feat: 12个月窗口+资金面/板块维度 — 修预热期+网格相位bug，EM行业体系86板块全历史，v6.2达82.1%胜率/回撤9.68%
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+130
-17
@@ -88,7 +88,7 @@ STRATEGIES = {
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"hh_only": True}},
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"exit": {"tp_pct": 0.15, "sl_atr": 1.5, "max_hold_days": 20},
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"sizing": {"kelly": True, "kelly_fraction": 0.5},
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"eval_step": 5,
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"eval_step": 1,
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},
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},
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"v4.1": {
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@@ -179,6 +179,22 @@ STRATEGIES.update({
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"消融结果：单放量比+10笔保73%胜率/盈亏比2.92，单放ATR+4笔保78%胜率——两者是唯一不稀释质量的放宽，融合期望20+笔且保住70%胜率",
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entry_overrides={"vol_ratio_min": 0.9, "vol_ratio_max": 2.0,
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"atr_pct_min": 2.8, "atr_pct_max": 6.5}),
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# E组: 资金面/板块增强
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"v6.0": _v40_branch("v6.0", "资金流过滤",
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"v4.0f + 资金5日净占比>-2.5(排除持续流出)",
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"资金流归因：flow_5d<-2.7的持续流出组胜率仅37.5%，其余各桶55-78%。排除主力持续出逃的标的；flow_delta>4虽77.8%但会过度砍样本暂不启用",
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entry_overrides={"vol_ratio_min": 0.9, "vol_ratio_max": 2.0,
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"flow_5d_min": -2.5}),
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"v6.1": _v40_branch("v6.1", "板块不追高",
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"v4.0f + 板块MA20斜率≤1.0(排除已强涨板块)",
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"板块归因：sector_slope>1.04的强涨板块入场胜率仅27.3%，而斜率≤0.08的回调/横盘板块胜率80%。与大盘/个股层面的'回调买'规律三层同构",
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entry_overrides={"vol_ratio_min": 0.9, "vol_ratio_max": 2.0,
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"sector_slope_max": 1.0}),
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"v6.2": _v40_branch("v6.2", "资金+板块双滤",
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"v4.0f + 资金5日净占比>-2.5 + 板块MA20斜率≤1.0",
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"资金流(37.5%→55-78%)与板块(27.3%→80%)两个独立维度的负向排除叠加，期望在v4.0f基础上再提升胜率且不显著减样本",
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entry_overrides={"vol_ratio_min": 0.9, "vol_ratio_max": 2.0,
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"flow_5d_min": -2.5, "sector_slope_max": 1.0}),
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})
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@@ -216,33 +232,63 @@ def prepare_market_context(start_date, end_date):
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}
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def prepare_sector_context(start_date, end_date):
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"""每日行业强度: 平均涨幅/净流入/当日排名 + 个股→行业映射"""
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"""行业上下文: sector_index_daily(全历史) 提供板块趋势; sector_snapshots(近期) 补充净流入"""
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global _SECTOR_CTX, _STOCK_SECTOR
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_SECTOR_CTX, _STOCK_SECTOR = {}, {}
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conn = sqlite3.connect(DB_PATH)
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try:
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rows = conn.execute("""
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# 1. 板块指数历史（全周期）
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try:
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idx_rows = conn.execute("""
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SELECT sector, date, close, change_pct FROM sector_index_daily
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WHERE date >= ? AND date <= ? ORDER BY sector, date
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""", (start_date, end_date)).fetchall()
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except sqlite3.OperationalError:
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idx_rows = []
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# 每板块计算 MA20 和斜率
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from collections import defaultdict
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by_sector = defaultdict(list)
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for sec, d, close, chg in idx_rows:
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by_sector[sec].append((d, close, chg))
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for sec, series in by_sector.items():
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closes = [c for _, c, _ in series]
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for i, (d, close, chg) in enumerate(series):
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above = slope = None
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if i >= 19:
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ma20 = sum(closes[i-19:i+1]) / 20
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above = close > ma20
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if i >= 24:
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ma20_5 = sum(closes[i-24:i-4]) / 20
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if ma20_5 > 0:
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slope = round((ma20 - ma20_5) / ma20_5 * 100, 3)
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_SECTOR_CTX.setdefault(d, {})[sec] = {
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'change': chg, 'above_ma20': above, 'slope': slope,
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}
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# 2. sector_snapshots 补充净流入和涨幅（近期，THS命名）
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snap_rows = conn.execute("""
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SELECT substr(m.timestamp,1,10) as d, s.name,
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AVG(s.change_pct), SUM(s.net_inflow)
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FROM sector_snapshots s JOIN market_snapshots m ON s.snapshot_id = m.id
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WHERE m.timestamp >= ? AND m.timestamp <= ?
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GROUP BY d, s.name
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""", (start_date, end_date + ' 23:59')).fetchall()
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# 优先 THS 源命名（与 sector_snapshots 同体系），证监会分类作兜底
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_STOCK_SECTOR = {}
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for d, name, chg, inflow in snap_rows:
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e = _SECTOR_CTX.setdefault(d, {}).setdefault(name, {})
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e['inflow'] = round(inflow or 0, 1)
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if 'change' not in e or e.get('change') is None:
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e['change'] = round(chg or 0, 2)
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# 3. 个股→行业映射：优先 EM 体系（覆盖全，与 sector_index_daily 对齐），THS 兜底
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try:
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for code, sec in conn.execute("SELECT code, sector FROM stock_sectors_em").fetchall():
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_STOCK_SECTOR[code] = sec
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except sqlite3.OperationalError:
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pass
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for code, sec, src in conn.execute(
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"SELECT code, sector_name, source FROM stock_sectors").fetchall():
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if src == 'ths' or code not in _STOCK_SECTOR:
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if code not in _STOCK_SECTOR and src == 'ths':
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_STOCK_SECTOR[code] = sec
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finally:
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conn.close()
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for d, name, chg, inflow in rows:
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_SECTOR_CTX.setdefault(d, {})[name] = {'change': round(chg or 0, 2), 'inflow': round(inflow or 0, 1)}
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for d in _SECTOR_CTX:
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ranked = sorted(_SECTOR_CTX[d].items(), key=lambda x: -(x[1]['change']))
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total = len(ranked)
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for rank, (name, v) in enumerate(ranked):
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v['rank_pct'] = round(rank / total, 3) if total else None # 0=最强
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def mkt_ctx(date):
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return _MKT_CTX.get(date, {})
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@@ -254,6 +300,55 @@ def sector_ctx(code, date):
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return _SECTOR_CTX.get(date, {}).get(sec, {})
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# ══════════════════════════════════════════════════════
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# 资金面上下文（stock_capital_flow 表）
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# ══════════════════════════════════════════════════════
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_FLOW_CTX = {} # code -> {date: main_pct}
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_FLOW_SORTED = {} # code -> sorted list of (date, main_pct)
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def prepare_flow_context(start_date, end_date):
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"""加载个股主力资金净流入净占比历史"""
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global _FLOW_CTX, _FLOW_SORTED
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_FLOW_CTX, _FLOW_SORTED = {}, {}
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conn = sqlite3.connect(DB_PATH)
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try:
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rows = conn.execute("""
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SELECT code, date, main_pct FROM stock_capital_flow
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WHERE date >= ? AND date <= ?
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""", (start_date, end_date)).fetchall()
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except sqlite3.OperationalError:
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rows = [] # 表不存在时容忍
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finally:
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conn.close()
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for code, d, pct in rows:
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_FLOW_CTX.setdefault(code, {})[d] = pct
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for code, dmap in _FLOW_CTX.items():
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_FLOW_SORTED[code] = sorted(dmap.items())
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def flow_ctx(code, date, bars_dates, idx):
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"""资金流因子: 当日净占比 / 5日均值 / 5日趋势"""
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series = _FLOW_SORTED.get(code)
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if not series:
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return {}
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dmap = _FLOW_CTX[code]
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# 找 date 之前(含)最近5个资金流数据点
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dates = [d for d, _ in series if d <= date]
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if not dates:
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return {}
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recent = dates[-5:]
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prior = dates[-10:-5]
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f = {'flow_pct': dmap.get(dates[-1])}
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if recent:
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vals = [dmap[d] for d in recent if dmap.get(d) is not None]
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f['flow_5d'] = round(sum(vals) / len(vals), 2) if vals else None
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if recent and prior:
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v5 = [dmap[d] for d in recent if dmap.get(d) is not None]
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p5 = [dmap[d] for d in prior if dmap.get(d) is not None]
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if v5 and p5:
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f['flow_delta'] = round(sum(v5)/len(v5) - sum(p5)/len(p5), 2)
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return f
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# ══════════════════════════════════════════════════════
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# 入场过滤器
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# ══════════════════════════════════════════════════════
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@@ -287,6 +382,12 @@ def pass_filters(factors, filters):
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# 行业
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if not chk('sector_change', filters.get('sector_change_min'), filters.get('sector_change_max')): return False
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if not chk('sector_rank_pct', None, filters.get('sector_rank_pct_max')): return False
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if not chk('sector_slope', filters.get('sector_slope_min'), filters.get('sector_slope_max')): return False
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if filters.get('sector_above_ma20') and factors.get('sector_above_ma20') is not True: return False
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# 资金面
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if not chk('flow_pct', filters.get('flow_pct_min'), filters.get('flow_pct_max')): return False
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if not chk('flow_5d', filters.get('flow_5d_min'), filters.get('flow_5d_max')): return False
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if not chk('flow_delta', filters.get('flow_delta_min'), filters.get('flow_delta_max')): return False
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return True
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@@ -345,8 +446,11 @@ def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=T
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filters = entry_cfg.get('filters', {})
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step = cfg.get('eval_step', 5)
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prepare_market_context(start_date, end_date)
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# 指标预热：取数往前多取120天，保证窗口首日 MA60/RSI/ATR 等已收敛
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fetch_start = (datetime.strptime(start_date, '%Y-%m-%d') - timedelta(days=120)).strftime('%Y-%m-%d')
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prepare_market_context(fetch_start, end_date)
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prepare_sector_context(start_date, end_date)
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prepare_flow_context(fetch_start, end_date)
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conn = sqlite3.connect(DB_PATH)
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stocks = conn.execute("""
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@@ -361,12 +465,16 @@ def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=T
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for code, name in stocks:
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screened += 1
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bars = _bars(code, start_date, end_date)
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bars = _bars(code, fetch_start, end_date)
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if not bars or len(bars) < 25:
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continue
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i = 20
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while i < len(bars):
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# 只在考察窗口内产生交易，预热期 bars 仅供指标计算
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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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window = bars[:i+1]
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sc = compute_single_score(window)
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if sc is None:
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@@ -390,6 +498,11 @@ def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=T
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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_inflow'] = sc_ctx.get('inflow')
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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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# 资金面因子
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fl = flow_ctx(code, date, None, i)
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factors.update(fl)
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if pass_filters(factors, filters):
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atr_val = last.get('atr') or 0
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@@ -516,9 +629,9 @@ def calc_summary(trades, capital):
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# ══════════════════════════════════════════════════════
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ANALYZE_FACTORS = ['rsi', 'adx', 'macd_hist', 'roc', 'atr_pct', 'dist_ma20', 'vol_ratio',
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'ma20_slope', 'macd_hist_delta', 'rsi_delta', 'mkt_slope', 'mkt_roc',
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'sector_change', 'sector_rank_pct', 'score']
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'sector_change', 'sector_rank_pct', 'sector_slope', 'flow_pct', 'flow_5d', 'flow_delta', 'score']
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BOOL_FACTORS = ['trend_aligned', 'hh_structure', 'hl_structure', 'adx_rising',
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'mkt_above_ma20', 'near_high_20d']
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'mkt_above_ma20', 'near_high_20d', 'sector_above_ma20']
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def analyze_trades(strategy_version):
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conn = sqlite3.connect(DB_PATH)
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