fix: 普适指标真实化——trades月份去重/温区总月份(原信号÷3估算使s2_panic虚高100分)

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 FROM strategy_regime_perf_by_period "
+            "positions_taken, sharpe_ratio, profit_factor, universality_months, universality_score "
+            "FROM strategy_regime_perf_by_period "
             "WHERE period_tag=? ORDER BY strategy, market, regime",
             (_pt_use,)).fetchall():
             _ver, _mkt, _reg = _r[0], _r[1], _r[2]
@@ -71,6 +72,8 @@ def _compute_regime_winrates_cached(pt, _approx_univ):
             _pt_v = _r[12]
             _sh_v = _r[13]
             _pf_v = _r[14]
+            _umon = _r[15]
+            _uscore = _r[16]
             _regime_winrates.setdefault(_ver, {})[_reg] = {
                 "trades": _r[4], "win_rate": _r[5], "avg_pnl": _r[6],
                 "avg_hold_days": _r[7],
@@ -83,7 +86,7 @@ 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]),
+                "universality": _approx_univ(_ver, _reg, _r[4], _umon, _uscore),
             }
         _c.close()
     except Exception:
@@ -625,8 +628,11 @@ 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):
-        """温区级普适近似：温区内信号月份≈trades/温区月均笔数，温区总月份占比（避免逐笔遍历性能问题）"""
+    def _approx_regime_universality(strategy, regime, trades, umon=None, uscore=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}
         try:
             import sqlite3 as _sq6
             _c6 = _sq6.connect(str(DATA_DIR / "mofin.db"), timeout=10)