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因子逻辑
仅在收盘价上涨的日子取20日收盘价标准差、其余为0，再做SMA平滑，类似相对波动指标RVI的上行部分；值越大表示上涨时的价格波动越大。
因子公式
SMA((CLOSE>DELAY(CLOSE,1)?STD(CLOSE,20):0),20,1)BigQuant SQL
SELECT date, instrument, m_nanavg( IF(close>m_lag(close,1), m_stddev(close,20), 0) ,20) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10413_strategy.py --name 'Alpha191 #174 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_174"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10413_strategy.pySELECT date, instrument, alpha_174 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_10413"], "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()