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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
vwap与80日均量17日相关的衰减均值排名，除以收盘与vwap均价3日变化的衰减均值排名；值越大表示量价相关偏高而短期价格涨幅偏小。
因子公式
(RANK(DECAYLINEAR(CORR((VWAP), MEAN(VOLUME,80),17), 20)) / RANK(DECAYLINEAR(DELTA(((CLOSE * 0.5)+ (VWAP * 0.5)), 3), 16)))BigQuant SQL
SELECT date, instrument, close*0.5+amount/volume*0.5 AS _pre, pct_rank_by(date, m_decay_linear(m_corr(amount/volume, m_avg(volume, 80), 17), 20))/pct_rank_by(date, m_decay_linear(_pre-m_lag(_pre, 3), 16)) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10315_strategy.py --name 'Alpha191 #125 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_125"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10315_strategy.pySELECT date, instrument, alpha_125 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_10315"], "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()