Exploration of Algorithmic Trading Strategies for the Bitcoin Market

TL;DR

This paper develops and tests machine learning-based algorithmic trading strategies for Bitcoin, demonstrating that models incorporating prediction confidence can outperform traditional buy-and-hold approaches in real-world trading scenarios.

Contribution

It introduces a novel approach combining internal and external features for Bitcoin price direction prediction and demonstrates improved profitability using confidence-based trading strategies.

Findings

Models achieved 86% profit matching buy-and-hold.

Incorporating risk tolerance increased profitability by 12.5%.

Real-world testing on unseen data validated the approach.

Abstract

Bitcoin is firmly becoming a mainstream asset in our global society. Its highly volatile nature has traders and speculators flooding into the market to take advantage of its significant price swings in the hope of making money. This work brings an algorithmic trading approach to the Bitcoin market to exploit the variability in its price on a day-to-day basis through the classification of its direction. Building on previous work, in this paper, we utilise both features internal to the Bitcoin network and external features to inform the prediction of various machine learning models. As an empirical test of our models, we evaluate them using a real-world trading strategy on completely unseen data collected throughout the first quarter of 2021. Using only a binary predictor, at the end of our three-month trading period, our models showed an average profit of 86\%, matching the results of the more traditional buy-and-hold strategy. However, after incorporating a risk tolerance score into our trading strategy by utilising the model's prediction confidence scores, our models were 12.5\% more profitable than the simple buy-and-hold strategy. These results indicate the credible potential that machine learning models have in extracting profit from the Bitcoin market and act as a front-runner for further research into real-world Bitcoin trading.