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因子逻辑
日价格差的9日SMA滞后后取12日与26日均值之差，再做10日SMA，结构类似MACD信号线；值越大表示短期价格动量相对长期越强。
因子公式
SMA(MEAN(DELAY(SMA(CLOSE-DELAY(CLOSE,1),9,1),1),12)-MEAN(DELAY(SMA(CLOSE-DELAY(CLOSE,1),9,1),1),26),10,1)BigQuant SQL
SELECT date, instrument, close-m_lag(close, 1) AS _front, (m_avg(_front, 9)*1 + m_lag(m_avg(_front, 9), 1)*(9-1)) / 9 AS _front1, m_avg(m_lag(_front1, 1), 12)-m_avg(m_lag(_front1, 1), 26) AS _front2, -1 * ( (m_avg(_front2, 10)*1 + m_lag(m_avg(_front2, 10), 1)*(10-1)) / 10 ) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10403_strategy.py --name 'Alpha191 #169 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_169"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10403_strategy.pySELECT date, instrument, alpha_169 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_10403"], "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()