This master thesis focuses on developing an AI model designed to generate profitable investment strategies that outperform market benchmarks. Specifically, the model constructs a portfolio of 500 US equities, determining optimal positions and allocations for each stock. The goal is to maximize returns while minimizing risk and transaction costs. The decision to focus on portfolio optimization, rather than individual assets, stems from the ability to reduce risk through diversification. This is further enhanced by introducing an additional input parameter, λ, which accounts for the investor's risk aversion. Building on the work of Zhang, Zohren, and Roberts (2020), the model leverages multiple data sources to create meaningful features.