ggx/templates/reports/sensitivity.html

{% extends "base.html" %}

{% block content %}

{% if analysis %}
<div class="callout {{ 'ok' if analysis.robust else 'fail' }}">
  <strong>敏感性结论：{{ analysis.verdict }}</strong><br>
  CAGR 跨度 <strong>{{ analysis.cagr_range_s }}</strong>
  （{{ analysis.cagr_min_s }} ~ {{ analysis.cagr_max_s }}），
  相邻档最大跳变 <strong>{{ analysis.max_jump_s }}</strong>，
  平滑度 <strong>{{ analysis.smoothness_s }}</strong>
  {% if analysis.spikes %}，检出 <strong>{{ analysis.spikes|length }}</strong> 处尖峰{% endif %}。
</div>
{% else %}
<div class="callout warn">扫描点数不足，无法评估参数敏感性。</div>
{% endif %}

<div class="kpi-grid">
  <div class="kpi"><div class="label">参数组合数</div><div class="value">{{ point_count }}</div>
    <div class="note">扫描网格 {{ sweep }}</div></div>
  <div class="kpi"><div class="label">CAGR 区间</div>
    <div class="value" style="font-size:17px">{{ analysis.cagr_min_s }} ~ {{ analysis.cagr_max_s }}</div>
    <div class="note">跨度 {{ analysis.cagr_range_s }}</div></div>
  <div class="kpi"><div class="label">平滑度</div>
    <div class="value {{ 'gain' if analysis.robust else 'loss' }}">{{ analysis.smoothness_s }}</div>
    <div class="note">越接近 1 越平滑（≥0.6 且无尖峰视为稳健）</div></div>
  <div class="kpi"><div class="label">尖峰数</div>
    <div class="value {{ 'loss' if analysis.spikes else 'gain' }}">{{ analysis.spikes|length if analysis.spikes else 0 }}</div>
    <div class="note">某点显著高于左右邻居即为尖峰</div></div>
</div>

<div class="callout">
  <strong>如何判读（plan.md §27）</strong>：若买入分位 P75→13%、P80→13.5%、P85→13.2%，
  说明策略对参数<strong>不敏感</strong>，结论相对可信；
  若 P79→18%、P80→20%、P81→8%，说明存在<strong>尖峰</strong>，很可能过拟合。
  平滑度指标把这一判断从主观观察变成了可量化的数字。
</div>

{% if point_count > 1 %}
<div class="card">
  <h2>绩效随参数的变化</h2>
  <div id="chart-sweep" class="chart"></div>
</div>
{% endif %}

<div class="card">
  <h2>扫描明细</h2>
  <div class="table-scroll">
  <table class="data">
    <thead>
      <tr>
        {% for k in sweep_keys %}<th>{{ k }}</th>{% endfor %}
        <th>CAGR</th><th>总收益</th><th>最大回撤</th>
        <th>Sharpe</th><th>Calmar</th><th>Sortino</th><th>成交</th><th>年换手</th>
      </tr>
    </thead>
    <tbody>
      {% for p in points %}
      <tr class="{{ 'spike-row' if p.is_spike else '' }}">
        {% for v in p.key_values %}<td class="mono">{{ v }}</td>{% endfor %}
        <td class="num {{ 'gain' if p.cagr_positive else 'loss' }}"><strong>{{ p.cagr }}</strong></td>
        <td class="num">{{ p.total_return }}</td>
        <td class="num loss">{{ p.max_drawdown }}</td>
        <td class="num">{{ p.sharpe }}</td>
        <td class="num">{{ p.calmar }}</td>
        <td class="num">{{ p.sortino }}</td>
        <td class="num">{{ p.trade_count }}</td>
        <td class="num">{{ p.turnover }}</td>
      </tr>
      {% endfor %}
    </tbody>
  </table>
  </div>
  {% if analysis.spikes %}
  <div class="callout fail">
    <strong>检出的尖峰：</strong>
    <ul style="margin:4px 0 0 18px">
      {% for s in analysis.spikes %}
      <li>第 {{ s.index }} 点 CAGR {{ s.cagr_s }}，高于左右邻居均值 {{ s.excess_s }}
        （邻居均值 {{ s.neighbour_mean_s }}）</li>
      {% endfor %}
    </ul>
    这意味着收益对参数的微小变化异常敏感，是过拟合的典型信号。
  </div>
  {% endif %}
</div>

<div class="card">
  <h2>性能与参数的关系</h2>
  <div class="chart-grid">
    <div><h3>年化收益 CAGR</h3><div id="chart-cagr" class="chart short"></div></div>
    <div><h3>最大回撤</h3><div id="chart-dd" class="chart short"></div></div>
    <div><h3>Sharpe</h3><div id="chart-sharpe" class="chart short"></div></div>
    <div><h3>成交笔数（换手）</h3><div id="chart-trades" class="chart short"></div></div>
  </div>
</div>

<div class="card">
  <h2>可复现性</h2>
  <div class="table-scroll">
  <table class="data">
    <thead><tr><th style="text-align:left">项</th><th style="text-align:left">值</th></tr></thead>
    <tbody>
      <tr><td style="text-align:left">敏感性 ID</td><td style="text-align:left" class="mono">{{ sens_id }}</td></tr>
      <tr><td style="text-align:left">基准策略</td><td style="text-align:left" class="mono">{{ strategy_id }} v{{ strategy_version }}</td></tr>
      <tr><td style="text-align:left">扫描网格</td><td style="text-align:left" class="mono">{{ sweep }}</td></tr>
      <tr><td style="text-align:left">区间</td><td style="text-align:left">{{ period }}</td></tr>
      <tr><td style="text-align:left">数据版本</td><td style="text-align:left" class="mono">{{ data_version }}</td></tr>
    </tbody>
  </table>
  </div>
  <div class="callout warn">
    <strong>注意</strong>：敏感性结论依赖扫描区间长度与股票池规模。
    若股票池仅数十只、区间仅数年，即使平滑度低也可能只是样本噪声 ——
    应结合 Walk-forward 的样本外结果共同判断。
  </div>
</div>

{% endblock %}

{% block scripts %}
<script>
(function () {
  if (typeof echarts === 'undefined') return;
  var C = {{ chart_colors | json }};
  var P = {{ param_values | json }};

  function lineChart(id, values, name, formatter, color, markSpikes) {
    var el = document.getElementById(id);
    if (!el) return;
    var c = echarts.init(el);
    c.setOption({
      color: [color || C[0]],
      grid: { left: 62, right: 20, top: 28, bottom: 40 },
      tooltip: { trigger: 'axis', valueFormatter: formatter },
      xAxis: { type: 'category', data: P, name: '{{ sweep_keys[0] if sweep_keys else "参数" }}',
               axisLabel: { color: '#64748B' } },
      yAxis: { type: 'value', axisLabel: { color: '#64748B', formatter: formatter },
               splitLine: { lineStyle: { color: '#E9EEF6' } } },
      series: [{
        name: name, type: 'line', data: values, showSymbol: true, symbolSize: 7,
        lineStyle: { width: 2 },
        markPoint: markSpikes ? {
          symbol: 'pin', symbolSize: 40,
          data: {{ spike_points | json }}.map(function (i) {
            return { coord: [i, values[i]], value: '尖峰', itemStyle: { color: '#DC2626' } };
          })
        } : undefined
      }]
    });
    window.addEventListener('resize', function () { c.resize(); });
  }

  var pct = function (v) { return v == null ? '—' : (v * 100).toFixed(2) + '%'; };
  var num = function (v) { return v == null ? '—' : Number(v).toFixed(2); };

  lineChart('chart-cagr', {{ charts.cagr | json }}, 'CAGR', pct, C[0], true);
  lineChart('chart-dd', {{ charts.max_drawdown | json }}, '最大回撤', pct, C[4], false);
  lineChart('chart-sharpe', {{ charts.sharpe | json }}, 'Sharpe', num, C[2], false);
  lineChart('chart-trades', {{ charts.trade_count | json }}, '成交笔数', num, C[1], false);

  var el = document.getElementById('chart-sweep');
  if (el) {
    var c = echarts.init(el);
    c.setOption({
      color: C,
      grid: { left: 66, right: 66, top: 44, bottom: 46 },
      tooltip: { trigger: 'axis' },
      legend: { top: 0, textStyle: { color: '#64748B' } },
      xAxis: { type: 'category', data: P, axisLabel: { color: '#64748B' } },
      yAxis: [
        { type: 'value', name: '收益/回撤', axisLabel: { color: '#64748B',
          formatter: function (v) { return (v * 100).toFixed(0) + '%'; } },
          splitLine: { lineStyle: { color: '#E9EEF6' } } },
        { type: 'value', name: 'Sharpe', axisLabel: { color: '#64748B' },
          splitLine: { show: false } }
      ],
      series: [
        { name: 'CAGR', type: 'line', data: {{ charts.cagr | json }},
          showSymbol: true, symbolSize: 7, lineStyle: { width: 2.4, color: C[0] } },
        { name: '最大回撤', type: 'line', data: {{ charts.max_drawdown | json }},
          showSymbol: true, symbolSize: 6, lineStyle: { width: 1.6, color: C[4] } },
        { name: 'Sharpe', type: 'line', yAxisIndex: 1, data: {{ charts.sharpe | json }},
          showSymbol: true, symbolSize: 6, lineStyle: { width: 1.6, type: 'dashed', color: C[2] } }
      ]
    });
    window.addEventListener('resize', function () { c.resize(); });
  }
})();
</script>
<style>
  tr.spike-row td { background: #FEF2F2; font-weight: 600; }
</style>
{% endblock %}