In this chapter, we covered the important topic of portfolio management, which involves the combination of investment positions with the objective of managing risk-return trade-offs. We introduced pyfolio to compute and visualize key risk and return metrics and to compare the performance of various algorithms.
We saw how important accurate predictions are to optimize portfolio weights and maximize diversification benefits. We also explored how ML can facilitate more effective portfolio construction by learning hierarchical relationships from the asset-returns covariance matrix.
We will now move on to the second part of this book, which focuses on the use of ML models. These models will produce more accurate predictions by making more effective use of more diverse information to capture more complex patterns than the simpler alpha factors that were most prominent so far...
