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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
比较开盘价距12日最低开盘价的截面排名，与中间价累计和与均量累计和相关系数排名的5次方再排名，前者小于后者取1、否则为0。是一个布尔信号，取1表示开盘价相对低点不高而量价相关较强。
因子公式
(RANK((OPEN -TSMIN(OPEN, 12))) < RANK((RANK(CORR(SUM(((HIGH + LOW) / 2), 19), SUM(MEAN(VOLUME,40), 19), 13))^5)))BigQuant SQL
SELECT date, instrument, m_corr(m_sum((high+low)/2, 19), m_sum(m_avg(volume, 40), 19), 13) AS _front, 1 * if(_front IS NULL, NULL, if(pct_rank_by(date, open-m_min(open, 12))<pct_rank_by(date, pct_rank_by(date, _front)^5), 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_10177_strategy.py --name 'Alpha191 #56 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_056"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10177_strategy.pySELECT date, instrument, alpha_056 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_10177"], "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()