feat: 策略实验室多版本体系 — v1~v4.1迭代+12维上下文(大盘/趋势变化)+因子归因+版本对比列表

server.py

@@ -533,28 +533,66 @@ def get_tracking():
 
 
 
-@app.route("/api/research/backtest")
-def get_research_backtest():
-    import sqlite3
-    from datetime import datetime, timedelta
-    period = request.args.get("period", "1y")
-    capital = float(request.args.get("capital", 1000000))
-    end_date = datetime.now().strftime("%Y-%m-%d")
-    if period == "6m":
-        start_date = (datetime.now() - timedelta(days=180)).strftime('%Y-%m-%d')
-    elif period == "1y":
-        start_date = (datetime.now() - timedelta(days=365)).strftime('%Y-%m-%d')
-    else:
-        start_date = (datetime.now() - timedelta(days=730)).strftime('%Y-%m-%d')
+@app.route("/api/research/strategies")
+def api_research_strategies():
+    """策略版本列表（含回测结果摘要）"""
     try:
-        from backtest_framework import run_strategy_research
-        result = run_strategy_research(start_date, end_date, capital)
-        from flask import jsonify
-        return jsonify(result)
+        from strategy_lab import list_strategies
+        return jsonify({'strategies': list_strategies()})
     except Exception as e:
-        from flask import jsonify
         return jsonify({'error': str(e)}), 500
 
+
+@app.route("/api/research/backtest")
+def api_research_backtest():
+    """运行指定策略版本的回测"""
+    from datetime import datetime, timedelta
+    version = request.args.get('strategy', 'v4.1')
+    period = request.args.get('period', '6m')
+    capital = float(request.args.get('capital', 1000000))
+    end_date = '2026-07-24'  # 数据完整截止日
+    days = {'1m': 30, '6m': 185, '1y': 365, '2y': 730}.get(period, 185)
+    start_date = (datetime.strptime(end_date, '%Y-%m-%d') - timedelta(days=days)).strftime('%Y-%m-%d')
+    try:
+        from strategy_lab import run_backtest, analyze_trade_list, save_analysis
+        result = run_backtest(version, start_date, end_date, capital)
+        analysis = analyze_trade_list(result.get('trades', []), version)
+        save_analysis(version, analysis)
+        result['insights'] = analysis.get('insights', [])
+        return jsonify(result)
+    except ValueError as e:
+        return jsonify({'error': str(e)}), 404
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+
+@app.route("/api/research/analysis")
+def api_research_analysis():
+    """指定策略版本的因子归因分析"""
+    version = request.args.get('strategy', 'v4.1')
+    try:
+        from strategy_lab import analyze_trades
+        return jsonify(analyze_trades(version))
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+
+@app.route("/api/research/trades")
+def api_research_trades():
+    """指定策略版本的交易明细"""
+    import sqlite3 as _sq, json as _json
+    version = request.args.get('strategy', 'v4.1')
+    conn = _sq.connect("/home/hmo/MoFin/data/mofin.db")
+    row = conn.execute(
+        "SELECT results_json FROM strategy_research WHERE version=? ORDER BY id DESC LIMIT 1",
+        (version,)).fetchone()
+    conn.close()
+    if not row:
+        return jsonify({'error': f'无 {version} 回测结果'}), 404
+    res = _json.loads(row[0])
+    return jsonify({'strategy': version, 'trades': res.get('trades', [])})
+
+
 @app.route("/api/overview")
 def api_overview():
     """概览数据"""

static/index.html

@@ -1929,84 +1929,156 @@ function inMd(t) {
 function renderResearch() {
   const el = document.getElementById('tab-research');
   if (!el) return;
-  el.innerHTML = '<div class="flex gap-2 items-center mb-4"><h2 class="text-lg font-bold">📋 策略研究 — 全流程回测</h2>' +
-    '<select id="btPeriod" class="bg-slate-800 text-sm rounded-lg px-2 py-1 border border-slate-700" onchange="runBacktest()">' +
-    '<option value="1m">近 1 个月</option>' +
-    '<option value="6m" selected>近 6 个月</option>' +
-    '<option value="1y">近 1 年</option>' +
-    '<option value="2y">近 2 年</option>' +
-    '</select>' +
-    '<button onclick="runBacktest()" class="px-3 py-1 bg-blue-600 hover:bg-blue-500 rounded-lg text-sm">▶ 运行</button></div>' +
-    '<div id="btResults" class="text-slate-400 text-sm">点击「运行」开始回测验证</div>';
-  window.btEl = el;
+  el.innerHTML = '<div class="flex gap-2 items-center mb-3 flex-wrap">' +
+    '<h2 class="text-lg font-bold">📋 策略研究 — 版本迭代对比</h2>' +
+    '<select id="btPeriod" class="bg-slate-800 text-sm rounded-lg px-2 py-1 border border-slate-700">' +
+    '<option value="1m">近 1 个月</option><option value="6m" selected>近 6 个月</option>' +
+    '<option value="1y">近 1 年</option></select>' +
+    '<span class="text-xs text-slate-500">每版策略基于上一版归因分析迭代 · 点击行展开明细</span></div>' +
+    '<div id="strategyList"><div class="text-slate-500 text-sm">加载中...</div></div>' +
+    '<div id="strategyDetail" class="mt-4"></div>';
+  loadStrategyList();
 }
 
-async function runBacktest() {
-  const period = document.getElementById('btPeriod')?.value || '6m';
-  const resultsEl = document.getElementById('btResults');
-  if (!resultsEl) return;
-  resultsEl.innerHTML = '<div class="text-blue-400 animate-pulse">⏳ 回测运行中（约 15-30 秒）...</div>';
+async function loadStrategyList() {
+  const listEl = document.getElementById('strategyList');
   try {
-    const resp = await fetch('/api/research/backtest?period=' + period);
+    const resp = await fetch('/api/research/strategies');
     const data = await resp.json();
-    if (data.error) { resultsEl.innerHTML = '<div class="text-red-400">错误: ' + data.error + '</div>'; return; }
-    displayBacktestResults(data);
+    if (data.error) { listEl.innerHTML = '<div class="text-red-400">' + data.error + '</div>'; return; }
+    renderStrategyTable(data.strategies || []);
   } catch(e) {
-    resultsEl.innerHTML = '<div class="text-red-400">请求失败: ' + e.message + '</div>';
+    listEl.innerHTML = '<div class="text-red-400">加载失败: ' + e.message + '</div>';
   }
 }
 
-function displayBacktestResults(data) {
-  const el = document.getElementById('btResults');
-  if (!el) return;
-  const s = data.summary || {};
-  const winRate = s.win_rate || 0;
-  const winRateClass = winRate >= 50 ? 'text-green-400' : winRate >= 40 ? 'text-yellow-400' : 'text-red-400';
-  const sharpeClass = (s.sharpe_ratio || 0) >= 1 ? 'text-green-400' : (s.sharpe_ratio || 0) >= 0 ? 'text-yellow-400' : 'text-red-400';
-  
-  let html = '<div class="grid grid-cols-2 md:grid-cols-4 gap-3 mb-4">' +
-    '<div class="bg-slate-800/50 rounded-lg p-3"><div class="text-xs text-slate-500">测试周期</div><div class="text-sm font-mono">' + (data.period || '') + '</div></div>' +
-    '<div class="bg-slate-800/50 rounded-lg p-3"><div class="text-xs text-slate-500">初始资金</div><div class="text-sm font-mono">¥' + (s.capital_end ? (data.capital || 1000000).toLocaleString() : '100万') + '</div></div>' +
-    '<div class="bg-slate-800/50 rounded-lg p-3"><div class="text-xs text-slate-500">最终资金</div><div class="text-sm font-mono">¥' + (s.capital_end ? s.capital_end.toLocaleString() : '-') + '</div></div>' +
-    '<div class="bg-slate-800/50 rounded-lg p-3"><div class="text-xs text-slate-500">总交易</div><div class="text-sm font-mono">' + (s.total_trades || 0) + ' 笔</div></div>' +
-    '<div class="bg-slate-800/50 rounded-lg p-3"><div class="text-xs text-slate-500">胜率</div><div class="text-lg font-bold ' + winRateClass + '">' + s.win_rate + '%</div></div>' +
-    '<div class="bg-slate-800/50 rounded-lg p-3"><div class="text-xs text-slate-500">平均收益</div><div class="text-lg font-bold ' + ((s.avg_profit_pct || 0) >= 0 ? 'text-green-400' : 'text-red-400') + '">' + (s.avg_profit_pct || 0) + '%</div></div>' +
-    '<div class="bg-slate-800/50 rounded-lg p-3"><div class="text-xs text-slate-500">夏普比率</div><div class="text-lg font-bold ' + sharpeClass + '">' + (s.sharpe_ratio || 0).toFixed(2) + '</div></div>' +
-    '<div class="bg-slate-800/50 rounded-lg p-3"><div class="text-xs text-slate-500">最大回撤</div><div class="text-lg font-bold text-red-400">' + (s.max_drawdown_pct || 0).toFixed(1) + '%</div></div>' +
-    '</div>' +
-    '<div class="grid grid-cols-4 gap-2 mb-4 text-xs">' +
-    '<div class="bg-green-900/20 rounded p-2"><span class="text-green-400">✅ 盈利: ' + (s.wins || 0) + '</span> <span class="text-slate-500">均+' + (s.avg_win_pct || 0).toFixed(1) + '%</span></div>' +
-    '<div class="bg-red-900/20 rounded p-2"><span class="text-red-400">❌ 亏损: ' + (s.losses || 0) + '</span> <span class="text-slate-500">均' + (s.avg_loss_pct || 0).toFixed(1) + '%</span></div>' +
-    '<div class="bg-slate-800/30 rounded p-2"><span class="text-yellow-400">📊 盈亏比: ' + (s.profit_factor || 0).toFixed(2) + '</span></div>' +
-    '<div class="bg-slate-800/30 rounded p-2"><span class="text-slate-300">📈 筛选: ' + (data.total_stocks_screened || 0) + ' 只</span></div>' +
-    '</div>';
-
-  // Trade list
-  const trades = data.trades || [];
-  html += '<div class="mb-2 text-sm font-bold text-slate-300">交易明细（前 50 笔）</div>' +
-    '<div class="overflow-x-auto"><table class="w-full text-xs"><thead><tr class="text-slate-500 border-b border-slate-700">' +
-    '<th class="text-left px-2 py-1">代码</th><th class="text-left px-2 py-1">名称</th><th class="text-left px-2 py-1">买入日</th>' +
-    '<th class="text-right px-2 py-1">买入价</th><th class="text-right px-2 py-1">卖出价</th><th class="text-right px-2 py-1">收益</th>' +
-    '<th class="text-center px-2 py-1">结果</th><th class="text-right px-2 py-1">持仓</th><th class="text-right px-2 py-1">评分</th></tr></thead><tbody>';
-  for (const t of trades) {
-    const pnlClass = (t.profit_pct || 0) >= 0 ? 'text-green-400' : 'text-red-400';
-    const reasonMap = { 'target': '🎯 止盈', 'stop': '🛑 止损', 'keep': '⏳ 持仓中' };
-    html += '<tr class="border-b border-slate-800/50 hover:bg-slate-800/30">' +
-      '<td class="px-2 py-1 font-mono">' + (t.code || '') + '</td>' +
-      '<td class="px-2 py-1">' + (t.name || '') + '</td>' +
-      '<td class="px-2 py-1 font-mono">' + (t.entry_date || '') + '</td>' +
-      '<td class="text-right px-2 py-1 font-mono">' + (t.entry_price || 0).toFixed(2) + '</td>' +
-      '<td class="text-right px-2 py-1 font-mono">' + (t.exit_price || 0).toFixed(2) + '</td>' +
-      '<td class="text-right px-2 py-1 font-mono ' + pnlClass + '">' + (t.profit_pct || 0).toFixed(1) + '%</td>' +
-      '<td class="text-center px-2 py-1">' + (reasonMap[t.exit_reason] || t.exit_reason || '') + '</td>' +
-      '<td class="text-right px-2 py-1">' + (t.hold_days || 0) + 'd</td>' +
-      '<td class="text-right px-2 py-1">' + (t.score || 0) + '</td></tr>';
+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; }
+  // 找每列最优值用于高亮
+  const best = { win_rate: -999, sharpe_ratio: -999, profit_factor: -999, avg_profit_pct: -999, max_drawdown_pct: 999 };
+  for (const s of strategies) {
+    const st = s.summary_stats || {};
+    if (st.win_rate != null) best.win_rate = Math.max(best.win_rate, st.win_rate);
+    if (st.sharpe_ratio != null) best.sharpe_ratio = Math.max(best.sharpe_ratio, st.sharpe_ratio);
+    if (st.profit_factor != null) best.profit_factor = Math.max(best.profit_factor, st.profit_factor);
+    if (st.avg_profit_pct != null) best.avg_profit_pct = Math.max(best.avg_profit_pct, st.avg_profit_pct);
+    if (st.max_drawdown_pct != null) best.max_drawdown_pct = Math.min(best.max_drawdown_pct, st.max_drawdown_pct);
   }
-  html += '</tbody></table></div>';
-  
+  const hl = (val, bestVal, invert) => {
+    if (val == null) return '';
+    const isBest = invert ? (val === bestVal && bestVal !== 999) : (val === bestVal && bestVal !== -999);
+    return isBest ? 'text-green-400 font-bold' : '';
+  };
+  let html = '<table class="w-full text-xs"><thead><tr class="text-slate-500 border-b border-slate-700">' +
+    '<th class="text-left px-2 py-1.5">版本</th><th class="text-left px-2 py-1.5">名称</th>' +
+    '<th class="text-left px-2 py-1.5">改进假设</th>' +
+    '<th class="text-right px-2 py-1.5">交易数</th><th class="text-right px-2 py-1.5">胜率</th>' +
+    '<th class="text-right px-2 py-1.5">平均收益</th><th class="text-right px-2 py-1.5">夏普</th>' +
+    '<th class="text-right px-2 py-1.5">盈亏比</th><th class="text-right px-2 py-1.5">最大回撤</th>' +
+    '<th class="text-center px-2 py-1.5">操作</th></tr></thead><tbody>';
+  for (const s of strategies) {
+    const st = s.summary_stats || {};
+    const hasResult = st.total_trades != null;
+    const smallSample = hasResult && st.total_trades < 40;
+    html += '<tr class="border-b border-slate-800/50 hover:bg-slate-800/30 cursor-pointer" onclick="showStrategyDetail(\'' + s.version + '\')">' +
+      '<td class="px-2 py-1.5 font-mono font-bold text-blue-400">' + s.version + '</td>' +
+      '<td class="px-2 py-1.5">' + (s.name || '') + '</td>' +
+      '<td class="px-2 py-1.5 text-slate-400 max-w-xs truncate" title="' + (s.hypothesis || '').replace(/"/g, '&quot;') + '">' + (s.hypothesis || s.summary || '') + '</td>' +
+      '<td class="text-right px-2 py-1.5 font-mono">' + (hasResult ? st.total_trades + (smallSample ? '⚠️' : '') : '—') + '</td>' +
+      '<td class="text-right px-2 py-1.5 font-mono ' + hl(st.win_rate, best.win_rate) + '">' + (st.win_rate != null ? st.win_rate + '%' : '—') + '</td>' +
+      '<td class="text-right px-2 py-1.5 font-mono ' + ((st.avg_profit_pct || 0) >= 0 ? 'text-green-400' : 'text-red-400') + '">' + (st.avg_profit_pct != null ? st.avg_profit_pct + '%' : '—') + '</td>' +
+      '<td class="text-right px-2 py-1.5 font-mono ' + hl(st.sharpe_ratio, best.sharpe_ratio) + '">' + (st.sharpe_ratio != null ? st.sharpe_ratio : '—') + '</td>' +
+      '<td class="text-right px-2 py-1.5 font-mono ' + hl(st.profit_factor, best.profit_factor) + '">' + (st.profit_factor != null ? st.profit_factor : '—') + '</td>' +
+      '<td class="text-right px-2 py-1.5 font-mono ' + hl(st.max_drawdown_pct, best.max_drawdown_pct, true) + '">' + (st.max_drawdown_pct != null ? st.max_drawdown_pct + '%' : '—') + '</td>' +
+      '<td class="text-center px-2 py-1.5" onclick="event.stopPropagation()">' +
+        '<button onclick="runStrategyBacktest(\'' + s.version + '\')" class="px-2 py-0.5 bg-blue-600/60 hover:bg-blue-500 rounded text-xs">▶回测</button>' +
+      '</td></tr>';
+  }
+  html += '</tbody></table>' +
+    '<div class="text-xs text-slate-600 mt-2">⚠️ = 样本量&lt;40笔，统计意义有限 · 绿色 = 该列最优 · 回测期间: 2026-01-21 ~ 2026-07-24</div>';
   el.innerHTML = html;
 }
 
+async function runStrategyBacktest(version) {
+  const period = document.getElementById('btPeriod')?.value || '6m';
+  const detail = document.getElementById('strategyDetail');
+  detail.innerHTML = '<div class="text-blue-400 animate-pulse text-sm">⏳ 正在回测 ' + version + '（约15-30秒）...</div>';
+  try {
+    const resp = await fetch('/api/research/backtest?strategy=' + version + '&period=' + period);
+    const data = await resp.json();
+    if (data.error) { detail.innerHTML = '<div class="text-red-400">' + data.error + '</div>'; return; }
+    await loadStrategyList();
+    showStrategyResult(data);
+  } catch(e) {
+    detail.innerHTML = '<div class="text-red-400">失败: ' + e.message + '</div>';
+  }
+}
+
+async function showStrategyDetail(version) {
+  const detail = document.getElementById('strategyDetail');
+  detail.innerHTML = '<div class="text-slate-500 text-sm">加载 ' + version + ' 明细...</div>';
+  try {
+    const [anaResp, tradesResp] = await Promise.all([
+      fetch('/api/research/analysis?strategy=' + version),
+      fetch('/api/research/trades?strategy=' + version)
+    ]);
+    const ana = await anaResp.json();
+    const tradesData = await tradesResp.json();
+    showStrategyResult({ strategy: version, trades: tradesData.trades || [], insights: (ana.insights || []), analysis: ana });
+  } catch(e) {
+    detail.innerHTML = '<div class="text-red-400">失败: ' + e.message + '</div>';
+  }
+}
+
+function showStrategyResult(data) {
+  const el = document.getElementById('strategyDetail');
+  const s = data.summary || {};
+  let html = '<div class="border border-slate-700 rounded-xl p-4 bg-slate-900/40">' +
+    '<div class="flex items-center gap-3 mb-3"><h3 class="font-bold text-blue-300">' + (data.strategy || '') + ' ' + (data.strategy_name || '') + '</h3>' +
+    '<span class="text-xs text-slate-500">' + (data.period || '') + '</span></div>';
+  if (s.total_trades != null) {
+    html += '<div class="grid grid-cols-3 md:grid-cols-6 gap-2 mb-3 text-center">' +
+      '<div class="bg-slate-800/50 rounded p-2"><div class="text-xs text-slate-500">交易</div><div class="font-mono">' + s.total_trades + '</div></div>' +
+      '<div class="bg-slate-800/50 rounded p-2"><div class="text-xs text-slate-500">胜率</div><div class="font-mono ' + (s.win_rate >= 50 ? 'text-green-400' : 'text-yellow-400') + '">' + s.win_rate + '%</div></div>' +
+      '<div class="bg-slate-800/50 rounded p-2"><div class="text-xs text-slate-500">平均收益</div><div class="font-mono ' + (s.avg_profit_pct >= 0 ? 'text-green-400' : 'text-red-400') + '">' + s.avg_profit_pct + '%</div></div>' +
+      '<div class="bg-slate-800/50 rounded p-2"><div class="text-xs text-slate-500">夏普</div><div class="font-mono">' + s.sharpe_ratio + '</div></div>' +
+      '<div class="bg-slate-800/50 rounded p-2"><div class="text-xs text-slate-500">盈亏比</div><div class="font-mono">' + (s.profit_factor || '—') + '</div></div>' +
+      '<div class="bg-slate-800/50 rounded p-2"><div class="text-xs text-slate-500">回撤</div><div class="font-mono text-red-400">' + s.max_drawdown_pct + '%</div></div></div>';
+  }
+  // 归因洞察
+  if (data.insights && data.insights.length) {
+    html += '<div class="mb-3"><div class="text-sm font-bold text-slate-300 mb-1">🔬 因子归因洞察（下一版迭代依据）</div>' +
+      '<div class="space-y-1">' + data.insights.map(i => '<div class="text-xs text-slate-400 bg-slate-800/30 rounded px-2 py-1">' + i + '</div>').join('') + '</div></div>';
+  }
+  // 交易明细
+  const trades = (data.trades || []).slice(0, 50);
+  if (trades.length) {
+    html += '<div class="text-sm font-bold text-slate-300 mb-1">交易明细（前50笔）</div>' +
+      '<div class="overflow-x-auto max-h-96 overflow-y-auto"><table class="w-full text-xs"><thead class="sticky top-0 bg-slate-900"><tr class="text-slate-500 border-b border-slate-700">' +
+      '<th class="text-left px-2 py-1">代码</th><th class="text-left px-2 py-1">名称</th><th class="text-left px-2 py-1">入场</th>' +
+      '<th class="text-right px-2 py-1">买入</th><th class="text-right px-2 py-1">卖出</th><th class="text-right px-2 py-1">收益</th>' +
+      '<th class="text-center px-2 py-1">结果</th><th class="text-right px-2 py-1">持仓</th><th class="text-right px-2 py-1">评分</th></tr></thead><tbody>';
+    const reasonMap = { target: '🎯止盈', stop: '🛑止损', keep: '⏳到期' };
+    for (const t of trades) {
+      const pc = (t.profit_pct || 0) >= 0 ? 'text-green-400' : 'text-red-400';
+      html += '<tr class="border-b border-slate-800/40">' +
+        '<td class="px-2 py-1 font-mono">' + t.code + '</td><td class="px-2 py-1">' + (t.name || '') + '</td>' +
+        '<td class="px-2 py-1 font-mono">' + (t.entry_date || '') + '</td>' +
+        '<td class="text-right px-2 py-1 font-mono">' + (t.entry_price || 0).toFixed(2) + '</td>' +
+        '<td class="text-right px-2 py-1 font-mono">' + (t.exit_price || 0).toFixed(2) + '</td>' +
+        '<td class="text-right px-2 py-1 font-mono ' + pc + '">' + (t.profit_pct || 0).toFixed(1) + '%</td>' +
+        '<td class="text-center px-2 py-1">' + (reasonMap[t.exit_reason] || t.exit_reason) + '</td>' +
+        '<td class="text-right px-2 py-1">' + (t.hold_days || 0) + 'd</td>' +
+        '<td class="text-right px-2 py-1">' + (t.score || 0) + '</td></tr>';
+    }
+    html += '</tbody></table></div>';
+  }
+  html += '</div>';
+  el.innerHTML = html;
+}
+
+
 </script>
 </body>
 </html>

strategy_lab.py

@@ -0,0 +1,628 @@
+#!/usr/bin/env python3
+"""MoFin 策略实验室 v2 — 多版本策略回测 + 12维上下文 + 因子归因
+   维度: 个股技术(水平+趋势变化) / 大盘状态 / 行业强度
+   每个策略版本 = 命名配置 + 元数据(名称/假设/父版本)"""
+import sqlite3, json, math, os
+from datetime import datetime, timedelta
+
+DB_PATH = "/home/hmo/MoFin/data/mofin.db"
+
+import sys
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+from backtest_framework import prepare_bars, compute_single_score, compute_kelly
+
+# ══════════════════════════════════════════════════════
+# 策略版本注册表
+# ══════════════════════════════════════════════════════
+STRATEGIES = {
+    "v1.0": {
+        "version": "v1.0",
+        "name": "多因子基线",
+        "summary": "五因子评分≥45 + 动量≥8，10%止盈 / 2×ATR止损，半Kelly",
+        "hypothesis": "基线版本：验证多因子评分体系的基础有效性",
+        "parent": None,
+        "created": "2026-07-28",
+        "config": {
+            "entry": {"min_score": 45, "min_momentum": 8, "filters": {}},
+            "exit": {"tp_pct": 0.10, "sl_atr": 2.0, "max_hold_days": 20},
+            "sizing": {"kelly": True, "kelly_fraction": 0.5},
+            "eval_step": 5,
+        },
+    },
+    "v2.0": {
+        "version": "v2.0",
+        "name": "趋势动能过滤",
+        "summary": "v1 + MACD柱>0 + ROC>2 + ADX≥20 + ATR%≥2.8 过滤弱势入场",
+        "hypothesis": "v1归因：MACD>0.69胜率54%vs33%，ROC>11胜率57%vs35%，ADX>43胜率50%，ATR%>4.3胜率49%vs31%。过滤无趋势/无动能/死鱼股",
+        "parent": "v1.0",
+        "created": "2026-07-28",
+        "config": {
+            "entry": {"min_score": 45, "min_momentum": 8,
+                      "filters": {"adx_min": 20, "atr_pct_min": 2.8, "roc_min": 2, "macd_hist_min": 0}},
+            "exit": {"tp_pct": 0.10, "sl_atr": 2.0, "max_hold_days": 20},
+            "sizing": {"kelly": True, "kelly_fraction": 0.5},
+            "eval_step": 5,
+        },
+    },
+    "v3.0": {
+        "version": "v3.0",
+        "name": "强动量+优盈亏比",
+        "summary": "v2 + ROC≥8 + 距MA20≥4% + 量比1.0~1.8；止盈15%/止损1.5×ATR（RR→2.2:1）",
+        "hypothesis": "v2归因：ROC>17.5胜率58.5%，距MA20>12.9胜率56.5%，量比1.12~1.45胜率55.1%。且v2平均亏损-9.14%≈止盈10%，RR仅1.1:1是盈亏比恶化主因→收紧止损放大止盈",
+        "parent": "v2.0",
+        "created": "2026-07-28",
+        "config": {
+            "entry": {"min_score": 45, "min_momentum": 8,
+                      "filters": {"adx_min": 20, "atr_pct_min": 2.8, "roc_min": 8,
+                                  "macd_hist_min": 0, "dist_ma20_min": 4,
+                                  "vol_ratio_min": 1.0, "vol_ratio_max": 1.8}},
+            "exit": {"tp_pct": 0.15, "sl_atr": 1.5, "max_hold_days": 20},
+            "sizing": {"kelly": True, "kelly_fraction": 0.5},
+            "eval_step": 5,
+        },
+    },
+    "v4.0": {
+        "version": "v4.0",
+        "name": "大盘回调+趋势结构",
+        "summary": "v3 + 大盘须在MA20上且MA20斜率<-0.05(上升中回调) + 个股更高高点结构 + ROC 10~25 + MACD柱<1.3(避追高)",
+        "hypothesis": "v3归因：大盘MA20斜率-1.76~-0.56时胜率58.8%vs平坡20.6%(差38pp最强信号)；大盘在MA20上胜率42%vs34%；hh结构+15pp；ROC甜区12.9~16.2胜率61%；MACD柱>1.33胜率仅28%(追高必死)；个股MA20斜率<1.5胜率56%vs≥1.5约35%(强势回调买)",
+        "parent": "v3.0",
+        "created": "2026-07-28",
+        "config": {
+            "entry": {"min_score": 45, "min_momentum": 8,
+                      "filters": {"adx_min": 20, "atr_pct_min": 3.5, "atr_pct_max": 5.5,
+                                  "roc_min": 10, "roc_max": 25,
+                                  "macd_hist_min": 0.25, "macd_hist_max": 1.3,
+                                  "dist_ma20_min": 4,
+                                  "vol_ratio_min": 1.2, "vol_ratio_max": 1.5,
+                                  "ma20_slope_max": 1.5,
+                                  "mkt_above_ma20": True, "mkt_slope_max": -0.05,
+                                  "hh_only": True}},
+            "exit": {"tp_pct": 0.15, "sl_atr": 1.5, "max_hold_days": 20},
+            "sizing": {"kelly": True, "kelly_fraction": 0.5},
+            "eval_step": 5,
+        },
+    },
+    "v4.1": {
+        "version": "v4.1",
+        "name": "大盘回调·宽松带",
+        "summary": "v4.0放宽：ROC 8~25 + MACD柱0~1.5 + ATR 2.8~6.0 + 量比1.0~1.8；保留大盘MA20上+斜率<0 + hh结构",
+        "hypothesis": "v4.0仅5笔交易=过滤器叠加过拟合(分桶样本仅36笔/桶)。保留归因最强的市场状态+趋势结构信号，放宽窄幅过滤器换取统计样本量",
+        "parent": "v4.0",
+        "created": "2026-07-28",
+        "config": {
+            "entry": {"min_score": 45, "min_momentum": 8,
+                      "filters": {"adx_min": 20, "atr_pct_min": 2.8, "atr_pct_max": 6.0,
+                                  "roc_min": 8, "roc_max": 25,
+                                  "macd_hist_min": 0, "macd_hist_max": 1.5,
+                                  "dist_ma20_min": 4,
+                                  "vol_ratio_min": 1.0, "vol_ratio_max": 1.8,
+                                  "ma20_slope_max": 1.5,
+                                  "mkt_above_ma20": True, "mkt_slope_max": 0,
+                                  "hh_only": True}},
+            "exit": {"tp_pct": 0.15, "sl_atr": 1.5, "max_hold_days": 20},
+            "sizing": {"kelly": True, "kelly_fraction": 0.5},
+            "eval_step": 5,
+        },
+    },
+}
+
+
+def get_strategy(version):
+    if version not in STRATEGIES:
+        raise ValueError(f"未知策略版本: {version}，可用: {list(STRATEGIES.keys())}")
+    return STRATEGIES[version]
+
+
+# ══════════════════════════════════════════════════════
+# 大盘 / 行业上下文
+# ══════════════════════════════════════════════════════
+_MKT_CTX = {}
+_SECTOR_CTX = {}
+_STOCK_SECTOR = {}
+
+def prepare_market_context(start_date, end_date):
+    """大盘指数(sh000001)每日状态: 是否在MA20上、MA20斜率、ROC"""
+    global _MKT_CTX
+    _MKT_CTX = {}
+    bars = prepare_bars('sh000001', start_date, end_date)
+    if not bars:
+        return
+    for i, b in enumerate(bars):
+        slope = None
+        if i >= 5:
+            m0, m1 = bars[i-5].get('ma20'), b.get('ma20')
+            if m0 and m1:
+                slope = round((m1 - m0) / m0 * 100, 3)
+        ma20 = b.get('ma20') or 0
+        _MKT_CTX[b['date']] = {
+            'above_ma20': (b.get('close') or 0) > ma20 if ma20 > 0 else None,
+            'ma20_slope': slope,
+            'roc': b.get('roc'),
+        }
+
+def prepare_sector_context(start_date, end_date):
+    """每日行业强度: 平均涨幅/净流入/当日排名 + 个股→行业映射"""
+    global _SECTOR_CTX, _STOCK_SECTOR
+    _SECTOR_CTX, _STOCK_SECTOR = {}, {}
+    conn = sqlite3.connect(DB_PATH)
+    try:
+        rows = conn.execute("""
+            SELECT substr(m.timestamp,1,10) as d, s.name,
+                   AVG(s.change_pct), SUM(s.net_inflow)
+            FROM sector_snapshots s JOIN market_snapshots m ON s.snapshot_id = m.id
+            WHERE m.timestamp >= ? AND m.timestamp <= ?
+            GROUP BY d, s.name
+        """, (start_date, end_date + ' 23:59')).fetchall()
+        # 优先 THS 源命名（与 sector_snapshots 同体系），证监会分类作兜底
+        _STOCK_SECTOR = {}
+        for code, sec, src in conn.execute(
+                "SELECT code, sector_name, source FROM stock_sectors").fetchall():
+            if src == 'ths' or code not in _STOCK_SECTOR:
+                _STOCK_SECTOR[code] = sec
+    finally:
+        conn.close()
+    for d, name, chg, inflow in rows:
+        _SECTOR_CTX.setdefault(d, {})[name] = {'change': round(chg or 0, 2), 'inflow': round(inflow or 0, 1)}
+    for d in _SECTOR_CTX:
+        ranked = sorted(_SECTOR_CTX[d].items(), key=lambda x: -(x[1]['change']))
+        total = len(ranked)
+        for rank, (name, v) in enumerate(ranked):
+            v['rank_pct'] = round(rank / total, 3) if total else None  # 0=最强
+
+def mkt_ctx(date):
+    return _MKT_CTX.get(date, {})
+
+def sector_ctx(code, date):
+    sec = _STOCK_SECTOR.get(code)
+    if not sec:
+        return {}
+    return _SECTOR_CTX.get(date, {}).get(sec, {})
+
+
+# ══════════════════════════════════════════════════════
+# 入场过滤器
+# ══════════════════════════════════════════════════════
+def pass_filters(factors, filters):
+    if not filters:
+        return True
+    def chk(key, vmin=None, vmax=None):
+        v = factors.get(key)
+        if vmin is not None and (v is None or v < vmin):
+            return False
+        if vmax is not None and v is not None and v > vmax:
+            return False
+        return True
+    if not chk('rsi', filters.get('rsi_min'), filters.get('rsi_max')): return False
+    if not chk('adx', filters.get('adx_min'), filters.get('adx_max')): return False
+    if not chk('dist_ma20', filters.get('dist_ma20_min'), filters.get('dist_ma20_max')): return False
+    if not chk('vol_ratio', filters.get('vol_ratio_min'), filters.get('vol_ratio_max')): return False
+    if not chk('roc', filters.get('roc_min'), filters.get('roc_max')): return False
+    if not chk('atr_pct', filters.get('atr_pct_min'), filters.get('atr_pct_max')): return False
+    if not chk('macd_hist', filters.get('macd_hist_min'), filters.get('macd_hist_max')): return False
+    # 趋势变化
+    if not chk('ma20_slope', filters.get('ma20_slope_min'), filters.get('ma20_slope_max')): return False
+    if not chk('macd_hist_delta', filters.get('macd_hist_delta_min'), filters.get('macd_hist_delta_max')): return False
+    if filters.get('adx_rising') and not factors.get('adx_rising'): return False
+    if filters.get('trend_only') and not factors.get('trend_aligned'): return False
+    if filters.get('hh_only') and not factors.get('hh_structure'): return False
+    if filters.get('no_new_high') and factors.get('near_high_20d'): return False
+    # 大盘
+    if filters.get('mkt_above_ma20') and factors.get('mkt_above_ma20') is not True: return False
+    if not chk('mkt_slope', filters.get('mkt_slope_min'), filters.get('mkt_slope_max')): return False
+    # 行业
+    if not chk('sector_change', filters.get('sector_change_min'), filters.get('sector_change_max')): return False
+    if not chk('sector_rank_pct', None, filters.get('sector_rank_pct_max')): return False
+    return True
+
+
+def calc_factors(bars, idx):
+    """个股因子: 水平值 + 趋势变化"""
+    b = bars[idx]
+    prev5 = bars[max(0, idx-5)]
+    close = b.get('close') or 0
+    ma20 = b.get('ma20') or 0
+    atr = b.get('atr') or 0
+    vol = b.get('volume') or 0
+    pvol = prev5.get('volume') or 0
+    window = bars[max(0, idx-19):idx+1]
+    high20 = max((x.get('high') or 0) for x in window) if window else 0
+    ma5, ma10 = b.get('ma5') or 0, b.get('ma10') or 0
+    f = {
+        'rsi': b.get('rsi'),
+        'adx': b.get('adx'),
+        'macd_hist': b.get('macd_hist'),
+        'roc': b.get('roc'),
+        'atr_pct': round(atr / close * 100, 2) if close > 0 and atr else None,
+        'dist_ma20': round((close - ma20) / ma20 * 100, 2) if ma20 > 0 else None,
+        'vol_ratio': round(vol / pvol, 2) if pvol > 0 else None,
+        'obv_delta': (b.get('obv') or 0) - (prev5.get('obv') or 0),
+        'trend_aligned': ma5 > ma10 > ma20 > 0,
+        'near_high_20d': close >= high20 * 0.98 if high20 > 0 else False,
+    }
+    # 趋势变化因子（不能孤立看点值，要看方向和变化）
+    if idx >= 5:
+        b5 = bars[idx-5]
+        m0, m1 = b5.get('ma20'), b.get('ma20')
+        f['ma20_slope'] = round((m1 - m0) / m0 * 100, 3) if m0 and m1 else None
+        h0, h1 = b5.get('macd_hist'), b.get('macd_hist')
+        f['macd_hist_delta'] = round(h1 - h0, 3) if h0 is not None and h1 is not None else None
+        a0, a1 = b5.get('adx'), b.get('adx')
+        f['adx_rising'] = (a1 > a0) if a0 is not None and a1 is not None else None
+        r0, r1 = b5.get('rsi'), b.get('rsi')
+        f['rsi_delta'] = round(r1 - r0, 2) if r0 is not None and r1 is not None else None
+    if idx >= 10:
+        h5 = max(x.get('high') or 0 for x in bars[idx-4:idx+1])
+        h10 = max(x.get('high') or 0 for x in bars[idx-9:idx-4])
+        l5 = min(x.get('low') or 1e9 for x in bars[idx-4:idx+1])
+        l10 = min(x.get('low') or 1e9 for x in bars[idx-9:idx-4])
+        f['hh_structure'] = h5 > h10          # 更高的高点 = 上升结构
+        f['hl_structure'] = l5 > l10          # 更高的低点 = 上升结构
+    return f
+
+
+# ══════════════════════════════════════════════════════
+# 回测引擎（配置驱动 + 12维上下文记录）
+# ══════════════════════════════════════════════════════
+def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=True):
+    strat = get_strategy(strategy_version)
+    cfg = strat['config']
+    entry_cfg, exit_cfg = cfg['entry'], cfg['exit']
+    filters = entry_cfg.get('filters', {})
+    step = cfg.get('eval_step', 5)
+
+    prepare_market_context(start_date, end_date)
+    prepare_sector_context(start_date, end_date)
+
+    conn = sqlite3.connect(DB_PATH)
+    stocks = conn.execute("""
+        SELECT DISTINCT sd.code, COALESCE(s.name, sd.code) as name
+        FROM stock_daily sd LEFT JOIN stocks s ON sd.code = s.code
+        WHERE sd.date>=? AND sd.date<=?
+    """, (start_date, end_date)).fetchall()
+    conn.close()
+
+    trades = []
+    screened = scored_n = 0
+
+    for code, name in stocks:
+        screened += 1
+        bars = prepare_bars(code, start_date, end_date)
+        if not bars or len(bars) < 25:
+            continue
+
+        i = 20
+        while i < len(bars):
+            window = bars[:i+1]
+            sc = compute_single_score(window)
+            if sc is None:
+                i += step
+                continue
+            total_score, comp = sc
+            scored_n += 1
+
+            last = bars[i]
+            close = last.get('close') or 0
+
+            if total_score >= entry_cfg['min_score'] and comp['momentum'] >= entry_cfg['min_momentum']:
+                factors = calc_factors(bars, i)
+                # 附加大盘/行业上下文
+                date = last.get('date')
+                mk = mkt_ctx(date)
+                sc_ctx = sector_ctx(code, date)
+                factors['mkt_above_ma20'] = mk.get('above_ma20')
+                factors['mkt_slope'] = mk.get('ma20_slope')
+                factors['mkt_roc'] = mk.get('roc')
+                factors['sector_change'] = sc_ctx.get('change')
+                factors['sector_rank_pct'] = sc_ctx.get('rank_pct')
+                factors['sector_inflow'] = sc_ctx.get('inflow')
+
+                if pass_filters(factors, filters):
+                    atr_val = last.get('atr') or 0
+                    if exit_cfg.get('tp_pct'):
+                        target = close * (1 + exit_cfg['tp_pct'])
+                    elif exit_cfg.get('tp_atr') and atr_val > 0:
+                        target = close + atr_val * exit_cfg['tp_atr']
+                    else:
+                        target = close * 1.10
+                    if exit_cfg.get('sl_atr') and atr_val > 0:
+                        stop = close - atr_val * exit_cfg['sl_atr']
+                    elif exit_cfg.get('sl_pct'):
+                        stop = close * (1 - exit_cfg['sl_pct'])
+                    else:
+                        stop = close * 0.93
+
+                    kelly = 0
+                    if cfg['sizing'].get('kelly'):
+                        kelly = compute_kelly(total_score, (target-close)/close if close>0 else 0.1,
+                                              (close-stop)/close if close>0 else 0.07)
+
+                    max_hold = exit_cfg.get('max_hold_days', 20)
+                    future = bars[i+1:i+1+max_hold]
+                    exit_price = exit_reason = None
+                    hold_days = 0
+                    for k, fb in enumerate(future):
+                        fh, fl, fc = fb.get('high') or 0, fb.get('low') or 0, fb.get('close') or 0
+                        if fh >= target:
+                            exit_price, exit_reason, hold_days = target, 'target', k+1
+                            break
+                        elif fl <= stop:
+                            exit_price, exit_reason, hold_days = fc, 'stop', k+1
+                            break
+                    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
+                    trades.append({
+                        'code': code, 'name': name,
+                        'entry_date': date,
+                        'entry_price': round(close, 2),
+                        'exit_price': round(exit_price, 2),
+                        'profit_pct': round(pnl, 2),
+                        'exit_reason': exit_reason,
+                        'hold_days': hold_days,
+                        'score': total_score,
+                        'score_comp': comp,
+                        'kelly': round(kelly, 3),
+                        'stop_loss': round(stop, 2),
+                        'target': round(target, 2),
+                        'factors': {k: (round(v, 3) if isinstance(v, float) else v)
+                                    for k, v in factors.items()},
+                    })
+            i += step
+
+    summary = calc_summary(trades, capital)
+    result = {
+        'strategy': strat['version'],
+        'strategy_name': strat['name'],
+        'period': f"{start_date} ~ {end_date}",
+        'capital': capital,
+        'total_stocks_screened': screened,
+        'scored_events': scored_n,
+        'trades': trades,
+        'summary': summary,
+    }
+    if save:
+        save_result(strat, result)
+    return result
+
+
+def calc_summary(trades, capital):
+    if not trades:
+        return {}
+    profits = [t['profit_pct'] for t in trades]
+    wins = [t for t in trades if t['profit_pct'] > 0]
+    losses = [t for t in trades if t['profit_pct'] <= 0]
+    win_rate = len(wins) / len(trades) * 100
+    avg_p = sum(profits) / len(profits)
+    avg_w = sum(t['profit_pct'] for t in wins) / len(wins) if wins else 0
+    avg_l = sum(t['profit_pct'] for t in losses) / len(losses) if losses else 0
+    mean_r = avg_p / 100
+    std_r = math.sqrt(sum((p/100 - mean_r)**2 for p in profits) / (len(profits)-1)) if len(profits) > 1 else 0
+    sharpe = mean_r / std_r * math.sqrt(252) if std_r > 0 else 0
+    curve = [capital]
+    for t in trades:
+        curve.append(curve[-1] * (1 + t['profit_pct']/100))
+    peak = capital
+    max_dd = 0
+    for c in curve:
+        peak = max(peak, c)
+        max_dd = max(max_dd, (peak - c) / peak * 100)
+    return {
+        'total_trades': len(trades),
+        'win_rate': round(win_rate, 1),
+        'avg_profit_pct': round(avg_p, 2),
+        'avg_win_pct': round(avg_w, 2),
+        'avg_loss_pct': round(avg_l, 2),
+        'sharpe_ratio': round(sharpe, 2),
+        'max_drawdown_pct': round(max_dd, 2),
+        'profit_factor': round(abs(avg_w/avg_l), 2) if avg_l != 0 else None,
+        'wins': len(wins), 'losses': len(losses),
+        'capital_end': round(curve[-1], 2),
+    }
+
+
+# ══════════════════════════════════════════════════════
+# 因子归因分析（连续分桶 + 布尔分组）
+# ══════════════════════════════════════════════════════
+ANALYZE_FACTORS = ['rsi', 'adx', 'macd_hist', 'roc', 'atr_pct', 'dist_ma20', 'vol_ratio',
+                   'ma20_slope', 'macd_hist_delta', 'rsi_delta', 'mkt_slope', 'mkt_roc',
+                   'sector_change', 'sector_rank_pct', 'score']
+BOOL_FACTORS = ['trend_aligned', 'hh_structure', 'hl_structure', 'adx_rising',
+                'mkt_above_ma20', 'near_high_20d']
+
+def analyze_trades(strategy_version):
+    conn = sqlite3.connect(DB_PATH)
+    row = conn.execute(
+        "SELECT results_json FROM strategy_research WHERE version=? ORDER BY id DESC LIMIT 1",
+        (strategy_version,)).fetchone()
+    conn.close()
+    if not row:
+        return {'error': f'无 {strategy_version} 的回测结果，请先运行回测'}
+    result = json.loads(row[0])
+    return analyze_trade_list(result.get('trades', []), strategy_version)
+
+
+def analyze_trade_list(trades, label=''):
+    if not trades:
+        return {'error': '无交易数据'}
+    wins = [t for t in trades if t['profit_pct'] > 0]
+    report = {
+        'label': label,
+        'total': len(trades), 'wins': len(wins), 'losses': len(trades) - len(wins),
+        'factors': {}, 'bool_factors': {}, 'exit_reasons': {}, 'hold_analysis': {}, 'insights': [],
+    }
+
+    # 连续因子: 五分桶胜率
+    for f in ANALYZE_FACTORS:
+        pairs = [(t['factors'].get(f), t['profit_pct'] > 0)
+                 for t in trades if t.get('factors', {}).get(f) is not None]
+        if len(pairs) < 30:
+            continue
+        vals = sorted(pairs, key=lambda x: x[0])
+        w_vals = [v for v, w in pairs if w]
+        l_vals = [v for v, w in pairs if not w]
+        buckets = []
+        n = len(vals)
+        for bi in range(5):
+            seg = vals[int(n*bi/5):int(n*(bi+1)/5)]
+            if seg:
+                wr = sum(1 for _, w in seg if w) / len(seg) * 100
+                buckets.append({'range': f"{seg[0][0]:.2f}~{seg[-1][0]:.2f}",
+                                'win_rate': round(wr, 1), 'count': len(seg)})
+        report['factors'][f] = {
+            'winner_mean': round(sum(w_vals)/len(w_vals), 3) if w_vals else None,
+            'loser_mean': round(sum(l_vals)/len(l_vals), 3) if l_vals else None,
+            'buckets': buckets,
+        }
+
+    # 布尔因子: True/False 分组胜率
+    for f in BOOL_FACTORS:
+        pairs = [(t['factors'].get(f), t['profit_pct'] > 0)
+                 for t in trades if t.get('factors', {}).get(f) is not None]
+        if len(pairs) < 30:
+            continue
+        t_grp = [w for v, w in pairs if v]
+        f_grp = [w for v, w in pairs if not v]
+        if t_grp and f_grp:
+            report['bool_factors'][f] = {
+                'true_win_rate': round(sum(t_grp)/len(t_grp)*100, 1), 'true_count': len(t_grp),
+                'false_win_rate': round(sum(f_grp)/len(f_grp)*100, 1), 'false_count': len(f_grp),
+            }
+
+    # 出场方式
+    for t in trades:
+        r = t['exit_reason']
+        report['exit_reasons'].setdefault(r, {'count': 0, 'total_pnl': 0, 'avg_hold': 0})
+        d = report['exit_reasons'][r]
+        d['count'] += 1
+        d['total_pnl'] += t['profit_pct']
+        d['avg_hold'] += t['hold_days']
+    for r, d in report['exit_reasons'].items():
+        d['avg_pnl'] = round(d['total_pnl'] / d['count'], 2)
+        d['avg_hold'] = round(d['avg_hold'] / d['count'], 1)
+        d['total_pnl'] = round(d['total_pnl'], 1)
+
+    # 持仓天数
+    hold_buckets = {}
+    for t in trades:
+        hb = '1-3天' if t['hold_days'] <= 3 else ('4-7天' if t['hold_days'] <= 7 else ('8-14天' if t['hold_days'] <= 14 else '15天+'))
+        hold_buckets.setdefault(hb, {'count': 0, 'wins': 0})
+        hold_buckets[hb]['count'] += 1
+        if t['profit_pct'] > 0:
+            hold_buckets[hb]['wins'] += 1
+    for hb, d in hold_buckets.items():
+        d['win_rate'] = round(d['wins'] / d['count'] * 100, 1)
+    report['hold_analysis'] = hold_buckets
+
+    # 自动洞察
+    ins = []
+    for f, d in report['factors'].items():
+        if len(d['buckets']) >= 4:
+            wrs = [b['win_rate'] for b in d['buckets']]
+            spread = max(wrs) - min(wrs)
+            if spread >= 12:
+                best = d['buckets'][wrs.index(max(wrs))]
+                worst = d['buckets'][wrs.index(min(wrs))]
+                ins.append(f"📌 {f} 区分度{spread:.0f}pp: [{best['range']}]胜率{best['win_rate']}% vs [{worst['range']}]胜率{worst['win_rate']}%")
+    for f, d in report['bool_factors'].items():
+        diff = d['true_win_rate'] - d['false_win_rate']
+        if abs(diff) >= 8:
+            arrow = '✅' if diff > 0 else '❌'
+            ins.append(f"{arrow} {f}=True 胜率{d['true_win_rate']}% vs False {d['false_win_rate']}% (差{abs(diff):.0f}pp)")
+    er = report['exit_reasons']
+    if 'stop' in er and er['stop']['count'] > er.get('target', {}).get('count', 0) * 2:
+        ins.append(f"⚠️ 止损({er['stop']['count']})远多于止盈({er.get('target',{}).get('count',0)}): 入场追高或止损过紧")
+    report['insights'] = ins
+    return report
+
+
+# ══════════════════════════════════════════════════════
+# 持久化
+# ══════════════════════════════════════════════════════
+def init_table():
+    conn = sqlite3.connect(DB_PATH)
+    conn.execute("""
+        CREATE TABLE IF NOT EXISTS strategy_research (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            version TEXT, name TEXT, summary TEXT, hypothesis TEXT, parent TEXT,
+            config_json TEXT, results_json TEXT, analysis_json TEXT,
+            period TEXT, created_at TEXT
+        )
+    """)
+    conn.commit()
+    conn.close()
+
+
+def save_result(strat, result):
+    init_table()
+    conn = sqlite3.connect(DB_PATH)
+    conn.execute("""
+        INSERT INTO strategy_research (version, name, summary, hypothesis, parent,
+                                       config_json, results_json, period, created_at)
+        VALUES (?,?,?,?,?,?,?,?,?)
+    """, (strat['version'], strat['name'], strat['summary'], strat['hypothesis'],
+          strat.get('parent'), json.dumps(strat['config'], ensure_ascii=False),
+          json.dumps(result, ensure_ascii=False), result['period'],
+          datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    conn.commit()
+    conn.close()
+
+
+def save_analysis(version, analysis):
+    init_table()
+    conn = sqlite3.connect(DB_PATH)
+    conn.execute("""
+        UPDATE strategy_research SET analysis_json=?
+        WHERE id = (SELECT id FROM strategy_research WHERE version=? ORDER BY id DESC LIMIT 1)
+    """, (json.dumps(analysis, ensure_ascii=False), version))
+    conn.commit()
+    conn.close()
+
+
+def list_strategies():
+    init_table()
+    conn = sqlite3.connect(DB_PATH)
+    conn.row_factory = sqlite3.Row
+    rows = conn.execute("""
+        SELECT sr.* FROM strategy_research sr
+        INNER JOIN (SELECT version, MAX(id) as max_id FROM strategy_research GROUP BY version) latest
+        ON sr.id = latest.max_id
+        ORDER BY sr.version
+    """).fetchall()
+    conn.close()
+    out = []
+    for r in rows:
+        d = dict(r)
+        res = json.loads(d['results_json']) if d.get('results_json') else {}
+        ana = json.loads(d['analysis_json']) if d.get('analysis_json') else None
+        d['summary_stats'] = res.get('summary', {})
+        d['insights'] = (ana or {}).get('insights', [])
+        d['trades_count'] = len(res.get('trades', []))
+        del d['results_json']
+        del d['analysis_json']
+        out.append(d)
+    existing = {d['version'] for d in out}
+    for v, s in STRATEGIES.items():
+        if v not in existing:
+            out.append({
+                'version': v, 'name': s['name'], 'summary': s['summary'],
+                'hypothesis': s['hypothesis'], 'parent': s.get('parent'),
+                'config_json': json.dumps(s['config'], ensure_ascii=False),
+                'summary_stats': {}, 'insights': [], 'created_at': s.get('created'),
+            })
+    out.sort(key=lambda x: x['version'])
+    return out
+
+
+if __name__ == '__main__':
+    import sys
+    ver = sys.argv[1] if len(sys.argv) > 1 else 'v3.0'
+    end = '2026-07-24'
+    start = '2026-01-21'
+    r = run_backtest(ver, start, end)
+    print(json.dumps(r['summary'], indent=2, ensure_ascii=False))
+    a = analyze_trade_list(r['trades'], ver)
+    save_analysis(ver, a)
+    print(json.dumps(a.get('insights', []), indent=2, ensure_ascii=False))