2569718930@qq.com
1199 lines
45 KiB
Python
1199 lines
45 KiB
Python
from __future__ import annotations
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import json
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import os
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import threading
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import time
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import hashlib
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from concurrent.futures import TimeoutError as FutureTimeoutError
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from datetime import datetime, timedelta
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from typing import Any, Dict, List, Optional
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import httpx
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from loguru import logger
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from web.analysis_service import _analyze, _build_city_market_scan_payload
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from web.core import CITIES
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_SCAN_TERMINAL_CACHE_LOCK = threading.Lock()
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_SCAN_TERMINAL_CACHE: Dict[str, Dict[str, Any]] = {}
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_SCAN_TERMINAL_REFRESHING: set[str] = set()
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_SCAN_TERMINAL_AI_CACHE_LOCK = threading.Lock()
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_SCAN_TERMINAL_AI_CACHE: Dict[str, Dict[str, Any]] = {}
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SCAN_TERMINAL_PAYLOAD_TTL_SEC = max(
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5,
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int(os.getenv("POLYWEATHER_SCAN_TERMINAL_PAYLOAD_TTL_SEC", "30")),
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)
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SCAN_TERMINAL_BUILD_TIMEOUT_SEC = max(
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8,
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int(os.getenv("POLYWEATHER_SCAN_TERMINAL_BUILD_TIMEOUT_SEC", "22")),
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)
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SCAN_AI_MODEL = str(
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os.getenv("POLYWEATHER_SCAN_AI_MODEL") or "deepseek-v4-flash"
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).strip()
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SCAN_AI_BASE_URL = str(
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os.getenv("POLYWEATHER_DEEPSEEK_BASE_URL") or "https://api.deepseek.com"
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).strip().rstrip("/")
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SCAN_AI_ENABLED = str(
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os.getenv("POLYWEATHER_SCAN_AI_ENABLED") or "false"
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).strip().lower() in {"1", "true", "yes", "on"}
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SCAN_AI_TIMEOUT_SEC = max(
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3,
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int(os.getenv("POLYWEATHER_SCAN_AI_TIMEOUT_SEC", "12")),
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)
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SCAN_AI_CACHE_TTL_SEC = max(
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30,
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int(os.getenv("POLYWEATHER_SCAN_AI_CACHE_TTL_SEC", "600")),
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)
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SCAN_AI_MAX_ROWS = max(
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1,
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int(os.getenv("POLYWEATHER_SCAN_AI_MAX_ROWS", "40")),
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)
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def _safe_float(value: Any) -> Optional[float]:
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try:
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if value is None or value == "":
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return None
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return float(value)
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except Exception:
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return None
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def _safe_int(value: Any, default: int) -> int:
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try:
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return int(value)
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except Exception:
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return int(default)
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def _normalize_scan_terminal_filters(
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raw_filters: Optional[Dict[str, Any]] = None,
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) -> Dict[str, Any]:
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raw = raw_filters if isinstance(raw_filters, dict) else {}
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min_price = _safe_float(raw.get("min_price"))
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max_price = _safe_float(raw.get("max_price"))
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if min_price is None:
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min_price = 0.05
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if max_price is None:
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max_price = 0.95
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min_price = max(0.0, min(1.0, min_price))
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max_price = max(0.0, min(1.0, max_price))
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if min_price > max_price:
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min_price, max_price = max_price, min_price
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high_liquidity_only = bool(raw.get("high_liquidity_only"))
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min_liquidity = _safe_float(raw.get("min_liquidity"))
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if min_liquidity is None:
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min_liquidity = 5000.0 if high_liquidity_only else 500.0
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if high_liquidity_only:
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min_liquidity = max(min_liquidity, 5000.0)
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return {
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"scan_mode": str(raw.get("scan_mode") or "tradable").strip().lower()
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or "tradable",
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"min_price": float(min_price),
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"max_price": float(max_price),
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"min_edge_pct": max(0.0, _safe_float(raw.get("min_edge_pct")) or 2.0),
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"min_liquidity": max(0.0, float(min_liquidity)),
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"high_liquidity_only": high_liquidity_only,
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"market_type": str(raw.get("market_type") or "maxtemp").strip().lower()
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or "maxtemp",
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"time_range": str(raw.get("time_range") or "today").strip().lower()
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or "today",
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"limit": max(1, min(_safe_int(raw.get("limit"), 25), 100)),
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"max_spread": max(0.0, _safe_float(raw.get("max_spread")) or 0.03),
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}
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def _scan_terminal_cache_key(filters: Dict[str, Any]) -> str:
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normalized = _normalize_scan_terminal_filters(filters)
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return json.dumps(normalized, ensure_ascii=True, sort_keys=True)
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def _get_cached_scan_terminal_payload(
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filters: Dict[str, Any],
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) -> Optional[Dict[str, Any]]:
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cache_key = _scan_terminal_cache_key(filters)
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now = time.time()
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with _SCAN_TERMINAL_CACHE_LOCK:
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cached = _SCAN_TERMINAL_CACHE.get(cache_key)
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if not cached:
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return None
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cached_at = float(cached.get("t") or 0.0)
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if now - cached_at >= float(SCAN_TERMINAL_PAYLOAD_TTL_SEC):
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return None
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payload = cached.get("payload")
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if not isinstance(payload, dict):
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return None
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return dict(payload)
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def _get_scan_terminal_cache_entry(filters: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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cache_key = _scan_terminal_cache_key(filters)
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with _SCAN_TERMINAL_CACHE_LOCK:
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cached = _SCAN_TERMINAL_CACHE.get(cache_key)
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if not isinstance(cached, dict):
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return None
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return dict(cached)
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def _build_scan_terminal_snapshot_id(
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filters: Dict[str, Any],
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rows: List[Dict[str, Any]],
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summary: Dict[str, Any],
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top_signal: Optional[Dict[str, Any]],
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) -> str:
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seed_payload = {
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"filters": filters,
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"summary": {
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"candidate_total": summary.get("candidate_total"),
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"tradable_market_count": summary.get("tradable_market_count"),
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"avg_edge_percent": summary.get("avg_edge_percent"),
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},
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"top_signal": {
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"id": (top_signal or {}).get("id"),
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"edge_percent": (top_signal or {}).get("edge_percent"),
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"final_score": (top_signal or {}).get("final_score"),
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},
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"rows": [
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{
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"id": row.get("id"),
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"edge_percent": row.get("edge_percent"),
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"final_score": row.get("final_score"),
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}
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for row in rows[:10]
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],
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}
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digest = hashlib.md5(
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json.dumps(seed_payload, ensure_ascii=True, sort_keys=True).encode("utf-8")
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).hexdigest()
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return f"scan-{digest[:10]}"
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def _set_cached_scan_terminal_payload(
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filters: Dict[str, Any],
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payload: Dict[str, Any],
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) -> None:
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cache_key = _scan_terminal_cache_key(filters)
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existing = _get_scan_terminal_cache_entry(filters) or {}
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with _SCAN_TERMINAL_CACHE_LOCK:
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_SCAN_TERMINAL_CACHE[cache_key] = {
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"t": time.time(),
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"payload": dict(payload),
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"success_t": time.time(),
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"success_payload": dict(payload),
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"last_error": existing.get("last_error"),
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"last_failed_at": existing.get("last_failed_at"),
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}
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def _set_scan_terminal_failure_state(
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filters: Dict[str, Any],
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*,
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error_message: str,
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) -> None:
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cache_key = _scan_terminal_cache_key(filters)
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with _SCAN_TERMINAL_CACHE_LOCK:
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existing = _SCAN_TERMINAL_CACHE.get(cache_key) or {}
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existing["last_error"] = error_message
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existing["last_failed_at"] = datetime.utcnow().isoformat() + "Z"
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_SCAN_TERMINAL_CACHE[cache_key] = existing
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def _start_scan_terminal_background_refresh(filters: Dict[str, Any]) -> bool:
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cache_key = _scan_terminal_cache_key(filters)
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with _SCAN_TERMINAL_CACHE_LOCK:
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if cache_key in _SCAN_TERMINAL_REFRESHING:
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return False
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_SCAN_TERMINAL_REFRESHING.add(cache_key)
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def _runner() -> None:
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try:
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_build_scan_terminal_payload_uncached(filters, force_refresh=True)
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except Exception as exc: # pragma: no cover - defensive background guard
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logger.warning("scan terminal background refresh failed: {}", exc)
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finally:
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with _SCAN_TERMINAL_CACHE_LOCK:
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_SCAN_TERMINAL_REFRESHING.discard(cache_key)
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thread = threading.Thread(
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target=_runner,
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name="polyweather-scan-terminal-refresh",
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daemon=True,
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)
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thread.start()
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return True
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def _build_stale_scan_terminal_payload(
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*,
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filters: Dict[str, Any],
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success_payload: Dict[str, Any],
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error_message: str,
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failed_at: Optional[str],
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) -> Dict[str, Any]:
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payload = dict(success_payload)
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payload["status"] = "stale"
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payload["stale"] = True
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payload["stale_reason"] = error_message
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payload["last_success_at"] = success_payload.get("generated_at")
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payload["last_failed_at"] = failed_at
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payload["filters"] = filters
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return payload
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def _build_failed_scan_terminal_payload(
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*,
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filters: Dict[str, Any],
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error_message: str,
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failed_at: Optional[str] = None,
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) -> Dict[str, Any]:
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return {
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"generated_at": datetime.utcnow().isoformat() + "Z",
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"snapshot_id": None,
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"status": "failed",
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"stale": False,
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"stale_reason": error_message,
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"last_success_at": None,
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"last_failed_at": failed_at or (datetime.utcnow().isoformat() + "Z"),
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"filters": filters,
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"summary": {
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"recommended_count": 0,
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"visible_count": 0,
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"candidate_total": 0,
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"avg_edge_percent": None,
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"avg_primary_confidence": None,
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"tradable_market_count": 0,
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"total_volume": 0.0,
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"resolved_market_type": "maxtemp",
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},
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"top_signal": None,
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"rows": [],
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}
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def _extract_ai_json_object(raw_text: str) -> Dict[str, Any]:
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text = str(raw_text or "").strip()
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if not text:
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raise ValueError("empty AI content")
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try:
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parsed = json.loads(text)
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if isinstance(parsed, dict):
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return parsed
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except Exception:
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pass
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start = text.find("{")
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end = text.rfind("}")
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if start >= 0 and end > start:
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parsed = json.loads(text[start : end + 1])
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if isinstance(parsed, dict):
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return parsed
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raise ValueError("AI content is not a JSON object")
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def _scan_ai_cache_key(snapshot_id: str, filters: Dict[str, Any]) -> str:
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raw = json.dumps(
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{
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"snapshot_id": snapshot_id,
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"filters": _normalize_scan_terminal_filters(filters),
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"model": SCAN_AI_MODEL,
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"max_rows": SCAN_AI_MAX_ROWS,
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},
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sort_keys=True,
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ensure_ascii=False,
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)
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return hashlib.sha256(raw.encode("utf-8")).hexdigest()
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def _get_cached_scan_ai_result(snapshot_id: str, filters: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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cache_key = _scan_ai_cache_key(snapshot_id, filters)
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now = time.time()
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with _SCAN_TERMINAL_AI_CACHE_LOCK:
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cached = _SCAN_TERMINAL_AI_CACHE.get(cache_key)
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if not cached:
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return None
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cached_at = float(cached.get("cached_at") or 0.0)
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if now - cached_at >= float(SCAN_AI_CACHE_TTL_SEC):
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return None
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||
result = cached.get("result")
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||
if isinstance(result, dict):
|
||
return dict(result)
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||
return None
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||
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def _set_cached_scan_ai_result(snapshot_id: str, filters: Dict[str, Any], result: Dict[str, Any]) -> None:
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cache_key = _scan_ai_cache_key(snapshot_id, filters)
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||
with _SCAN_TERMINAL_AI_CACHE_LOCK:
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_SCAN_TERMINAL_AI_CACHE[cache_key] = {
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"cached_at": time.time(),
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"result": result,
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}
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def _compact_ai_candidate(row: Dict[str, Any]) -> Dict[str, Any]:
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return {
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"id": row.get("id"),
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"action": row.get("action"),
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||
"side": row.get("side"),
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||
"target_label": row.get("target_label"),
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||
"target_value": row.get("target_value"),
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||
"target_threshold": row.get("target_threshold"),
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||
"target_unit": row.get("target_unit"),
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||
"model_probability": row.get("model_probability"),
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"market_probability": row.get("market_probability"),
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"model_event_probability": row.get("model_event_probability"),
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"market_event_probability": row.get("market_event_probability"),
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"yes_ask": row.get("yes_ask"),
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"no_ask": row.get("no_ask"),
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"ask": row.get("ask"),
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"spread": row.get("spread"),
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"quote_age_ms": row.get("quote_age_ms"),
|
||
"edge_percent": row.get("edge_percent"),
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||
"final_score": row.get("final_score"),
|
||
"cluster_role": row.get("cluster_role"),
|
||
"trend_alignment": row.get("trend_alignment"),
|
||
"tradable": row.get("tradable"),
|
||
"accepting_orders": row.get("accepting_orders"),
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||
}
|
||
|
||
|
||
def _normalize_ai_city_key(value: Any) -> str:
|
||
return str(value or "").strip().lower().replace(" ", "").replace("-", "").replace("_", "")
|
||
|
||
|
||
def _compact_ai_distribution(row: Dict[str, Any]) -> List[Dict[str, Any]]:
|
||
raw_items = row.get("distribution_full") or row.get("distribution_preview") or []
|
||
if not isinstance(raw_items, list):
|
||
return []
|
||
out: List[Dict[str, Any]] = []
|
||
for item in raw_items:
|
||
if not isinstance(item, dict):
|
||
continue
|
||
out.append(
|
||
{
|
||
"label": item.get("label"),
|
||
"value": item.get("value"),
|
||
"unit": item.get("unit") or row.get("target_unit") or row.get("temp_symbol"),
|
||
"model_probability": item.get("model_probability"),
|
||
"market_probability": item.get("market_probability"),
|
||
"highlighted": item.get("highlighted"),
|
||
}
|
||
)
|
||
return out
|
||
|
||
|
||
def _compact_ai_model_sources(row: Dict[str, Any]) -> List[Dict[str, Any]]:
|
||
raw_sources = row.get("model_cluster_sources")
|
||
if not isinstance(raw_sources, dict):
|
||
return []
|
||
sources: List[Dict[str, Any]] = []
|
||
for name, value in raw_sources.items():
|
||
if _safe_float(value) is None:
|
||
continue
|
||
sources.append({"model": str(name), "value": value})
|
||
return sources[:12]
|
||
|
||
|
||
def _compact_ai_city_group(rows: List[Dict[str, Any]]) -> Dict[str, Any]:
|
||
first = rows[0]
|
||
distribution = _compact_ai_distribution(first)
|
||
peak = next((item for item in distribution if item.get("highlighted")), None)
|
||
if not peak and distribution:
|
||
peak = max(
|
||
distribution,
|
||
key=lambda item: _safe_float(item.get("model_probability")) or -1.0,
|
||
)
|
||
return {
|
||
"city": first.get("city"),
|
||
"city_display_name": first.get("city_display_name") or first.get("display_name") or first.get("city"),
|
||
"selected_date": first.get("selected_date") or first.get("local_date"),
|
||
"local_time": first.get("local_time"),
|
||
"temp_symbol": first.get("temp_symbol") or first.get("target_unit"),
|
||
"current_temp": first.get("current_temp"),
|
||
"current_max_so_far": first.get("current_max_so_far"),
|
||
"deb_prediction": first.get("deb_prediction"),
|
||
"window_phase": first.get("window_phase"),
|
||
"remaining_window_minutes": first.get("remaining_window_minutes"),
|
||
"emos_distribution": distribution,
|
||
"emos_peak": {
|
||
"label": (peak or {}).get("label"),
|
||
"value": (peak or {}).get("value"),
|
||
"probability": (peak or {}).get("model_probability"),
|
||
},
|
||
"model_cluster": {
|
||
"core_low": first.get("cluster_core_low"),
|
||
"core_high": first.get("cluster_core_high"),
|
||
"median": first.get("cluster_median"),
|
||
"deb_reference": first.get("cluster_deb_reference"),
|
||
"model_count": first.get("cluster_model_count"),
|
||
"sources": _compact_ai_model_sources(first),
|
||
},
|
||
"contracts": [_compact_ai_candidate(row) for row in rows],
|
||
}
|
||
|
||
|
||
def _build_scan_ai_prompt(payload: Dict[str, Any]) -> Dict[str, Any]:
|
||
raw_rows = [
|
||
row
|
||
for row in (payload.get("rows") or [])[:SCAN_AI_MAX_ROWS]
|
||
if isinstance(row, dict) and row.get("id")
|
||
]
|
||
grouped: Dict[str, List[Dict[str, Any]]] = {}
|
||
for row in raw_rows:
|
||
key = "|".join(
|
||
[
|
||
_normalize_ai_city_key(row.get("city") or row.get("city_display_name")),
|
||
str(row.get("selected_date") or row.get("local_date") or ""),
|
||
]
|
||
)
|
||
grouped.setdefault(key, []).append(row)
|
||
cities = [_compact_ai_city_group(rows) for rows in grouped.values() if rows]
|
||
sent_contracts = sum(len(city.get("contracts") or []) for city in cities)
|
||
return {
|
||
"schema_version": "city_group_v2",
|
||
"snapshot_id": payload.get("snapshot_id"),
|
||
"generated_at": payload.get("generated_at"),
|
||
"summary": payload.get("summary") or {},
|
||
"filters": payload.get("filters") or {},
|
||
"city_count": len(cities),
|
||
"candidate_row_count": len(raw_rows),
|
||
"cities": cities,
|
||
"_polyweather_input_meta": {
|
||
"sent_cities": len(cities),
|
||
"sent_contracts": sent_contracts,
|
||
},
|
||
}
|
||
|
||
|
||
def _call_deepseek_scan_ai(ai_input: Dict[str, Any]) -> Dict[str, Any]:
|
||
api_key = str(os.getenv("POLYWEATHER_DEEPSEEK_API_KEY") or "").strip()
|
||
if not api_key:
|
||
raise RuntimeError("POLYWEATHER_DEEPSEEK_API_KEY is not configured")
|
||
|
||
system_prompt = (
|
||
"你是 PolyWeather 的付费 V4-Flash 市场扫描员。你只能基于用户提供的 JSON 快照做判断，"
|
||
"不得编造城市、价格、概率、盘口或天气数据。输入已经按城市分组，每城包含 EMOS 分布、"
|
||
"DEB、模型集群、当前实测和候选合约。你的任务不是复述 edge，而是形成 city thesis，"
|
||
"再决定哪些合约值得推荐、哪些必须排除、哪些只降级观察。"
|
||
"重点规则：最高温市场一天只会落入一个桶；如果 DEB/模型集群/EMOS 主峰集中在更高温区，"
|
||
"低温 YES 即使有表面 edge 也通常应 veto，优先考虑相邻尾部 NO；若价格过期、盘口太宽或窗口不支持，也要降级。"
|
||
"只能引用输入中的 row id。必须输出 JSON object。"
|
||
)
|
||
model_snapshot = dict(ai_input)
|
||
model_snapshot.pop("_polyweather_input_meta", None)
|
||
user_payload = {
|
||
"task": (
|
||
"Analyze each city first, then contract decisions. Return strict JSON only with: "
|
||
"summary_zh, summary_en, city_theses, recommendations, vetoed, downgraded, watchlist. "
|
||
"city_theses items need city, thesis_zh, thesis_en, model_cluster_note, confidence, "
|
||
"recommended_row_ids, vetoed_row_ids. recommendations items need row_id, rank, decision, "
|
||
"confidence, reason_zh, reason_en, model_cluster_note. vetoed/downgraded items need row_id, "
|
||
"reason_zh/reason_en. watchlist items are optional and need row_id plus reason."
|
||
),
|
||
"snapshot": model_snapshot,
|
||
}
|
||
with httpx.Client(timeout=float(SCAN_AI_TIMEOUT_SEC)) as client:
|
||
response = client.post(
|
||
f"{SCAN_AI_BASE_URL}/chat/completions",
|
||
headers={
|
||
"Authorization": f"Bearer {api_key}",
|
||
"Content-Type": "application/json",
|
||
},
|
||
json={
|
||
"model": SCAN_AI_MODEL,
|
||
"temperature": 0.1,
|
||
"max_tokens": 2200,
|
||
"response_format": {"type": "json_object"},
|
||
"messages": [
|
||
{"role": "system", "content": system_prompt},
|
||
{
|
||
"role": "user",
|
||
"content": json.dumps(user_payload, ensure_ascii=False),
|
||
},
|
||
],
|
||
},
|
||
)
|
||
response.raise_for_status()
|
||
data = response.json()
|
||
content = (
|
||
((data.get("choices") or [{}])[0].get("message") or {}).get("content")
|
||
if isinstance(data, dict)
|
||
else None
|
||
)
|
||
parsed = _extract_ai_json_object(str(content or ""))
|
||
if isinstance(data, dict):
|
||
parsed["_polyweather_meta"] = {
|
||
"usage": data.get("usage"),
|
||
"finish_reason": ((data.get("choices") or [{}])[0] or {}).get("finish_reason"),
|
||
}
|
||
return parsed
|
||
|
||
|
||
def _normalize_ai_items(raw_items: Any) -> List[Dict[str, Any]]:
|
||
if not isinstance(raw_items, list):
|
||
return []
|
||
out: List[Dict[str, Any]] = []
|
||
for item in raw_items:
|
||
if isinstance(item, str):
|
||
out.append({"row_id": item})
|
||
elif isinstance(item, dict):
|
||
row_id = str(item.get("row_id") or item.get("id") or "").strip()
|
||
if row_id:
|
||
out.append({**item, "row_id": row_id})
|
||
return out
|
||
|
||
|
||
def _normalize_ai_city_theses(raw_items: Any) -> List[Dict[str, Any]]:
|
||
if not isinstance(raw_items, list):
|
||
return []
|
||
out: List[Dict[str, Any]] = []
|
||
for item in raw_items:
|
||
if not isinstance(item, dict):
|
||
continue
|
||
city = str(item.get("city") or item.get("city_name") or "").strip()
|
||
if not city:
|
||
continue
|
||
out.append({**item, "city": city})
|
||
return out
|
||
|
||
|
||
def _merge_scan_ai_result(
|
||
payload: Dict[str, Any],
|
||
ai_raw: Dict[str, Any],
|
||
*,
|
||
cached: bool = False,
|
||
duration_ms: Optional[int] = None,
|
||
input_rows: Optional[int] = None,
|
||
) -> Dict[str, Any]:
|
||
rows = [dict(row) for row in (payload.get("rows") or []) if isinstance(row, dict)]
|
||
by_id = {str(row.get("id")): row for row in rows if row.get("id")}
|
||
recommendations = _normalize_ai_items(ai_raw.get("recommendations"))
|
||
vetoed = _normalize_ai_items(ai_raw.get("vetoed"))
|
||
downgraded = _normalize_ai_items(ai_raw.get("downgraded"))
|
||
watchlist = _normalize_ai_items(ai_raw.get("watchlist"))
|
||
city_theses = _normalize_ai_city_theses(ai_raw.get("city_theses"))
|
||
|
||
veto_ids = {str(item.get("row_id")) for item in vetoed}
|
||
downgrade_ids = {str(item.get("row_id")) for item in downgraded}
|
||
recommended_ids: set[str] = set()
|
||
watchlist_ids = {str(item.get("row_id")) for item in watchlist}
|
||
|
||
thesis_by_city: Dict[str, Dict[str, Any]] = {}
|
||
for item in city_theses:
|
||
key = _normalize_ai_city_key(item.get("city"))
|
||
if key:
|
||
thesis_by_city[key] = item
|
||
|
||
for row in rows:
|
||
thesis = thesis_by_city.get(_normalize_ai_city_key(row.get("city"))) or thesis_by_city.get(
|
||
_normalize_ai_city_key(row.get("city_display_name"))
|
||
)
|
||
if not thesis:
|
||
continue
|
||
row["ai_city_thesis_zh"] = thesis.get("thesis_zh") or thesis.get("summary_zh")
|
||
row["ai_city_thesis_en"] = thesis.get("thesis_en") or thesis.get("summary_en")
|
||
row["ai_city_confidence"] = thesis.get("confidence")
|
||
row["ai_city_model_cluster_note"] = thesis.get("model_cluster_note")
|
||
|
||
for item in vetoed:
|
||
row = by_id.get(str(item.get("row_id")))
|
||
if not row:
|
||
continue
|
||
row["ai_decision"] = "veto"
|
||
row["ai_reason_zh"] = item.get("reason_zh") or item.get("reason")
|
||
row["ai_reason_en"] = item.get("reason_en")
|
||
for item in downgraded:
|
||
row = by_id.get(str(item.get("row_id")))
|
||
if not row:
|
||
continue
|
||
row["ai_decision"] = "downgrade"
|
||
row["ai_reason_zh"] = item.get("reason_zh") or item.get("reason")
|
||
row["ai_reason_en"] = item.get("reason_en")
|
||
for item in watchlist:
|
||
row = by_id.get(str(item.get("row_id")))
|
||
if not row:
|
||
continue
|
||
row["ai_watchlist_reason_zh"] = item.get("reason_zh") or item.get("reason")
|
||
row["ai_watchlist_reason_en"] = item.get("reason_en")
|
||
for fallback_rank, item in enumerate(recommendations, start=1):
|
||
row_id = str(item.get("row_id"))
|
||
row = by_id.get(row_id)
|
||
if not row:
|
||
continue
|
||
if row_id in veto_ids:
|
||
continue
|
||
recommended_ids.add(row_id)
|
||
row["ai_decision"] = str(item.get("decision") or "approve").strip().lower() or "approve"
|
||
row["ai_rank"] = _safe_int(item.get("rank"), fallback_rank)
|
||
row["ai_confidence"] = item.get("confidence")
|
||
row["ai_reason_zh"] = item.get("reason_zh") or item.get("reason")
|
||
row["ai_reason_en"] = item.get("reason_en")
|
||
row["ai_model_cluster_note"] = item.get("model_cluster_note")
|
||
|
||
for row in rows:
|
||
row_id = str(row.get("id"))
|
||
if row_id not in recommended_ids and row_id not in veto_ids and row_id not in downgrade_ids:
|
||
row["ai_decision"] = row.get("ai_decision") or "neutral"
|
||
if row_id in watchlist_ids and row.get("ai_decision") == "neutral":
|
||
row["ai_decision"] = "watchlist"
|
||
|
||
def _ai_sort_key(row: Dict[str, Any]) -> tuple:
|
||
decision = str(row.get("ai_decision") or "").lower()
|
||
if decision == "veto":
|
||
tier = 3
|
||
elif decision == "downgrade":
|
||
tier = 2
|
||
elif row.get("ai_rank") is not None:
|
||
tier = 0
|
||
else:
|
||
tier = 1
|
||
return (
|
||
tier,
|
||
_safe_int(row.get("ai_rank"), 999),
|
||
-float(row.get("final_score") or 0.0),
|
||
-float(row.get("edge_percent") or 0.0),
|
||
)
|
||
|
||
rows.sort(key=_ai_sort_key)
|
||
top_signal = next(
|
||
(row for row in rows if str(row.get("ai_decision") or "").lower() != "veto"),
|
||
rows[0] if rows else None,
|
||
)
|
||
input_meta = ai_raw.get("_polyweather_input_meta")
|
||
sent_cities = input_meta.get("sent_cities") if isinstance(input_meta, dict) else None
|
||
sent_contracts = input_meta.get("sent_contracts") if isinstance(input_meta, dict) else None
|
||
ai_scan = {
|
||
"status": "ready",
|
||
"stage": "completed",
|
||
"model": SCAN_AI_MODEL,
|
||
"cached": cached,
|
||
"generated_at": datetime.utcnow().isoformat() + "Z",
|
||
"snapshot_id": payload.get("snapshot_id"),
|
||
"input_rows": input_rows if input_rows is not None else len(payload.get("rows") or []),
|
||
"sent_rows": sent_contracts if sent_contracts is not None else min(len(payload.get("rows") or []), SCAN_AI_MAX_ROWS),
|
||
"sent_cities": sent_cities,
|
||
"sent_contracts": sent_contracts,
|
||
"duration_ms": duration_ms,
|
||
"timeout_sec": SCAN_AI_TIMEOUT_SEC,
|
||
"cache_ttl_sec": SCAN_AI_CACHE_TTL_SEC,
|
||
"provider": "deepseek",
|
||
"base_url": SCAN_AI_BASE_URL,
|
||
"summary_zh": ai_raw.get("summary_zh"),
|
||
"summary_en": ai_raw.get("summary_en"),
|
||
"city_theses": city_theses,
|
||
"watchlist": watchlist,
|
||
"recommended_count": sum(1 for row in rows if row.get("ai_rank") is not None),
|
||
"vetoed_count": sum(1 for row in rows if row.get("ai_decision") == "veto"),
|
||
"downgraded_count": sum(1 for row in rows if row.get("ai_decision") == "downgrade"),
|
||
"watchlist_count": sum(1 for row in rows if row.get("ai_decision") == "watchlist"),
|
||
}
|
||
meta = ai_raw.get("_polyweather_meta")
|
||
if isinstance(meta, dict):
|
||
ai_scan["usage"] = meta.get("usage")
|
||
ai_scan["finish_reason"] = meta.get("finish_reason")
|
||
merged = {
|
||
**payload,
|
||
"rows": rows,
|
||
"top_signal": top_signal,
|
||
"ai_scan": ai_scan,
|
||
}
|
||
return merged
|
||
|
||
|
||
def _build_scan_ai_unavailable_payload(
|
||
payload: Dict[str, Any],
|
||
*,
|
||
status: str,
|
||
reason: str,
|
||
duration_ms: Optional[int] = None,
|
||
) -> Dict[str, Any]:
|
||
return {
|
||
**payload,
|
||
"ai_scan": {
|
||
"status": status,
|
||
"stage": "fallback",
|
||
"model": SCAN_AI_MODEL,
|
||
"cached": False,
|
||
"generated_at": datetime.utcnow().isoformat() + "Z",
|
||
"snapshot_id": payload.get("snapshot_id"),
|
||
"input_rows": len(payload.get("rows") or []),
|
||
"sent_rows": min(len(payload.get("rows") or []), SCAN_AI_MAX_ROWS),
|
||
"duration_ms": duration_ms,
|
||
"timeout_sec": SCAN_AI_TIMEOUT_SEC,
|
||
"cache_ttl_sec": SCAN_AI_CACHE_TTL_SEC,
|
||
"provider": "deepseek",
|
||
"base_url": SCAN_AI_BASE_URL,
|
||
"reason": reason,
|
||
},
|
||
}
|
||
|
||
|
||
def _resolve_time_range_dates(data: Dict[str, Any], time_range: str) -> List[str]:
|
||
local_date = str(data.get("local_date") or "").strip()
|
||
multi_model_daily = data.get("multi_model_daily") or {}
|
||
available_dates = sorted(
|
||
str(date_key).strip()
|
||
for date_key in (multi_model_daily.keys() if isinstance(multi_model_daily, dict) else [])
|
||
if str(date_key).strip()
|
||
)
|
||
|
||
if not local_date:
|
||
return available_dates[:1]
|
||
if time_range == "today":
|
||
return [local_date]
|
||
|
||
try:
|
||
local_dt = datetime.fromisoformat(local_date)
|
||
except Exception:
|
||
return available_dates[:7] if time_range == "week" else available_dates[:1]
|
||
|
||
if time_range == "tomorrow":
|
||
target = (local_dt + timedelta(days=1)).strftime("%Y-%m-%d")
|
||
if target in available_dates:
|
||
return [target]
|
||
future_dates = [date_key for date_key in available_dates if date_key > local_date]
|
||
return future_dates[:1]
|
||
|
||
if time_range == "week":
|
||
target_dates = [date_key for date_key in available_dates if date_key >= local_date]
|
||
if local_date not in target_dates:
|
||
target_dates.insert(0, local_date)
|
||
deduped: List[str] = []
|
||
for date_key in target_dates:
|
||
if date_key not in deduped:
|
||
deduped.append(date_key)
|
||
if len(deduped) >= 7:
|
||
break
|
||
return deduped
|
||
|
||
return [local_date]
|
||
|
||
|
||
def _build_terminal_row(
|
||
*,
|
||
city: str,
|
||
data: Dict[str, Any],
|
||
scan: Dict[str, Any],
|
||
row: Dict[str, Any],
|
||
) -> Dict[str, Any]:
|
||
current = data.get("current") or {}
|
||
multi_model_daily = data.get("multi_model_daily") or {}
|
||
selected_date = str(row.get("selected_date") or scan.get("selected_date") or data.get("local_date") or "").strip()
|
||
daily_entry = multi_model_daily.get(selected_date) if isinstance(multi_model_daily, dict) else {}
|
||
if not isinstance(daily_entry, dict):
|
||
daily_entry = {}
|
||
|
||
display_name = str(data.get("display_name") or city).strip() or city
|
||
market_slug = str(row.get("market_slug") or "").strip()
|
||
side = str(row.get("side") or "").strip().lower()
|
||
edge_percent = _safe_float(row.get("edge_percent"))
|
||
final_score = _safe_float(row.get("final_score"))
|
||
volume = _safe_float(row.get("volume")) or 0.0
|
||
primary_signal = scan.get("primary_signal") or {}
|
||
|
||
return {
|
||
**row,
|
||
"id": str(row.get("id") or f"{city}|{selected_date}|{market_slug}|{side}"),
|
||
"city": city,
|
||
"city_display_name": display_name,
|
||
"selected_date": selected_date or None,
|
||
"local_date": data.get("local_date"),
|
||
"local_time": data.get("local_time"),
|
||
"temp_symbol": data.get("temp_symbol"),
|
||
"current_temp": current.get("temp"),
|
||
"current_max_so_far": current.get("max_so_far"),
|
||
"deb_prediction": ((daily_entry.get("deb") or {}).get("prediction") if isinstance(daily_entry.get("deb"), dict) else None)
|
||
or ((data.get("deb") or {}).get("prediction") if isinstance(data.get("deb"), dict) else None),
|
||
"display_name": display_name,
|
||
"airport": ((data.get("risk") or {}).get("airport") if isinstance(data.get("risk"), dict) else None),
|
||
"risk_level": ((data.get("risk") or {}).get("level") if isinstance(data.get("risk"), dict) else None),
|
||
"distribution_bias": scan.get("distribution_bias"),
|
||
"distribution_preview": scan.get("distribution_preview") or row.get("distribution_preview") or [],
|
||
"distribution_full": scan.get("distribution_full") or scan.get("distribution_preview") or row.get("distribution_preview") or [],
|
||
"model_cluster_sources": daily_entry.get("models") if isinstance(daily_entry.get("models"), dict) else data.get("multi_model"),
|
||
"window_phase": row.get("window_phase") or scan.get("window_phase"),
|
||
"window_score": row.get("window_score") if row.get("window_score") is not None else scan.get("window_score"),
|
||
"signal_status": scan.get("signal_status"),
|
||
"candidate_count": scan.get("candidate_count"),
|
||
"resolved_market_type": scan.get("resolved_market_type") or "maxtemp",
|
||
"market_key": f"{city}|{selected_date}|{market_slug}",
|
||
"is_primary_signal": bool(primary_signal and primary_signal.get("id") == row.get("id")),
|
||
"signal_confidence": final_score,
|
||
"edge_percent": edge_percent,
|
||
"final_score": final_score,
|
||
"volume": volume,
|
||
}
|
||
|
||
|
||
def _scan_city_terminal_rows(
|
||
city: str,
|
||
filters: Dict[str, Any],
|
||
*,
|
||
force_refresh: bool = False,
|
||
) -> Dict[str, Any]:
|
||
data = _analyze(
|
||
city,
|
||
force_refresh=force_refresh,
|
||
include_llm_commentary=False,
|
||
detail_mode="market",
|
||
)
|
||
target_dates = _resolve_time_range_dates(data, filters["time_range"])
|
||
rows: List[Dict[str, Any]] = []
|
||
primary_scores: List[float] = []
|
||
candidate_total = 0
|
||
|
||
for target_date in target_dates:
|
||
payload = _build_city_market_scan_payload(
|
||
data,
|
||
market_slug=None,
|
||
target_date=target_date,
|
||
lite=True,
|
||
scan_filters=filters,
|
||
)
|
||
scan = payload.get("market_scan") or {}
|
||
candidate_total += int(scan.get("candidate_count") or 0)
|
||
raw_rows = scan.get("scan_rows")
|
||
if not isinstance(raw_rows, list) or not raw_rows:
|
||
raw_rows = [scan.get("primary_signal")] if isinstance(scan.get("primary_signal"), dict) else []
|
||
if not raw_rows:
|
||
continue
|
||
for raw_row in raw_rows:
|
||
if not isinstance(raw_row, dict) or not raw_row:
|
||
continue
|
||
row = _build_terminal_row(
|
||
city=city,
|
||
data=data,
|
||
scan=scan,
|
||
row=raw_row,
|
||
)
|
||
rows.append(row)
|
||
score = _safe_float(row.get("final_score"))
|
||
if score is not None and row.get("is_primary_signal"):
|
||
primary_scores.append(score)
|
||
|
||
return {
|
||
"city": city,
|
||
"rows": rows,
|
||
"candidate_total": candidate_total,
|
||
"primary_scores": primary_scores,
|
||
}
|
||
|
||
|
||
def _build_scan_terminal_payload_uncached(
|
||
filters: Dict[str, Any],
|
||
*,
|
||
force_refresh: bool = False,
|
||
) -> Dict[str, Any]:
|
||
cached_entry = _get_scan_terminal_cache_entry(filters) or {}
|
||
|
||
try:
|
||
city_names = list(CITIES.keys())
|
||
max_workers = max(1, min(4, len(city_names)))
|
||
city_results: List[Dict[str, Any]] = []
|
||
failed_cities: List[str] = []
|
||
failed_reasons: List[str] = []
|
||
|
||
timed_out = False
|
||
timeout_message: Optional[str] = None
|
||
executor = ThreadPoolExecutor(max_workers=max_workers)
|
||
future_map = {
|
||
executor.submit(
|
||
_scan_city_terminal_rows,
|
||
city_name,
|
||
filters,
|
||
force_refresh=force_refresh,
|
||
): city_name
|
||
for city_name in city_names
|
||
}
|
||
try:
|
||
try:
|
||
completed = as_completed(
|
||
future_map,
|
||
timeout=float(SCAN_TERMINAL_BUILD_TIMEOUT_SEC),
|
||
)
|
||
for future in completed:
|
||
city_name = future_map[future]
|
||
try:
|
||
city_results.append(future.result())
|
||
except Exception as exc:
|
||
failed_cities.append(city_name)
|
||
failed_reasons.append(str(exc))
|
||
logger.warning("scan terminal city failed city={}: {}", city_name, exc)
|
||
except FutureTimeoutError:
|
||
timed_out = True
|
||
timeout_message = (
|
||
f"scan terminal build timed out after "
|
||
f"{SCAN_TERMINAL_BUILD_TIMEOUT_SEC}s"
|
||
)
|
||
failed_reasons.append(timeout_message)
|
||
for future, city_name in future_map.items():
|
||
if not future.done():
|
||
future.cancel()
|
||
failed_cities.append(city_name)
|
||
logger.warning(
|
||
"{}; completed={}/{}",
|
||
timeout_message,
|
||
len(city_results),
|
||
len(city_names),
|
||
)
|
||
finally:
|
||
executor.shutdown(wait=False)
|
||
|
||
if city_names and len(failed_cities) >= len(city_names):
|
||
error_message = failed_reasons[0] if failed_reasons else "all city market scans failed"
|
||
_set_scan_terminal_failure_state(filters, error_message=error_message)
|
||
failed_entry = _get_scan_terminal_cache_entry(filters) or {}
|
||
success_payload = failed_entry.get("success_payload")
|
||
failed_at = failed_entry.get("last_failed_at")
|
||
if isinstance(success_payload, dict) and success_payload:
|
||
return _build_stale_scan_terminal_payload(
|
||
filters=filters,
|
||
success_payload=success_payload,
|
||
error_message=error_message,
|
||
failed_at=failed_at,
|
||
)
|
||
return _build_failed_scan_terminal_payload(
|
||
filters=filters,
|
||
error_message=error_message,
|
||
failed_at=failed_at,
|
||
)
|
||
|
||
primary_rows: List[Dict[str, Any]] = []
|
||
primary_scores: List[float] = []
|
||
candidate_total = 0
|
||
|
||
for result in city_results:
|
||
candidate_total += int(result.get("candidate_total") or 0)
|
||
primary_rows.extend(result.get("rows") or [])
|
||
primary_scores.extend(result.get("primary_scores") or [])
|
||
|
||
primary_rows.sort(
|
||
key=lambda row: (
|
||
float(row.get("final_score") or 0.0),
|
||
float(row.get("edge_percent") or 0.0),
|
||
),
|
||
reverse=True,
|
||
)
|
||
|
||
ranked_rows: List[Dict[str, Any]] = []
|
||
for index, row in enumerate(primary_rows[: filters["limit"]], start=1):
|
||
ranked_rows.append(
|
||
{
|
||
**row,
|
||
"rank": index,
|
||
}
|
||
)
|
||
|
||
unique_market_volume: Dict[str, float] = {}
|
||
for row in primary_rows:
|
||
market_key = str(row.get("market_key") or row.get("id") or "").strip()
|
||
if not market_key:
|
||
continue
|
||
unique_market_volume[market_key] = max(
|
||
unique_market_volume.get(market_key, 0.0),
|
||
float(row.get("volume") or 0.0),
|
||
)
|
||
|
||
avg_edge = None
|
||
if primary_rows:
|
||
edge_values = [
|
||
float(row.get("edge_percent") or 0.0)
|
||
for row in primary_rows
|
||
if _safe_float(row.get("edge_percent")) is not None
|
||
]
|
||
if edge_values:
|
||
avg_edge = sum(edge_values) / len(edge_values)
|
||
|
||
avg_confidence = None
|
||
if primary_scores:
|
||
avg_confidence = sum(primary_scores) / len(primary_scores)
|
||
|
||
top_signal = ranked_rows[0] if ranked_rows else None
|
||
summary = {
|
||
"recommended_count": len(primary_rows),
|
||
"visible_count": len(ranked_rows),
|
||
"candidate_total": candidate_total,
|
||
"avg_edge_percent": avg_edge,
|
||
"avg_primary_confidence": avg_confidence,
|
||
"tradable_market_count": len(unique_market_volume),
|
||
"total_volume": sum(unique_market_volume.values()),
|
||
"resolved_market_type": "maxtemp",
|
||
"total_city_count": len(city_names),
|
||
"scanned_city_count": len(city_results),
|
||
"failed_city_count": len(failed_cities),
|
||
}
|
||
payload = {
|
||
"generated_at": datetime.utcnow().isoformat() + "Z",
|
||
"filters": filters,
|
||
"summary": summary,
|
||
"top_signal": top_signal,
|
||
"rows": ranked_rows,
|
||
"status": "partial" if timed_out else "ready",
|
||
"stale": False,
|
||
"stale_reason": timeout_message,
|
||
"last_success_at": None,
|
||
"last_failed_at": None,
|
||
}
|
||
payload["snapshot_id"] = _build_scan_terminal_snapshot_id(
|
||
filters,
|
||
ranked_rows,
|
||
summary,
|
||
top_signal,
|
||
)
|
||
|
||
_set_cached_scan_terminal_payload(filters, payload)
|
||
return payload
|
||
except Exception as exc:
|
||
error_message = str(exc)
|
||
logger.exception("scan terminal payload build failed: {}", error_message)
|
||
_set_scan_terminal_failure_state(filters, error_message=error_message)
|
||
success_payload = cached_entry.get("success_payload")
|
||
failed_at = _get_scan_terminal_cache_entry(filters).get("last_failed_at") if _get_scan_terminal_cache_entry(filters) else None
|
||
if isinstance(success_payload, dict) and success_payload:
|
||
return _build_stale_scan_terminal_payload(
|
||
filters=filters,
|
||
success_payload=success_payload,
|
||
error_message=error_message,
|
||
failed_at=failed_at,
|
||
)
|
||
return _build_failed_scan_terminal_payload(
|
||
filters=filters,
|
||
error_message=error_message,
|
||
failed_at=failed_at,
|
||
)
|
||
|
||
|
||
def build_scan_terminal_payload(
|
||
raw_filters: Optional[Dict[str, Any]] = None,
|
||
*,
|
||
force_refresh: bool = False,
|
||
) -> Dict[str, Any]:
|
||
filters = _normalize_scan_terminal_filters(raw_filters)
|
||
if not force_refresh:
|
||
cached = _get_cached_scan_terminal_payload(filters)
|
||
if cached is not None:
|
||
return cached
|
||
|
||
cached_entry = _get_scan_terminal_cache_entry(filters) or {}
|
||
success_payload = cached_entry.get("success_payload")
|
||
if isinstance(success_payload, dict) and success_payload:
|
||
started = _start_scan_terminal_background_refresh(filters)
|
||
return _build_stale_scan_terminal_payload(
|
||
filters=filters,
|
||
success_payload=success_payload,
|
||
error_message=(
|
||
"正在后台刷新市场扫描快照"
|
||
if started
|
||
else "市场扫描快照正在刷新中"
|
||
),
|
||
failed_at=cached_entry.get("last_failed_at"),
|
||
)
|
||
|
||
return _build_scan_terminal_payload_uncached(filters, force_refresh=force_refresh)
|
||
|
||
|
||
def build_scan_terminal_ai_payload(
|
||
raw_filters: Optional[Dict[str, Any]] = None,
|
||
*,
|
||
snapshot_id: Optional[str] = None,
|
||
) -> Dict[str, Any]:
|
||
ai_started_at = time.time()
|
||
filters = _normalize_scan_terminal_filters(raw_filters)
|
||
payload = build_scan_terminal_payload(filters, force_refresh=False)
|
||
current_snapshot_id = str(payload.get("snapshot_id") or "").strip()
|
||
requested_snapshot_id = str(snapshot_id or "").strip()
|
||
if requested_snapshot_id and current_snapshot_id and requested_snapshot_id != current_snapshot_id:
|
||
return _build_scan_ai_unavailable_payload(
|
||
payload,
|
||
status="snapshot_mismatch",
|
||
reason="scan snapshot changed; refresh the scan before running AI review",
|
||
)
|
||
if not current_snapshot_id:
|
||
return _build_scan_ai_unavailable_payload(
|
||
payload,
|
||
status="no_snapshot",
|
||
reason="no scan snapshot is available for AI review",
|
||
)
|
||
if not payload.get("rows"):
|
||
return _build_scan_ai_unavailable_payload(
|
||
payload,
|
||
status="no_rows",
|
||
reason="no candidate rows are available for AI review",
|
||
)
|
||
if not SCAN_AI_ENABLED:
|
||
return _build_scan_ai_unavailable_payload(
|
||
payload,
|
||
status="disabled",
|
||
reason="POLYWEATHER_SCAN_AI_ENABLED is not enabled",
|
||
)
|
||
if not str(os.getenv("POLYWEATHER_DEEPSEEK_API_KEY") or "").strip():
|
||
return _build_scan_ai_unavailable_payload(
|
||
payload,
|
||
status="missing_key",
|
||
reason="POLYWEATHER_DEEPSEEK_API_KEY is not configured",
|
||
)
|
||
|
||
cached = _get_cached_scan_ai_result(current_snapshot_id, filters)
|
||
if cached is not None:
|
||
logger.info(
|
||
"scan terminal AI cache hit snapshot={} rows={}",
|
||
current_snapshot_id,
|
||
len(payload.get("rows") or []),
|
||
)
|
||
return _merge_scan_ai_result(
|
||
payload,
|
||
cached,
|
||
cached=True,
|
||
duration_ms=0,
|
||
input_rows=len(payload.get("rows") or []),
|
||
)
|
||
|
||
try:
|
||
ai_input = _build_scan_ai_prompt(payload)
|
||
input_meta = ai_input.get("_polyweather_input_meta") if isinstance(ai_input, dict) else {}
|
||
sent_rows = int((input_meta or {}).get("sent_contracts") or 0)
|
||
sent_cities = int((input_meta or {}).get("sent_cities") or 0)
|
||
logger.info(
|
||
"scan terminal AI review start snapshot={} rows={} sent_cities={} sent_contracts={} model={}",
|
||
current_snapshot_id,
|
||
len(payload.get("rows") or []),
|
||
sent_cities,
|
||
sent_rows,
|
||
SCAN_AI_MODEL,
|
||
)
|
||
ai_raw = _call_deepseek_scan_ai(ai_input)
|
||
ai_raw["_polyweather_input_meta"] = input_meta
|
||
_set_cached_scan_ai_result(current_snapshot_id, filters, ai_raw)
|
||
duration_ms = int((time.time() - ai_started_at) * 1000)
|
||
logger.info(
|
||
"scan terminal AI review complete snapshot={} duration_ms={} recommendations={} vetoed={} downgraded={}",
|
||
current_snapshot_id,
|
||
duration_ms,
|
||
len(_normalize_ai_items(ai_raw.get("recommendations"))),
|
||
len(_normalize_ai_items(ai_raw.get("vetoed"))),
|
||
len(_normalize_ai_items(ai_raw.get("downgraded"))),
|
||
)
|
||
return _merge_scan_ai_result(
|
||
payload,
|
||
ai_raw,
|
||
cached=False,
|
||
duration_ms=duration_ms,
|
||
input_rows=len(payload.get("rows") or []),
|
||
)
|
||
except Exception as exc:
|
||
duration_ms = int((time.time() - ai_started_at) * 1000)
|
||
logger.warning(
|
||
"scan terminal AI review failed snapshot={} duration_ms={} error={}",
|
||
current_snapshot_id,
|
||
duration_ms,
|
||
exc,
|
||
)
|
||
return _build_scan_ai_unavailable_payload(
|
||
payload,
|
||
status="failed",
|
||
reason=str(exc),
|
||
duration_ms=duration_ms,
|
||
)
|