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因子逻辑
按日比较高低价之和与前一日的大小，取12日内价格重心下移日的波动幅度占全部方向波动的比例，类似 DMI 的方向强度。值越大（0到1），表示下行方向的波动越占主导。
因子公式
SUM(((HIGH+LOW)>=(DELAY(HIGH,1)+DELAY(LOW,1))?0:MAX(ABS(HIGH-DELAY(HIGH,1)),ABS(LOW-DELAY(LOW,1)))),12)/(SUM(((HIGH+LOW)>=(DELAY(HIGH,1)+DELAY(LOW,1))?0:MAX(ABS(HIGH-DELAY(HIGH,1)),ABS(LOW-DELAY(LOW,1)))),12)+SUM(((HIGH+LOW)<=(DELAY(HIGH,1)+DELAY(LOW,1))?0:MAX(ABS(HIGH-DELAY(HIGH,1)),ABS(LOW-DELAY(LOW,1)))),12))BigQuant SQL
SELECT date, instrument, high+low AS _pre1, m_lag(high, 1)+m_lag(low, 1) AS _pre2, -1 * ( 1+m_sum(if(_pre2 IS NULL, NULL, if(_pre1<=_pre2, 0, greatest(ABS(high-m_lag(high, 1)), ABS(low-m_lag(low, 1))))), 12)) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10162_strategy.py --name 'Alpha191 #49 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_049"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10162_strategy.pySELECT date, instrument, alpha_049 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_10162"], "2021-01-01", "2026-09-22")report = factor.analyze( values, start_date="2021-01-01", end_date="2026-09-22", universe=("all", "hs300"), neutralize=(None, "size+industry"), depth="deep",)report.render()