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因子逻辑
vwap与10日平均成交额长期累计和相关性的四次方排名经14日累乘取对数后排名，若小于vwap与成交量排名5日相关的排名则取-1，表示短期量价相关的截面位置更高。
因子公式
((rank(Log(product(rank((rank(correlation(vwap, sum(adv10, 49.6054), 8.47743))^4)), 14.9655))) < rank(correlation(rank(vwap), rank(volume), 5.07914))) * -1)BigQuant SQL
WITHfactor_data AS ( SELECT date, instrument, pct_rank_by(date, LOG(m_product(pct_rank_by(date, (pct_rank_by(date, m_corr(amount/volume, m_sum(m_avg(amount, 10), 49.6054), 8.47743)))^4), 14.9655))) AS factor_1, pct_rank_by(date, m_corr(pct_rank_by(date, amount/volume), pct_rank_by(date, volume), 5.07914)) AS factor_2 FROM cn_stock_bar1d)SELECT date, instrument, IF(factor_1 IS NULL, NULL, -1 * IF(factor_1 < factor_2, -1, 0)) AS _factor_temp, -1 * CAST(_factor_temp AS DOUBLE) AS factor,FROM factor_dataORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10081_strategy.py --name 'Alpha#81 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_081"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-17"' \ && python bqf_10081_strategy.pySELECT date, instrument, alpha_081 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_10081"], "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()