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因子逻辑
收盘价10日变化取负后的线性衰减加权多层排名、3日价格变化取负的排名，以及成交额与最低价12日相关的符号三者相加；值越大表示中短期跌幅越大。
因子公式
((rank(rank(rank(decay_linear((-1 * rank(rank(delta(close, 10)))), 10)))) + rank((-1 * delta(close, 3)))) + sign(scale(correlation(adv20, low, 12))))BigQuant SQL
SELECT date, instrument, pct_rank_by(date, pct_rank_by(date, pct_rank_by(date, m_decay_linear(-1 * pct_rank_by(date, pct_rank_by(date, close-m_lag(close, 10))), 10)))) + pct_rank_by(date, -1 * (close - m_lag(close, 3))) + SIGN(ABS(m_corr(m_avg(amount, 20), low, 12))/(SUM(ABS(m_corr(m_avg(amount, 20), low, 12))) OVER (PARTITION BY date))) AS _factor_temp, 1 * _factor_temp AS factor,FROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10031_strategy.py --name 'Alpha#31 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_031"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10031_strategy.pySELECT date, instrument, alpha_031 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_10031"], "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()