Stochastic Neural Networks-Based Algorithmic Trading for the Cryptocurrency Market

Abstract

Throughout the history of modern finance, very few financial instruments have been as strikingly volatile as cryptocurrencies. The long-term prospects of cryptocurrencies remain uncertain; however, taking advantage of recent advances in neural networks and volatility, we show that the trading algorithms reinforced by short-term price predictions are bankable. Traditional trading algorithms and indicators are often based on mean reversal strategies that do not advantage price predictions. Furthermore, deterministic models cannot capture market volatility even after incorporating price predictions. Thus motivated by these issues, we integrate randomness in the price prediction models to simulate stochastic behavior. This paper proposes hybrid trading strategies that take advantage of the traditional mean reversal strategies alongside robust price predictions from stochastic neural networks. We trained stochastic neural networks to predict prices based on market data and social sentiment. The backtesting was conducted on three cryptocurrencies: Bitcoin, Ethereum, and Litecoin, for over 600 days from August 2017 to December 2019. We show that the proposed trading algorithms are better when compared to the traditional buy and hold strategy in terms of both stability and returns.

Enhanced Genetic-Algorithm-Driven Triple Barrier Labeling Method and Machine Learning Approach for Pair Trading Strategy in Cryptocurrency Markets

“…Borges et al [9] proposed a machine-learning-based system that uses a novel financial series resampling method based on closing price thresholds and four machine learning algorithms (Logistic Regression, Random Forest, Support Vector Classifier, and Gradient Boosting) to create high-return, low-risk trading strategies in the cryptocurrency market. Kalariya et al [10] introduced a new trading strategy that combines mean reversion and stochastic neural networks for cryptocurrency price prediction. Longterm backtesting showed that it outperforms traditional buy-and-hold strategies in terms of stability and returns.…”

“…This approach focuses on optimizing profitability and reducing risk, with its effectiveness validated by empirical market data analysis. Conversely, the latter category leverages advanced AI techniques such as machine learning and deep learning to forecast trends in cryptocurrency prices or the price spread between paired assets, thereby providing a more refined basis for making trading decisions (as referenced in [8][9][10][11][12][13]). Our analysis reveals that relying solely on historical cryptocurrency price data often leads to inadequate accuracy in identifying trading opportunities, particularly given the high volatility of the cryptocurrency market.…”

Modelo de análisis multivariante aplicado a la predicción de la tendencia del tipo de cambio euro-dólar

“…Entre los indicadores Bill Williams se destacan: Oscilador Acelerador, "Accelerator Oscillator" [382], Alligator [383], Oscilador Asombroso (AO), "Bill Williams's Awesome Oscillator" [384], Fractales, "Fractals" [385], Oscilador Gator, "Gator Oscillator" [386], Índice de Facilidad del Mercado (MFI), "Market Facilitation Index" [387],…”