Portfolio Optimization and Performance Evaluation

Signals generated by alpha factors are converted into trades through algorithmic trading. The market then becomes long and short as a result of these trades. In order for the strategy to succeed, it must deliver positive returns and take on a level of risk.

It is necessary to replicate trades and assess their effectiveness before putting a strategy into action in the real market. A strategy is evaluated by performing backtests using historical data to fine-tune its parameters and by performing forward testing using new data. We want to make sure false conclusions are not drawn as a result of tailoring a strategy to suit specific past situations.

When assets generate positive returns, they can balance out negative price fluctuations in a portfolio. When two positions do not have a high correlation, gains from one will usually compensate for losses from the other. In 1952, Harry Markowitz developed the theory of modern portfolio management, which emphasizes diversification and considers how portfolio risk is impacted by position covariance. In response to this theory, means-variance optimization was developed, which determines how to allocate weights to different assets to minimize risk while targeting a desired outcome.

A capital asset pricing model (CAPM) includes a risk premium that represents the additional expected return over a risk-free investment. It is used to compensate for exposure to a systematic and non-diverse risk factor, namely the market.

In recent decades, risk management has evolved thanks to new risk factors and more detailed exposure management options. Using Kelly’s criterion to optimize portfolios involves a series of long-term strategic decisions. Thorp adapted it to stock market use in 1968 after initially developing it for gambling.

Machine learning (ML) has been applied to understand hierarchical relationships between assets, thereby enhancing portfolios. Through ML, the portfolio can be distinguished into complements and substitutes according to their risk profiles.

We will discuss the following topics in this article: — The measurement of portfolio risk and return — The management of portfolio weights through mean-variance optimization and other methods — Utilizing machine learning to optimize asset allocation in a portfolio setting — Creating a portfolio and simulating trades using Zipline — Using Python to assess portfolio performance

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