227 lines
7.1 KiB
Python
227 lines
7.1 KiB
Python
"""
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影子模式半自动化：读取 config/current_suggestions.json，在开仓前应用黑名单/差时段/杠杆倍数建议。
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与 aggregate_trade_stats 的 STATS_* 互补：本模块面向 daily_report / 影子分析产出的 JSON。
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"""
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from __future__ import annotations
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import json
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import logging
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import os
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import time
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Tuple
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logger = logging.getLogger(__name__)
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# 项目根目录（trading_system/ 的上一级）
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_PROJECT_ROOT = Path(__file__).resolve().parent.parent
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def _project_root() -> Path:
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return _PROJECT_ROOT
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def _safe_read_json(path: Path) -> Optional[dict]:
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try:
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if not path.is_file():
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return None
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with open(path, "r", encoding="utf-8") as f:
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return json.load(f)
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except Exception as e:
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logger.debug(f"读取 JSON 失败 {path}: {e}")
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return None
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_suggestions_cache: Tuple[float, Optional[dict]] = (0.0, None)
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_tracking_cache: Tuple[float, Optional[dict]] = (0.0, None)
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_CACHE_TTL_SEC = 45.0
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def _get_suggestions(trading_config: dict) -> dict:
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global _suggestions_cache
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path_str = trading_config.get("SHADOW_MODE_SUGGESTIONS_PATH") or "config/current_suggestions.json"
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path = Path(path_str)
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if not path.is_absolute():
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path = _project_root() / path
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now = time.time()
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if _suggestions_cache[1] is not None and now - _suggestions_cache[0] < _CACHE_TTL_SEC:
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return _suggestions_cache[1]
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data = _safe_read_json(path)
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if data is None:
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data = {
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"blacklist": [],
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"increase_position": [],
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"decrease_position": [],
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"worst_hours": [],
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}
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_suggestions_cache = (now, data)
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return data
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def _get_tracking_confidence(trading_config: dict) -> float:
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global _tracking_cache
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path_str = trading_config.get("SHADOW_MODE_TRACKING_PATH") or "config/shadow_mode_tracking.json"
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path = Path(path_str)
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if not path.is_absolute():
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path = _project_root() / path
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now = time.time()
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if _tracking_cache[1] is not None and now - _tracking_cache[0] < _CACHE_TTL_SEC:
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data = _tracking_cache[1]
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else:
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data = _safe_read_json(path) or {}
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_tracking_cache = (now, data)
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# 兼容多种字段名
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for key in ("accuracy", "rolling_accuracy", "confidence", "min_confidence"):
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v = data.get(key)
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if v is not None:
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try:
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return float(v)
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except (TypeError, ValueError):
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pass
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return 1.0
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def _norm_symbol(s: str) -> str:
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return (s or "").strip().upper()
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def _find_blacklist_entry(suggestions: dict, symbol: str) -> Optional[dict]:
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sym = _norm_symbol(symbol)
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for item in suggestions.get("blacklist") or []:
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if not isinstance(item, dict):
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continue
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if _norm_symbol(str(item.get("symbol", ""))) == sym:
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return item
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return None
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def _in_worst_hours(suggestions: dict, hour_bj: int) -> Optional[dict]:
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for item in suggestions.get("worst_hours") or []:
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if not isinstance(item, dict):
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continue
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h = item.get("hour")
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try:
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hi = int(h)
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except (TypeError, ValueError):
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continue
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if hi == int(hour_bj):
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return item
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return None
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def _position_list_has(suggestions: dict, key: str, symbol: str) -> Optional[dict]:
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sym = _norm_symbol(symbol)
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for item in suggestions.get(key) or []:
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if not isinstance(item, dict):
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continue
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if _norm_symbol(str(item.get("symbol", ""))) == sym:
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return item
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return None
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def evaluate_shadow_mode(
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symbol: str,
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hour_bj: int,
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trading_config: dict,
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) -> Dict[str, Any]:
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"""
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评估影子模式：是否允许自动开仓、杠杆乘子、写入 entry_context 的 shadow_mode_applied。
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当 SHADOW_MODE_AUTO_APPLY=False 或置信度不足时：不拦截、不调整杠杆，但标记 skipped。
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"""
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auto = bool(trading_config.get("SHADOW_MODE_AUTO_APPLY", False))
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min_conf = float(trading_config.get("SHADOW_MODE_MIN_CONFIDENCE", 0.7) or 0.0)
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min_conf = max(0.0, min(1.0, min_conf))
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inc_mul = float(trading_config.get("SHADOW_MODE_INCREASE_LEVERAGE_MULT", 1.5) or 1.5)
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dec_mul = float(trading_config.get("SHADOW_MODE_DECREASE_LEVERAGE_MULT", 0.5) or 0.5)
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inc_mul = max(0.1, min(inc_mul, 3.0))
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dec_mul = max(0.1, min(dec_mul, 2.0))
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applied = {
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"blacklist_check": "skipped",
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"hour_check": "skipped",
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"position_adjustment": "none",
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"suggestion_source": "current_suggestions.json",
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}
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if not auto:
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return {
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"allowed": True,
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"skip_reason": None,
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"leverage_multiplier": 1.0,
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"shadow_mode_applied": {**applied, "reason": "SHADOW_MODE_AUTO_APPLY=false"},
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}
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conf = _get_tracking_confidence(trading_config)
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if conf < min_conf:
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logger.info(
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f"影子模式：跟踪置信度 {conf:.2f} < 最小要求 {min_conf:.2f}，本笔不自动应用建议"
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)
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return {
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"allowed": True,
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"skip_reason": None,
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"leverage_multiplier": 1.0,
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"shadow_mode_applied": {
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**applied,
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"reason": f"confidence {conf:.2f} < {min_conf:.2f}",
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},
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}
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suggestions = _get_suggestions(trading_config)
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# 黑名单
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bl = _find_blacklist_entry(suggestions, symbol)
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if bl is not None:
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applied["blacklist_check"] = "blocked"
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reason = bl.get("reason") or "shadow blacklist"
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return {
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"allowed": False,
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"skip_reason": reason,
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"leverage_multiplier": 1.0,
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"shadow_mode_applied": {**applied, "blacklist_detail": bl},
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}
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applied["blacklist_check"] = "passed"
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# 差时段
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wh = _in_worst_hours(suggestions, hour_bj)
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if wh is not None:
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applied["hour_check"] = "blocked"
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avg_pnl = wh.get("avg_pnl", wh.get("avgPnl", ""))
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return {
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"allowed": False,
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"skip_reason": f"worst_hour_{hour_bj}",
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"leverage_multiplier": 1.0,
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"shadow_mode_applied": {**applied, "worst_hour_detail": wh},
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"log_avg_pnl": avg_pnl,
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}
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applied["hour_check"] = "passed"
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# 杠杆：减仓优先于加仓（同时命中时更保守）
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mult = 1.0
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dec_item = _position_list_has(suggestions, "decrease_position", symbol)
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inc_item = _position_list_has(suggestions, "increase_position", symbol)
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if dec_item is not None:
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mult *= dec_mul
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applied["position_adjustment"] = "decrease"
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applied["decrease_detail"] = dec_item
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elif inc_item is not None:
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mult *= inc_mul
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applied["position_adjustment"] = "increase"
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applied["increase_detail"] = inc_item
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return {
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"allowed": True,
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"skip_reason": None,
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"leverage_multiplier": mult,
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"shadow_mode_applied": applied,
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}
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def invalidate_cache() -> None:
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"""配置热更新时可调用（可选）。"""
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global _suggestions_cache, _tracking_cache
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_suggestions_cache = (0.0, None)
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_tracking_cache = (0.0, None)
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