# Leveraging Explainable AI to Support Cryptocurrency Investors
## 6 Experimental Results
### Empirical Outcome O1: Feature Importance across Cryptocurrencies  
The pie charts in Figures 2-5 show the feature importance scores (returned by the XGBoost ML model) computed over all cryptocurrencies (see Figure 2) and separately for BTCUSD, BCHUSD, and ETHUSD (see Figures 3-5). BTCUSD is, by far, the most famous cryptocurrency. BCHUSD is a fork of BTCUSD, whereas ETHUSD is another extremely popular cryptocurrency.  
Figure 2: Hierarchical mean feature importance over all the analyzed cryptocurrencies.  
Figure 4: Hierarchical mean feature importance for BCHUSD.  
Figure 3: Hierarchical mean feature importance for BTCUSD.  
The outermost circular crown of the pie chart in Figure 2 reports the average importance scores per feature by considering all cryptocurrencies. Specific price-related features, such as _close_resid_ (i.e., the Seasonal-Trend decomposition using LOESS of the closing price series [30], have shown to be the most relevant to predict future cryptocurrency prices). However, the selected features are not the same for all cryptocurrencies and also include blockchain-related ones. For example, _hashrate_pct_, which indicates the amount of computational operations that a miner or the network of miners is capable of carrying out, is particularly relevant to BitCoin cash(BCH), which has been created to specifically address efficiency issues of the most established BTC cryptocurrency. Conversely, it is not relevant to Ethereum (ETH) because ETH is known to be weakly correlated to BTC.  
To have a higher-level view of which features are more discriminating for a given cryptocurrency, we also aggregate the feature importance scores per subcategory and category (see the two inner crowns in Figure 2 and the bar charts). The most relevant features are those belonging to category _Blockchain_ (average score 0.48), which is followed by _Market data_ (0.46) and _Technical analysis_ features (0.16). This means that to drive their investments, cryptocurrency traders should closely monitor blockchain-related features first rather than simply analyzing price-related features (e.g., moving averages, momentum [27]).  
Focusing on the most influential subcategories, they encompass the properties of the supply chain, namely _Supply_ (BC category), the historical cryptocurrency prices, i.e., _History_ (MD category), and the blockchain network activity metrics, namely _Addresses_ (BC category). It is worth noticing that restricting the in-depth analysis to these feature subsets allows experts to ignore almost 70% of the original features.  
The variability in feature importance across different cryptocurrencies is also quite significant (see Figures 3-5). For example, for ETHUSD, the blockchain-related features turn out to be slightly less significant than for BTCUSD and BCHUSD, which is possibly due to the primary influence of the blockchain architecture on the price movements of the BiTCoin-related assets. Ethereum (ETH) is partly uncorrelated with BC and weakly dependent on blockchain-related properties such as hash rate and transaction counts.  
Figure 5: Hierarchical mean feature importance for ETHUSD.