask is None and yes_ask is not None:
|
||
no_ask = _clamp_probability(1.0 - yes_bid) if yes_bid is not None else None
|
||
if no_bid is None and yes_bid is not None:
|
||
no_bid = _clamp_probability(1.0 - yes_ask) if yes_ask is not None else None
|
||
spread = _extract_price(yes_quote.get("spread"))
|
||
if spread is None and yes_ask is not None and yes_bid is not None:
|
||
spread = max(0.0, yes_ask - yes_bid)
|
||
|
||
entry["market_event_probability"] = market_event_probability
|
||
entry["yes_ask"] = yes_ask
|
||
entry["yes_bid"] = yes_bid
|
||
entry["no_ask"] = no_ask
|
||
entry["no_bid"] = no_bid
|
||
entry["midpoint"] = _extract_price(yes_quote.get("midpoint")) or market_event_probability
|
||
entry["spread"] = spread
|
||
entry["yes_book_liquidity"] = _extract_price(yes_quote.get("book_liquidity"))
|
||
entry["no_book_liquidity"] = _extract_price(no_quote.get("book_liquidity"))
|
||
entry["quote_source"] = yes_quote.get("quote_source") or no_quote.get("quote_source")
|
||
entry["quote_age_ms"] = _safe_int(
|
||
yes_quote.get("quote_age_ms") if yes_quote.get("quote_age_ms") is not None else no_quote.get("quote_age_ms"),
|
||
0,
|
||
)
|
||
|
||
model_event_probability = _clamp_probability(_safe_float(entry.get("model_event_probability")))
|
||
if (
|
||
model_event_probability is not None
|
||
and market_event_probability is not None
|
||
and entry["market_direction"] in {"above", "below"}
|
||
):
|
||
gap = model_event_probability - market_event_probability
|
||
signed_gap = -gap if entry["market_direction"] == "below" else gap
|
||
bias_inputs.append((max(model_event_probability, 0.08), signed_gap))
|
||
|
||
distribution_bias_value = None
|
||
distribution_bias_score = 0.0
|
||
distribution_bias_direction = "balanced"
|
||
if len(bias_inputs) >= 3:
|
||
total_weight = sum(weight for weight, _signed_gap in bias_inputs)
|
||
if total_weight > 0:
|
||
distribution_bias_value = sum(weight * signed_gap for weight, signed_gap in bias_inputs) / total_weight
|
||
distribution_bias_score = max(0.0, min(abs(distribution_bias_value) / 0.08, 1.0)) * 100.0
|
||
if distribution_bias_value >= 0.015:
|
||
distribution_bias_direction = "hotter"
|
||
elif distribution_bias_value <= -0.015:
|
||
distribution_bias_direction = "colder"
|
||
|
||
distribution_bias = {
|
||
"available": len(bias_inputs) >= 3 and distribution_bias_value is not None,
|
||
"value": distribution_bias_value,
|
||
"direction": distribution_bias_direction,
|
||
"score": distribution_bias_score,
|
||
"valid_markets": len(bias_inputs),
|
||
}
|
||
|
||
current_reference_raw = _safe_float(
|
||
(scan_context or {}).get("current_max_so_far")
|
||
or (scan_context or {}).get("current_temp")
|
||
)
|
||
|
||
def _row_from_entry(entry: Dict[str, Any], side: str) -> Optional[Dict[str, Any]]:
|
||
model_event_probability = _clamp_probability(_safe_float(entry.get("model_event_probability")))
|
||
market_event_probability = _clamp_probability(_safe_float(entry.get("market_event_probability")))
|
||
ask = _clamp_probability(_safe_float(entry.get("yes_ask") if side == "yes" else entry.get("no_ask")))
|
||
bid = _clamp_probability(_safe_float(entry.get("yes_bid") if side == "yes" else entry.get("no_bid")))
|
||
if model_event_probability is None or ask is None:
|
||
return None
|
||
|
||
model_probability = (
|
||
model_event_probability
|
||
if side == "yes"
|
||
else _clamp_probability(1.0 - model_event_probability)
|
||
)
|
||
market_probability = (
|
||
market_event_probability
|
||
if side == "yes"
|
||
else _clamp_probability(1.0 - market_event_probability)
|
||
)
|
||
if model_probability is None:
|
||
return None
|
||
|
||
market = entry["market"]
|
||
target_threshold = _safe_float(entry.get("target_threshold"))
|
||
bucket_range = entry.get("bucket_range")
|
||
market_unit = bucket_range[2] if bucket_range else ("F" if self._is_fahrenheit_symbol(temp_symbol) else "C")
|
||
current_reference = self._convert_temp_to_market_unit(
|
||
current_reference_raw,
|
||
source_symbol=temp_symbol,
|
||
market_unit=market_unit,
|
||
)
|
||
gap_to_target = (
|
||
target_threshold - current_reference
|
||
if target_threshold is not None and current_reference is not None
|
||
else None
|
||
)
|
||
temperature_direction = self._resolve_temperature_direction(
|
||
side=side,
|
||
market_direction=str(entry.get("market_direction") or "exact"),
|
||
target_threshold=target_threshold,
|
||
current_reference=current_reference,
|
||
)
|
||
trend_alignment = self._is_trend_aligned(
|
||
temperature_direction=temperature_direction,
|
||
trend_info=(scan_context or {}).get("trend"),
|
||
network_lead_signal=(scan_context or {}).get("network_lead_signal"),
|
||
)
|
||
edge = model_probability - ask
|
||
edge_percent = edge * 100.0
|
||
liquidity_reference = max(
|
||
_safe_float(
|
||
entry.get("yes_book_liquidity") if side == "yes" else entry.get("no_book_liquidity")
|
||
) or 0.0,
|
||
_safe_float(entry.get("market_liquidity")) or 0.0,
|
||
)
|
||
if liquidity_reference >= 10000:
|
||
liquidity_score = 1.0
|
||
elif liquidity_reference >= 5000:
|
||
liquidity_score = 0.8
|
||
elif liquidity_reference >= 1000:
|
||
liquidity_score = 0.6
|
||
else:
|
||
liquidity_score = 0.4
|
||
if 0.10 <= ask <= 0.90:
|
||
price_usefulness_score = 1.0
|
||
elif 0.05 <= ask < 0.10 or 0.90 < ask <= 0.95:
|
||
price_usefulness_score = 0.7
|
||
else:
|
||
price_usefulness_score = 0.0
|
||
bias_score = 0.0
|
||
if distribution_bias["available"]:
|
||
if distribution_bias_direction == "balanced" or distribution_bias_direction == temperature_direction:
|
||
bias_score = distribution_bias_score / 100.0
|
||
spread = _safe_float(entry.get("spread"))
|
||
spread_penalty = max(
|
||
0.0,
|
||
min(((spread or 0.0) - 0.01) / 0.02, 1.0),
|
||
) * 15.0
|
||
edge_score = max(0.0, min(edge_percent / 12.0, 1.0))
|
||
final_score = 100.0 * (
|
||
0.35 * edge_score
|
||
+ 0.25 * bias_score
|
||
+ 0.20 * float(window_meta.get("score") or 0.0)
|
||
+ 0.10 * liquidity_score
|
||
+ 0.10 * price_usefulness_score
|
||
) - spread_penalty
|
||
market_slug = str(market.get("slug") or "").strip()
|
||
target_label = str(entry.get("target_label") or "").strip()
|
||
action = f"BUY {'YES' if side == 'yes' else 'NO'}"
|
||
if target_label:
|
||
action = f"{action} {target_label}"
|
||
return {
|
||
"id": f"{city_key}|{target_date}|{market_slug}|{side}",
|
||
"city": city_key,
|
||
"selected_date": target_date,
|
||
"market_slug": market_slug or None,
|
||
"market_question": market.get("question") or market.get("title"),
|
||
"market_url": self._build_market_url(market),
|
||
"side": side,
|
||
"action": action,
|
||
"market_direction": entry.get("market_direction"),
|
||
"temperature_direction": temperature_direction,
|
||
"target_label": entry.get("target_label"),
|
||
"target_value": entry.get("bucket_temp"),
|
||
"target_threshold": target_threshold,
|
||
"target_lower": bucket_range[0] if bucket_range else None,
|
||
"target_upper": bucket_range[1] if bucket_range else None,
|
||
"target_unit": market_unit,
|
||
"model_probability": model_probability,
|
||
"market_probability": market_probability,
|
||
"model_event_probability": model_event_probability,
|
||
"market_event_probability": market_event_probability,
|
||
"gap": (
|
||
model_event_probability - market_event_probability
|
||
if model_event_probability is not None and market_event_probability is not None
|
||
else None
|
||
),
|
||
"signed_gap": (
|
||
-1.0 * (model_event_probability - market_event_probability)
|
||
if model_event_probability is not None
|
||
and market_event_probability is not None
|
||
and entry.get("market_direction") == "below"
|
||
else (
|
||
model_event_probability - market_event_probability
|
||
if model_event_probability is not None and market_event_probability is not None
|
||
else None
|
||
)
|
||
),
|
||
"yes_token_id": entry.get("yes_token_id"),
|
||
"no_token_id": entry.get("no_token_id"),
|
||
"yes_ask": entry.get("yes_ask"),
|
||
"yes_bid": entry.get("yes_bid"),
|
||
"no_ask": entry.get("no_ask"),
|
||
"no_bid": entry.get("no_bid"),
|
||
"ask": ask,
|
||
"bid": bid,
|
||
"midpoint": entry.get("midpoint"),
|
||
"spread": spread,
|
||
"book_liquidity": _safe_float(
|
||
entry.get("yes_book_liquidity") if side == "yes" else entry.get("no_book_liquidity")
|
||
),
|
||
"market_liquidity": entry.get("market_liquidity"),
|
||
"volume": entry.get("volume"),
|
||
"quote_source": entry.get("quote_source"),
|
||
"quote_age_ms": entry.get("quote_age_ms"),
|
||
"edge": edge,
|
||
"edge_percent": edge_percent,
|
||
"edge_score": edge_score,
|
||
"bias_score": bias_score,
|
||
"window_phase": window_meta.get("phase"),
|
||
"window_score": window_meta.get("score"),
|
||
"remaining_window_minutes": window_meta.get("remaining_minutes"),
|
||
"liquidity_score": liquidity_score,
|
||
"price_usefulness_score": price_usefulness_score,
|
||
"spread_penalty": spread_penalty,
|
||
"final_score": final_score,
|
||
"distribution_bias_direction": distribution_bias_direction,
|
||
"distribution_bias_score": distribution_bias_score,
|
||
"distribution_bias_available": distribution_bias["available"],
|
||
"current_reference": current_reference,
|
||
"gap_to_target": gap_to_target,
|
||
"touch_distance": abs(gap_to_target) if gap_to_target is not None else None,
|
||
"trend_alignment": trend_alignment,
|
||
"tradable": bool(entry["trade_state"].get("tradable")),
|
||
"active": entry["trade_state"].get("active"),
|
||
"closed": entry["trade_state"].get("closed"),
|
||
"accepting_orders": entry["trade_state"].get("accepting_orders"),
|
||
"enable_order_book": entry.get("enable_order_book"),
|
||
"is_primary_market": bool(
|
||
str(primary_market.get("slug") or "").strip().lower()
|
||
and market_slug
|
||
and str(primary_market.get("slug") or "").strip().lower() == market_slug.lower()
|
||
),
|
||
}
|
||
|
||
preliminary_rows: List[Dict[str, Any]] = []
|
||
for entry in market_entries:
|
||
row_yes = _row_from_entry(entry, "yes")
|
||
row_no = _row_from_entry(entry, "no")
|
||
if row_yes:
|
||
preliminary_rows.append(row_yes)
|
||
if row_no:
|
||
preliminary_rows.append(row_no)
|
||
|
||
preliminary_rows.sort(key=lambda row: float(row.get("final_score") or 0.0), reverse=True)
|
||
shortlist_market_slugs = []
|
||
seen_slugs = set()
|
||
for row in preliminary_rows:
|
||
market_slug = str(row.get("market_slug") or "").strip()
|
||
if not market_slug or market_slug in seen_slugs:
|
||
continue
|
||
seen_slugs.add(market_slug)
|
||
shortlist_market_slugs.append(market_slug)
|
||
if len(shortlist_market_slugs) >= 10:
|
||
break
|
||
|
||
shortlisted_tokens: List[str] = []
|
||
for entry in market_entries:
|
||
market_slug = str(entry["market"].get("slug") or "").strip()
|
||
if market_slug not in seen_slugs:
|
||
continue
|
||
shortlisted_tokens.extend([entry["yes_token_id"], entry["no_token_id"]])
|
||
|
||
precise_quotes = self._batch_get_token_market_data(shortlisted_tokens, include_books=True)
|
||
for entry in market_entries:
|
||
market_slug = str(entry["market"].get("slug") or "").strip()
|
||
if market_slug not in seen_slugs:
|
||
continue
|
||
yes_quote = precise_quotes.get(entry["yes_token_id"], {})
|
||
no_quote = precise_quotes.get(entry["no_token_id"], {})
|
||
if yes_quote:
|
||
entry["yes_ask"] = _extract_price(yes_quote.get("buy")) or entry.get("yes_ask")
|
||
entry["yes_bid"] = _extract_price(yes_quote.get("sell")) or entry.get("yes_bid")
|
||
entry["midpoint"] = _extract_price(yes_quote.get("midpoint")) or entry.get("midpoint")
|
||
entry["spread"] = _extract_price(yes_quote.get("spread")) or entry.get("spread")
|
||
entry["yes_book_liquidity"] = _extract_price(yes_quote.get("book_liquidity")) or entry.get("yes_book_liquidity")
|
||
entry["quote_source"] = yes_quote.get("quote_source") or entry.get("quote_source")
|
||
if no_quote:
|
||
entry["no_ask"] = _extract_price(no_quote.get("buy")) or entry.get("no_ask")
|
||
entry["no_bid"] = _extract_price(no_quote.get("sell")) or entry.get("no_bid")
|
||
entry["no_book_liquidity"] = _extract_price(no_quote.get("book_liquidity")) or entry.get("no_book_liquidity")
|
||
entry["quote_source"] = no_quote.get("quote_source") or entry.get("quote_source")
|
||
if entry.get("spread") is None and entry.get("yes_ask") is not None and entry.get("yes_bid") is not None:
|
||
entry["spread"] = max(0.0, float(entry["yes_ask"]) - float(entry["yes_bid"]))
|
||
|
||
final_rows: List[Dict[str, Any]] = []
|
||
for entry in market_entries:
|
||
for side in ("yes", "no"):
|
||
row = _row_from_entry(entry, side)
|
||
if row:
|
||
final_rows.append(row)
|
||
|
||
def _passes_hard_filters(row: Dict[str, Any]) -> bool:
|
||
ask = _safe_float(row.get("ask"))
|
||
edge_percent = _safe_float(row.get("edge_percent"))
|
||
spread = _safe_float(row.get("spread"))
|
||
liquidity = max(
|
||
_safe_float(row.get("book_liquidity")) or 0.0,
|
||
_safe_float(row.get("market_liquidity")) or 0.0,
|
||
)
|
||
if ask is None or edge_percent is None:
|
||
return False
|
||
if not row.get("tradable") or row.get("accepting_orders") is False:
|
||
return False
|
||
if row.get("enable_order_book") is False:
|
||
return False
|
||
if ask < filters["min_price"] or ask > filters["max_price"]:
|
||
return False
|
||
if edge_percent < filters["min_edge_pct"]:
|
||
return False
|
||
if spread is None or spread > filters["max_spread"]:
|
||
return False
|
||
if liquidity < filters["min_liquidity"]:
|
||
return False
|
||
return True
|
||
|
||
def _passes_mode_filters(row: Dict[str, Any]) -> bool:
|
||
scan_mode = filters["scan_mode"]
|
||
if scan_mode == "tradable":
|
||
return float(row.get("window_score") or 0.0) >= 0.65
|
||
if scan_mode == "early":
|
||
return str(row.get("window_phase") or "") in {"tomorrow", "week_ahead", "early_today"}
|
||
if scan_mode == "touch":
|
||
return (
|
||
bool(window_meta.get("same_day"))
|
||
and str(row.get("window_phase") or "") in {"setup_today", "active_peak"}
|
||
and (_safe_float(row.get("touch_distance")) is not None)
|
||
and float(row.get("touch_distance")) <= 2.0
|
||
)
|
||
if scan_mode == "trend":
|
||
return bool(row.get("trend_alignment"))
|
||
return True
|
||
|
||
filtered_rows = [
|
||
row
|
||
for row in final_rows
|
||
if _passes_hard_filters(row) and _passes_mode_filters(row)
|
||
]
|
||
filtered_rows.sort(
|
||
key=lambda row: (
|
||
float(row.get("final_score") or 0.0),
|
||
float(row.get("edge_percent") or 0.0),
|
||
),
|
||
reverse=True,
|
||
)
|
||
|
||
primary_signal = filtered_rows[0] if filtered_rows else None
|
||
signal_status = "ready" if primary_signal else "no_signal"
|
||
return {
|
||
"rows": filtered_rows[: filters["limit"]],
|
||
"distribution_bias": distribution_bias,
|
||
"primary_signal": primary_signal,
|
||
"signal_status": signal_status,
|
||
"candidate_count": len(filtered_rows),
|
||
"window_phase": window_meta.get("phase"),
|
||
"window_score": window_meta.get("score"),
|
||
"resolved_market_type": "maxtemp",
|
||
}
|