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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
开盘价分别减去前一日最高价、收盘价、最低价后的三个排名相乘并取负，值越大（越接近0）表示开盘相对前一日价格区间越低，即低开程度越大。
因子公式
(((-1 * rank((open - delay(high, 1)))) * rank((open - delay(close, 1)))) * rank((open - delay(low, 1))))BigQuant SQL
WITHrank_data AS ( SELECT date, instrument, -1 * pct_rank_by(date, (open - m_lag(high, 1))) AS rank_1, pct_rank_by(date, (open - m_lag(close, 1))) AS rank_2, pct_rank_by(date, (open - m_lag(low, 1))) AS rank_3 FROM cn_stock_bar1d)SELECT date, instrument, ( rank_1 * rank_2 * rank_3 ) AS _factor_temp, -1 * _factor_temp AS factor,FROM rank_dataORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10020_strategy.py --name 'Alpha#20 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_020"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10020_strategy.pySELECT date, instrument, alpha_020 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_10020"], "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()