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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
近7日价格变化方向取反，再乘以1加过去250日累计收益的排名；值越大表示近7日下跌，且过去一年累计收益越高，体现短期反转与长期动量的结合。
因子公式
((-1 * sign(((close - delay(close, 7)) + delta(close, 7)))) * (1 + rank((1 + sum(returns, 250)))))BigQuant SQL
WITHfactor_pre AS ( SELECT date, instrument, -1 * SIGN((close - m_lag(close, 7)) + (close - m_lag(close, 7))) AS pre_factor1, 1 + m_sum((close/m_lag(close, 1) - 1), 250) AS pre_factor2 FROM cn_stock_bar1d)SELECT date, instrument, pre_factor1 * pct_rank_by(date, pre_factor2) AS _factor_temp, 1 * _factor_temp AS factor,FROM factor_preORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10019_strategy.py --name 'Alpha#19 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_019"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10019_strategy.pySELECT date, instrument, alpha_019 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_10019"], "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()