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AI 原生量化投研平台AI-native quantitative research platform
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因子逻辑
vwap 4日变化衰减排名与开盘最低加权价2日跌幅衰减时序排名取较大者后取负，值越大表示两者都较低，即均价近期涨幅小且加权价近期未明显回落。
因子公式
(max(rank(decay_linear(delta(vwap, 4.72775), 2.91864)), Ts_Rank(decay_linear(((delta(((open * 0.147155) + (low * (1 - 0.147155))), 2.03608) / ((open * 0.147155) + (low * (1 - 0.147155)))) * -1), 3.33829), 16.7411)) * -1)BigQuant SQL
WITHfactor_data AS ( SELECT date, instrument, pct_rank_by(date, m_decay_linear(amount/volume - m_lag(amount/volume, 4.72775), 2.91864)) AS factor_1, open * 0.147155 + low * (1-0.147155) AS factor_2, m_rank(m_decay_linear((factor_2 - m_lag(factor_2, 2.03608))/factor_2, 3.33829), 16.7411)/16.7411 AS factor_3 FROM cn_stock_bar1d)SELECT date, instrument, IF (factor_3 IS NULL, NULL, IF(factor_1 < factor_3, factor_3, factor_1)) AS _factor_temp, -1 * _factor_temp AS factor,FROM factor_dataORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10073_strategy.py --name 'Alpha#73 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_073"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-17"' \ && python bqf_10073_strategy.pySELECT date, instrument, alpha_073 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_10073"], "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()