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dolphin-dev/mssql.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
" import sqlalchemy as sa\n",
" engine = sa.create_engine(\n",
" 'mssql+pyodbc://sa:passw0rd!@192.168.1.7/master?driver=ODBC+Driver+18+for+SQL+Server',\n",
" connect_args = {\n",
" \"TrustServerCertificate\": \"yes\"\n",
" }, echo=False)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"with engine.connect() as conn:\n",
" stat = \"select distinct S_INFO_WINDCODE, TRADE_DT from Level2BytesKLine.dbo.KLine\"\n",
" rs = conn.execute(stat)\n",
" stock_date_list = [(stock_name, date) for stock_name, date in rs.fetchall()]"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"stock_list, trade_list = zip(*stock_date_list)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4843"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(set(stock_list))"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'blob' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/home/guofu/Workspaces/dolphin-dev/mssql.ipynb Cell 3\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2B7b22686f73744e616d65223a2247756f66752d5043227d/home/guofu/Workspaces/dolphin-dev/mssql.ipynb#W2sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mgzip\u001b[39;00m\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2B7b22686f73744e616d65223a2247756f66752d5043227d/home/guofu/Workspaces/dolphin-dev/mssql.ipynb#W2sdnNjb2RlLXJlbW90ZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mProtoBuffEntitys\u001b[39;00m \u001b[39mimport\u001b[39;00m TranseMessage_pb2\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2B7b22686f73744e616d65223a2247756f66752d5043227d/home/guofu/Workspaces/dolphin-dev/mssql.ipynb#W2sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m f_all \u001b[39m=\u001b[39m gzip\u001b[39m.\u001b[39mdecompress(blob)\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2B7b22686f73744e616d65223a2247756f66752d5043227d/home/guofu/Workspaces/dolphin-dev/mssql.ipynb#W2sdnNjb2RlLXJlbW90ZQ%3D%3D?line=4'>5</a>\u001b[0m dataArray \u001b[39m=\u001b[39m TranseMessage_pb2\u001b[39m.\u001b[39mTranseArray()\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2B7b22686f73744e616d65223a2247756f66752d5043227d/home/guofu/Workspaces/dolphin-dev/mssql.ipynb#W2sdnNjb2RlLXJlbW90ZQ%3D%3D?line=5'>6</a>\u001b[0m dataArray\u001b[39m.\u001b[39mParseFromString(f_all)\n",
"\u001b[0;31mNameError\u001b[0m: name 'blob' is not defined"
]
}
],
"source": [
"import gzip\n",
"from ProtoBuffEntitys import TranseMessage_pb2\n",
"\n",
"f_all = gzip.decompress(blob)\n",
"dataArray = TranseMessage_pb2.TranseArray()\n",
"dataArray.ParseFromString(f_all)\n",
"\n",
"print(dataArray.dataArray)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"code 600843\n",
"m_nDate 20130104\n",
"m_nTime 92506510\n",
"m_nIndex 0\n",
"m_chFunctionCode 0\n",
"m_chOrderKind 0\n",
"m_chBSFlag 83\n",
"m_nTradePrice 6.8\n",
"m_nTradeVolume 200\n",
"m_nAskOrder 0\n",
"m_nBidOrder 0\n"
]
}
],
"source": [
"for item in dataArray.dataArray:\n",
" fields = item.ListFields()\n",
" for desc, val in fields:\n",
" print(desc.name, val)\n",
" break\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'sqlalchemy.engine.row.LegacyRow'>\n"
]
}
],
"source": [
"with engine.connect() as conn:\n",
" stat = \"select top 1 * from Level2BytesTickQue.dbo.TickQue\"\n",
" rs = conn.execute(stat)\n",
"\n",
" for row in rs.fetchall():\n",
" print(type(row))\n",
" blob = row[2]\n"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1499694"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import gzip\n",
"from ProtoBuffEntitys import TickQueueMessage_pb2\n",
"\n",
"f_all = gzip.decompress(blob)\n",
"dataArray = TickQueueMessage_pb2.TickQueueArray()\n",
"dataArray.ParseFromString(f_all)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"<class 'google.protobuf.pyext._message.RepeatedScalarContainer'>\n",
"[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
]
}
],
"source": [
"import numpy as np\n",
"print(dataArray.dataArray[0].m_nABVolume)\n",
"print(type(dataArray.dataArray[0].m_nABVolume))\n",
"print(np.array(dataArray.dataArray[0].m_nABVolume))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import urllib\n",
"\n",
"server = 'serverName\\instanceName,port' # to specify an alternate port\n",
"database = 'mydb' \n",
"username = 'myusername' \n",
"password = 'mypassword'\n",
"\n",
"params = urllib.parse.quote_plus(\"'DRIVER={ODBC Driver 17 for SQL Server};SERVER='+server+';DATABASE='+database+';UID='+username+';PWD='+ password\")\n",
"\n",
"engine = sa.ceate_engine(\"mssql+pyodbc:///?odbc_connect=%s\" % params)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"from ProtoBuffEntitys import KLineMessage_pb2, OrderMessage_pb2, TickMessage_pb2, TickQueueMessage_pb2, TranseMessage_pb2"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"HFT_TYPE_LIST = [\n",
" 'KLine', 'Order', 'Tick', 'TickQueue', 'Transe'\n",
"]\n",
"PROTOBUFF_NAME_LIST = [f\"{name}Message_pb2\" for name in PROTOBUFF_NAME_LIST]\n",
"\n",
"import importlib\n",
"\n",
"PROTOBUFF_MODULE_LIST = [importlib.import_module(f\".{name}\", package='ProtoBuffEntitys') for name in PROTOBUFF_MODULE_LIST]"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (99226286.py, line 1)",
"output_type": "error",
"traceback": [
"\u001b[0;36m Input \u001b[0;32mIn [35]\u001b[0;36m\u001b[0m\n\u001b[0;31m from ProtoBuffEntitys import KLineMessage_pb2.KLineArray.KLineData.DESCRIPTOR\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"import ProtoBuffEntitys.KLineMessage_pb2.KLineArray.KLineData.DESCRIPTOR "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"'code'\n",
"'m_nDate'\n",
"'m_nTime'\n",
"'m_nOpen'\n",
"'m_nHigh'\n",
"'m_nLow'\n",
"'m_nClose'\n",
"'m_iVolume'\n",
"'m_iTurover'\n",
"'m_nMatchItems'\n"
]
}
],
"source": [
"from pprint import pprint\n",
"\n",
"for field in KLineMessage_pb2.KLineArray.KLineData.DESCRIPTOR.fields:\n",
" pprint(field.name)\n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\n db_ts_stock.createPartitionedTable(\\n table = t, \\n tableName = abd, \\n partitionColumns = `code`m_nDate, \\n sortColumns = `code`m_nDate`m_nTime,\\n compressMethods = {m_nDate:\"delta\", m_nTime:\"delta\"}\\n )\\n'"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
" db_ts_stock.createPartitionedTable(\n",
" table = t, \n",
" tableName = {hft_type_name}, \n",
" partitionColumns = `code`m_nDate, \n",
" sortColumns = `code`m_nDate`m_nTime,\n",
" compressMethods = {{m_nDate:\"delta\", m_nTime:\"delta\"}}\n",
" )\n",
"\"\"\".format(\n",
" hft_type_name = \"abd\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('600519.SH', '20210531', b'\\x1f\\x8b\\x08\\x00\\x00\\x00\\x00\\x00\\x04\\x00\\x8c\\xddy\\\\^Yz\\x1f\\xf8tuUw\\xed%$\\xb4K\\x15\\x12\\x97+\\xd7V}\\x9cr\\xb7\\xa7\\xd3\\xd3^\\xd2\\xe5`\\xbb\\xd3\\xe9\\xe9\\xd8\\ ... (1454851 characters truncated) ... 17\\xb2\\x87\\xe4\\xce\\x94T\\xe8\\xe2O\\xc8\\xe6\\x10\\x1c=\\r\\xd9+\\x02\\xcc\\x9c!\\xc56\\xdb\\xef\\xb3uf\\xe5,d\\x9f\\xc8\\xd1\\xbc$\\xe7~)\\xe4\\xff\\x075\\xea@$i\\x1c\\x1a\\x00')\n"
]
}
],
"source": [
"with engine.connect() as conn:\n",
" stat = \"select * from Level2BytesOrder.dbo.[Order] where S_INFO_WINDCODE='600519.SH'\"\n",
" rs = conn.execute(stat)\n",
" for row in rs.fetchall():\n",
" print(row)\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(4708,)\n"
]
}
],
"source": [
"with engine.connect() as conn:\n",
" stat = \"select count(*) from Level2BytesKLine.dbo.KLine where TRADE_DT='20220608'\"\n",
" rs = conn.execute(stat)\n",
" for row in rs.fetchall():\n",
" print(row)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('NonClusteredIndex-Order', 'nonclustered located on PRIMARY', 'S_INFO_WINDCODE, TRADE_DT')\n"
]
}
],
"source": [
"engine = sa.create_engine(\n",
" 'mssql+pyodbc://sa:passw0rd!@192.168.1.7/Level2BytesOrder?driver=ODBC+Driver+18+for+SQL+Server',\n",
" connect_args = {\n",
" \"TrustServerCertificate\": \"yes\"\n",
" }, echo=False)\n",
" \n",
"with engine.connect() as conn:\n",
" stat = \"EXEC sp_helpindex 'Level2BytesOrder.dbo.[Order]'\"\n",
" rs = conn.execute(stat)\n",
" for row in rs.fetchall():\n",
" print(row)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('master', 1, datetime.datetime(2003, 4, 8, 9, 13, 36, 390000))\n",
"('tempdb', 2, datetime.datetime(2022, 8, 3, 4, 47, 56, 987000))\n",
"('model', 3, datetime.datetime(2003, 4, 8, 9, 13, 36, 390000))\n",
"('msdb', 4, datetime.datetime(2022, 5, 29, 16, 33, 42, 60000))\n",
"('test', 5, datetime.datetime(2022, 8, 3, 4, 52, 46, 450000))\n",
"('Level1BytesIndexFutureKLine', 6, datetime.datetime(2022, 8, 3, 5, 2, 17, 660000))\n",
"('Level2BytesConvBondKLine', 7, datetime.datetime(2022, 8, 3, 5, 2, 30, 837000))\n",
"('Level2BytesConvBondOrder', 8, datetime.datetime(2022, 8, 3, 5, 2, 39, 987000))\n",
"('Level2BytesConvBondTick', 9, datetime.datetime(2022, 8, 3, 5, 2, 54, 587000))\n",
"('Level2BytesConvBondTickQue', 10, datetime.datetime(2022, 8, 3, 5, 3, 58, 270000))\n",
"('Level2BytesConvBondTranse', 11, datetime.datetime(2022, 8, 3, 5, 4, 14, 500000))\n",
"('Level2BytesETFKLine', 12, datetime.datetime(2022, 8, 3, 5, 4, 27, 270000))\n",
"('Level2BytesETFOrder', 13, datetime.datetime(2022, 8, 3, 5, 4, 43, 457000))\n",
"('Level2BytesTick', 14, datetime.datetime(2022, 8, 3, 8, 51, 40, 633000))\n",
"('Level2BytesTickQue', 15, datetime.datetime(2022, 8, 3, 8, 51, 58, 650000))\n",
"('Level2BytesTranse', 16, datetime.datetime(2022, 8, 3, 8, 52, 14, 103000))\n",
"('Level2BytesOrder', 17, datetime.datetime(2022, 8, 3, 8, 52, 27, 740000))\n",
"('Level2BytesKLine', 18, datetime.datetime(2022, 8, 3, 8, 52, 44, 610000))\n",
"('Level2BytesIndexTick', 19, datetime.datetime(2022, 8, 3, 9, 22, 36, 850000))\n",
"('Level2BytesIndexKLine', 20, datetime.datetime(2022, 8, 3, 9, 22, 57, 527000))\n",
"('Level2BytesETFTranse', 21, datetime.datetime(2022, 8, 3, 9, 23, 53, 713000))\n",
"('Level2BytesETFTickQue', 22, datetime.datetime(2022, 8, 3, 9, 24, 9, 87000))\n",
"('Level2BytesETFTick', 23, datetime.datetime(2022, 8, 3, 9, 24, 26, 267000))\n"
]
}
],
"source": [
"stat = \"\"\"SELECT name, database_id, create_date FROM sys.databases; \n",
"\"\"\"\n",
"\n",
"with engine.connect() as conn:\n",
" rs = conn.execute(stat)\n",
" for row in rs.fetchall():\n",
" print(row)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"a = [1,2,3]\n",
"b = {x : (x + 1 if x != 3 else x + 2) for x in a}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"\n",
"with engine.connect() as conn:\n",
" stat = \"select distinct S_INFO_WINDCODE, TRADE_DT from Level2BytesKline.dbo.KLine\"\n",
" rs = conn.execute(stat)\n",
" stock_date_list = [(stock_name, date) for stock_name, date in rs.fetchall()]\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>000001.SZ</td>\n",
" <td>20130104</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>000001.SZ</td>\n",
" <td>20130107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>000001.SZ</td>\n",
" <td>20130108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>000001.SZ</td>\n",
" <td>20130109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>000001.SZ</td>\n",
" <td>20130110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7379201</th>\n",
" <td>689009.SH</td>\n",
" <td>20220704</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7379202</th>\n",
" <td>689009.SH</td>\n",
" <td>20220705</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7379203</th>\n",
" <td>689009.SH</td>\n",
" <td>20220706</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7379204</th>\n",
" <td>689009.SH</td>\n",
" <td>20220707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7379205</th>\n",
" <td>689009.SH</td>\n",
" <td>20220708</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>7379206 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" code m_nDate\n",
"0 000001.SZ 20130104\n",
"1 000001.SZ 20130107\n",
"2 000001.SZ 20130108\n",
"3 000001.SZ 20130109\n",
"4 000001.SZ 20130110\n",
"... ... ...\n",
"7379201 689009.SH 20220704\n",
"7379202 689009.SH 20220705\n",
"7379203 689009.SH 20220706\n",
"7379204 689009.SH 20220707\n",
"7379205 689009.SH 20220708\n",
"\n",
"[7379206 rows x 2 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(stock_date_list, columns=['code', 'm_nDate'])"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv('ddb_dump_journal.csv')"
]
},
{
"cell_type": "code",
"execution_count": 52,
"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>type_name</th>\n",
" <th>stock_id</th>\n",
" <th>status</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [type_name, stock_id, status]\n",
"Index: []"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_259767/4140820348.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
" df = df.append({'type_name':'KLine', 'stock_id':'000001.SZ', 'status':'OK'},ignore_index=True)\n"
]
}
],
"source": [
"df = df.append({'type_name':'KLine', 'stock_id':'000001.SZ', 'status':'OK'},ignore_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"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></th>\n",
" <th></th>\n",
" </tr>\n",
" <tr>\n",
" <th>type_name</th>\n",
" <th>stock_id</th>\n",
" <th>status</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>KLine</th>\n",
" <th>000001.SZ</th>\n",
" <th>OK</th>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: []\n",
"Index: [(KLine, 000001.SZ, OK)]"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.set_index(['type_name', 'stock_id', 'status'], inplace=True)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Series([], Name: (KLine, 000001.SZ, OK), dtype: float64)"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc[('KLine', '000001.SZ', 'OK')]"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"('KLine', '000001.SZ', 'OK') in df.index"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"import pickle as pkl\n",
"with open('tmp.pkl', 'rb') as fin:\n",
" stock_list, date_list = pkl.load(fin)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('20130104',\n",
" '20130107',\n",
" '20130108',\n",
" '20130109',\n",
" '20130110',\n",
" '20130111',\n",
" '20130114',\n",
" '20130115',\n",
" '20130116',\n",
" '20130117')"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"date_list[:10]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.13"
},
"vscode": {
"interpreter": {
"hash": "5a0c795ff324b912f12ad95b94c9d776ccc7a75bdf6a126a4f44b3067472979e"
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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