{"id":9032,"date":"2017-12-15T09:00:54","date_gmt":"2017-12-15T14:00:54","guid":{"rendered":"https:\/\/blogs.mathworks.com\/pick\/?p=9032"},"modified":"2017-12-15T15:28:59","modified_gmt":"2017-12-15T20:28:59","slug":"download-stock-data","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/pick\/2017\/12\/15\/download-stock-data\/","title":{"rendered":"Download Stock Data"},"content":{"rendered":"

Jiro<\/a>‘s pick this week is “Historical Stock Data Download (alternate Method)”<\/a> by Captain Awesome<\/a>.<\/p>

I’m not an expert in stocks, but I stumbled upon this entry when I received a question from a training customer asking about the now removed Datafeed Toolbox<\/a> function, yahoo<\/tt>. Earlier this year, Yahoo! changed their API, which prevented the function from connecting to the service. As of R2017a, the yahoo<\/tt> function has been removed from the toolbox.<\/p>

My customer wanted to know alternatives to fetching stock data from Yahoo!. The workaround I heard was to use lower-level functions to read the raw data from the website and scrape for the necessary data. As I was writing some sample code, I decided to see if there were any useful functions on the File Exchange. While there are a number of entries<\/a> for fetching stock data from the web, this entry by Captain Awesome was the first one that I came across that worked out of the box. Captain Awesome had accounted for the API change.<\/p>

Here is an example of fetching daily Google stock data from Jan 2010.<\/p>

stock = get_yahoo_stockdata3('GOOG'<\/span>,'2010\/1\/1'<\/span>)\r\n<\/pre>
stock = \r\n  struct with fields:\r\n\r\n           ticker: 'GOOG'\r\n       dataSource: 'Yahoo Finance'\r\n       dataUpdate: '14-Dec-2017 23:42:24'\r\n         errorMsg: ''\r\n         dataFreq: 'd'\r\n        dividends: []\r\n           splits: [1×1 struct]\r\n            range: '04-Jan-2010 to 13-Dec-2017'\r\n         varnotes: {7×4 cell}\r\n         DateTime: [2002×1 double]\r\n        openPrice: [2002×1 double]\r\n        highPrice: [2002×1 double]\r\n         lowPrice: [2002×1 double]\r\n       closePrice: [2002×1 double]\r\n           volume: [2002×1 double]\r\n    adjClosePrice: [2002×1 double]\r\n<\/pre>
If you look at the code, you’ll notice that it’s simply reading the website with the appropriate queries, and doing a bunch of text processing to extract data.<\/p>
Comments<\/b><\/p>
Give it a try and let us know what you think here<\/a> or leave a comment<\/a> for Captain Awesome.<\/p>