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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
取中间价减 vwap 的20日线性衰减均值排名，与中间价和40日均量3日相关系数衰减均值的排名，两者取较小值（式中两处最高价可约去）。值越大，表示中间价持续高于均价且价与长期量同步。
因子公式
MIN(RANK(DECAYLINEAR(((((HIGH + LOW) / 2) + HIGH) -(VWAP + HIGH)), 20)),RANK(DECAYLINEAR(CORR(((HIGH + LOW) / 2), MEAN(VOLUME,40), 3), 6)))BigQuant SQL
SELECT date, instrument, -1 * (least(pct_rank_by(date, m_decay_linear((high+low)/2-amount/volume, 20)), pct_rank_by(date, m_decay_linear(m_corr((high+low)/2, m_avg(volume, 40), 3), 6)))) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10219_strategy.py --name 'Alpha191 #77 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_077"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10219_strategy.pySELECT date, instrument, alpha_077 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_10219"], "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()