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因子逻辑
最高价排名与成交量排名的5日协方差做截面排名后取反，值越大表示最高价与成交量的同向共变越弱。
因子公式
(-1 * rank(covariance(rank(high), rank(volume), 5)))BigQuant SQL
SELECT t1.date, t1.instrument, -pct_rank_by(t1.date, t1.cov) AS _factor_temp, 1 * _factor_temp AS factor,FROM ( SELECT t.date, t.instrument, m_covar_pop(t.rank_high, t.rank_volume, 5) AS cov FROM ( SELECT date, instrument, pct_rank_by(date, high) AS rank_high, pct_rank_by(date, volume) AS rank_volume FROM cn_stock_bar1d ) t) t1ORDER BY t1.date, t1.instrumentbq examples new strategies/multi_factor.py bqf_10016_strategy.py --name 'Alpha#16 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_016"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-21"' \ && python bqf_10016_strategy.pySELECT date, instrument, alpha_016 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_10016"], "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()