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因子逻辑
最高价与成交量5日相关的5日变化，乘以收盘价20日波动率的排名后取反，值越大表示高价与成交量的相关性下降越多，且在高波动股票上幅度更大。
因子公式
(-1 * (delta(correlation(high, volume, 5), 5) * rank(stddev(close, 20))))BigQuant SQL
SELECT date, instrument, (m_corr(high, volume, 5)-m_lag(m_corr(high, volume, 5), 5))* pct_rank_by(date, m_stddev(close, 20)) AS _factor_temp, -1 * _factor_temp AS factor,FROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10022_strategy.py --name 'Alpha#22 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_022"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10022_strategy.pySELECT date, instrument, alpha_022 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_10022"], "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()