# Simulated Random Walk in MATLAB

Put in simple terms, a Random Walk basically involves taking successive random steps and tracing the trajectory. I adapted the example from part one of Wilmott on Quantitative Finance and wrote a quick MATLAB script to simulate a simple random walk.

Basically flip a coin if it is heads multiply the equity seed by 1.01 and if its tails multiply by 0.99.

## MATLAB Simulation Results

The graphs below show successive runs of the script. I simulated 10,000 coin tosses in each test.

Sim One: You flipped 5069 heads & 4931 tails Sim Two: You flipped 4946 heads & 5054 tails ## MATLAB Code

Writing the code in MATLAB is very straightforward I wrote the code in less then five minutes so it is a bit rough around the edges. I probably could refactor it but it does the job.

```% Simple Simulated Random Walk Adapted
% from part 1 Paul Wilmott on Quant Fin
% create matrix with random int either 1 or 2
randMat = randi(2,10000,1);

randEquity = 1:10000;
headCount = 0;
tailCount = 0;
equitySeed = 100.00;

for i=1:10000
if randMat(i)==1
% we flipped a heads
headCount = headCount + 1;
equitySeed = 1.01 * equitySeed;
randEquity(i) = equitySeed;
else
% must have flipped tails
tailCount = tailCount + 1;
equitySeed = 0.99 * equitySeed;
randEquity(i) = equitySeed;
end
end
fprintf('You flipped %i heads & %i tails\n', headCount, tailCount);

plot(randEquity);
title('Simulated Random Walk');
xlabel('Number of Coin Tosses');
ylabel('Price in (\$)');

% clean-up
clear equitySeed headCount tailCount randMat i;```

MATLAB makes it very easy to quickly conduct experiments and run simulations. It would be far to time consuming to manually flip a coin 10,000 times. This quick example highlights how a simple random walk can look very similar to stock or security charts. It also shows how powerful MATLAB is as a learning and prototyping tool

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