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因子逻辑
收盘价减vwap，再除以30日最高收盘价截面排名的线性衰减均值；值越大表示收盘相对当日成交均价越强，并按近期高点排名做缩放。
因子公式
(CLOSE -VWAP) / DECAYLINEAR(RANK(TSMAX(CLOSE, 30)),2)BigQuant SQL
SELECT date, instrument, (close-amount/volume)/m_decay_linear(pct_rank_by(date, m_max(close, 30)), 2) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10449_strategy.py --name 'Alpha191 #124 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_124"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-23"' \ && python bqf_10449_strategy.pySELECT date, instrument, alpha_124 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_10449"], "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()