Optimisation of Cryptocurrency Trading Using the Fractal Market Hypothesis with Symbolic Regression

Jonathan Blackledge () and Anton Blackledge
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Jonathan Blackledge: Science Foundation Ireland, Three Park Place, Hatch Street Upper, Saint Kevin’s, D02 FX65 Dublin, Ireland
Anton Blackledge: Department of Electronic and Electrical Engineering, Faculty of Engineering and Design, University of Bath, Claverton Down, Bath BA2 7AY, UK

Commodities, 2025, vol. 4, issue 4, 1-58

Abstract: Cryptocurrencies such as Bitcoin can be classified as commodities under the Commodity Exchange Act (CEA), giving the Commodity Futures Trading Commission (CFTC) jurisdiction over those cryptocurrencies deemed commodities, particularly in the context of futures trading. This paper presents a method for predicting both long- and short-term trends in selected cryptocurrencies based on the Fractal Market Hypothesis (FMH). The FMH applies the self-affine properties of fractal stochastic fields to model financial time series. After introducing the underlying theory and mathematical framework, a fundamental analysis of Bitcoin and Ethereum exchange rates against the U.S. dollar is conducted. The analysis focuses on changes in the polarity of the ‘Beta-to-Volatility’ and ‘Lyapunov-to-Volatility’ ratios as indicators of impending shifts in Bitcoin/Ethereum price trends. These signals are used to recommend long, short, or hold trading positions, with corresponding algorithms (implemented in Matlab R2023b) developed and back-tested. An optimisation of these algorithms identifies ideal parameter ranges that maximise both accuracy and profitability, thereby ensuring high confidence in the predictions. The resulting trading strategy provides actionable guidance for cryptocurrency investment and quantifies the likelihood of bull or bear market dominance. Under stable market conditions, machine learning (using the ‘TuringBot’ platform) is shown to produce reliable short-horizon estimates of future price movements and fluctuations. This reduces trading delays caused by data filtering and increases returns by identifying optimal positions within rapid ‘micro-trends’ that would otherwise remain undetected—yielding gains of up to approximately 10%. Empirical results confirm that Bitcoin and Ethereum exchanges behave as self-affine (fractal) stochastic fields with Lévy distributions, exhibiting a Hurst exponent of roughly 0.32, a fractal dimension of about 1.68, and a Lévy index near 1.22. These findings demonstrate that the Fractal Market Hypothesis and its associated indices provide a robust market model capable of generating investment returns that consistently outperform standard Buy-and-Hold strategies.

Keywords: cryptocurrencies; fractal market hypothesis; symbolic regression; optimisation; return on investments (search for similar items in EconPapers)
JEL-codes: C0 C1 C2 C3 C4 C5 C6 C7 C8 C9 D4 E3 E6 F0 F1 F3 F4 F5 F6 G1 O1 O5 Q1 Q2 Q4 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jcommo:v:4:y:2025:i:4:p:22-:d:1764730

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