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因子逻辑
vwap减收盘价在过去3日的最大值与最小值的排名之和，乘以成交量3日变化的排名；值越大表示收盘相对成交均价越弱，且近期成交量增加越多。
因子公式
((rank(ts_max((vwap - close), 3)) + rank(ts_min((vwap - close), 3))) * rank(delta(volume, 3)))BigQuant SQL
SELECT date, instrument, -1 * ( (pct_rank_by(date, m_max((amount/volume)-close, 3))+pct_rank_by(date, m_min((amount/volume)-close, 3))) * pct_rank_by(date, (volume - m_lag(volume, 3)))) AS _factor_temp, 1 * _factor_temp AS factor,FROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10011_strategy.py --name 'Alpha#11 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_011"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-21"' \ && python bqf_10011_strategy.pySELECT date, instrument, alpha_011 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_10011"], "2021-01-01", "2026-09-21")report = factor.analyze( values, start_date="2021-01-01", end_date="2026-09-21", universe=("all", "hs300"), neutralize=(None, "size+industry"), depth="deep",)report.render()