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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
以中间价(高低均值)与 vwap 按0.2/0.8加权得到参考价，取其4日变化的相反数做截面排名。值越大，表示该参考价在4日内跌得越多，刻画短期反转。
因子公式
RANK(DELTA(((((HIGH + LOW) / 2) * 0.2) + (VWAP * 0.8)), 4) * -1)BigQuant SQL
SELECT date, instrument, pct_rank_by(date, -1*(((high+low)/2*0.2+0.8*amount/volume)-m_lag(((high+low)/2*0.2+0.8*amount/volume), 4))) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10108_strategy.py --name 'Alpha191 #8 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_008"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-21"' \ && python bqf_10108_strategy.pySELECT date, instrument, alpha_008 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_10108"], "2021-01-01", "2026-09-21")report = factor.analyze( values, start_date="2021-01-01", end_date="2026-09-21", universe=("all", "hs300"), neutralize=(None, "size+industry"), depth="deep",)report.render()