diff --git a/deploy/profile-scripts/regime_perf_by_period.py b/deploy/profile-scripts/regime_perf_by_period.py index d38d64cb..538cdad0 100644 --- a/deploy/profile-scripts/regime_perf_by_period.py +++ b/deploy/profile-scripts/regime_perf_by_period.py @@ -57,7 +57,10 @@ def create_table(conn): sharpe_ratio REAL, profit_factor REAL, universality_months INTEGER, + universality_years INTEGER, + universality_valid_years INTEGER, universality_score REAL, + universality_leave1 REAL, updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, PRIMARY KEY (strategy, market, regime, period_tag) ) @@ -160,24 +163,45 @@ def process_period(conn, market, period_tag): cagr = round(((1 + ret / 100) ** (365 / span_days) - 1) * 100, 1) else: cagr = None - # 普适：该温区 trades 的 entry_date 去重月份数 / 该温区总月份数（2026-08-16 修复： - # 原 server 端信号数÷3估算 → s2_panic 2255信号估算751月=100分,实际只2个月） + # 普适（2026-08-17 重构，老莫）：跨独立年份的有效性，而非覆盖广度 + # 核心：策略不是靠某次历史特例（如某次大反弹）才成立，而是多个独立时段都有效 _uniq_months = len({t.get("entry_date", "")[:7] for t in reg_trades if t.get("entry_date")}) - _regime_months_all = {d[:7] for d, r in rmap.items() if r == reg} - _regime_total_months = len(_regime_months_all) - _univ_score = round(min(_uniq_months / max(_regime_total_months, 1) * 100, 100)) if _uniq_months else 0 + _univ_score = _univ_years = _univ_valid = _univ_leave1 = 0 + _ed_list = [t.get("entry_date", "")[:4] for t in reg_trades if t.get("entry_date")] + if len(_ed_list) >= 5: + from collections import defaultdict + _yr = defaultdict(list) + for _y, _t in zip(_ed_list, reg_trades): + _yr[_y].append(_t.get("profit_pct", 0)) + _yearly = {_y: {"n": len(_v), "wr": sum(1 for p in _v if p > 0) / len(_v) * 100, + "avg": sum(_v) / len(_v)} for _y, _v in _yr.items()} + # 只统计有足够样本的年（>=5笔） + _stat_years = {_y: _d for _y, _d in _yearly.items() if _d["n"] >= 5} + if _stat_years: + _univ_years = len(_stat_years) + _univ_valid = sum(1 for _d in _stat_years.values() if _d["wr"] > 50) + # 覆盖因子：>=3年给满覆盖分，<3年按比例 + _coverage = min(_univ_years / 3.0, 1.0) + # 稳定性：有效年占比 + _stability = _univ_valid / _univ_years + _univ_score = round(100 * _coverage * _stability) + # 特例剔除：去掉最好一年的平均收益，剩余平均是否仍 > 0 + _avgs = sorted((_d["avg"] for _d in _stat_years.values()), reverse=True) + if len(_avgs) >= 2: + _univ_leave1 = round(sum(_avgs[1:]) / (len(_avgs) - 1), 2) conn.execute( """INSERT OR REPLACE INTO strategy_regime_perf_by_period (strategy, market, regime, period_tag, trades, win_rate, avg_pnl, avg_hold_days, total_return_pct, cagr_pct, portfolio_max_dd_pct, capital_final, - positions_taken, sharpe_ratio, profit_factor, universality_months, universality_score, updated_at) - VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)""", + positions_taken, sharpe_ratio, profit_factor, universality_months, universality_years, + universality_valid_years, universality_score, universality_leave1, updated_at) + VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)""", (v, market, reg, period_tag, len(reg_trades), extra.get("win_rate"), extra.get("avg_pnl"), extra.get("avg_hold_days"), ret, cagr, sim.get("portfolio_max_dd_pct"), sim.get("capital_final"), sim.get("positions_taken"), extra.get("sharpe_ratio"), extra.get("profit_factor"), - _uniq_months, _univ_score, now)) + _uniq_months, _univ_years, _univ_valid, _univ_score, _univ_leave1, now)) written += 1 return written diff --git a/server.py b/server.py index 470a4628..30e8570b 100644 --- a/server.py +++ b/server.py @@ -60,7 +60,8 @@ def _compute_regime_winrates_cached(pt, _approx_univ): for _r in _c.execute( "SELECT strategy, market, regime, period_tag, trades, win_rate, avg_pnl, " "avg_hold_days, total_return_pct, cagr_pct, portfolio_max_dd_pct, capital_final, " - "positions_taken, sharpe_ratio, profit_factor, universality_months, universality_score " + "positions_taken, sharpe_ratio, profit_factor, universality_months, universality_years, " + "universality_valid_years, universality_score, universality_leave1 " "FROM strategy_regime_perf_by_period " "WHERE period_tag=? ORDER BY strategy, market, regime", (_pt_use,)).fetchall(): @@ -73,7 +74,10 @@ def _compute_regime_winrates_cached(pt, _approx_univ): _sh_v = _r[13] _pf_v = _r[14] _umon = _r[15] - _uscore = _r[16] + _uyears = _r[16] + _uvalid = _r[17] + _uscore = _r[18] + _uleave1 = _r[19] _regime_winrates.setdefault(_ver, {})[_reg] = { "trades": _r[4], "win_rate": _r[5], "avg_pnl": _r[6], "avg_hold_days": _r[7], @@ -86,7 +90,7 @@ def _compute_regime_winrates_cached(pt, _approx_univ): "portfolio_max_dd_pct": _dd_v, "capital_final": _cf_v, "positions_taken": _pt_v, "sharpe_ratio": _sh_v, "profit_factor": _pf_v}, - "universality": _approx_univ(_ver, _reg, _r[4], _umon, _uscore), + "universality": _approx_univ(_ver, _reg, _r[4], _umon, _uscore, _uyears, _uvalid, _uleave1), } _c.close() except Exception: @@ -628,11 +632,13 @@ def get_tracking(): @app.route("/api/research/strategies") def api_research_strategies(): """策略版本列表（含回测结果摘要，支持 period_tag 区间过滤）""" - def _approx_regime_universality(strategy, regime, trades, umon=None, uscore=None): + def _approx_regime_universality(strategy, regime, trades, umon=None, uscore=None, uyears=None, uvalid=None, uleave1=None): """温区级普适：优先用预计算真实值（trades entry_date 去重月份/温区总月份）， 缺失时退回旧近似（信号数÷3估算，2026-08-16 修复——原估算对集中信号虚高）""" if umon is not None and uscore is not None: - return {"months": umon, "score": uscore, "regime_total_months": 0} + return {"months": umon, "score": uscore, "years": uyears or 0, + "valid_years": uvalid or 0, "leave1_avg": uleave1, + "regime_total_months": 0} try: import sqlite3 as _sq6 _c6 = _sq6.connect(str(DATA_DIR / "mofin.db"), timeout=10)