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因子逻辑
布尔信号，比较vwap与成交量4日相关的排名和最低价排名与50日平均成交额排名12日相关的排名；为1表示短期均价与成交量的相关性相对较弱。
因子公式
(rank(correlation(vwap, volume, 4.24304)) < rank(correlation(rank(low), rank(adv50), 12.4413)))BigQuant SQL
SELECT date, instrument, IF(m_lag(m_avg(amount, 50), 12.4413) IS NULL, NULL, IF(pct_rank_by(date, m_corr(amount/volume, volume, 4.24304)) < pct_rank_by(date, m_corr(pct_rank_by(date, low), pct_rank_by(date, m_avg(amount, 50)), 12.4413)), 1, 0)) AS _factor_temp, 1 * CAST(_factor_temp AS DOUBLE) AS factor,FROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10075_strategy.py --name 'Alpha#75 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_075"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-17"' \ && python bqf_10075_strategy.pySELECT date, instrument, alpha_075 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_10075"], "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()