Data Science Portfolio

Scrape Historical OHLC (Open-High-Low-Close) Stock Prices

Overview

• Create a basic web scraper to extract historical OHLC stock price data for a given ticker symbol
• Use the Alpha Vantage API to obtain the stock price data
• Alpha Vantage API provides last two years’ worth of data
• Save the data to a pandas dataframe

Obtain a unique API key here: https://www.alphavantage.co/support/#api-key

Import libraries

import io
import os
import sys
import math
import csv
import time
import itertools
import bisect
import string
import requests
import random
import requests
import json
import numpy
import pandas as pd
import alpha_vantage
import bs4
from bs4 import BeautifulSoup

Define stock scraper method

• Use requests.get() with the Alpha Vantage API url in order to obtain response data
• Add appropriate time interval to base url
  • DAILY
  • WEEKLY
  • MONTHLY
• Add desired stock symbol to base url
• Add unique API key to base url

def ohlc_stock_price_scraper(symbol, interval, api_key):
    # Use GET request to access data through API, using custom API key and desired symbol/interval options
    data = requests.get('https://www.alphavantage.co/query?function=TIME_SERIES_' + interval + '&symbol=' + symbol + '&apikey=' + api_key + '=csv')
    
    return data

Test scraper method

• Test symbol: AAPL
• Test interval: DAILY

data = ohlc_stock_price_scraper('AAPL', 'DAILY', 'WTX6IKTWWR57LOIQ&datatype')

Store raw data in new variable

• Store data in pandas dataframe with read_csv() method
• Decode data content using ‘utf-8’ decoding
• Pass decoded content into io.StringIO() method before passing into read_csv

raw_data = pd.read_csv(io.StringIO(data.content.decode('utf-8')))

View the first 10 rows of data

raw_data.head(10)

	timestamp	open	high	low	close	volume
0	2019-04-05	196.450	197.100	195.93	197.00	18472107
1	2019-04-04	194.790	196.370	193.14	195.69	19114275
2	2019-04-03	193.250	196.500	193.15	195.35	23271830
3	2019-04-02	191.090	194.460	191.05	194.02	22765732
4	2019-04-01	191.640	191.680	188.38	191.24	27861964
5	2019-03-29	189.830	190.080	188.54	189.95	23563961
6	2019-03-28	188.950	189.559	187.53	188.72	20780363
7	2019-03-27	188.750	189.760	186.55	188.47	29848427
8	2019-03-26	191.664	192.880	184.58	186.79	49800538
9	2019-03-25	191.510	191.980	186.60	188.74	43845293