# Applying Artificial Intelligence in Cryptocurrency Markets: A Survey
## 3 Artificial Intelligence and Cryptocurrencies
### Application of Machine Learning in Cryptocurrency  
Computer scientists, mathematicians, statisticians, and data scientists have been developing and refining various ML algorithms to extract high-quality knowledge from data to develop trustable and accurate price and movement predictors to perform profitable trading in cryptocurrencies. In this section, we focus on the literature on supervised and unsupervised learning applications in cryptocurrencies. RL in cryptocurrencies will be discussed later in Section 3.2.2. Portfolio performance in financial markets is affected by various factors, including the quality of input data, the data granularity, market maturity, and forecasting models [50]. Thus, in the following, the papers reviewed in supervised and unsupervised learning models are catalogued and summarised based on these metrics in addition to features including baseline methods, trade frequencies, coin types, and data sources (eight attributes in total) in Tables 4 and 5. In addition, Table 4 has been created for the purpose of quick access to the references of the papers in Table 5.  
Figure 4: A generic categorisation of AI concepts and their subcategories. Hybrid ML models are widely used in the cryptocurrency domain. Hybrid ML models are constructed by combining ML methods that make use of the relative advantages of each model on its own to enhance the overall model's performance [61]. [PERSON] et al. [28] propose a hybrid model of long short-term memory (LSTM) and gated recurrent unit (GRU) networks to predict the price of two less common altcoins, namely, Litecoin and Monero. Although the proposed model effectively predicts daily prices, it is not effective for longer periods, for example, a seven-day prediction. They also demonstrate that LSTM-based methods are more suitable for short-term prediction. Furthermore, ref. [54] uses a combination of recurrent neural networks (RNN) and LSTM methods to forecast the price of Bitcoin, and a random forest (RF) for feature engineering. Since different exchanges provide different prices of cryptocurrencies based on their supply and demand factors, the study considers the average closing price of five major exchanges instead of one specific exchange. Thus, the data are less noisy, and they lead to a more generalised model. As a result, the LSTM performance is considerably better for long periods, such as 100 days, while it is not the case for RNN. In another study incorporating LSTM, [PERSON] et al. [56] build an investment portfolio system to predict the return on investment by using three supervised learning methods, two gradient-based boosting decision trees, and one LSTM. First, two regression models relying on XGBoost are applied to all cryptocurrencies, and then different models separately are checked on each currency. LSTM performs best; however, gradient-boosting decision trees allow better interpretation. Interestingly, based on their findings, the models have a better performance when the coins' prices are fed based on Bitcoin price rather than their worth converted into the US dollar value. Although cryptocurrencies are theoretically recognised as tools for economic decentralisation, they rely on fiat currencies such as the US dollar to be traded. Thus, whether traditional financial assets affect the price of cryptocurrencies is an open question. [PERSON] and [PERSON] [36] implement Bayesian networks (BNs) to predict the price of Bitcoin. As an advantage of the proposed model, blockchain information and microeconomic factors are incorporated to forecast the price. Blockchain-related variables, ten global macroeconomic indices, and five international exchange rates are the inputs of the BN model. Log price and volatility of Bitcoin are compared to linear regression (LR) and support vector regression (SVR) as baseline models. The Bayesian network model demonstrates better predictive performance.