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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日变化取反后经多层排名、缩放、对数等嵌套处理并取近5日最小值，再加上6日前收益取反的5日时序排名；值越大表示近期价格跌幅相对越大且滞后收益越低。
因子公式
(min(product(rank(rank(scale(log(sum(ts_min(rank(rank((-1 * rank(delta((close - 1), 5))))), 2), 1))))), 1), 5) + ts_rank(delay((-1 * returns), 6), 5))BigQuant SQL
WITHscale_computation AS ( SELECT date, instrument, close, m_min(pct_rank_by(date, pct_rank_by(date, -1 * pct_rank_by(date, close - m_lag(close, 5)))), 2) AS sum_min, LOG(sum_min) AS pre_scale, ABS(pre_scale)/SUM(ABS(pre_scale)) OVER (PARTITION BY date) AS after_scale, pct_rank_by(date, pct_rank_by(date, after_scale)) AS factor1 FROM cn_stock_bar1d)SELECT date, instrument, (IF(after_scale IS NULL, NULL, IF(factor1 < 5, factor1, 5)) + m_rank(m_lag(-1 * (close/m_lag(close, 1) - 1), 6), 5)/5) AS _factor_temp, -1 * _factor_temp AS factor,FROM scale_computationORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10029_strategy.py --name 'Alpha#29 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_029"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10029_strategy.pySELECT date, instrument, alpha_029 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_10029"], "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()