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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
若100日均线的100日变化率不超过5%，取收盘价与100日最低价距离的相反数，否则取3日价格变化的相反数；值越大表示越接近长期低点或近3日跌幅越大。
因子公式
((((delta((sum(close, 100) / 100), 100) / delay(close, 100)) < 0.05) || ((delta((sum(close, 100) / 100), 100) / delay(close, 100)) == 0.05)) ? (-1 * (close - ts_min(close, 100))) : (-1 * delta(close, 3)))BigQuant SQL
SELECT date, instrument, IF(m_lag(close, 100) IS NULL, NULL, IF(((m_avg(close, 100) - m_lag(m_avg(close, 100), 100)) / m_lag(close, 100) <= 0.05), -1 * (close - m_min(close, 100)), -1 * (close - m_lag(close, 3)))) AS _factor_temp, 1 * _factor_temp AS factor,FROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10024_strategy.py --name 'Alpha#24 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_024"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10024_strategy.pySELECT date, instrument, alpha_024 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_10024"], "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()