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因子逻辑
以 vwap 与近15日 vwap 最大值之差（即距近期高点的回撤）做截面排名，再以5日收盘价变化为指数进行幂运算。式子含义较特殊，大致结合了均价相对近期高点的位置与短期价格变化方向。
因子公式
RANK((VWAP -MAX(VWAP, 15)))^DELTA(CLOSE, 5)BigQuant SQL
SELECT date, instrument, pct_rank_by(date, amount/volume-greatest(amount/volume, 15))^(close-m_lag(close, 5)) AS _factor_temp, 1 * CAST(_factor_temp AS DOUBLE) AS factor,FROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10448_strategy.py --name 'Alpha191 #17 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_017"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-23"' \ && python bqf_10448_strategy.pySELECT date, instrument, alpha_017 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_10448"], "2021-01-01", "2026-09-23")report = factor.analyze( values, start_date="2021-01-01", end_date="2026-09-23", universe=("all", "hs300"), neutralize=(None, "size+industry"), depth="deep",)report.render()