清理旧决策卡片、市场总览、跑道面板等已废弃组件
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2569718930@qq.com
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"""Anomaly detection — pure math, no AI call.
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Flags cities where current observations deviate from model predictions.
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"""
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from __future__ import annotations
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from typing import Any, Dict, List, Optional
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from web.scan_city_ai_helpers import _safe_float
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def _check_city_anomaly(
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data: Dict[str, Any],
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*,
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high_temp_threshold: float = 2.0,
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) -> Optional[Dict[str, Any]]:
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"""Return anomaly flag if current observation breaks model cluster bounds."""
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current = data.get("current") if isinstance(data.get("current"), dict) else {}
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airport = data.get("airport_current") if isinstance(data.get("airport_current"), dict) else {}
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multi = data.get("multi_model") if isinstance(data.get("multi_model"), dict) else {}
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deb = data.get("deb") if isinstance(data.get("deb"), dict) else {}
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observed = _safe_float(current.get("temp") or airport.get("temp"))
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if observed is None:
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return None
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model_highs = [
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_safe_float(v)
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for v in multi.values()
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if _safe_float(v) is not None
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]
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deb_pred = _safe_float(deb.get("prediction"))
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if deb_pred is not None:
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model_highs.append(deb_pred)
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if not model_highs:
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return None
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model_max = max(model_highs)
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model_min = min(model_highs)
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model_median = sorted(model_highs)[len(model_highs) // 2]
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delta_above_max = observed - model_max
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delta_below_min = model_min - observed
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delta_from_median = observed - model_median
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anomaly: Optional[Dict[str, Any]] = None
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if delta_above_max > high_temp_threshold:
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anomaly = {
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"level": "breakout_above",
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"observed": observed,
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"model_max": model_max,
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"delta": round(delta_above_max, 1),
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"model_count": len(model_highs),
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}
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elif delta_below_min > high_temp_threshold:
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anomaly = {
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"level": "breakout_below",
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"observed": observed,
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"model_min": model_min,
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"delta": round(delta_below_min, 1),
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"model_count": len(model_highs),
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}
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elif abs(delta_from_median) > 1.5:
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anomaly = {
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"level": "deviation",
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"observed": observed,
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"model_median": model_median,
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"delta": round(delta_from_median, 1),
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"model_count": len(model_highs),
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}
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if anomaly:
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anomaly.update(
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{
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"city": data.get("name") or data.get("city"),
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"local_date": data.get("local_date"),
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"temp_unit": data.get("temp_symbol", "°C"),
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"deb_prediction": deb_pred,
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}
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)
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return anomaly
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def detect_scan_terminal_anomalies(
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rows: List[Dict[str, Any]],
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*,
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high_temp_threshold: float = 2.0,
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) -> List[Dict[str, Any]]:
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"""Scan all terminal rows and return anomaly flags."""
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anomalies = []
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for row in rows:
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if not isinstance(row, dict):
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continue
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city_data = row.get("city_data") or row
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flag = _check_city_anomaly(city_data, high_temp_threshold=high_temp_threshold)
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if flag:
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flag["row_id"] = row.get("row_id") or row.get("id")
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anomalies.append(flag)
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return anomalies
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@@ -1,169 +0,0 @@
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"""Deterministic market overview for scan terminal rows, cached 10 minutes."""
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from __future__ import annotations
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import hashlib
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import json
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import threading
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import time
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from datetime import datetime
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from typing import Any, Dict, List, Optional
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from src.utils.refresh_policy import MARKET_OVERVIEW_TTL_SEC
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_OVERVIEW_CACHE: Dict[str, Dict[str, Any]] = {}
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_OVERVIEW_CACHE_LOCK = threading.Lock()
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OVERVIEW_CACHE_TTL_SEC = MARKET_OVERVIEW_TTL_SEC
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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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number = float(value)
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except Exception:
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return None
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return number if number == number else None
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def _row_city(row: Dict[str, Any]) -> str:
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return str(row.get("display_name") or row.get("city") or row.get("name") or "").strip()
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def _row_edge(row: Dict[str, Any]) -> Optional[float]:
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return (
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_safe_float(row.get("edge_percent"))
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or _safe_float(row.get("edge_pct"))
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or _safe_float(row.get("edge"))
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)
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def _row_score(row: Dict[str, Any]) -> float:
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edge = _row_edge(row) or 0.0
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final_score = _safe_float(row.get("final_score")) or 0.0
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liquidity = _safe_float(row.get("liquidity")) or _safe_float(row.get("liquidity_num")) or 0.0
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return edge * 10.0 + final_score + min(liquidity / 1000.0, 25.0)
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def _row_liquidity(row: Dict[str, Any]) -> float:
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return _safe_float(row.get("liquidity")) or _safe_float(row.get("liquidity_num")) or 0.0
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def _row_prob_gap(row: Dict[str, Any]) -> Optional[float]:
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model_prob = _safe_float(row.get("model_probability"))
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market_prob = _safe_float(row.get("market_probability"))
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if model_prob is None or market_prob is None:
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return None
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return model_prob - market_prob
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def _cache_key(rows: List[Dict[str, Any]], locale: str) -> str:
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finger = {
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"locale": locale,
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"rows": [
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{
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"city": row.get("city") or row.get("name") or "",
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"edge": _row_edge(row),
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"score": _safe_float(row.get("final_score")),
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"liquidity": _row_liquidity(row),
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"status": row.get("status") or row.get("signal_status") or "",
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}
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for row in rows
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if isinstance(row, dict)
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],
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}
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raw = json.dumps(finger, sort_keys=True, ensure_ascii=False, default=str)
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return "overview:" + hashlib.sha256(raw.encode("utf-8")).hexdigest()
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def _build_highlights(rows: List[Dict[str, Any]]) -> List[Dict[str, str]]:
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ranked = sorted(
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(row for row in rows if isinstance(row, dict) and _row_city(row)),
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key=lambda item: (_row_score(item), _row_liquidity(item)),
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reverse=True,
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)
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highlights: List[Dict[str, str]] = []
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for row in ranked[:5]:
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city = _row_city(row)
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edge = _row_edge(row)
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liquidity = _row_liquidity(row)
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gap = _row_prob_gap(row)
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edge_text = f"{edge:.1f}%" if edge is not None else "--"
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gap_text = f"{gap * 100:.1f}pp" if gap is not None else "--"
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liquidity_text = f"{liquidity:,.0f}" if liquidity else "--"
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highlights.append(
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{
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"city": city,
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"note_zh": f"edge {edge_text}，模型/市场概率差 {gap_text}，流动性 {liquidity_text}。",
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"note_en": f"edge {edge_text}, model-market gap {gap_text}, liquidity {liquidity_text}.",
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}
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)
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return highlights
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def _build_payload(rows: List[Dict[str, Any]]) -> Dict[str, Any]:
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clean_rows = [row for row in rows if isinstance(row, dict)]
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total = len(clean_rows)
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tradable = sum(1 for row in clean_rows if not row.get("closed") and not row.get("stale_for_today"))
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high_risk = sum(
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1
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for row in clean_rows
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if str(row.get("risk_level") or row.get("risk") or "").lower() in {"high", "danger", "red"}
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)
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avg_edge_values = [_row_edge(row) for row in clean_rows]
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avg_edge_nums = [value for value in avg_edge_values if value is not None]
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avg_edge = sum(avg_edge_nums) / len(avg_edge_nums) if avg_edge_nums else 0.0
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total_liquidity = sum(_row_liquidity(row) for row in clean_rows)
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highlights = _build_highlights(clean_rows)
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overview_zh = (
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f"当前区域共有 {total} 个天气合约，{tradable} 个可交易；"
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f"高风险 {high_risk} 个，平均 edge {avg_edge:.1f}%，总流动性 {total_liquidity:,.0f}。"
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"优先查看 edge、final score 与流动性同时靠前的城市。"
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)
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overview_en = (
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f"{total} weather contracts are in scope, {tradable} tradable; "
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f"{high_risk} high-risk rows, average edge {avg_edge:.1f}%, total liquidity {total_liquidity:,.0f}. "
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"Prioritize rows where edge, final score and liquidity align."
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)
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return {
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"overview_zh": overview_zh,
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"overview_en": overview_en,
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"highlights": highlights,
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"generated_at": datetime.utcnow().isoformat() + "Z",
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"cache_ttl_sec": OVERVIEW_CACHE_TTL_SEC,
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"source": "deterministic",
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}
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def build_market_overview_payload(
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rows: List[Dict[str, Any]],
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*,
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locale: str = "zh-CN",
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force_refresh: bool = False,
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) -> Dict[str, Any]:
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if not rows:
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return {
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"overview_zh": "",
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"overview_en": "",
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"highlights": [],
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"generated_at": None,
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"cache_ttl_sec": OVERVIEW_CACHE_TTL_SEC,
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"source": "deterministic",
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}
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key = _cache_key(rows, locale)
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if not force_refresh:
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with _OVERVIEW_CACHE_LOCK:
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cached = _OVERVIEW_CACHE.get(key)
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if cached and cached.get("expires_at", 0) >= time.time():
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return cached["payload"]
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payload = _build_payload(rows)
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with _OVERVIEW_CACHE_LOCK:
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_OVERVIEW_CACHE[key] = {
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"expires_at": time.time() + OVERVIEW_CACHE_TTL_SEC,
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"payload": payload,
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}
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return payload
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