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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
取加权价（0.35收盘+0.65vwap）2日变化衰减均值的截面排名，与180日均量和收盘价13日相关系数绝对值衰减均值的15日时序排名，两者取较大值后取反。值越大，表示近期价格涨幅偏小且价与长期成交量的关联偏弱。
因子公式
(MAX(RANK(DECAYLINEAR(DELTA(((CLOSE * 0.35) + (VWAP *0.65)), 2), 3)),TSRANK(DECAYLINEAR(ABS(CORR((MEAN(VOLUME,180)), CLOSE, 13)), 5), 15)) * -1)BigQuant SQL
SELECT date, instrument, close * 0.35 + amount/volume * 0.65 AS _front, _front - m_lag(_front, 2) AS _front2, greatest(pct_rank_by(date, m_decay_linear(_front2, 3)), m_rank(m_decay_linear(ABS(m_corr(m_avg(volume, 180), close, 13)), 5), 15)) * -1 AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10250_strategy.py --name 'Alpha191 #92 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_092"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10250_strategy.pySELECT date, instrument, alpha_092 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_10250"], "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()