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TslDataFeed/qlib-demo.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "a4ee5431-61a4-45ba-8075-c9bc4981c3d1",
"metadata": {},
"outputs": [],
"source": [
"from pprint import pprint\n",
"from pathlib import Path\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7e6b5b5d-9556-4e32-97db-598170e51e8f",
"metadata": {},
"outputs": [],
"source": [
"import qlib\n",
"from qlib.constant import REG_CN"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "462a4caa-030b-4c5a-bdaf-6da2e2a7018f",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[64026:MainThread](2022-06-23 17:30:31,270) INFO - qlib.Initialization - [config.py:413] - default_conf: client.\n",
"[64026:MainThread](2022-06-23 17:30:31,395) INFO - qlib.Initialization - [__init__.py:74] - qlib successfully initialized based on client settings.\n",
"[64026:MainThread](2022-06-23 17:30:31,395) INFO - qlib.Initialization - [__init__.py:76] - data_path={'__DEFAULT_FREQ': PosixPath('/home/guofu/Workspaces/guofu/TslDataFeed/qlib-data/target/market/k-daily')}\n"
]
}
],
"source": [
"provider_uri = 'qlib-data/target/market/k-daily/'\n",
"qlib.init(provider_uri=provider_uri, region=REG_CN)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d17953b5-f84d-4d07-b348-9f2e3058e09c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([Timestamp('2010-01-04 00:00:00'), Timestamp('2010-01-05 00:00:00')],\n",
" dtype=object)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from qlib.data import D\n",
"D.calendar(start_time='2010-01-01', end_time='2019-12-31', freq='day')[:2]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "3110bbee-95e5-4077-8bf7-bbf130a8e395",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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"\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>$close</th>\n",
" <th>$factor</th>\n",
" <th>$volume</th>\n",
" </tr>\n",
" <tr>\n",
" <th>instrument</th>\n",
" <th>datetime</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"19\" valign=\"top\">SH600519</th>\n",
" <th>2022-05-05</th>\n",
" <td>1837.000000</td>\n",
" <td>0.038907</td>\n",
" <td>3366094.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-06</th>\n",
" <td>1793.000000</td>\n",
" <td>0.038907</td>\n",
" <td>2859618.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-09</th>\n",
" <td>1752.000000</td>\n",
" <td>0.038907</td>\n",
" <td>3035182.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-10</th>\n",
" <td>1766.189941</td>\n",
" <td>0.038907</td>\n",
" <td>4011919.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-11</th>\n",
" <td>1768.000000</td>\n",
" <td>0.038907</td>\n",
" <td>3031267.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-12</th>\n",
" <td>1762.670044</td>\n",
" <td>0.038907</td>\n",
" <td>2203823.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-13</th>\n",
" <td>1778.239990</td>\n",
" <td>0.038907</td>\n",
" <td>2073052.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-16</th>\n",
" <td>1758.000000</td>\n",
" <td>0.038907</td>\n",
" <td>2776531.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-17</th>\n",
" <td>1768.000000</td>\n",
" <td>0.038907</td>\n",
" <td>2014474.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-18</th>\n",
" <td>1761.000000</td>\n",
" <td>0.038907</td>\n",
" <td>2485044.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-19</th>\n",
" <td>1756.000000</td>\n",
" <td>0.038907</td>\n",
" <td>1977626.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-20</th>\n",
" <td>1800.010010</td>\n",
" <td>0.038907</td>\n",
" <td>4194960.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-23</th>\n",
" <td>1781.000000</td>\n",
" <td>0.038907</td>\n",
" <td>2215713.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-24</th>\n",
" <td>1760.000000</td>\n",
" <td>0.038907</td>\n",
" <td>2734222.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-25</th>\n",
" <td>1755.510010</td>\n",
" <td>0.038907</td>\n",
" <td>2238869.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-26</th>\n",
" <td>1742.800049</td>\n",
" <td>0.038907</td>\n",
" <td>2805344.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-27</th>\n",
" <td>1755.160034</td>\n",
" <td>0.038907</td>\n",
" <td>2511958.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-30</th>\n",
" <td>1778.410034</td>\n",
" <td>0.038907</td>\n",
" <td>3446569.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-31</th>\n",
" <td>1804.030029</td>\n",
" <td>0.038907</td>\n",
" <td>4075082.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" $close $factor $volume\n",
"instrument datetime \n",
"SH600519 2022-05-05 1837.000000 0.038907 3366094.0\n",
" 2022-05-06 1793.000000 0.038907 2859618.0\n",
" 2022-05-09 1752.000000 0.038907 3035182.0\n",
" 2022-05-10 1766.189941 0.038907 4011919.0\n",
" 2022-05-11 1768.000000 0.038907 3031267.0\n",
" 2022-05-12 1762.670044 0.038907 2203823.0\n",
" 2022-05-13 1778.239990 0.038907 2073052.0\n",
" 2022-05-16 1758.000000 0.038907 2776531.0\n",
" 2022-05-17 1768.000000 0.038907 2014474.0\n",
" 2022-05-18 1761.000000 0.038907 2485044.0\n",
" 2022-05-19 1756.000000 0.038907 1977626.0\n",
" 2022-05-20 1800.010010 0.038907 4194960.0\n",
" 2022-05-23 1781.000000 0.038907 2215713.0\n",
" 2022-05-24 1760.000000 0.038907 2734222.0\n",
" 2022-05-25 1755.510010 0.038907 2238869.0\n",
" 2022-05-26 1742.800049 0.038907 2805344.0\n",
" 2022-05-27 1755.160034 0.038907 2511958.0\n",
" 2022-05-30 1778.410034 0.038907 3446569.0\n",
" 2022-05-31 1804.030029 0.038907 4075082.0"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"D.features(['SH600519'], \n",
" ['$close', '$factor', '$volume'], \n",
" start_time='2022-05-01', end_time='2022-05-31'\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "130b595e-1025-44d5-a1a0-e83a5594f026",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" $open $high $low $close $volume $factor\n",
"instrument datetime \n",
"SH601216 2020-05-06 2.85 2.91 2.82 2.89 41379160.0 0.079303\n",
" 2020-05-07 2.89 2.90 2.85 2.86 29762070.0 0.079303\n",
" 2020-05-08 2.87 2.90 2.86 2.87 28355788.0 0.079303\n",
" 2020-05-11 2.88 2.89 2.86 2.87 23293712.0 0.079303\n",
" 2020-05-12 2.87 2.89 2.86 2.87 19887894.0 0.079303\n",
" 2020-05-13 2.87 2.87 2.85 2.87 19220766.0 0.079303\n",
" 2020-05-14 2.86 2.87 2.83 2.83 27414002.0 0.079303\n",
" 2020-05-15 2.84 2.85 2.80 2.81 17712920.0 0.079303\n",
" 2020-05-18 2.83 2.87 2.81 2.83 24515376.0 0.079303\n",
" 2020-05-19 2.84 2.86 2.82 2.83 15435646.0 0.079303\n",
" 2020-05-20 2.88 3.00 2.88 2.96 93234496.0 0.079303\n",
" 2020-05-21 2.98 3.00 2.94 2.96 49795752.0 0.079303\n",
" 2020-05-22 2.96 2.97 2.91 2.96 46242076.0 0.079303\n",
" 2020-05-25 2.99 3.01 2.94 2.99 71138352.0 0.079303\n",
" 2020-05-26 2.63 2.63 2.51 2.55 80402464.0 0.070020\n",
" 2020-05-27 2.53 2.54 2.48 2.49 38057112.0 0.070020\n",
" 2020-05-28 2.47 2.52 2.47 2.49 21264260.0 0.070020\n",
" 2020-05-29 2.49 2.49 2.46 2.48 22057360.0 0.070020\n",
" 2020-06-01 2.49 2.54 2.48 2.53 28091710.0 0.070020\n",
" 2020-06-02 2.55 2.55 2.50 2.53 23823118.0 0.070020\n",
" 2020-06-03 2.53 2.57 2.53 2.53 26374100.0 0.070020\n",
" 2020-06-04 2.53 2.54 2.51 2.52 17998960.0 0.070020\n",
" 2020-06-05 2.53 2.53 2.50 2.52 17636240.0 0.070020\n",
" 2020-06-08 2.52 2.54 2.51 2.53 19058552.0 0.070020\n",
" 2020-06-09 2.53 2.54 2.51 2.53 17052228.0 0.070020\n",
" 2020-06-10 2.53 2.53 2.50 2.51 13044420.0 0.070020\n",
"SH601226 2020-05-06 3.79 3.83 3.76 3.82 1787352.0 0.653196\n",
" 2020-05-07 3.83 3.86 3.80 3.81 1464540.0 0.653196\n",
" 2020-05-08 3.83 3.84 3.81 3.83 1811100.0 0.653196\n",
" 2020-05-11 3.84 3.85 3.80 3.82 1609700.0 0.653196\n",
" 2020-05-12 3.82 3.82 3.74 3.79 2060700.0 0.653196\n",
" 2020-05-13 3.79 3.80 3.75 3.79 1685700.0 0.653196\n",
" 2020-05-14 3.78 3.78 3.75 3.76 1505500.0 0.653196\n",
" 2020-05-15 3.76 3.78 3.76 3.77 1067100.0 0.653196\n",
" 2020-05-18 3.77 3.79 3.73 3.79 1886750.0 0.653196\n",
" 2020-05-19 3.80 3.83 3.77 3.79 1430700.0 0.653196\n",
" 2020-05-20 3.79 3.79 3.73 3.73 2318142.0 0.653196\n",
" 2020-05-21 3.74 3.75 3.72 3.73 1677042.0 0.653196\n",
" 2020-05-22 3.73 3.73 3.65 3.65 2952700.0 0.653196\n",
" 2020-05-25 3.65 3.66 3.60 3.61 2197650.0 0.653196\n",
" 2020-05-26 3.61 3.68 3.61 3.67 1395300.0 0.653196\n",
" 2020-05-27 3.67 3.70 3.64 3.69 1506800.0 0.653196\n",
" 2020-05-28 3.68 3.70 3.62 3.66 1596750.0 0.653196\n",
" 2020-05-29 3.64 3.71 3.63 3.67 1506901.0 0.653196\n",
" 2020-06-01 3.68 3.77 3.68 3.75 2344991.0 0.653196\n",
" 2020-06-02 3.73 3.79 3.73 3.77 1902000.0 0.653196\n",
" 2020-06-03 3.78 3.80 3.75 3.75 1723665.0 0.653196\n",
" 2020-06-04 3.77 3.78 3.74 3.77 1351150.0 0.653196\n",
" 2020-06-05 3.77 3.78 3.75 3.78 1308801.0 0.653196\n",
" 2020-06-08 3.80 3.80 3.75 3.76 1219050.0 0.653196\n",
" 2020-06-09 3.77 3.93 3.74 3.84 3872250.0 0.653196\n",
" 2020-06-10 3.81 3.84 3.74 3.76 2186850.0 0.653196\n"
]
}
],
"source": [
"df = D.features(['SH601216', 'SH601226'], \n",
" ['$open', '$high', '$low', '$close', '$volume', '$factor'], \n",
" start_time='2020-05-01', end_time='2020-06-10'\n",
" )\n",
"pprint(df)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "09e1e518-0874-4f76-a0e0-591a71935d13",
"metadata": {},
"outputs": [
{
"data": {
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"config": {
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},
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"type": "candlestick",
"x": [
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"2020-05-07T00:00:00",
"2020-05-08T00:00:00",
"2020-05-11T00:00:00",
"2020-05-12T00:00:00",
"2020-05-13T00:00:00",
"2020-05-14T00:00:00",
"2020-05-15T00:00:00",
"2020-05-18T00:00:00",
"2020-05-19T00:00:00",
"2020-05-20T00:00:00",
"2020-05-21T00:00:00",
"2020-05-22T00:00:00",
"2020-05-25T00:00:00",
"2020-05-26T00:00:00",
"2020-05-27T00:00:00",
"2020-05-28T00:00:00",
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",
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"fig = go.Figure(data=[go.Candlestick(x=df.index.get_level_values(\"datetime\"),\n",
" open=df['$open'],\n",
" high=df['$high'],\n",
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},
{
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"id": "0fd2ef53-4f8a-4633-8625-c37478ce9e53",
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.graph_objects as go\n",
"fig = go.Figure(data=[go.Candlestick(x=df.index.get_level_values(\"datetime\"),\n",
" open=df['$open'] / df['$factor'],\n",
" high=df['$high'] / df['$factor'],\n",
" low=df['$low'] / df['$factor'],\n",
" close=df['$close'] / df['$factor'])])\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "9dc26721-c180-4346-91c9-d3ce01625825",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'market': 'csi100', 'filter_pipe': []}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"D.instruments('csi100')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "d6949cac-2f51-44d9-b3ac-0b73644715cc",
"metadata": {},
"outputs": [],
"source": [
"p = Path('qlib-data/target/financial').expanduser()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "2f89ccce-1e40-4599-b873-d61944fc5b44",
"metadata": {},
"outputs": [],
"source": [
"instruments = [\"sh600519\", \"sz000725\"]\n",
"data = D.features(instruments, ['P($$roewa_q)'], start_time=\"2019-01-01\", end_time=\"2019-07-19\", freq=\"day\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "26a494b1-81fe-4ac3-987a-82f27cc1a21d",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>P($$roewa_q)</th>\n",
" </tr>\n",
" <tr>\n",
" <th>instrument</th>\n",
" <th>datetime</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">sh600519</th>\n",
" <th>2019-01-02</th>\n",
" <td>0.255220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-03</th>\n",
" <td>0.255220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-04</th>\n",
" <td>0.255220</td>\n",
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" <tr>\n",
" <th>2019-01-07</th>\n",
" <td>0.255220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-08</th>\n",
" <td>0.255220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">sz000725</th>\n",
" <th>2019-07-15</th>\n",
" <td>0.012164</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-16</th>\n",
" <td>0.012164</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-17</th>\n",
" <td>0.012164</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-18</th>\n",
" <td>0.012164</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-07-19</th>\n",
" <td>0.012164</td>\n",
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"</table>\n",
"<p>266 rows × 1 columns</p>\n",
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],
"text/plain": [
" P($$roewa_q)\n",
"instrument datetime \n",
"sh600519 2019-01-02 0.255220\n",
" 2019-01-03 0.255220\n",
" 2019-01-04 0.255220\n",
" 2019-01-07 0.255220\n",
" 2019-01-08 0.255220\n",
"... ...\n",
"sz000725 2019-07-15 0.012164\n",
" 2019-07-16 0.012164\n",
" 2019-07-17 0.012164\n",
" 2019-07-18 0.012164\n",
" 2019-07-19 0.012164\n",
"\n",
"[266 rows x 1 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "43aabad1-4718-4a30-a549-5707de7b1e0b",
"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
}
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