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dolphin-dev/ipynb/ddb.ipynb

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1. Add `DDBDailyFactor.py`, which creates daily factors from HFT data. 2. Correct some typo in `DDBLoader.py`, but shouldn't affect its normal behaviour.
2 years ago
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "7246e0c8-61cd-4cbf-a978-aa0dc0172d6d",
"metadata": {},
"outputs": [],
"source": [
"import dolphindb as ddb"
]
},
{
"cell_type": "code",
1. `DDBDailyFactor.py` => `DDBFactor.py` 2. 在`DDBLoader.py`中增加了`DDBPITLoader`类，专门用于向DolphinDB里面添加PIT数据 3. 其他一些优化
2 years ago
"execution_count": 66,
1. Add `DDBDailyFactor.py`, which creates daily factors from HFT data. 2. Correct some typo in `DDBLoader.py`, but shouldn't affect its normal behaviour.
2 years ago
"id": "5d0f471e-682e-43cc-abdb-7e52f3bbd707",
"metadata": {},
"outputs": [],
"source": [
"sess = ddb.session('192.168.1.7', 8848)\n",
"sess.login('admin', '123456')"
]
},
{
"cell_type": "code",
1. `DDBDailyFactor.py` => `DDBFactor.py` 2. 在`DDBLoader.py`中增加了`DDBPITLoader`类，专门用于向DolphinDB里面添加PIT数据 3. 其他一些优化
2 years ago
"execution_count": 67,
1. Add `DDBDailyFactor.py`, which creates daily factors from HFT data. 2. Correct some typo in `DDBLoader.py`, but shouldn't affect its normal behaviour.
2 years ago
"id": "ed17fd0b-9b36-47e4-9ab6-11459a3621fb",
"metadata": {},
"outputs": [],
"source": [
"# backup(backup_path, sql_obj, force, parallel)\n",
"code = \"\"\"\n",
1. `DDBDailyFactor.py` => `DDBFactor.py` 2. 在`DDBLoader.py`中增加了`DDBPITLoader`类，专门用于向DolphinDB里面添加PIT数据 3. 其他一些优化
2 years ago
" backup('/data/dolphindb/backup/', <select * from loadTable(\"dfs://hft_stock_ts\", \"OrderPartitioned\")>, true, true)\n",
1. Add `DDBDailyFactor.py`, which creates daily factors from HFT data. 2. Correct some typo in `DDBLoader.py`, but shouldn't affect its normal behaviour.
2 years ago
"\"\"\""
]
},
{
"cell_type": "code",
1. `DDBDailyFactor.py` => `DDBFactor.py` 2. 在`DDBLoader.py`中增加了`DDBPITLoader`类，专门用于向DolphinDB里面添加PIT数据 3. 其他一些优化
2 years ago
"execution_count": 68,
1. Add `DDBDailyFactor.py`, which creates daily factors from HFT data. 2. Correct some typo in `DDBLoader.py`, but shouldn't affect its normal behaviour.
2 years ago
"id": "8b7dae3d-aef1-4c50-92b2-460d4fea0a96",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
1. `DDBDailyFactor.py` => `DDBFactor.py` 2. 在`DDBLoader.py`中增加了`DDBPITLoader`类，专门用于向DolphinDB里面添加PIT数据 3. 其他一些优化
2 years ago
"114350"
1. Add `DDBDailyFactor.py`, which creates daily factors from HFT data. 2. Correct some typo in `DDBLoader.py`, but shouldn't affect its normal behaviour.
2 years ago
]
},
1. `DDBDailyFactor.py` => `DDBFactor.py` 2. 在`DDBLoader.py`中增加了`DDBPITLoader`类，专门用于向DolphinDB里面添加PIT数据 3. 其他一些优化
2 years ago
"execution_count": 68,
1. Add `DDBDailyFactor.py`, which creates daily factors from HFT data. 2. Correct some typo in `DDBLoader.py`, but shouldn't affect its normal behaviour.
2 years ago
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sess.run(code)"
]
},
1. `DDBDailyFactor.py` => `DDBFactor.py` 2. 在`DDBLoader.py`中增加了`DDBPITLoader`类，专门用于向DolphinDB里面添加PIT数据 3. 其他一些优化
2 years ago
{
"cell_type": "code",
"execution_count": 69,
"id": "bf3bc38e-74cb-4549-bdca-6a3d2a601488",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2287.0"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"114350 / 50"
]
},
1. Add `DDBDailyFactor.py`, which creates daily factors from HFT data. 2. Correct some typo in `DDBLoader.py`, but shouldn't affect its normal behaviour.
2 years ago
{
"cell_type": "code",
"execution_count": 6,
"id": "11275975-af43-4123-973a-f75096eb4e43",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2311.0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"115550 / 50"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "155735c7-bdad-4c64-a2a1-514fe54e088c",
"metadata": {},
"outputs": [],
"source": [
"import dolphindb as ddb\n",
"\n",
"sess = ddb.session('localhost', 8848)\n",
"sess.login('admin', '123456')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a2e95bc5-c1d5-47f5-9488-9b8702947afc",
"metadata": {},
"outputs": [],
"source": [
"# migrate(backupDir, [backupDBPath], [backupTableName], [newDBPath=backupDBPath], [newTableName=backupTableName])\n",
"code = \"\"\"\n",
" migrate('/data/dolphindb/backup/',\"dfs://hft_stock_ts\", \"KLinePartitioned\")\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8a6198ef-e7f1-4f58-ba24-32dc429c9803",
"metadata": {},
"outputs": [],
"source": [
"sess.run(code)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "60f71516-33fc-4147-9740-c493ccb6b6a8",
"metadata": {},
"outputs": [],
"source": [
"def make_kurto_code_partition(partition_num):\n",
" code = \"\"\"\n",
" tbl = loadTable(\"dfs://hft_stock_ts\", \"KLinePartitioned\");\n",
" ret = select \n",
" code, m_nDate, eachPre(\\, m_nClose)-1.0 as ret \n",
" from tbl \n",
" where partition(code, {partition_num}) \n",
" context by m_nDate;\n",
" kurto = select \n",
" code, m_nDate, sqrt(239) * sum(pow(ret, 3)) / pow(sum(pow(ret, 2)), 1.5) as kurto \n",
" from ret \n",
" group by code, m_nDate;\n",
" \"\"\".format(\n",
" partition_num = partition_num\n",
" )\n",
" sess.run(code)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "1397c1b7-f81a-465e-868a-894b3ef818fc",
"metadata": {},
"outputs": [
{
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" <td>656</td>\n",
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"<p>99 rows × 2 columns</p>\n",
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" code count\n",
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"4 000731.SZ 2311\n",
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"94 688039.SH 624\n",
"95 688052.SH 52\n",
"96 688091.SH 198\n",
"97 688368.SH 656\n",
"98 688396.SH 575\n",
"\n",
"[99 rows x 2 columns]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sess.run(\"\"\"\n",
" select code, count(*) from kurto group by code\n",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "59758901-3728-4355-bbb8-15b3eb537cdf",
"metadata": {},
"outputs": [],
"source": [
"def create_daily_ddb_database():\n",
" code = \"\"\"\n",
" daily_stock_ts = database(\n",
" \"dfs://daily_stock_ts\", \n",
" HASH, [SYMBOL, 50], \n",
" engine = 'TSDB'\n",
" )\n",
" \"\"\"\n",
" sess.run(code)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "d8fa94ae-6ca4-41f3-8b13-47ab8876ba95",
"metadata": {},
"outputs": [],
"source": [
"def load_daily_ddb_database():\n",
" code = \"\"\"\n",
" daily_stock_ts = database(\"dfs://daily_stock_ts\")\n",
" \"\"\"\n",
" sess.run(code)\n",
"\n",
"load_daily_ddb_database()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "84eb237e-959c-419f-bce1-6779acda86d4",
"metadata": {},
"outputs": [],
"source": [
"def create_kurto_partition_table():\n",
" # createPartitionedTable(dbHandle, table, tableName, [partitionColumns], [compressMethods], [sortColumns], [keepDuplicates=ALL], [sortKeyMappingFunction])\n",
" code = \"\"\"\n",
" dropTable(daily_stock_ts, \"kurto_partitioned\");\n",
" kurto_partitioned = createPartitionedTable(\n",
" daily_stock_ts,\n",
" kurto, \n",
" \"kurto_partitioned\",\n",
" partitionColumns = 'code',\n",
" compressMethods = {'m_nDate' : 'delta'},\n",
" sortColumns = `code`m_nDate\n",
" );\n",
" \"\"\"\n",
" sess.run(code)\n",
" \n",
"create_kurto_partition_table()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "0d93b3d1-39b7-4e23-8abf-eda86dcd2a23",
"metadata": {},
"outputs": [
{
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" <td>15.443339</td>\n",
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" <td>2013-01-04</td>\n",
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" <td>000789.SZ</td>\n",
" <td>2013-01-04</td>\n",
" <td>-14.801720</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>000903.SZ</td>\n",
" <td>2013-01-04</td>\n",
" <td>-15.405085</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>000912.SZ</td>\n",
" <td>2013-01-04</td>\n",
" <td>14.755241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>000935.SZ</td>\n",
" <td>2013-01-04</td>\n",
" <td>15.413214</td>\n",
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" <th>9</th>\n",
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" code m_nDate kurto\n",
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"8 000935.SZ 2013-01-04 15.413214\n",
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]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sess.run(\"\"\"\n",
" kurto_partitioned.tableInsert(kurto);\n",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "eea5cb0b-8452-41a9-a0ad-7cf52c856c25",
"metadata": {},
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{
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"execution_count": 41,
"metadata": {},
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],
"source": [
"sess.run(\"\"\"\n",
" select count(*) from kurto_partitioned;\n",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "9e7bc856-517b-4bf9-866d-4fe85b9705a3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'partitionType': 5,\n",
" 'partitionColumnType': 17,\n",
" 'partitionColumnIndex': 0,\n",
" 'chunkPath': None,\n",
" 'colDefs': name typeString typeInt comment\n",
" 0 code SYMBOL 17 \n",
" 1 m_nDate DATE 6 \n",
" 2 kurto DOUBLE 16 ,\n",
" 'chunkGranularity': 'TABLE',\n",
" 'partitionTypeName': 'HASH',\n",
" 'keepDuplicates': 'ALL',\n",
" 'engineType': 'TSDB',\n",
" 'partitionColumnName': 'code',\n",
" 'partitionSchema': 50,\n",
" 'sortColumns': array(['code', 'm_nDate'], dtype=object),\n",
" 'partitionSites': None}"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sess.run(\"\"\"\n",
" schema(loadTable(\"dfs://daily_stock_ts\", \"kurto_partitioned\"));\n",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "94ae00b5-ba42-48b9-b114-012cf306d871",
"metadata": {},
"outputs": [],
"source": [
"sess.run(\"m_table = table(kurto)\")"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "e30134f9-5110-421d-8f71-1bef25e7ef17",
"metadata": {},
"outputs": [
{
"data": {
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" <th>154948</th>\n",
" <td>688396.SH</td>\n",
" <td>2022-07-04</td>\n",
" <td>-15.420192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>154949</th>\n",
" <td>688396.SH</td>\n",
" <td>2022-07-05</td>\n",
" <td>-15.426922</td>\n",
" </tr>\n",
" <tr>\n",
" <th>154950</th>\n",
" <td>688396.SH</td>\n",
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" <th>154951</th>\n",
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" <td>-15.411602</td>\n",
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" <tr>\n",
" <th>154952</th>\n",
" <td>688396.SH</td>\n",
" <td>2022-07-08</td>\n",
" <td>-15.401453</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>154953 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" code m_nDate kurto\n",
"0 000400.SZ 2013-01-04 -0.317991\n",
"1 000400.SZ 2013-01-07 0.790872\n",
"2 000400.SZ 2013-01-08 0.655005\n",
"3 000400.SZ 2013-01-09 0.965054\n",
"4 000400.SZ 2013-01-10 -0.116488\n",
"... ... ... ...\n",
"154948 688396.SH 2022-07-04 -15.420192\n",
"154949 688396.SH 2022-07-05 -15.426922\n",
"154950 688396.SH 2022-07-06 -15.389434\n",
"154951 688396.SH 2022-07-07 -15.411602\n",
"154952 688396.SH 2022-07-08 -15.401453\n",
"\n",
"[154953 rows x 3 columns]"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sess.run(\"m_table\")"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "947e3346-15f6-4fb5-b2d6-b97b013a3b36",
"metadata": {},
"outputs": [],
"source": [
"code = \"\"\"\n",
" // 保证创建新的分区表不会和已经存在表冲突\n",
" if (existsTable(\"{ddb_daily_path}\", \"{partition_tbl_name}\")) {{\n",
" dropTable({ddb_daily_dbname}, \"{partition_tbl_name}\");\n",
" }}\n",
"\n",
" {partition_tbl_name} = createPartitionedTable(\n",
" dbHandle = {ddb_daily_dbname},\n",
" table = {memory_tbl_name}, \n",
" \"{partition_tbl_name}\",\n",
" partitionColumns = 'code',\n",
" compressMethods = {{'m_nDate' : 'delta'}},\n",
" sortColumns = `code`m_nDate\n",
" );\n",
"\"\"\".format(\n",
" ddb_daily_path = \"A\",\n",
" ddb_daily_dbname = \"B\",\n",
" partition_tbl_name = \"C\",\n",
" memory_tbl_name = \"D\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "5d758c68-301a-47a7-9cd2-b7dd7edf1a55",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('\\n'\n",
" ' // 保证创建新的分区表不会和已经存在表冲突\\n'\n",
" ' if (existsTable(\"A\", \"C\")) {\\n'\n",
" ' dropTable(B, \"C\");\\n'\n",
" ' }\\n'\n",
" '\\n'\n",
" ' C = createPartitionedTable(\\n'\n",
" ' dbHandle = B,\\n'\n",
" ' table = D, \\n'\n",
" ' \"C\",\\n'\n",
" \" partitionColumns = 'code',\\n\"\n",
" \" compressMethods = {'m_nDate' : 'delta'},\\n\"\n",
" ' sortColumns = `code`m_nDate\\n'\n",
" ' );\\n')\n"
]
}
],
"source": [
"from pprint import pprint\n",
"pprint(code)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "400424d9-9c58-4417-94e4-fe1569522b03",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'chunkGranularity': 'TABLE',\n",
" 'chunkPath': None,\n",
" 'colDefs': name typeString typeInt comment\n",
"0 code SYMBOL 17 \n",
"1 m_nDate DATE 6 \n",
"2 kurto DOUBLE 16 ,\n",
" 'engineType': 'TSDB',\n",
" 'keepDuplicates': 'ALL',\n",
" 'partitionColumnIndex': 0,\n",
" 'partitionColumnName': 'code',\n",
" 'partitionColumnType': 17,\n",
" 'partitionSchema': 50,\n",
" 'partitionSites': None,\n",
" 'partitionType': 5,\n",
" 'partitionTypeName': 'HASH',\n",
" 'sortColumns': array(['code', 'm_nDate'], dtype=object)}\n"
]
}
],
"source": [
"pprint(\n",
" sess.run(\"\"\"\n",
" schema(loadTable(\"dfs://daily_stock_ts\", \"hft_daily_factor\"));\n",
" \"\"\")\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "68e74de8-359c-4c47-a361-2c006896211c",
"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>count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>309906</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count\n",
"0 309906"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sess.run(\"\"\"\n",
" select count(*) from loadTable(\"dfs://daily_stock_ts\", \"hft_daily_factor\");\n",
"\"\"\")"
]
},
{
"cell_type": "code",
reorganize the directory structure
2 years ago
"execution_count": 21,
1. Add `DDBDailyFactor.py`, which creates daily factors from HFT data. 2. Correct some typo in `DDBLoader.py`, but shouldn't affect its normal behaviour.
2 years ago
"id": "b1bd7605-c347-44f2-a90f-bc949cde0ef0",
"metadata": {},
reorganize the directory structure
2 years ago
"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_in_period</th>\n",
" <th>appear_at_date</th>\n",
" <th>TOT_OPER_REV</th>\n",
" <th>OPER_REV</th>\n",
" <th>INT_INC</th>\n",
" <th>INSUR_PREM_UNEARNED</th>\n",
" <th>HANDLING_CHRG_COMM_INC</th>\n",
" <th>NET_INC_OTHER_OPS</th>\n",
" <th>...</th>\n",
" <th>OTHER_INCOME</th>\n",
" <th>MEMO</th>\n",
" <th>ASSET_DISPOSAL_INCOME</th>\n",
" <th>CONTINUED_NET_PROFIT</th>\n",
" <th>END_NET_PROFIT</th>\n",
" <th>CREDIT_IMPAIRMENT_LOSS</th>\n",
" <th>RD_EXPENSE</th>\n",
" <th>STMNOTE_FINEXP</th>\n",
" <th>FIN_EXP_INT_INC</th>\n",
" <th>TOT_OPER_COST2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>000677.SZ</td>\n",
" <td>2009-12-31</td>\n",
" <td>2009-12-31</td>\n",
" <td>2010-04-26</td>\n",
" <td>3.514495e+09</td>\n",
" <td>3.514495e+09</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>26392229.68</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>nan</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.474455e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>000677.SZ</td>\n",
" <td>2009-12-31</td>\n",
" <td>2009-12-31</td>\n",
" <td>2011-12-10</td>\n",
" <td>3.514495e+09</td>\n",
" <td>3.514495e+09</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>26392229.68</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>nan</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.460335e+09</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 69 columns</p>\n",
"</div>"
],
"text/plain": [
" code report_period appear_in_period appear_at_date TOT_OPER_REV \\\n",
"0 000677.SZ 2009-12-31 2009-12-31 2010-04-26 3.514495e+09 \n",
"1 000677.SZ 2009-12-31 2009-12-31 2011-12-10 3.514495e+09 \n",
"\n",
" OPER_REV INT_INC INSUR_PREM_UNEARNED HANDLING_CHRG_COMM_INC \\\n",
"0 3.514495e+09 0.0 0.0 0.0 \n",
"1 3.514495e+09 0.0 0.0 0.0 \n",
"\n",
" NET_INC_OTHER_OPS ... OTHER_INCOME MEMO ASSET_DISPOSAL_INCOME \\\n",
"0 26392229.68 ... 0.0 nan 0.0 \n",
"1 26392229.68 ... 0.0 nan 0.0 \n",
"\n",
" CONTINUED_NET_PROFIT END_NET_PROFIT CREDIT_IMPAIRMENT_LOSS RD_EXPENSE \\\n",
"0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 \n",
"\n",
" STMNOTE_FINEXP FIN_EXP_INT_INC TOT_OPER_COST2 \n",
"0 0.0 0.0 3.474455e+09 \n",
"1 0.0 0.0 3.460335e+09 \n",
"\n",
"[2 rows x 69 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import dolphindb as ddb \n",
"\n",
"sess = ddb.session('192.168.1.167', 8848)\n",
"sess.login('admin', '123456')\n",
"\n",
"sess.run(\"\"\"\n",
" is_common_adj = loadTable(\"dfs://pit_stock_ts\", \"is_common_adj\")\n",
" is_common_ori = loadTable(\"dfs://pit_stock_ts\", \"is_common_ori\")\n",
" select * from is_common_ori where code='000677.SZ' and report_period=2009.12.31\n",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "f0b3928d-c06f-4976-b568-091d98293a6e",
"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_in_period</th>\n",
" <th>appear_at_date</th>\n",
" <th>TOT_OPER_REV</th>\n",
" <th>OPER_REV</th>\n",
" <th>INT_INC</th>\n",
" <th>INSUR_PREM_UNEARNED</th>\n",
" <th>HANDLING_CHRG_COMM_INC</th>\n",
" <th>NET_INC_OTHER_OPS</th>\n",
" <th>...</th>\n",
" <th>OTHER_INCOME</th>\n",
" <th>MEMO</th>\n",
" <th>ASSET_DISPOSAL_INCOME</th>\n",
" <th>CONTINUED_NET_PROFIT</th>\n",
" <th>END_NET_PROFIT</th>\n",
" <th>CREDIT_IMPAIRMENT_LOSS</th>\n",
" <th>RD_EXPENSE</th>\n",
" <th>STMNOTE_FINEXP</th>\n",
" <th>FIN_EXP_INT_INC</th>\n",
" <th>TOT_OPER_COST2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>000677.SZ</td>\n",
" <td>2009-12-31</td>\n",
" <td>2010-12-31</td>\n",
" <td>2011-04-29</td>\n",
" <td>3.514495e+09</td>\n",
" <td>3.514495e+09</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>26392229.68</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>nan</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.474455e+09</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows × 69 columns</p>\n",
"</div>"
],
"text/plain": [
" code report_period appear_in_period appear_at_date TOT_OPER_REV \\\n",
"0 000677.SZ 2009-12-31 2010-12-31 2011-04-29 3.514495e+09 \n",
"\n",
" OPER_REV INT_INC INSUR_PREM_UNEARNED HANDLING_CHRG_COMM_INC \\\n",
"0 3.514495e+09 0.0 0.0 0.0 \n",
"\n",
" NET_INC_OTHER_OPS ... OTHER_INCOME MEMO ASSET_DISPOSAL_INCOME \\\n",
"0 26392229.68 ... 0.0 nan 0.0 \n",
"\n",
" CONTINUED_NET_PROFIT END_NET_PROFIT CREDIT_IMPAIRMENT_LOSS RD_EXPENSE \\\n",
"0 0.0 0.0 0.0 0.0 \n",
"\n",
" STMNOTE_FINEXP FIN_EXP_INT_INC TOT_OPER_COST2 \n",
"0 0.0 0.0 3.474455e+09 \n",
"\n",
"[1 rows x 69 columns]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sess.run(\"select * from is_common_adj where code='000677.SZ' and report_period=2009.12.31\")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "d68ea326-82c3-4a7c-97cf-c04dd8aee56b",
"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>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",
" </tr>\n",
" </thead>\n",
" <tbody>\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]"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sess = ddb.session('192.168.1.7', 8848)\n",
"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",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "0e6e949b-ff25-40eb-a70a-72a3a6639aec",
"metadata": {},
"outputs": [
{
"ename": "RuntimeError",
"evalue": "<Exception> in run: Failed to read response header from the socket with IO error type 13",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [31]\u001b[0m, in \u001b[0;36m<cell line: 15>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m code \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124mtbl = loadTable(\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdfs://hft_stock_ts\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTickPartitioned\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m)\u001b[39m\n\u001b[1;32m 3\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;124m}\u001b[39m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[0;32m---> 15\u001b[0m \u001b[43msess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcode\u001b[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: Failed to read response header from the socket with IO error type 13"
]
}
],
"source": [
"code = \"\"\"\n",
"tbl = loadTable(\"dfs://hft_stock_ts\", 'TickPartitioned')\n",
"\n",
"for (code in ['000666.SZ' , '000667.SZ', '000668.SZ', '000669.SZ']) {\n",
"\tdel_date = 2013.01.01\n",
"\t//end_date = 2013.01.31\n",
"\tdo {\n",
"\t\t//print('Deleting ' + code + ' from ' + start_date + ' to ' + end_date);\n",
"\t\tprint('Deleting ' + code + ' of ' + del_date);\n",
"\t\tdelete from tbl where m_nDate=del_date, code=code;\n",
"\t\tdel_date = temporalAdd(del_date, 1, \"d\");\n",
"\t} while (del_date <= 2022.12.31)\n",
"}\n",
"\"\"\"\n",
"sess.run(code)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ea0501e7-d416-45ce-add5-e443c55f158c",
"metadata": {},
1. Add `DDBDailyFactor.py`, which creates daily factors from HFT data. 2. Correct some typo in `DDBLoader.py`, but shouldn't affect its normal behaviour.
2 years ago
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