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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（混入少量开盘价）与60日平均成交额累计和的6日相关排名，若小于开盘价距13日最低开盘价的排名则取-1，表示开盘离近期低点的相对距离超过量价相关性。
因子公式
((rank(correlation(((open * 0.00817205) + (vwap * (1 - 0.00817205))), sum(adv60, 8.6911), 6.40374)) < rank((open - ts_min(open, 13.635)))) * -1)BigQuant SQL
SELECT date, instrument, IF(m_sum(m_avg(amount, 60), 8.6911) IS NULL, NULL, IF(pct_rank_by(date, m_corr(open*0.00817205+amount/volume*(1-0.00817205), m_sum(m_avg(amount, 60), 8.6911), 6.40374)) < pct_rank_by(date, open - m_min(open, 13.635)), -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_10065_strategy.py --name 'Alpha#65 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_065"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10065_strategy.pySELECT date, instrument, alpha_065 FROM cn_stock_factors_alpha_101带入当前因子，在 Cowork 中继续研究。
from bigquant import factor
values = factor.values(["BQF_10065"], "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()