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May 22, 2017 21:16
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Getting started with TA-Lib
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Getting Started with TA-Lib\n", | |
"\n", | |
"1. brew install ta-lib\n", | |
"2. pip install TA-Lib" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 55, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"import quandl\n", | |
"import json\n", | |
"import numpy as np\n", | |
"import talib as ta\n", | |
"\n", | |
"import matplotlib.pyplot as plt\n", | |
"%matplotlib inline" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"# set my api key\n", | |
"with open('./apikey.json') as f: \n", | |
" quandl.ApiConfig.api_key = json.load(f)['apikey']" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"# get historical data of Facebook stock price\n", | |
"data = quandl.get_table('WIKI/PRICES', ticker='FB', paginate=True)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 61, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"data_simple = data[['date', 'close']].reset_index(drop=True)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 62, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/html": [ | |
"<div>\n", | |
"<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>date</th>\n", | |
" <th>close</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td>2012-05-18</td>\n", | |
" <td>38.2318</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1</th>\n", | |
" <td>2012-05-21</td>\n", | |
" <td>34.0300</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2</th>\n", | |
" <td>2012-05-22</td>\n", | |
" <td>31.0000</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" date close\n", | |
"0 2012-05-18 38.2318\n", | |
"1 2012-05-21 34.0300\n", | |
"2 2012-05-22 31.0000" | |
] | |
}, | |
"execution_count": 62, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"data_simple.head(3)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 63, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"# 100 day simple moving average\n", | |
"data_simple[\"SMA100\"] = ta.SMA(np.array(data_simple[\"close\"]), timeperiod=100)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 64, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.axes._subplots.AxesSubplot at 0x11a183d68>" | |
] | |
}, | |
"execution_count": 64, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
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CATRNuw54ppVXAa5mqBeSViaEeMDdiL+Bh51Hgc6xtdTnm6dLMRg4etgCu7R9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\ngg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x11a147fd0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"data_simple[[\"SMA100\", \"close\"]].plot()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 3", | |
"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": "3.5.2" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 0 | |
} |
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