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因子逻辑
上涨日取涨幅的倒数、否则为1，减去其12日最小值后除以日内振幅，再做SMA平滑；值越大大致表示上涨幅度越小或振幅越窄，公式形式较特殊。
因子公式
SMA((((CLOSE>DELAY(CLOSE,1))?1/(CLOSE-DELAY(CLOSE,1)):1)-MIN(((CLOSE>DELAY(CLOSE,1))?1/(CLOSE-DELAY(CLOSE,1)):1),12))/(HIGH-LOW)*100,13,2)BigQuant SQL
SELECT date, instrument, if(m_lag(close, 1) IS NULL, NULL, if(close>m_lag(close, 1), 1/(close-m_lag(close, 1)), 1)) AS _front, (_front-least(_front, 12))/(high-low)*100 AS _front1, (m_avg(_front1, 13)*2 + m_lag(m_avg(_front1, 13), 1)*(13-2)) / 13 AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10393_strategy.py --name 'Alpha191 #164 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_164"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10393_strategy.pySELECT date, instrument, alpha_164 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_10393"], "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()