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因子逻辑
按日累加6天的多空力量：上涨日计收盘价减去最低价与昨收中的较小者，下跌日计收盘价减去最高价与昨收中的较大者，平盘记0，类似 Williams 累积/派发(A/D)指标。值越大，说明近期收盘相对真实区间偏强。
因子公式
SUM((CLOSE=DELAY(CLOSE,1)?0:CLOSE-(CLOSE>DELAY(CLOSE,1)?MIN(LOW,DELAY(CLOSE,1)):MAX(HIGH,DELAY(CLOSE,1)))),6)BigQuant SQL
SELECT date, instrument, -1 * m_sum(if(m_lag(close, 1) IS NULL, NULL, if(close=m_lag(close, 1), 0, close-if(close>m_lag(close, 1), least(low, m_lag(close, 1)), greatest(high, m_lag(close, 1))))), 6) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10103_strategy.py --name 'Alpha191 #3 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_003"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-21"' \ && python bqf_10103_strategy.pySELECT date, instrument, alpha_003 FROM cn_stock_factors_alpha_191带入当前因子，在 Cowork 中继续研究。
from bigquant import factor
values = factor.values(["BQF_10103"], "2021-01-01", "2026-09-21")report = factor.analyze( values, start_date="2021-01-01", end_date="2026-09-21", universe=("all", "hs300"), neutralize=(None, "size+industry"), depth="deep",)report.render()