This project aims to explore and develop alpha strategies in quantitative finance by leveraging a combination of financial time series data and natural language processing (NLP) techniques applied to news articles and social media data.

(1) Collect and preprocess historical financial data, including price, trading volume, and other relevant metrics, along with news articles and social media data related to financial markets.

(2) Engineer features from the financial data and apply NLP techniques to extract sentiment scores, event detection, and named entity recognition (NER). (3) Develop predictive models that correlate events and sentiment with asset price. (4) Test the alpha signals on historical financial data to assess their performance.

(5) Validate models using out-of-sample data to assess real-world performance. (6) Produce a comprehensive report outlining findings, methodology, and results