Martin Pellon Consunji
ABSTRACT
Due to the rise in popularity of Bitcoin as both a store of wealth and speculative investment, there is an ever-growing demand for automated trading tools to gain an advantage over the market. Although traditionally trading was done by professionals, nowadays a majority of market participants are market-data processing bots due to their inherent advantages in processing large amounts of data, lack of emotions of fear or greed, and predicting market prices through artificial intelligence. A large number of approaches have been brought forward to tackle this task, many of which rely on specially engineered deep learning methods with a focus on specific market conditions. The general limitation of these approaches, however, is the reliance on customized gradient-based methods which limit the scope of possible solutions and don't necessarily generalize well when solving similar problems. This paper proposes a method which uses neuroevolutionary techniques capable of automatically customizing offspring neural networks, generating entire populations of solutions and more thoroughly exploring and parallelizing potential solutions. Our approach uses evolutionary algorithms to evolve increasingly improved populations of neural networks which, based on sentimental and technical analysis data, efficiently predict future market price movements. The effectiveness of this approach is validated by testing the system on both live and historical trading scenarios, and its robustness is tested on other cryptocurrency and stock markets. Experimental results during a 30-day live-trading period show that this method outperformed the buy and hold strategy by over 260%, even while factoring in standard trading fees.
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