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因子逻辑
在6、12、24日三个窗口上计算收盘价相对真实低点的累计位置占真实波幅的比例，再加权合成，类似终极振荡指标UO；值越大表示收盘价越靠近多周期区间上沿。
因子公式
((CLOSE-SUM(MIN(LOW,DELAY(CLOSE,1)),6))/SUM(MAX(HGIH,DELAY(CLOSE,1))-MIN(LOW,DELAY(CLOSE,1)),6)*12*24+(CLOSE-SUM(MIN(LOW,DELAY(CLOSE,1)),12))/SUM(MAX(HGIH,DELAY(CLOSE,1))-MIN(LOW,DELAY(CLOSE,1)),12)*6*24+(CLOSE-SUM(MIN(LOW,DELAY(CLOSE,1)),24))/SUM(MAX(HGIH,DELAY(CLOSE,1))-MIN(LOW,DELAY(CLOSE,1)),24)*6*24)*100/(6*12+6*24+12*24)BigQuant SQL
SELECT date, instrument, close-m_sum(least(low, m_lag(close, 1)), 6) AS _front1, m_sum(greatest(high, m_lag(close, 1))-least(low, m_lag(close, 1)), 6) AS _front2, close-m_sum(least(low, m_lag(close, 1)), 12) AS _front3, m_sum(greatest(high, m_lag(close, 1))-least(low, m_lag(close, 1)), 12) AS _front4, close-m_sum(least(low, m_lag(close, 1)), 24) AS _front5, m_sum(greatest(high, m_lag(close, 1))-least(low, m_lag(close, 1)), 24) AS _front6, -1 * ( (_front1/_front2*12*24+_front3/_front4*6*24+_front5/_front6*6*24)*100/(6*12+6*24+12*24) ) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10383_strategy.py --name 'Alpha191 #159 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_159"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10383_strategy.pySELECT date, instrument, alpha_159 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_10383"], "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()