Algorithmic Trading of Bitcoin Utilizing Momentum Effects
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In this thesis we seek to utilize time series momentum to design a profitable trading strategy in the highly trending and volatile Bitcoin market. this study is interesting to both financial academics and traders alike as it explores modern financial concepts in the context of applied finance. Our contribution is to develop an algorithmic trading strategy based on signs of time series momentum effects present in the Bitcoin asset, by using data from the GDAX exchange data 2015-2018. Our long strategy is able to outperform the Bitcoin market by a factor of 14. Furthermore, with the inclusion of short trading we outperform the Bitcoin market by a factor of 87, which equals a compounded return of 229 000 \% over three years. We show that the results of our strategy are significant to a 5 \% level and not obtained by taking excessive risk. Lastly, we conclude that the Bitcoin asset may profitably be traded on, and that this breaks with the results of earlier studies of market efficiency in the Bitcoin asset.