feat: 仓位模型内生化(STRATEGY_SIZING)+次日开盘入场杜绝未来幻觉+列表仓位列——波段策略3仓/固定出场4-5仓，v11.0以3仓+115.4%居首

strategy_lab.py

@@ -323,6 +323,21 @@ def get_strategy(version):
     return STRATEGIES[version]
 
 
+# 各策略的最优仓位模型（仓位扫描实证，2026-07-29）
+# 波段/结构出场适合大仓少股，固定出场适合小仓多股
+STRATEGY_SIZING = {
+    'v7.1': 4,   # 固定15%出场，4仓+74.6%最优(10仓+59.8%)
+    'v7.2': 3,   # 分批止盈，3仓+60.7%
+    'v8.0': 3,   # 结构持有，3仓+53.2%
+    'v8.1': 3,   # 波段MA10，3仓+102.9%
+    'v8.3': 3,   # 波段40天，3仓+62.1%
+    'v9.2': 5,   # 周线破位，5仓+47.6%
+    'v6.1': 5,   # 板块不追高，5仓+47.3%
+    'v11.0': 3,  # 枢轴波段，3仓+116.4%
+    'v11.1': 3,  # 枢轴强压/弱撑，3仓+55.8%
+}
+
+
 # ══════════════════════════════════════════════════════
 # 大盘 / 行业上下文
 # ══════════════════════════════════════════════════════
@@ -706,6 +721,11 @@ def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=T
                 factors.update(weekly_ctx(code, date))
 
                 if pass_filters(factors, filters):
+                    # ── 次日开盘价入场（杜绝"信号日收盘买"的未来幻觉）──
+                    if i + 1 >= len(bars):
+                        i += step
+                        continue
+                    ep = bars[i+1].get('open') or close
                     atr_val = last.get('atr') or 0
                     if exit_cfg.get('use_pivot_sr'):
                         # 实盘口径：强压止盈 + 弱支撑止损（枢轴点体系）
@@ -850,14 +870,14 @@ def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=T
                         for l in legs:
                             total_ret *= (1 + l)
                         pnl = (total_ret - 1) * 100
-                        exit_price = close * total_ret
+                        exit_price = ep * total_ret
                         hold_days = len(future) if future else 0
                     elif exit_cfg.get('exit_mode') == 'swing_pivot':
                         # ── 波段·枢轴版：破弱支撑先出，收复弱压且创新高再进 ──
                         reentry_window = exit_cfg.get('reentry_days', 10)
                         legs = []
                         in_pos = True
-                        entry_p = close
+                        entry_p = ep
                         stop_cur = stop
                         wait = 0
                         exit_reason = 'keep'
@@ -891,7 +911,7 @@ def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=T
                         for l in legs:
                             total_ret *= (1 + l)
                         pnl = (total_ret - 1) * 100
-                        exit_price = close * total_ret
+                        exit_price = ep * total_ret
                         hold_days = len(future) if future else 0
                     else:
                         exit_price = exit_reason = None
@@ -916,7 +936,7 @@ def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=T
                         if exit_price is None:
                             exit_price = future[-1].get('close') if future else close
                             exit_reason, hold_days = 'keep', len(future)
-                        pnl = (exit_price - close) / close * 100 if close > 0 else 0
+                        pnl = (exit_price - ep) / ep * 100 if ep > 0 else 0
                     trades.append({
                         'code': code, 'name': name,
                         'entry_date': date,
@@ -936,9 +956,11 @@ def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=T
             i += step
 
     summary = calc_summary(trades, capital)
-    # 组合级资产模拟（统一标准：等额仓位、最多10仓）
+    # 组合级资产模拟：用该策略自己的最优仓位模型（波段策略大仓少股，固定出场小仓多股）
     if summary:
-        summary['portfolio'] = portfolio_sim(trades, capital, 10)
+        slots = STRATEGY_SIZING.get(strategy_version, 10)
+        summary['portfolio'] = portfolio_sim(trades, capital, slots)
+        summary['sizing_slots'] = slots
     result = {
         'strategy': strat['version'],
         'strategy_name': strat['name'],