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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
开盘价与60日均量9日累计的6日相关排名，若低于开盘价相对14日最低开盘涨幅的排名则取-1，否则为0；-1表示开盘价抬升相对量价相关更突出。
因子公式
((RANK(CORR((OPEN), SUM(MEAN(VOLUME,60), 9), 6)) < RANK((OPEN -TSMIN(OPEN, 14)))) * -1)BigQuant SQL
SELECT date, instrument, m_corr(open, m_sum(m_avg(volume, 60), 9), 6) AS _front, -1 * ( if(_front IS NULL, NULL, if(_front<pct_rank_by(date, open-m_min(open, 14)), -1, 0)) ) AS _factor_temp, 1 * CAST(_factor_temp AS DOUBLE) AS factor,FROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10361_strategy.py --name 'Alpha191 #148 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_148"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10361_strategy.pySELECT date, instrument, alpha_148 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_10361"], "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()