matus-jan-lavko/ReinforcementLearning-vs-EW
RL vs. 1/n and Mean-Variance in the Portfolio Allocation Problem. A Bachelor's thesis at Utrecht University.
This project helps financial professionals assess different portfolio allocation strategies. By inputting historical price data for U.S. and EU listed equities, it evaluates whether advanced Reinforcement Learning algorithms can outperform simpler methods like 1/n allocation or mean-variance optimization. The output shows the evolving weights suggested by various algorithms, allowing portfolio managers and quantitative analysts to compare performance.
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Use this if you are a quantitative analyst or portfolio manager interested in exploring how Reinforcement Learning can enhance your portfolio allocation strategies compared to traditional methods.
Not ideal if you are looking for an out-of-the-box, production-ready portfolio management system, as this is a research project comparing algorithmic performance.
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May 20, 2021
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