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MATLAB for R Users in Computational Finance

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MATLAB for R Users in Computational Finance

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23 Jul 2013 (Updated )

Learn how to use MATLAB and R together to tackle your computational needs

Backtest Moving Average RSI Combo Strategy

Backtest Moving Average RSI Combo Strategy

This script demonstrates testing a simple technical indicator strategy and backtesting to optimize its parameters

Contents

Data Import

Import data from database using auto-generated file. If the database is not installed, the data can instead be loaded from the MAT-file provided

try
    data = getBundData;
catch
    load bund1min
end

Sub-sample the series if necessary

step = 20;
Close = data.Close(1:step:end);

Test MA+RSI Strategy

N = 200;
M = 50;
[sh pnl pos] = marisa(Close, N, M, 0.01);

posPNLPlot(Close, pos, pnl);

fprintf('Sharpe''s Ratio: %0.2f\n\n', sh * sqrt(60*11/step));
Sharpe's Ratio: 1.56

Run Backtest

Select a range of values for the EMA and RSI parameters for the strategy Run a parameter sweep and compute Sharpe's ratios

N = 10:10:300;
M = 10:5:200;
cost = .01;

SH = zeros(length(N),length(M));
SHrow = zeros(1,length(M));
% loop over N,M
tic;
for i = 1:length(N)
    SHrow = zeros(1,length(M));
    for j = 1:length(M)
        SHrow(j) = marisa(Close, N(i), M(j), cost);
    end
    SH(i,:) = SHrow;
end
toc
SH = SH * sqrt(60*11/step);
Elapsed time is 4.804719 seconds.

Visualize Backtest Results

The Sharpe's ratios can be used to compare the performance of the strategy for different parameters. A 3D surface plot shows the relationship between the EMA, RSI parameters and the resultant Sharpe's ratio

clf
surfc(M,N,SH); shading interp; lighting phong; light
ylabel('EMA Parameter (N)');
xlabel('RSI Parameter (M)');
zlabel('Sharpe''s Ratio');

% Select Optimal Parameters
[I,J] = find(SH == max(max(SH)));
fprintf('\nOptimal Sharpe''s ratio of %0.2f was found for N = %d, M = %d\n', SH(I,J), N(I), M(J));

hold on;
plot3(M(J), N(I), SH(I,J), 'c*', 'MarkerSize', 8)
hold off;
Optimal Sharpe's ratio of 1.92 was found for N = 290, M = 55

Rerun strategy for optimal parameters

[sh, pnl, pos] = marisa(Close, N(I), M(J), cost);
sh = sqrt(60*11/step) * sh;

posPNLPlot(Close, pos, pnl);
title(['Cumulative PNL. Sharpe = ',num2str(sh),', N=',num2str(N(I)),', M=',num2str(M(J))])

Analyze Strategy Returns

The Sharpe's ratio may not be sufficient information on the strategy. Here we extract individual positions and report on their statistics

cpnl = cumsum(pnl);
[maxdd, period] = maxdrawdown(cpnl, 'arithmetic');
% Extract PNL for individual positions
ind = find(diff(pos)) + 1;
posPNL = diff([0;cpnl(ind)]);

% Display Histogram of Position Returns
histPNLPlot(cpnl, posPNL, period);

fprintf('\nTotal number of positions = %d\n', length(posPNL));
fprintf('Average position duration = %0.2f periods\n', mean(diff(ind)));
fprintf('Average profit per position = $%0.2f\n', mean(posPNL));
fprintf('Maximum Drawdown = $%0.2f\n\n', maxdd);
Total number of positions = 880
Average position duration = 24.14 periods
Average profit per position = $0.02
Maximum Drawdown = $2.50

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