feat(portfolio): 组合回测结果增强: 基准对比曲线(对齐交易日+归一化)+回撤序列+扩展指标(波动/Sortino/Calmar/超额/Alpha/Beta), worker与API透传, 结果页净值对比+回撤图 [vps]

frontend/src/api/portfolio.ts

@@ -51,6 +51,24 @@ export interface PortfolioMetrics {
   win_rate_daily: number | null
   win_rate_trade: number | null
   trading_days: number | null
+  // 扩展指标(后端 _compute_extended_metrics,可能缺失)
+  annual_volatility?: number | null
+  sortino?: number | null
+  calmar?: number | null
+  benchmark_return?: number | null
+  excess_return?: number | null
+  alpha?: number | null
+  beta?: number | null
+}
+
+export interface BenchmarkPoint {
+  date: string
+  benchmark: number
+}
+
+export interface DrawdownPoint {
+  date: string
+  drawdown: number
 }
 
 export interface PortfolioBacktestResult {
@@ -60,6 +78,8 @@ export interface PortfolioBacktestResult {
   trades: PortfolioTrade[]
   equity_curve: EquityPoint[]
   metrics: PortfolioMetrics
+  benchmark_curve?: BenchmarkPoint[]
+  drawdown_curve?: DrawdownPoint[]
   raw_summary?: Record<string, unknown>
 }
 

frontend/src/views/backtest/PortfolioBacktest.vue

@@ -13,6 +13,8 @@ import {
   type StockPicked,
   type PortfolioTrade,
   type PortfolioMetrics,
+  type BenchmarkPoint,
+  type DrawdownPoint,
 } from '@/api/portfolio'
 
 // 两态:表单(发起) / 查看(route.query.task_id 历史结果)。任务跟踪统一在「历史任务」页(任务中心)。
@@ -21,6 +23,8 @@ const router = useRouter()
 const viewTaskId = computed(() => (route.query.task_id as string) || '')
 const result = ref<PortfolioBacktestResult | null>(null)
 const equityCurve = ref<EquityPoint[]>([])
+const benchmarkCurve = ref<BenchmarkPoint[]>([])
+const drawdownCurve = ref<DrawdownPoint[]>([])
 const stocks = ref<StockPicked[]>([])
 const trades = ref<PortfolioTrade[]>([])
 const metrics = ref<PortfolioMetrics | null>(null)
@@ -91,23 +95,57 @@ const { setOption: setEquityOption } = useChart(equityEl)
 function renderEquity(): void {
   if (!equityCurve.value.length) return
   const dates = equityCurve.value.map((p) => p.date)
-  const values = equityCurve.value.map((p) => p.equity)
+  // 策略净值归一化(首日=1),与基准同轴对比
+  const base = equityCurve.value[0]?.equity || 1
+  const values = equityCurve.value.map((p) => p.equity / base)
+  const hasBench = benchmarkCurve.value.length === equityCurve.value.length
+  const series: EChartsCoreOption['series'] = [
+    {
+      type: 'line', name: '策略净值', smooth: true, showSymbol: false,
+      lineStyle: { color: '#c23531', width: 1.6 },
+      areaStyle: { color: '#c23531', opacity: 0.12 },
+      data: values.map((v) => Number(v.toFixed(4))),
+    },
+  ]
+  if (hasBench) {
+    series.push({
+      type: 'line', name: '基准净值', smooth: true, showSymbol: false,
+      lineStyle: { color: '#4b7bce', width: 1.4, type: 'dashed' },
+      data: benchmarkCurve.value.map((p) => Number((p.benchmark ?? 1).toFixed(4))),
+    })
+  }
   const option: EChartsCoreOption = {
-    title: darkTitle('净值曲线'),
-    tooltip: darkTooltip(),
+    title: darkTitle(hasBench ? '净值对比(归一化)' : '净值曲线'),
+    tooltip: { ...darkTooltip(), trigger: 'axis' },
+    legend: { ...darkAxis(), top: 4, right: 8, textStyle: { color: 'var(--text-3, #aaa)' } },
     grid: darkGrid(),
     xAxis: { type: 'category', data: dates, ...darkAxis() },
-    yAxis: { type: 'value', scale: true, name: '净值(元)', ...darkAxis() },
+    yAxis: { type: 'value', scale: true, name: '净值', ...darkAxis() },
+    series,
+  }
+  setEquityOption(option)
+}
+
+const ddEl = ref<HTMLDivElement>()
+const { setOption: setDdOption } = useChart(ddEl)
+function renderDrawdown(): void {
+  if (!drawdownCurve.value.length) return
+  const option: EChartsCoreOption = {
+    title: darkTitle('回撤(%)'),
+    tooltip: { ...darkTooltip(), trigger: 'axis' },
+    grid: darkGrid(),
+    xAxis: { type: 'category', data: drawdownCurve.value.map((p) => p.date), ...darkAxis() },
+    yAxis: { type: 'value', name: '回撤%', ...darkAxis() },
     series: [
       {
-        type: 'line', name: '策略净值', smooth: true, showSymbol: false,
-        lineStyle: { color: '#c23531', width: 1.6 },
-        areaStyle: { color: '#c23531', opacity: 0.12 },
-        data: values,
+        type: 'line', name: '回撤', smooth: true, showSymbol: false,
+        lineStyle: { color: '#6f42c1', width: 1.4 },
+        areaStyle: { color: '#6f42c1', opacity: 0.18 },
+        data: drawdownCurve.value.map((p) => Number((p.drawdown ?? 0).toFixed(2))),
       },
     ],
   }
-  setEquityOption(option)
+  setDdOption(option)
 }
 
 async function loadResult(tid: string): Promise<void> {
@@ -115,12 +153,15 @@ async function loadResult(tid: string): Promise<void> {
     const r = await getPortfolioResult(tid)
     result.value = r
     equityCurve.value = r.equity_curve || []
+    benchmarkCurve.value = r.benchmark_curve || []
+    drawdownCurve.value = r.drawdown_curve || []
     stocks.value = r.stocks_selected || []
     trades.value = r.trades || []
     metrics.value = r.metrics || null
     period.value = r.period || null
     await nextTick()
     renderEquity()
+    renderDrawdown()
   } catch {
     ElMessage.error('加载结果失败')
   }
@@ -154,6 +195,7 @@ onMounted(() => {
   if (viewTaskId.value) loadResult(viewTaskId.value)
 })
 watch(equityCurve, renderEquity, { deep: true, flush: 'post' })
+watch(drawdownCurve, renderDrawdown, { deep: true, flush: 'post' })
 
 function fmtPct(v: number | null | undefined): string {
   if (v === null || v === undefined || !Number.isFinite(v)) return '-'
@@ -269,14 +311,25 @@ function fmtNum(v: number | null | undefined, digits = 2): string {
           <div class="metric-cell"><div class="metric-label">夏普比率</div><div class="metric-value">{{ fmtNum(metrics?.sharpe) }}</div></div>
           <div class="metric-cell"><div class="metric-label">日胜率</div><div class="metric-value">{{ fmtPct(metrics?.win_rate_daily) }}</div></div>
           <div class="metric-cell"><div class="metric-label">交易胜率</div><div class="metric-value">{{ fmtPct(metrics?.win_rate_trade) }}</div></div>
+          <div class="metric-cell"><div class="metric-label">年化波动</div><div class="metric-value">{{ fmtPct(metrics?.annual_volatility) }}</div></div>
+          <div class="metric-cell"><div class="metric-label">Sortino</div><div class="metric-value">{{ fmtNum(metrics?.sortino) }}</div></div>
+          <div class="metric-cell"><div class="metric-label">Calmar</div><div class="metric-value">{{ fmtNum(metrics?.calmar) }}</div></div>
+          <div class="metric-cell"><div class="metric-label">基准收益</div><div class="metric-value" :class="(metrics?.benchmark_return ?? 0) >= 0 ? 'up' : 'down'">{{ fmtPct(metrics?.benchmark_return) }}</div></div>
+          <div class="metric-cell"><div class="metric-label">超额收益</div><div class="metric-value" :class="(metrics?.excess_return ?? 0) >= 0 ? 'up' : 'down'">{{ fmtPct(metrics?.excess_return) }}</div></div>
+          <div class="metric-cell"><div class="metric-label">Alpha / Beta</div><div class="metric-value">{{ fmtNum(metrics?.alpha, 2) }} / {{ fmtNum(metrics?.beta, 2) }}</div></div>
         </div>
       </el-card>
 
       <el-card v-if="result" class="blk" shadow="never">
-        <template #header><span class="section-title">净值曲线</span></template>
+        <template #header><span class="section-title">净值对比</span></template>
         <div ref="equityEl" class="chart-box" />
       </el-card>
 
+      <el-card v-if="result && drawdownCurve.length" class="blk" shadow="never">
+        <template #header><span class="section-title">回撤曲线</span></template>
+        <div ref="ddEl" class="chart-box dd-box" />
+      </el-card>
+
       <el-card v-if="result" class="blk" shadow="never">
         <template #header><span class="section-title">选股名单(末日持仓)</span></template>
         <el-table :data="stocks" stripe size="small" empty-text="无持仓数据">
@@ -313,6 +366,7 @@ function fmtNum(v: number | null | undefined, digits = 2): string {
 .submit-bar { padding: 4px 0 8px; }
 .result-actions { display: flex; gap: 8px; padding: 0 0 4px; }
 .chart-box { width: 100%; height: 360px; }
+.dd-box { height: 240px; }
 .metric-row { display: grid; grid-template-columns: repeat(auto-fit, minmax(140px, 1fr)); gap: 12px; }
 .metric-cell { background: var(--bg-hover); border: 1px solid var(--border-2); border-radius: 6px; padding: 12px 14px; }
 .metric-label { font-size: 12px; color: var(--text-3); margin-bottom: 6px; }

sanguo_api/routes_portfolio.py

@@ -104,6 +104,8 @@ def get_portfolio_result(task_id: str):
         "equity_curve": _df_to_records(r.equity_curve),
         "trades": _df_to_records(r.trades),
         "stocks_selected": stocks_selected,
+        "benchmark_curve": stats.get("benchmark_curve", []),
+        "drawdown_curve": stats.get("drawdown_curve", []),
     }
 
 

sanguo_orchestrator/portfolio_worker.py

@@ -116,6 +116,8 @@ def run_portfolio_task(spec: dict) -> Any:
             "metrics": data.get("metrics", {}),
             "raw_summary": data.get("raw_summary", {}),
             "stocks_selected": data.get("stocks_selected", []),
+            "benchmark_curve": data.get("benchmark_curve", []),
+            "drawdown_curve": data.get("drawdown_curve", []),
             "period": period,
         },
         equity_curve=pd.DataFrame(equity_list) if equity_list else None,

sanguo_portfolio/runner_backtest.py

@@ -288,6 +288,14 @@ def run_backtest(args: argparse.Namespace) -> Dict[str, Any]:
     result = engine.run()
     print("[runner] RUN_DONE type=%s" % type(result).__name__, flush=True)
 
+    # 引擎不把基准序列放进 results——这里带出(引擎已按区间加载 benchmark_data)
+    try:
+        bd = getattr(engine, "benchmark_data", None)
+        if bd is not None and len(bd) and isinstance(result, dict):
+            result["benchmark_curve"] = _extract_benchmark_curve(bd)
+    except Exception as exc:
+        logger.warning("提取基准曲线失败: %s", exc)
+
     # 输出结果摘要到 markdown(JSON 模式时 result_file="" 跳过)
     if getattr(args, "result_file", ""):
         _write_result_md(result, args.result_file, args)
@@ -380,6 +388,11 @@ def run_backtest_json(params: Dict[str, Any]) -> Dict[str, Any]:
     # 净值曲线:daily_records 是 DataFrame,index=date,列含 total_value
     equity_curve = _extract_equity_curve(raw.get("daily_records"))
 
+    # 基准曲线(对齐策略交易日、归一化) + 回撤序列 + 扩展指标
+    benchmark_curve = _align_benchmark(raw.get("benchmark_curve"), equity_curve)
+    drawdown_curve = _extract_drawdown(equity_curve)
+    metrics.update(_compute_extended_metrics(equity_curve, benchmark_curve))
+
     # 选股(末日持仓):daily_positions 最后一日
     stocks_selected = _extract_last_positions(raw.get("daily_positions"))
 
@@ -397,6 +410,8 @@ def run_backtest_json(params: Dict[str, Any]) -> Dict[str, Any]:
         "stocks_selected": stocks_selected,
         "trades": trades,
         "equity_curve": equity_curve,
+        "benchmark_curve": benchmark_curve,
+        "drawdown_curve": drawdown_curve,
         "metrics": metrics,
         "raw_summary": summary,
     }
@@ -454,6 +469,128 @@ def _extract_equity_curve(daily_records: Any) -> list[Dict[str, Any]]:
     return out
 
 
+def _extract_benchmark_curve(bd: Any) -> list[Dict[str, Any]]:
+    """engine.benchmark_data → [{date, close}]。jq 风格 DataFrame(index=date,含 close)或 Series。"""
+    out: list[Dict[str, Any]] = []
+    try:
+        import pandas as pd  # type: ignore
+        if isinstance(bd, pd.Series):
+            df = bd.to_frame(name="close").reset_index()
+            df.columns = ["date", "close"]
+        elif isinstance(bd, pd.DataFrame) and "close" in bd.columns:
+            df = bd[["close"]].reset_index()
+            df.columns = ["date", "close"]
+        else:
+            return out
+        for _, row in df.iterrows():
+            d = row["date"]
+            close = row["close"]
+            if close is None or str(close) == "nan":
+                continue
+            out.append({
+                "date": getattr(d, "strftime", lambda f: str(d))("%Y-%m-%d"),
+                "close": float(close),
+            })
+    except Exception as exc:
+        logger.warning("解析 benchmark_curve 失败: %s", exc)
+    return out
+
+
+def _align_benchmark(benchmark: Any, equity_curve: list[Dict[str, Any]]) -> list[Dict[str, Any]]:
+    """基准收盘对齐策略交易日(前向填充)并归一化为净值 1.0 起。
+
+    基准日历(指数)与策略交易日历基本一致;不一致时用最近一日基准价填充,
+    首日之前无基准则从首个可得日起以该日为 1.0。
+    """
+    if not benchmark or not equity_curve:
+        return []
+    close_by_date: Dict[str, float] = {}
+    for p in benchmark:
+        try:
+            close_by_date[p["date"]] = float(p["close"])
+        except (KeyError, TypeError, ValueError):
+            continue
+    out: list[Dict[str, Any]] = []
+    last_close: float | None = None
+    base: float | None = None
+    for point in equity_curve:
+        d = point["date"]
+        c = close_by_date.get(d)
+        if c is None or c <= 0:
+            c = last_close
+        else:
+            last_close = c
+        if c is None or c <= 0:
+            out.append({"date": d, "benchmark": 1.0})  # 基准缺头几天:先垫 1.0
+            continue
+        if base is None:
+            base = c
+        out.append({"date": d, "benchmark": c / base})
+    return out
+
+
+def _extract_drawdown(equity_curve: list[Dict[str, Any]]) -> list[Dict[str, Any]]:
+    """净值 → 回撤序列(%,负值):dd = equity/历史峰值 - 1。"""
+    out: list[Dict[str, Any]] = []
+    peak: float | None = None
+    for point in equity_curve:
+        v = float(point.get("equity", 0) or 0)
+        if peak is None or v > peak:
+            peak = v
+        dd = (v / peak - 1) * 100 if peak else 0.0
+        out.append({"date": point["date"], "drawdown": dd})
+    return out
+
+
+def _compute_extended_metrics(
+    equity_curve: list[Dict[str, Any]],
+    benchmark_curve: list[Dict[str, Any]],
+) -> Dict[str, float]:
+    """从净值/基准序列算扩展指标(纯 python,不引 numpy)。
+
+    惯例与 _extract_metrics 一致:比率类原值、百分比类用百分数字面值。
+    """
+    out: Dict[str, float] = {}
+    vals = [float(p["equity"]) for p in equity_curve]
+    if len(vals) < 2:
+        return out
+    rets = [vals[i] / vals[i - 1] - 1 for i in range(1, len(vals)) if vals[i - 1] > 0]
+    n = len(rets)
+    if n == 0:
+        return out
+    mean = sum(rets) / n
+    var = sum((r - mean) ** 2 for r in rets) / max(n - 1, 1)
+    vol = (var ** 0.5) * (252 ** 0.5)
+    out["annual_volatility"] = vol * 100
+    downside = [r for r in rets if r < 0]
+    if downside:
+        dstd = (sum(r * r for r in downside) / len(downside)) ** 0.5
+        if dstd > 0:
+            out["sortino"] = (mean / dstd) * (252 ** 0.5)
+    days = len(equity_curve)
+    ann_s = (vals[-1] / vals[0]) ** (252 / days) - 1 if vals[0] > 0 and vals[-1] > 0 else None
+    total_dd = min(p["drawdown"] for p in _extract_drawdown(equity_curve)) if days else None
+    if total_dd is not None and total_dd < 0 and ann_s is not None:
+        out["calmar"] = ann_s / abs(total_dd / 100)
+
+    bench = [float(p.get("benchmark", 1.0) or 1.0) for p in benchmark_curve] if benchmark_curve else []
+    if len(bench) == days and bench[0] > 0:
+        brets = [bench[i] / bench[i - 1] - 1 for i in range(1, len(bench)) if bench[i - 1] > 0]
+        if brets:
+            out["benchmark_return"] = (bench[-1] - 1) * 100
+            out["excess_return"] = (vals[-1] / vals[0] - 1) * 100 - out["benchmark_return"]
+            bmean = sum(brets) / len(brets)
+            bvar = sum((r - bmean) ** 2 for r in brets) / max(len(brets) - 1, 1)
+            if bvar > 0:
+                cov = sum((rets[i] - mean) * (brets[i] - bmean) for i in range(min(n, len(brets)))) / max(min(n, len(brets)) - 1, 1)
+                beta = cov / bvar
+                out["beta"] = beta
+                ann_b = (bench[-1] / bench[0]) ** (252 / days) - 1
+                if ann_s is not None:
+                    out["alpha"] = (ann_s - beta * ann_b) * 100
+    return out
+
+
 def _extract_last_positions(daily_positions: Any) -> list[Dict[str, Any]]:
     """daily_positions: DataFrame,列含 date/code/amount/avg_cost/price/value。
     取最后一日的非零持仓作为选股名单。"""

tests/portfolio/test_runner_curves.py

@@ -0,0 +1,66 @@
+"""runner_backtest 基准对齐/回撤/扩展指标 纯函数单测(B1)。"""
+import pytest
+
+from sanguo_portfolio.runner_backtest import (
+    _align_benchmark,
+    _compute_extended_metrics,
+    _extract_drawdown,
+)
+
+
+EQ = [
+    {"date": "2024-01-01", "equity": 100.0},
+    {"date": "2024-01-02", "equity": 110.0},
+    {"date": "2024-01-03", "equity": 99.0},
+    {"date": "2024-01-04", "equity": 120.0},
+    {"date": "2024-01-05", "equity": 90.0},
+]
+BD = [
+    {"date": "2024-01-01", "close": 200.0},
+    {"date": "2024-01-02", "close": 220.0},
+    {"date": "2024-01-03", "close": 210.0},
+    {"date": "2024-01-04", "close": 260.0},
+    {"date": "2024-01-05", "close": 208.0},
+]
+
+
+def test_drawdown_series():
+    dd = _extract_drawdown(EQ)
+    vals = [p["drawdown"] for p in dd]
+    assert vals[0] == 0.0 and vals[1] == 0.0 and vals[3] == 0.0
+    assert vals[2] == pytest.approx(-10.0)
+    assert vals[4] == pytest.approx(-25.0)
+
+
+def test_align_benchmark_normalizes_and_matches_length():
+    bench = _align_benchmark(BD, EQ)
+    assert len(bench) == len(EQ)
+    assert bench[0]["benchmark"] == pytest.approx(1.0)
+    assert bench[4]["benchmark"] == pytest.approx(1.04)
+
+
+def test_align_benchmark_ffill_missing_dates():
+    bench = _align_benchmark(BD[:2], EQ)
+    # 后 3 天无基准数据 → 前向填充 1.1
+    assert [p["benchmark"] for p in bench] == [1.0, 1.1, 1.1, 1.1, 1.1]
+
+
+def test_align_benchmark_empty_inputs():
+    assert _align_benchmark([], EQ) == []
+    assert _align_benchmark(BD, []) == []
+
+
+def test_extended_metrics_values():
+    bench = _align_benchmark(BD, EQ)
+    m = _compute_extended_metrics(EQ, bench)
+    assert m["benchmark_return"] == pytest.approx(4.0)
+    assert m["excess_return"] == pytest.approx(-14.0)  # -10% 策略 - +4% 基准
+    assert m["beta"] == pytest.approx(1.0874, abs=1e-3)
+    assert "annual_volatility" in m
+    assert "sortino" in m
+    assert "calmar" in m
+
+
+def test_extended_metrics_short_series():
+    assert _compute_extended_metrics([{"date": "d", "equity": 1.0}], []) == {}
+    assert _compute_extended_metrics([], []) == {}