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因子逻辑
取两项较小者：开盘与最低排名之和减最高与收盘排名之和的衰减排名，与收盘和60日平均成交额时序排名相关的衰减时序排名；值越大表示日内收盘偏弱且价额时序同步度较高。
因子公式
min(rank(decay_linear(((rank(open) + rank(low)) - (rank(high) + rank(close))), 8.06882)), Ts_Rank(decay_linear(correlation(Ts_Rank(close, 8.44728), Ts_Rank(adv60, 20.6966), 8.01266), 6.65053), 2.61957))BigQuant SQL
WITHfactor_data AS ( SELECT date, instrument, pct_rank_by(date, m_decay_linear(pct_rank_by(date, open) + pct_rank_by(date, low) - pct_rank_by(date, high) - pct_rank_by(date, close), 8.06882)) AS factor_1, m_rank(m_decay_linear(m_corr(m_rank(close, 8.44728), m_rank(m_avg(amount, 60), 20.6966), 8.01266), 6.65053), 2.61957)/2.61957 AS factor_2 FROM cn_stock_bar1d)SELECT date, instrument, IF(factor_2 IS NULL, NULL, IF(factor_1 < factor_2, factor_1, factor_2)) AS _factor_temp, 1 * _factor_temp AS factor,FROM factor_dataORDER BY date, instrumentbq examples new strategies/multi_factor.py bqf_10087_strategy.py --name 'Alpha#88 选股' \ --set 'TABLES=["cn_stock_factors_alpha_101"]' --set 'FACTORS=["alpha_088"]' --set 'HOLD=30' \ --set 'EVERY=20' --set 'BENCHMARK="000300.SH"' --set 'START="2021-01-01"' \ --set 'END="2026-09-17"' \ && python bqf_10087_strategy.pySELECT date, instrument, alpha_088 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_10087"], "2021-01-01", "2026-09-17")report = factor.analyze( values, start_date="2021-01-01", end_date="2026-09-17", universe=("all", "hs300"), neutralize=(None, "size+industry"), depth="deep",)report.render()