BTC YTD Cumulative Return based on MA strategy – It says it is time to Sell!

November 29, 2017
cryptoadmin
Opinion
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Algorithmic Trading with MATLAB®: Simple Lead/Lag EMA

This demo is an introduction to using MATLAB to develop and test a simple trading strategy using an exponential moving average.

Load in some data (Excel)

Load YTD BTC prices from coinmarketcap.com into workspace

data = csvread(‘data/BTCYTD2.csv’,1,1);
testPts = floor(0.8*length(data(:,4)));
BTCClose = data(1:testPts,4);
BTCCloseV = data(testPts+1:end,4);

Develop a simple lead/lag technical indicator

We’ll use two exponentially weighted moving averages

[lead,lag]=movavg(BTCClose,10,20,‘e’);

plot([BTCClose,lead,lag]), grid on

legend(‘Close’,‘Lead’,‘Lag’,‘Location’,‘Best’)

Develop a trading signal and performance measures. We’ll assume 250 trading days per year.

s = zeros(size(BTCClose));
s(lead>lag) = 1; % Buy (long)
s(lead<lag) = -1; % Sell (short)
r = [0; s(1:end-1).*diff(BTCClose)]; % Return
sh = sqrt(250)*sharpe(r,0); % Annual Sharpe Ratio

Plot results

ax(1) = subplot(2,1,1);

plot([BTCClose,lead,lag]); grid on

legend(‘Close’,‘Lead’,‘Lag’,‘Location’,‘Best’)

title([‘First Pass Results, Annual Sharpe Ratio = ‘,num2str(sh,3)])

ax(2) = subplot(2,1,2);

plot([s,cumsum(r)]); grid on

legend(‘Position’,‘Cumulative Return’,‘Location’,‘Best’)

linkaxes(ax,‘x’)

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