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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
收盘价2日变化线性衰减均值的排名，减去加权价（0.3vwap+0.7开盘价）与长期成交量累计值相关系数衰减均值的排名，整体取反。值越大，表示近期价格上涨较弱，或价与长期量的相关性较高。
因子公式
((RANK(DECAYLINEAR(DELTA((CLOSE), 2),8)) -RANK(DECAYLINEAR(CORR(((VWAP * 0.3) + (OPEN * 0.7)),SUM(MEAN(VOLUME,180), 37), 14), 12))) * -1)BigQuant SQL
SELECT date, instrument, -1*(pct_rank_by(date, m_decay_linear(close-m_lag(close, 2), 8))-pct_rank_by(date, m_decay_linear(m_corr(amount/volume*0.3+open*0.7, m_sum(m_avg(volume, 180), 37), 14), 12))) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10138_strategy.py --name 'Alpha191 #39 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_039"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10138_strategy.pySELECT date, instrument, alpha_039 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_10138"], "2021-01-01", "2026-09-22")report = factor.analyze( values, start_date="2021-01-01", end_date="2026-09-22", universe=("all", "hs300"), neutralize=(None, "size+industry"), depth="deep",)report.render()