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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
细分行业中性化的收盘区间位置乘成交量排名的1.5倍缩放值，减去中性化的价额相关与30日低点位置之差的缩放值，乘量比后取负；值越大表示收盘偏向日内低位的量能越强且当日相对放量。
因子公式
(0 - (1 * (((1.5 * scale(indneutralize(indneutralize(rank(((((close - low) - (high - close)) / (high - low)) * volume)), IndClass.subindustry), IndClass.subindustry))) - scale(indneutralize((correlation(close, rank(adv20), 5) - rank(ts_argmin(close, 30))), IndClass.subindustry))) * (volume / adv20))))BigQuant SQL
WITHindustry_data AS ( SELECT date, instrument, industry_level1_code FROM cn_stock_industry_component WHERE industry = 'sw2021'),windows AS (bq examples new strategies/multi_factor.py bqf_10099_strategy.py --name 'Alpha#100 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_100"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-17"' \ && python bqf_10099_strategy.pySELECT date, instrument, alpha_100 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_10099"], "2021-01-01", "2026-09-17")report = factor.analyze( values, start_date="2021-01-01", end_date="2026-09-17", universe=("all", "hs300"), neutralize=(None, "size+industry"), depth="deep",)report.render()