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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
先看100日均价的百日变化率是否不超过5%：若是，取收盘价相对100日最低收盘的涨幅的负值，否则取近3日价格变化的负值；属条件化反转度量，值越大表示相应窗口涨幅越小。
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 -TSMIN(CLOSE, 100))) : (-1 * DELTA(CLOSE, 3)))BigQuant SQL
SELECT date, instrument, m_avg(close, 100) / 100 AS _front, IF(m_lag(_front, 100) IS NULL, NULL, IF((_front - m_lag(_front, 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 factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10262_strategy.py --name 'Alpha191 #98 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_098"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10262_strategy.pySELECT date, instrument, alpha_098 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_10262"], "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()