test: v9多周期消融(否决) — 周线趋势过滤与回调买入逻辑冲突(weekly_up=True胜率60.9% vs False 78.4%)，证据存档不并入
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@@ -242,6 +242,17 @@ STRATEGIES.update({
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entry_overrides={"vol_ratio_min": 0.9, "vol_ratio_max": 2.0, "sector_slope_max": 1.0,
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"hl_only": True, "rsi_delta_min": 6},
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exit_overrides={"tp_pct": None, "exit_mode": "swing", "sl_atr": 1.5, "max_hold_days": 40, "reentry_days": 10}),
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# H组: 多周期维度
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"v9.0": _v40_branch("v9.0", "周线趋势过滤",
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"v7.1 + 周线收盘须站上周线MA10（中期趋势向上才买）",
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"L2多周期维度消融：日线级的回调买点若周线趋势已坏则是下跌中继；周线MA10上方=中期趋势完好",
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entry_overrides={"vol_ratio_min": 0.9, "vol_ratio_max": 2.0, "sector_slope_max": 1.0,
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"hl_only": True, "rsi_delta_min": 6, "weekly_up": True}),
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"v9.1": _v40_branch("v9.1", "周线多头排列",
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"v7.1 + 周线MA10>MA20（周线多头排列，更严的中期趋势要求）",
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"比weekly_up更严的变体：不仅要求价在线上，还要求周线均线本身多头排列",
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entry_overrides={"vol_ratio_min": 0.9, "vol_ratio_max": 2.0, "sector_slope_max": 1.0,
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"hl_only": True, "rsi_delta_min": 6, "weekly_aligned": True}),
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})
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@@ -396,6 +407,61 @@ def flow_ctx(code, date, bars_dates, idx):
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return f
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# ══════════════════════════════════════════════════════
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# 多周期（周线）上下文
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# ══════════════════════════════════════════════════════
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_WEEKLY_CTX = {} # code -> sorted [(date, close, ma10w, ma20w)]
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def prepare_weekly_context(start_date, end_date):
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"""加载 stock_weekly，计算每周 MA10/MA20"""
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global _WEEKLY_CTX
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_WEEKLY_CTX = {}
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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, close FROM stock_weekly
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WHERE date >= ? AND date <= ? ORDER BY code, date
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""", (start_date, end_date)).fetchall()
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except sqlite3.OperationalError:
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rows = []
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finally:
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conn.close()
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from collections import defaultdict
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by_code = defaultdict(list)
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for code, d, close in rows:
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by_code[code].append((d, close))
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for code, series in by_code.items():
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closes = [c for _, c in series]
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out = []
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for i, (d, close) in enumerate(series):
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ma10 = sum(closes[max(0, i-9):i+1]) / len(closes[max(0, i-9):i+1]) if i >= 4 else None
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ma20 = sum(closes[max(0, i-19):i+1]) / len(closes[max(0, i-19):i+1]) if i >= 10 else None
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out.append((d, close, ma10, ma20))
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_WEEKLY_CTX[code] = out
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def weekly_ctx(code, date):
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"""取 date 之前最近一根完整周线的状态"""
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series = _WEEKLY_CTX.get(code)
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if not series:
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return {}
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last = None
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for d, close, ma10, ma20 in series:
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if d < date: # 只用已完成的周线（不含当周）
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last = (d, close, ma10, ma20)
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else:
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break
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if not last:
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return {}
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_, close, ma10, ma20 = last
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f = {}
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if ma10:
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f['weekly_up'] = close > ma10 # 周线站上MA10 = 中期趋势向上
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f['weekly_dist'] = round((close - ma10) / ma10 * 100, 2)
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if ma10 and ma20:
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f['weekly_aligned'] = ma10 > ma20 # 周线多头排列
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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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@@ -437,6 +503,10 @@ def pass_filters(factors, filters):
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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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# 多周期
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if filters.get('weekly_up') and factors.get('weekly_up') is not True: return False
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if filters.get('weekly_aligned') and factors.get('weekly_aligned') is not True: return False
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if not chk('weekly_dist', filters.get('weekly_dist_min'), filters.get('weekly_dist_max')): return False
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return True
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@@ -500,6 +570,7 @@ def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=T
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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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prepare_weekly_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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@@ -552,6 +623,8 @@ def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=T
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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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# 周线因子
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factors.update(weekly_ctx(code, date))
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if pass_filters(factors, filters):
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atr_val = last.get('atr') or 0
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@@ -866,9 +939,10 @@ 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', 'sector_slope', 'flow_pct', 'flow_5d', 'flow_delta', 'score']
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'sector_change', 'sector_rank_pct', 'sector_slope', 'flow_pct', 'flow_5d', 'flow_delta',
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'weekly_dist', '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', 'sector_above_ma20']
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'mkt_above_ma20', 'near_high_20d', 'sector_above_ma20', 'weekly_up', 'weekly_aligned']
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def analyze_trades(strategy_version):
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conn = sqlite3.connect(DB_PATH)
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