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因子逻辑
近5日日内实体绝对值的波动率、当日收盘减开盘、收盘与开盘的10日相关三项相加后排名取反，值越大表示日内涨幅、实体波动与开收盘相关性整体越低。
因子公式
(-1 * rank(((stddev(abs((close - open)), 5) + (close - open)) + correlation(close, open, 10))))BigQuant SQL
WITHfactor_pre AS ( SELECT date, instrument, m_stddev(abs(close - open), 5) + (close - open) + m_corr(close, open, 10) AS pre_factor FROM cn_stock_bar1d)SELECT date, instrument, pct_rank_by(date, pre_factor) AS _factor_temp, -1 * _factor_temp AS factor,FROM factor_preORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10018_strategy.py --name 'Alpha#18 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_018"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10018_strategy.pySELECT date, instrument, alpha_018 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_10018"], "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()