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因子逻辑
7日价格变化乘以1减量比9日衰减均值的排名，截面排名取负后再乘以1加250日累计收益排名；值越大表示近7日缩量下跌越明显，且过去一年累计收益越高。
因子公式
((-1 * rank((delta(close, 7) * (1 - rank(decay_linear((volume / adv20), 9)))))) * (1 + rank(sum(returns, 250))))BigQuant SQL
SELECT date, instrument, pct_rank_by(date, (close - m_lag(close, 7)) * (1 - pct_rank_by(date, m_decay_linear(volume/m_avg(amount, 20), 9)))) * (1 + pct_rank_by(date, m_sum(close/m_lag(close, 1)-1, 250))) AS _factor_temp, -1 * _factor_temp AS factor,FROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10039_strategy.py --name 'Alpha#39 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_039"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10039_strategy.pySELECT date, instrument, alpha_039 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_10039"], "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()