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因子逻辑
计算日收益相对其61日SMA的偏离，将偏离的20日均值与当日偏离相乘，再除以一个平方项的60日平滑；大致刻画收益相对趋势的偏离及其标准化，分母写法较特殊。
因子公式
MEAN((CLOSE-DELAY(CLOSE,1))/DELAY(CLOSE,1)-SMA((CLOSE-DELAY(CLOSE,1))/DELAY(CLOSE,1),61,2),20)*((CLOSE-DELAY(CLOSE,1))/DELAY(CLOSE,1)-SMA((CLOSE-DELAY(CLOSE,1))/DELAY(CLOSE,1),61,2))/SMA(((CLOSE-DELAY(CLOSE,1))/DELAY(CLOSE,1)-((CLOSE-DELAY(CLOSE,1))/DELAY(CLOSE,1)-SMA((CLOSE-DELAY(CLOSE,1))/DELAY(CLOSE,1),61,2)))^2,60)BigQuant SQL
SELECT date, instrument, (close - m_lag(close,1)) / m_lag(close,1) AS _a, m_ta_wma(_a, 61) AS _b, _a - _b AS _c, _b^2 AS _d, m_avg(_c, 20) / m_avg(_d, 60) AS _factor_temp, -1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10357_strategy.py --name 'Alpha191 #146 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_146"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10357_strategy.pySELECT date, instrument, alpha_146 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_10357"], "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()