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因子逻辑
取开盘价日变化线性衰减均值的排名，与成交量和开盘价17日相关系数衰减均值的排名，两者取较小值后取反（式中0.65与0.35加权的都是开盘价）。值越大，表示开盘价走势偏弱或量价相关偏低。
因子公式
(MIN(RANK(DECAYLINEAR(DELTA(OPEN, 1), 15)), RANK(DECAYLINEAR(CORR((VOLUME), ((OPEN * 0.65) +(OPEN *0.35)), 17),7))) * -1)BigQuant SQL
SELECT date, instrument, least(pct_rank_by(date, m_decay_linear(open-m_lag(open, 1), 15)), pct_rank_by(date, m_decay_linear(m_corr(volume, open, 17), 7)))*-1 AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10134_strategy.py --name 'Alpha191 #35 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_035"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10134_strategy.pySELECT date, instrument, alpha_035 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_10134"], "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()