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因子逻辑
两项线性衰减均值的排名之差：vwap与累计均量的相关，以及开盘价与15日均量排名相关的时序排名；值越大表示前一种量价相关相对后一种越强。
因子公式
(RANK(DECAYLINEAR(CORR(VWAP, SUM(MEAN(VOLUME,5), 26), 5), 7)) -RANK(DECAYLINEAR(TSRANK(MIN(CORR(RANK(OPEN), RANK(MEAN(VOLUME,15)), 21), 9), 7), 8)))BigQuant SQL
SELECT date, instrument, -1 * (pct_rank_by(date, m_decay_linear(m_corr(amount/volume, m_sum(m_avg(volume, 5), 26), 5), 7))-pct_rank_by(date, m_decay_linear(m_rank(least(m_corr(pct_rank_by(date, open), pct_rank_by(date, m_avg(volume, 15)), 21), 9), 7), 8)) ) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10305_strategy.py --name 'Alpha191 #119 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_119"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10305_strategy.pySELECT date, instrument, alpha_119 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_10305"], "2021-01-01", "2026-09-18")report = factor.analyze( values, start_date="2021-01-01", end_date="2026-09-18", universe=("all", "hs300"), neutralize=(None, "size+industry"), depth="deep",)report.render()