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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
以中间价变化乘以振幅、除以成交量，经 SMA(15,2) 平滑，是9号因子的长周期版本，类似 EMV 指标。值越大，表示价格以较低成交量实现较大上移。
因子公式
SMA(((HIGH+LOW)/2-(DELAY(HIGH,1)+DELAY(LOW,1))/2)*(HIGH-LOW)/VOLUME,15,2)BigQuant SQL
SELECT date, instrument, ((high+low)/2-(m_lag(high, 1)+m_lag(low, 1))/2)*(high-low)/volume AS _front, -1 * ((m_avg(_front, 15)*2 + m_lag(m_avg(_front, 15), 1)*(15-2)) / 15) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10200_strategy.py --name 'Alpha191 #68 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_068"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10200_strategy.pySELECT date, instrument, alpha_068 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_10200"], "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()