Stock-Crypto-App – Recommendation System for Stock and Cryptocurrency Market Using Cutting Edge Machine Learning Technology

Abstract

In the dynamic world of financial markets, the prediction of stock performance and bitcoin trading is undergoing a significant transformation due to the integration of advanced technologies and novel methodologies. The incorporation of Transformer models alongside Time Embeddings significantly improves the precision of stock market predictions by effectively capturing intricate temporal relationships and mitigating the presence of overly simplistic assumptions. The integration of real-time social media data with sentiment analysis based on BERT provides significant value in understanding investor sentiment. Additionally, the application of language model pre-training, as exemplified by BERT, brings about a transformative impact on text classification for predicting stock prices. Within the domain of cryptocurrency, sophisticated algorithms such as Transformers, Long Short-Term Memory (LSTM), Deep Convolutional LSTM (DC-LSTM), and Neural Networks (NN) have demonstrated enhanced capabilities in predicting price movements. These algorithms are further bolstered by the implementation of a comprehensive trading strategy. Automated systems for bitcoin trading introduce elements of personalization and adaptability to the trading process, thereby facilitating broader access to a diverse group of traders. The progress highlights the significant importance of the integration of technology and methodologies in the field of financial analysis. This integration enables investors and traders to possess the necessary resources for making well-informed choices within the ever-changing landscape of financial markets.