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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
收盘价5日变化取负后经多层排名、对数、时序最小值等变换得到反转项，再加上滞后6日负收益的5日时序排名；整体刻画短期价格反转，值越大表示近期跌幅越大。
因子公式
(MIN(PROD(RANK(RANK(LOG(SUM(TSMIN(RANK(RANK((-1 * RANK(DELTA((CLOSE -1), 5))))), 2), 1)))), 1), 5) +TSRANK(DELAY((-1 * RET), 6), 5))BigQuant SQL
SELECT date, instrument, close/m_lag(close, 1)-1 AS _RET, LOG(m_min(pct_rank_by(date, pct_rank_by(date, -1*pct_rank_by(date, close-1-m_lag(close-1, 5)))), 2)) AS _front, least(pct_rank_by(date, pct_rank_by(date, _front)), 5)+m_rank(-1*_RET-m_lag(-1*_RET, 6), 5) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10379_strategy.py --name 'Alpha191 #157 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_157"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10379_strategy.pySELECT date, instrument, alpha_157 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_10379"], "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()