From 0298aa7660b7c2ee9ea08343e9013a92207fad30 Mon Sep 17 00:00:00 2001 From: hmo Date: Wed, 29 Jul 2026 01:17:13 +0800 Subject: [PATCH] =?UTF-8?q?feat:=2012=E4=B8=AA=E6=9C=88=E7=AA=97=E5=8F=A3+?= =?UTF-8?q?=E8=B5=84=E9=87=91=E9=9D=A2/=E6=9D=BF=E5=9D=97=E7=BB=B4?= =?UTF-8?q?=E5=BA=A6=20=E2=80=94=20=E4=BF=AE=E9=A2=84=E7=83=AD=E6=9C=9F+?= =?UTF-8?q?=E7=BD=91=E6=A0=BC=E7=9B=B8=E4=BD=8Dbug=EF=BC=8CEM=E8=A1=8C?= =?UTF-8?q?=E4=B8=9A=E4=BD=93=E7=B3=BB86=E6=9D=BF=E5=9D=97=E5=85=A8?= =?UTF-8?q?=E5=8E=86=E5=8F=B2=EF=BC=8Cv6.2=E8=BE=BE82.1%=E8=83=9C=E7=8E=87?= =?UTF-8?q?/=E5=9B=9E=E6=92=A49.68%?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- strategy_lab.py | 147 ++++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 130 insertions(+), 17 deletions(-) diff --git a/strategy_lab.py b/strategy_lab.py index 1d4d799c..22d904c4 100644 --- a/strategy_lab.py +++ b/strategy_lab.py @@ -88,7 +88,7 @@ STRATEGIES = { "hh_only": True}}, "exit": {"tp_pct": 0.15, "sl_atr": 1.5, "max_hold_days": 20}, "sizing": {"kelly": True, "kelly_fraction": 0.5}, - "eval_step": 5, + "eval_step": 1, }, }, "v4.1": { @@ -179,6 +179,22 @@ STRATEGIES.update({ "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", entry_overrides={"vol_ratio_min": 0.9, "vol_ratio_max": 2.0, "atr_pct_min": 2.8, "atr_pct_max": 6.5}), + # E组: 资金面/板块增强 + "v6.0": _v40_branch("v6.0", "资金流过滤", + "v4.0f + 资金5日净占比>-2.5(排除持续流出)", + "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", + entry_overrides={"vol_ratio_min": 0.9, "vol_ratio_max": 2.0, + "flow_5d_min": -2.5}), + "v6.1": _v40_branch("v6.1", "板块不追高", + "v4.0f + 板块MA20斜率≤1.0(排除已强涨板块)", + "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", + entry_overrides={"vol_ratio_min": 0.9, "vol_ratio_max": 2.0, + "sector_slope_max": 1.0}), + "v6.2": _v40_branch("v6.2", "资金+板块双滤", + "v4.0f + 资金5日净占比>-2.5 + 板块MA20斜率≤1.0", + "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", + entry_overrides={"vol_ratio_min": 0.9, "vol_ratio_max": 2.0, + "flow_5d_min": -2.5, "sector_slope_max": 1.0}), }) @@ -216,33 +232,63 @@ def prepare_market_context(start_date, end_date): } def prepare_sector_context(start_date, end_date): - """每日行业强度: 平均涨幅/净流入/当日排名 + 个股→行业映射""" + """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""" global _SECTOR_CTX, _STOCK_SECTOR _SECTOR_CTX, _STOCK_SECTOR = {}, {} conn = sqlite3.connect(DB_PATH) try: - rows = conn.execute(""" + # 1. 板块指数历史（全周期） + try: + idx_rows = conn.execute(""" + SELECT sector, date, close, change_pct FROM sector_index_daily + WHERE date >= ? AND date <= ? ORDER BY sector, date + """, (start_date, end_date)).fetchall() + except sqlite3.OperationalError: + idx_rows = [] + # 每板块计算 MA20 和斜率 + from collections import defaultdict + by_sector = defaultdict(list) + for sec, d, close, chg in idx_rows: + by_sector[sec].append((d, close, chg)) + for sec, series in by_sector.items(): + closes = [c for _, c, _ in series] + for i, (d, close, chg) in enumerate(series): + above = slope = None + if i >= 19: + ma20 = sum(closes[i-19:i+1]) / 20 + above = close > ma20 + if i >= 24: + ma20_5 = sum(closes[i-24:i-4]) / 20 + if ma20_5 > 0: + slope = round((ma20 - ma20_5) / ma20_5 * 100, 3) + _SECTOR_CTX.setdefault(d, {})[sec] = { + 'change': chg, 'above_ma20': above, 'slope': slope, + } + # 2. sector_snapshots 补充净流入和涨幅（近期，THS命名） + snap_rows = conn.execute(""" SELECT substr(m.timestamp,1,10) as d, s.name, AVG(s.change_pct), SUM(s.net_inflow) FROM sector_snapshots s JOIN market_snapshots m ON s.snapshot_id = m.id WHERE m.timestamp >= ? AND m.timestamp <= ? GROUP BY d, s.name """, (start_date, end_date + ' 23:59')).fetchall() - # 优先 THS 源命名（与 sector_snapshots 同体系），证监会分类作兜底 - _STOCK_SECTOR = {} + for d, name, chg, inflow in snap_rows: + e = _SECTOR_CTX.setdefault(d, {}).setdefault(name, {}) + e['inflow'] = round(inflow or 0, 1) + if 'change' not in e or e.get('change') is None: + e['change'] = round(chg or 0, 2) + # 3. 个股→行业映射：优先 EM 体系（覆盖全，与 sector_index_daily 对齐），THS 兜底 + try: + for code, sec in conn.execute("SELECT code, sector FROM stock_sectors_em").fetchall(): + _STOCK_SECTOR[code] = sec + except sqlite3.OperationalError: + pass for code, sec, src in conn.execute( "SELECT code, sector_name, source FROM stock_sectors").fetchall(): - if src == 'ths' or code not in _STOCK_SECTOR: + if code not in _STOCK_SECTOR and src == 'ths': _STOCK_SECTOR[code] = sec finally: conn.close() - for d, name, chg, inflow in rows: - _SECTOR_CTX.setdefault(d, {})[name] = {'change': round(chg or 0, 2), 'inflow': round(inflow or 0, 1)} - for d in _SECTOR_CTX: - ranked = sorted(_SECTOR_CTX[d].items(), key=lambda x: -(x[1]['change'])) - total = len(ranked) - for rank, (name, v) in enumerate(ranked): - v['rank_pct'] = round(rank / total, 3) if total else None # 0=最强 def mkt_ctx(date): return _MKT_CTX.get(date, {}) @@ -254,6 +300,55 @@ def sector_ctx(code, date): return _SECTOR_CTX.get(date, {}).get(sec, {}) +# ══════════════════════════════════════════════════════ +# 资金面上下文（stock_capital_flow 表） +# ══════════════════════════════════════════════════════ +_FLOW_CTX = {} # code -> {date: main_pct} +_FLOW_SORTED = {} # code -> sorted list of (date, main_pct) + +def prepare_flow_context(start_date, end_date): + """加载个股主力资金净流入净占比历史""" + global _FLOW_CTX, _FLOW_SORTED + _FLOW_CTX, _FLOW_SORTED = {}, {} + conn = sqlite3.connect(DB_PATH) + try: + rows = conn.execute(""" + SELECT code, date, main_pct FROM stock_capital_flow + WHERE date >= ? AND date <= ? + """, (start_date, end_date)).fetchall() + except sqlite3.OperationalError: + rows = [] # 表不存在时容忍 + finally: + conn.close() + for code, d, pct in rows: + _FLOW_CTX.setdefault(code, {})[d] = pct + for code, dmap in _FLOW_CTX.items(): + _FLOW_SORTED[code] = sorted(dmap.items()) + +def flow_ctx(code, date, bars_dates, idx): + """资金流因子: 当日净占比 / 5日均值 / 5日趋势""" + series = _FLOW_SORTED.get(code) + if not series: + return {} + dmap = _FLOW_CTX[code] + # 找 date 之前(含)最近5个资金流数据点 + dates = [d for d, _ in series if d <= date] + if not dates: + return {} + recent = dates[-5:] + prior = dates[-10:-5] + f = {'flow_pct': dmap.get(dates[-1])} + if recent: + vals = [dmap[d] for d in recent if dmap.get(d) is not None] + f['flow_5d'] = round(sum(vals) / len(vals), 2) if vals else None + if recent and prior: + v5 = [dmap[d] for d in recent if dmap.get(d) is not None] + p5 = [dmap[d] for d in prior if dmap.get(d) is not None] + if v5 and p5: + f['flow_delta'] = round(sum(v5)/len(v5) - sum(p5)/len(p5), 2) + return f + + # ══════════════════════════════════════════════════════ # 入场过滤器 # ══════════════════════════════════════════════════════ @@ -287,6 +382,12 @@ def pass_filters(factors, filters): # 行业 if not chk('sector_change', filters.get('sector_change_min'), filters.get('sector_change_max')): return False if not chk('sector_rank_pct', None, filters.get('sector_rank_pct_max')): return False + if not chk('sector_slope', filters.get('sector_slope_min'), filters.get('sector_slope_max')): return False + if filters.get('sector_above_ma20') and factors.get('sector_above_ma20') is not True: return False + # 资金面 + if not chk('flow_pct', filters.get('flow_pct_min'), filters.get('flow_pct_max')): return False + if not chk('flow_5d', filters.get('flow_5d_min'), filters.get('flow_5d_max')): return False + if not chk('flow_delta', filters.get('flow_delta_min'), filters.get('flow_delta_max')): return False return True @@ -345,8 +446,11 @@ def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=T filters = entry_cfg.get('filters', {}) step = cfg.get('eval_step', 5) - prepare_market_context(start_date, end_date) + # 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 + fetch_start = (datetime.strptime(start_date, '%Y-%m-%d') - timedelta(days=120)).strftime('%Y-%m-%d') + prepare_market_context(fetch_start, end_date) prepare_sector_context(start_date, end_date) + prepare_flow_context(fetch_start, end_date) conn = sqlite3.connect(DB_PATH) stocks = conn.execute(""" @@ -361,12 +465,16 @@ def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=T for code, name in stocks: screened += 1 - bars = _bars(code, start_date, end_date) + bars = _bars(code, fetch_start, end_date) if not bars or len(bars) < 25: continue i = 20 while i < len(bars): + # 只在考察窗口内产生交易，预热期 bars 仅供指标计算 + if bars[i].get('date', '') < start_date: + i += 1 + continue window = bars[:i+1] sc = compute_single_score(window) if sc is None: @@ -390,6 +498,11 @@ def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=T factors['sector_change'] = sc_ctx.get('change') factors['sector_rank_pct'] = sc_ctx.get('rank_pct') factors['sector_inflow'] = sc_ctx.get('inflow') + factors['sector_above_ma20'] = sc_ctx.get('above_ma20') + factors['sector_slope'] = sc_ctx.get('slope') + # 资金面因子 + fl = flow_ctx(code, date, None, i) + factors.update(fl) if pass_filters(factors, filters): atr_val = last.get('atr') or 0 @@ -516,9 +629,9 @@ def calc_summary(trades, capital): # ══════════════════════════════════════════════════════ ANALYZE_FACTORS = ['rsi', 'adx', 'macd_hist', 'roc', 'atr_pct', 'dist_ma20', 'vol_ratio', 'ma20_slope', 'macd_hist_delta', 'rsi_delta', 'mkt_slope', 'mkt_roc', - 'sector_change', 'sector_rank_pct', 'score'] + 'sector_change', 'sector_rank_pct', 'sector_slope', 'flow_pct', 'flow_5d', 'flow_delta', 'score'] BOOL_FACTORS = ['trend_aligned', 'hh_structure', 'hl_structure', 'adx_rising', - 'mkt_above_ma20', 'near_high_20d'] + 'mkt_above_ma20', 'near_high_20d', 'sector_above_ma20'] def analyze_trades(strategy_version): conn = sqlite3.connect(DB_PATH)