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因子逻辑
以滞后2日的振幅率排名乘成交量排名，再除以当日振幅率与vwap减收盘之比；综合振幅变化、成交量和收盘相对均价的位置，取值方向依赖vwap与收盘的相对高低。
因子公式
((RANK(DELAY(((HIGH -LOW) / (SUM(CLOSE, 5) / 5)), 2)) * RANK(RANK(VOLUME))) / (((HIGH -LOW) /(SUM(CLOSE, 5) / 5)) / (VWAP -CLOSE)))BigQuant SQL
SELECT date, instrument, (pct_rank_by(date, m_lag((high-low)/(m_sum(close, 5)/5), 2))*pct_rank_by(date, pct_rank_by(date, volume)))/((high-low)/((m_sum(close, 5)/5))/(amount/volume-close)) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10295_strategy.py --name 'Alpha191 #114 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_114"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10295_strategy.pySELECT date, instrument, alpha_114 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_10295"], "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()