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因子逻辑
近3日涨跌方向之和做排名后用1减去，再乘以5日成交量占20日成交量的比例；值越大表示近3日下跌天数越多，且近期成交量占比越高。
因子公式
(((1.0 - rank(((sign((close - delay(close, 1))) + sign((delay(close, 1) - delay(close, 2)))) + sign((delay(close, 2) - delay(close, 3)))))) * sum(volume, 5)) / sum(volume, 20))BigQuant SQL
SELECT date, instrument, (1.0 - pct_rank_by(date, (sign(close - m_lag(close, 1)) + sign(m_lag(close, 1) - m_lag(close, 2)) + sign(m_lag(close, 2) - m_lag(close, 3))))) * (m_sum(volume, 5) / m_sum(volume, 20)) AS _factor_temp, 1 * _factor_temp AS factor,FROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10030_strategy.py --name 'Alpha#30 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_030"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10030_strategy.pySELECT date, instrument, alpha_030 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_10030"], "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()