feat: 自我进化模块——health_monitor+lesson_extractor+evolution_api+Dashboard+auto_iterator

evolution/__init__.py

@@ -0,0 +1,70 @@
+"""
+evolution/__init__.py — 自我进化模块
+Loop Engineering: 策略健康度监控 + 教训提取 + 自动迭代
+"""
+import sqlite3, os
+
+DB = os.environ.get('MOFIN_DB', '/home/hmo/MoFin/data/mofin.db')
+
+def init_evolution_tables(conn=None):
+    """初始化进化模块数据表"""
+    close_conn = False
+    if conn is None:
+        conn = sqlite3.connect(DB)
+        close_conn = True
+    
+    # 策略健康度每日快照
+    conn.execute("""
+        CREATE TABLE IF NOT EXISTS strategy_health (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            strategy_version TEXT NOT NULL,
+            date TEXT NOT NULL,
+            live_trades INTEGER DEFAULT 0,
+            live_wins INTEGER DEFAULT 0,
+            live_return_pct REAL DEFAULT 0,
+            backtest_wr REAL DEFAULT 0,
+            backtest_avg_ret REAL DEFAULT 0,
+            deviation REAL DEFAULT 0,
+            health_score REAL DEFAULT 0,
+            created_at TEXT DEFAULT (datetime('now','localtime')),
+            UNIQUE(strategy_version, date)
+        )
+    """)
+    conn.execute("CREATE INDEX IF NOT EXISTS idx_health_version_date ON strategy_health(strategy_version, date)")
+    
+    # 教训库
+    conn.execute("""
+        CREATE TABLE IF NOT EXISTS strategy_lessons (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            strategy_version TEXT NOT NULL,
+            trade_id INTEGER,
+            lesson_type TEXT NOT NULL,
+            lesson_text TEXT NOT NULL,
+            confidence REAL DEFAULT 0.5,
+            applied INTEGER DEFAULT 0,
+            created_at TEXT DEFAULT (datetime('now','localtime'))
+        )
+    """)
+    conn.execute("CREATE INDEX IF NOT EXISTS idx_lessons_version ON strategy_lessons(strategy_version, applied)")
+    
+    # 策略迭代历史
+    conn.execute("""
+        CREATE TABLE IF NOT EXISTS strategy_evolution (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            parent_version TEXT NOT NULL,
+            child_version TEXT NOT NULL,
+            change_description TEXT,
+            backtest_result TEXT,
+            promoted INTEGER DEFAULT 0,
+            created_at TEXT DEFAULT (datetime('now','localtime'))
+        )
+    """)
+    conn.execute("CREATE INDEX IF NOT EXISTS idx_evolution_parent ON strategy_evolution(parent_version, promoted)")
+    
+    conn.commit()
+    if close_conn:
+        conn.close()
+
+if __name__ == '__main__':
+    init_evolution_tables()
+    print("进化模块数据表初始化完成")

evolution/auto_iterator.py

@@ -0,0 +1,125 @@
+"""
+evolution/auto_iterator.py — 策略自动迭代
+健康度低时生成参数变体，跑回测，记录结果
+"""
+import sys, os, json, sqlite3, copy
+from datetime import datetime
+
+sys.path.insert(0, '/home/hmo/MoFin')
+import strategy_lab as lab
+
+DB = '/home/hmo/MoFin/data/mofin.db'
+
+# 可迭代的参数空间
+PARAM_SPACE = {
+    'max_hold_days': [40, 60, 80, 100, 120],
+    'reentry_days': [5, 10, 15, 20],
+    'sl_atr': [1.0, 1.5, 2.0, 2.5],
+}
+
+
+def get_current_health(version='v_next4', days=7):
+    """获取当前策略健康度"""
+    conn = sqlite3.connect(DB)
+    conn.row_factory = sqlite3.Row
+    rows = conn.execute("""
+        SELECT health_score, date FROM strategy_health 
+        WHERE strategy_version=? ORDER BY date DESC LIMIT ?
+    """, (version, days)).fetchall()
+    conn.close()
+    if not rows:
+        return 50  # 无数据，中性
+    return round(sum(r['health_score'] for r in rows) / len(rows), 1)
+
+
+def propose_variants(parent_version, health):
+    """根据健康度生成变体参数建议"""
+    if health >= 70:
+        print(f"健康度{health}≥70，无需迭代", flush=True)
+        return []
+    
+    variants = []
+    severity = 'minor' if health >= 50 else 'major'
+    
+    if severity == 'minor':
+        # 小幅调参
+        variants.append({
+            'parent': parent_version,
+            'params': {'max_hold_days': 80, 'reentry_days': 15, 'sl_atr': 1.5},
+            'description': '微调：确保当前最优参数',
+        })
+    else:
+        # 大幅调参（扫参数网格）
+        base_hold = 60
+        base_reentry = 10
+        for hold in PARAM_SPACE['max_hold_days']:
+            for reentry in PARAM_SPACE['reentry_days']:
+                variants.append({
+                    'parent': parent_version,
+                    'params': {'max_hold_days': hold, 'reentry_days': reentry, 'sl_atr': 1.5},
+                    'description': f'网格扫描: h{hold}/r{reentry}',
+                })
+    
+    return variants
+
+
+def test_variant(variant):
+    """测试单个变体"""
+    name = f"auto_h{variant['params']['max_hold_days']}_r{variant['params']['reentry_days']}"
+    # 克隆基座配置
+    base = lab.STRATEGIES.get(variant['parent'])
+    if not base:
+        return None
+    cfg = copy.deepcopy(base)
+    cfg['version'] = name
+    cfg['name'] = f"自进化-{variant['description']}"
+    for k, v in variant['params'].items():
+        cfg['config']['exit'][k] = v
+    lab.STRATEGIES[name] = cfg
+    
+    results = {}
+    for tag, start, end in [('5y', '2021-07-01', '2026-07-24')]:
+        r = lab.run_backtest(name, start, end, 913000, save=False, universe='a', period_tag=tag)
+        pf = r['summary'].get('portfolio_full', {})
+        results[tag] = {
+            'full': pf.get('total_return_pct'),
+            'cagr': pf.get('cagr_pct'),
+            'dd': pf.get('portfolio_max_dd_pct'),
+        }
+    return {'name': name, 'description': variant['description'], 'results': results}
+
+
+def run_iteration(version='v_next4'):
+    """执行一次迭代检查"""
+    health = get_current_health(version)
+    print(f"{version} 健康度: {health}", flush=True)
+    
+    variants = propose_variants(version, health)
+    if not variants:
+        return []
+    
+    conn = sqlite3.connect(DB)
+    results = []
+    for v in variants[:3]:  # 最多测3个
+        print(f"测试: {v['description']}", flush=True)
+        r = test_variant(v)
+        if r:
+            results.append(r)
+            # 记录到 evolution 表
+            conn.execute("""
+                INSERT INTO strategy_evolution (parent_version, child_version, change_description, backtest_result, promoted)
+                VALUES (?, ?, ?, ?, 0)
+            """, (version, r['name'], r['description'], json.dumps(r['results'], ensure_ascii=False)))
+            conn.commit()
+    
+    # 打印对比
+    for r in results:
+        rs = r['results'].get('5y', {})
+        print(f"  {r['name']}: full={rs.get('full')}% cagr={rs.get('cagr')}% dd={rs.get('dd')}%", flush=True)
+    
+    conn.close()
+    return results
+
+
+if __name__ == '__main__':
+    run_iteration()

evolution/evolution_api.py

@@ -0,0 +1,97 @@
+"""
+evolution/evolution_api.py — 进化模块 API 接口
+供 dashboard 查询健康度、教训、迭代历史
+"""
+import sys, os, json, sqlite3
+
+sys.path.insert(0, '/home/hmo/MoFin')
+sys.path.insert(0, '/home/hmo/MoFin/deploy/profile-scripts')
+
+DB = os.environ.get('MOFIN_DB', '/home/hmo/MoFin/data/mofin.db')
+
+
+def get_evolution_dashboard():
+    """进化模块 Dashboard 数据"""
+    conn = sqlite3.connect(DB)
+    conn.row_factory = sqlite3.Row
+    
+    # 最近健康度（近30天）
+    health = []
+    for r in conn.execute("""
+        SELECT strategy_version, date, live_trades, live_wins, live_return_pct,
+               backtest_wr, backtest_avg_ret, deviation, health_score
+        FROM strategy_health ORDER BY date DESC LIMIT 30
+    """).fetchall():
+        health.append(dict(r))
+    
+    # 最近教训（近20条）
+    lessons = []
+    for r in conn.execute("""
+        SELECT strategy_version, lesson_type, lesson_text, confidence, applied, created_at
+        FROM strategy_lessons ORDER BY id DESC LIMIT 20
+    """).fetchall():
+        lessons.append(dict(r))
+    
+    # 迭代历史
+    evolution = []
+    for r in conn.execute("""
+        SELECT parent_version, child_version, change_description, promoted, created_at
+        FROM strategy_evolution ORDER BY id DESC LIMIT 20
+    """).fetchall():
+        evolution.append(dict(r))
+    
+    # 当前策略基线
+    baseline = {}
+    for v in ['v_next4', 'v_next3', 'v8.1']:
+        r = conn.execute("""
+            SELECT results_json FROM strategy_research 
+            WHERE version=? AND period_tag='5y' ORDER BY id DESC LIMIT 1
+        """, (v,)).fetchone()
+        if r:
+            res = json.loads(r[0])
+            s = res.get('summary', {})
+            pf = s.get('portfolio_full', {})
+            baseline[v] = {
+                'win_rate': s.get('win_rate', 0),
+                'total_return': pf.get('total_return_pct', 0),
+                'cagr': pf.get('cagr_pct', 0),
+                'max_dd': pf.get('portfolio_max_dd_pct', 0),
+            }
+    
+    conn.close()
+    
+    return {
+        'health': health,
+        'lessons': lessons,
+        'evolution': evolution,
+        'baseline': baseline,
+    }
+
+
+def get_health_trend(version='v_next4', days=30):
+    """健康度趋势"""
+    conn = sqlite3.connect(DB)
+    conn.row_factory = sqlite3.Row
+    rows = conn.execute("""
+        SELECT date, health_score, deviation, live_trades
+        FROM strategy_health WHERE strategy_version=? ORDER BY date DESC LIMIT ?
+    """, (version, days)).fetchall()
+    conn.close()
+    return [dict(r) for r in rows]
+
+
+def record_evolution(parent, child, description, backtest_result=None, promoted=0):
+    """记录一次策略迭代"""
+    conn = sqlite3.connect(DB)
+    conn.execute("""
+        INSERT INTO strategy_evolution (parent_version, child_version, change_description, backtest_result, promoted)
+        VALUES (?, ?, ?, ?, ?)
+    """, (parent, child, description, json.dumps(backtest_result) if backtest_result else None, promoted))
+    conn.commit()
+    conn.close()
+
+
+if __name__ == '__main__':
+    d = get_evolution_dashboard()
+    print(f"健康度: {len(d['health'])}条, 教训: {len(d['lessons'])}条, 迭代: {len(d['evolution'])}条")
+    print(f"基线: {list(d['baseline'].keys())}")

evolution/health_monitor.py

@@ -0,0 +1,142 @@
+"""
+evolution/health_monitor.py — 策略健康度监控
+对比实盘交易 vs 回测预期，计算健康分，偏差过大时报警
+"""
+import sys, os, json, sqlite3
+from datetime import datetime, timedelta
+
+sys.path.insert(0, '/home/hmo/MoFin')
+sys.path.insert(0, '/home/hmo/MoFin/deploy/profile-scripts')
+
+DB = os.environ.get('MOFIN_DB', '/home/hmo/MoFin/data/mofin.db')
+CURRENT_STRATEGY = 'v_next4'
+
+
+def get_backtest_baseline(conn, version):
+    """从 strategy_research 取回测基线"""
+    r = conn.execute("""
+        SELECT results_json FROM strategy_research 
+        WHERE version=? AND period_tag='5y' ORDER BY id DESC LIMIT 1
+    """, (version,)).fetchone()
+    if not r:
+        return None
+    res = json.loads(r[0])
+    s = res.get('summary', {})
+    return {
+        'win_rate': s.get('win_rate', 0),
+        'avg_profit_pct': s.get('avg_profit_pct', 0),
+        'total_trades': s.get('total_trades', 0),
+    }
+
+
+def get_live_trades(conn, days=7):
+    """取近N天实盘交易（holding_strategies 全部，计算盈亏）"""
+    since = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
+    try:
+        rows = conn.execute("""
+            SELECT code, name, price as current_price, avg_price as entry_price,
+                   timing_signal as signal, updated_at,
+                   CASE WHEN avg_price > 0 THEN round((price - avg_price) / avg_price * 100, 2) ELSE 0 END as profit_pct
+            FROM holding_strategies 
+            WHERE updated_at >= ? AND avg_price > 0
+            ORDER BY updated_at DESC
+        """, (since,)).fetchall()
+        return rows
+    except sqlite3.OperationalError as e:
+        print(f"查询失败: {e}", flush=True)
+        return []
+
+
+def calc_health_score(live_wr, live_ret, backtest_wr, backtest_ret):
+    """计算健康分 (0-100)
+    健康分 = 100 - 偏差惩罚
+    偏差 = |实盘胜率-回测胜率| + |实盘收益-回测收益|/2
+    """
+    if backtest_wr == 0:
+        return 50  # 无基线，中性分
+    
+    wr_dev = abs(live_wr - backtest_wr)
+    ret_dev = abs(live_ret - backtest_ret) / 2
+    deviation = wr_dev + ret_dev
+    
+    # 偏差越大，健康分越低
+    health = max(0, 100 - deviation * 2)
+    return round(health, 1)
+
+
+def run_health_check(strategy_version=None):
+    """执行健康度检查"""
+    version = strategy_version or CURRENT_STRATEGY
+    conn = sqlite3.connect(DB)
+    conn.row_factory = sqlite3.Row
+    
+    # 回测基线
+    baseline = get_backtest_baseline(conn, version)
+    if not baseline:
+        print(f"无 {version} 回测基线", flush=True)
+        conn.close()
+        return None
+    
+    # 实盘交易（近7天）
+    live = get_live_trades(conn, days=7)
+    today = datetime.now().strftime('%Y-%m-%d')
+    
+    if not live:
+        # 无实盘数据，记录中性健康分
+        health = 50
+        deviation = 0
+        live_wr = live_ret = 0
+        print(f"{version}: 近7天无实盘交易，健康分=50(中性)", flush=True)
+    else:
+        wins = sum(1 for t in live if (t.get('profit_pct') or 0) > 0)
+        total = len(live)
+        live_wr = round(100 * wins / total, 1) if total else 0
+        live_ret = round(sum(t.get('profit_pct') or 0 for t in live) / total, 2) if total else 0
+        
+        health = calc_health_score(live_wr, live_ret, baseline['win_rate'], baseline['avg_profit_pct'])
+        deviation = abs(live_wr - baseline['win_rate'])
+    
+    # 写入 strategy_health 表
+    conn.execute("""
+        INSERT OR REPLACE INTO strategy_health 
+        (strategy_version, date, live_trades, live_wins, live_return_pct, 
+         backtest_wr, backtest_avg_ret, deviation, health_score)
+        VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
+    """, (version, today, len(live), sum(1 for t in live if (t.get('profit_pct') or 0) > 0),
+          live_ret, baseline['win_rate'], baseline['avg_profit_pct'], deviation, health))
+    conn.commit()
+    
+    # 报警判断
+    alert = None
+    if health < 40:
+        alert = f"🔴 策略健康度严重下降: {health}分 (偏差{deviation}pp)"
+    elif health < 60:
+        alert = f"🟡 策略健康度偏低: {health}分 (偏差{deviation}pp)"
+    
+    result = {
+        'version': version,
+        'date': today,
+        'live_trades': len(live),
+        'live_wr': live_wr,
+        'live_ret': live_ret,
+        'backtest_wr': baseline['win_rate'],
+        'backtest_ret': baseline['avg_profit_pct'],
+        'deviation': deviation,
+        'health_score': health,
+        'alert': alert,
+    }
+    
+    print(f"{version} 健康度: {health}分 (实盘{live_wr}%/{live_ret}% vs 回测{baseline['win_rate']}%/{baseline['avg_profit_pct']}%)", flush=True)
+    if alert:
+        print(f"  {alert}", flush=True)
+    
+    conn.close()
+    return result
+
+
+if __name__ == '__main__':
+    import sys
+    sys.path.insert(0, '/home/hmo/MoFin/evolution')
+    from __init__ import init_evolution_tables
+    init_evolution_tables()
+    run_health_check()

evolution/lesson_extractor.py

@@ -0,0 +1,106 @@
+"""
+evolution/lesson_extractor.py — 交易后教训提取
+分析已平仓交易，用LLM提取"为什么赢/亏"的教训
+"""
+import sys, os, json, sqlite3
+from datetime import datetime, timedelta
+
+sys.path.insert(0, '/home/hmo/MoFin')
+sys.path.insert(0, '/home/hmo/MoFin/deploy/profile-scripts')
+
+DB = os.environ.get('MOFIN_DB', '/home/hmo/MoFin/data/mofin.db')
+
+
+def get_closed_trades(conn, days=30):
+    """取近N天已平仓的交易（从strategy_research的trades里取最近的，或从holding_strategies推断）"""
+    # 从最近的回测结果取交易（作为样本分析）
+    r = conn.execute("""
+        SELECT results_json FROM strategy_research 
+        WHERE version='v_next4' AND period_tag='5y' ORDER BY id DESC LIMIT 1
+    """).fetchone()
+    if not r:
+        return []
+    res = json.loads(r[0])
+    trades = res.get('trades', [])
+    # 按日期排序，取最近的
+    recent = sorted(trades, key=lambda x: x.get('entry_date', ''), reverse=True)[:10]
+    return recent
+
+
+def analyze_trade(trade):
+    """分析单笔交易的成败原因（规则化，非LLM）"""
+    profit = trade.get('profit_pct', 0)
+    hold_days = trade.get('hold_days', 0)
+    factors = trade.get('factors', {})
+    
+    lessons = []
+    
+    # 盈利交易的共性
+    if profit > 15:
+        if factors.get('mkt_adx', 0) > 25:
+            lessons.append(('win_pattern', f"大盘趋势强(ADX={factors['mkt_adx']:.0f})时盈利{profit:.1f}%", 0.8))
+        if factors.get('sector_above_ma20'):
+            lessons.append(('win_pattern', f"板块在MA20上方时盈利{profit:.1f}%", 0.7))
+        if trade.get('dna'):
+            lessons.append(('win_pattern', f"动量基因(DNA)票盈利{profit:.1f}%", 0.9))
+    
+    # 亏损交易的共性
+    if profit < -5:
+        if factors.get('mkt_slope', 0) < -0.5:
+            lessons.append(('loss_pattern', f"大盘斜率负({factors['mkt_slope']:.2f})时亏损{profit:.1f}%", 0.7))
+        if not factors.get('sector_above_ma20'):
+            lessons.append(('loss_pattern', f"板块在MA20下方时亏损{profit:.1f}%", 0.6))
+        if hold_days < 5:
+            lessons.append(('loss_pattern', f"持仓{hold_days}天短于5天时亏损{profit:.1f}%", 0.5))
+    
+    # 长持盈利
+    if profit > 10 and hold_days > 30:
+        lessons.append(('win_pattern', f"长持{hold_days}天盈利{profit:.1f}%", 0.85))
+    
+    return lessons
+
+
+def extract_lessons(days=30):
+    """提取近N天交易的教训"""
+    conn = sqlite3.connect(DB)
+    conn.row_factory = sqlite3.Row
+    
+    trades = get_closed_trades(conn, days)
+    if not trades:
+        print("无交易数据", flush=True)
+        conn.close()
+        return []
+    
+    all_lessons = []
+    for t in trades:
+        lessons = analyze_trade(t)
+        for lesson_type, text, confidence in lessons:
+            all_lessons.append({
+                'strategy_version': 'v_next4',
+                'trade_id': t.get('id', 0),
+                'lesson_type': lesson_type,
+                'lesson_text': text,
+                'confidence': confidence,
+                'profit_pct': t.get('profit_pct', 0),
+                'entry_date': t.get('entry_date', ''),
+            })
+    
+    # 写入 strategy_lessons 表
+    for l in all_lessons:
+        conn.execute("""
+            INSERT INTO strategy_lessons (strategy_version, trade_id, lesson_type, lesson_text, confidence, applied)
+            VALUES (?, ?, ?, ?, ?, 0)
+        """, (l['strategy_version'], l['trade_id'], l['lesson_type'], l['lesson_text'], l['confidence']))
+    conn.commit()
+    
+    # 打印
+    print(f"分析 {len(trades)} 笔交易, 提取 {len(all_lessons)} 条教训", flush=True)
+    for l in all_lessons[:5]:
+        print(f"  [{l['lesson_type']}] {l['lesson_text']} (置信度{l['confidence']})", flush=True)
+    
+    conn.close()
+    return all_lessons
+
+
+if __name__ == '__main__':
+    extract_lessons()

server.py

@@ -1799,6 +1799,30 @@ def api_prd():
 register_routes(app)
 
 
+@app.route("/api/evolution/dashboard")
+def api_evolution_dashboard():
+    """进化模块 Dashboard"""
+    try:
+        sys.path.insert(0, '/home/hmo/MoFin/evolution')
+        from evolution_api import get_evolution_dashboard
+        return jsonify(get_evolution_dashboard())
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+
+@app.route("/api/evolution/health_trend")
+def api_evolution_health_trend():
+    """健康度趋势"""
+    try:
+        version = request.args.get('version', 'v_next4')
+        days = int(request.args.get('days', 30))
+        sys.path.insert(0, '/home/hmo/MoFin/evolution')
+        from evolution_api import get_health_trend
+        return jsonify({'trend': get_health_trend(version, days)})
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+
 if __name__ == "__main__":
     port = int(os.environ.get("PORT", 8899))
     print(f"🚀 MoFin Dashboard → http://0.0.0.0:{port}")

static/index.html

@@ -1929,7 +1929,8 @@ function inMd(t) {
 function renderResearch() {
   const el = document.getElementById('tab-research');
   if (!el) return;
-  el.innerHTML = '<div class="flex gap-2 items-center mb-3 flex-wrap">' +
+  el.innerHTML = '<div id="evo-box" class="mb-3 bg-slate-800/60 border border-slate-700 rounded-lg p-3"><div class="text-xs text-slate-500">🧬 进化模块加载中...</div></div>' +
+    '<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" onchange="saveBtPeriod()" 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">近 6 个月</option>' +
@@ -1972,6 +1973,34 @@ async function loadStrategyList() {
 }
 
 const ASC_DEFAULT = new Set(['max_dd']);  // 回撤类：小=好，默认升序
+async function loadEvolutionBox() {
+  const box = document.getElementById('evo-box');
+  if (!box) return;
+  try {
+    const r = await fetch('/api/evolution/dashboard');
+    const d = await r.json();
+    let h = '<div class="flex items-center justify-between mb-1"><span class="text-xs font-semibold text-emerald-400">🧬 策略自我进化</span><button onclick="loadEvolutionBox()" class="text-[10px] text-blue-400">刷新</button></div>';
+    if (d.baseline) {
+      h += '<div class="grid grid-cols-3 gap-2 mb-2">';
+      for (const [v, b] of Object.entries(d.baseline)) {
+        const clr = v === 'v_next4' ? 'text-emerald-400' : 'text-slate-300';
+        h += '<div class="bg-slate-900/50 rounded px-2 py-1"><span class="'+clr+' font-mono font-bold text-xs">'+v+'</span><br><span class="text-slate-500 text-[10px]">胜率'+b.win_rate+'% 收益'+b.total_return+'%</span></div>';
+      }
+      h += '</div>';
+    }
+    if (d.health && d.health.length > 0) { const hs = d.health[0]; const sc = hs.health_score;
+      const color = sc >= 70 ? 'text-emerald-400' : sc >= 50 ? 'text-amber-400' : 'text-red-400';
+      h += '<div class="text-xs '+color+'">健康度: '+sc+'分 (实盘'+hs.live_trades+'笔)</div>'; }
+    if (d.lessons && d.lessons.length > 0) {
+      h += '<div class="text-xs text-slate-500 mt-1">最近教训:</div>';
+      for (const l of d.lessons.slice(0,3)) h += '<div class="text-xs text-slate-400">'+(l.lesson_type==='win_pattern'?'✅':'❌')+' '+l.lesson_text+'</div>';
+    }
+    box.innerHTML = h;
+  } catch (e) { box.innerHTML = '<div class="text-xs text-red-400">加载失败</div>'; }
+}
+// 研究Tab活跃时加载进化模块
+new MutationObserver(()=>{if(!document.getElementById('tab-research')?.classList?.contains('hidden'))loadEvolutionBox();}).observe(document.body,{attributes:true,subtree:true});
+
 function showDesc(version) {
   const s = (window._strats || []).find(x => x.version === version);
   const d = s && s.description;