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1. Reorganized the directory structure.

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2. Add `DDBBase` class to decrease repeated class-level constant definitino.
3. Add `DDBExpression` class for single quarter calculation and yoy claculation.
main
Guofu Li 2 years ago
parent 85522c064c
commit bd9363f6fc
17 changed files with 1312 additions and 64 deletions
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0
dolphindb/project/scripts/pit2.dos → dolphindb/project/scripts/single_quarter.dos
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86
ipynb/ddb.ipynb
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@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"id": "7246e0c8-61cd-4cbf-a978-aa0dc0172d6d",
"metadata": {},
"outputs": [],
@ -12,15 +12,27 @@
},
{
"cell_type": "code",
"execution_count": 66,
"execution_count": 3,
"id": "5d0f471e-682e-43cc-abdb-7e52f3bbd707",
"metadata": {},
"outputs": [],
"source": [
"sess = ddb.session('192.168.1.7', 8848)\n",
"sess = ddb.session('localhost', 8848)\n",
"sess.login('admin', '123456')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fca58bdc-2aa1-4610-9a94-67d55f97a6e1",
"metadata": {},
"outputs": [],
"source": [
"sess.run(\"\"\"\n",
" migrate('/data/dolphindb/backup/', \"dfs://hft_stock_ts\", \"OrderPartitioned\")\n",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": 67,
@ -1101,7 +1113,7 @@
},
{
"cell_type": "code",
"execution_count": 33,
"execution_count": 27,
"id": "d68ea326-82c3-4a7c-97cf-c04dd8aee56b",
"metadata": {},
"outputs": [
@ -1126,44 +1138,24 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>code</th>\n",
" <th>m_nDate</th>\n",
" <th>m_nTime</th>\n",
" <th>m_nPrice</th>\n",
" <th>m_iVolume</th>\n",
" <th>m_iTurover</th>\n",
" <th>m_nMatchItems</th>\n",
" <th>m_chTradeFlag</th>\n",
" <th>m_chBSFlag</th>\n",
" <th>m_iAccVolume</th>\n",
" <th>...</th>\n",
" <th>m_nOpen</th>\n",
" <th>m_nPreClose</th>\n",
" <th>m_nAskPrice</th>\n",
" <th>m_nAskVolume</th>\n",
" <th>m_nBidPrice</th>\n",
" <th>m_nBidVolume</th>\n",
" <th>m_nAskAvPrice</th>\n",
" <th>m_nBidAvPrice</th>\n",
" <th>m_iTotalAskVolume</th>\n",
" <th>m_iTotalBidVolume</th>\n",
" <th>sum_cnt</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>0 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [code, m_nDate, m_nTime, m_nPrice, m_iVolume, m_iTurover, m_nMatchItems, m_chTradeFlag, m_chBSFlag, m_iAccVolume, m_iAccTurover, m_nHigh, m_nLow, m_nOpen, m_nPreClose, m_nAskPrice, m_nAskVolume, m_nBidPrice, m_nBidVolume, m_nAskAvPrice, m_nBidAvPrice, m_iTotalAskVolume, m_iTotalBidVolume]\n",
"Index: []\n",
"\n",
"[0 rows x 23 columns]"
" sum_cnt\n",
"0 0"
]
},
"execution_count": 33,
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
@ -1173,7 +1165,35 @@
"sess.login('admin', '123456')\n",
"sess.run(\"\"\"\n",
"tbl = loadTable(\"dfs://hft_stock_ts\", 'TickPartitioned');\n",
"select * from tbl where code='000666.SZ', m_nDate <= 2013.02.26;\n",
"select sum(cnt) from (select count(*) as cnt from tbl map);\n",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "7cbc4906-7756-424a-9ce5-9d2b6d1bab4b",
"metadata": {},
"outputs": [
{
"ename": "RuntimeError",
"evalue": "<Exception> in run: Server response: 'tbl = loadTable(\"dfs://hft_stock_ts\", \"TickPartitioned\") => getFileBlocksMeta on path '/hft_stock_ts/TickPartitioned.tbl' failed, reason: path does not exist' script: '\ntbl = loadTable(\"dfs://hft_stock_ts\", 'TickPartitioned');\nselect sum(cnt) from (select count(*) as cnt from tbl map);\n'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [28]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m sess \u001b[38;5;241m=\u001b[39m ddb\u001b[38;5;241m.\u001b[39msession(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m192.168.1.167\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m8848\u001b[39m)\n\u001b[1;32m 2\u001b[0m sess\u001b[38;5;241m.\u001b[39mlogin(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madmin\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m123456\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m \u001b[43msess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\"\"\u001b[39;49m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124;43mtbl = loadTable(\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdfs://hft_stock_ts\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m, \u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTickPartitioned\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m);\u001b[39;49m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124;43mselect sum(cnt) from (select count(*) as cnt from tbl map);\u001b[39;49m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124;43m\"\"\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/.venv/tinysoft/lib/python3.8/site-packages/dolphindb/session.py:161\u001b[0m, in \u001b[0;36msession.run\u001b[0;34m(self, script, *args, **kwargs)\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfetchSize\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m 160\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m BlockReader(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcpp\u001b[38;5;241m.\u001b[39mrunBlock(script, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs))\n\u001b[0;32m--> 161\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcpp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mscript\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[0;31mRuntimeError\u001b[0m: <Exception> in run: Server response: 'tbl = loadTable(\"dfs://hft_stock_ts\", \"TickPartitioned\") => getFileBlocksMeta on path '/hft_stock_ts/TickPartitioned.tbl' failed, reason: path does not exist' script: '\ntbl = loadTable(\"dfs://hft_stock_ts\", 'TickPartitioned');\nselect sum(cnt) from (select count(*) as cnt from tbl map);\n'"
]
}
],
"source": [
"sess = ddb.session('192.168.1.167', 8848)\n",
"sess.login('admin', '123456')\n",
"sess.run(\"\"\"\n",
"tbl = loadTable(\"dfs://hft_stock_ts\", 'TickPartitioned');\n",
"select sum(cnt) from (select count(*) as cnt from tbl map);\n",
"\"\"\")"
]
},

870
ipynb/ddb_pit.ipynb
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@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 3,
"id": "139fd1cb-aedf-4186-8408-4d630ba69599",
"metadata": {},
"outputs": [],
@ -639,7 +639,7 @@
},
{
"cell_type": "code",
"execution_count": 67,
"execution_count": 70,
"id": "4cd4cd6e-f34e-43a8-98de-c468a54d8081",
"metadata": {},
"outputs": [],
@ -656,7 +656,10 @@
"\treport_period = report_period_list;\n",
"\t\n",
"\tquery_table = table(report_period, m_nDate);\n",
"\tquery_table_exp = select * from cj(query_table, select code from source_table where partition(code, code_partition_id) group by code map);\n",
"\tquery_table_exp = select * from cj(\n",
" query_table, \n",
" select code from source_table where partition(code, code_partition_id\n",
" ) group by code map);\n",
"\t\n",
"\tcol_list = sqlCol(['code', 'report_period', 'appear_at_date', col_name]);\n",
" from_tbl = <ej(source_table, query_table_exp, `code`report_period)>;\n",
@ -672,7 +675,7 @@
},
{
"cell_type": "code",
"execution_count": 69,
"execution_count": 71,
"id": "943b760a-ab39-4291-8a93-81b3e38a70b7",
"metadata": {},
"outputs": [
@ -798,7 +801,7 @@
"[91 rows x 1 columns]"
]
},
"execution_count": 69,
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
@ -978,10 +981,865 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 181,
"id": "3c246940-1ad6-414f-b461-2d8ca7cd87f1",
"metadata": {},
"outputs": [],
"source": [
"sess.run(\"\"\"\n",
" def single_quarter(table_name, col_list) {\n",
" \n",
" sel_col = array(ANY, 4 + size(col_list));\n",
" sel_col[0] = sqlCol('code');\n",
" sel_col[2] = sqlColAlias(<year(report_period)>, 'year');\n",
" \n",
" for (i in 0..(size(col_list) - 1)) {\n",
" sel_col[i + 4] = sqlCol(col_list[i]);\n",
" }\n",
"\n",
" // 当季累计数据，作为下一季基准\n",
" sel_col[1] = sqlColAlias(sqlCol('report_period'), 'base_report_period');\n",
" sel_col[3] = sqlColAlias(<quarterOfYear(report_period) + 1>, 'quarter_of_year');\n",
" tbl_quarter_accum_base = sql(\n",
" select = sel_col,\n",
" from = table_name,\n",
" where = <quarterOfYear(report_period) < 4>\n",
" ).eval();\n",
"\n",
" // 从第二季开始，需要去匹配前一季累计基数\n",
" sel_col[1] = sqlColAlias(sqlCol('report_period'), 'current_report_period');\n",
" sel_col[3] = sqlColAlias(<quarterOfYear(report_period)>, 'quarter_of_year');\n",
" tbl_quarter_accum = sql(\n",
" select = sel_col,\n",
" from = table_name,\n",
" where = <quarterOfYear(report_period) > 1>\n",
" ).eval();\n",
"\n",
" // 单季流量，把非第一季的季报都要扣除前一季度的基数\n",
" sel_col[1] = sqlColAlias(sqlCol('current_report_period'), 'report_period');\n",
" sel_col[2] = sqlCol('year');\n",
" sel_col[3] = sqlCol('quarter_of_year');\n",
" for (i in 0..(size(col_list) - 1)) {\n",
" sel_col[i + 4] = sqlColAlias(\n",
" expr(\n",
" sqlCol('tbl_quarter_accum_' + col_list[i]),\n",
" -,\n",
" sqlCol(col_list[i])\n",
" )\n",
" , col_list[i] + '_flux');\n",
" }\n",
" from_obj = ej(\n",
" tbl_quarter_accum_base, \n",
" tbl_quarter_accum, \n",
" `code`year`quarter_of_year, \n",
" `code`year`quarter_of_year);\n",
" tbl_quarter_flux = sql(\n",
" select = sel_col, \n",
" from = from_obj\n",
" ).eval();\n",
"\n",
" // 每年第一个季度\n",
" sel_col[1] = sqlCol('report_period');\n",
" sel_col[2] = sqlColAlias(<year(report_period)>, 'year');\n",
" sel_col[3] = sqlColAlias(<quarterOfYear(report_period)>, 'quarter_of_year');\n",
" for (i in 0..(size(col_list) - 1)) {\n",
" sel_col[i + 4] = sqlColAlias(sqlCol(col_list[i]), col_list[i] + '_flux');\n",
" }\n",
" tbl_quarter1 = sql(\n",
" select = sel_col,\n",
" from = table_name,\n",
" where = <quarterOfYear(report_period) == 1>\n",
" ).eval();\n",
" \n",
" // 再拼接回第一季度（无需扣除基数的数据）\n",
" tbl_quarter_flux = unionAll(\n",
" tbl_quarter1, \n",
" tbl_quarter_flux\n",
" );\n",
" \n",
" return tbl_quarter_flux;\n",
" }\n",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": 182,
"id": "981c8b85-a750-4d6b-bed9-c2567a95b2a3",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>code</th>\n",
" <th>report_period</th>\n",
" <th>year</th>\n",
" <th>quarter_of_year</th>\n",
" <th>TOT_OPER_REV_flux</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>000400.SZ</td>\n",
" <td>2019-03-31</td>\n",
" <td>2019</td>\n",
" <td>1</td>\n",
" <td>1.032565e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>000400.SZ</td>\n",
" <td>2019-06-30</td>\n",
" <td>2019</td>\n",
" <td>2</td>\n",
" <td>2.021923e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>000400.SZ</td>\n",
" <td>2019-09-30</td>\n",
" <td>2019</td>\n",
" <td>3</td>\n",
" <td>2.183729e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>000400.SZ</td>\n",
" <td>2019-12-31</td>\n",
" <td>2019</td>\n",
" <td>4</td>\n",
" <td>4.917866e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>000558.SZ</td>\n",
" <td>2019-03-31</td>\n",
" <td>2019</td>\n",
" <td>1</td>\n",
" <td>2.361816e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>321</th>\n",
" <td>688396.SH</td>\n",
" <td>2019-12-31</td>\n",
" <td>2019</td>\n",
" <td>4</td>\n",
" <td>1.610869e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>833874.NE</td>\n",
" <td>2019-03-31</td>\n",
" <td>2019</td>\n",
" <td>1</td>\n",
" <td>3.720237e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>325</th>\n",
" <td>833874.NE</td>\n",
" <td>2019-06-30</td>\n",
" <td>2019</td>\n",
" <td>2</td>\n",
" <td>4.538674e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>324</th>\n",
" <td>833874.NE</td>\n",
" <td>2019-09-30</td>\n",
" <td>2019</td>\n",
" <td>3</td>\n",
" <td>4.776034e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>323</th>\n",
" <td>833874.NE</td>\n",
" <td>2019-12-31</td>\n",
" <td>2019</td>\n",
" <td>4</td>\n",
" <td>4.816188e+07</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>326 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" code report_period year quarter_of_year TOT_OPER_REV_flux\n",
"0 000400.SZ 2019-03-31 2019 1 1.032565e+09\n",
"82 000400.SZ 2019-06-30 2019 2 2.021923e+09\n",
"81 000400.SZ 2019-09-30 2019 3 2.183729e+09\n",
"80 000400.SZ 2019-12-31 2019 4 4.917866e+09\n",
"1 000558.SZ 2019-03-31 2019 1 2.361816e+07\n",
".. ... ... ... ... ...\n",
"321 688396.SH 2019-12-31 2019 4 1.610869e+09\n",
"79 833874.NE 2019-03-31 2019 1 3.720237e+07\n",
"325 833874.NE 2019-06-30 2019 2 4.538674e+07\n",
"324 833874.NE 2019-09-30 2019 3 4.776034e+07\n",
"323 833874.NE 2019-12-31 2019 4 4.816188e+07\n",
"\n",
"[326 rows x 5 columns]"
]
},
"execution_count": 182,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sess.run(\"single_quarter(tmp_table, ['TOT_OPER_REV'])\").sort_values(['code', 'report_period'])"
]
},
{
"cell_type": "code",
"execution_count": 144,
"id": "1ceea0a7-7218-4aa7-be8a-ebb655dcbc93",
"metadata": {},
"outputs": [],
"source": [
"sess.run(\"\"\"\n",
" tmp_table = select * from pit_col_at_date(\n",
" \"is_common_ori\", \n",
" \"TOT_OPER_REV\", \n",
" 2021.03.14, \n",
" [2019.12.31, 2019.09.30, 2019.06.30, 2019.03.31], \n",
" 0) order by code\n",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": 145,
"id": "d01d6fc2-15ed-4e40-9181-0f3e3a96d2cb",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>code</th>\n",
" <th>report_period</th>\n",
" <th>appear_at_date</th>\n",
" <th>TOT_OPER_REV</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>000400.SZ</td>\n",
" <td>2019-12-31</td>\n",
" <td>2020-04-10</td>\n",
" <td>1.015608e+10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>000400.SZ</td>\n",
" <td>2019-09-30</td>\n",
" <td>2019-10-12</td>\n",
" <td>5.238217e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>000400.SZ</td>\n",
" <td>2019-06-30</td>\n",
" <td>2019-08-24</td>\n",
" <td>3.054488e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>000400.SZ</td>\n",
" <td>2019-03-31</td>\n",
" <td>2019-04-26</td>\n",
" <td>1.032565e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>000558.SZ</td>\n",
" <td>2019-12-31</td>\n",
" <td>2020-08-25</td>\n",
" <td>1.378479e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>333</th>\n",
" <td>831010.NE</td>\n",
" <td>2019-06-30</td>\n",
" <td>2019-08-09</td>\n",
" <td>1.970813e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>334</th>\n",
" <td>833874.NE</td>\n",
" <td>2019-12-31</td>\n",
" <td>2020-06-29</td>\n",
" <td>1.785113e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>335</th>\n",
" <td>833874.NE</td>\n",
" <td>2019-09-30</td>\n",
" <td>2019-10-22</td>\n",
" <td>1.303494e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>336</th>\n",
" <td>833874.NE</td>\n",
" <td>2019-06-30</td>\n",
" <td>2019-08-16</td>\n",
" <td>8.258911e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>337</th>\n",
" <td>833874.NE</td>\n",
" <td>2019-03-31</td>\n",
" <td>2019-04-30</td>\n",
" <td>3.720237e+07</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>338 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" code report_period appear_at_date TOT_OPER_REV\n",
"0 000400.SZ 2019-12-31 2020-04-10 1.015608e+10\n",
"1 000400.SZ 2019-09-30 2019-10-12 5.238217e+09\n",
"2 000400.SZ 2019-06-30 2019-08-24 3.054488e+09\n",
"3 000400.SZ 2019-03-31 2019-04-26 1.032565e+09\n",
"4 000558.SZ 2019-12-31 2020-08-25 1.378479e+08\n",
".. ... ... ... ...\n",
"333 831010.NE 2019-06-30 2019-08-09 1.970813e+08\n",
"334 833874.NE 2019-12-31 2020-06-29 1.785113e+08\n",
"335 833874.NE 2019-09-30 2019-10-22 1.303494e+08\n",
"336 833874.NE 2019-06-30 2019-08-16 8.258911e+07\n",
"337 833874.NE 2019-03-31 2019-04-30 3.720237e+07\n",
"\n",
"[338 rows x 4 columns]"
]
},
"execution_count": 145,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sess.run(\"tmp_table\")"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "5ac22809-9ba9-4a01-b76a-591558c4cc52",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'< select symbol,date,cumsum(volume) as cumVol from tmp_tablef0e858a3837f0000 >'"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sess.run(\"\"\"\n",
"sql(select=sqlCol(['symbol', 'date', 'cumsum(volume) as cumVol']), from = tmp_table)\n",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "dc36ca56-07e0-4912-b5d2-da192a1c3c62",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'< select symbol,date,cumsum(volume) as cumVol from tmp_tablef0e858a3837f0000 >'"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sess.run(\"\"\"\n",
"sql(select=(sqlCol(`symbol),sqlCol(`date),sqlColAlias(<cumsum(volume)>, `cumVol)), from=tmp_table)\n",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": 211,
"id": "c3be05b5-c9b9-4df5-8481-b3092c25125e",
"metadata": {},
"outputs": [],
"source": [
"sess.run(\"\"\"\n",
" def yoy(table_name, col_list) {\n",
" sel_col = array(ANY, 4 + size(col_list));\n",
" sel_col[0] = sqlCol('code');\n",
" sel_col[3] = sqlColAlias(<quarterOfYear(report_period)>, 'quarter_of_year');\n",
" \n",
" // 上一年数据\n",
" for (i in 0..(size(col_list) - 1)) {\n",
" sel_col[i + 4] = sqlColAlias(sqlCol(col_list[i]), col_list[i] + '_base');\n",
" }\n",
" sel_col[1] = sqlColAlias(sqlCol('report_period'), 'report_period_last_year');\n",
" sel_col[2] = sqlColAlias(<year(report_period) + 1>, 'year');\n",
" tbl_last_year = sql(\n",
" select = sel_col,\n",
" from = table_name\n",
" ).eval();\n",
"\n",
" // 本年度数据\n",
" for (i in 0..(size(col_list) - 1)) {\n",
" sel_col[i + 4] = sqlCol(col_list[i]);\n",
" }\n",
" sel_col[1] = sqlColAlias(sqlCol('report_period'), 'report_period_current_year');\n",
" sel_col[2] = sqlColAlias(<year(report_period)>, 'year');\n",
" tbl_this_year = sql(\n",
" select = sel_col,\n",
" from = table_name\n",
" ).eval();\n",
" \n",
" // 计算同比增长率\n",
" sel_col = array(ANY, 4 + (3 * size(col_list)));\n",
"\n",
" sel_col[0] = sqlCol('code');\n",
" sel_col[1] = sqlColAlias(sqlCol('report_period_current_year'), 'report_period');\n",
" sel_col[2] = sqlCol('year');\n",
" sel_col[3] = sqlCol('quarter_of_year');\n",
" \n",
" for (i in 0..(size(col_list) - 1)) {\n",
" sel_col[3 * i + 4] = sqlCol(col_list[i] + '_base');\n",
" sel_col[3 * i + 5] = sqlCol(col_list[i]);\n",
" sel_col[3 * i + 6] = sqlColAlias(\n",
" expr(expr(sqlCol(col_list[i]), -, sqlCol(col_list[i] + '_base')), /, expr(abs, sqlCol(col_list[i] + '_base'))), \n",
" col_list[i] + '_yoy');\n",
" }\n",
" from_obj = ej(\n",
" tbl_last_year, \n",
" tbl_this_year, \n",
" `code`year`quarter_of_year, \n",
" `code`year`quarter_of_year);\n",
" \n",
" return sql(\n",
" select = sel_col,\n",
" from = from_obj).eval();\n",
" }\n",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": 212,
"id": "63492d0c-c348-4e8b-a08b-5feb279cee5e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>code</th>\n",
" <th>report_period</th>\n",
" <th>year</th>\n",
" <th>quarter_of_year</th>\n",
" <th>TOT_OPER_REV_base</th>\n",
" <th>TOT_OPER_REV</th>\n",
" <th>TOT_OPER_REV_yoy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>000400.SZ</td>\n",
" <td>2019-12-31</td>\n",
" <td>2019</td>\n",
" <td>4</td>\n",
" <td>8.216559e+09</td>\n",
" <td>1.015608e+10</td>\n",
" <td>0.236051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>000400.SZ</td>\n",
" <td>2018-12-31</td>\n",
" <td>2018</td>\n",
" <td>4</td>\n",
" <td>1.033072e+10</td>\n",
" <td>8.216559e+09</td>\n",
" <td>-0.204648</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>000558.SZ</td>\n",
" <td>2019-12-31</td>\n",
" <td>2019</td>\n",
" <td>4</td>\n",
" <td>7.024741e+08</td>\n",
" <td>1.378479e+08</td>\n",
" <td>-0.803768</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>000558.SZ</td>\n",
" <td>2018-12-31</td>\n",
" <td>2018</td>\n",
" <td>4</td>\n",
" <td>1.324249e+09</td>\n",
" <td>7.024741e+08</td>\n",
" <td>-0.469530</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>000677.SZ</td>\n",
" <td>2019-12-31</td>\n",
" <td>2019</td>\n",
" <td>4</td>\n",
" <td>7.068198e+08</td>\n",
" <td>7.906742e+08</td>\n",
" <td>0.118636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>177</th>\n",
" <td>688396.SH</td>\n",
" <td>2018-12-31</td>\n",
" <td>2018</td>\n",
" <td>4</td>\n",
" <td>5.875590e+09</td>\n",
" <td>6.270797e+09</td>\n",
" <td>0.067262</td>\n",
" </tr>\n",
" <tr>\n",
" <th>178</th>\n",
" <td>831010.NE</td>\n",
" <td>2019-12-31</td>\n",
" <td>2019</td>\n",
" <td>4</td>\n",
" <td>2.936331e+08</td>\n",
" <td>3.552313e+08</td>\n",
" <td>0.209780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>179</th>\n",
" <td>831010.NE</td>\n",
" <td>2018-12-31</td>\n",
" <td>2018</td>\n",
" <td>4</td>\n",
" <td>2.209252e+08</td>\n",
" <td>2.936331e+08</td>\n",
" <td>0.329106</td>\n",
" </tr>\n",
" <tr>\n",
" <th>180</th>\n",
" <td>833874.NE</td>\n",
" <td>2019-12-31</td>\n",
" <td>2019</td>\n",
" <td>4</td>\n",
" <td>1.974978e+08</td>\n",
" <td>1.785113e+08</td>\n",
" <td>-0.096135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>181</th>\n",
" <td>833874.NE</td>\n",
" <td>2018-12-31</td>\n",
" <td>2018</td>\n",
" <td>4</td>\n",
" <td>1.986075e+08</td>\n",
" <td>1.974978e+08</td>\n",
" <td>-0.005588</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>182 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" code report_period year quarter_of_year TOT_OPER_REV_base \\\n",
"0 000400.SZ 2019-12-31 2019 4 8.216559e+09 \n",
"1 000400.SZ 2018-12-31 2018 4 1.033072e+10 \n",
"2 000558.SZ 2019-12-31 2019 4 7.024741e+08 \n",
"3 000558.SZ 2018-12-31 2018 4 1.324249e+09 \n",
"4 000677.SZ 2019-12-31 2019 4 7.068198e+08 \n",
".. ... ... ... ... ... \n",
"177 688396.SH 2018-12-31 2018 4 5.875590e+09 \n",
"178 831010.NE 2019-12-31 2019 4 2.936331e+08 \n",
"179 831010.NE 2018-12-31 2018 4 2.209252e+08 \n",
"180 833874.NE 2019-12-31 2019 4 1.974978e+08 \n",
"181 833874.NE 2018-12-31 2018 4 1.986075e+08 \n",
"\n",
" TOT_OPER_REV TOT_OPER_REV_yoy \n",
"0 1.015608e+10 0.236051 \n",
"1 8.216559e+09 -0.204648 \n",
"2 1.378479e+08 -0.803768 \n",
"3 7.024741e+08 -0.469530 \n",
"4 7.906742e+08 0.118636 \n",
".. ... ... \n",
"177 6.270797e+09 0.067262 \n",
"178 3.552313e+08 0.209780 \n",
"179 2.936331e+08 0.329106 \n",
"180 1.785113e+08 -0.096135 \n",
"181 1.974978e+08 -0.005588 \n",
"\n",
"[182 rows x 7 columns]"
]
},
"execution_count": 212,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sess.run(\"yoy(tmp_table, ['TOT_OPER_REV'])\")"
]
},
{
"cell_type": "code",
"execution_count": 183,
"id": "5273ebfc-25bd-4b38-9605-d3109cf2280f",
"metadata": {},
"outputs": [],
"source": [
"sess.run(\"\"\"\n",
" tmp_table = select * from pit_col_at_date(\n",
" \"is_common_ori\", \n",
" \"TOT_OPER_REV\", \n",
" 2021.03.14, \n",
" [2019.12.31, 2018.12.31, 2017.12.31], \n",
" 0) order by code\n",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": 184,
"id": "99af13cd-5918-4d21-9067-d3da86d19c15",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>code</th>\n",
" <th>report_period</th>\n",
" <th>appear_at_date</th>\n",
" <th>TOT_OPER_REV</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>000400.SZ</td>\n",
" <td>2019-12-31</td>\n",
" <td>2020-04-10</td>\n",
" <td>1.015608e+10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>000400.SZ</td>\n",
" <td>2018-12-31</td>\n",
" <td>2019-03-29</td>\n",
" <td>8.216559e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>000400.SZ</td>\n",
" <td>2017-12-31</td>\n",
" <td>2018-03-24</td>\n",
" <td>1.033072e+10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>000558.SZ</td>\n",
" <td>2019-12-31</td>\n",
" <td>2020-08-25</td>\n",
" <td>1.378479e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>000558.SZ</td>\n",
" <td>2018-12-31</td>\n",
" <td>2019-04-26</td>\n",
" <td>7.024741e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>268</th>\n",
" <td>831010.NE</td>\n",
" <td>2018-12-31</td>\n",
" <td>2020-07-01</td>\n",
" <td>2.936331e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>269</th>\n",
" <td>831010.NE</td>\n",
" <td>2017-12-31</td>\n",
" <td>2020-07-01</td>\n",
" <td>2.209252e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>270</th>\n",
" <td>833874.NE</td>\n",
" <td>2019-12-31</td>\n",
" <td>2020-06-29</td>\n",
" <td>1.785113e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>271</th>\n",
" <td>833874.NE</td>\n",
" <td>2018-12-31</td>\n",
" <td>2020-06-29</td>\n",
" <td>1.974978e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>272</th>\n",
" <td>833874.NE</td>\n",
" <td>2017-12-31</td>\n",
" <td>2020-06-29</td>\n",
" <td>1.986075e+08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>273 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" code report_period appear_at_date TOT_OPER_REV\n",
"0 000400.SZ 2019-12-31 2020-04-10 1.015608e+10\n",
"1 000400.SZ 2018-12-31 2019-03-29 8.216559e+09\n",
"2 000400.SZ 2017-12-31 2018-03-24 1.033072e+10\n",
"3 000558.SZ 2019-12-31 2020-08-25 1.378479e+08\n",
"4 000558.SZ 2018-12-31 2019-04-26 7.024741e+08\n",
".. ... ... ... ...\n",
"268 831010.NE 2018-12-31 2020-07-01 2.936331e+08\n",
"269 831010.NE 2017-12-31 2020-07-01 2.209252e+08\n",
"270 833874.NE 2019-12-31 2020-06-29 1.785113e+08\n",
"271 833874.NE 2018-12-31 2020-06-29 1.974978e+08\n",
"272 833874.NE 2017-12-31 2020-06-29 1.986075e+08\n",
"\n",
"[273 rows x 4 columns]"
]
},
"execution_count": 184,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sess.run(\"tmp_table\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "87820a2a-1ddf-4950-b0d4-a3e7d4a61b16",
"metadata": {},
"outputs": [],
"source": []
}
],

18
src/DDBBase.py
Unescape View File

@ -0,0 +1,18 @@
import dolphindb as ddb
class DDBBase(object):
ddb_config = {
'host' : '192.168.1.167',
'username' : 'admin',
'password' : '123456',
}
def __init__(self):
self.ddb_sess = ddb.session(self.ddb_config['host'], 8848)
self.login(self.ddb_config['username'], self.ddb_config['password'])

365
src/DDBExpression.py
Unescape View File

@ -0,0 +1,365 @@
from tqdm import tqdm
import numpy as np
import pandas as pd
import dolphindb as ddb
from DDBBase import DDBBase
from DDBLoader import DDBPITLoader
class DDBExpression(DDBBase):
"""
调用事先定义好的DolphinDB的Function或者FunctionView来进行表达式计算。
定义DolphinDB中的Function或者FunctionView的具体逻辑在`DDBExpreCreator`中。
"""
def pit_at_date(self, table_name, date, report_period_list):
"""
获得任何时间点上，能看到的某个财报最新可见数据。
实现方式：
通过调用事先在DolpinDB中创建的Function View。
Args:
table_name: 财务报表名字，包含是否为调整报表的后缀名（非stem名）
date: 观察日期，使用DolphinDB的日期格式，即YYYY.MM.DD，无需加引号
report_period_list: 报告期列表，列表中每一个日期为一个DolphinDb的日期格式
Return:
DataFrame: 在观察日期`date`日能看到的，所有公司最近能看到的，报告期在`report_period_list`中的，财务报告表`table_name`的截面数据。
"""
df_list = []
for i in range(DDBPITLoader.num_code_partition):
_df = self.ddb_sess.run("""
select * from pit_at_date("{table_name}", {date}, [{report_period_list}], {code_partition_id});
""".format(
table_name = table_name,
date = date,
report_period_list = ','.join(report_period_list),
code_parition_id = i
))
_df.set_index(['code', 'report_period', 'appear_at_date'], inplace=True)
df_list.append(_df)
return pd.concat(df_list, axis=1)
def pit_col_at_date(self, table_name, col_name, date, report_period_list):
"""
获得任何时间点上，能看到的某个财报最新可见数据。
实现方式：
通过调用事先在DolpinDB中创建的Function View。
Args:
table_name: 财务报表名字，包含是否为调整报表的后缀名（非stem名）
col_name: 列名，一般为具体财务科目名字。
date: 观察日期，使用DolphinDB的日期格式，即YYYY.MM.DD，无需加引号
report_period_list: 报告期列表，列表中每一个日期为一个DolphinDb的日期格式
Return:
DataFrame: 在观察日期`date`日能看到的，所有公司最近能看到的，报告期在`report_period_list`中的，财务报告表`table_name`中``col_name`指标的截面数据。
"""
df_list = []
for i in range(DDBPITLoader.num_code_partition):
_df = self.ddb_sess.run("""
select * from pit_col_at_date("{table_name}", "{col_name}", {date}, [{report_period_list}], {code_partition_id});
""".format(
table_name = table_name,
col_name = col_name,
date = date,
report_period_list = ','.join(report_period_list),
code_parition_id = i
))
_df.set_index(['code', 'report_period', 'appear_at_date'], inplace=True)
df_list.append(_df)
return pd.concat(df_list, axis=1)
def yoy(self, dataframe, col_list):
self.ddb_sess.upload({'table_yoy' : dataframe })
self.ddb_sess.run("""
yoy({table_name}, [{col_list}]);
""".format(
table_name = 'table_yoy',
col_list = ','.join([f"'{col_name}'" for col_name in col_list])
))
def single_quarter(self, dataframe, col_list):
self.ddb_sess.upload({'table_single_quarter' : dataframe})
self.ddb_sess.run("""
single_quarter({table_name}, [{col_list}])
""".format(
table_name = table_name,
col_list = ','.join([f"'{col_name}'" for col_name in col_list])
))
class DDBExpreCreator(DDBBase):
"""
定义DolphinDB中的各种基础操作，因此这个类中的大部分实质性代码都是DolphinDB自己的语言，而不是Python语言编写的。
`Expre`为`Expression`的简写。
"""
pit_dbpath = "dfs://pit_stock_ts"
pit_dbname = "db_pit_stock"
daily_dbpath = "dfs://daily_stock_ts"
daily_dbname = "db_daily_stock"
def create_function_views(self):
self._func_view_pit_at_date()
self._func_view_pit_col_at_date()
def create_functions(self):
self._fun_yoy();
self._func_single_quarter()
def _func_single_quarter(self):
code = """
/**
* Args:
* table: DolphinDB的表，可以是一个内存表，也可以是一个分区表。
* col_list: 需要进行单季转换的字段名列表。
*/
def single_quarter(table_name, col_list) {
sel_col = array(ANY, 4 + size(col_list));
sel_col[0] = sqlCol('code');
sel_col[2] = sqlColAlias(<year(report_period)>, 'year');
for (i in 0..(size(col_list) - 1)) {
sel_col[i + 4] = sqlCol(col_list[i]);
}
// 当季累计数据，作为下一季基准
sel_col[1] = sqlColAlias(sqlCol('report_period'), 'base_report_period');
sel_col[3] = sqlColAlias(<quarterOfYear(report_period) + 1>, 'quarter_of_year');
tbl_quarter_accum_base = sql(
select = sel_col,
from = table_name,
where = <quarterOfYear(report_period) < 4>
).eval();
// 从第二季开始，需要去匹配前一季累计基数
sel_col[1] = sqlColAlias(sqlCol('report_period'), 'current_report_period');
sel_col[3] = sqlColAlias(<quarterOfYear(report_period)>, 'quarter_of_year');
tbl_quarter_accum = sql(
select = sel_col,
from = table_name,
where = <quarterOfYear(report_period) > 1>
).eval();
// 单季流量，把非第一季的季报都要扣除前一季度的基数
sel_col[1] = sqlColAlias(sqlCol('current_report_period'), 'report_period');
sel_col[2] = sqlCol('year');
sel_col[3] = sqlCol('quarter_of_year');
for (i in 0..(size(col_list) - 1)) {
sel_col[i + 4] = sqlColAlias(
expr(
sqlCol('tbl_quarter_accum_' + col_list[i]),
-,
sqlCol(col_list[i])
)
, col_list[i] + '_flux');
}
from_obj = ej(
tbl_quarter_accum_base,
tbl_quarter_accum,
`code`year`quarter_of_year,
`code`year`quarter_of_year);
tbl_quarter_flux = sql(
select = sel_col,
from = from_obj
).eval();
// 每年第一个季度
sel_col[1] = sqlCol('report_period');
sel_col[2] = sqlColAlias(<year(report_period)>, 'year');
sel_col[3] = sqlColAlias(<quarterOfYear(report_period)>, 'quarter_of_year');
for (i in 0..(size(col_list) - 1)) {
sel_col[i + 4] = sqlColAlias(sqlCol(col_list[i]), col_list[i] + '_flux');
}
tbl_quarter1 = sql(
select = sel_col,
from = table_name,
where = <quarterOfYear(report_period) == 1>
).eval();
// 再拼接回第一季度（无需扣除基数的数据）
tbl_quarter_flux = unionAll(
tbl_quarter1,
tbl_quarter_flux
);
return tbl_quarter_flux;
}
"""
self.ddb_sess.run(code)
def _func_yoy(self):
code = """
/**
* Args:
* table: DolphinDB的表，可以是一个内存表，也可以是一个分区表。
* col_list: 需要进行同比转换的字段名。
*/
def yoy(table_name, col_list) {
sel_col = array(ANY, 4 + size(col_list));
sel_col[0] = sqlCol('code');
sel_col[3] = sqlColAlias(<quarterOfYear(report_period)>, 'quarter_of_year');
// 上一年数据
for (i in 0..(size(col_list) - 1)) {
sel_col[i + 4] = sqlColAlias(sqlCol(col_list[i]), col_list[i] + '_base');
}
sel_col[1] = sqlColAlias(sqlCol('report_period'), 'report_period_last_year');
sel_col[2] = sqlColAlias(<year(report_period) + 1>, 'year');
tbl_last_year = sql(
select = sel_col,
from = table_name
).eval();
// 本年度数据
for (i in 0..(size(col_list) - 1)) {
sel_col[i + 4] = sqlCol(col_list[i]);
}
sel_col[1] = sqlColAlias(sqlCol('report_period'), 'report_period_current_year');
sel_col[2] = sqlColAlias(<year(report_period)>, 'year');
tbl_this_year = sql(
select = sel_col,
from = table_name
).eval();
// 计算同比增长率
sel_col = array(ANY, 4 + (3 * size(col_list)));
sel_col[0] = sqlCol('code');
sel_col[1] = sqlColAlias(sqlCol('report_period_current_year'), 'report_period');
sel_col[2] = sqlCol('year');
sel_col[3] = sqlCol('quarter_of_year');
for (i in 0..(size(col_list) - 1)) {
sel_col[3 * i + 4] = sqlCol(col_list[i] + '_base');
sel_col[3 * i + 5] = sqlCol(col_list[i]);
sel_col[3 * i + 6] = sqlColAlias(
expr(expr(sqlCol(col_list[i]), -, sqlCol(col_list[i] + '_base')), /, expr(abs, sqlCol(col_list[i] + '_base'))),
col_list[i] + '_yoy');
}
from_obj = ej(
tbl_last_year,
tbl_this_year,
`code`year`quarter_of_year,
`code`year`quarter_of_year);
return sql(
select = sel_col,
from = from_obj).eval();
}
"""
self.ddb_sess.run(code)
def _func_view_pit_at_date(self):
"""
创建`pit_at_date`，获得任何时间点上，能看到的某个财报最新可见数据。
"""
self.ddb_sess.run("""
dropFunctionView('pit_at_date');
""")
self.ddb_sess.run("""
/**
* Example of use:
* pit_at_date("is_common_ori", "TOT_OPER_REV", 2021.03.14, [2019.12.31, 2019.09.30, 2019.06.30], 0)
*/
def pit_at_date(table_name, date, report_period_list, code_partition_id) {
source_table = loadTable("{pit_dbpath}", table_name);
m_nDate = take(date, size(report_period_list));
report_period = report_period_list;
query_table = table(report_period, m_nDate);
query_table_exp = select * from cj(
query_table,
select code
from source_table
where partition(code, code_partition_id)
group by code map
);
source_table_part = select source_table.* from ej(
source_table, query_table_exp, `code`report_period
) where partition(code, code_partition_id);
return select source_table_part.* from aj(
query_table_exp,
source_table_part,
`code`report_period`m_nDate,
`code`report_period`appear_at_date
) where not isNull(source_table_part.code);
}
addFunctionView(pit_at_date);
""".format(
pit_dbpath = pit_dbpath
))
def _func_view_pit_col_at_date(self):
"""
创建`pit_col_at_date`，获得任何日期点上，能看到的财报中某一个指标的最新可见数据。
与`pit_at_date`的区别在于，`pit_col_at_date`返回返回一个序列，而`pit_at_date`返回一整张表。
"""
# 先把原有的View Function删除
self.ddb_sess.run("""
dropFunctionView('pit_col_at_date');
""")
self.ddb_sess.run("""
/**
* Example of use:
* pit_col_at_date("is_common_ori", "TOT_OPER_REV", 2021.03.14, [2019.12.31, 2019.09.30, 2019.06.30], 0)
*/
def pit_col_at_date(table_name, col_name, date, report_period_list, code_partition_id) {
source_table = loadTable("{pit_dbpath}", table_name);
m_nDate = take(date, size(report_period_list));
report_period = report_period_list;
query_table = table(report_period, m_nDate);
query_table_exp = select * from cj(
query_table,
select code
from source_table
where partition(code, code_partition_id)
group by code map
);
col_list = sqlCol(['code', 'report_period', 'appear_at_date', col_name]);
from_tbl = <ej(source_table, query_table_exp, `code`report_period)>;
where_conditions = [<partition(code, code_partition_id)>];
source_table_part = sql(select=col_list, from=from_tbl, where=where_conditions).eval();
return select source_table_part.* from aj(
query_table_exp,
source_table_part,
`code`report_period`m_nDate,
`code`report_period`appear_at_date
) where not isNull(source_table_part.code)
}
addFunctionView(pit_col_at_date);
""".format(
pit_dbpath = self.pit_dbpath
))

14
src/DDBFactor.py
Unescape View File

@ -5,10 +5,10 @@ import functools
import dolphindb as ddb
from DDBBase import DDBBase
from DDBLoader import DDBLoader
def load_ddb_table(hft_tbl_name):
"""
这是一个用来简化载入分区表过程的语法糖，但似乎需要预先调用这个函数的场景并不多，简化效果不是很明显。
@ -30,27 +30,17 @@ def load_ddb_table(hft_tbl_name):
return decorator
class DailyFactor(object):
class DDBDailyFactor(DDBBase):
#ddb_hft_path = "dfs://hft_stock_ts"
#ddb_hft_dbname = "db_hft_stock"
ddb_daily_path = "dfs://daily_stock_ts"
ddb_daily_dbname = "db_daily_stock"
ddb_config = {
'host' : '192.168.1.167',
'username' : 'admin',
'password' : '123456',
}
# 这里的partition数量未必需要和hft表的一致
# 当读取hft表的时候，需要使用DDBLoader中的`num_code_partition`，而不是此字段
num_code_partition = 50
def __init__(self):
self.ddb_sess = ddb.session(self.ddb_config['host'], 8848)
self.ddb_sess.login(self.ddb_config['username'], self.ddb_config['password'])
def create_ddb_database(self):
"""

23
src/DDBLoader.py
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@ -23,9 +23,10 @@ import sqlalchemy as sa
import ProtoBuffEntitys
from DDBBase import DDBBase
class DDBLoader(object):
class DDBLoader(DDBBase):
"""
- 放了几个公用的配置字段，包括：
1. SQL-Server的链接参数
@ -42,14 +43,8 @@ class DDBLoader(object):
'password' : 'passw0rd!'
}
ddb_config = {
'host' : '192.168.1.167',
'username' : 'admin',
'password' : '123456'
}
def __init__(self):
super().__init__()
self.mssql_engine = sa.create_engine(
"mssql+pyodbc://{username}:{password}@{host}/master?driver=ODBC+Driver+18+for+SQL+Server".format(**self.mssql_config),
connect_args = {
@ -57,9 +52,6 @@ class DDBLoader(object):
}, echo=False
)
self.ddb_sess = ddb.session(self.ddb_config['host'], 8848)
self.ddb_sess.login(self.ddb_config['username'], self.ddb_config['password'])
@abc.abstractmethod
def create_ddb_database(self, *args, **kwargs):
@ -552,7 +544,7 @@ class DDBHFTLoader(DDBLoader):
num_workers = 8
default_table_capacity = 10000
ddb_dump_journal_fname = 'ddb_dump_journal.csv'
ddb_dump_journal_fname = '../assets/ddb_dump_journal.csv'
def init_ddb_database(self, df_calendar):
@ -913,7 +905,9 @@ class DDBHFTLoader(DDBLoader):
ddb_sess.login(DDBLoader.ddb_config['username'], DDBLoader.ddb_config['password'])
ddb_sess.upload({df_table_name : df})
ddb_sess.run("tableInsert(loadTable('{dbPath}', `{partitioned_table_name}), {df_table_name})".format(
# 因为在做Tick数据的时候，偶然发生'CHUNK[xxx] does not exist.'，所以在这里使用`append!`函数代换一下试试
ddb_sess.run("append!(loadTable('{dbPath}', `{partitioned_table_name}), {df_table_name})".format(
#ddb_sess.run("tableInsert(loadTable('{dbPath}', `{partitioned_table_name}), {df_table_name})".format(
dbPath = DDBLoader.ddb_path,
partitioned_table_name = type_name + DDBLoader.ddb_partition_table_suffix,
df_table_name = df_table_name
@ -923,6 +917,9 @@ class DDBHFTLoader(DDBLoader):
def main():
# TODO:
# 可以使用`Fire`库，对函数调用再做一次封装，就可以避免每次运行不同参数时候需要修改内部多处的代码。
# PIT基本面数据
loader = DDBPITLoader()
loader.create_ddb_database()

0
ProtoBuffEntitys/HFDataTableMessage_pb2.py → src/ProtoBuffEntitys/HFDataTableMessage_pb2.py
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ProtoBuffEntitys/IndexFutureKLineMessage_pb2.py → src/ProtoBuffEntitys/IndexFutureKLineMessage_pb2.py
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ProtoBuffEntitys/IndexFutureL1TickMessage_pb2.py → src/ProtoBuffEntitys/IndexFutureL1TickMessage_pb2.py
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ProtoBuffEntitys/IndexKLineMessage_pb2.py → src/ProtoBuffEntitys/IndexKLineMessage_pb2.py
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ProtoBuffEntitys/IndexTickMessage_pb2.py → src/ProtoBuffEntitys/IndexTickMessage_pb2.py
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ProtoBuffEntitys/KLineMessage_pb2.py → src/ProtoBuffEntitys/KLineMessage_pb2.py
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ProtoBuffEntitys/OrderMessage_pb2.py → src/ProtoBuffEntitys/OrderMessage_pb2.py
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ProtoBuffEntitys/TickMessage_pb2.py → src/ProtoBuffEntitys/TickMessage_pb2.py
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ProtoBuffEntitys/TickQueueMessage_pb2.py → src/ProtoBuffEntitys/TickQueueMessage_pb2.py
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ProtoBuffEntitys/TranseMessage_pb2.py → src/ProtoBuffEntitys/TranseMessage_pb2.py
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