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因子逻辑
收盘价与成交量10日相关系数经两次线性衰减后的5日时序排名，减去 vwap 与30日均量相关系数衰减均值的截面排名，整体取反。值越大，表示近期价量相关在时序上偏低，或均价与成交量的相关在截面上偏高。
因子公式
((TSRANK(DECAYLINEAR(DECAYLINEAR(CORR((CLOSE), VOLUME, 10), 16), 4), 5) - RANK(DECAYLINEAR(CORR(VWAP, MEAN(VOLUME,30), 4),3))) * -1)BigQuant SQL
SELECT date, instrument, -1 * (m_rank(m_decay_linear(m_decay_linear(m_corr(close, volume, 10), 16), 4), 5) - pct_rank_by(date, m_decay_linear(m_corr(amount/volume, m_avg(volume, 30), 4), 3))) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10211_strategy.py --name 'Alpha191 #73 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_073"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10211_strategy.pySELECT date, instrument, alpha_073 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_10211"], "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()