"""
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()