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因子逻辑
vwap与成交量排名相关的衰减时序排名，与收盘和60日平均成交额时序相关峰值出现位置的衰减时序排名取较大者后取负；值越大表示这两项都处于近期低位。
因子公式
(max(Ts_Rank(decay_linear(correlation(rank(vwap), rank(volume), 3.83878), 4.16783), 8.38151), Ts_Rank(decay_linear(Ts_ArgMax(correlation(Ts_Rank(close, 7.45404), Ts_Rank(adv60, 4.13242), 3.65459), 12.6556), 14.0365), 13.4143)) * -1)BigQuant SQL
WITHwindows AS( SELECT date, instrument, pct_rank_by(date, amount/volume) AS a, pct_rank_by(date, volume) AS b, IF(m_corr(a, b, 3.83878) IS NULL, 0, m_corr(a, b, 3.83878)) AS c, m_rank(m_decay_linear(c, 4.16783), 8.38151) AS factor_1, m_rank(close, 7.45404) AS d,bq examples new strategies/multi_factor.py bqf_10095_strategy.py --name 'Alpha#96 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_096"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-17"' \ && python bqf_10095_strategy.pySELECT date, instrument, alpha_096 FROM cn_stock_factors_alpha_101带入当前因子，在 Cowork 中继续研究。
from bigquant import factor
values = factor.values(["BQF_10095"], "2021-01-01", "2026-09-17")report = factor.analyze( values, start_date="2021-01-01", end_date="2026-09-17", universe=("all", "hs300"), neutralize=(None, "size+industry"), depth="deep",)report.render()