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因子逻辑
日价格变化与前一日价格变化的250日相关乘当日价格变化再除以收盘价，按细分行业中性化，再除以250日日收益平方和；衡量价格变化自相关方向与当日涨跌的组合，并按长期波动标准化。
因子公式
(indneutralize(((correlation(delta(close, 1), delta(delay(close, 1), 1), 250) * delta(close, 1)) / close), IndClass.subindustry) / sum(((delta(close, 1) / delay(close, 1))^2), 250))BigQuant SQL
WITHindustry_data AS ( SELECT date, instrument, industry_level1_code FROM cn_stock_industry_component WHERE industry = 'sw2021'),factor_data AS(bq examples new strategies/multi_factor.py bqf_10048_strategy.py --name 'Alpha#48 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_048"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10048_strategy.pySELECT date, instrument, alpha_048 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_10048"], "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()