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AI 原生量化投研平台AI-native quantitative research platform
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因子逻辑
衡量成交均价 vwap 与收盘价之间的偏离（取近3日的最大值与最小值，各自截面排名后相加），再乘以3日成交量变化的截面排名。值越大，表示 vwap 相对收盘价偏高（收盘弱于全天均价）且近期成交量增加。
因子公式
((RANK(MAX((VWAP -CLOSE), 3)) + RANK(MIN((VWAP -CLOSE), 3))) * RANK(DELTA(VOLUME, 3)))BigQuant SQL
SELECT date, instrument, -1 * (pct_rank_by(date, greatest(amount/volume-close, 3))+pct_rank_by(date, least(amount/volume, 3)))*pct_rank_by(date, volume-m_lag(volume, 3)) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10107_strategy.py --name 'Alpha191 #7 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_007"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-21"' \ && python bqf_10107_strategy.pySELECT date, instrument, alpha_007 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_10107"], "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()