I proposed an adaptive learning model to trade a single stock under the reinforcement learning framework. This area of machine learning consists in training an agent by reward and punishment without needing to specify the expected action. The agent learns from its experience and develops a strategy that maximizes its profits. Lastly, I presented the best solution achieved, discussed its strengths and weaknesses, and scope for future work.

I mostly used the Python libraries Pandas and Sklearn to wrangle and built the model and Seaborn to visualize the data. This project is connected to the Machine Learning Engineer Nanodegree program, from Udacity.