feat: 参数寻优新增买入持有基准对比 — 两个寻优接口返回同口径 buy_hold（run_buy_hold_benchmark），全局最佳/最优结果总收益旁展示 [买入持有：xx.xx%]，涨红跌绿

src/easy_tdx/web/backtest_schemas.py

@@ -360,6 +360,9 @@ class OptimizeAllResult(BaseModel):
     best: OptimizeAllRankEntry | None = None
     per_strategy: dict[str, OptimizeAllRankEntry] = {}  # 策略名 → 最优点
     total_grid_points: int = 0  # 所有策略网格点合计
+    # 买入持有基准（同区间/同费率/同资金；含 total_return 等 6 项指标），
+    # 供前端在全局最佳旁直观对比「策略 vs 买入不动」。老版本结果可能缺省。
+    buy_hold: dict[str, Any] | None = None
 
 
 # ── 已保存策略（策略库 / StrategyLibrary）───────────────────────────────────────

src/easy_tdx/web/routers/backtest.py

@@ -1184,6 +1184,7 @@ def _run_multi_strategy_evaluate(
 
 def _run_optimize(df: pd.DataFrame, req: OptimizeBacktestRequest) -> dict[str, Any]:
     """执行参数网格寻优并返回清洗后的结果字典（后台线程内调用）。"""
+    from easy_tdx.backtest.benchmark import run_buy_hold_benchmark
     from easy_tdx.backtest.optimizer import ParamGridOptimizer
 
     optimizer = ParamGridOptimizer(
@@ -1197,7 +1198,17 @@ def _run_optimize(df: pd.DataFrame, req: OptimizeBacktestRequest) -> dict[str, A
         workers=req.workers,
     )
     result = optimizer.run()
-    return result.to_dict()
+    out = result.to_dict()
+    # 买入持有基准（同区间/同费率/同资金，与一条龙评估同口径），
+    # 供前端在最优结果旁直观对比「策略 vs 买入不动」。
+    out["buy_hold"] = run_buy_hold_benchmark(
+        df,
+        cash=req.cash,
+        commission=req.commission,
+        slippage=req.slippage,
+        execution=req.execution,
+    )
+    return out
 
 
 def _optimize_one_strategy(
@@ -1263,6 +1274,7 @@ def _run_optimize_all(df: pd.DataFrame, req: OptimizeAllBacktestRequest) -> dict
     ``workers`` 为 0 或 1 时串行。进程池在函数内 ``with`` 创建/销毁，对前端
     轮询与 task_runner 透明。
     """
+    from easy_tdx.backtest.benchmark import run_buy_hold_benchmark
     from easy_tdx.backtest.strategies import get_registry
     from easy_tdx.backtest.strategies.presets import STRATEGY_PRESETS
 
@@ -1338,6 +1350,14 @@ def _run_optimize_all(df: pd.DataFrame, req: OptimizeAllBacktestRequest) -> dict
         best=best,
         per_strategy=per_strategy,
         total_grid_points=total_grid,
+        # 买入持有基准（同区间/同费率/同资金），供前端在全局最佳旁直观对比
+        buy_hold=run_buy_hold_benchmark(
+            df,
+            cash=req.cash,
+            commission=req.commission,
+            slippage=req.slippage,
+            execution=req.execution,
+        ),
     )
     return result_obj.model_dump()
 

web-ui/src/types.ts

@@ -257,12 +257,24 @@ export interface OptimizeHeatmap {
   data: Array<[number, number, number | null]>
 }
 
+/** 买入持有基准（同区间/同费率，首根买入持有到末根，与一条龙评估同口径） */
+export interface BuyHoldBenchmark {
+  total_return: number
+  annual_return: number
+  max_drawdown: number
+  sharpe: number
+  calmar: number
+  volatility: number
+}
+
 export interface OptimizeResult {
   strategy: string
   param_names: string[]
   results: GridPointResult[]
   best: GridPointResult | null
   heatmap: OptimizeHeatmap | null
+  /** 买入持有基准，供直观对比「策略 vs 买入不动」（老版本后端结果可能缺省） */
+  buy_hold?: BuyHoldBenchmark | null
 }
 
 // ── 一键寻优所有策略（Phase 6） ──────────────────────────────────────────────
@@ -299,6 +311,8 @@ export interface OptimizeAllResult {
   best: OptimizeAllRankEntry | null
   per_strategy: Record<string, OptimizeAllRankEntry>
   total_grid_points: number
+  /** 买入持有基准，供直观对比「策略 vs 买入不动」（老版本后端结果可能缺省） */
+  buy_hold?: BuyHoldBenchmark | null
 }
 
 // ── 错误响应（后端 ApiErrorResponse） ─────────────────────────────────────────

web-ui/src/views/OptimizeView.vue

@@ -196,6 +196,10 @@ function onViewAll(strategyName: string, params: Record<string, number | string>
 function pct(v: number | null | undefined): string {
   return v !== null && v !== undefined && Number.isFinite(v) ? `${(v * 100).toFixed(2)}%` : '-'
 }
+/** 买入持有的盈亏配色（A 股习惯：涨红跌绿；非数值不加色） */
+function bhClass(v: number | null | undefined): string {
+  return v !== null && v !== undefined && Number.isFinite(v) ? (v >= 0 ? 'pos' : 'neg') : ''
+}
 function num(v: number | null | undefined, d = 2): string {
   return v !== null && v !== undefined && Number.isFinite(v) ? v.toFixed(d) : '-'
 }
@@ -316,6 +320,11 @@ const rankingGrades = computed<GradeResult[]>(() =>
             <span class="best-return pos">
               {{ (store.optimizeResult.best.total_return! * 100).toFixed(2) }}%
             </span>
+            <span v-if="store.optimizeResult.buy_hold" class="best-bh">
+              [买入持有：<span :class="bhClass(store.optimizeResult.buy_hold.total_return)">{{
+                pct(store.optimizeResult.buy_hold.total_return)
+              }}</span>]
+            </span>
             <span class="best-meta">
               夏普 {{ store.optimizeResult.best.sharpe?.toFixed(2) }} · 回撤
               {{ (store.optimizeResult.best.max_drawdown! * 100).toFixed(2) }}%
@@ -351,6 +360,11 @@ const rankingGrades = computed<GradeResult[]>(() =>
             <span class="best-return pos">
               {{ (store.optimizeAllResult.best.total_return! * 100).toFixed(2) }}%
             </span>
+            <span v-if="store.optimizeAllResult.buy_hold" class="best-bh">
+              [买入持有：<span :class="bhClass(store.optimizeAllResult.buy_hold.total_return)">{{
+                pct(store.optimizeAllResult.buy_hold.total_return)
+              }}</span>]
+            </span>
             <span class="best-meta">
               夏普 {{ store.optimizeAllResult.best.sharpe?.toFixed(2) }} · 回撤
               {{ (store.optimizeAllResult.best.max_drawdown! * 100).toFixed(2) }}% · 胜率
@@ -542,6 +556,12 @@ const rankingGrades = computed<GradeResult[]>(() =>
   font-weight: 700;
   font-family: var(--font-mono);
 }
+/* 买入持有对比项：标签/括号弱化不抢焦点，百分比按盈亏走 .pos/.neg 红绿 */
+.best-bh {
+  color: var(--text-dim);
+  font-size: 13px;
+  font-family: var(--font-mono);
+}
 .best-meta {
   color: var(--text-dim);
   font-size: 12px;