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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
成交量13日与27日SMA之差，再减去该差值的10日SMA，即成交量版MACD柱状值；值越大表示成交量短期放大的动能越强。
因子公式
SMA(VOLUME,13,2)-SMA(VOLUME,27,2)-SMA(SMA(VOLUME,13,2)-SMA(VOLUME,27,2),10,2)BigQuant SQL
SELECT date, instrument, (m_avg(volume, 13)*2 + m_lag(m_avg(volume, 13), 1)*(13-2)) / 13 AS _front1, (m_avg(volume, 27)*2 + m_lag(m_avg(volume, 27), 1)*(27-2)) / 27 AS _front2, (m_avg(_front1-_front2, 10)*2 + m_lag(m_avg(_front1-_front2, 10), 1)*(10-2)) / 10 AS _front3, -1 * ( _front1-_front2-_front3 ) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10375_strategy.py --name 'Alpha191 #155 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_155"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10375_strategy.pySELECT date, instrument, alpha_155 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_10375"], "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()