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因子逻辑
用过去60日个股收益对市场、规模、价值三因子回归，取残差平方后做20日加权移动平均，衡量特质波动率。值越大，表示无法被三因子解释的个股特有波动越大。
因子公式
WMA((REGRESI(CLOSE/DELAY(CLOSE)-1,MKT,SMB,HML，60))^2,20)BigQuant SQL
WITHdata1 AS ( SELECT date, instrument, change_ratio, FROM cn_stock_factors),data2 AS ( SELECTbq examples new strategies/multi_factor.py bqf_10129_strategy.py --name 'Alpha191 #30 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --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_10129_strategy.pySELECT date, instrument, alpha_030 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_10129"], "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()