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因子逻辑
由日内涨幅与前日成交量相关、开盘减收盘、滞后收益反转、vwap与成交额相关强度、价格相对200日均线位置乘日内涨幅五项排名加权组成，值越大表示加权综合得分越高。
因子公式
(((((2.21 * rank(correlation((close - open), delay(volume, 1), 15))) + (0.7 * rank((open - close)))) + (0.73 * rank(Ts_Rank(delay((-1 * returns), 6), 5)))) + rank(abs(correlation(vwap, adv20, 6)))) + (0.6 * rank((((sum(close, 200) / 200) - open) * (close - open)))))BigQuant SQL
SELECT date, instrument, 2.21 * pct_rank_by(date, m_corr((close - open), m_lag(volume, 1), 15)) + 0.7 * pct_rank_by(date, open - close) + 0.73 * pct_rank_by(date, m_rank(m_lag(-1 * (close/m_lag(close, 1)-1), 6), 5)) + pct_rank_by(date, ABS(m_corr(amount/volume, m_avg(amount, 20), 6))) + 0.6 * pct_rank_by(date, (m_avg(close, 200) - open)*(close - open)) AS _factor_temp, 1 * _factor_temp AS factor,FROM cn_stock_bar1dORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10036_strategy.py --name 'Alpha#36 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_036"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-22"' \ && python bqf_10036_strategy.pySELECT date, instrument, alpha_036 FROM cn_stock_factors_alpha_101带入当前因子，在 Cowork 中继续研究。
from bigquant import factor
values = factor.values(["BQF_10036"], "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()