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@@ -0,0 +1,572 @@
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+module fundit::rankingCalculator
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+
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+use fundit::sqlUtilities
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+use fundit::dataPuller
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+use fundit::dataSaver
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+
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+
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+
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+def cal_ranking() {
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+
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+}
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+
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+/*
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+ * 计算收益率排名
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+ *
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+ *
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+ */
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+def cal_ret_ranking(entity_type, entity_info, end_date, isFromMySQL) {
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+
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+ table_desc = get_performance_table_description(entity_type);
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+
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+ tb_data = get_monthly_indicator_data(table_desc.table_name[0], end_date, isFromMySQL);
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+ sec_id_col = table_desc.sec_id_col[0];
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+ tb_data.rename!(sec_id_col, 'entity_id');
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+
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+ tb_strategy = get_strategy_list();
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+ tb_substrategy = get_substrategy_list();
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+
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+ t = SELECT *
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+ FROM entity_info en
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+ INNER JOIN tb_data d ON en.entity_id = d.entity_id
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+ WHERE en.strategy IS NOT NULL
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+ AND (en.entity_id LIKE 'MF%' OR en.entity_id LIKE 'HF%')
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+
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+ // 按照 MySQL 字段建表
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+ t_s = create_entity_indicator_ranking(false);
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+ t_s_num = create_entity_indicator_ranking_num(false);
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+ t_ss = create_entity_indicator_substrategy_ranking(false);
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+ t_ss_num = create_entity_indicator_substrategy_ranking_num(false);
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+ v_tables = [t_s, t_s_num, t_ss, t_ss_num];
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+
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+ v_tables[0] = SELECT entity_id, end_date, strategy, 1 AS indicator_id,
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+ ret_1m AS indicator_1m, ret_1m.rank(false) AS absrank_1m, (ret_1m.rank(false, percent=true)*100).round(0) AS perrank_1m,
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+ ret_3m AS indicator_3m, ret_3m.rank(false) AS absrank_3m, (ret_3m.rank(false, percent=true)*100).round(0) AS perrank_3m,
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+ ret_6m AS indicator_6m, ret_6m.rank(false) AS absrank_6m, (ret_6m.rank(false, percent=true)*100).round(0) AS perrank_6m,
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+ ret_1y AS indicator_1y, ret_1y.rank(false) AS absrank_1y, (ret_1y.rank(false, percent=true)*100).round(0) AS perrank_1y,
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+ ret_2y AS indicator_2y, ret_2y.rank(false) AS absrank_2y, (ret_2y.rank(false, percent=true)*100).round(0) AS perrank_2y,
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+ ret_3y AS indicator_3y, ret_3y.rank(false) AS absrank_3y, (ret_3y.rank(false, percent=true)*100).round(0) AS perrank_3y,
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+ ret_5y AS indicator_5y, ret_5y.rank(false) AS absrank_5y, (ret_5y.rank(false, percent=true)*100).round(0) AS perrank_5y,
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+ ret_10y AS indicator_10y, ret_10y.rank(false) AS absrank_10y, (ret_10y.rank(false, percent=true)*100).round(0) AS perrank_10y,
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+ ret_ytd AS indicator_ytd, ret_ytd.rank(false) AS absrank_ytd, (ret_ytd.rank(false, percent=true)*100).round(0) AS perrank_ytd
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+ FROM t CONTEXT BY strategy, end_date;
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+
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+ v_tables[1] = SELECT t.end_date, t.strategy, s.raise_type[0], 1 AS indicator_id,
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+ ret_1m.mean() AS avg_1m, ret_1m.count() AS avg_1m_cnt, ret_1m.percentile(95) AS perrank_percent_5_1m,
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+ ret_1m.percentile(90) AS perrank_percent_10_1m, ret_1m.percentile(75) AS perrank_percent_25_1m,
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+ ret_1m.percentile(50) AS perrank_percent_50_1m, ret_1m.percentile(25) AS perrank_percent_75_1m,
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+ ret_1m.percentile(10) AS perrank_percent_90_1m, ret_1m.percentile(5) AS perrank_percent_95_1m,
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+ ret_1m.max() AS best_1m, ret_1m.min() AS worst_1m,
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+ ret_3m.mean() AS avg_3m, ret_3m.count() AS avg_3m_cnt, ret_3m.percentile(95) AS perrank_percent_5_3m,
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+ ret_3m.percentile(90) AS perrank_percent_10_3m, ret_3m.percentile(75) AS perrank_percent_25_3m,
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+ ret_3m.percentile(50) AS perrank_percent_50_3m, ret_3m.percentile(25) AS perrank_percent_75_3m,
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+ ret_3m.percentile(10) AS perrank_percent_90_3m, ret_3m.percentile(5) AS perrank_percent_95_3m,
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+ ret_3m.max() AS best_3m, ret_3m.min() AS worst_3m,
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+ ret_6m.mean() AS avg_6m, ret_6m.count() AS avg_6m_cnt, ret_6m.percentile(95) AS perrank_percent_5_6m,
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+ ret_6m.percentile(90) AS perrank_percent_10_6m, ret_6m.percentile(75) AS perrank_percent_25_6m,
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+ ret_6m.percentile(50) AS perrank_percent_50_6m, ret_6m.percentile(25) AS perrank_percent_75_6m,
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+ ret_6m.percentile(10) AS perrank_percent_90_6m, ret_6m.percentile(5) AS perrank_percent_95_6m,
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+ ret_6m.max() AS best_6m, ret_6m.min() AS worst_6m,
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+ ret_1y.mean() AS avg_1y, ret_1y.count() AS avg_1y_cnt, ret_1y.percentile(95) AS perrank_percent_5_1y,
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+ ret_1y.percentile(90) AS perrank_percent_10_1y, ret_1y.percentile(75) AS perrank_percent_25_1y,
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+ ret_1y.percentile(50) AS perrank_percent_50_1y, ret_1y.percentile(25) AS perrank_percent_75_1y,
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+ ret_1y.percentile(10) AS perrank_percent_90_1y, ret_1y.percentile(5) AS perrank_percent_95_1y,
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+ ret_1y.max() AS best_1y, ret_1y.min() AS worst_1y,
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+ ret_2y.mean() AS avg_2y, ret_2y.count() AS avg_2y_cnt, ret_2y.percentile(95) AS perrank_percent_5_2y,
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+ ret_2y.percentile(90) AS perrank_percent_10_2y, ret_2y.percentile(75) AS perrank_percent_25_2y,
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+ ret_2y.percentile(50) AS perrank_percent_50_2y, ret_2y.percentile(25) AS perrank_percent_75_2y,
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+ ret_2y.percentile(10) AS perrank_percent_90_2y, ret_2y.percentile(5) AS perrank_percent_95_2y,
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+ ret_2y.max() AS best_2y, ret_2y.min() AS worst_2y,
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+ ret_3y.mean() AS avg_3y, ret_3y.count() AS avg_3y_cnt, ret_3y.percentile(95) AS perrank_percent_5_3y,
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+ ret_3y.percentile(90) AS perrank_percent_10_3y, ret_3y.percentile(75) AS perrank_percent_25_3y,
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+ ret_3y.percentile(50) AS perrank_percent_50_3y, ret_3y.percentile(25) AS perrank_percent_75_3y,
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+ ret_3y.percentile(10) AS perrank_percent_90_3y, ret_3y.percentile(5) AS perrank_percent_95_3y,
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+ ret_3y.max() AS best_3y, ret_3y.min() AS worst_3y,
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+ ret_5y.mean() AS avg_5y, ret_5y.count() AS avg_5y_cnt, ret_5y.percentile(95) AS perrank_percent_5_5y,
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+ ret_5y.percentile(90) AS perrank_percent_10_5y, ret_5y.percentile(75) AS perrank_percent_25_5y,
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+ ret_5y.percentile(50) AS perrank_percent_50_5y, ret_5y.percentile(25) AS perrank_percent_75_5y,
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+ ret_5y.percentile(10) AS perrank_percent_90_5y, ret_5y.percentile(5) AS perrank_percent_95_5y,
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+ ret_5y.max() AS best_5y, ret_5y.min() AS worst_5y,
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+ ret_10y.mean() AS avg_10y, ret_10y.count() AS avg_10y_cnt, ret_10y.percentile(95) AS perrank_percent_5_10y,
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+ ret_10y.percentile(90) AS perrank_percent_10_10y, ret_10y.percentile(75) AS perrank_percent_25_10y,
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+ ret_10y.percentile(50) AS perrank_percent_50_10y, ret_10y.percentile(25) AS perrank_percent_75_10y,
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+ ret_10y.percentile(10) AS perrank_percent_90_10y, ret_10y.percentile(5) AS perrank_percent_95_10y,
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+ ret_10y.max() AS best_10y, ret_10y.min() AS worst_10y,
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+ ret_ytd.mean() AS avg_ytd, ret_ytd.count() AS avg_ytd_cnt, ret_ytd.percentile(95) AS perrank_percent_5_ytd,
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+ ret_ytd.percentile(90) AS perrank_percent_10_ytd, ret_ytd.percentile(75) AS perrank_percent_25_ytd,
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+ ret_ytd.percentile(50) AS perrank_percent_50_ytd, ret_ytd.percentile(25) AS perrank_percent_75_ytd,
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+ ret_ytd.percentile(10) AS perrank_percent_90_ytd, ret_ytd.percentile(5) AS perrank_percent_95_ytd,
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+ ret_ytd.max() AS best_ytd, ret_ytd.min() AS worst_ytd
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+ FROM t
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+ INNER JOIN tb_strategy s ON t.strategy = s.strategy_id
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+ GROUP BY t.strategy, t.end_date;
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+
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+
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+ v_tables[2] = SELECT entity_id, end_date, substrategy, 1 AS indicator_id,
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+ ret_1m AS indicator_1m, ret_1m.rank(false) AS absrank_1m, (ret_1m.rank(false, percent=true)*100).round(0) AS perrank_1m,
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+ ret_3m AS indicator_3m, ret_3m.rank(false) AS absrank_3m, (ret_3m.rank(false, percent=true)*100).round(0) AS perrank_3m,
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+ ret_6m AS indicator_6m, ret_6m.rank(false) AS absrank_6m, (ret_6m.rank(false, percent=true)*100).round(0) AS perrank_6m,
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+ ret_1y AS indicator_1y, ret_1y.rank(false) AS absrank_1y, (ret_1y.rank(false, percent=true)*100).round(0) AS perrank_1y,
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+ ret_2y AS indicator_2y, ret_2y.rank(false) AS absrank_2y, (ret_2y.rank(false, percent=true)*100).round(0) AS perrank_2y,
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+ ret_3y AS indicator_3y, ret_3y.rank(false) AS absrank_3y, (ret_3y.rank(false, percent=true)*100).round(0) AS perrank_3y,
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+ ret_5y AS indicator_5y, ret_5y.rank(false) AS absrank_5y, (ret_5y.rank(false, percent=true)*100).round(0) AS perrank_5y,
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+ ret_10y AS indicator_10y, ret_10y.rank(false) AS absrank_10y, (ret_10y.rank(false, percent=true)*100).round(0) AS perrank_10y,
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+ ret_ytd AS indicator_ytd, ret_ytd.rank(false) AS absrank_ytd, (ret_ytd.rank(false, percent=true)*100).round(0) AS perrank_ytd
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+ FROM t CONTEXT BY substrategy, end_date;
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+
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+ v_tables[3] = SELECT t.end_date, t.substrategy, s.raise_type[0], 1 AS indicator_id,
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+ ret_1m.mean() AS avg_1m, ret_1m.count() AS avg_1m_cnt, ret_1m.percentile(95) AS perrank_percent_5_1m,
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+ ret_1m.percentile(90) AS perrank_percent_10_1m, ret_1m.percentile(75) AS perrank_percent_25_1m,
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+ ret_1m.percentile(50) AS perrank_percent_50_1m, ret_1m.percentile(25) AS perrank_percent_75_1m,
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+ ret_1m.percentile(10) AS perrank_percent_90_1m, ret_1m.percentile(5) AS perrank_percent_95_1m,
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+ ret_1m.max() AS best_1m, ret_1m.min() AS worst_1m,
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+ ret_3m.mean() AS avg_3m, ret_3m.count() AS avg_3m_cnt, ret_3m.percentile(95) AS perrank_percent_5_3m,
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+ ret_3m.percentile(90) AS perrank_percent_10_3m, ret_3m.percentile(75) AS perrank_percent_25_3m,
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+ ret_3m.percentile(50) AS perrank_percent_50_3m, ret_3m.percentile(25) AS perrank_percent_75_3m,
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+ ret_3m.percentile(10) AS perrank_percent_90_3m, ret_3m.percentile(5) AS perrank_percent_95_3m,
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+ ret_3m.max() AS best_3m, ret_3m.min() AS worst_3m,
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+ ret_6m.mean() AS avg_6m, ret_6m.count() AS avg_6m_cnt, ret_6m.percentile(95) AS perrank_percent_5_6m,
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+ ret_6m.percentile(90) AS perrank_percent_10_6m, ret_6m.percentile(75) AS perrank_percent_25_6m,
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+ ret_6m.percentile(50) AS perrank_percent_50_6m, ret_6m.percentile(25) AS perrank_percent_75_6m,
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+ ret_6m.percentile(10) AS perrank_percent_90_6m, ret_6m.percentile(5) AS perrank_percent_95_6m,
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+ ret_6m.max() AS best_6m, ret_6m.min() AS worst_6m,
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+ ret_1y.mean() AS avg_1y, ret_1y.count() AS avg_1y_cnt, ret_1y.percentile(95) AS perrank_percent_5_1y,
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+ ret_1y.percentile(90) AS perrank_percent_10_1y, ret_1y.percentile(75) AS perrank_percent_25_1y,
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+ ret_1y.percentile(50) AS perrank_percent_50_1y, ret_1y.percentile(25) AS perrank_percent_75_1y,
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+ ret_1y.percentile(10) AS perrank_percent_90_1y, ret_1y.percentile(5) AS perrank_percent_95_1y,
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+ ret_1y.max() AS best_1y, ret_1y.min() AS worst_1y,
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+ ret_2y.mean() AS avg_2y, ret_2y.count() AS avg_2y_cnt, ret_2y.percentile(95) AS perrank_percent_5_2y,
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+ ret_2y.percentile(90) AS perrank_percent_10_2y, ret_2y.percentile(75) AS perrank_percent_25_2y,
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+ ret_2y.percentile(50) AS perrank_percent_50_2y, ret_2y.percentile(25) AS perrank_percent_75_2y,
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+ ret_2y.percentile(10) AS perrank_percent_90_2y, ret_2y.percentile(5) AS perrank_percent_95_2y,
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+ ret_2y.max() AS best_2y, ret_2y.min() AS worst_2y,
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+ ret_3y.mean() AS avg_3y, ret_3y.count() AS avg_3y_cnt, ret_3y.percentile(95) AS perrank_percent_5_3y,
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+ ret_3y.percentile(90) AS perrank_percent_10_3y, ret_3y.percentile(75) AS perrank_percent_25_3y,
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+ ret_3y.percentile(50) AS perrank_percent_50_3y, ret_3y.percentile(25) AS perrank_percent_75_3y,
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+ ret_3y.percentile(10) AS perrank_percent_90_3y, ret_3y.percentile(5) AS perrank_percent_95_3y,
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+ ret_3y.max() AS best_3y, ret_3y.min() AS worst_3y,
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+ ret_5y.mean() AS avg_5y, ret_5y.count() AS avg_5y_cnt, ret_5y.percentile(95) AS perrank_percent_5_5y,
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+ ret_5y.percentile(90) AS perrank_percent_10_5y, ret_5y.percentile(75) AS perrank_percent_25_5y,
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+ ret_5y.percentile(50) AS perrank_percent_50_5y, ret_5y.percentile(25) AS perrank_percent_75_5y,
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+ ret_5y.percentile(10) AS perrank_percent_90_5y, ret_5y.percentile(5) AS perrank_percent_95_5y,
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+ ret_5y.max() AS best_5y, ret_5y.min() AS worst_5y,
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+ ret_10y.mean() AS avg_10y, ret_10y.count() AS avg_10y_cnt, ret_10y.percentile(95) AS perrank_percent_5_10y,
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+ ret_10y.percentile(90) AS perrank_percent_10_10y, ret_10y.percentile(75) AS perrank_percent_25_10y,
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+ ret_10y.percentile(50) AS perrank_percent_50_10y, ret_10y.percentile(25) AS perrank_percent_75_10y,
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+ ret_10y.percentile(10) AS perrank_percent_90_10y, ret_10y.percentile(5) AS perrank_percent_95_10y,
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+ ret_10y.max() AS best_10y, ret_10y.min() AS worst_10y,
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+ ret_ytd.mean() AS avg_ytd, ret_ytd.count() AS avg_ytd_cnt, ret_ytd.percentile(95) AS perrank_percent_5_ytd,
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+ ret_ytd.percentile(90) AS perrank_percent_10_ytd, ret_ytd.percentile(75) AS perrank_percent_25_ytd,
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+ ret_ytd.percentile(50) AS perrank_percent_50_ytd, ret_ytd.percentile(25) AS perrank_percent_75_ytd,
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+ ret_ytd.percentile(10) AS perrank_percent_90_ytd, ret_ytd.percentile(5) AS perrank_percent_95_ytd,
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+ ret_ytd.max() AS best_ytd, ret_ytd.min() AS worst_ytd
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+ FROM t
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+ INNER JOIN tb_substrategy s ON t.substrategy = s.substrategy_id
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+ GROUP BY t.substrategy, t.end_date;
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+
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+ return v_tables;
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+}
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+
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+/*
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+ * 自定义百分位计算
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+ *
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+ */
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+defg perRank(x, is_ASC) {
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+
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+ return (100 * x.rank(ascending=is_ASC, percent=true)).round(0);
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+
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+}
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+
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+/*
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+ * 动态生成用于排序的SQL脚本
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+ *
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+ * @param indicator_name <STRING>: 指标字段名
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+ * @param indicator_id <INT>：指标ID
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+ * @param is_ASC <BOOL>: 是否排正序
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+ * @param ranking_by <STRING>: 'strategy', 'substrategy', 'factor', 'catavg'
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+ *
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+ */
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+def gen_ranking_sql(data_table, indicator_name, indicator_id, is_ASC, ranking_by) {
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+
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+ // 近1月和近3月排名仅对收益有效，为了满足表结构的要求，需要建立几个”假”字段，并用NULL赋值
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+ t_tmp = table(1000:0, ['indicator_id', 'indicator_1m', 'absrank_1m', 'perrank_1m',
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+ 'indicator_3m', 'absrank_3m', 'perrank_3m'],
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+ [INT, DOUBLE, INT, INT, DOUBLE, INT, INT]);
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+ INSERT INTO t_tmp VALUES (indicator_id, double(NULL), int(NULL), int(NULL), double(NULL), int(NULL), int(NULL));
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+
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+ // 因为 parseExpr 没法将表 data_table 传入，所以用 sql()
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+ t_ranking = sql(select = (sqlCol('entity_id'), sqlCol('end_date'), sqlCol(ranking_by), sqlCol('indicator_id'),
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+ sqlCol('indicator_1m'), sqlCol('absrank_1m'), sqlCol('perrank_1m'),
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+ sqlCol('indicator_3m'), sqlCol('absrank_3m'), sqlCol('perrank_3m'),
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+ // 与 MySQL 不同，这里统一把近4年和成立以来的排名去掉
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+ sqlCol(indicator_name + '_6m',,'indicator_6m'),
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+ sqlCol(indicator_name + '_6m', rank{, is_ASC}, 'absrank_6m'),
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+ sqlCol(indicator_name + '_6m', perRank{, is_ASC}, 'perrank_6m'),
|
|
|
+ sqlCol(indicator_name + '_1y',,'indicator_1y'),
|
|
|
+ sqlCol(indicator_name + '_1y', rank{, is_ASC}, 'absrank_1y'),
|
|
|
+ sqlCol(indicator_name + '_1y', perRank{, is_ASC}, 'perrank_1y'),
|
|
|
+ sqlCol(indicator_name + '_2y',,'indicator_2y'),
|
|
|
+ sqlCol(indicator_name + '_2y', rank{, is_ASC}, 'absrank_2y'),
|
|
|
+ sqlCol(indicator_name + '_2y', perRank{, is_ASC}, 'perrank_2y'),
|
|
|
+ sqlCol(indicator_name + '_3y',,'indicator_3y'),
|
|
|
+ sqlCol(indicator_name + '_3y', rank{, is_ASC}, 'absrank_3y'),
|
|
|
+ sqlCol(indicator_name + '_3y', perRank{, is_ASC}, 'perrank_3y'),
|
|
|
+ sqlCol(indicator_name + '_5y',,'indicator_5y'),
|
|
|
+ sqlCol(indicator_name + '_5y', rank{, is_ASC}, 'absrank_5y'),
|
|
|
+ sqlCol(indicator_name + '_5y', perRank{, is_ASC}, 'perrank_5y'),
|
|
|
+ sqlCol(indicator_name + '_10y',,'indicator_10y'),
|
|
|
+ sqlCol(indicator_name + '_10y', rank{, is_ASC}, 'absrank_10y'),
|
|
|
+ sqlCol(indicator_name + '_10y', perRank{, is_ASC}, 'perrank_10y'),
|
|
|
+ sqlCol(indicator_name + '_ytd',,'indicator_ytd'),
|
|
|
+ sqlCol(indicator_name + '_ytd', rank{, is_ASC}, 'absrank_ytd'),
|
|
|
+ sqlCol(indicator_name + '_ytd', perRank{, is_ASC}, 'perrank_ytd')
|
|
|
+ ),
|
|
|
+ from = cj(data_table, t_tmp),
|
|
|
+ where = <_$ranking_by IS NOT NULL>,
|
|
|
+ groupBy = (sqlCol(ranking_by), sqlCol('end_date')),
|
|
|
+ groupFlag = 0 ).eval(); // context by
|
|
|
+
|
|
|
+
|
|
|
+ // 近1月和近3月排名仅对收益有效，为了满足表结构的要求，需要建立几个”假”字段，并用NULL赋值
|
|
|
+ t_tmp = table(1000:0, ['indicator_id', 'avg_1m', 'avg_1m_cnt', 'perrank_percent_5_1m', 'perrank_percent_10_1m', 'perrank_percent_25_1m',
|
|
|
+ 'perrank_percent_50_1m', 'perrank_percent_75_1m', 'perrank_percent_90_1m', 'perrank_percent_95_1m', 'best_1m', 'worst_1m',
|
|
|
+ 'avg_3m', 'avg_3m_cnt', 'perrank_percent_5_3m', 'perrank_percent_10_3m', 'perrank_percent_25_3m',
|
|
|
+ 'perrank_percent_50_3m', 'perrank_percent_75_3m', 'perrank_percent_90_3m', 'perrank_percent_95_3m', 'best_3m', 'worst_3m'],
|
|
|
+ [INT, DOUBLE, INT, DOUBLE, DOUBLE, DOUBLE,
|
|
|
+ DOUBLE, DOUBLE, DOUBLE, DOUBLE, DOUBLE, DOUBLE,
|
|
|
+ DOUBLE, INT, DOUBLE, DOUBLE, DOUBLE,
|
|
|
+ DOUBLE, DOUBLE, DOUBLE, DOUBLE, DOUBLE, DOUBLE]);
|
|
|
+
|
|
|
+ INSERT INTO t_tmp VALUES (indicator_id, double(NULL), int(NULL), double(NULL), double(NULL), double(NULL),
|
|
|
+ double(NULL), double(NULL), double(NULL), double(NULL), double(NULL), double(NULL),
|
|
|
+ double(NULL), int(NULL), double(NULL), double(NULL), double(NULL),
|
|
|
+ double(NULL), double(NULL), double(NULL), double(NULL), double(NULL), double(NULL));
|
|
|
+
|
|
|
+ t_ranking_num = sql(select = (sqlCol('end_date'), sqlCol(ranking_by), sqlCol('raise_type', mean, 'raise_type'), sqlCol('indicator_id', mean,'indicator_id'),
|
|
|
+ sqlCol('avg_1m', mean, 'avg_1m'), sqlCol('avg_1m_cnt', mean, 'avg_1m_cnt'),
|
|
|
+ sqlCol('perrank_percent_5_1m', mean, 'perrank_percent_5_1m'),
|
|
|
+ sqlCol('perrank_percent_10_1m', mean, 'perrank_percent_10_1m'),
|
|
|
+ sqlCol('perrank_percent_25_1m', mean, 'perrank_percent_25_1m'),
|
|
|
+ sqlCol('perrank_percent_50_1m', mean, 'perrank_percent_50_1m'),
|
|
|
+ sqlCol('perrank_percent_75_1m', mean, 'perrank_percent_75_1m'),
|
|
|
+ sqlCol('perrank_percent_90_1m', mean, 'perrank_percent_90_1m'),
|
|
|
+ sqlCol('perrank_percent_95_1m', mean, 'perrank_percent_95_1m'),
|
|
|
+ sqlCol('best_1m', mean, 'best_1m'), sqlCol('worst_1m', mean, 'worst_1m'),
|
|
|
+ sqlCol('avg_3m', mean, 'avg_3m'), sqlCol('avg_3m_cnt', mean, 'avg_3m_cnt'),
|
|
|
+ sqlCol('perrank_percent_5_3m', mean, 'perrank_percent_5_3m'),
|
|
|
+ sqlCol('perrank_percent_10_3m', mean, 'perrank_percent_10_3m'),
|
|
|
+ sqlCol('perrank_percent_25_3m', mean, 'perrank_percent_25_3m'),
|
|
|
+ sqlCol('perrank_percent_50_3m', mean, 'perrank_percent_50_3m'),
|
|
|
+ sqlCol('perrank_percent_75_3m', mean, 'perrank_percent_75_3m'),
|
|
|
+ sqlCol('perrank_percent_90_3m', mean, 'perrank_percent_90_3m'),
|
|
|
+ sqlCol('perrank_percent_95_3m', mean, 'perrank_percent_95_3m'),
|
|
|
+ sqlCol('best_3m', mean, 'best_3m'), sqlCol('worst_3m', mean, 'worst_3m'),
|
|
|
+ // 与 MySQL 不同，这里统一把近4年和成立以来的排名去掉
|
|
|
+ sqlCol(indicator_name + '_6m', mean, 'avg_6m'), sqlCol(indicator_name + '_6m', count, 'avg_6m_cnt'),
|
|
|
+ sqlCol(indicator_name + '_6m', percentile{, iif(is_ASC, 5, 95)}, 'perrank_percent_5_6m'),
|
|
|
+ sqlCol(indicator_name + '_6m', percentile{, iif(is_ASC, 10, 90)}, 'perrank_percent_10_6m'),
|
|
|
+ sqlCol(indicator_name + '_6m', percentile{, iif(is_ASC, 25, 75)}, 'perrank_percent_25_6m'),
|
|
|
+ sqlCol(indicator_name + '_6m', percentile{, iif(is_ASC, 50, 50)}, 'perrank_percent_50_6m'),
|
|
|
+ sqlCol(indicator_name + '_6m', percentile{, iif(is_ASC, 75, 25)}, 'perrank_percent_75_6m'),
|
|
|
+ sqlCol(indicator_name + '_6m', percentile{, iif(is_ASC, 90, 10)}, 'perrank_percent_90_6m'),
|
|
|
+ sqlCol(indicator_name + '_6m', percentile{, iif(is_ASC, 95, 5)}, 'perrank_percent_95_6m'),
|
|
|
+ sqlCol(indicator_name + '_6m', iif(is_ASC, min, max), 'best_6m'),
|
|
|
+ sqlCol(indicator_name + '_6m', iif(is_ASC, max, min), 'worst_6m'),
|
|
|
+ sqlCol(indicator_name + '_1y', mean, 'avg_1y'), sqlCol(indicator_name + '_1y', count, 'avg_1y_cnt'),
|
|
|
+ sqlCol(indicator_name + '_1y', percentile{, iif(is_ASC, 5, 95)}, 'perrank_percent_5_1y'),
|
|
|
+ sqlCol(indicator_name + '_1y', percentile{, iif(is_ASC, 10, 90)}, 'perrank_percent_10_1y'),
|
|
|
+ sqlCol(indicator_name + '_1y', percentile{, iif(is_ASC, 25, 75)}, 'perrank_percent_25_1y'),
|
|
|
+ sqlCol(indicator_name + '_1y', percentile{, iif(is_ASC, 50, 50)}, 'perrank_percent_50_1y'),
|
|
|
+ sqlCol(indicator_name + '_1y', percentile{, iif(is_ASC, 75, 25)}, 'perrank_percent_75_1y'),
|
|
|
+ sqlCol(indicator_name + '_1y', percentile{, iif(is_ASC, 90, 10)}, 'perrank_percent_90_1y'),
|
|
|
+ sqlCol(indicator_name + '_1y', percentile{, iif(is_ASC, 95, 5)}, 'perrank_percent_95_1y'),
|
|
|
+ sqlCol(indicator_name + '_1y', iif(is_ASC, min, max), 'best_1y'),
|
|
|
+ sqlCol(indicator_name + '_1y', iif(is_ASC, max, min), 'worst_1y'),
|
|
|
+ sqlCol(indicator_name + '_2y', mean, 'avg_2y'), sqlCol(indicator_name + '_2y', count, 'avg_2y_cnt'),
|
|
|
+ sqlCol(indicator_name + '_2y', percentile{, iif(is_ASC, 5, 95)}, 'perrank_percent_5_2y'),
|
|
|
+ sqlCol(indicator_name + '_2y', percentile{, iif(is_ASC, 10, 90)}, 'perrank_percent_10_2y'),
|
|
|
+ sqlCol(indicator_name + '_2y', percentile{, iif(is_ASC, 25, 75)}, 'perrank_percent_25_2y'),
|
|
|
+ sqlCol(indicator_name + '_2y', percentile{, iif(is_ASC, 50, 50)}, 'perrank_percent_50_2y'),
|
|
|
+ sqlCol(indicator_name + '_2y', percentile{, iif(is_ASC, 75, 25)}, 'perrank_percent_75_2y'),
|
|
|
+ sqlCol(indicator_name + '_2y', percentile{, iif(is_ASC, 90, 10)}, 'perrank_percent_90_2y'),
|
|
|
+ sqlCol(indicator_name + '_2y', percentile{, iif(is_ASC, 95, 5)}, 'perrank_percent_95_2y'),
|
|
|
+ sqlCol(indicator_name + '_2y', iif(is_ASC, min, max), 'best_2y'),
|
|
|
+ sqlCol(indicator_name + '_2y', iif(is_ASC, max, min), 'worst_2y'),
|
|
|
+ sqlCol(indicator_name + '_3y', mean, 'avg_3y'), sqlCol(indicator_name + '_3y', count, 'avg_3y_cnt'),
|
|
|
+ sqlCol(indicator_name + '_3y', percentile{, iif(is_ASC, 5, 95)}, 'perrank_percent_5_3y'),
|
|
|
+ sqlCol(indicator_name + '_3y', percentile{, iif(is_ASC, 10, 90)}, 'perrank_percent_10_3y'),
|
|
|
+ sqlCol(indicator_name + '_3y', percentile{, iif(is_ASC, 25, 75)}, 'perrank_percent_25_3y'),
|
|
|
+ sqlCol(indicator_name + '_3y', percentile{, iif(is_ASC, 50, 50)}, 'perrank_percent_50_3y'),
|
|
|
+ sqlCol(indicator_name + '_3y', percentile{, iif(is_ASC, 75, 25)}, 'perrank_percent_75_3y'),
|
|
|
+ sqlCol(indicator_name + '_3y', percentile{, iif(is_ASC, 90, 10)}, 'perrank_percent_90_3y'),
|
|
|
+ sqlCol(indicator_name + '_3y', percentile{, iif(is_ASC, 95, 5)}, 'perrank_percent_95_3y'),
|
|
|
+ sqlCol(indicator_name + '_3y', iif(is_ASC, min, max), 'best_3y'),
|
|
|
+ sqlCol(indicator_name + '_3y', iif(is_ASC, max, min), 'worst_3y'),
|
|
|
+ sqlCol(indicator_name + '_5y', mean, 'avg_5y'), sqlCol(indicator_name + '_5y', count, 'avg_5y_cnt'),
|
|
|
+ sqlCol(indicator_name + '_5y', percentile{, iif(is_ASC, 5, 95)}, 'perrank_percent_5_5y'),
|
|
|
+ sqlCol(indicator_name + '_5y', percentile{, iif(is_ASC, 10, 90)}, 'perrank_percent_10_5y'),
|
|
|
+ sqlCol(indicator_name + '_5y', percentile{, iif(is_ASC, 25, 75)}, 'perrank_percent_25_5y'),
|
|
|
+ sqlCol(indicator_name + '_5y', percentile{, iif(is_ASC, 50, 50)}, 'perrank_percent_50_5y'),
|
|
|
+ sqlCol(indicator_name + '_5y', percentile{, iif(is_ASC, 75, 25)}, 'perrank_percent_75_5y'),
|
|
|
+ sqlCol(indicator_name + '_5y', percentile{, iif(is_ASC, 90, 10)}, 'perrank_percent_90_5y'),
|
|
|
+ sqlCol(indicator_name + '_5y', percentile{, iif(is_ASC, 95, 5)}, 'perrank_percent_95_5y'),
|
|
|
+ sqlCol(indicator_name + '_5y', iif(is_ASC, min, max), 'best_5y'),
|
|
|
+ sqlCol(indicator_name + '_5y', iif(is_ASC, max, min), 'worst_5y'),
|
|
|
+ sqlCol(indicator_name + '_10y', mean, 'avg_10y'), sqlCol(indicator_name + '_10y', count, 'avg_10y_cnt'),
|
|
|
+ sqlCol(indicator_name + '_10y', percentile{, iif(is_ASC, 5, 95)}, 'perrank_percent_5_10y'),
|
|
|
+ sqlCol(indicator_name + '_10y', percentile{, iif(is_ASC, 10, 90)}, 'perrank_percent_10_10y'),
|
|
|
+ sqlCol(indicator_name + '_10y', percentile{, iif(is_ASC, 25, 75)}, 'perrank_percent_25_10y'),
|
|
|
+ sqlCol(indicator_name + '_10y', percentile{, iif(is_ASC, 50, 50)}, 'perrank_percent_50_10y'),
|
|
|
+ sqlCol(indicator_name + '_10y', percentile{, iif(is_ASC, 75, 25)}, 'perrank_percent_75_10y'),
|
|
|
+ sqlCol(indicator_name + '_10y', percentile{, iif(is_ASC, 90, 10)}, 'perrank_percent_90_10y'),
|
|
|
+ sqlCol(indicator_name + '_10y', percentile{, iif(is_ASC, 95, 5)}, 'perrank_percent_95_10y'),
|
|
|
+ sqlCol(indicator_name + '_10y', iif(is_ASC, min, max), 'best_10y'),
|
|
|
+ sqlCol(indicator_name + '_10y', iif(is_ASC, max, min), 'worst_10y'),
|
|
|
+ sqlCol(indicator_name + '_ytd', mean, 'avg_ytd'), sqlCol(indicator_name + '_ytd', count, 'avg_ytd_cnt'),
|
|
|
+ sqlCol(indicator_name + '_ytd', percentile{, iif(is_ASC, 5, 95)}, 'perrank_percent_5_ytd'),
|
|
|
+ sqlCol(indicator_name + '_ytd', percentile{, iif(is_ASC, 10, 90)}, 'perrank_percent_10_ytd'),
|
|
|
+ sqlCol(indicator_name + '_ytd', percentile{, iif(is_ASC, 25, 75)}, 'perrank_percent_25_ytd'),
|
|
|
+ sqlCol(indicator_name + '_ytd', percentile{, iif(is_ASC, 50, 50)}, 'perrank_percent_50_ytd'),
|
|
|
+ sqlCol(indicator_name + '_ytd', percentile{, iif(is_ASC, 75, 25)}, 'perrank_percent_75_ytd'),
|
|
|
+ sqlCol(indicator_name + '_ytd', percentile{, iif(is_ASC, 90, 10)}, 'perrank_percent_90_ytd'),
|
|
|
+ sqlCol(indicator_name + '_ytd', percentile{, iif(is_ASC, 95, 5)}, 'perrank_percent_95_ytd'),
|
|
|
+ sqlCol(indicator_name + '_ytd', iif(is_ASC, min, max), 'best_ytd'),
|
|
|
+ sqlCol(indicator_name + '_ytd', iif(is_ASC, max, min), 'worst_ytd')
|
|
|
+ ),
|
|
|
+ from = cj(data_table, t_tmp),
|
|
|
+ where = <_$ranking_by IS NOT NULL>,
|
|
|
+ groupBy = (sqlCol(ranking_by), sqlCol('end_date')),
|
|
|
+ groupFlag = 1).eval(); // group by
|
|
|
+
|
|
|
+ return t_ranking, t_ranking_num;
|
|
|
+}
|
|
|
+
|
|
|
+/*
|
|
|
+ * 运行排名SQL脚本
|
|
|
+ *
|
|
|
+ *
|
|
|
+ */
|
|
|
+def run_ranking_sql(data_table, indicator_name, indicator_id, is_ASC, mutable v_tables) {
|
|
|
+/*
|
|
|
+ entity_type = 'MF'
|
|
|
+ end_date = 2024.09M
|
|
|
+ isFromMySQL = true;
|
|
|
+ indicator_name = 'alpha'
|
|
|
+ indicator_id = 11
|
|
|
+ is_ASC = false;
|
|
|
+ groupby_field = 'strategy';
|
|
|
+ data_table = t;
|
|
|
+ v_tables = v_ranking_tables
|
|
|
+*/
|
|
|
+
|
|
|
+ tb_strategy_ranking = gen_ranking_sql(data_table, indicator_name, indicator_id, is_ASC, 'strategy')[0];
|
|
|
+ v_tables[0].tableInsert(tb_strategy_ranking);
|
|
|
+
|
|
|
+ tb_strategy_ranking_num = gen_ranking_sql(data_table, indicator_name, indicator_id, is_ASC, 'strategy')[1];
|
|
|
+ v_tables[1].tableInsert(tb_strategy_ranking_num);
|
|
|
+
|
|
|
+ tb_substrategy_ranking = gen_ranking_sql(data_table, indicator_name, indicator_id, is_ASC, 'substrategy')[0];
|
|
|
+ v_tables[2].tableInsert(tb_substrategy_ranking);
|
|
|
+
|
|
|
+ tb_substrategy_ranking_num = gen_ranking_sql(data_table, indicator_name, indicator_id, is_ASC, 'substrategy')[1];
|
|
|
+ v_tables[3].tableInsert(tb_substrategy_ranking_num);
|
|
|
+
|
|
|
+}
|
|
|
+
|
|
|
+/*
|
|
|
+ * 计算风险指标排名
|
|
|
+ *
|
|
|
+ *
|
|
|
+ */
|
|
|
+def cal_risk_ranking(entity_type, entity_info, end_date, isFromMySQL) {
|
|
|
+
|
|
|
+ table_desc = get_risk_stats_table_description(entity_type);
|
|
|
+
|
|
|
+ tb_data = get_monthly_indicator_data(table_desc.table_name[0], end_date, isFromMySQL);
|
|
|
+ sec_id_col = table_desc.sec_id_col[0];
|
|
|
+ tb_data.rename!(sec_id_col, 'entity_id');
|
|
|
+
|
|
|
+ t = SELECT *
|
|
|
+ FROM entity_info en
|
|
|
+ INNER JOIN tb_data d ON en.entity_id = d.entity_id
|
|
|
+ WHERE en.strategy IS NOT NULL;
|
|
|
+
|
|
|
+ // 按照 MySQL 字段建表
|
|
|
+ t_s = create_entity_indicator_ranking(false);
|
|
|
+ t_s_num = create_entity_indicator_ranking_num(false);
|
|
|
+ t_ss = create_entity_indicator_substrategy_ranking(false);
|
|
|
+ t_ss_num = create_entity_indicator_substrategy_ranking_num(false);
|
|
|
+
|
|
|
+ v_ranking_tables = [t_s, t_s_num, t_ss, t_ss_num];
|
|
|
+
|
|
|
+ // 最大回撤
|
|
|
+ run_ranking_sql(t, 'maxdrawdown', 2, true, v_ranking_tables);
|
|
|
+
|
|
|
+ // 峰度
|
|
|
+ run_ranking_sql(t, 'kurtosis', 6, true, v_ranking_tables);
|
|
|
+
|
|
|
+ // 偏度
|
|
|
+ run_ranking_sql(t, 'skewness', 9, false, v_ranking_tables);
|
|
|
+
|
|
|
+ // 标准差
|
|
|
+ run_ranking_sql(t, 'stddev', 10, true, v_ranking_tables);
|
|
|
+
|
|
|
+ // Alpha
|
|
|
+ run_ranking_sql(t, 'alpha', 11, false, v_ranking_tables);
|
|
|
+
|
|
|
+ // Beta
|
|
|
+ run_ranking_sql(t, 'beta', 12, false, v_ranking_tables);
|
|
|
+
|
|
|
+ // 下行标准差
|
|
|
+ run_ranking_sql(t, 'downsidedev', 21, true, v_ranking_tables);
|
|
|
+
|
|
|
+ // 月最大回撤 dolphin 未计算
|
|
|
+ // run_ranking_sql(t, 'maxdrawdown_months', 50, true, v_ranking_tables);
|
|
|
+
|
|
|
+ // 最大回撤修复月份数 dolphin 未计算
|
|
|
+ //run_ranking_sql(t, 'maxdrawdown_recoverymonths', 52, true, v_ranking_tables);
|
|
|
+
|
|
|
+ // 胜率
|
|
|
+ run_ranking_sql(t, 'winrate', 59, false, v_ranking_tables);
|
|
|
+
|
|
|
+ return v_ranking_tables;
|
|
|
+}
|
|
|
+
|
|
|
+
|
|
|
+/*
|
|
|
+ * 计算风险调整收益指标排名
|
|
|
+ *
|
|
|
+ *
|
|
|
+ */
|
|
|
+def cal_risk_adj_return_ranking(entity_type, entity_info, end_date, isFromMySQL) {
|
|
|
+
|
|
|
+ table_desc = get_riskadjret_stats_table_description(entity_type);
|
|
|
+
|
|
|
+ tb_data = get_monthly_indicator_data(table_desc.table_name[0], end_date, isFromMySQL);
|
|
|
+ sec_id_col = table_desc.sec_id_col[0];
|
|
|
+ tb_data.rename!(sec_id_col, 'entity_id');
|
|
|
+
|
|
|
+ t = SELECT *
|
|
|
+ FROM entity_info en
|
|
|
+ INNER JOIN tb_data d ON en.entity_id = d.entity_id
|
|
|
+ WHERE en.strategy IS NOT NULL;
|
|
|
+
|
|
|
+ // 按照 MySQL 字段建表
|
|
|
+ t_s = create_entity_indicator_ranking(false);
|
|
|
+ t_s_num = create_entity_indicator_ranking_num(false);
|
|
|
+ t_ss = create_entity_indicator_substrategy_ranking(false);
|
|
|
+ t_ss_num = create_entity_indicator_substrategy_ranking_num(false);
|
|
|
+
|
|
|
+ v_ranking_tables = [t_s, t_s_num, t_ss, t_ss_num];
|
|
|
+
|
|
|
+ // Kappa ratio
|
|
|
+ run_ranking_sql(t, 'kapparatio', 14, false, v_ranking_tables);
|
|
|
+
|
|
|
+ // Treynor ratio
|
|
|
+ run_ranking_sql(t, 'treynorratio', 15, false, v_ranking_tables);
|
|
|
+
|
|
|
+ // Jensen
|
|
|
+ run_ranking_sql(t, 'jensen', 16, false, v_ranking_tables);
|
|
|
+
|
|
|
+ // Omega ratio
|
|
|
+ run_ranking_sql(t, 'omegaratio', 17, false, v_ranking_tables);
|
|
|
+
|
|
|
+ // Sharpe ratio
|
|
|
+ run_ranking_sql(t, 'sharperatio', 18, false, v_ranking_tables);
|
|
|
+
|
|
|
+ // MAR Sortino ratio dolphin 未计算
|
|
|
+ //run_ranking_sql(t, 'sortinoratio_MAR', 19, false, v_ranking_tables);
|
|
|
+
|
|
|
+ // Calmar ratio
|
|
|
+ run_ranking_sql(t, 'calmarratio', 40, false, v_ranking_tables);
|
|
|
+
|
|
|
+ // Sortino ratio
|
|
|
+ run_ranking_sql(t, 'sortinoratio', 58, false, v_ranking_tables);
|
|
|
+
|
|
|
+ return v_ranking_tables;
|
|
|
+}
|
|
|
+
|
|
|
+
|
|
|
+/*
|
|
|
+ * 计算杂项指标排名
|
|
|
+ *
|
|
|
+ *
|
|
|
+ */
|
|
|
+def cal_other_indicator_ranking(entity_type, entity_info, end_date, isFromMySQL) {
|
|
|
+
|
|
|
+ table_desc = get_indicator_table_description(entity_type);
|
|
|
+
|
|
|
+ tb_data = get_monthly_indicator_data(table_desc.table_name[0], end_date, isFromMySQL);
|
|
|
+ sec_id_col = table_desc.sec_id_col[0];
|
|
|
+ tb_data.rename!(sec_id_col, 'entity_id');
|
|
|
+
|
|
|
+ t = SELECT *
|
|
|
+ FROM entity_info en
|
|
|
+ INNER JOIN tb_data d ON en.entity_id = d.entity_id
|
|
|
+ WHERE en.strategy IS NOT NULL;
|
|
|
+
|
|
|
+ // 按照 MySQL 字段建表
|
|
|
+ t_s = create_entity_indicator_ranking(false);
|
|
|
+ t_s_num = create_entity_indicator_ranking_num(false);
|
|
|
+ t_ss = create_entity_indicator_substrategy_ranking(false);
|
|
|
+ t_ss_num = create_entity_indicator_substrategy_ranking_num(false);
|
|
|
+
|
|
|
+ v_ranking_tables = [t_s, t_s_num, t_ss, t_ss_num];
|
|
|
+
|
|
|
+ // 风格一致性 dolphin 未计算
|
|
|
+ //run_ranking_sql(t, 'per_con', 37, false, v_ranking_tables);
|
|
|
+
|
|
|
+ // Information ratio
|
|
|
+ run_ranking_sql(t, 'info_ratio', 38, false, v_ranking_tables);
|
|
|
+
|
|
|
+ // Value at Risk
|
|
|
+ run_ranking_sql(t, 'var', 41, true, v_ranking_tables);
|
|
|
+
|
|
|
+ // Conditional Value at Risk
|
|
|
+ run_ranking_sql(t, 'cvar', 42, true, v_ranking_tables);
|
|
|
+
|
|
|
+ // SMDD 模型下的 VaR dolphin 未计算
|
|
|
+ //run_ranking_sql(t, 'smddvar', 43, true, v_ranking_tables);
|
|
|
+
|
|
|
+ // SMDD 模型下的 CVaR dolphin 未计算
|
|
|
+ //run_ranking_sql(t, 'smddcvar', 44, true, v_ranking_tables);
|
|
|
+
|
|
|
+ // SMDD 模型下的 LPM1 dolphin 未计算
|
|
|
+ //run_ranking_sql(t, 'smdd_lpm1', 45, true, v_ranking_tables);
|
|
|
+
|
|
|
+ // SMDD 模型下的 LPM2 dolphin 未计算
|
|
|
+ //run_ranking_sql(t, 'smdd_lpm2', 46, true, v_ranking_tables);
|
|
|
+
|
|
|
+ // SMDD 模型下的下行风险 dolphin 未计算
|
|
|
+ //run_ranking_sql(t, 'smdd_downside_dev', 47, true, v_ranking_tables);
|
|
|
+
|
|
|
+ // 跟踪误差
|
|
|
+ run_ranking_sql(t, 'tracking_error', 48, true, v_ranking_tables);
|
|
|
+
|
|
|
+ // M2
|
|
|
+ run_ranking_sql(t, 'm2', 49, false, v_ranking_tables);
|
|
|
+
|
|
|
+ return v_ranking_tables;
|
|
|
+}
|
|
|
+
|
|
|
+/*
|
|
|
+ * 排名数据入库
|
|
|
+ *
|
|
|
+ * @param ranking_tables <VECTOR>: 包含4个数据表的向量，分别是一级策略排名，一级策略排名阈值，二级策略排名，二级策略排名阈值
|
|
|
+ */
|
|
|
+def save_ranking_tables(ranking_tables) {
|
|
|
+
|
|
|
+ if(ranking_tables.isVoid()) return;
|
|
|
+
|
|
|
+ ranking_tables[0].rename!('entity_id', 'fund_id');
|
|
|
+ save_and_sync(ranking_tables[0], 'raw_db.pf_fund_indicator_ranking', 'raw_db.pf_fund_indicator_ranking');
|
|
|
+
|
|
|
+ save_and_sync(ranking_tables[1], 'raw_db.pf_fund_indicator_ranking_num', 'raw_db.pf_fund_indicator_ranking_num');
|
|
|
+
|
|
|
+ ranking_tables[2].rename!('entity_id', 'fund_id');
|
|
|
+ save_and_sync(ranking_tables[2], 'raw_db.pf_fund_indicator_substrategy_ranking', 'raw_db.pf_fund_indicator_substrategy_ranking');
|
|
|
+
|
|
|
+ save_and_sync(ranking_tables[3], 'raw_db.pf_fund_indicator_substrategy_ranking_num', 'raw_db.pf_fund_indicator_substrategy_ranking_num');
|
|
|
+
|
|
|
+}
|
|
|
+
|
