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因子逻辑
用最高价、最低价和收盘价计算9日随机值RSV，再做两次SMA平滑，类似KDJ指标的D值；值越大表示收盘价越靠近近9日价格区间的上沿。
因子公式
SMA(SMA((CLOSE-TSMIN(LOW,9))/(TSMAX(HIGH,9)-TSMIN(LOW,9))*100,3,1),3,1)BigQuant SQL
SELECT date, instrument, (close - m_min(low, 9)) / (m_max(high, 9) - m_min(low, 9)) * 100 AS _front, (m_avg(_front, 3) * 1 + m_lag(m_avg(_front, 3), 1) * (3 - 1)) / 3 AS _front2, -1 * ((m_avg(_front2, 3) * 1 + m_lag(m_avg(_front2, 3), 1) * (3 - 1)) / 3) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10258_strategy.py --name 'Alpha191 #96 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_096"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-18"' \ && python bqf_10258_strategy.pySELECT date, instrument, alpha_096 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_10258"], "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()