Crypto Price Guessing: How Smart Computers Beat the Market

Cryptocurrencies have become a hot topic in finance, offering plenty of room for research. Predicting their prices accurately is a big deal for both researchers and investors. However, the unpredictable nature of cryptocurrency markets makes this a tough job. Many studies have tried to use deep learning to forecast crypto prices. Three major cryptocurrencies, Bitcoin, Ethereum, and Litecoin, were the focus of this research. Two types of neural networks, LSTM and GRU, were used to make these predictions. To measure how well these networks performed, four different tests were used: mean squared error, mean absolute error, mean absolute percentage error, and root mean squared error. The results showed that the GRU model did better than LSTM. The GRU model was set up with two layers and used a method called Adam optimization to improve its accuracy. A technique called dropout was also used to prevent the model from becoming too complex and overfitting the data. The model was trained using historical price data that was normalized and split into training and testing sets. The GRU model showed the best results for predicting crypto prices. The mean absolute percentage error values for Bitcoin, Litecoin, and Ethereum were 0. 03540, 0. 08703, and 0. 04415, respectively. These results suggest that the GRU model provides the most reliable forecasts compared to LSTM. Such prediction models can be very useful for traders and investors, helping them make more informed decisions. However, there is still room for improvement. Future research should consider other factors that might affect crypto prices, such as social media trends and trading volumes. By including more variables, these models could become even more accurate and helpful.