This project employs machine learning methods to forecast stock prices by merging various data inputs, including historical price records, technical indicators, sentiment analysis derived from news and social media, and macroeconomic metrics. The goal is to develop a reliable predictive model that utilizes extensive data features to achieve accurate stock price predictions.
Utilizes TA-Lib and pandas-ta libraries to compute essential technical indicators such as SMA, EMA, RSI, and MACD.
Integrates sentiment scores derived from news articles (via NewsAPI), Twitter posts (via Twitter API), and Reddit discussions (via Reddit API) to gauge market sentiment.
Includes macroeconomic indicators such as GDP from FRED (Federal Reserve Economic Data) and World Bank Open Data to capture broader economic trends.
Implements machine learning models like Random Forests to train and predict stock prices based on the combined dataset of technical, sentiment, and macroeconomic features.
Python, pandas, numpy, scikit-learn, TA-Lib, pandas-ta, newsapi-python, tweepy, praw, fredapi, wbdata.
Contributions are welcome! If you'd like to contribute to this project, please fork the repository and submit a pull request with your improvements.
Inspired by the need to integrate diverse data sources for accurate stock price prediction. Special thanks to the creators and maintainers of TA-Lib, pandas-ta, NewsAPI, Twitter API, Reddit API, FRED, and World Bank Open Data for their valuable data and APIs.