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因子逻辑
收盘价偏离48日均值的累计和取最大值减最小值，并与48日标准差相除，类似重标极差R/S分析；原记法运算优先级有歧义，值越大大致表示价格累计偏离越大。
因子公式
MAX(SUMAC(CLOSE-MEAN(CLOSE,48)))-MIN(SUMAC(CLOSE-MEAN(CLOSE,48)))/STD(CLOSE,48)BigQuant SQL
SELECT date, instrument, close - m_avg(close, 48) AS _a, SUM(_a) OVER (ORDER BY date) AS _b, (m_max(_b, 48) - m_min(_b, 48)) / m_stddev(close, 48) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10395_strategy.py --name 'Alpha191 #165 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_165"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10395_strategy.pySELECT date, instrument, alpha_165 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_10395"], "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()