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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
当日最高价相对近2日低位的抬升幅度排名，以vwap与120日均量6日相关的排名为指数做幂运算，再取负；值越大大致表示高点抬升与量价同步性越弱。
因子公式
((RANK((HIGH -MIN(HIGH, 2)))^RANK(CORR((VWAP), (MEAN(VOLUME,120)), 6))) * -1)BigQuant SQL
SELECT date, instrument, pct_rank_by(date, high-least(high, 2))^pct_rank_by(date, m_corr(amount/volume, m_avg(volume, 120), 6))*-1 AS _factor_temp, -1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10283_strategy.py --name 'Alpha191 #108 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_108"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10283_strategy.pySELECT date, instrument, alpha_108 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_10283"], "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()