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因子逻辑
最低与vwap加权价19日累计和与40日平均成交额累计和的相关排名，以vwap与成交量排名5日相关的排名为指数；值越大表示价格与成交额的累计同步程度越高。
因子公式
(rank(correlation(sum(((low * 0.352233) + (vwap * (1 - 0.352233))), 19.7428), sum(adv40, 19.7428), 6.83313))^rank(correlation(rank(vwap), rank(volume), 5.77492)))BigQuant SQL
SELECT date, instrument, pct_rank_by(date, m_corr(m_sum(low*0.352233+amount/volume*(1-0.352233), 19.7428), m_sum(m_avg(amount, 40), 19.7428), 6.83313)) ^ pct_rank_by(date, m_corr(pct_rank_by(date, amount/volume), pct_rank_by(date, volume), 5.77492)) AS _factor_temp, -1 * _factor_temp AS factor,FROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10078_strategy.py --name 'Alpha#78 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_078"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-17"' \ && python bqf_10078_strategy.pySELECT date, instrument, alpha_078 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_10078"], "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()