Intraday trading of cryptocurrencies using polynomial auto regression

Abstract

This research attempts to fit a polynomial auto regression (PAR) model to intraday price data of four major cryptocurrencies and convert the model into a real-time profitable automated trading system. A PAR model was constructed to fit cryptocurrencies' behavior and to attempt to predict their short-term trends and trade them profitably. We used machine learning (ML) procedures enabling our system to train using minutes' data for six months and perform actual trading and reporting for the next six months. Results have shown that our system has dramatically outperformed the naive buy and hold (B & H) strategy for all four examined cryptocurrencies. Results show that our system's best performances were achieved trading Ethereum and Bitcoin and worse trading Cardano. The highest net profit (NP) for Bitcoin trades was 15.58%, achieved by using 67 minutes bars to form the prediction model, compared to −44.8% for the B & H strategy. Trading Ethereum, the system generated 16.98% NP, compared to −33.6% for the B & H strategy, 61 minutes bars. Moreover, the highest NPs achieved trading Binance Coin (BNB) and Cardano were 9.33% and 4.26%, compared to 0.28% and −41.8% for the B & H strategy, respectively. Furthermore, the system better predicted Ethereum and Cardano uptrends than downtrends while it better predicted Bitcoin and BNB downtrends than uptrends.

Keywords

• cryptocurrencies
• intraday trading
• algorithmic
• artificial intelligence