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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from sqlalchemy import create_engine\n",
"import pandas as pd\n",
"from datetime import timedelta\n",
"import jqdatasdk as jq\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# load existing portfolio"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# check if database need update\n",
"def fetch_data_for_table():\n",
" '''\n",
" return None if no update needed\n",
" else return (starttime, endtime, frequency)\n",
" '''\n",
" pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Need to fetch stock price periodically from jqdatasdk, also need to check if portfolio updated, but this case can be handled seperately \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# fetch stock price from api\n",
"def fetch_stock_price():\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"auth success \n"
]
}
],
"source": [
"# TODO auth, remove later\n",
"user_name = \"13126862272\"\n",
"password = \"862272\"\n",
"jq.auth(user_name, password)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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>open</th>\n",
" <th>close</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>volume</th>\n",
" <th>money</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2021-01-05 09:31:00</th>\n",
" <td>32.54</td>\n",
" <td>31.73</td>\n",
" <td>32.54</td>\n",
" <td>31.72</td>\n",
" <td>1205715.0</td>\n",
" <td>38788451.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-01-05 09:32:00</th>\n",
" <td>31.68</td>\n",
" <td>32.00</td>\n",
" <td>32.00</td>\n",
" <td>31.39</td>\n",
" <td>1084879.0</td>\n",
" <td>34358448.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-01-05 09:33:00</th>\n",
" <td>31.91</td>\n",
" <td>31.62</td>\n",
" <td>31.91</td>\n",
" <td>31.61</td>\n",
" <td>811350.0</td>\n",
" <td>25785154.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-01-05 09:34:00</th>\n",
" <td>31.62</td>\n",
" <td>32.07</td>\n",
" <td>32.07</td>\n",
" <td>31.62</td>\n",
" <td>760611.0</td>\n",
" <td>24135183.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-01-05 09:35:00</th>\n",
" <td>32.11</td>\n",
" <td>31.98</td>\n",
" <td>32.16</td>\n",
" <td>31.98</td>\n",
" <td>655989.0</td>\n",
" <td>21046238.0</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",
" </tr>\n",
" <tr>\n",
" <th>2021-01-05 11:26:00</th>\n",
" <td>32.25</td>\n",
" <td>32.29</td>\n",
" <td>32.29</td>\n",
" <td>32.20</td>\n",
" <td>118071.0</td>\n",
" <td>3807211.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-01-05 11:27:00</th>\n",
" <td>32.29</td>\n",
" <td>32.30</td>\n",
" <td>32.30</td>\n",
" <td>32.21</td>\n",
" <td>114251.0</td>\n",
" <td>3686602.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-01-05 11:28:00</th>\n",
" <td>32.26</td>\n",
" <td>32.24</td>\n",
" <td>32.30</td>\n",
" <td>32.21</td>\n",
" <td>60077.0</td>\n",
" <td>1938244.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-01-05 11:29:00</th>\n",
" <td>32.25</td>\n",
" <td>32.28</td>\n",
" <td>32.30</td>\n",
" <td>32.22</td>\n",
" <td>65634.0</td>\n",
" <td>2117204.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-01-05 11:30:00</th>\n",
" <td>32.23</td>\n",
" <td>32.30</td>\n",
" <td>32.30</td>\n",
" <td>32.23</td>\n",
" <td>49052.0</td>\n",
" <td>1582947.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>120 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" open close high low volume money\n",
"2021-01-05 09:31:00 32.54 31.73 32.54 31.72 1205715.0 38788451.0\n",
"2021-01-05 09:32:00 31.68 32.00 32.00 31.39 1084879.0 34358448.0\n",
"2021-01-05 09:33:00 31.91 31.62 31.91 31.61 811350.0 25785154.0\n",
"2021-01-05 09:34:00 31.62 32.07 32.07 31.62 760611.0 24135183.0\n",
"2021-01-05 09:35:00 32.11 31.98 32.16 31.98 655989.0 21046238.0\n",
"... ... ... ... ... ... ...\n",
"2021-01-05 11:26:00 32.25 32.29 32.29 32.20 118071.0 3807211.0\n",
"2021-01-05 11:27:00 32.29 32.30 32.30 32.21 114251.0 3686602.0\n",
"2021-01-05 11:28:00 32.26 32.24 32.30 32.21 60077.0 1938244.0\n",
"2021-01-05 11:29:00 32.25 32.28 32.30 32.22 65634.0 2117204.0\n",
"2021-01-05 11:30:00 32.23 32.30 32.30 32.23 49052.0 1582947.0\n",
"\n",
"[120 rows x 6 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## exam return type of each api \n",
"\n",
"# range of stock price in different resolution\n",
"single_stock_df = jq.get_price('002709.XSHE',start_date='2021-01-05 9:00:00', end_date='2021-01-05 12:00:00', frequency='1m')\n",
"single_stock_df\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
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" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>time</th>\n",
" <th>code</th>\n",
" <th>open</th>\n",
" <th>close</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>volume</th>\n",
" <th>money</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2021-01-05 09:31:00</td>\n",
" <td>600409.XSHG</td>\n",
" <td>9.23</td>\n",
" <td>9.16</td>\n",
" <td>9.23</td>\n",
" <td>9.08</td>\n",
" <td>1417593.0</td>\n",
" <td>13000397.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2021-01-05 09:32:00</td>\n",
" <td>600409.XSHG</td>\n",
" <td>9.17</td>\n",
" <td>9.17</td>\n",
" <td>9.17</td>\n",
" <td>9.15</td>\n",
" <td>394595.0</td>\n",
" <td>3613283.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2021-01-05 09:33:00</td>\n",
" <td>600409.XSHG</td>\n",
" <td>9.16</td>\n",
" <td>9.15</td>\n",
" <td>9.16</td>\n",
" <td>9.13</td>\n",
" <td>492224.0</td>\n",
" <td>4501667.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2021-01-05 09:34:00</td>\n",
" <td>600409.XSHG</td>\n",
" <td>9.14</td>\n",
" <td>9.21</td>\n",
" <td>9.22</td>\n",
" <td>9.14</td>\n",
" <td>451578.0</td>\n",
" <td>4152355.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2021-01-05 09:35:00</td>\n",
" <td>600409.XSHG</td>\n",
" <td>9.20</td>\n",
" <td>9.20</td>\n",
" <td>9.22</td>\n",
" <td>9.20</td>\n",
" <td>503040.0</td>\n",
" <td>4631781.0</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",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>715</th>\n",
" <td>2021-01-05 11:26:00</td>\n",
" <td>600415.XSHG</td>\n",
" <td>5.87</td>\n",
" <td>5.87</td>\n",
" <td>5.87</td>\n",
" <td>5.87</td>\n",
" <td>279118.0</td>\n",
" <td>1639550.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>716</th>\n",
" <td>2021-01-05 11:27:00</td>\n",
" <td>600415.XSHG</td>\n",
" <td>5.87</td>\n",
" <td>5.87</td>\n",
" <td>5.87</td>\n",
" <td>5.87</td>\n",
" <td>253667.0</td>\n",
" <td>1490048.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>717</th>\n",
" <td>2021-01-05 11:28:00</td>\n",
" <td>600415.XSHG</td>\n",
" <td>5.87</td>\n",
" <td>5.87</td>\n",
" <td>5.87</td>\n",
" <td>5.87</td>\n",
" <td>137293.0</td>\n",
" <td>806465.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>718</th>\n",
" <td>2021-01-05 11:29:00</td>\n",
" <td>600415.XSHG</td>\n",
" <td>5.87</td>\n",
" <td>5.87</td>\n",
" <td>5.87</td>\n",
" <td>5.87</td>\n",
" <td>218351.0</td>\n",
" <td>1282600.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>719</th>\n",
" <td>2021-01-05 11:30:00</td>\n",
" <td>600415.XSHG</td>\n",
" <td>5.87</td>\n",
" <td>5.87</td>\n",
" <td>5.87</td>\n",
" <td>5.87</td>\n",
" <td>248941.0</td>\n",
" <td>1462285.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>720 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" time code open close high low volume \\\n",
"0 2021-01-05 09:31:00 600409.XSHG 9.23 9.16 9.23 9.08 1417593.0 \n",
"1 2021-01-05 09:32:00 600409.XSHG 9.17 9.17 9.17 9.15 394595.0 \n",
"2 2021-01-05 09:33:00 600409.XSHG 9.16 9.15 9.16 9.13 492224.0 \n",
"3 2021-01-05 09:34:00 600409.XSHG 9.14 9.21 9.22 9.14 451578.0 \n",
"4 2021-01-05 09:35:00 600409.XSHG 9.20 9.20 9.22 9.20 503040.0 \n",
".. ... ... ... ... ... ... ... \n",
"715 2021-01-05 11:26:00 600415.XSHG 5.87 5.87 5.87 5.87 279118.0 \n",
"716 2021-01-05 11:27:00 600415.XSHG 5.87 5.87 5.87 5.87 253667.0 \n",
"717 2021-01-05 11:28:00 600415.XSHG 5.87 5.87 5.87 5.87 137293.0 \n",
"718 2021-01-05 11:29:00 600415.XSHG 5.87 5.87 5.87 5.87 218351.0 \n",
"719 2021-01-05 11:30:00 600415.XSHG 5.87 5.87 5.87 5.87 248941.0 \n",
"\n",
" money \n",
"0 13000397.0 \n",
"1 3613283.0 \n",
"2 4501667.0 \n",
"3 4152355.0 \n",
"4 4631781.0 \n",
".. ... \n",
"715 1639550.0 \n",
"716 1490048.0 \n",
"717 806465.0 \n",
"718 1282600.0 \n",
"719 1462285.0 \n",
"\n",
"[720 rows x 8 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# range of multiple stocks price in different resolution\n",
"mul_stocks_df = jq.get_..0n63\n",
"\n",
"\n",
"price(['002709.XSHE',\n",
" '002920.XSHE',\n",
" '300274.XSHE',\n",
" '600409.XSHG',\n",
" '600415.XSHG',\n",
" '603882.XSHG'],start_date='2021-01-05 9:00:00', end_date='2021-01-05 12:00:00', frequency='1m')\n",
"mul_stocks_df\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>weight</th>\n",
" <th>display_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>000008.XSHE</th>\n",
" <td>2020-12-31</td>\n",
" <td>0.088</td>\n",
" <td>神州高铁</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000009.XSHE</th>\n",
" <td>2020-12-31</td>\n",
" <td>0.344</td>\n",
" <td>中国宝安</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000012.XSHE</th>\n",
" <td>2020-12-31</td>\n",
" <td>0.180</td>\n",
" <td>南玻A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000021.XSHE</th>\n",
" <td>2020-12-31</td>\n",
" <td>0.297</td>\n",
" <td>深科技</td>\n",
" </tr>\n",
" <tr>\n",
" <th>001872.XSHE</th>\n",
" <td>2020-12-31</td>\n",
" <td>0.030</td>\n",
" <td>招商港口</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>688002.XSHG</th>\n",
" <td>2020-12-31</td>\n",
" <td>0.438</td>\n",
" <td>睿创微纳</td>\n",
" </tr>\n",
" <tr>\n",
" <th>688099.XSHG</th>\n",
" <td>2020-12-31</td>\n",
" <td>0.287</td>\n",
" <td>晶晨股份</td>\n",
" </tr>\n",
" <tr>\n",
" <th>688088.XSHG</th>\n",
" <td>2020-12-31</td>\n",
" <td>0.252</td>\n",
" <td>虹软科技</td>\n",
" </tr>\n",
" <tr>\n",
" <th>688321.XSHG</th>\n",
" <td>2020-12-31</td>\n",
" <td>0.134</td>\n",
" <td>微芯生物</td>\n",
" </tr>\n",
" <tr>\n",
" <th>688029.XSHG</th>\n",
" <td>2020-12-31</td>\n",
" <td>0.130</td>\n",
" <td>南微医学</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>500 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" date weight display_name\n",
"000008.XSHE 2020-12-31 0.088 神州高铁\n",
"000009.XSHE 2020-12-31 0.344 中国宝安\n",
"000012.XSHE 2020-12-31 0.180 南玻A\n",
"000021.XSHE 2020-12-31 0.297 深科技\n",
"001872.XSHE 2020-12-31 0.030 招商港口\n",
"... ... ... ...\n",
"688002.XSHG 2020-12-31 0.438 睿创微纳\n",
"688099.XSHG 2020-12-31 0.287 晶晨股份\n",
"688088.XSHG 2020-12-31 0.252 虹软科技\n",
"688321.XSHG 2020-12-31 0.134 微芯生物\n",
"688029.XSHG 2020-12-31 0.130 南微医学\n",
"\n",
"[500 rows x 3 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# composition of benchmark\n",
"jq.get_index_weights('000905.XSHG',date='2021-01-20 9:00:00')\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'600409.XSHG': {'sw_l1': {'industry_code': '801030', 'industry_name': '化工I'},\n",
" 'sw_l2': {'industry_code': '801033', 'industry_name': '化学原料II'},\n",
" 'sw_l3': {'industry_code': '850321', 'industry_name': '纯碱III'},\n",
" 'zjw': {'industry_code': 'C26', 'industry_name': '化学原料和化学制品制造业'},\n",
" 'jq_l2': {'industry_code': 'HY02107', 'industry_name': '粘胶'},\n",
" 'jq_l1': {'industry_code': 'HY002', 'industry_name': '原材料'}},\n",
" '603882.XSHG': {'sw_l1': {'industry_code': '801150',\n",
" 'industry_name': '医药生物I'},\n",
" 'sw_l2': {'industry_code': '801156', 'industry_name': '医疗服务II'},\n",
" 'sw_l3': {'industry_code': '851562', 'industry_name': '诊断服务III'},\n",
" 'zjw': {'industry_code': 'Q83', 'industry_name': '卫生'},\n",
" 'jq_l2': {'industry_code': 'HY06103', 'industry_name': '体外诊断'},\n",
" 'jq_l1': {'industry_code': 'HY006', 'industry_name': '医药卫生'}},\n",
" '300274.XSHE': {'sw_l1': {'industry_code': '801730',\n",
" 'industry_name': '电气设备I'},\n",
" 'sw_l2': {'industry_code': '801735', 'industry_name': '光伏设备II'},\n",
" 'sw_l3': {'industry_code': '857353', 'industry_name': '逆变器III'},\n",
" 'zjw': {'industry_code': 'C38', 'industry_name': '电气机械和器材制造业'},\n",
" 'jq_l2': {'industry_code': 'HY03111', 'industry_name': '光伏设备'},\n",
" 'jq_l1': {'industry_code': 'HY003', 'industry_name': '工业'}},\n",
" '002709.XSHE': {'sw_l1': {'industry_code': '801730',\n",
" 'industry_name': '电气设备I'},\n",
" 'sw_l2': {'industry_code': '801737', 'industry_name': '电池II'},\n",
" 'sw_l3': {'industry_code': '857372', 'industry_name': '电池化学品III'},\n",
" 'zjw': {'industry_code': 'C26', 'industry_name': '化学原料和化学制品制造业'},\n",
" 'jq_l2': {'industry_code': 'HY03119', 'industry_name': '电池部件及材料'},\n",
" 'jq_l1': {'industry_code': 'HY003', 'industry_name': '工业'}},\n",
" '002920.XSHE': {'sw_l1': {'industry_code': '801750', 'industry_name': '计算机I'},\n",
" 'sw_l2': {'industry_code': '801104', 'industry_name': '软件开发II'},\n",
" 'sw_l3': {'industry_code': '851041', 'industry_name': '垂直应用软件III'},\n",
" 'zjw': {'industry_code': 'C36', 'industry_name': '汽车制造业'},\n",
" 'jq_l2': {'industry_code': 'HY04103', 'industry_name': '汽车电子'},\n",
" 'jq_l1': {'industry_code': 'HY004', 'industry_name': '可选消费'}},\n",
" '600415.XSHG': {'sw_l1': {'industry_code': '801200',\n",
" 'industry_name': '商业贸易I'},\n",
" 'sw_l2': {'industry_code': '801203', 'industry_name': '一般零售II'},\n",
" 'sw_l3': {'industry_code': '852034', 'industry_name': '商业物业经营III'},\n",
" 'zjw': {'industry_code': 'L72', 'industry_name': '商务服务业'},\n",
" 'jq_l2': {'industry_code': 'HY03147', 'industry_name': '市场服务'},\n",
" 'jq_l1': {'industry_code': 'HY003', 'industry_name': '工业'}}}"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sector information of each stock\n",
"jq.get_industry(['002709.XSHE',\n",
" '002920.XSHE',\n",
" '300274.XSHE',\n",
" '600409.XSHG',\n",
" '600415.XSHG',\n",
" '603882.XSHG'])\n",
"# display name of each stock"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([datetime.date(2005, 1, 4), datetime.date(2005, 1, 5),\n",
" datetime.date(2005, 1, 6), ..., datetime.date(2025, 8, 11),\n",
" datetime.date(2025, 8, 12), datetime.date(2025, 8, 13)],\n",
" dtype=object)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get all trading days\n",
"jq.get_all_trade_days()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>display_name</th>\n",
" <th>name</th>\n",
" <th>start_date</th>\n",
" <th>end_date</th>\n",
" <th>type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>000001.XSHE</th>\n",
" <td>平安银行</td>\n",
" <td>PAYH</td>\n",
" <td>1991-04-03</td>\n",
" <td>2200-01-01</td>\n",
" <td>stock</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000002.XSHE</th>\n",
" <td>万科A</td>\n",
" <td>WKA</td>\n",
" <td>1991-01-29</td>\n",
" <td>2200-01-01</td>\n",
" <td>stock</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000004.XSHE</th>\n",
" <td>国华网安</td>\n",
" <td>GHWA</td>\n",
" <td>1990-12-01</td>\n",
" <td>2200-01-01</td>\n",
" <td>stock</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000005.XSHE</th>\n",
" <td>ST星源</td>\n",
" <td>STXY</td>\n",
" <td>1990-12-10</td>\n",
" <td>2200-01-01</td>\n",
" <td>stock</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000006.XSHE</th>\n",
" <td>深振业A</td>\n",
" <td>SZYA</td>\n",
" <td>1992-04-27</td>\n",
" <td>2200-01-01</td>\n",
" <td>stock</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>688799.XSHG</th>\n",
" <td>华纳药厂</td>\n",
" <td>HNYC</td>\n",
" <td>2021-07-13</td>\n",
" <td>2200-01-01</td>\n",
" <td>stock</td>\n",
" </tr>\n",
" <tr>\n",
" <th>688800.XSHG</th>\n",
" <td>瑞可达</td>\n",
" <td>RKD</td>\n",
" <td>2021-07-22</td>\n",
" <td>2200-01-01</td>\n",
" <td>stock</td>\n",
" </tr>\n",
" <tr>\n",
" <th>688819.XSHG</th>\n",
" <td>天能股份</td>\n",
" <td>TNGF</td>\n",
" <td>2021-01-18</td>\n",
" <td>2200-01-01</td>\n",
" <td>stock</td>\n",
" </tr>\n",
" <tr>\n",
" <th>688981.XSHG</th>\n",
" <td>中芯国际</td>\n",
" <td>ZXGJ</td>\n",
" <td>2020-07-16</td>\n",
" <td>2200-01-01</td>\n",
" <td>stock</td>\n",
" </tr>\n",
" <tr>\n",
" <th>689009.XSHG</th>\n",
" <td>九号公司</td>\n",
" <td>JHGS</td>\n",
" <td>2020-10-29</td>\n",
" <td>2200-01-01</td>\n",
" <td>stock</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5248 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" display_name name start_date end_date type\n",
"000001.XSHE 平安银行 PAYH 1991-04-03 2200-01-01 stock\n",
"000002.XSHE 万科A WKA 1991-01-29 2200-01-01 stock\n",
"000004.XSHE 国华网安 GHWA 1990-12-01 2200-01-01 stock\n",
"000005.XSHE ST星源 STXY 1990-12-10 2200-01-01 stock\n",
"000006.XSHE 深振业A SZYA 1992-04-27 2200-01-01 stock\n",
"... ... ... ... ... ...\n",
"688799.XSHG 华纳药厂 HNYC 2021-07-13 2200-01-01 stock\n",
"688800.XSHG 瑞可达 RKD 2021-07-22 2200-01-01 stock\n",
"688819.XSHG 天能股份 TNGF 2021-01-18 2200-01-01 stock\n",
"688981.XSHG 中芯国际 ZXGJ 2020-07-16 2200-01-01 stock\n",
"689009.XSHG 九号公司 JHGS 2020-10-29 2200-01-01 stock\n",
"\n",
"[5248 rows x 5 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"jq.get_all_securities()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'engine' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[4], line 9\u001b[0m\n\u001b[1;32m 7\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39mdate\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mto_datetime(df[\u001b[39m'\u001b[39m\u001b[39mdate\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[1;32m 8\u001b[0m \u001b[39mreturn\u001b[39;00m df\n\u001b[0;32m----> 9\u001b[0m get_most_recent_profile(\u001b[39m'\u001b[39;49m\u001b[39mportfolio\u001b[39;49m\u001b[39m'\u001b[39;49m)\n",
"Cell \u001b[0;32mIn[4], line 5\u001b[0m, in \u001b[0;36mget_most_recent_profile\u001b[0;34m(type)\u001b[0m\n\u001b[1;32m 3\u001b[0m table_name \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mbenchmark_profile\u001b[39m\u001b[39m'\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39mtype\u001b[39m \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mbenchmark\u001b[39m\u001b[39m'\u001b[39m \u001b[39melse\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39mportfolio_profile\u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m 4\u001b[0m query \u001b[39m=\u001b[39m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mSELECT * FROM \u001b[39m\u001b[39m{\u001b[39;00mtable_name\u001b[39m}\u001b[39;00m\u001b[39m WHERE date = (SELECT MAX(date) FROM \u001b[39m\u001b[39m{\u001b[39;00mtable_name\u001b[39m}\u001b[39;00m\u001b[39m)\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m----> 5\u001b[0m df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mread_sql(query, con\u001b[39m=\u001b[39mengine)\n\u001b[1;32m 6\u001b[0m \u001b[39m# convert date to datetime object\u001b[39;00m\n\u001b[1;32m 7\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39mdate\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mto_datetime(df[\u001b[39m'\u001b[39m\u001b[39mdate\u001b[39m\u001b[39m'\u001b[39m])\n",
"\u001b[0;31mNameError\u001b[0m: name 'engine' is not defined"
]
}
],
"source": [
"# when update stock price just need the latest portfolio frame\n",
"def get_most_recent_profile(type):\n",
" table_name = 'benchmark_profile' if type == 'benchmark' else 'portfolio_profile'\n",
" query = f\"SELECT * FROM {table_name} WHERE date = (SELECT MAX(date) FROM {table_name})\"\n",
" with create_engine(db_url).connect() as conn:\n",
" df = pd.read_sql(query, con=engine)\n",
" # convert date to datetime object\n",
" df['date'] = pd.to_datetime(df['date'])\n",
" return df\n",
"get_most_recent_profile('portfolio')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "portfolio_risk_assesment",
"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": "3.11.4"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
|