  Asset allocation is an investment strategy that aims to balance risk and
reward by constantly redistributing the portfolio's assets according to certain
goals, risk tolerance, and investment horizon. Unfortunately, there is no
simple formula that can find the right allocation for every individual. As a
result, investors may use different asset allocations' strategy to try to
fulfil their financial objectives. In this work, we conduct an extensive
benchmark study to determine the efficacy and reliability of a number of
optimization techniques. In particular, we focus on traditional approaches
based on Modern Portfolio Theory, and on machine-learning approaches based on
deep reinforcement learning. We assess the model's performance under different
market tendency, i.e., both bullish and bearish markets. For reproducibility,
we provide the code implementation code in this repository.
