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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
两项取小：开盘与最低价排名和减最高与收盘价排名和的衰减均值排名，以及收盘与60日均量时序排名相关的衰减时序排名；值越大要求日内价格偏弱与量价相关两项同时偏高。
因子公式
MIN(RANK(DECAYLINEAR(((RANK(OPEN) + RANK(LOW)) -(RANK(HIGH) + RANK(CLOSE))), 8)),TSRANK(DECAYLINEAR(CORR(TSRANK(CLOSE, 8), TSRANK(MEAN(VOLUME,60), 20), 8), 7), 3))BigQuant SQL
SELECT date, instrument, pct_rank_by(date, open)+pct_rank_by(date, low)-pct_rank_by(date, high)-pct_rank_by(date, close) AS _front, least(pct_rank_by(date, m_decay_linear(_front, 8)), m_rank(m_decay_linear(m_corr(m_rank(close, 8), m_rank(m_avg(volume, 60), 20), 20), 7), 3)) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10345_strategy.py --name 'Alpha191 #140 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_140"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10345_strategy.pySELECT date, instrument, alpha_140 FROM cn_stock_factors_alpha_191带入当前因子，在 Cowork 中继续研究。
from bigquant import factor
values = factor.values(["BQF_10345"], "2021-01-01", "2026-09-18")report = factor.analyze( values, start_date="2021-01-01", end_date="2026-09-18", universe=("all", "hs300"), neutralize=(None, "size+industry"), depth="deep",)report.render()