加载中...
AI 原生量化投研平台AI-native quantitative research platform
正在加载…Loading…
页面仍在加载，可以继续等待，或重新加载。The page is still loading. You can keep waiting or reload.
部分页面资源未能加载，请重新加载。Some page resources could not load. Please reload.
设备当前处于离线状态，恢复连接后可以继续等待或重新加载。Your device is offline. Once connected, you can keep waiting or reload.
因子逻辑
将6日均线对时间序列1至6做回归取斜率，衡量均线在近6日的线性趋势。值越大，表示短期均线上行越陡，价格趋势向上越明显。
因子公式
REGBETA(MEAN(CLOSE,6),SEQUENCE(6))BigQuant SQL
SELECT date, instrument, m_avg(close, 6) AS _A, row_number() OVER (PARTITION BY date) AS _B, m_ta_beta(_A, _B, 6) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10120_strategy.py --name 'Alpha191 #21 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_021"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10120_strategy.pySELECT date, instrument, alpha_021 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_10120"], "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()