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TslDataFeed/qlib-demo.ipynb

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init
2 years ago
{
"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",
" }\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>$close</th>\n",
" <th>$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', '$factor'], \n",
" start_time='2020-05-01', end_time='2020-06-10'\n",
" )\n",
"pprint(df)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "09e1e518-0874-4f76-a0e0-591a71935d13",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
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"open": [
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"type": "candlestick",
"x": [
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"2020-05-07T00:00:00",
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"2020-05-11T00:00:00",
"2020-05-12T00:00:00",
"2020-05-13T00:00:00",
"2020-05-14T00:00:00",
"2020-05-15T00:00:00",
"2020-05-18T00:00:00",
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"2020-05-20T00:00:00",
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"2020-05-28T00:00:00",
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"2020-06-01T00:00:00",
"2020-06-02T00:00:00",
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],
"source": [
"import plotly.graph_objects as go\n",
"fig = go.Figure(data=[go.Candlestick(x=df.index.get_level_values(\"datetime\"),\n",
" open=df['$open'],\n",
" high=df['$high'],\n",
" low=df['$low'],\n",
" close=df['$close'])])\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "0fd2ef53-4f8a-4633-8625-c37478ce9e53",
"metadata": {},
"outputs": [
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.graph_objects as go\n",
"fig = go.Figure(data=[go.Candlestick(x=df.index.get_level_values(\"datetime\"),\n",
" open=df['$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($$roewa_q)'], start_time=\"2019-01-01\", end_time=\"2019-07-19\", freq=\"day\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "26a494b1-81fe-4ac3-987a-82f27cc1a21d",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>P($$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",
" </tr>\n",
" <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",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>266 rows × 1 columns</p>\n",
"</div>"
],
"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": []
}
],
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