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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
将日内涨跌幅绝对值的标准差、收盘减开盘的日内涨跌，以及收盘与开盘10日相关系数三项相加，截面排名后取反（标准差未写明窗口）。值越大，表示日内波动较小、日内涨幅偏低或开收价相关较低。
因子公式
(-1 * RANK((STD(ABS(CLOSE -OPEN)) + (CLOSE -OPEN)) + CORR(CLOSE, OPEN,10)))BigQuant SQL
SELECT date, instrument, -1 * c_pct_rank(m_stddev(abs(close - open), 10) + (close - open) + m_corr(close, open, 10)) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10173_strategy.py --name 'Alpha191 #54 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_054"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10173_strategy.pySELECT date, instrument, alpha_054 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_10173"], "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()