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

2026-08-01 00:30:02 +08:00
parent 114bdc577a
commit a89e9fa8c0
7 changed files with 594 additions and 1 deletions
@@ -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()