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因子逻辑
行业中性化vwap与81日平均成交额相关衰减后的时序排名，除以收盘与vwap混合价2日变化衰减均值的排名；值越大表示均价与成交额相关处于近期高位而近期价格上涨相对较少。
因子公式
(Ts_Rank(decay_linear(correlation(IndNeutralize(vwap, IndClass.industry), adv81, 17.4193), 19.848), 7.54455) / rank(decay_linear(delta(((close * 0.524434) + (vwap * (1 - 0.524434))), 2.77377), 16.2664)))BigQuant SQL
WITHindustry_data AS ( SELECT date, instrument, industry_level1_code FROM cn_stock_industry_component WHERE industry = 'sw2021'),factor_data AS (bq examples new strategies/multi_factor.py bqf_10092_strategy.py --name 'Alpha#93 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_093"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-17"' \ && python bqf_10092_strategy.pySELECT date, instrument, alpha_093 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_10092"], "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()