|
|
@@ -38,165 +38,6 @@ def get_indicator_info() {
|
|
|
|
|
|
}
|
|
|
|
|
|
-/*
|
|
|
- * 计算收益率排名
|
|
|
- *
|
|
|
- * TODO: 整合入 gen_ranking_sql
|
|
|
- */
|
|
|
-def cal_ret_ranking(entity_type, entity_info, end_date, isFromMySQL) {
|
|
|
-
|
|
|
- // 当前只对基金做排名， 其它类型参考基金排名做相对排名
|
|
|
- if(!(entity_type in ['MF', 'HF'])) return null;
|
|
|
-
|
|
|
- table_desc = get_performance_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');
|
|
|
-
|
|
|
- tb_strategy = get_strategy_list();
|
|
|
- tb_substrategy = get_substrategy_list();
|
|
|
-
|
|
|
- t = SELECT *
|
|
|
- FROM entity_info en
|
|
|
- INNER JOIN tb_data d ON en.entity_id = d.entity_id
|
|
|
- WHERE en.strategy IS NOT NULL
|
|
|
- AND (en.entity_id LIKE 'MF%' OR en.entity_id LIKE 'HF%')
|
|
|
-
|
|
|
- // 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_tables = [t_s, t_s_num, t_ss, t_ss_num];
|
|
|
-
|
|
|
- v_tables[0] = SELECT entity_id, end_date, strategy, 1 AS indicator_id,
|
|
|
- ret_1m AS indicator_1m, ret_1m.rank(false) AS absrank_1m, (ret_1m.rank(false, percent=true)*100).round(0) AS perrank_1m,
|
|
|
- ret_3m AS indicator_3m, ret_3m.rank(false) AS absrank_3m, (ret_3m.rank(false, percent=true)*100).round(0) AS perrank_3m,
|
|
|
- ret_6m AS indicator_6m, ret_6m.rank(false) AS absrank_6m, (ret_6m.rank(false, percent=true)*100).round(0) AS perrank_6m,
|
|
|
- ret_1y AS indicator_1y, ret_1y.rank(false) AS absrank_1y, (ret_1y.rank(false, percent=true)*100).round(0) AS perrank_1y,
|
|
|
- ret_2y AS indicator_2y, ret_2y.rank(false) AS absrank_2y, (ret_2y.rank(false, percent=true)*100).round(0) AS perrank_2y,
|
|
|
- ret_3y AS indicator_3y, ret_3y.rank(false) AS absrank_3y, (ret_3y.rank(false, percent=true)*100).round(0) AS perrank_3y,
|
|
|
- ret_5y AS indicator_5y, ret_5y.rank(false) AS absrank_5y, (ret_5y.rank(false, percent=true)*100).round(0) AS perrank_5y,
|
|
|
- ret_10y AS indicator_10y, ret_10y.rank(false) AS absrank_10y, (ret_10y.rank(false, percent=true)*100).round(0) AS perrank_10y,
|
|
|
- ret_ytd AS indicator_ytd, ret_ytd.rank(false) AS absrank_ytd, (ret_ytd.rank(false, percent=true)*100).round(0) AS perrank_ytd
|
|
|
- FROM t CONTEXT BY strategy, end_date;
|
|
|
-
|
|
|
- v_tables[1] = SELECT t.end_date, t.strategy, s.raise_type[0], 1 AS indicator_id,
|
|
|
- ret_1m.mean() AS avg_1m, ret_1m.count() AS avg_1m_cnt, ret_1m.percentile(95) AS perrank_percent_5_1m,
|
|
|
- ret_1m.percentile(90) AS perrank_percent_10_1m, ret_1m.percentile(75) AS perrank_percent_25_1m,
|
|
|
- ret_1m.percentile(50) AS perrank_percent_50_1m, ret_1m.percentile(25) AS perrank_percent_75_1m,
|
|
|
- ret_1m.percentile(10) AS perrank_percent_90_1m, ret_1m.percentile(5) AS perrank_percent_95_1m,
|
|
|
- ret_1m.max() AS best_1m, ret_1m.min() AS worst_1m,
|
|
|
- ret_3m.mean() AS avg_3m, ret_3m.count() AS avg_3m_cnt, ret_3m.percentile(95) AS perrank_percent_5_3m,
|
|
|
- ret_3m.percentile(90) AS perrank_percent_10_3m, ret_3m.percentile(75) AS perrank_percent_25_3m,
|
|
|
- ret_3m.percentile(50) AS perrank_percent_50_3m, ret_3m.percentile(25) AS perrank_percent_75_3m,
|
|
|
- ret_3m.percentile(10) AS perrank_percent_90_3m, ret_3m.percentile(5) AS perrank_percent_95_3m,
|
|
|
- ret_3m.max() AS best_3m, ret_3m.min() AS worst_3m,
|
|
|
- ret_6m.mean() AS avg_6m, ret_6m.count() AS avg_6m_cnt, ret_6m.percentile(95) AS perrank_percent_5_6m,
|
|
|
- ret_6m.percentile(90) AS perrank_percent_10_6m, ret_6m.percentile(75) AS perrank_percent_25_6m,
|
|
|
- ret_6m.percentile(50) AS perrank_percent_50_6m, ret_6m.percentile(25) AS perrank_percent_75_6m,
|
|
|
- ret_6m.percentile(10) AS perrank_percent_90_6m, ret_6m.percentile(5) AS perrank_percent_95_6m,
|
|
|
- ret_6m.max() AS best_6m, ret_6m.min() AS worst_6m,
|
|
|
- ret_1y.mean() AS avg_1y, ret_1y.count() AS avg_1y_cnt, ret_1y.percentile(95) AS perrank_percent_5_1y,
|
|
|
- ret_1y.percentile(90) AS perrank_percent_10_1y, ret_1y.percentile(75) AS perrank_percent_25_1y,
|
|
|
- ret_1y.percentile(50) AS perrank_percent_50_1y, ret_1y.percentile(25) AS perrank_percent_75_1y,
|
|
|
- ret_1y.percentile(10) AS perrank_percent_90_1y, ret_1y.percentile(5) AS perrank_percent_95_1y,
|
|
|
- ret_1y.max() AS best_1y, ret_1y.min() AS worst_1y,
|
|
|
- ret_2y.mean() AS avg_2y, ret_2y.count() AS avg_2y_cnt, ret_2y.percentile(95) AS perrank_percent_5_2y,
|
|
|
- ret_2y.percentile(90) AS perrank_percent_10_2y, ret_2y.percentile(75) AS perrank_percent_25_2y,
|
|
|
- ret_2y.percentile(50) AS perrank_percent_50_2y, ret_2y.percentile(25) AS perrank_percent_75_2y,
|
|
|
- ret_2y.percentile(10) AS perrank_percent_90_2y, ret_2y.percentile(5) AS perrank_percent_95_2y,
|
|
|
- ret_2y.max() AS best_2y, ret_2y.min() AS worst_2y,
|
|
|
- ret_3y.mean() AS avg_3y, ret_3y.count() AS avg_3y_cnt, ret_3y.percentile(95) AS perrank_percent_5_3y,
|
|
|
- ret_3y.percentile(90) AS perrank_percent_10_3y, ret_3y.percentile(75) AS perrank_percent_25_3y,
|
|
|
- ret_3y.percentile(50) AS perrank_percent_50_3y, ret_3y.percentile(25) AS perrank_percent_75_3y,
|
|
|
- ret_3y.percentile(10) AS perrank_percent_90_3y, ret_3y.percentile(5) AS perrank_percent_95_3y,
|
|
|
- ret_3y.max() AS best_3y, ret_3y.min() AS worst_3y,
|
|
|
- ret_5y.mean() AS avg_5y, ret_5y.count() AS avg_5y_cnt, ret_5y.percentile(95) AS perrank_percent_5_5y,
|
|
|
- ret_5y.percentile(90) AS perrank_percent_10_5y, ret_5y.percentile(75) AS perrank_percent_25_5y,
|
|
|
- ret_5y.percentile(50) AS perrank_percent_50_5y, ret_5y.percentile(25) AS perrank_percent_75_5y,
|
|
|
- ret_5y.percentile(10) AS perrank_percent_90_5y, ret_5y.percentile(5) AS perrank_percent_95_5y,
|
|
|
- ret_5y.max() AS best_5y, ret_5y.min() AS worst_5y,
|
|
|
- ret_10y.mean() AS avg_10y, ret_10y.count() AS avg_10y_cnt, ret_10y.percentile(95) AS perrank_percent_5_10y,
|
|
|
- ret_10y.percentile(90) AS perrank_percent_10_10y, ret_10y.percentile(75) AS perrank_percent_25_10y,
|
|
|
- ret_10y.percentile(50) AS perrank_percent_50_10y, ret_10y.percentile(25) AS perrank_percent_75_10y,
|
|
|
- ret_10y.percentile(10) AS perrank_percent_90_10y, ret_10y.percentile(5) AS perrank_percent_95_10y,
|
|
|
- ret_10y.max() AS best_10y, ret_10y.min() AS worst_10y,
|
|
|
- ret_ytd.mean() AS avg_ytd, ret_ytd.count() AS avg_ytd_cnt, ret_ytd.percentile(95) AS perrank_percent_5_ytd,
|
|
|
- ret_ytd.percentile(90) AS perrank_percent_10_ytd, ret_ytd.percentile(75) AS perrank_percent_25_ytd,
|
|
|
- ret_ytd.percentile(50) AS perrank_percent_50_ytd, ret_ytd.percentile(25) AS perrank_percent_75_ytd,
|
|
|
- ret_ytd.percentile(10) AS perrank_percent_90_ytd, ret_ytd.percentile(5) AS perrank_percent_95_ytd,
|
|
|
- ret_ytd.max() AS best_ytd, ret_ytd.min() AS worst_ytd
|
|
|
- FROM t
|
|
|
- INNER JOIN tb_strategy s ON t.strategy = s.strategy_id
|
|
|
- GROUP BY t.strategy, t.end_date;
|
|
|
-
|
|
|
-
|
|
|
- v_tables[2] = SELECT entity_id, end_date, substrategy, 1 AS indicator_id,
|
|
|
- ret_1m AS indicator_1m, ret_1m.rank(false) AS absrank_1m, (ret_1m.rank(false, percent=true)*100).round(0) AS perrank_1m,
|
|
|
- ret_3m AS indicator_3m, ret_3m.rank(false) AS absrank_3m, (ret_3m.rank(false, percent=true)*100).round(0) AS perrank_3m,
|
|
|
- ret_6m AS indicator_6m, ret_6m.rank(false) AS absrank_6m, (ret_6m.rank(false, percent=true)*100).round(0) AS perrank_6m,
|
|
|
- ret_1y AS indicator_1y, ret_1y.rank(false) AS absrank_1y, (ret_1y.rank(false, percent=true)*100).round(0) AS perrank_1y,
|
|
|
- ret_2y AS indicator_2y, ret_2y.rank(false) AS absrank_2y, (ret_2y.rank(false, percent=true)*100).round(0) AS perrank_2y,
|
|
|
- ret_3y AS indicator_3y, ret_3y.rank(false) AS absrank_3y, (ret_3y.rank(false, percent=true)*100).round(0) AS perrank_3y,
|
|
|
- ret_5y AS indicator_5y, ret_5y.rank(false) AS absrank_5y, (ret_5y.rank(false, percent=true)*100).round(0) AS perrank_5y,
|
|
|
- ret_10y AS indicator_10y, ret_10y.rank(false) AS absrank_10y, (ret_10y.rank(false, percent=true)*100).round(0) AS perrank_10y,
|
|
|
- ret_ytd AS indicator_ytd, ret_ytd.rank(false) AS absrank_ytd, (ret_ytd.rank(false, percent=true)*100).round(0) AS perrank_ytd
|
|
|
- FROM t CONTEXT BY substrategy, end_date;
|
|
|
-
|
|
|
- v_tables[3] = SELECT t.end_date, t.substrategy, s.raise_type[0], 1 AS indicator_id,
|
|
|
- ret_1m.mean() AS avg_1m, ret_1m.count() AS avg_1m_cnt, ret_1m.percentile(95) AS perrank_percent_5_1m,
|
|
|
- ret_1m.percentile(90) AS perrank_percent_10_1m, ret_1m.percentile(75) AS perrank_percent_25_1m,
|
|
|
- ret_1m.percentile(50) AS perrank_percent_50_1m, ret_1m.percentile(25) AS perrank_percent_75_1m,
|
|
|
- ret_1m.percentile(10) AS perrank_percent_90_1m, ret_1m.percentile(5) AS perrank_percent_95_1m,
|
|
|
- ret_1m.max() AS best_1m, ret_1m.min() AS worst_1m,
|
|
|
- ret_3m.mean() AS avg_3m, ret_3m.count() AS avg_3m_cnt, ret_3m.percentile(95) AS perrank_percent_5_3m,
|
|
|
- ret_3m.percentile(90) AS perrank_percent_10_3m, ret_3m.percentile(75) AS perrank_percent_25_3m,
|
|
|
- ret_3m.percentile(50) AS perrank_percent_50_3m, ret_3m.percentile(25) AS perrank_percent_75_3m,
|
|
|
- ret_3m.percentile(10) AS perrank_percent_90_3m, ret_3m.percentile(5) AS perrank_percent_95_3m,
|
|
|
- ret_3m.max() AS best_3m, ret_3m.min() AS worst_3m,
|
|
|
- ret_6m.mean() AS avg_6m, ret_6m.count() AS avg_6m_cnt, ret_6m.percentile(95) AS perrank_percent_5_6m,
|
|
|
- ret_6m.percentile(90) AS perrank_percent_10_6m, ret_6m.percentile(75) AS perrank_percent_25_6m,
|
|
|
- ret_6m.percentile(50) AS perrank_percent_50_6m, ret_6m.percentile(25) AS perrank_percent_75_6m,
|
|
|
- ret_6m.percentile(10) AS perrank_percent_90_6m, ret_6m.percentile(5) AS perrank_percent_95_6m,
|
|
|
- ret_6m.max() AS best_6m, ret_6m.min() AS worst_6m,
|
|
|
- ret_1y.mean() AS avg_1y, ret_1y.count() AS avg_1y_cnt, ret_1y.percentile(95) AS perrank_percent_5_1y,
|
|
|
- ret_1y.percentile(90) AS perrank_percent_10_1y, ret_1y.percentile(75) AS perrank_percent_25_1y,
|
|
|
- ret_1y.percentile(50) AS perrank_percent_50_1y, ret_1y.percentile(25) AS perrank_percent_75_1y,
|
|
|
- ret_1y.percentile(10) AS perrank_percent_90_1y, ret_1y.percentile(5) AS perrank_percent_95_1y,
|
|
|
- ret_1y.max() AS best_1y, ret_1y.min() AS worst_1y,
|
|
|
- ret_2y.mean() AS avg_2y, ret_2y.count() AS avg_2y_cnt, ret_2y.percentile(95) AS perrank_percent_5_2y,
|
|
|
- ret_2y.percentile(90) AS perrank_percent_10_2y, ret_2y.percentile(75) AS perrank_percent_25_2y,
|
|
|
- ret_2y.percentile(50) AS perrank_percent_50_2y, ret_2y.percentile(25) AS perrank_percent_75_2y,
|
|
|
- ret_2y.percentile(10) AS perrank_percent_90_2y, ret_2y.percentile(5) AS perrank_percent_95_2y,
|
|
|
- ret_2y.max() AS best_2y, ret_2y.min() AS worst_2y,
|
|
|
- ret_3y.mean() AS avg_3y, ret_3y.count() AS avg_3y_cnt, ret_3y.percentile(95) AS perrank_percent_5_3y,
|
|
|
- ret_3y.percentile(90) AS perrank_percent_10_3y, ret_3y.percentile(75) AS perrank_percent_25_3y,
|
|
|
- ret_3y.percentile(50) AS perrank_percent_50_3y, ret_3y.percentile(25) AS perrank_percent_75_3y,
|
|
|
- ret_3y.percentile(10) AS perrank_percent_90_3y, ret_3y.percentile(5) AS perrank_percent_95_3y,
|
|
|
- ret_3y.max() AS best_3y, ret_3y.min() AS worst_3y,
|
|
|
- ret_5y.mean() AS avg_5y, ret_5y.count() AS avg_5y_cnt, ret_5y.percentile(95) AS perrank_percent_5_5y,
|
|
|
- ret_5y.percentile(90) AS perrank_percent_10_5y, ret_5y.percentile(75) AS perrank_percent_25_5y,
|
|
|
- ret_5y.percentile(50) AS perrank_percent_50_5y, ret_5y.percentile(25) AS perrank_percent_75_5y,
|
|
|
- ret_5y.percentile(10) AS perrank_percent_90_5y, ret_5y.percentile(5) AS perrank_percent_95_5y,
|
|
|
- ret_5y.max() AS best_5y, ret_5y.min() AS worst_5y,
|
|
|
- ret_10y.mean() AS avg_10y, ret_10y.count() AS avg_10y_cnt, ret_10y.percentile(95) AS perrank_percent_5_10y,
|
|
|
- ret_10y.percentile(90) AS perrank_percent_10_10y, ret_10y.percentile(75) AS perrank_percent_25_10y,
|
|
|
- ret_10y.percentile(50) AS perrank_percent_50_10y, ret_10y.percentile(25) AS perrank_percent_75_10y,
|
|
|
- ret_10y.percentile(10) AS perrank_percent_90_10y, ret_10y.percentile(5) AS perrank_percent_95_10y,
|
|
|
- ret_10y.max() AS best_10y, ret_10y.min() AS worst_10y,
|
|
|
- ret_ytd.mean() AS avg_ytd, ret_ytd.count() AS avg_ytd_cnt, ret_ytd.percentile(95) AS perrank_percent_5_ytd,
|
|
|
- ret_ytd.percentile(90) AS perrank_percent_10_ytd, ret_ytd.percentile(75) AS perrank_percent_25_ytd,
|
|
|
- ret_ytd.percentile(50) AS perrank_percent_50_ytd, ret_ytd.percentile(25) AS perrank_percent_75_ytd,
|
|
|
- ret_ytd.percentile(10) AS perrank_percent_90_ytd, ret_ytd.percentile(5) AS perrank_percent_95_ytd,
|
|
|
- ret_ytd.max() AS best_ytd, ret_ytd.min() AS worst_ytd
|
|
|
- FROM t
|
|
|
- INNER JOIN tb_substrategy s ON t.substrategy = s.substrategy_id
|
|
|
- GROUP BY t.substrategy, t.end_date;
|
|
|
-
|
|
|
- return v_tables;
|
|
|
-}
|
|
|
|
|
|
/*
|
|
|
* 自定义百分位计算
|
|
|
@@ -211,186 +52,17 @@ defg perRank(x, is_ASC) {
|
|
|
/*
|
|
|
* 动态生成用于排序的SQL脚本
|
|
|
*
|
|
|
- * @param indicator_name <STRING>: 指标字段名
|
|
|
- * @param indicator_id <INT>：指标ID
|
|
|
- * @param is_ASC <BOOL>: 是否排正序
|
|
|
- * @param ranking_by <STRING>: 'strategy', 'substrategy', 'factor_id', 'catavg'
|
|
|
+ * @param data_table <TABLE>: 指标横表
|
|
|
+ * @param indicator_table <TABLE>: 指标表，有 id, name, is_ASC 字段
|
|
|
*
|
|
|
* TODO: portfolio, cf, manager, company,
|
|
|
* TODO: bfi & category
|
|
|
*
|
|
|
*/
|
|
|
-def gen_ranking_sql0(data_table, indicator_table, ranking_by) {
|
|
|
+def gen_ranking_sql(data_table, indicator_table) {
|
|
|
|
|
|
- for(indicator in indicator_table) {
|
|
|
-
|
|
|
- // 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', 'indicator_1m', 'absrank_1m', 'perrank_1m',
|
|
|
- 'indicator_3m', 'absrank_3m', 'perrank_3m'],
|
|
|
- [INT, DOUBLE, INT, INT, DOUBLE, INT, INT]);
|
|
|
- INSERT INTO t_tmp VALUES (indicator.id, double(NULL), int(NULL), int(NULL), double(NULL), int(NULL), int(NULL));
|
|
|
-
|
|
|
- t_ranking = sql(select = (sqlCol('entity_id'), sqlCol('end_date'), sqlCol(ranking_by), sqlCol('indicator_id'),
|
|
|
- sqlCol('indicator_1m'), sqlCol('absrank_1m'), sqlCol('perrank_1m'),
|
|
|
- sqlCol('indicator_3m'), sqlCol('absrank_3m'), sqlCol('perrank_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',,'indicator_6m'),
|
|
|
- sqlCol(indicator.name + '_6m', rank{, indicator.is_ASC}, 'absrank_6m'),
|
|
|
- sqlCol(indicator.name + '_6m', perRank{, indicator.is_ASC}, 'perrank_6m'),
|
|
|
- sqlCol(indicator.name + '_1y',,'indicator_1y'),
|
|
|
- sqlCol(indicator.name + '_1y', rank{, indicator.is_ASC}, 'absrank_1y'),
|
|
|
- sqlCol(indicator.name + '_1y', perRank{, indicator.is_ASC}, 'perrank_1y'),
|
|
|
- sqlCol(indicator.name + '_2y',,'indicator_2y'),
|
|
|
- sqlCol(indicator.name + '_2y', rank{, indicator.is_ASC}, 'absrank_2y'),
|
|
|
- sqlCol(indicator.name + '_2y', perRank{, indicator.is_ASC}, 'perrank_2y'),
|
|
|
- sqlCol(indicator.name + '_3y',,'indicator_3y'),
|
|
|
- sqlCol(indicator.name + '_3y', rank{, indicator.is_ASC}, 'absrank_3y'),
|
|
|
- sqlCol(indicator.name + '_3y', perRank{, indicator.is_ASC}, 'perrank_3y'),
|
|
|
- sqlCol(indicator.name + '_5y',,'indicator_5y'),
|
|
|
- sqlCol(indicator.name + '_5y', rank{, indicator.is_ASC}, 'absrank_5y'),
|
|
|
- sqlCol(indicator.name + '_5y', perRank{, indicator.is_ASC}, 'perrank_5y'),
|
|
|
- sqlCol(indicator.name + '_10y',,'indicator_10y'),
|
|
|
- sqlCol(indicator.name + '_10y', rank{, indicator.is_ASC}, 'absrank_10y'),
|
|
|
- sqlCol(indicator.name + '_10y', perRank{, indicator.is_ASC}, 'perrank_10y'),
|
|
|
- sqlCol(indicator.name + '_ytd',,'indicator_ytd'),
|
|
|
- sqlCol(indicator.name + '_ytd', rank{, indicator.is_ASC}, 'absrank_ytd'),
|
|
|
- sqlCol(indicator.name + '_ytd', perRank{, indicator.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(indicator.is_ASC, 5, 95)}, 'perrank_percent_5_6m'),
|
|
|
- sqlCol(indicator.name + '_6m', percentile{, iif(indicator.is_ASC, 10, 90)}, 'perrank_percent_10_6m'),
|
|
|
- sqlCol(indicator.name + '_6m', percentile{, iif(indicator.is_ASC, 25, 75)}, 'perrank_percent_25_6m'),
|
|
|
- sqlCol(indicator.name + '_6m', percentile{, iif(indicator.is_ASC, 50, 50)}, 'perrank_percent_50_6m'),
|
|
|
- sqlCol(indicator.name + '_6m', percentile{, iif(indicator.is_ASC, 75, 25)}, 'perrank_percent_75_6m'),
|
|
|
- sqlCol(indicator.name + '_6m', percentile{, iif(indicator.is_ASC, 90, 10)}, 'perrank_percent_90_6m'),
|
|
|
- sqlCol(indicator.name + '_6m', percentile{, iif(indicator.is_ASC, 95, 5)}, 'perrank_percent_95_6m'),
|
|
|
- sqlCol(indicator.name + '_6m', iif(indicator.is_ASC, min, max), 'best_6m'),
|
|
|
- sqlCol(indicator.name + '_6m', iif(indicator.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(indicator.is_ASC, 5, 95)}, 'perrank_percent_5_1y'),
|
|
|
- sqlCol(indicator.name + '_1y', percentile{, iif(indicator.is_ASC, 10, 90)}, 'perrank_percent_10_1y'),
|
|
|
- sqlCol(indicator.name + '_1y', percentile{, iif(indicator.is_ASC, 25, 75)}, 'perrank_percent_25_1y'),
|
|
|
- sqlCol(indicator.name + '_1y', percentile{, iif(indicator.is_ASC, 50, 50)}, 'perrank_percent_50_1y'),
|
|
|
- sqlCol(indicator.name + '_1y', percentile{, iif(indicator.is_ASC, 75, 25)}, 'perrank_percent_75_1y'),
|
|
|
- sqlCol(indicator.name + '_1y', percentile{, iif(indicator.is_ASC, 90, 10)}, 'perrank_percent_90_1y'),
|
|
|
- sqlCol(indicator.name + '_1y', percentile{, iif(indicator.is_ASC, 95, 5)}, 'perrank_percent_95_1y'),
|
|
|
- sqlCol(indicator.name + '_1y', iif(indicator.is_ASC, min, max), 'best_1y'),
|
|
|
- sqlCol(indicator.name + '_1y', iif(indicator.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(indicator.is_ASC, 5, 95)}, 'perrank_percent_5_2y'),
|
|
|
- sqlCol(indicator.name + '_2y', percentile{, iif(indicator.is_ASC, 10, 90)}, 'perrank_percent_10_2y'),
|
|
|
- sqlCol(indicator.name + '_2y', percentile{, iif(indicator.is_ASC, 25, 75)}, 'perrank_percent_25_2y'),
|
|
|
- sqlCol(indicator.name + '_2y', percentile{, iif(indicator.is_ASC, 50, 50)}, 'perrank_percent_50_2y'),
|
|
|
- sqlCol(indicator.name + '_2y', percentile{, iif(indicator.is_ASC, 75, 25)}, 'perrank_percent_75_2y'),
|
|
|
- sqlCol(indicator.name + '_2y', percentile{, iif(indicator.is_ASC, 90, 10)}, 'perrank_percent_90_2y'),
|
|
|
- sqlCol(indicator.name + '_2y', percentile{, iif(indicator.is_ASC, 95, 5)}, 'perrank_percent_95_2y'),
|
|
|
- sqlCol(indicator.name + '_2y', iif(indicator.is_ASC, min, max), 'best_2y'),
|
|
|
- sqlCol(indicator.name + '_2y', iif(indicator.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(indicator.is_ASC, 5, 95)}, 'perrank_percent_5_3y'),
|
|
|
- sqlCol(indicator.name + '_3y', percentile{, iif(indicator.is_ASC, 10, 90)}, 'perrank_percent_10_3y'),
|
|
|
- sqlCol(indicator.name + '_3y', percentile{, iif(indicator.is_ASC, 25, 75)}, 'perrank_percent_25_3y'),
|
|
|
- sqlCol(indicator.name + '_3y', percentile{, iif(indicator.is_ASC, 50, 50)}, 'perrank_percent_50_3y'),
|
|
|
- sqlCol(indicator.name + '_3y', percentile{, iif(indicator.is_ASC, 75, 25)}, 'perrank_percent_75_3y'),
|
|
|
- sqlCol(indicator.name + '_3y', percentile{, iif(indicator.is_ASC, 90, 10)}, 'perrank_percent_90_3y'),
|
|
|
- sqlCol(indicator.name + '_3y', percentile{, iif(indicator.is_ASC, 95, 5)}, 'perrank_percent_95_3y'),
|
|
|
- sqlCol(indicator.name + '_3y', iif(indicator.is_ASC, min, max), 'best_3y'),
|
|
|
- sqlCol(indicator.name + '_3y', iif(indicator.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(indicator.is_ASC, 5, 95)}, 'perrank_percent_5_5y'),
|
|
|
- sqlCol(indicator.name + '_5y', percentile{, iif(indicator.is_ASC, 10, 90)}, 'perrank_percent_10_5y'),
|
|
|
- sqlCol(indicator.name + '_5y', percentile{, iif(indicator.is_ASC, 25, 75)}, 'perrank_percent_25_5y'),
|
|
|
- sqlCol(indicator.name + '_5y', percentile{, iif(indicator.is_ASC, 50, 50)}, 'perrank_percent_50_5y'),
|
|
|
- sqlCol(indicator.name + '_5y', percentile{, iif(indicator.is_ASC, 75, 25)}, 'perrank_percent_75_5y'),
|
|
|
- sqlCol(indicator.name + '_5y', percentile{, iif(indicator.is_ASC, 90, 10)}, 'perrank_percent_90_5y'),
|
|
|
- sqlCol(indicator.name + '_5y', percentile{, iif(indicator.is_ASC, 95, 5)}, 'perrank_percent_95_5y'),
|
|
|
- sqlCol(indicator.name + '_5y', iif(indicator.is_ASC, min, max), 'best_5y'),
|
|
|
- sqlCol(indicator.name + '_5y', iif(indicator.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(indicator.is_ASC, 5, 95)}, 'perrank_percent_5_10y'),
|
|
|
- sqlCol(indicator.name + '_10y', percentile{, iif(indicator.is_ASC, 10, 90)}, 'perrank_percent_10_10y'),
|
|
|
- sqlCol(indicator.name + '_10y', percentile{, iif(indicator.is_ASC, 25, 75)}, 'perrank_percent_25_10y'),
|
|
|
- sqlCol(indicator.name + '_10y', percentile{, iif(indicator.is_ASC, 50, 50)}, 'perrank_percent_50_10y'),
|
|
|
- sqlCol(indicator.name + '_10y', percentile{, iif(indicator.is_ASC, 75, 25)}, 'perrank_percent_75_10y'),
|
|
|
- sqlCol(indicator.name + '_10y', percentile{, iif(indicator.is_ASC, 90, 10)}, 'perrank_percent_90_10y'),
|
|
|
- sqlCol(indicator.name + '_10y', percentile{, iif(indicator.is_ASC, 95, 5)}, 'perrank_percent_95_10y'),
|
|
|
- sqlCol(indicator.name + '_10y', iif(indicator.is_ASC, min, max), 'best_10y'),
|
|
|
- sqlCol(indicator.name + '_10y', iif(indicator.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(indicator.is_ASC, 5, 95)}, 'perrank_percent_5_ytd'),
|
|
|
- sqlCol(indicator.name + '_ytd', percentile{, iif(indicator.is_ASC, 10, 90)}, 'perrank_percent_10_ytd'),
|
|
|
- sqlCol(indicator.name + '_ytd', percentile{, iif(indicator.is_ASC, 25, 75)}, 'perrank_percent_25_ytd'),
|
|
|
- sqlCol(indicator.name + '_ytd', percentile{, iif(indicator.is_ASC, 50, 50)}, 'perrank_percent_50_ytd'),
|
|
|
- sqlCol(indicator.name + '_ytd', percentile{, iif(indicator.is_ASC, 75, 25)}, 'perrank_percent_75_ytd'),
|
|
|
- sqlCol(indicator.name + '_ytd', percentile{, iif(indicator.is_ASC, 90, 10)}, 'perrank_percent_90_ytd'),
|
|
|
- sqlCol(indicator.name + '_ytd', percentile{, iif(indicator.is_ASC, 95, 5)}, 'perrank_percent_95_ytd'),
|
|
|
- sqlCol(indicator.name + '_ytd', iif(indicator.is_ASC, min, max), 'best_ytd'),
|
|
|
- sqlCol(indicator.name + '_ytd', iif(indicator.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;
|
|
|
-}
|
|
|
-
|
|
|
-
|
|
|
-/*
|
|
|
- * 动态生成用于排序的SQL脚本
|
|
|
- *
|
|
|
- * @param indicator_name <STRING>: 指标字段名
|
|
|
- * @param indicator_id <INT>：指标ID
|
|
|
- * @param is_ASC <BOOL>: 是否排正序
|
|
|
- * @param ranking_by <STRING>: 'strategy', 'substrategy', 'factor_id', 'catavg'
|
|
|
- *
|
|
|
- * TODO: portfolio, cf, manager, company,
|
|
|
- * TODO: bfi & category
|
|
|
- *
|
|
|
- */
|
|
|
-def gen_ranking_sql(data_table, indicator_table, ranking_by) {
|
|
|
+ ranking = create_entity_indicator_ranking();
|
|
|
+ ranking_num = create_entity_indicator_ranking_num();
|
|
|
|
|
|
for(indicator in indicator_table) {
|
|
|
|
|
|
@@ -416,13 +88,16 @@ def gen_ranking_sql(data_table, indicator_table, ranking_by) {
|
|
|
// 为了满足表结构的要求, 非收益的指标要补上1m和3m的字段，虽然都是NULL
|
|
|
if(indicator.id != 1) {
|
|
|
|
|
|
- t_tmp = table(1000:0,
|
|
|
- ['indicator_' + v_missing_trailing, 'absrank_' + v_missing_trailing, 'perrank_' + v_missing_trailing].flatten(),
|
|
|
- [take(DOUBLE, v_missing_trailing.size()), take(INT, v_missing_trailing.size()), take(INT, v_missing_trailing.size())].flatten()
|
|
|
- );
|
|
|
-
|
|
|
- t_ranking = SELECT * FROM cj(t_ranking, t_tmp);
|
|
|
+ v_tmp_col = ['indicator_' + v_missing_trailing, 'absrank_' + v_missing_trailing, 'perrank_' + v_missing_trailing].flatten();
|
|
|
+ v_tmp_type = [take(DOUBLE, v_missing_trailing.size()), take(INT, v_missing_trailing.size()), take(INT, v_missing_trailing.size())].flatten();
|
|
|
+
|
|
|
+ t_ranking.addColumn(v_tmp_col, v_tmp_type);
|
|
|
+
|
|
|
}
|
|
|
+
|
|
|
+ t_ranking.reorderColumns!(ranking.colNames());
|
|
|
+ ranking.tableInsert(t_ranking);
|
|
|
+
|
|
|
|
|
|
// 平均值、集合数量、各分位的阈值
|
|
|
t_ranking_num = sql(select =(sqlCol(['end_date', 'category_id']),
|
|
|
@@ -447,174 +122,147 @@ def gen_ranking_sql(data_table, indicator_table, ranking_by) {
|
|
|
// 为了满足表结构的要求, 非收益的指标要补上1m和3m的字段，虽然都是NULL
|
|
|
if(indicator.id != 1) {
|
|
|
|
|
|
- t_tmp = table(1000:0,
|
|
|
- ['avg_' + v_missing_trailing, 'avg_' + v_missing_trailing + '_cnt', 'perrank_percent_5_' + v_missing_trailing,
|
|
|
- 'perrank_percent_10_' + v_missing_trailing, 'perrank_percent_25_' + v_missing_trailing,
|
|
|
- 'perrank_percent_50_' + v_missing_trailing, 'perrank_percent_75_' + v_missing_trailing,
|
|
|
- 'perrank_percent_90_' + v_missing_trailing, 'perrank_percent_95_' + v_missing_trailing,
|
|
|
- 'best_' + v_missing_trailing, 'worst_' + v_missing_trailing ].flatten(),
|
|
|
- [take(DOUBLE, v_missing_trailing.size()), take(INT, v_missing_trailing.size()), take(DOUBLE, v_missing_trailing.size()),
|
|
|
- take(DOUBLE, v_missing_trailing.size()), take(DOUBLE, v_missing_trailing.size()),
|
|
|
- take(DOUBLE, v_missing_trailing.size()), take(DOUBLE, v_missing_trailing.size()),
|
|
|
- take(DOUBLE, v_missing_trailing.size()), take(DOUBLE, v_missing_trailing.size()),
|
|
|
- take(DOUBLE, v_missing_trailing.size()),take(DOUBLE, v_missing_trailing.size())].flatten()
|
|
|
- );
|
|
|
-
|
|
|
- t_ranking = SELECT * FROM cj(t_ranking, t_tmp);
|
|
|
+ v_tmp_col = ['avg_' + v_missing_trailing, 'avg_' + v_missing_trailing + '_cnt', 'perrank_percent_5_' + v_missing_trailing,
|
|
|
+ 'perrank_percent_10_' + v_missing_trailing, 'perrank_percent_25_' + v_missing_trailing,
|
|
|
+ 'perrank_percent_50_' + v_missing_trailing, 'perrank_percent_75_' + v_missing_trailing,
|
|
|
+ 'perrank_percent_90_' + v_missing_trailing, 'perrank_percent_95_' + v_missing_trailing,
|
|
|
+ 'best_' + v_missing_trailing, 'worst_' + v_missing_trailing
|
|
|
+ ].flatten();
|
|
|
+ v_tmp_type = [take(DOUBLE, v_missing_trailing.size()), take(INT, v_missing_trailing.size()), take(DOUBLE, v_missing_trailing.size()),
|
|
|
+ take(DOUBLE, v_missing_trailing.size()), take(DOUBLE, v_missing_trailing.size()),
|
|
|
+ take(DOUBLE, v_missing_trailing.size()), take(DOUBLE, v_missing_trailing.size()),
|
|
|
+ take(DOUBLE, v_missing_trailing.size()), take(DOUBLE, v_missing_trailing.size()),
|
|
|
+ take(DOUBLE, v_missing_trailing.size()),take(DOUBLE, v_missing_trailing.size())
|
|
|
+ ].flatten();
|
|
|
+
|
|
|
+ t_ranking_num.addColumn(v_tmp_col, v_tmp_type);
|
|
|
}
|
|
|
+
|
|
|
+ t_ranking_num.reorderColumns!(ranking_num.colNames());
|
|
|
+ ranking_num.tableInsert(t_ranking_num);
|
|
|
}
|
|
|
|
|
|
- return t_ranking, t_ranking_num;
|
|
|
+ return ranking, ranking_num;
|
|
|
}
|
|
|
|
|
|
/*
|
|
|
* 运行排名SQL脚本
|
|
|
*
|
|
|
- * NOTE: 没有用 parseExpr 来生成动态脚本的原因是数据表无法传入
|
|
|
+ *
|
|
|
*/
|
|
|
-def run_ranking_sql(data_table, indicator_table, mutable v_tables) {
|
|
|
+def run_ranking_sql(cal_type, mutable data_table, indicator_table) {
|
|
|
|
|
|
- tb_strategy_ranking = gen_ranking_sql(data_table, indicator_table, 'strategy')[0];
|
|
|
- v_tables[0].tableInsert(tb_strategy_ranking);
|
|
|
+// data_table = t
|
|
|
+// v_tables = v_ranking_tables
|
|
|
+// cal_type = 'strategy'
|
|
|
|
|
|
- tb_strategy_ranking_num = gen_ranking_sql(data_table, indicator_table, 'strategy')[1];
|
|
|
- v_tables[1].tableInsert(tb_strategy_ranking_num);
|
|
|
+ ret = array(ANY, 0);
|
|
|
|
|
|
- tb_substrategy_ranking = gen_ranking_sql(data_table, indicator_table, 'substrategy')[0];
|
|
|
- v_tables[2].tableInsert(tb_substrategy_ranking);
|
|
|
+ if(cal_type == 'bfi') {
|
|
|
|
|
|
- tb_substrategy_ranking_num = gen_ranking_sql(data_table, indicator_table, 'substrategy')[1];
|
|
|
- v_tables[3].tableInsert(tb_substrategy_ranking_num);
|
|
|
+ UPDATE data_table SET category_id = factor_id;
|
|
|
|
|
|
-}
|
|
|
+ v_ranking = gen_ranking_sql(data_table, indicator_table);
|
|
|
+
|
|
|
+ ret.append!(v_ranking[0]); // ranking table
|
|
|
+ ret.append!(v_ranking[1]); // ranking_num table
|
|
|
+
|
|
|
+ } else {
|
|
|
|
|
|
-/*
|
|
|
- * 计算风险指标排名
|
|
|
- *
|
|
|
- *
|
|
|
- */
|
|
|
-def cal_risk_ranking(entity_type, entity_info, end_date, isFromMySQL) {
|
|
|
+ // 策略排名
|
|
|
+ UPDATE data_table SET category_id = strategy$STRING;
|
|
|
|
|
|
- // 当前只对基金做排名， 其它类型参考基金排名做相对排名
|
|
|
- if(!(entity_type in ['MF', 'HF'])) return null;
|
|
|
+ v_ranking = gen_ranking_sql(data_table, indicator_table);
|
|
|
|
|
|
- table_desc = get_risk_stats_table_description(entity_type);
|
|
|
+ ret.append!(v_ranking[0]); // ranking table
|
|
|
+ ret.append!(v_ranking[1]); // ranking_num table
|
|
|
|
|
|
- tb_data = get_monthly_indicator_data(table_desc.table_name[0], end_date, isFromMySQL);
|
|
|
- sec_id_col = table_desc.sec_id_col[0];
|
|
|
- tb_data.rename!(sec_id_col, 'entity_id');
|
|
|
-
|
|
|
- t = SELECT *
|
|
|
- FROM entity_info en
|
|
|
- INNER JOIN tb_data d ON en.entity_id = d.entity_id
|
|
|
- WHERE en.strategy IS NOT NULL;
|
|
|
-
|
|
|
- // 按照 MySQL 字段建表
|
|
|
- t_s = create_entity_indicator_ranking(false);
|
|
|
- t_s_num = create_entity_indicator_ranking_num(false);
|
|
|
- t_ss = create_entity_indicator_substrategy_ranking(false);
|
|
|
- t_ss_num = create_entity_indicator_substrategy_ranking_num(false);
|
|
|
-
|
|
|
- v_ranking_tables = [t_s, t_s_num, t_ss, t_ss_num];
|
|
|
+ // 子策略排名
|
|
|
+ UPDATE data_table SET category_id = substrategy$STRING;
|
|
|
|
|
|
- // 50, 52 dolphin 未计算
|
|
|
- indicator_table = SELECT * FROM get_indicator_info() WHERE id in [2, 6, 9, 10, 11, 12, 21, 59];
|
|
|
-
|
|
|
- run_ranking_sql(t, indicator_table, v_ranking_tables);
|
|
|
+ v_ranking = gen_ranking_sql(data_table, indicator_table);
|
|
|
+
|
|
|
+ ret.append!(v_ranking[0]); // ranking table
|
|
|
+ ret.append!(v_ranking[1]); // ranking_num table
|
|
|
+ }
|
|
|
|
|
|
- return v_ranking_tables;
|
|
|
+ return ret;
|
|
|
}
|
|
|
|
|
|
|
|
|
/*
|
|
|
- * 计算风险调整收益指标排名
|
|
|
- *
|
|
|
+ * 通用指标排名计算
|
|
|
+ *
|
|
|
+ * @param cal_type <STRING>: strategy, bfi
|
|
|
*
|
|
|
*/
|
|
|
-def cal_risk_adj_return_ranking(entity_type, entity_info, end_date, isFromMySQL) {
|
|
|
+def cal_indicator_ranking(cal_type, entity_type, entity_info, end_date, isFromMySQL) {
|
|
|
|
|
|
// 当前只对基金做排名， 其它类型参考基金排名做相对排名
|
|
|
if(!(entity_type in ['MF', 'HF'])) return null;
|
|
|
|
|
|
+ // return
|
|
|
+ table_desc = get_performance_table_description(entity_type);
|
|
|
+ tb_data_return = get_monthly_indicator_data(table_desc.table_name[0], end_date, isFromMySQL);
|
|
|
+ // risk
|
|
|
+ table_desc = get_risk_stats_table_description(entity_type);
|
|
|
+ tb_data_risk_stats = get_monthly_indicator_data(table_desc.table_name[0], end_date, isFromMySQL);
|
|
|
+ // risk adjusted return
|
|
|
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];
|
|
|
-
|
|
|
- // 19 (MAR Sortino ratio) dolphin 未计算
|
|
|
- indicator_table = SELECT * FROM get_indicator_info() WHERE id in [14, 15, 16, 17, 18, 40, 58];
|
|
|
-
|
|
|
- run_ranking_sql(t, indicator_table, 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) {
|
|
|
-
|
|
|
- // 当前只对基金做排名， 其它类型参考基金排名做相对排名
|
|
|
- if(!(entity_type in ['MF', 'HF'])) return null;
|
|
|
-
|
|
|
+ tb_data_riskadjret_stats = get_monthly_indicator_data(table_desc.table_name[0], end_date, isFromMySQL);
|
|
|
+ // others
|
|
|
table_desc = get_indicator_table_description(entity_type);
|
|
|
+ tb_data_indicator_stats = get_monthly_indicator_data(table_desc.table_name[0], end_date, isFromMySQL);
|
|
|
|
|
|
- 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');
|
|
|
+ tb_data = SELECT *
|
|
|
+ FROM tb_data_return d1
|
|
|
+ LEFT JOIN tb_data_indicator_stats d2 ON d1.fund_id = d2.fund_id AND d1.end_date = d2.end_date
|
|
|
+ LEFT JOIN tb_data_risk_stats d3 ON d1.fund_id = d3.fund_id AND d1.end_date = d3.end_date
|
|
|
+ LEFT JOIN tb_data_riskadjret_stats d4 ON d1.fund_id = d4.fund_id AND d1.end_date = d4.end_date;
|
|
|
|
|
|
- t = SELECT *
|
|
|
- FROM entity_info en
|
|
|
- INNER JOIN tb_data d ON en.entity_id = d.entity_id
|
|
|
- WHERE en.strategy IS NOT NULL;
|
|
|
+ if(cal_type == 'bfi') {
|
|
|
|
|
|
- // 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);
|
|
|
+ // bfi (as benchmark) indicator
|
|
|
+ table_desc = get_bfi_indicator_table_description(entity_type);
|
|
|
+ tb_data_bfi_indicator = get_monthly_indicator_data(table_desc.table_name[0], end_date, isFromMySQL);
|
|
|
|
|
|
- v_ranking_tables = [t_s, t_s_num, t_ss, t_ss_num];
|
|
|
+ // 去掉被移到 fund_ty_bfi_bm_indicator 表中的重复字段
|
|
|
+ v_dups = [38, 48, 11, 12, 59, 16];
|
|
|
+ v_dup_col = EXEC name + suffix
|
|
|
+ FROM cj(get_indicator_info(), table(['_6m', '_1y', '_2y', '_3y', '_5y', '_10y', '_ytd'] AS suffix))
|
|
|
+ WHERE id IN v_dups;
|
|
|
+
|
|
|
+ tb_data.dropColumns!(v_dup_col);
|
|
|
|
|
|
- // 37 (per_con), 43, 44, 45, 46, 47 (smdd模型) dolphin 未计算
|
|
|
- indicator_table = SELECT * FROM get_indicator_info() WHERE id in [38, 41, 42, 48, 49];
|
|
|
+ tb_data = SELECT * FROM tb_data d1
|
|
|
+ LEFT JOIN tb_data_bfi_indicator d2 ON d1.fund_id = d2.fund_id AND d1.end_date = d2.end_date;
|
|
|
|
|
|
- run_ranking_sql(t, indicator_table, v_ranking_tables);
|
|
|
+ v_indicator_id = [1, // 对应 fund_performance, 取消39（年化收益) 因为没有意义
|
|
|
+ 41, 42, 49, // 对应 fund_indicator, 取消37 (per_con), 43, 44, 45, 46, 47 (smdd模型) 因为dolphin 未计算
|
|
|
+ 2, 6, 9, 10, 21, // 对应 fund_risk_stats, 取消50, 52 因为 dolphin 未计算
|
|
|
+ 14, 15, 17, 18, 40, 58, // 对应 fund_riskadjret_stats 取消19 (MAR Sortino ratio) 因为 dolphin 未计算
|
|
|
+ 11, 12, 16, 33, 34, 35, 36, 38, 48, 59 // 对应 fund_ty_bfi_bm_indicator
|
|
|
+ ]; // 取消 pf_fund_factor_stability 66 （stabiliy) 因为 dolphin 未计算
|
|
|
+ // 取消 fund_rbsa_style 53, 54, 55, 56, 57（风格稳定性) 因为 dolphin 未计算
|
|
|
|
|
|
- return v_ranking_tables;
|
|
|
-}
|
|
|
+ } else {
|
|
|
|
|
|
+ // upside/downside capture
|
|
|
+ table_desc = get_capture_style_table_description(entity_type);
|
|
|
+ tb_data_capture_stats = get_monthly_indicator_data(table_desc.table_name[0], end_date, isFromMySQL);
|
|
|
|
|
|
-/*
|
|
|
- * 计算上下行指标排名
|
|
|
- *
|
|
|
- *
|
|
|
- */
|
|
|
-def cal_capture_style_ranking(entity_type, entity_info, end_date, isFromMySQL) {
|
|
|
+ tb_data = SELECT * FROM tb_data d1
|
|
|
+ LEFT JOIN tb_data_capture_stats d2 ON d1.fund_id = d2.fund_id AND d1.end_date = d2.end_date;
|
|
|
|
|
|
- // 当前只对基金做排名， 其它类型参考基金排名做相对排名
|
|
|
- if(!(entity_type in ['MF', 'HF'])) return null;
|
|
|
+ v_indicator_id = [1, // 对应 fund_performance, 取消39（年化收益) 因为没有意义
|
|
|
+ 38, 41, 42, 48, 49, // 对应 fund_indicator, 取消37 (per_con), 43, 44, 45, 46, 47 (smdd模型) 因为dolphin 未计算
|
|
|
+ 2, 6, 9, 10, 11, 12, 21, 59, // 对应 fund_risk_stats, 取消50, 52 因为 dolphin 未计算
|
|
|
+ 14, 15, 16, 17, 18, 40, 58, // 对应 fund_riskadjret_stats 取消19 (MAR Sortino ratio) 因为 dolphin 未计算
|
|
|
+ 33, 34, 35, 36 // 对应 fund_style_stats
|
|
|
+ ];
|
|
|
|
|
|
- table_desc = get_capture_style_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');
|
|
|
|
|
|
@@ -622,82 +270,15 @@ def cal_capture_style_ranking(entity_type, entity_info, end_date, isFromMySQL) {
|
|
|
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];
|
|
|
-
|
|
|
- indicator_table = SELECT * FROM get_indicator_info() WHERE id in [33, 34, 35, 36];
|
|
|
-
|
|
|
- run_ranking_sql(t, indicator_table, v_ranking_tables);
|
|
|
-
|
|
|
- return v_ranking_tables;
|
|
|
-}
|
|
|
-
|
|
|
-
|
|
|
-/*
|
|
|
- * 计算BFI指标排名
|
|
|
- *
|
|
|
- * TODO: return
|
|
|
- */
|
|
|
-def cal_bfi_indicator_ranking(entity_type, entity_info, end_date, isFromMySQL) {
|
|
|
-
|
|
|
- // 当前只对基金做排名， 其它类型参考基金排名做相对排名
|
|
|
- if(!(entity_type in ['MF', 'HF'])) return null;
|
|
|
-
|
|
|
- table_desc = get_bfi_indicator_table_description(entity_type);
|
|
|
- sec_id_col = table_desc.sec_id_col[0];
|
|
|
- tb_data_bfi_indicator = get_monthly_indicator_data(table_desc.table_name[0], end_date, isFromMySQL);
|
|
|
- tb_data_bfi_indicator.rename!(sec_id_col, 'entity_id');
|
|
|
-
|
|
|
- table_desc = get_risk_stats_table_description(entity_type);
|
|
|
- sec_id_col = table_desc.sec_id_col[0];
|
|
|
- tb_data_risk_stats = get_monthly_indicator_data(table_desc.table_name[0], end_date, isFromMySQL);
|
|
|
- tb_data_risk_stats.rename!(sec_id_col, 'entity_id');
|
|
|
-
|
|
|
- table_desc = get_riskadjret_stats_table_description(entity_type);
|
|
|
- sec_id_col = table_desc.sec_id_col[0];
|
|
|
- tb_data_riskadjret_stats = get_monthly_indicator_data(table_desc.table_name[0], end_date, isFromMySQL);
|
|
|
- tb_data_riskadjret_stats.rename!(sec_id_col, 'entity_id');
|
|
|
-
|
|
|
- table_desc = get_indicator_table_description(entity_type);
|
|
|
- sec_id_col = table_desc.sec_id_col[0];
|
|
|
- tb_data_indicator_stats = get_monthly_indicator_data(table_desc.table_name[0], end_date, isFromMySQL);
|
|
|
- tb_data_indicator_stats.rename!(sec_id_col, 'entity_id');
|
|
|
-
|
|
|
- t = SELECT *
|
|
|
- FROM entity_info en
|
|
|
- INNER JOIN tb_data_bfi_indicator d2 ON en.entity_id = d2.entity_id
|
|
|
- INNER JOIN tb_data_risk_stats d3 ON en.entity_id = d3.entity_id
|
|
|
- INNER JOIN tb_data_riskadjret_stats d4 ON en.entity_id = d4.entity_id
|
|
|
- INNER JOIN tb_data_indicator_stats d5 ON en.entity_id = d5.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);
|
|
|
+ indicator_table = SELECT * FROM get_indicator_info() WHERE id IN v_indicator_id;
|
|
|
|
|
|
- v_ranking_tables = [t_s, t_s_num, t_ss, t_ss_num];
|
|
|
-
|
|
|
- // 取消 39, 53, 54, 55, 57, 57
|
|
|
- v_indicator_id = [11, 12, 16, 33, 34, 35, 36, 38, 48, 59,
|
|
|
- 2, 6, 9, 10, 21, 50, 52,
|
|
|
- 14, 15, 16, 17, 18, 19, 58, 21, 40,
|
|
|
- 37, 41, 42, 43, 44, 45, 46, 47, 49
|
|
|
- ];
|
|
|
- indicator_table = SELECT * FROM get_indicator_info() WHERE id in v_indicator_id;
|
|
|
-
|
|
|
- run_ranking_sql(t, indicator_table, v_ranking_tables);
|
|
|
+ v_ranking_tables = run_ranking_sql(cal_type, t, indicator_table);
|
|
|
|
|
|
return v_ranking_tables;
|
|
|
}
|
|
|
|
|
|
+
|
|
|
/*
|
|
|
* 将源指标表横表变竖表，以方便排名计算
|
|
|
*
|
|
|
@@ -801,7 +382,7 @@ def transform_return_for_ranking (entity_type, entity_info, end_date, ranking_by
|
|
|
/*
|
|
|
* 将源风险指标表横表变竖表，以方便排名计算
|
|
|
*
|
|
|
- * TODO: 一直缺 portfolio bfi indicator 计算！mysql 里的 pf_fund_bfi_bm_indicator_ranking 是错的...
|
|
|
+ *
|
|
|
*/
|
|
|
def transform_risk_stats_for_ranking (entity_type, entity_info, end_date, ranking_by, isFromMySQL=true) {
|
|
|
|
|
|
@@ -990,40 +571,52 @@ def cal_relative_ranking(benchmark_ranking, mutable entity_ranking, isFromMySQL=
|
|
|
/*
|
|
|
* 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
|
|
|
+ * @param cal_type
|
|
|
+ * @param ranking_tables <VECTOR>: 当 cal_type = 'strategy' 时包含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
|
|
|
+ * cal_type = 'bfi' 时包含2个数据表的向量，分别是bfi策略排名，bfi策略排名阈值
|
|
|
*/
|
|
|
-def save_ranking_tables(entity_type, ranking_tables) {
|
|
|
-
|
|
|
+def save_ranking_tables(cal_type, ranking_tables) {
|
|
|
+//cal_type = 'bfi'
|
|
|
+//ranking_tables=v_ranking_tables
|
|
|
if(ranking_tables.isVoid()) return;
|
|
|
|
|
|
- source_table = '';
|
|
|
- target_table = '';
|
|
|
-
|
|
|
- if(entity_type IN ['MF', 'HF']) {
|
|
|
+ entity_id_col = 'fund_id';
|
|
|
+
|
|
|
+ if(cal_type == 'bfi') {
|
|
|
+
|
|
|
+ source_table = 'raw_db.pf_fund_bfi_bm_indicator_ranking';
|
|
|
+ target_table = 'raw_db.pf_fund_bfi_bm_indicator_ranking';
|
|
|
+ category_id_col = 'factor_id';
|
|
|
+
|
|
|
+ } else {
|
|
|
+ source_table = 'raw_db.pf_fund_indicator_ranking';
|
|
|
+ target_table = 'raw_db.pf_fund_indicator_ranking';
|
|
|
+ category_id_col = 'strategy';
|
|
|
+ }
|
|
|
|
|
|
- entity_id_col = 'fund_id';
|
|
|
- source_table = 'raw_db.pf_fund_indicator_ranking';
|
|
|
- target_table = 'raw_db.pf_fund_indicator_ranking'
|
|
|
-
|
|
|
- }
|
|
|
+ t = ranking_tables[0];
|
|
|
+ save_and_sync(t.rename!(['entity_id', 'category_id'], [entity_id_col, category_id_col]), source_table, target_table);
|
|
|
|
|
|
- ranking_tables[0].rename!('entity_id', entity_id_col);
|
|
|
- save_and_sync(ranking_tables[0], source_table, target_table);
|
|
|
+ t = ranking_tables[1];
|
|
|
+ save_and_sync(t.rename!('category_id', category_id_col), source_table + '_num', target_table + '_num');
|
|
|
|
|
|
- save_and_sync(ranking_tables[1], source_table + '_num', target_table + '_num');
|
|
|
-
|
|
|
- source_table = source_table.strReplace('_ranking', '_substrategy_ranking');
|
|
|
- target_table = target_table.strReplace('_ranking', '_substrategy_ranking');
|
|
|
+ if(cal_type == 'strategy') {
|
|
|
|
|
|
- ranking_tables[2].rename!('entity_id', entity_id_col);
|
|
|
- save_and_sync(ranking_tables[2], source_table, target_table);
|
|
|
+ source_table = source_table.strReplace('_ranking', '_substrategy_ranking');
|
|
|
+ target_table = target_table.strReplace('_ranking', '_substrategy_ranking');
|
|
|
+ category_id_col = 'substrategy';
|
|
|
+
|
|
|
+ t = ranking_tables[2];
|
|
|
+ save_and_sync(t.rename!(['entity_id', 'category_id'], [entity_id_col, category_id_col]), source_table, target_table);
|
|
|
|
|
|
- save_and_sync(ranking_tables[3], source_table + '_num', target_table + '_num');
|
|
|
+ t = ranking_tables[3];
|
|
|
+ save_and_sync(t.rename!('category_id', category_id_col), source_table + '_num', target_table + '_num');
|
|
|
+ }
|
|
|
|
|
|
}
|
|
|
|
|
|
/*
|
|
|
- * 参考排名数据入库AND a.indicator_id NOT IN (50, 52, 59, 46)
|
|
|
+ * 参考排名数据入库
|
|
|
|
|
|
*
|
|
|
* @param ranking_tables <TABLE>:
|
