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因子逻辑
统计近3日涨跌方向符号之和的截面排名，乘以5日与20日成交量之比，整体取反。值越大，表示近期下跌天数越多或近期成交量相对缩小。
因子公式
(-1*((RANK(((SIGN((CLOSE -DELAY(CLOSE, 1))) + SIGN((DELAY(CLOSE, 1) -DELAY(CLOSE, 2)))) +SIGN((DELAY(CLOSE, 2) -DELAY(CLOSE, 3)))))) * SUM(VOLUME, 5)) / SUM(VOLUME, 20))BigQuant SQL
SELECT date, instrument, -1*pct_rank_by(date, SIGN(close-m_lag(close, 1))+SIGN(m_lag(close, 1)-m_lag(close, 2))+SIGN(m_lag(close, 2)-m_lag(close, 3)))*m_sum(volume, 5)/m_sum(volume, 20) AS _factor_temp, 1 * _factor_temp AS factorFROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10147_strategy.py --name 'Alpha191 #48 选股' \ --set 'TABLES=["cn_stock_factors_alpha_191"]' --set 'FACTORS=["alpha_048"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10147_strategy.pySELECT date, instrument, alpha_048 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_10147"], "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()