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因子逻辑
成交量排名与vwap排名的6日相关取2日均值后做截面排名，高于0.5取-1，否则取1；为二值信号，1表示量价相关性在截面上处于下半部分。
因子公式
((0.5 < rank((sum(correlation(rank(volume), rank(vwap), 6), 2) / 2.0))) ? (-1 * 1) : 1)BigQuant SQL
SELECT date, instrument, IF(m_avg(m_corr(pct_rank_by(date, volume), pct_rank_by(date, amount/volume), 6), 2) IS NULL, NULL, IF(pct_rank_by(date, m_avg(m_corr(pct_rank_by(date, volume), pct_rank_by(date, amount/volume), 6), 2))>0.5, -1, 1)) 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_10027_strategy.py --name 'Alpha#27 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_027"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10027_strategy.pySELECT date, instrument, alpha_027 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_10027"], "2021-01-01", "2026-09-22")report = factor.analyze( values, start_date="2021-01-01", end_date="2026-09-22", universe=("all", "hs300"), neutralize=(None, "size+industry"), depth="deep",)report.render()