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因子逻辑
以典型价格乘成交量作为资金流，按14日内上涨日与下跌日资金流之比换算到0至100，即资金流量指标MFI；值越大表示近期资金流入力量越强。
因子公式
100-(100/(1+SUM(((HIGH+LOW+CLOSE)/3>DELAY((HIGH+LOW+CLOSE)/3,1)?(HIGH+LOW+CLOSE)/3*VOLUME:0),14)/SUM(((HIGH+LOW+CLOSE)/3<DELAY((HIGH+LOW+CLOSE)/3,1)?(HIGH+LOW+CLOSE)/3*VOLUME:0),14)))BigQuant SQL
SELECT date, instrument, (high+low+close)/3 AS _front, -1 * (100-(100/(1+m_sum(if(m_lag(_front, 1) IS NULL, NULL, if(_front>m_lag(_front, 1), _front*volume, 0)), 14)/m_sum(if(m_lag(_front, 1) IS NULL, NULL, if(_front<m_lag(_front, 1), _front*volume, 0)), 14))) ) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10321_strategy.py --name 'Alpha191 #128 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_128"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10321_strategy.pySELECT date, instrument, alpha_128 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_10321"], "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()