2024 Automated Stock Trading with AI 

I explored the intersection of deep learning and stock price prediction, focusing on Tesla (TSLA) to challenge AI and volatile stock prices. The goal was to harness cutting-edge technology to forecast market trends more accurately.

I began by preparing and cleaning stock price data to address any biases. I then implemented advanced techniques, including Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN), using Python libraries like TensorFlow and Scikit-learn. I incorporated sentiment analysis from news articles, using tools like TextBlob, to see how market sentiment influences stock movements predictions.

The project was powered by technologies such as Python 3, TensorFlow, Keras, and Jupyter Notebooks, with data sourced from the Polygon API. One of the key takeaways was understanding how deep learning can offer actionable insights into financial markets. Despite challenges, like TensorFlow compatibility issues and the need for data normalization, the work demonstrated potential for predictive accuracy.

This project illustrates the potential of AI in financial forecasting, showing how data-driven insights can help making  investment decisions.