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
最高价与最低价分别减去同一条15日SMA后相减，结果等于当日振幅，再除以收盘价，实为日内振幅率；值越大表示当日波动相对价格越大。
因子公式
((HIGH-SMA(CLOSE,15,2))-(LOW-SMA(CLOSE,15,2)))/CLOSEBigQuant SQL
SELECT date, instrument, (m_avg(close, 15)*2 + m_lag(m_avg(close, 15), 1)*(15-2)) / 15 AS _front, -1 * ( (high-low)/close ) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10381_strategy.py --name 'Alpha191 #158 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_158"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10381_strategy.pySELECT date, instrument, alpha_158 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_10381"], "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()