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因子逻辑
以20日平均日收益为界，比较高于与低于该水平的天数及偏离平方和，取比值的对数，刻画收益向上与向下偏离的不对称；值越大表示向下偏离的波动相对越大。
因子公式
LOG((COUNT(CLOSE/DELAY(CLOSE)-1>((CLOSE/DELAY(CLOSE,19))^(1/20)-1),20)-1)*(SUMIF(((CLOSE/DELAY(CLOSE)-1-(CLOSE/DELAY(CLOSE,19))^(1/20)-1))^2,20,CLOSE/DELAY(CLOSE)-1<(CLOSE/DELAY(CLOSE,19))^(1/20)-1))/((COUNT((CLOSE/DELAY(CLOSE)-1<(CLOSE/DELAY(CLOSE,19))^(1/20)-1),20))*(SUMIF((CLOSE/DELAY(CLOSE)-1-((CLOSE/DELAY(CLOSE,19))^(1/20)-1))^2,20,CLOSE/DELAY(CLOSE)-1>(CLOSE/DELAY(CLOSE,19))^(1/20)-1))))BigQuant SQL
SELECT date, instrument, close / m_lag(close,1) - 1 AS _a, (close / m_lag(close,19))^(1/20) - 1 AS _b, IF(_a > _b, 1, 0) AS _c1, IF(_a > _b, 0, 1) AS _c2, (_a-_b)^2 AS _d, m_sum(_c1,20) * m_sum(IF(_c1, _d, 0),20) AS _e1, m_sum(_c2,20) * m_sum(IF(_c1, _d, 0),20) As _e2, log(_e1 / _e2) AS _factor_temp, -1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10445_strategy.py --name 'Alpha191 #190 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_190"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10445_strategy.pySELECT date, instrument, alpha_190 FROM cn_stock_factors_alpha_191带入当前因子，在 Cowork 中继续研究。
from bigquant import factor
values = factor.values(["BQF_10445"], "2021-01-01", "2026-09-18")report = factor.analyze( values, start_date="2021-01-01", end_date="2026-09-18", universe=("all", "hs300"), neutralize=(None, "size+industry"), depth="deep",)report.render()