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因子逻辑
收盘价排名与成交量排名的5日协方差，再做截面排名并取反，值越大表示价格与成交量的同向共变越弱。
因子公式
(-1 * rank(covariance(rank(close), rank(volume), 5)))BigQuant SQL
WITHcov_table AS ( SELECT date, instrument, pct_rank_by(date, close) AS close_rank, pct_rank_by(date, volume) AS volume_rank, m_covar_pop(close_rank, volume_rank, 5) AS covar_close_volumeFROM cn_stock_bar1d)SELECT date, instrument, pct_rank_by(date, covar_close_volume) AS _factor_temp, -1 * _factor_temp AS factor,FROM cov_tableORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10013_strategy.py --name 'Alpha#13 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_013"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-21"' \ && python bqf_10013_strategy.pySELECT date, instrument, alpha_013 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_10013"], "2021-01-01", "2026-09-21")report = factor.analyze( values, start_date="2021-01-01", end_date="2026-09-21", universe=("all", "hs300"), neutralize=(None, "size+industry"), depth="deep",)report.render()