feat: v_mr 均值回复策略 v2——10y全市场验证通过(21690笔/53.6%WR/avg+3.34%)
- 独立均值回复引擎: MA60下>10%+RSI≤42+60日跌>15%+小盘≤1500万+RSI止跌≥2 - 出场: 止盈+18%/止损-8%/25天, 移除MA20反弹截断(v1毒药) - backtest_framework: prepare_bars 补 amount 字段(千元) - 大盘上下文: sh000001 2016-2020 数据从 sector_index_daily 回填 stock_daily - 验证: sideways(ADX)过滤有害(206.6%→147.9%), mkt_mode=any 最优 - docs/v_mr_strategy.md 完整策略文档
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#!/usr/bin/env python3
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"""mr_engine_v2: 修复版独立均值回复引擎
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修复1: 移除 MA20 反弹出场（超跌票 MA20 太近，反弹到 MA20 只赚 2-3%，盈亏比倒挂）
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修复2: bias_min/ret_min 支持 None=单边不限制（离线验证用的是单边 ≤-10）
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"""
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import sys, os, sqlite3, json, math
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from datetime import datetime, timedelta
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sys.path.insert(0, '/home/hmo/MoFin')
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import strategy_lab as lab
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def run_mr_v2(strategy_version, start_date, end_date, capital=913000,
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save=True, universe='all', period_tag='10y'):
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strat = lab.get_strategy(strategy_version)
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cfg = strat['config']
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entry_cfg, exit_cfg = cfg['entry'], cfg['exit']
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mr = entry_cfg.get('mr', {})
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fetch_start = (datetime.strptime(start_date, '%Y-%m-%d') - timedelta(days=200)).strftime('%Y-%m-%d')
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lab.prepare_market_context(fetch_start, end_date)
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lab.prepare_sector_context(start_date, end_date)
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lab.prepare_flow_context(fetch_start, end_date)
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lab.prepare_weekly_context(fetch_start, end_date)
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lab.prepare_news_context(start_date, end_date)
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conn = sqlite3.connect(lab.DB_PATH)
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stocks = conn.execute("""
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SELECT DISTINCT sd.code, COALESCE(s.name, sd.code) as name
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FROM stock_daily sd LEFT JOIN stocks s ON sd.code = s.code
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WHERE sd.date>=? AND sd.date<=?
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""", (start_date, end_date)).fetchall()
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conn.close()
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if universe == 'hk':
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stocks = [(c, n) for c, n in stocks if lab.is_hk_code(c)]
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elif universe == 'a':
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stocks = [(c, n) for c, n in stocks if not lab.is_hk_code(c)]
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trades = []
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screened = 0
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skip_stats = {'no_bars': 0, 'bias': 0, 'rsi': 0, 'ret60': 0, 'mom20': 0,
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'amount': 0, 'rsi_delta': 0, 'mkt': 0, 'next_open': 0}
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for code, name in stocks:
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screened += 1
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bars = lab._bars(code, fetch_start, end_date)
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if not bars or len(bars) < 70:
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skip_stats['no_bars'] += 1
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continue
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i = 60
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while i < len(bars):
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if bars[i].get('date', '') < start_date:
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i += 1
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continue
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b = bars[i]
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close = b.get('close') or 0
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ma60 = b.get('ma60') or 0
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rsi = b.get('rsi')
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if close <= 0 or ma60 <= 0 or rsi is None:
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i += 1
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continue
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# 1. MA60 下方超跌（单边）
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bias60 = (close - ma60) / ma60 * 100
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bmin = mr.get('bias_min')
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bmax = mr.get('bias_max')
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if bmin is not None and bias60 < bmin:
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skip_stats['bias'] += 1; i += 1; continue
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if bmax is not None and bias60 > bmax:
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skip_stats['bias'] += 1; i += 1; continue
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# 2. RSI 超卖
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if rsi > mr.get('rsi_max', 50):
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skip_stats['rsi'] += 1; i += 1; continue
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# 3. 近期下跌（单边）
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if i >= 60:
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prev60 = bars[i-60].get('close') or 0
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prev_ret60 = (close - prev60) / prev60 * 100 if prev60 > 0 else 0
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else:
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prev_ret60 = 0
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rmin = mr.get('ret_min')
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rmax = mr.get('ret_max')
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if rmin is not None and prev_ret60 < rmin:
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skip_stats['ret60'] += 1; i += 1; continue
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if rmax is not None and prev_ret60 > rmax:
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skip_stats['ret60'] += 1; i += 1; continue
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# 4. 低动量
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prev20 = bars[i-20].get('close') or 0
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mom20 = (close - prev20) / prev20 * 100 if prev20 > 0 else 0
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if mom20 > mr.get('mom20_max', 5):
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skip_stats['mom20'] += 1; i += 1; continue
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# 5. 小盘（20日均成交额，百万元）
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amt20 = [x.get('amount') or 0 for x in bars[max(0, i-19):i+1]]
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amt_valid = [a for a in amt20 if a > 0]
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amount_ma20 = sum(amt_valid) / len(amt_valid) if amt_valid else 0
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amount_ma20_m = amount_ma20 / 1000.0 # 千元→百万元
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if mr.get('amount_max') is not None and amount_ma20_m > mr['amount_max']:
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skip_stats['amount'] += 1; i += 1; continue
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# 6. 止跌回升确认（RSI 5日回升）
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if i >= 5:
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rsi0 = bars[i-5].get('rsi')
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rsi_delta = (rsi - rsi0) if rsi0 is not None else 0
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else:
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rsi_delta = 0
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if rsi_delta < mr.get('rsi_delta_min', -2):
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skip_stats['rsi_delta'] += 1; i += 1; continue
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# 7. 大盘状态
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date = b.get('date')
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mk = lab.mkt_ctx(date, code)
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mkt_adx = mk.get('adx')
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mkt_above = mk.get('above_ma20')
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mkt_mode = mr.get('mkt_mode', 'any')
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if mkt_mode == 'sideways':
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if mkt_adx is not None and mkt_adx >= 25 and mkt_above:
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skip_stats['mkt'] += 1; i += 1; continue
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elif mkt_mode == 'bear':
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if mkt_above is True:
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skip_stats['mkt'] += 1; i += 1; continue
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# 次日开盘入场
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if i + 1 >= len(bars):
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skip_stats['next_open'] += 1; i += 1; continue
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ep = bars[i+1].get('open') or close
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if ep <= 0:
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skip_stats['next_open'] += 1; i += 1; continue
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# ── 出场（均值回归：让利润跑向止盈，无 MA20 截断）──
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tp_pct = exit_cfg.get('tp_pct', 0.18)
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sl_pct = exit_cfg.get('sl_pct', 0.08)
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max_hold = exit_cfg.get('max_hold_days', 25)
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target = ep * (1 + tp_pct)
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stop = ep * (1 - sl_pct)
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future = bars[i+1:i+1+max_hold]
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exit_price = exit_reason = None
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hold_days = 0
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for k, fb in enumerate(future):
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fh, fl, fc = fb.get('high') or 0, fb.get('low') or 0, fb.get('close') or 0
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if fl <= stop:
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exit_price, exit_reason, hold_days = stop, 'stop', k+1
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break
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if fh >= target:
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exit_price, exit_reason, hold_days = target, 'target', k+1
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break
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if exit_price is None:
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exit_price = future[-1].get('close') if future else ep
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exit_reason, hold_days = 'time', len(future)
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pnl = (exit_price - ep) / ep * 100 if ep > 0 else 0
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factors = lab.calc_factors(bars, i)
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factors.update({
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'bias60': round(bias60, 2),
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'prev_ret60': round(prev_ret60, 2),
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'mom20': round(mom20, 2),
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'amount_ma20': round(amount_ma20_m, 2),
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'mkt_adx': mkt_adx,
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'mkt_above_ma20': mkt_above,
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'rsi_delta': round(rsi_delta, 2),
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})
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sc_ctx = lab.sector_ctx(code, date)
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factors['sector_change'] = sc_ctx.get('change')
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factors['sector_rank_pct'] = sc_ctx.get('rank_pct')
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factors['sector_adx'] = sc_ctx.get('adx')
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factors['sector_above_ma20'] = sc_ctx.get('above_ma20')
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factors['sector_slope'] = sc_ctx.get('slope')
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trades.append({
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'code': code, 'name': name,
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'entry_date': date,
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'entry_price': round(ep, 2),
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'exit_price': round(exit_price, 2),
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'profit_pct': round(pnl, 2),
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'exit_reason': exit_reason,
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'hold_days': hold_days,
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'score': 0, 'score_comp': {},
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'kelly': 0, 'stop_loss': round(stop, 2),
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'target': round(target, 2),
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'dna': False,
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'factors': {k: (round(v, 3) if isinstance(v, float) else v)
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for k, v in factors.items()},
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})
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i += 1
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print(f"skip_stats: {skip_stats}")
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summary = lab.calc_summary(trades, capital)
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if summary:
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_y0 = datetime.strptime(start_date, '%Y-%m-%d')
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_y1 = datetime.strptime(end_date, '%Y-%m-%d')
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_bt_years = max((_y1 - _y0).days / 365.0, 0.5)
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slots = lab.STRATEGY_SIZING.get(strategy_version, 5)
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for t in trades:
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t['boost'] = 1.0
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summary['portfolio'] = lab.portfolio_sim(trades, capital, slots)
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summary['sizing_slots'] = slots
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summary['portfolio_full'] = lab.portfolio_sim_full(trades, capital)
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_tr = summary['portfolio'].get('total_return_pct', 0) / 100
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_tf = summary['portfolio_full'].get('total_return_pct', 0) / 100
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summary['portfolio']['cagr_pct'] = round((((1 + _tr) ** (1 / _bt_years)) - 1) * 100, 1)
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summary['portfolio_full']['cagr_pct'] = round((((1 + _tf) ** (1 / _bt_years)) - 1) * 100, 1)
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result = {
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'strategy': strat['version'],
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'strategy_name': strat['name'],
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'market': universe,
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'period': f"{start_date} ~ {end_date}",
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'period_tag': period_tag,
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'capital': capital,
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'total_stocks_screened': screened,
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'scored_events': len(trades),
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'trades': trades,
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'summary': summary,
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}
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if save:
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lab.save_result(strat, result)
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return result
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if __name__ == '__main__':
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ST, EN = '2016-07-01', '2026-07-24'
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mr_cfg = {
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'bias_max': -10, # 单边：MA60 下方超过 10%（无下限）
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'rsi_max': 42,
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'ret_max': -15, # 单边：60日跌超 15%（无下限）
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'mom20_max': 5,
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'amount_max': 15, # 百万元 = 1500万日成交额
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'rsi_delta_min': 2,
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'mkt_mode': 'any',
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}
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exit_cfg = {'tp_pct': 0.18, 'sl_pct': 0.08, 'max_hold_days': 25}
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lab.register_mr_strategy('v_mr', 'v_mr: 小盘深超跌均值回复',
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'独立均值回复引擎v2: MA60下>10%+RSI≤42+60日跌>15%+小盘(≤1500万)+RSI止跌≥2; 止盈18%/止损8%/25天; 无MA20截断',
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'翻倍股77%诞生于震荡市; 深超跌+小盘+止跌回升是均值回复金矿; 离线重放9082笔 avg3.16%/wr54.8%',
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mr_cfg, exit_cfg, slots=5)
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r = run_mr_v2('v_mr', ST, EN, 913000, save=True, universe='a', period_tag='10y')
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s = r['summary']
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pf = s.get('portfolio_full', {})
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p = s.get('portfolio', {})
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print(f"v_mr: trades={s['total_trades']} wr={s['win_rate']}% avg={s['avg_profit_pct']}% "
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f"avg_win={s['avg_win_pct']}% avg_loss={s['avg_loss_pct']}% hold={s['avg_hold_days']}d")
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print(f" portfolio: ret={p.get('total_return_pct')}% cagr={p.get('cagr_pct')}% dd={p.get('portfolio_max_dd_pct')}%")
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print(f" full: ret={pf.get('total_return_pct')}% cagr={pf.get('cagr_pct')}% dd={pf.get('portfolio_max_dd_pct')}%")
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print(f" months={s['universality']['months']} peak={s['universality']['peak_pct']}% uni={s['universality']['score']}")
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a = lab.analyze_trade_list(r['trades'], 'v_mr')
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print(f" exit: {a['exit_reasons']}")
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lab.save_analysis('v_mr', a)
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