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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 vwap 与成交量排名的4日相关衰减均值排名，与收盘价和60日均量排名相关系数（经最大化与衰减）的排名，二者取较大值后取反。值越大，表示短期与中长期的量价相关性都越低。
因子公式
(MAX(RANK(DECAYLINEAR(CORR(RANK(VWAP), RANK(VOLUME), 4), 4)),RANK(DECAYLINEAR(MAX(CORR(RANK(CLOSE), RANK(MEAN(VOLUME,60)), 4), 13), 14))) * -1)BigQuant SQL
SELECT date, instrument, greatest(pct_rank_by(date, m_decay_linear(m_corr(pct_rank_by(date, amount/volume), pct_rank_by(date, volume), 4), 4)), pct_rank_by(date, m_decay_linear(greatest(m_corr(pct_rank_by(date, close), pct_rank_by(date, m_avg(volume, 60)), 4), 13), 14))) * -1 AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10192_strategy.py --name 'Alpha191 #64 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_064"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10192_strategy.pySELECT date, instrument, alpha_064 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_10192"], "2021-01-01", "2026-09-22")report = factor.analyze( values, start_date="2021-01-01", end_date="2026-09-22", universe=("all", "hs300"), neutralize=(None, "size+industry"), depth="deep",)report.render()