# Leveraging Explainable AI to Support Cryptocurrency Investors
## 1 Introduction  
Cryptocurrencies are digital assets whose transfers and accounting are cryptographically established through the blockchain [1]. Even though they are not backed by any physical asset, they have become popular financial assets for online trading. After bitcoin, which is the first and most famous cryptocurrency [2], many different cryptocurrencies have been created, thus increasing the options to invest in cryptoassess significantly. Following the recent trends on algorithmic trading, many research efforts have been devoted to designing cryptocurrency trading systems based on Machine Learning (ML) and Artificial Intelligence (AI). Existing methods rely on algorithms that span from classical classification and regression methods (e.g., [3; 4; 5; 6; 7]) to Deep Learning architectures (e.g., [8; 9; 10; 11]). The aim is to learn predictive models from historical data related to cryptocurrency assets (e.g., markets, blockchain-related data, news) and apply them to forecast the future price directions. A recent survey on cryptocurrency trading can be found in [12]. Although Machine Learning-based solutions have shown to achieve better performance than simpler heuristic methods [12], they suffer from the lack of transparency. In fact, most state-of-the-art classification models, including all the Neural Network-based approaches, are not explainable; i.e., domain experts cannot gain insights into the model decisions. Cryptocurrency markets can be influenced by a large variety of factors, including the underlying market trends, the characteristics of the blockchain, and the sentiment of financial investors on the virtual assets. This prompts the need for new approaches aimed at explaining ML reasoning in cryptocurrency trading. This work focuses on leveraging an established eXplainable AI (XAI) method, namely SHapley Additive exPlanations (SHAP) [13], to provide domain experts with an effective visualization of the ML reasoning behind cryptocurrency trading. SHAP quantifies the contribution of different features on classifier predictions, thus highlighting the contributionof different factors to the decisions of the ML-based system. We aim at addressing the following research questions:  
* **Q1**: What are the most discriminative features for cryptocurrency price prediction? * **Q2**: How can cryptocurrency investors be provided with quantitative estimates of the influence of specific features and feature categories on machine learning-based cryptocurrency predictions? * **Q3**: How can we evaluate the statistical dependency of the Machine Learning (ML) feature ranks returned by SHAP in different time periods and on different cryptocurrencies? To address Q1, this paper explores a large variety of features computed on the daily price series of 21 different cryptocurrencies. The analyzed features are established for cryptocurrency trading [12] and encompass the price- and volume-related series, the technical indicators summarizing the momentum, volatility, and moving averages of the original price series, and the blockchain-related features. The latter are particularly relevant to the scope of the present study because they are peculiar to cryptoassets. To address Q2, we use eXplainable AI methods based on the Shapley value [14] to provide cryptocurrency traders with evidence of the main factors influencing algorithmic trading. We present a new eXplainable AI tool for visualizing and monitoring the activities of Machine Learning-based systems, with particular attention paid to the blockchain-based features influencing the decision process. To tackle Q3, we apply the Rank Biased Overlap similarity measure [15] to quantify the pairwise agreement between the top-10 features shortlisted by SHAP. We also performed the experiment using feature subcategories and categories rather than individual features. The experiments carried out on a 8-year period produce the following main outcomes:  
* **O1**: The high variability of the feature importance across different cryptocurrencies.