refactor: 温区数据改读预计算表(strategy_regime_perf_by_period)——删除现场重算+内存缓存,数据加工层每日预计算,server只读表毫秒级
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xxm
@@ -45,25 +45,11 @@ _regime_winrates_cache = {"slots": {}} # slots: {period_tag: (data_version_key,
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def _compute_regime_winrates_cached(pt, _approx_univ):
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"""按 period_tag 从 strategy_research 对应周期 trades 现场温区归因（带缓存）"""
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"""按 period_tag 读预计算表 strategy_regime_perf_by_period（2026-08-15 数据加工层）
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数据由 regime_perf_by_period.py 每日盘后预计算（含线性年化），server 只读表，毫秒级。
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无对应周期记录时回退到最近更长周期（1m/6m→1y，2y→2y，5y→5y，10y→10y）。
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"""
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import sqlite3 as _sq
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import math as _mth
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from collections import defaultdict as _dd
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from flask import request as _req
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# 缓存键
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_cache_key = None
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try:
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_cc = _sq.connect(str(DATA_DIR / "mofin.db"), timeout=5)
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_ver_s = _cc.execute("SELECT MAX(created_at) FROM strategy_research").fetchone()[0]
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_cc.close()
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_cache_key = (pt or "2y") + "|" + str(_ver_s)
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except Exception as _e:
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_cache_key = None
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_slots = _regime_winrates_cache.get("slots") or {}
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_slot = _slots.get(pt or "2y")
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if _cache_key and _slot and _slot[0] == _cache_key:
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return _slot[1]
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_pt_chain = {'1m': '1y', '6m': '1y', '1y': '1y', '2y': '2y', '5y': '5y', '10y': '10y'}
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_pt_use = _pt_chain.get(pt or '2y', '2y')
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@@ -71,132 +57,39 @@ def _compute_regime_winrates_cached(pt, _approx_univ):
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try:
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_c = _sq.connect(str(DATA_DIR / "mofin.db"), timeout=10)
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_c.execute("PRAGMA busy_timeout=10000")
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_row_map = {}
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for _r in _c.execute(
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"SELECT version, market, period_tag, results_json FROM strategy_research "
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"ORDER BY version, market, "
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"CASE period_tag WHEN '1y' THEN 1 WHEN '2y' THEN 2 WHEN '5y' THEN 3 WHEN '10y' THEN 4 ELSE 5 END"
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).fetchall():
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_k = (_r[0], _r[1])
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if _k not in _row_map:
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_row_map[_k] = {}
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_row_map[_k][_r[2]] = _r[3]
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_rmap_a = dict(_c.execute("SELECT date, regime FROM market_regime WHERE market='a'").fetchall())
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_rmap_hk = dict(_c.execute("SELECT date, regime FROM market_regime WHERE market='hk'").fetchall())
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"SELECT strategy, market, regime, period_tag, trades, win_rate, avg_pnl, "
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"avg_hold_days, total_return_pct, cagr_pct, portfolio_max_dd_pct, capital_final, "
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"positions_taken, sharpe_ratio, profit_factor FROM strategy_regime_perf_by_period "
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"WHERE period_tag=? ORDER BY strategy, market, regime",
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(_pt_use,)).fetchall():
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_ver, _mkt, _reg = _r[0], _r[1], _r[2]
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_cagr_v = _r[9]
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_ret_v = _r[8]
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_dd_v = _r[10]
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_cf_v = _r[11]
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_pt_v = _r[12]
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_sh_v = _r[13]
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_pf_v = _r[14]
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_regime_winrates.setdefault(_ver, {})[_reg] = {
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"trades": _r[4], "win_rate": _r[5], "avg_pnl": _r[6],
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"avg_hold_days": _r[7],
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"total_return_pct": _ret_v, "cagr_pct": _cagr_v,
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"max_dd_pct": _dd_v, "capital_final": _cf_v,
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"positions_taken": _pt_v, "sharpe_ratio": _sh_v,
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"profit_factor": _pf_v,
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"period_tag": _pt_use,
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"portfolio": {"cagr_pct": _cagr_v, "total_return_pct": _ret_v,
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"portfolio_max_dd_pct": _dd_v, "capital_final": _cf_v,
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"positions_taken": _pt_v, "sharpe_ratio": _sh_v,
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"profit_factor": _pf_v},
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"universality": _approx_univ(_ver, _reg, _r[4]),
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}
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_c.close()
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def _calc_extra(_trades):
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if not _trades:
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return {}
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_profits = [t.get("profit_pct", 0) for t in _trades]
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_wins = [p for p in _profits if p > 0]
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_losses = [p for p in _profits if p <= 0]
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_wr = len(_wins) / len(_profits) * 100 if _profits else 0
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_avg = sum(_profits) / len(_profits) if _profits else 0
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_avg_w = sum(_wins) / len(_wins) if _wins else 0
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_avg_l = abs(sum(_losses) / len(_losses)) if _losses else 1
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_pf = _avg_w / _avg_l if _avg_l > 0 else 0
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_mr = _avg / 100
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_std = _mth.sqrt(sum((p / 100 - _mr) ** 2 for p in _profits) / (len(_profits) - 1)) if len(_profits) > 1 else 0
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_sh = _mr / _std * _mth.sqrt(252) if _std > 0 else 0
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_holds = [t.get("hold_days", 0) for t in _trades if t.get("hold_days")]
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_ah = sum(_holds) / len(_holds) if _holds else 0
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return {"win_rate": round(_wr, 1), "avg_pnl": round(_avg, 2),
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"avg_hold_days": round(_ah, 1), "sharpe_ratio": round(_sh, 2),
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"profit_factor": round(_pf, 2)}
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def _portfolio_sim(_trades, _cap=1000000, _slots=10):
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if not _trades:
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return {}
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try:
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from strategy_lab import portfolio_sim
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return portfolio_sim(_trades, capital=_cap, max_positions=_slots, cost=True)
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except Exception:
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return {}
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for (_ver, _mkt), _periods in _row_map.items():
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_js = None
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_cand = None
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for _p in [_pt_use, '5y', '10y']:
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if _p in _periods and _periods[_p]:
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_cand = _periods[_p]
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break
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if not _cand:
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continue
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try:
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_trades = json.loads(_cand).get("trades", [])
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except Exception:
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continue
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if not _trades:
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continue
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_rmap = _rmap_a if _mkt != 'hk' else _rmap_hk
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# ── 温区天数（该策略 trades 窗口内，线性年化用）──
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_pt_days = {"total": 0, "regimes": {}}
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try:
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_eds = [t.get("entry_date", "") for t in _trades if t.get("entry_date")]
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_eds = [d for d in _eds if d in _rmap]
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if _eds:
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_d_min, _d_max = min(_eds), max(_eds)
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_cnt = {}
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_tot = 0
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for _d, _reg in _rmap.items():
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if _d_min <= _d <= _d_max:
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_tot += 1
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_cnt[_reg] = _cnt.get(_reg, 0) + 1
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_pt_days = {"total": _tot, "regimes": _cnt}
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except Exception:
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pass
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_by_regime = _dd(list)
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for _t in _trades:
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_ed = _t.get("entry_date", "")
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if _ed in _rmap:
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_by_regime[_rmap[_ed]].append(_t)
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for _reg, _reg_trades in _by_regime.items():
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if len(_reg_trades) < 2:
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continue
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_extra = _calc_extra(_reg_trades)
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_sim = _portfolio_sim(_reg_trades)
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if not _sim:
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continue
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_wr_v = _extra.get("win_rate")
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# 2026-08-15 温区年化线性放大：收益 × (窗口总天数/该温区天数)，消除复利爆炸
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_ret_v = _sim.get("total_return_pct")
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_reg_days = (_pt_days.get("regimes") or {}).get(_reg, 0)
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_pt_total = _pt_days.get("total", 0)
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if _ret_v is not None and _reg_days > 0 and _pt_total > 0:
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_cagr_v = round(_ret_v * (_pt_total / _reg_days), 1)
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else:
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_cagr_v = None
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_dd_v = _sim.get("portfolio_max_dd_pct")
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_cf_v = _sim.get("capital_final")
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_pt_v = _sim.get("positions_taken")
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_sh_v = _extra.get("sharpe_ratio")
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_pf_v = _extra.get("profit_factor")
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_regime_winrates.setdefault(_ver, {})[_reg] = {
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"trades": len(_reg_trades),
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"win_rate": _wr_v, "avg_pnl": _extra.get("avg_pnl"),
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"avg_hold_days": _extra.get("avg_hold_days"),
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"total_return_pct": _ret_v, "cagr_pct": _cagr_v,
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"max_dd_pct": _dd_v, "capital_final": _cf_v,
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"positions_taken": _pt_v, "sharpe_ratio": _sh_v,
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"profit_factor": _pf_v,
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"period_tag": _pt_use,
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"portfolio": {"cagr_pct": _cagr_v, "total_return_pct": _ret_v,
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"portfolio_max_dd_pct": _dd_v, "capital_final": _cf_v,
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"positions_taken": _pt_v, "sharpe_ratio": _sh_v,
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"profit_factor": _pf_v},
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"universality": _approx_univ(_ver, _reg, len(_reg_trades)),
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}
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except Exception:
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pass
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if _cache_key:
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try:
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(_regime_winrates_cache.setdefault("slots", {}))[pt or "2y"] = (_cache_key, _regime_winrates)
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except Exception:
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pass
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return _regime_winrates
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def _chk_http(host, port, path, timeout=3):
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try:
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url = f"http://{host}:{port}{path}"
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