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AI 原生量化投研平台AI-native quantitative research platform
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因子逻辑
3倍收盘价SMA减2倍二次SMA，再加对数收盘价的三重SMA，类似三重指数平滑均线TEMA的变形；反映平滑后的价格水平，取值随股价高低变化。
因子公式
3*SMA(CLOSE,13,2)-2*SMA(SMA(CLOSE,13,2),13,2)+SMA(SMA(SMA(LOG(CLOSE),13,2),13,2),13,2)BigQuant SQL
SELECT date, instrument, (m_avg(close, 13)*2 + m_lag(m_avg(close, 13), 1)*(13-2)) / 13 AS _front, (m_avg(LOG(close), 13)*2 + m_lag(m_avg(LOG(close), 13), 1)*(13-2)) / 13 AS _front1, (m_avg(_front1, 13)*2 + m_lag(m_avg(_front1, 13), 1)*(13-2)) / 13 AS _front2, (m_avg(_front2, 13)*2 + m_lag(m_avg(_front2, 13), 1)*(13-2)) / 13 AS _front3, (m_avg(_front, 13)*2 + m_lag(m_avg(_front, 13), 1)*(13-2)) / 13 AS _frontt, 3*_front-2*_frontt+_front3 AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10411_strategy.py --name 'Alpha191 #173 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_173"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10411_strategy.pySELECT date, instrument, alpha_173 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_10411"], "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()