Quantitative Analysis, Risk Management, Modelling, Algo Trading, and Big Data Analysis

Create a Portfolio of Stocks based on Google Finance Data fed by Quandl

There is an old saying Be careful what you wish for. Many quants and traders wished for a long time for a better quality of publicly available data. It wasn’t an easy task to accomplish but with a minimum of effort, it has become possible to get all what you truly dreamt of. When I started my experience with daily backtesting of my algo strategies I initially relied on Yahoo! data. Yeah, easily accessible but of low quality. Since the inception of Google Finance the same dream had been reborn. More precise data, covering more aspects of trading, platforms, markets, instruments. But how to get them all?


Quandl.com provides us with an enormous access to an enormous database. Just visit the webpage to find out what I mean by barley putting my thoughts all together in one sentence. In fact, the portal’s accessibility via different programming languages (e.g. Matlab, C++, Python, etc.) makes that database more alive than ever. The space of parameters the quants get an access to becomes astronomical in number!

Alleluja! Let’s ride this horse. Enough of watching the online charts changing a direction of our investing moods with a one-mintute time resolution. We were born to be smarter than stochastic processes taking control over our daily strategies. In this short post, making use of the Matlab R2013a platform, I want to show you how quickly you can construct any portfolio of US stocks taken from the Dow Jones Index universe (as an example). We will use the Google Finance stock data fed by Quandl.com.

Google Finance Stock Data for Model Feeding

Similar to Yahoo! Finance’s resources, Quandl.com has an own system of coding the names of all US stocks. Therefore, we need to know it before doing any further step. The .csv file containing all of the codes for Quandl’s stock data can be obtained via API for Stock Data webpage. The file contains the header of the form:

Ticker      Stock Name      Price Code      Ratios Code      In Market?

linking Google Finance ticker with Quandl’s code (Price Code). Doing some extra job with data filtering, I created the pre-processed set of all tickers excluding those non-active in the markets and removing all invalid data. You can download it here QuandlStockCodeListUS.xlsx as an Excel workbook.

The second list we need to have is a list of potential stock we want to deal with in our portfolio. As for today, the current list of Dow Jones Index’s components you can download here: dowjones.lst. Let’s start with it:

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% Create a Portfolio from Dow Jones Stocks based on Google Finance
%  Data fed by Quandl.com
%
% (c) 2013 QuantAtRisk.com, by Pawel Lachowicz
 
 
close all; clear all; clc;
 
% Read the list of Dow Jones components
fileID = fopen('dowjones.lst');
tmp = textscan(fileID, '%s');
fclose(fileID);
dowjc=tmp{1};  % a list as a cell array

The guys from Mathworks suggest to make a new and good practice of using textscan command (lines 10-12) instead of dataread for reading data from the text files. The latter will be removed from the next Matlab release.

Next, we need to import the current list of tickers for US stocks and their corresponding codes in the Quandl’s database. We will make the use of my pre-processed data set of QuandlStockCodeListUS.xlsx as mentioned earlier in the following way:

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% Read in the list of tickers and internal code from Quandl.com
[ndata, text, alldata] = xlsread('QuandlStockCodeListUS.xlsx');
quandlc=text(:,1); % again, as a list in a cell array

For the simplicity of our example, we will be considering all 30 stocks as a complete sample set of stocks in our portfolio. A portfolio construction is not solely a selection of stocks. It usually links a time period of the past performance of each individual stock. In the following move, we will collect daily stock returns over last 365 calendar year (corresponding to about 252 trading days).

This framework is pretty handful. Why? If you build or run live a model, most probably your wish is to update your database when US markets are closed. You fetch Quandl.com for data. Now, if your model employs risk management or portfolio optimisation as an integrated building-block, it’s very likely, you are interested in risk(-return) estimation, e.g. by calculating Value-at-Risk (VaR) or similar risk measures. Of course, the choice of the time-frame is up to you based on your strategy.

Having that said,

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% fetch stock data for last 365 days
date2=datestr(today,'yyyy-mm-dd')     % from
date1=datestr(today-365,'yyyy-mm-dd') % to

what employs an improved Matlab’s command of datestr with formatting as a parameter. It’s really useful when it comes to data fetching from Quandl.com as the format of the date they like to see in your code is yyyy-mm-dd. The above two commands return in Matlab the following values:

date2 =
  2013-11-03
date1 =
  2012-11-03

as for the time of writing this article (string type).

Get an Access to Quandl’s Resources


Excellent! Let’s catch some fishes in the pond! Before doing it we need to make sure that Quandl’s software is installed and linked to our Matlab console. In order to download Quandl Matlab package just visit this website and follow the installation procedure at the top of the page. Add proper path in Matlab environment too. The best practice is to register yourself on Quandl. That allows you to rise the daily number of data call from 50 to 500. If you think you will need more data requests per day, from the same webpage you can send your request.

When registered, in your Profile section on Quandi, you will gain an access to your individual Authorization Token, for example:
qtokenYou will need to replace ‘yourauthenticationtoken’ in the Matlab code (see line 35) with your token in order to get the data.

Fetching Stock Data

From this point the road stops to wind. For all 30 stocks belonging to the current composition of Dow Jones Index, we get their Open, High, Low, Close prices (as retrieved from Google Finance) in the following way:

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% create an empty array for storing stock data
stockd={};
% scan the Dow Jones tickers and fetch the data from Quandl.com
for i=1:length(dowjc)
    for j=1:length(quandlc)
        if(strcmp(dowjc{i},quandlc{j}))
            fprintf('%4.0f %s\n',i,quandlc{j});
            % fetch the data of the stock from Quandl
            % using recommanded Quandl's command and
            % saving them directly into Matlab's FTS object (fts)
            fts=0;
            [fts,headers]=Quandl.get(quandlcode{j},'type','fints', ...
                          'authcode','yourauthenticationtoken',...
                          'start_date',date1,'end_date',date2);
            stockd{i}=fts; % entire FTS object in an array's cell
        end
    end
end
% use 'save' command to save all actual variables from memory on the disc,
%  if you need it for multiple code running
% save data

A code in line 35 to 37 is an example how you call Quandi for data and fetch them into FINTS (financial time series object from the Financial Toolbox in Matlab). So easy. If you wish to change the time frequency, apply transformation or get the data not in FINTS but .csv format, all is available for you as a parameter at the level of calling Quandl.get() command (explore all possibilities here).

Having that, you can easily plot the Google Finance data, for instance, of IBM stock straight away:

% diplay High-Low Chart for IBM
highlow(stockd{11})
title('IBM');
ylabel('Stock Price ($)');

to get:
quandl-fig01

or prepare a return-risk plot for your further portfolio optimisation

ret=[];
for i=1:length(stockd)
    % extract the Close Price
    tmp=fts2mat(stockd{i}.Close,0);
    % calculate a vector of daily returns
    r=tmp(2:end)./tmp(1:end-1)-1;
    ret=[ret r];
end
% calculate the annualized mean returns for 30 stocks
mret=mean(ret)*sqrt(250);
% and corresponding standard deviations
msig=std(ret)*sqrt(250);
 
% plot return-risk diagram
p=100*[msig; mret]'
plot(p(:,1),p(:,2),'b.');
xlim([0 30]);
xlabel('Annualized Risk of Stock (%)')
ylabel('Annualized Mean Return (%)');

providing us with
quandl-fig02

Forum: Further Discussions

You can discuss the current post with other quants within a new Quant At Risk Forum (Data Handling and Pre-Processing section).

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