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因子逻辑
开盘价相对过去10日vwap均值的偏离排名，乘以收盘价偏离当日vwap程度排名的负值；值越大（越接近0）表示开盘相对均价越低、收盘与成交均价越接近。
因子公式
(rank((open - (sum(vwap, 10) / 10))) * (-1 * abs(rank((close - vwap)))))BigQuant SQL
SELECT date, instrument, pct_rank_by(date, (open - m_avg(amount/volume, 10))) * (-1 * abs(pct_rank_by(date, (close - amount/volume)))) AS _factor_temp, 1 * _factor_temp AS factor,FROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10005_strategy.py --name 'Alpha#5 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_005"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-21"' \ && python bqf_10005_strategy.pySELECT date, instrument, alpha_005 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_10005"], "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()