2569718930@qq.com
360 lines
12 KiB
Python
360 lines
12 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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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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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_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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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 _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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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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}
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def _resolve_time_range_dates(data: Dict[str, Any], time_range: str) -> List[str]:
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local_date = str(data.get("local_date") or "").strip()
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multi_model_daily = data.get("multi_model_daily") or {}
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available_dates = sorted(
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str(date_key).strip()
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for date_key in (multi_model_daily.keys() if isinstance(multi_model_daily, dict) else [])
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if str(date_key).strip()
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)
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if not local_date:
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return available_dates[:1]
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if time_range == "today":
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return [local_date]
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try:
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local_dt = datetime.fromisoformat(local_date)
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except Exception:
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return available_dates[:7] if time_range == "week" else available_dates[:1]
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if time_range == "tomorrow":
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target = (local_dt + timedelta(days=1)).strftime("%Y-%m-%d")
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if target in available_dates:
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return [target]
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future_dates = [date_key for date_key in available_dates if date_key > local_date]
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return future_dates[:1]
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if time_range == "week":
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target_dates = [date_key for date_key in available_dates if date_key >= local_date]
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if local_date not in target_dates:
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target_dates.insert(0, local_date)
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deduped: List[str] = []
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for date_key in target_dates:
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if date_key not in deduped:
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deduped.append(date_key)
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if len(deduped) >= 7:
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break
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return deduped
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return [local_date]
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def _build_terminal_row(
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*,
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city: str,
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data: Dict[str, Any],
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scan: Dict[str, Any],
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row: Dict[str, Any],
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) -> Dict[str, Any]:
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current = data.get("current") or {}
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multi_model_daily = data.get("multi_model_daily") or {}
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selected_date = str(row.get("selected_date") or scan.get("selected_date") or data.get("local_date") or "").strip()
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daily_entry = multi_model_daily.get(selected_date) if isinstance(multi_model_daily, dict) else {}
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if not isinstance(daily_entry, dict):
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daily_entry = {}
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display_name = str(data.get("display_name") or city).strip() or city
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market_slug = str(row.get("market_slug") or "").strip()
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side = str(row.get("side") or "").strip().lower()
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edge_percent = _safe_float(row.get("edge_percent"))
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final_score = _safe_float(row.get("final_score"))
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volume = _safe_float(row.get("volume")) or 0.0
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primary_signal = scan.get("primary_signal") or {}
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return {
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**row,
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"id": str(row.get("id") or f"{city}|{selected_date}|{market_slug}|{side}"),
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"city": city,
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"city_display_name": display_name,
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"selected_date": selected_date or None,
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"local_date": data.get("local_date"),
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"local_time": data.get("local_time"),
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"temp_symbol": data.get("temp_symbol"),
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"current_temp": current.get("temp"),
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"current_max_so_far": current.get("max_so_far"),
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"deb_prediction": ((daily_entry.get("deb") or {}).get("prediction") if isinstance(daily_entry.get("deb"), dict) else None)
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or ((data.get("deb") or {}).get("prediction") if isinstance(data.get("deb"), dict) else None),
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"display_name": display_name,
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"airport": ((data.get("risk") or {}).get("airport") if isinstance(data.get("risk"), dict) else None),
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"risk_level": ((data.get("risk") or {}).get("level") if isinstance(data.get("risk"), dict) else None),
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"distribution_bias": scan.get("distribution_bias"),
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"distribution_preview": scan.get("distribution_preview") or row.get("distribution_preview") or [],
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"window_phase": row.get("window_phase") or scan.get("window_phase"),
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"window_score": row.get("window_score") if row.get("window_score") is not None else scan.get("window_score"),
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"signal_status": scan.get("signal_status"),
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"candidate_count": scan.get("candidate_count"),
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"resolved_market_type": scan.get("resolved_market_type") or "maxtemp",
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"market_key": f"{city}|{selected_date}|{market_slug}",
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"is_primary_signal": bool(primary_signal and primary_signal.get("id") == row.get("id")),
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"signal_confidence": final_score,
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"edge_percent": edge_percent,
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"final_score": final_score,
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"volume": volume,
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}
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def _scan_city_terminal_rows(
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city: str,
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filters: Dict[str, Any],
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*,
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force_refresh: bool = False,
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) -> Dict[str, Any]:
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data = _analyze(
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city,
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force_refresh=force_refresh,
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include_llm_commentary=False,
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detail_mode="market",
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)
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target_dates = _resolve_time_range_dates(data, filters["time_range"])
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rows: List[Dict[str, Any]] = []
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primary_scores: List[float] = []
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candidate_total = 0
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for target_date in target_dates:
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payload = _build_city_market_scan_payload(
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data,
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market_slug=None,
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target_date=target_date,
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lite=True,
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scan_filters=filters,
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)
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scan = payload.get("market_scan") or {}
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candidate_total += int(scan.get("candidate_count") or 0)
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primary_signal = scan.get("primary_signal")
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if not isinstance(primary_signal, dict) or not primary_signal:
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continue
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row = _build_terminal_row(
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city=city,
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data=data,
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scan=scan,
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row=primary_signal,
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)
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rows.append(row)
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score = _safe_float(row.get("final_score"))
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if score is not None:
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primary_scores.append(score)
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return {
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"city": city,
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"rows": rows,
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"candidate_total": candidate_total,
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"primary_scores": primary_scores,
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}
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def build_scan_terminal_payload(
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raw_filters: Optional[Dict[str, Any]] = None,
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*,
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force_refresh: bool = False,
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) -> Dict[str, Any]:
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filters = _normalize_scan_terminal_filters(raw_filters)
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if not force_refresh:
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cached = _get_cached_scan_terminal_payload(filters)
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if cached is not None:
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return cached
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city_names = list(CITIES.keys())
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max_workers = max(1, min(6, len(city_names)))
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city_results: List[Dict[str, Any]] = []
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with ThreadPoolExecutor(max_workers=max_workers) as executor:
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future_map = {
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executor.submit(
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_scan_city_terminal_rows,
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city_name,
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filters,
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force_refresh=force_refresh,
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): city_name
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for city_name in city_names
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}
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for future in as_completed(future_map):
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city_name = future_map[future]
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try:
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city_results.append(future.result())
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except Exception as exc:
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logger.warning("scan terminal city failed city={}: {}", city_name, exc)
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primary_rows: List[Dict[str, Any]] = []
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primary_scores: List[float] = []
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candidate_total = 0
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for result in city_results:
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candidate_total += int(result.get("candidate_total") or 0)
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primary_rows.extend(result.get("rows") or [])
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primary_scores.extend(result.get("primary_scores") or [])
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primary_rows.sort(
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key=lambda row: (
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float(row.get("final_score") or 0.0),
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float(row.get("edge_percent") or 0.0),
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),
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reverse=True,
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)
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ranked_rows: List[Dict[str, Any]] = []
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for index, row in enumerate(primary_rows[: filters["limit"]], start=1):
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ranked_rows.append(
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{
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**row,
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"rank": index,
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}
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)
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unique_market_volume: Dict[str, float] = {}
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for row in primary_rows:
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market_key = str(row.get("market_key") or row.get("id") or "").strip()
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if not market_key:
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continue
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unique_market_volume[market_key] = max(
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unique_market_volume.get(market_key, 0.0),
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float(row.get("volume") or 0.0),
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)
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avg_edge = None
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if primary_rows:
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edge_values = [
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float(row.get("edge_percent") or 0.0)
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for row in primary_rows
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if _safe_float(row.get("edge_percent")) is not None
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]
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if edge_values:
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avg_edge = sum(edge_values) / len(edge_values)
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avg_confidence = None
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if primary_scores:
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avg_confidence = sum(primary_scores) / len(primary_scores)
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top_signal = ranked_rows[0] if ranked_rows else None
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payload = {
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"generated_at": datetime.utcnow().isoformat() + "Z",
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"filters": filters,
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"summary": {
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"recommended_count": len(primary_rows),
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"visible_count": len(ranked_rows),
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"candidate_total": candidate_total,
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"avg_edge_percent": avg_edge,
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"avg_primary_confidence": avg_confidence,
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"tradable_market_count": len(unique_market_volume),
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"total_volume": sum(unique_market_volume.values()),
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"resolved_market_type": "maxtemp",
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},
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"top_signal": top_signal,
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"rows": ranked_rows,
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}
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_set_cached_scan_terminal_payload(filters, payload)
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return payload
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