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因子逻辑
7日均价减收盘价的截面缩放值，加上20倍vwap与5日前收盘价230日相关的缩放值；前者衡量收盘低于短期均线的程度，后者衡量价格长期自相关的强弱。
因子公式
(scale(((sum(close, 7) / 7) - close)) + (20 * scale(correlation(vwap, delay(close, 5), 230))))BigQuant SQL
SELECT date, instrument, ( ABS(m_avg(close, 7)-close)/(SUM(ABS(m_avg(close, 7)-close)) OVER (PARTITION BY date)) + 20 * ABS(m_corr(amount/volume, m_lag(close, 5), 230))/(SUM(m_corr(amount/volume, m_lag(close, 5), 230)) OVER (PARTITION BY date))) AS _factor_temp, -1 * _factor_temp AS factor,FROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10032_strategy.py --name 'Alpha#32 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_032"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10032_strategy.pySELECT date, instrument, alpha_032 FROM cn_stock_factors_alpha_101带入当前因子，在 Cowork 中继续研究。
from bigquant import factor
values = factor.values(["BQF_10032"], "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()