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Bitcoin is firmly becoming a mainstream asset in our global society. Itshighly volatile nature has traders and speculators flooding into the market totake advantage of its significant price swings in the hope of making money.This work brings an algorithmic trading approach to the Bitcoin market toexploit the variability in its price on a day-to-day basis through theclassification of its direction. Building on previous work, in this paper, weutilise both features internal to the Bitcoin network and external features toinform the prediction of various machine learning models. As an empirical testof our models, we evaluate them using a real-world trading strategy oncompletely unseen data collected throughout the first quarter of 2021. Usingonly a binary predictor, at the end of our three-month trading period, ourmodels showed an average profit of 86\%, matching the results of the moretraditional buy-and-hold strategy. However, after incorporating a risktolerance score into our trading strategy by utilising the model's predictionconfidence scores, our models were 12.5\% more profitable than the simplebuy-and-hold strategy. These results indicate the credible potential thatmachine learning models have in extracting profit from the Bitcoin market andact as a front-runner for further research into real-world Bitcoin trading.

Exploration of Algorithmic Trading Strategies for the Bitcoin Market