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因子逻辑
比较两个量价相关的截面排名：收盘价与长期累计均量的15日相关，以及高价与vwap加权价同成交量的11日相关；前者排名更低时取-1，否则为0。
因子公式
((RANK(CORR(CLOSE, SUM(MEAN(VOLUME,30), 37), 15)) < RANK(CORR(RANK(((HIGH * 0.1) + (VWAP * 0.9))),RANK(VOLUME), 11))) * -1)BigQuant SQL
SELECT date, instrument, pct_rank_by(date, m_corr(close, m_sum(m_avg(volume, 30), 37), 15)) AS _front, pct_rank_by(date, m_corr(pct_rank_by(date, high*0.1+amount/volume*0.9), pct_rank_by(date, volume), 11)) AS _front2, if(_front IS NULL, NULL, if(_front<_front2, 1, 0))*-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_10268_strategy.py --name 'Alpha191 #101 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_101"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10268_strategy.pySELECT date, instrument, alpha_101 FROM cn_stock_factors_alpha_191带入当前因子，在 Cowork 中继续研究。
from bigquant import factor
values = factor.values(["BQF_10268"], "2021-01-01", "2026-09-18")report = factor.analyze( values, start_date="2021-01-01", end_date="2026-09-18", universe=("all", "hs300"), neutralize=(None, "size+industry"), depth="deep",)report.render()