Automate Stock and Crypto Research Using Python and Machine Learning

Abstract

This paper focuses on automating the analysis of financial news for stocks and cryptocurrencies, thereby providing traders and analysts with actionable insights. In today's fast-paced markets, being up-to-date is the need of the hour; however, manual analysis takes time. To overcome this challenge, the paper employs Python and deep learning tools to make the process of collecting, summarizing, and conducting sentiment analysis on relevant news articles much easier. The key assets analyzed are popular stocks and cryptocurrencies, such as Tesla, Gamestop, Bitcoin, and Ethereum. The system uses Hugging Face's Pegasus model, which is a state-of-the-art transformer, to summarize lengthy financial news into concise, manageable summaries. This reduces the effort required to sift through large amounts of information while preserving essential details. Moreover, the system applies pre-trained sentiment analysis models to gauge the market's overall sentiment-positive, negative, or neutral-toward specific assets, thus making quick and informed trading decisions possible. The paper workflow includes automatically scraping web sources such as Google News and Yahoo Finance, cleaning and processing the data, and exporting results in structured CSV files for further analysis. The files include the ticker symbols, sentiment scores, confidence levels, URLs, and summary text. It is scalable and flexible so that users can input their stock tickers and run real-time analysis with changes in market conditions. Overall, this paper provides an efficient, end-to-end solution for financial news analysis, allowing users to make informed decisions with reduced time and effort on data gathering and interpretation.