{ "cells": [ { "cell_type": "code", "execution_count": 2, "id": "8721fe9f-083b-4ba3-8380-31dc4e541e27", "metadata": {}, "outputs": [], "source": [ "import time\n", "from tqdm import tqdm\n", "import itertools\n", "\n", "import numpy as np\n", "import pandas as pd\n", "from pprint import pprint\n", "\n", "import pyTSL\n", "\n", "TINYSOFT_HOSTNAME = 'tsl.tinysoft.com.cn'\n", "TINYSOFT_PORT = 443\n", "TINYSOFT_USERNAME = 'xunuo2005'\n", "TINYSOFT_PASSWORD = '20220613'\n", "\n", "TINYSOFT_DATA_PATH = 'tinysoft-data'\n", "\n", "\n", "class tsl():\n", " \n", " def __enter__(self):\n", " self.c = pyTSL.Client(TINYSOFT_USERNAME, TINYSOFT_PASSWORD, TINYSOFT_HOSTNAME, 443)\n", " self.c.login()\n", " return self\n", " \n", " \n", " def __exit__(self, exc_type, exc_val, exc_tb):\n", " self.c.logout()\n", " \n", " \n", " def get_stock_list(self, date, bk_name='A股'):\n", " code = \"\"\"\n", " bkName := \"%s\";\n", " endT := %d;\n", " return getAbkbydate(bkName,endT);\n", " \"\"\" % (bk_name, date)\n", " r = self.c.exec(code)\n", " \n", " return r.value()\n", " \n", "\n", " def get_cmp_report(self, table_id, stock_id, start_date):\n", " code = \"\"\"\n", " SetSysParam('ReportMode', -1);\n", " return Select * \n", " from infotable {table_id} of '{stock_id}' \n", " where ['截止日'] >= {start_date}\n", " end;\n", " \"\"\".format(\n", " table_id=table_id, \n", " stock_id=stock_id, \n", " start_date=start_date\n", " )\n", " r = self.c.exec(code)\n", "\n", " df = pd.DataFrame(r.value())\n", " return df\n", " \n", " \n", " def get_cmp_indicator(self, stock_id, start_year, indicator_config_fname):\n", " index_list = ['StockID', '报告期']\n", " \n", " year_list = range(start_year, 2022, 1)\n", " quarter_list = ['0331', '0630', '0930', '1231']\n", " report_date_list = [str(t[0]) + t[1] for t in itertools.product(year_list, quarter_list)]\n", " \n", " config_df = pd.read_csv(indicator_config_fname)\n", " config_df['indicator_id'] = config_df['indicator_id'].astype('string')\n", " indicator_dict = config_df.set_index('indicator_id')['indicator_name'].to_dict()\n", " indicator_id_list = list(indicator_dict.keys())\n", " indicator_name_list = [\"'%s'\" % indicator_dict[indicator_id].strip() for indicator_id in indicator_id_list]\n", " \n", " code = \"\"\"\n", " SetSysParam('ReportMode', -1);\n", " SetSysParam(pn_reportmode(), -1);\n", " SetSysParam(pn_stock(), '{stock_id}');\n", "\n", " date_array := Array({date_list_input});\n", " indicator_array := Array({indicator_id_list_input});\n", " indicator_name_array := Array({indicator_name_list_input});\n", " r := Array();\n", " \n", " for i:=0 to length(date_array)-1 do\n", " begin\n", " r[i]['StockID'] := '{stock_id}';\n", " r[i]['报告期'] := date_array[i];\n", " for j:=0 to length(indicator_array)-1 do\n", " begin\n", " r[i][indicator_name_array[j]] := ReportOfAll(indicator_array[j], date_array[i]);\n", " end\n", " end\n", " return r;\n", " \"\"\".format(\n", " stock_id=stock_id, \n", " date_list_input=', '.join(report_date_list),\n", " indicator_id_list_input=', '.join(indicator_id_list),\n", " indicator_name_list_input=', '.join(indicator_name_list)\n", " )\n", " r = self.c.exec(code)\n", " r = r.value()\n", " df = pd.DataFrame(r, index=range(len(r)))\n", " df.set_index(index_list, inplace=True)\n", " return df\n", "\n", " \n", " def get_mkt_trading_days(self, start_date, end_date):\n", " # TradeDays: \n", " # nday3: 返回用第二个参数计算N日的一维数字下标数组。\n", " # SpecDate: 临时修改当前时间的快捷方法\n", " # Spec: 指定StockID股票为当前股票，计算表达式的值返回\n", " code = \"\"\"\n", " begt:=inttodate(%d);\n", " endt:=inttodate(%d);\n", " return spec(specdate(nday3(tradedays(begt,endt),DateToInt(sp_time())),endt),'SH000001');\n", " \"\"\" % (start_date, end_date);\n", " r = self.c.exec(code)\n", " \n", " return r.value()\n", " \n", " \n", " def get_mkt_stock_k_daily(self, start_date, end_date, stock_id): \n", " # vol: 成交量\n", " # amount: 成交金额\n", " # cjbs: 成交笔数\n", " # yclose: 上次价\n", " code = \"\"\"\n", " SetSysParam(pn_cycle(),cy_day());\n", " return \n", " select \n", " ['StockID'],\n", " datetoint(['date']) as 'date',\n", " ['open'],\n", " ['high'],\n", " ['low'],\n", " ['close'], \n", " ['vol'], \n", " ['amount'], \n", " ['cjbs'],\n", " ['yclose']\n", " from markettable \n", " datekey {start_date}T to {end_date}T+0.999 \n", " of \\'{stock_id}\\'\n", " end;\n", " \"\"\".format(\n", " stock_id=stock_id, \n", " start_date=start_date,\n", " end_date=end_date\n", " )\n", " r = self.c.exec(code)\n", " df = pd.DataFrame(r.value())\n", " df.set_index(['StockID', 'date'], inplace=True)\n", " return df\n", " \n", "\n", " def get_mkt_stock_k_daily_ext(self, start_date, end_date, stock_id):\n", " \n", " date_list = self.get_mkt_trading_days(start_date, end_date)\n", " \n", " # IsSt_3: 是否是ST(指定日)，判断当前股票在指定日是否是ST股。函数主要是通过名称变更进行判断。 \n", " # Stockzf3：涨幅(%) =（收盘-昨收）/昨收*100%（已复权）\n", " # http://www.tinysoft.com.cn/tsdn/helpdoc/display.tsl?id=12582\n", " # AuthorityFactor: 以上市日为复权基准日，向后复权，得到指定日比例复权方式的复权因子值，即复权方式为1。调用该函数需注意设置当前股票和当前时间。\n", " code = \"\"\"\n", " SetSysParam(pn_cycle(),cy_day());\n", " SetSysParam(pn_stock(), \\'{stock_id}\\');\n", " \n", " date_array := Array({date_list_input});\n", " r := Array();\n", " \n", " for i:=0 to length(date_array)-1 do\n", " begin\n", " SetSysParam(PN_Date(), inttodate(date_array[i]));\n", " \n", " r[i]['StockID'] := \\'{stock_id}\\';\n", " r[i]['date'] := date_array[i];\n", " r[i]['PctChg'] := StockZf3();\n", " r[i]['IsST'] := IsST_3(date_array[i]);\n", " r[i]['FloatShares'] := StockNegotiableShares(date_array[i]);\n", " r[i]['Factor'] := AuthorityFactor();\n", " end;\n", " return r;\n", " \"\"\".format(\n", " stock_id=stock_id,\n", " date_list_input=', '.join([str(date) for date in date_list])\n", " )\n", " r = self.c.exec(code)\n", "\n", " df = pd.DataFrame(r.value(), index=range(len(r.value())))\n", " df.set_index(['StockID', 'date'], inplace=True)\n", " return df\n", " \n", " \n", " def get_fund_info(self, table_id, fund_id):\n", " code = \"\"\"\n", " SetSysParam('ReportMode', -1);\n", " return Select * \n", " from infotable {table_id} of '{fund_id}' \n", " end;\n", " \"\"\".format(\n", " table_id=table_id, \n", " fund_id=fund_id\n", " )\n", " r = self.c.exec(code)\n", "\n", " df = pd.DataFrame(r.value())\n", " return df\n", " \n", " \n", " def get_fund_list(self, date):\n", " pass\n", "\n" ] }, { "cell_type": "code", "execution_count": 22, "id": "f71302c9-af94-4cc4-942a-281e76468470", "metadata": {}, "outputs": [], "source": [ "with tsl() as t:\n", " df1 = t.get_mkt_stock_k_daily(20220501, 20220609, 'SZ000012')\n", " df2 = t.get_mkt_stock_k_daily_ext(20220501, 20220609, 'SZ000012')\n", " df = pd.concat([df1, df2], axis=1)\n" ] }, { "cell_type": "code", "execution_count": 23, "id": "9ff6c11f-6038-43cf-bd80-a2a3fa09e733", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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closevollowyclosecjbsamounthighopenIsSTFactorPctChgFloatShares
StockIDdate
SZ000012202205055.7852548342.05.615.6247277.03.036327e+085.885.6300.0306672.8469751.956586e+09
202205065.6430434955.05.555.7837648.01.721508e+085.775.6500.030667-2.4221451.956586e+09
202205095.6826512956.05.615.6431115.01.513519e+085.815.6200.0306670.7092201.956586e+09
202205105.7644880370.05.555.6839085.02.556482e+085.775.5800.0306671.4084511.956586e+09
202205115.8263441266.05.805.7650303.03.741822e+086.025.8100.0306671.0416671.956586e+09
202205125.8340711757.05.725.8230591.02.373038e+085.935.8200.0306670.1718211.956586e+09
202205135.9544013643.05.815.8332703.02.597157e+085.975.8900.0306672.0583191.956586e+09
202205165.8051952217.05.765.9546334.03.046104e+086.056.0000.030667-2.5210081.956586e+09
202205175.8837310901.05.675.8033021.02.155254e+085.885.8200.0306671.3793101.956586e+09
202205185.9243071593.05.825.8836175.02.553750e+086.025.8700.0306670.6802721.956586e+09
202205196.0971322634.05.775.9255347.04.292484e+086.205.8100.0306672.8716221.956586e+09
202205206.1563335796.06.036.0947644.03.862153e+086.166.1100.0306670.9852221.956586e+09
202205236.1744809479.06.106.1533057.02.758453e+086.236.1900.0306670.3252031.956586e+09
202205245.9367558001.05.916.1748715.04.162481e+086.376.1800.030667-3.8897891.956586e+09
202205256.0237408360.05.895.9328801.02.233981e+086.025.9700.0306671.5177071.956586e+09
202205266.0631951823.05.916.0228125.01.925206e+086.116.0400.0306670.6644521.956586e+09
202205276.0731871879.05.986.0626731.01.931219e+086.126.0900.0306670.1650171.956586e+09
202205306.1227955000.06.036.0723213.01.700274e+086.136.1100.0306670.8237231.956586e+09
202205316.2250331033.06.066.1237964.03.103057e+086.246.1700.0306671.6339871.956586e+09
202206016.1535835989.06.116.2229394.02.208082e+086.226.2200.030667-1.1254021.956586e+09
202206026.3157818462.06.086.1539913.03.613558e+086.356.1300.0306672.6016261.956586e+09
202206066.3549949153.06.236.3140078.03.153994e+086.386.2900.0306670.6339141.956586e+09
202206076.3968554508.06.326.3546616.04.418079e+086.596.4400.0306670.6299211.956586e+09
202206086.3355527059.06.206.3938473.03.512041e+086.426.3500.030667-0.9389671.956586e+09
202206096.1834466429.06.136.3328828.02.143454e+086.326.3200.030667-2.3696681.956586e+09
\n", "
" ], "text/plain": [ " close vol low yclose cjbs amount \\\n", "StockID date \n", "SZ000012 20220505 5.78 52548342.0 5.61 5.62 47277.0 3.036327e+08 \n", " 20220506 5.64 30434955.0 5.55 5.78 37648.0 1.721508e+08 \n", " 20220509 5.68 26512956.0 5.61 5.64 31115.0 1.513519e+08 \n", " 20220510 5.76 44880370.0 5.55 5.68 39085.0 2.556482e+08 \n", " 20220511 5.82 63441266.0 5.80 5.76 50303.0 3.741822e+08 \n", " 20220512 5.83 40711757.0 5.72 5.82 30591.0 2.373038e+08 \n", " 20220513 5.95 44013643.0 5.81 5.83 32703.0 2.597157e+08 \n", " 20220516 5.80 51952217.0 5.76 5.95 46334.0 3.046104e+08 \n", " 20220517 5.88 37310901.0 5.67 5.80 33021.0 2.155254e+08 \n", " 20220518 5.92 43071593.0 5.82 5.88 36175.0 2.553750e+08 \n", " 20220519 6.09 71322634.0 5.77 5.92 55347.0 4.292484e+08 \n", " 20220520 6.15 63335796.0 6.03 6.09 47644.0 3.862153e+08 \n", " 20220523 6.17 44809479.0 6.10 6.15 33057.0 2.758453e+08 \n", " 20220524 5.93 67558001.0 5.91 6.17 48715.0 4.162481e+08 \n", " 20220525 6.02 37408360.0 5.89 5.93 28801.0 2.233981e+08 \n", " 20220526 6.06 31951823.0 5.91 6.02 28125.0 1.925206e+08 \n", " 20220527 6.07 31871879.0 5.98 6.06 26731.0 1.931219e+08 \n", " 20220530 6.12 27955000.0 6.03 6.07 23213.0 1.700274e+08 \n", " 20220531 6.22 50331033.0 6.06 6.12 37964.0 3.103057e+08 \n", " 20220601 6.15 35835989.0 6.11 6.22 29394.0 2.208082e+08 \n", " 20220602 6.31 57818462.0 6.08 6.15 39913.0 3.613558e+08 \n", " 20220606 6.35 49949153.0 6.23 6.31 40078.0 3.153994e+08 \n", " 20220607 6.39 68554508.0 6.32 6.35 46616.0 4.418079e+08 \n", " 20220608 6.33 55527059.0 6.20 6.39 38473.0 3.512041e+08 \n", " 20220609 6.18 34466429.0 6.13 6.33 28828.0 2.143454e+08 \n", "\n", " high open IsST Factor PctChg FloatShares \n", "StockID date \n", "SZ000012 20220505 5.88 5.63 0 0.030667 2.846975 1.956586e+09 \n", " 20220506 5.77 5.65 0 0.030667 -2.422145 1.956586e+09 \n", " 20220509 5.81 5.62 0 0.030667 0.709220 1.956586e+09 \n", " 20220510 5.77 5.58 0 0.030667 1.408451 1.956586e+09 \n", " 20220511 6.02 5.81 0 0.030667 1.041667 1.956586e+09 \n", " 20220512 5.93 5.82 0 0.030667 0.171821 1.956586e+09 \n", " 20220513 5.97 5.89 0 0.030667 2.058319 1.956586e+09 \n", " 20220516 6.05 6.00 0 0.030667 -2.521008 1.956586e+09 \n", " 20220517 5.88 5.82 0 0.030667 1.379310 1.956586e+09 \n", " 20220518 6.02 5.87 0 0.030667 0.680272 1.956586e+09 \n", " 20220519 6.20 5.81 0 0.030667 2.871622 1.956586e+09 \n", " 20220520 6.16 6.11 0 0.030667 0.985222 1.956586e+09 \n", " 20220523 6.23 6.19 0 0.030667 0.325203 1.956586e+09 \n", " 20220524 6.37 6.18 0 0.030667 -3.889789 1.956586e+09 \n", " 20220525 6.02 5.97 0 0.030667 1.517707 1.956586e+09 \n", " 20220526 6.11 6.04 0 0.030667 0.664452 1.956586e+09 \n", " 20220527 6.12 6.09 0 0.030667 0.165017 1.956586e+09 \n", " 20220530 6.13 6.11 0 0.030667 0.823723 1.956586e+09 \n", " 20220531 6.24 6.17 0 0.030667 1.633987 1.956586e+09 \n", " 20220601 6.22 6.22 0 0.030667 -1.125402 1.956586e+09 \n", " 20220602 6.35 6.13 0 0.030667 2.601626 1.956586e+09 \n", " 20220606 6.38 6.29 0 0.030667 0.633914 1.956586e+09 \n", " 20220607 6.59 6.44 0 0.030667 0.629921 1.956586e+09 \n", " 20220608 6.42 6.35 0 0.030667 -0.938967 1.956586e+09 \n", " 20220609 6.32 6.32 0 0.030667 -2.369668 1.956586e+09 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 14, "id": "cbbd95e0-0734-46c7-a314-a11e0a882bbc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dateclosevollowycloseStockIDcjbsamounthighopen
0202205055.7852548342.05.615.62SZ00001247277.03.036327e+085.885.63
1202205065.6430434955.05.555.78SZ00001237648.01.721508e+085.775.65
2202205095.6826512956.05.615.64SZ00001231115.01.513519e+085.815.62
3202205105.7644880370.05.555.68SZ00001239085.02.556482e+085.775.58
4202205115.8263441266.05.805.76SZ00001250303.03.741822e+086.025.81
5202205125.8340711757.05.725.82SZ00001230591.02.373038e+085.935.82
6202205135.9544013643.05.815.83SZ00001232703.02.597157e+085.975.89
7202205165.8051952217.05.765.95SZ00001246334.03.046104e+086.056.00
8202205175.8837310901.05.675.80SZ00001233021.02.155254e+085.885.82
9202205185.9243071593.05.825.88SZ00001236175.02.553750e+086.025.87
10202205196.0971322634.05.775.92SZ00001255347.04.292484e+086.205.81
11202205206.1563335796.06.036.09SZ00001247644.03.862153e+086.166.11
12202205236.1744809479.06.106.15SZ00001233057.02.758453e+086.236.19
13202205245.9367558001.05.916.17SZ00001248715.04.162481e+086.376.18
14202205256.0237408360.05.895.93SZ00001228801.02.233981e+086.025.97
15202205266.0631951823.05.916.02SZ00001228125.01.925206e+086.116.04
16202205276.0731871879.05.986.06SZ00001226731.01.931219e+086.126.09
17202205306.1227955000.06.036.07SZ00001223213.01.700274e+086.136.11
18202205316.2250331033.06.066.12SZ00001237964.03.103057e+086.246.17
19202206016.1535835989.06.116.22SZ00001229394.02.208082e+086.226.22
20202206026.3157818462.06.086.15SZ00001239913.03.613558e+086.356.13
21202206066.3549949153.06.236.31SZ00001240078.03.153994e+086.386.29
22202206076.3968554508.06.326.35SZ00001246616.04.418079e+086.596.44
23202206086.3355527059.06.206.39SZ00001238473.03.512041e+086.426.35
24202206096.1834466429.06.136.33SZ00001228828.02.143454e+086.326.32
\n", "
" ], "text/plain": [ " date close vol low yclose StockID cjbs \\\n", "0 20220505 5.78 52548342.0 5.61 5.62 SZ000012 47277.0 \n", "1 20220506 5.64 30434955.0 5.55 5.78 SZ000012 37648.0 \n", "2 20220509 5.68 26512956.0 5.61 5.64 SZ000012 31115.0 \n", "3 20220510 5.76 44880370.0 5.55 5.68 SZ000012 39085.0 \n", "4 20220511 5.82 63441266.0 5.80 5.76 SZ000012 50303.0 \n", "5 20220512 5.83 40711757.0 5.72 5.82 SZ000012 30591.0 \n", "6 20220513 5.95 44013643.0 5.81 5.83 SZ000012 32703.0 \n", "7 20220516 5.80 51952217.0 5.76 5.95 SZ000012 46334.0 \n", "8 20220517 5.88 37310901.0 5.67 5.80 SZ000012 33021.0 \n", "9 20220518 5.92 43071593.0 5.82 5.88 SZ000012 36175.0 \n", "10 20220519 6.09 71322634.0 5.77 5.92 SZ000012 55347.0 \n", "11 20220520 6.15 63335796.0 6.03 6.09 SZ000012 47644.0 \n", "12 20220523 6.17 44809479.0 6.10 6.15 SZ000012 33057.0 \n", "13 20220524 5.93 67558001.0 5.91 6.17 SZ000012 48715.0 \n", "14 20220525 6.02 37408360.0 5.89 5.93 SZ000012 28801.0 \n", "15 20220526 6.06 31951823.0 5.91 6.02 SZ000012 28125.0 \n", "16 20220527 6.07 31871879.0 5.98 6.06 SZ000012 26731.0 \n", "17 20220530 6.12 27955000.0 6.03 6.07 SZ000012 23213.0 \n", "18 20220531 6.22 50331033.0 6.06 6.12 SZ000012 37964.0 \n", "19 20220601 6.15 35835989.0 6.11 6.22 SZ000012 29394.0 \n", "20 20220602 6.31 57818462.0 6.08 6.15 SZ000012 39913.0 \n", "21 20220606 6.35 49949153.0 6.23 6.31 SZ000012 40078.0 \n", "22 20220607 6.39 68554508.0 6.32 6.35 SZ000012 46616.0 \n", "23 20220608 6.33 55527059.0 6.20 6.39 SZ000012 38473.0 \n", "24 20220609 6.18 34466429.0 6.13 6.33 SZ000012 28828.0 \n", "\n", " amount high open \n", "0 3.036327e+08 5.88 5.63 \n", "1 1.721508e+08 5.77 5.65 \n", "2 1.513519e+08 5.81 5.62 \n", "3 2.556482e+08 5.77 5.58 \n", "4 3.741822e+08 6.02 5.81 \n", "5 2.373038e+08 5.93 5.82 \n", "6 2.597157e+08 5.97 5.89 \n", "7 3.046104e+08 6.05 6.00 \n", "8 2.155254e+08 5.88 5.82 \n", "9 2.553750e+08 6.02 5.87 \n", "10 4.292484e+08 6.20 5.81 \n", "11 3.862153e+08 6.16 6.11 \n", "12 2.758453e+08 6.23 6.19 \n", "13 4.162481e+08 6.37 6.18 \n", "14 2.233981e+08 6.02 5.97 \n", "15 1.925206e+08 6.11 6.04 \n", "16 1.931219e+08 6.12 6.09 \n", "17 1.700274e+08 6.13 6.11 \n", "18 3.103057e+08 6.24 6.17 \n", "19 2.208082e+08 6.22 6.22 \n", "20 3.613558e+08 6.35 6.13 \n", "21 3.153994e+08 6.38 6.29 \n", "22 4.418079e+08 6.59 6.44 \n", "23 3.512041e+08 6.42 6.35 \n", "24 2.143454e+08 6.32 6.32 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1" ] }, { "cell_type": "code", "execution_count": 7, "id": "8ebd92ac-9831-4f66-9614-8cf06a73033c", "metadata": {}, "outputs": [], "source": [ "def dump_to_csv(path_template, path_args={}):\n", " def inner(func):\n", " def func_wrapper(*args, **kwargs):\n", " r = func(*args, **kwargs)\n", " print(path_template.format(**path_args))\n", " print('Will dump to a csv file...')\n", " return r\n", " return func_wrapper\n", " return inner" ] }, { "cell_type": "code", "execution_count": 8, "id": "36926197-5016-459b-a91d-5f2486ff600b", "metadata": {}, "outputs": [], "source": [ "@dump_to_csv(path_template='aaa{name}dd', path_args={'name':'ccc'})\n", "def dump_test():\n", " print('do test')" ] }, { "cell_type": "code", "execution_count": 9, "id": "389c2c4a-6e7d-45ad-9da1-780dffab3371", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "do test\n", "aaacccdd\n", "Will dump to a csv file...\n" ] } ], "source": [ "dump_test()" ] }, { "cell_type": "code", "execution_count": 34, "id": "0c5b725b-7ad7-4466-ba3c-ec6624c2575d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Inside decorator\n", "Inside inner function\n", "I like geeksforgeeks\n", "Inside actual function\n" ] } ], "source": [ "def decorator(*args, **kwargs):\n", " print(\"Inside decorator\")\n", " \n", " def inner(func):\n", " \n", " # code functionality here\n", " print(\"Inside inner function\")\n", " print(\"I like\", kwargs['like'])\n", " \n", " func()\n", " \n", " # returning inner function \n", " return inner\n", " \n", "@decorator(like = \"geeksforgeeks\")\n", "def my_func():\n", " print(\"Inside actual function\")" ] }, { "cell_type": "code", "execution_count": 17, "id": "c7ec2d92-5d6b-4b0c-afa0-d0210b64e0df", "metadata": {}, "outputs": [], "source": [ "with tsl() as t:\n", " date_list = t.get_mkt_trading_days(20000101,20220620)\n", " s = pd.Series(date_list, dtype=np.int64, name='date')" ] }, { "cell_type": "code", "execution_count": 19, "id": "dd10b07e-a170-4428-a6d7-19a7ac06dd18", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 20000104\n", "1 20000105\n", "2 20000106\n", "3 20000107\n", "4 20000110\n", " ... \n", "5436 20220614\n", "5437 20220615\n", "5438 20220616\n", "5439 20220617\n", "5440 20220620\n", "Name: date, Length: 5441, dtype: int64\n" ] } ], "source": [ "pprint(s)" ] }, { "cell_type": "code", "execution_count": 24, "id": "0a34a466-141f-42ec-8d84-7ca4f28efd7e", "metadata": {}, "outputs": [], "source": [ "def dump_mkt_calendar(start_date, end_date, stock_pool_date):\n", " \n", " def _dump_df_to_csv(series):\n", " dump_path = '{}/行情数据/calendar.csv'.format(\n", " TINYSOFT_DATA_PATH\n", " )\n", " series.to_csv(dump_path, index=False)\n", " \n", " with tsl() as t:\n", " date_list = t.get_mkt_trading_days(20000101,20220620)\n", " \n", " s = pd.Series(date_list, dtype=np.int64, name='date')\n", " _dump_df_to_csv(s)\n", " " ] }, { "cell_type": "code", "execution_count": 25, "id": "74b4d3d1-fbbc-4fbd-be36-790901b9270c", "metadata": {}, "outputs": [], "source": [ "dump_mkt_calendar(20200520, 20220620, 20220531)" ] }, { "cell_type": "code", "execution_count": 28, "id": "fde93bc5-bef0-4a3a-a30a-ebe04d65a474", "metadata": {}, "outputs": [], "source": [ "def dump_mkt_k_daily(start_date, end_date, stock_pool_date):\n", " \n", " def _dump_df_to_csv(stock_id, df):\n", " dump_path = '{}/行情数据/k-daily/{}.csv'.format(\n", " TINYSOFT_DATA_PATH, stock_id\n", " )\n", " df.to_csv(dump_path)\n", " \n", " with tsl() as t:\n", " stock_list = t.get_stock_list(stock_pool_date)\n", " print('正在获取日频行情数据：')\n", " \n", " with tqdm(stock_list) as pbar:\n", " for stock_id in pbar:\n", " pbar.set_description(stock_id)\n", " df1 = t.get_mkt_stock_k_daily(start_date, end_date, stock_id)\n", " df2 = t.get_mkt_stock_k_daily_ext(start_date, end_date, stock_id)\n", " df = pd.concat([df1, df2], axis=1)\n", " \n", " _dump_df_to_csv(stock_id, df)\n", " time.sleep(0.001)\n", " " ] }, { "cell_type": "code", "execution_count": 29, "id": "bde355b6-bfc9-45b5-9b8f-3d640306647f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "正在获取日频行情数据：\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "SH600021: 2%|█▌ | 113/4822 [00:37<26:17, 2.98it/s]\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Input \u001b[1;32mIn [29]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdump_mkt_k_daily\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m20200610\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m20220620\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m20220531\u001b[39;49m\u001b[43m)\u001b[49m\n", "Input \u001b[1;32mIn [28]\u001b[0m, in \u001b[0;36mdump_mkt_k_daily\u001b[1;34m(start_date, end_date, stock_pool_date)\u001b[0m\n\u001b[0;32m 15\u001b[0m pbar\u001b[38;5;241m.\u001b[39mset_description(stock_id)\n\u001b[0;32m 16\u001b[0m df1 \u001b[38;5;241m=\u001b[39m t\u001b[38;5;241m.\u001b[39mget_mkt_stock_k_daily(start_date, end_date, stock_id)\n\u001b[1;32m---> 17\u001b[0m df2 \u001b[38;5;241m=\u001b[39m \u001b[43mt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_mkt_stock_k_daily_ext\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_date\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_date\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstock_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 18\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat([df1, df2], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 20\u001b[0m _dump_df_to_csv(stock_id, df)\n", "Input \u001b[1;32mIn [16]\u001b[0m, in \u001b[0;36mtsl.get_mkt_stock_k_daily_ext\u001b[1;34m(self, start_date, end_date, stock_id)\u001b[0m\n\u001b[0;32m 159\u001b[0m \u001b[38;5;66;03m# IsSt_3: 是否是ST(指定日)，判断当前股票在指定日是否是ST股。函数主要是通过名称变更进行判断。 \u001b[39;00m\n\u001b[0;32m 160\u001b[0m \u001b[38;5;66;03m# Stockzf3：涨幅(%) =（收盘-昨收）/昨收*100%（已复权）\u001b[39;00m\n\u001b[0;32m 161\u001b[0m \u001b[38;5;66;03m# http://www.tinysoft.com.cn/tsdn/helpdoc/display.tsl?id=12582\u001b[39;00m\n\u001b[0;32m 162\u001b[0m \u001b[38;5;66;03m# AuthorityFactor: 以上市日为复权基准日，向后复权，得到指定日比例复权方式的复权因子值，即复权方式为1。调用该函数需注意设置当前股票和当前时间。\u001b[39;00m\n\u001b[0;32m 163\u001b[0m code \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[0;32m 164\u001b[0m \u001b[38;5;124m SetSysParam(pn_cycle(),cy_day());\u001b[39m\n\u001b[0;32m 165\u001b[0m \u001b[38;5;124m SetSysParam(pn_stock(), \u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;132;01m{stock_id}\u001b[39;00m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124m);\u001b[39m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 184\u001b[0m date_list_input\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin([\u001b[38;5;28mstr\u001b[39m(date) \u001b[38;5;28;01mfor\u001b[39;00m date \u001b[38;5;129;01min\u001b[39;00m date_list])\n\u001b[0;32m 185\u001b[0m )\n\u001b[1;32m--> 186\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 188\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(r\u001b[38;5;241m.\u001b[39mvalue(), index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(r\u001b[38;5;241m.\u001b[39mvalue())))\n\u001b[0;32m 189\u001b[0m df\u001b[38;5;241m.\u001b[39mset_index([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mStockID\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m'\u001b[39m], inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "dump_mkt_k_daily(20200610, 20220620, 20220531)" ] }, { "cell_type": "code", "execution_count": 38, "id": "93a4db53-fe61-4b80-983d-01c6434dd33a", "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('../TslDataFeed/tinysoft-data/行情数据/k-daily/NE430047.csv')" ] }, { "cell_type": "code", "execution_count": 39, "id": "0dc92194-f2fb-432b-a081-ba70688ec766", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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StockIDdateclosevollowyclosecjbsamounthighopenIsSTFactorPctChgFloatShares
0NE4300472015030926.9840000.026.526.480.01079070.027.426.501.0000001.888218149259448.0
1NE4300472015031027.509000.027.526.980.0247500.027.527.501.0000001.927354149259448.0
2NE4300472015031127.6029000.027.427.500.0797310.027.627.401.0000000.363636149259448.0
3NE4300472015031327.8031000.027.527.600.0861900.028.027.601.0000000.724638149259448.0
4NE4300472015031628.00110000.028.027.800.03099050.028.828.001.0000000.719424149259448.0
.............................................
5436NE43004720201117NaNNaNNaNNaNNaNNaNNaNNaN00.2119110.000000149259448.0
5437NE43004720201118NaNNaNNaNNaNNaNNaNNaNNaN00.2119110.000000149259448.0
5438NE43004720201119NaNNaNNaNNaNNaNNaNNaNNaN00.2119110.000000149259448.0
5439NE43004720201120NaNNaNNaNNaNNaNNaNNaNNaN00.2119110.000000149259448.0
5440NE43004720201123NaNNaNNaNNaNNaNNaNNaNNaN00.2119110.000000149259448.0
\n", "

5441 rows × 14 columns

\n", "
" ], "text/plain": [ " StockID date close vol low yclose cjbs amount \\\n", "0 NE430047 20150309 26.98 40000.0 26.5 26.48 0.0 1079070.0 \n", "1 NE430047 20150310 27.50 9000.0 27.5 26.98 0.0 247500.0 \n", "2 NE430047 20150311 27.60 29000.0 27.4 27.50 0.0 797310.0 \n", "3 NE430047 20150313 27.80 31000.0 27.5 27.60 0.0 861900.0 \n", "4 NE430047 20150316 28.00 110000.0 28.0 27.80 0.0 3099050.0 \n", "... ... ... ... ... ... ... ... ... \n", "5436 NE430047 20201117 NaN NaN NaN NaN NaN NaN \n", "5437 NE430047 20201118 NaN NaN NaN NaN NaN NaN \n", "5438 NE430047 20201119 NaN NaN NaN NaN NaN NaN \n", "5439 NE430047 20201120 NaN NaN NaN NaN NaN NaN \n", "5440 NE430047 20201123 NaN NaN NaN NaN NaN NaN \n", "\n", " high open IsST Factor PctChg FloatShares \n", "0 27.4 26.5 0 1.000000 1.888218 149259448.0 \n", "1 27.5 27.5 0 1.000000 1.927354 149259448.0 \n", "2 27.6 27.4 0 1.000000 0.363636 149259448.0 \n", "3 28.0 27.6 0 1.000000 0.724638 149259448.0 \n", "4 28.8 28.0 0 1.000000 0.719424 149259448.0 \n", "... ... ... ... ... ... ... \n", "5436 NaN NaN 0 0.211911 0.000000 149259448.0 \n", "5437 NaN NaN 0 0.211911 0.000000 149259448.0 \n", "5438 NaN NaN 0 0.211911 0.000000 149259448.0 \n", "5439 NaN NaN 0 0.211911 0.000000 149259448.0 \n", "5440 NaN NaN 0 0.211911 0.000000 149259448.0 \n", "\n", "[5441 rows x 14 columns]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 10, "id": "6c924316-cd8c-445b-808d-74b8e4e84131", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "df = pd.DataFrame(columns=['a'])" ] }, { "cell_type": "code", "execution_count": 11, "id": "f4fe5b31-b287-4338-8b8e-a46454f9b307", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['a'], dtype='object')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 13, "id": "9aea4161-4b67-4133-b778-97d8a1328096", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'a' in df.columns" ] }, { "cell_type": "code", "execution_count": 14, "id": "fbc79e1d-21a9-40aa-abbc-c123a53a1513", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(df.index)" ] }, { "cell_type": "code", "execution_count": 3, "id": "f510f510-03da-4663-a584-176fa76d1ff0", "metadata": {}, "outputs": [], "source": [ "code = \"\"\"\n", "return getAbkbydate('A股',20220630T); \n", "\"\"\"\n", "\n", "with tsl() as t:\n", " r = t.c.exec(code)" ] }, { "cell_type": "code", "execution_count": 5, "id": "cbd25e2b-69dc-4c9b-9c34-a184d282c479", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4817" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(r.value())" ] }, { "cell_type": "code", "execution_count": 17, "id": "240086e1-673d-4ebd-ad18-d397c43ac1b6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5012" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "code = \"\"\"\n", "return getbk('A股;暂停上市;终止上市');\n", "\"\"\"\n", "\n", "with tsl() as t:\n", " r = t.c.exec(code)\n", " \n", "len(r.value())" ] }, { "cell_type": "code", "execution_count": 22, "id": "2104d772-8224-4310-84df-48f2bd7f1d20", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2023" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from datetime import datetime\n", "datetime.now().year + 1" ] }, { "cell_type": "code", "execution_count": null, "id": "1b7eede1-b09f-4de5-b531-b28487005afa", "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" } }, "nbformat": 4, "nbformat_minor": 5 }