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因子逻辑
收盘价与20日平均成交额累计和6日相关的20日时序排名，若小于收盘减vwap的排名则取-1，表示收盘相对均价偏高的截面位置超过量价相关性的近期位置。
因子公式
((Ts_Rank(correlation(close, sum(adv20, 14.7444), 6.00049), 20.4195) < rank(((open + close) - (vwap + open)))) * -1)BigQuant SQL
SELECT date, instrument, IF(m_rank(m_corr(close, m_sum(m_avg(amount, 20), 14.7444), 6.00049), 20.4195) IS NULL, NULL, IF(m_rank(m_corr(close, m_sum(m_avg(amount, 20), 14.7444), 6.00049), 20.4195)/20.4195 < pct_rank_by(date, close - amount/volume), -1, 0)) 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_10085_strategy.py --name 'Alpha#86 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_086"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-17"' \ && python bqf_10085_strategy.pySELECT date, instrument, alpha_086 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_10085"], "2021-01-01", "2026-09-17")report = factor.analyze( values, start_date="2021-01-01", end_date="2026-09-17", universe=("all", "hs300"), neutralize=(None, "size+industry"), depth="deep",)report.render()