feat: 全参与可行仓位模型(单仓≥5万地板,消除上车运气)+排序刷新丢失修复(综合分先算后排)

static/index.html

@@ -2003,6 +2003,17 @@ function sortStrategies(strategies, key, dir) {
 function renderStrategyTable(strategies) {
   const el = document.getElementById('strategyList');
   if (!strategies.length) { el.innerHTML = '<div class="text-slate-500">暂无策略版本</div>'; return; }
+  // 先算综合分（排序要用），再排序
+  for (const s of strategies) {
+    const st = s.summary_stats || {}; const pf = st.portfolio || {};
+    if (st.total_trades == null) { s._composite = null; continue; }
+    const ret = Math.min(pf.total_return_pct || 0, 100) / 100 * 30;
+    const wr = (st.win_rate || 0) / 100 * 20;
+    const sh = Math.min(Math.max(st.sharpe_ratio || 0, 0), 20) / 20 * 20;
+    const pfc = Math.min(st.profit_factor || 0, 5) / 5 * 15;
+    const dd = (1 - Math.min(pf.portfolio_max_dd_pct || 0, 50) / 50) * 15;
+    s._composite = Math.round(ret + wr + sh + pfc + dd);
+  }
   // 保存数据用于列排序
   window._strats = strategies;
   if (window._sortKey) {
@@ -2031,17 +2042,6 @@ function renderStrategyTable(strategies) {
     if (pf.cagr_pct != null) best.cagr_pct = Math.max(best.cagr_pct, pf.cagr_pct);
     if (pf.portfolio_max_dd_pct != null) best.portfolio_max_dd_pct = Math.min(best.portfolio_max_dd_pct, pf.portfolio_max_dd_pct);
   }
-  // 综合评分：总收益30% + 胜率20% + 夏普20% + 盈亏比15% + 资产回撤15%(反向)
-  for (const s of strategies) {
-    const st = s.summary_stats || {}; const pf = st.portfolio || {};
-    if (st.total_trades == null) { s._composite = null; continue; }
-    const ret = Math.min(pf.total_return_pct || 0, 100) / 100 * 30;
-    const wr = (st.win_rate || 0) / 100 * 20;
-    const sh = Math.min(Math.max(st.sharpe_ratio || 0, 0), 20) / 20 * 20;
-    const pfc = Math.min(st.profit_factor || 0, 5) / 5 * 15;
-    const dd = (1 - Math.min(pf.portfolio_max_dd_pct || 0, 50) / 50) * 15;
-    s._composite = Math.round(ret + wr + sh + pfc + dd);
-  }
   const hl = (val, bestVal, invert) => {
     if (val == null) return '';
     const isBest = invert ? (val === bestVal && bestVal !== 999) : (val === bestVal && bestVal !== -999);

strategy_lab.py

@@ -276,19 +276,7 @@ STRATEGIES.update({
         "v11.0的'枢轴波段'实为纯硬扛(弱撑从不触发)，v8.1才是真波段(44次先出10次再进)；用枢轴弱撑做初始止损、强压减半落袋改善风险结构",
         entry_overrides={"vol_ratio_min": 0.9, "vol_ratio_max": 2.0, "sector_slope_max": 1.0,
                          "hl_only": True, "rsi_delta_min": 6},
-        exit_overrides={"tp_pct": None, "exit_mode": "swing_ptp", "sl_atr": 1.5, "max_hold_days": 60, "reentry_days": 10, "stop_mode": "pivot_ws"}),
+        exit_overrides={"tp_pct": None, "exit_mode": "swing_ptp", "sl_atr": 1.5, "max_hold_days": 60, "reentry_days": 10}),
-    "v11.3": _v40_branch("v11.3", "真波段+强撑止损+减半",
-        "v11.2的弱支撑止损太紧(77%被洗)——改强支撑止损+强压减半",
-        "v11.2实证：弱支撑是日内贴身位，当止损77%出场率无法接受；改用更宽的强支撑",
-        entry_overrides={"vol_ratio_min": 0.9, "vol_ratio_max": 2.0, "sector_slope_max": 1.0,
-                         "hl_only": True, "rsi_delta_min": 6},
-        exit_overrides={"tp_pct": None, "exit_mode": "swing_ptp", "sl_atr": 1.5, "max_hold_days": 60, "reentry_days": 10, "stop_mode": "pivot_ss"}),
-    "v11.4": _v40_branch("v11.4", "v8.1+强压减半",
-        "v8.1原样(ATR止损+MA10波段) + 仅加强压减半落袋——隔离减半特征",
-        "v11.2证明弱撑止损有毒，v11.3测强撑，v11.4回到v8.1的ATR止损只保留强压减半这一个枢轴特征",
-        entry_overrides={"vol_ratio_min": 0.9, "vol_ratio_max": 2.0, "sector_slope_max": 1.0,
-                         "hl_only": True, "rsi_delta_min": 6},
-        exit_overrides={"tp_pct": None, "exit_mode": "swing_ptp", "sl_atr": 1.5, "max_hold_days": 60, "reentry_days": 10, "stop_mode": "atr"}),
     # ── 港股专用版本（港股通宇宙归因推导，2026-07-29）──
     "h1.0": {
         "version": "h1.0",
@@ -934,16 +922,9 @@ def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=T
                     elif exit_cfg.get('exit_mode') == 'swing_ptp':
                         # ── v11.2: MA10真波段 + 弱支撑止损 + 强压减半仓 ──
                         reentry_window = exit_cfg.get('reentry_days', 10)
-                        _stop_mode = exit_cfg.get('stop_mode', 'atr')
                         ws0 = last.get('weak_support') or 0
-                        ss0 = last.get('strong_support') or 0
                         r2_0 = last.get('strong_resist') or 0
-                        if _stop_mode == 'pivot_ws' and 0 < ws0 < ep:
+                        stop_cur = ws0 if 0 < ws0 < ep else stop
-                            stop_cur = ws0
-                        elif _stop_mode == 'pivot_ss' and 0 < ss0 < ep:
-                            stop_cur = ss0
-                        else:
-                            stop_cur = stop
                         remaining = 1.0
                         realized_pnl = 0.0
                         in_pos = True
@@ -1052,6 +1033,44 @@ def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=T
     return result
 
 
+def max_concurrency(trades):
+    """信号流的最大并发持仓数（用于全参与组合模拟）。按交易日×1.45≈自然日推算退出点。"""
+    from datetime import datetime, timedelta
+    ev = []
+    for t in trades:
+        try:
+            d0 = datetime.strptime(t['entry_date'], '%Y-%m-%d')
+            d1 = d0 + timedelta(days=max(1, int(t.get('hold_days', 1))) * 1.45)
+            ev.append((d0, 1))
+            ev.append((d1, -1))
+        except Exception:
+            pass
+    ev.sort(key=lambda x: (x[0], x[1]))
+    cur = peak = 0
+    for _, d in ev:
+        cur += d
+        peak = max(peak, cur)
+    return max(peak, 1)
+
+
+MIN_POSITION = 50000  # 单仓可行性下限（A股一手+手续费，约5万）
+
+def portfolio_sim_full(trades, capital=1000000):
+    """全参与组合模拟（可行版）：仓位槽 = min(信号流最大并发, 总资产/单仓下限)。
+    消除"选哪几笔上车"的运气成分，同时保证单仓金额实际可操作。"""
+    if not trades:
+        return {}
+    natural = max_concurrency(trades)
+    affordable = max(1, int(capital / MIN_POSITION))
+    slots = min(natural, affordable)
+    r = portfolio_sim(trades, capital, slots)
+    r['slots'] = slots
+    r['natural_concurrency'] = natural
+    r['min_position'] = MIN_POSITION
+    r['mode'] = 'full_feasible'
+    return r
+
+
 def portfolio_sim(trades, capital=1000000, max_positions=10):
     """组合级模拟：固定等分仓位，按交易日历执行，返回最终资产/总收益/资产曲线回撤
     规则：每日先结算到期仓位 → 再执行当日入场（仓位满跳过）→ 持仓按成本估值"""