Thursday, April 29, 2010

Getting Data: Real-Time Quotes

As promised I'll discuss the options for real-time data... it seems that getting real-time data has become increasingly easier and some brokerages actually have APIs that help you get real-time data or they allow you to download small interval historical prices (minutes, seconds or even ticks).

In this post I'll discuss how to get free "real-time" data and how you can use that data to build your own historical prices database. Yahoo offers delayed quotes (up to 15 minutes), but it also has "real-time" data and we can take advantage of that with a little programming.

First I'd like to introduce you to the Yahoo "API", which is nothing more than a collection of tags. You can see all the available tags here:

Getting real-time quotes is much like getting historical data from Yahoo: you will build a URL with the tags you're interested in and you'll use that URL to get a CSV file containing a quote. Here is how to construct a URL: + STOCK_SYMBOL(S) + &f= + TAG(S)

Let's try it with one symbol and one tag: the symbol will be "GOOG" (Google) and the tag wil be "g" (day's low):

If we want to get multiple tags we can just string them together (will return a CSV file with the high and the low of the day):

Let's fetch a CSV file containing Google's high and low values for the day on the first line and Microsoft's high and low values for the day on the next line:

Here is an example that downloads all the data for the specified tags and prints them to the screen alongside their description:

// A dictionary with tags where the key is the tag
// and the value is the description of the tag.
private Dictionary _tags;

private void DownloadData(String symbol)
string url = String.Format(
"{0}&f=", symbol);

//Get page showing the table with the chosen indices
HttpWebRequest request = null;
DFDataSet ds = new DFDataSet();
Random rand = new Random(DateTime.Now.Millisecond);
while (_running)
foreach (String key in _tags.Keys)
lock (_sync)
request = (HttpWebRequest)WebRequest.CreateDefault(
new Uri(url + key));
request.Timeout = 30000;

using (var response = (HttpWebResponse)request.GetResponse())
using (StreamReader input = new StreamReader(
Console.WriteLine(String.Format("{0} {1} = {2}",
symbol, _tags[key], input.ReadLine());
Console.WriteLine(Thread.CurrentThread.Name + " running.");
Thread.Sleep(60*1000); // 60 seconds
catch (Exception exc)

At this point you should have everything you need to build your own historical database:
1. Download the CSV file with all the tags you're interested in.
2. Open a connection to your database.
3. Bulk insert the CSV file into your database.

Friday, April 23, 2010

Getting Data: EOD Historical Prices

One of the first issues that people encounter when trying to experiment with ML/AI algorithms in the trading field is getting (free) training data. It's pretty easy to get end of day (EOD) prices from google and yahoo- both providers allow you to download historical data in a csv format.

Let's take a look at both providers:

  • Posts the EOD data earlier than Yahoo (2-3 hours before Yahoo posts the data).
  • Sample URL:
  • Has a limit on how many requests you can make per minute (don't know the exact figure, but if you request data too often google will block you for a period of time).
  • Good if you download the data manually and automatically (at low frequency).
  • Posts the EOD data a little later (around 7:00 p.m. CST).
  • Sample URL:
  • Has no limit on how many requests you can make at all!
  • Good if you download the data both manually and automatically (no frequency limit).
The entire set of up-to-date EOD prices from Yahoo for all the stocks on all of the major exchanges is around 4GB. Google's database size is about the same.

I chose to use Yahoo, because it was important that there is no limit on the number of requests I can make... this allowed me to automate the process of downloading the data and populating my database.

Here is sample code on how to download the data:

void DownloadDataFromWeb(string symbol)
DateTime startDate = DateTime.Parse("1900-01-01");

string baseURL = "";
string queryText = BuildHistoricalDataRequest(symbol, startDate, DateTime.Today);
string url = string.Format("{0}{1}", baseURL, queryText);

//Get page showing the table with the chosen indices
HttpWebRequest request = null;
HttpWebResponse response = null;
StreamReader stReader = null;

//csv content
string docText = string.Empty;
string csvLine = null;
request = (HttpWebRequest)WebRequest.CreateDefault(new Uri(url));
request.Timeout = 300000;

response = (HttpWebResponse)request.GetResponse();

stReader = new StreamReader(response.GetResponseStream(), true);

stReader.ReadLine();//skip the first (header row)
while ((csvLine = stReader.ReadLine()) != null)
string[] sa = csvLine.Split(new char[] { ',' });

DateTime date = DateTime.Parse(sa[0].Trim('"'));
Double open = double.Parse(sa[1]);
Double high = double.Parse(sa[2]);
Double low = double.Parse(sa[3]);
Double close = double.Parse(sa[4]);
Double volume = double.Parse(sa[5]);
Double adjClose = double.Parse(sa[6]);
// Process the data (e.g. insert into DB)
catch (Exception e)
throw e;

string BuildHistoricalDataRequest(string symbol, DateTime startDate, DateTime endDate)
// We're subtracting 1 from the month because yahoo
// counts the months from 0 to 11 not from 1 to 12.
StringBuilder request = new StringBuilder();
request.AppendFormat("s={0}", symbol);
request.AppendFormat("&a={0}", startDate.Month-1);
request.AppendFormat("&b={0}", startDate.Day);
request.AppendFormat("&c={0}", startDate.Year);
request.AppendFormat("&d={0}", endDate.Month-1);
request.AppendFormat("&e={0}", endDate.Day);
request.AppendFormat("&f={0}", endDate.Year);
request.AppendFormat("&g={0}", "d"); //daily

return request.ToString();

Thursday, April 22, 2010

Hello World!

Since the topic is Machine Learning and Artificial Intelligence applied to trading, why don't we start by looking at how nature deals with trading:

I thought it was an interesting concept... they're essentially letting nature evolve trading strategies. While rat's tend to reproduce quite fast it's still quite slow compared to what your average computer can do.

In the spirit of evolution I made my own application that uses Genetic Programming algorithms to create trading strategies and I use them to trade on the stock market. I post my results on twitter in order to get a time-stamp:

So far the results have been quite promising: from 09-09-09 to 4-13-2010 we have accumulated about 25% profit when the stocks we trade have only gone up 18.77%. The S&P 500 has gone up 16.76% in the same period, so we're outperforming both the Buy & Hold strategy for our stocks and the S&P 500.