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因子逻辑
布尔信号，比较vwap距16日最低值的排名与vwap和180日平均成交额18日相关的排名；为1表示均价离近期低点的相对距离小于量价相关性的相对水平。
因子公式
(rank((vwap - ts_min(vwap, 16.1219))) < rank(correlation(vwap, adv180, 17.9282)))BigQuant SQL
SELECT date, instrument, IF(m_avg(amount, 180) IS NULL, NULL, IF(m_lag(amount/volume, 17.9282) IS NULL, NULL, IF(pct_rank_by(date, amount/volume - m_min(amount/volume, 16.1219)) < pct_rank_by(date, m_corr(amount/volume, m_avg(amount, 180), 17.9282)), 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_10061_strategy.py --name 'Alpha#61 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_061"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-10-25"' \ && python bqf_10061_strategy.pySELECT date, instrument, alpha_061 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_10061"], "2021-01-01", "2026-10-25")report = factor.analyze( values, start_date="2021-01-01", end_date="2026-10-25", universe=("all", "hs300"), neutralize=(None, "size+industry"), depth="deep",)report.render()