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Highlights from
Algorithmic Trading with Bloomberg EMSX and MATLAB

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Algorithmic Trading with Bloomberg EMSX and MATLAB

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Files used in the webinar which can be viewed at http://www.optinum.co.za/webinars/contact_main.php

Algorithmic Trading with MATLAB: Intraday trading

Algorithmic Trading with MATLAB: Intraday trading

This demo develops and tests a simple exponential moving average trading strategy. It encorporates obtaining data from the Bloomberg BLP datafeed and executing trades in EMSX, based on the strategy. % %% Pre-trading tasks % Add blpapi3.jar to java path javaaddpath('C:\blp\API\blpapi3.jar')

Contents

Fetch equity data from Bloomberg BLP datafeed

This time get intraday data instead of daily data

equity = 'SBK'; % Ticker symbol for equity around which we will develop the strategy. This can be changed.

startTime = today - 3 ; % 3 days ago (Friday)
endTime = floor(now);
annualScaling = sqrt(250*7*60);

c = blp; % Connect to Bloomberg V3 Communications Server

Ticker = strcat(equity,' Equity');

% rather than daily data, get raw tick data:
intradayData = timeseries(c,Ticker,{startTime,endTime}); % gives back trade, time stamp, price, quantity

intradayDates = cell2mat(intradayData(:,2));
intradayPrices = cell2mat(intradayData(:,3));

Open a parallel computing environment

We will be performing many more backtests on a larger data set than before, so we would like to take advantage of as many processors as we can in order to speed up the computation. MATLAB's Parallel Computing Toolbox makes this straightforward. First, we open a pool of parallel workers:

% Use all the cores on my laptop
if matlabpool('size') == 0
    matlabpool local
end

% Then we use MATLAB's |parfor| construct to parallelize our |for|-loops:

Perform the parameter sweep

We will sweep not just across many combinations of leading and lagging averages, but we will furthermore sweep across many different granularities of data in an effort to find the 'best' frequency to use. The variable 'ts' below is the sampling time and varies from 1 tick up to 2 ticks (i.e.: about 1 hour). We could sweep through in minutes, but for convenience we will use ticks.

seq = generateSpacedInts(1, 200, 25);

range = {seq,seq};
llfun =@(x) leadlagFun(x,intradayPrices,annualScaling);

tic
[~,param,sh,xyz] = parameterSweep(llfun,range);
toc


leadlag(intradayPrices,param(1),param(2),...
            annualScaling)
xlabel('Frequency')
Elapsed time is 0.153043 seconds.

The ideal values are a leading indicator of 34 and a lagging indicator of 43 ticks

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