Robust optimisation in algorithmic investment strategies

This research develops a portfolio of four algorithmic strategies that produce Long/Short signals based on t+1 close price predictions of the underlying instrument. The main instrument used is S&P 500 index, and the data covers the period from 1990-01-01 to 2021-04-23. Each strategy is based on a different theory and aims to perform well in different market regimes. The objective is to have a set of uncorrelated investment strategies based on different logics such as trend-following, contrarian approach, statistical methods, and macro-economic news. Each strategy was individually generated following a personalized Walk-Forward optimisation, in which the model seeks to choose the most robust combination of parameters rather than the best one, in terms of risk-adjusted returns. The robustness of all strategies was tested by changing all parameters selected at the beginning of the optimisation. Additionally, the robustness of the portfolio of strategies is tested by applying it to another American index, Nasdaq Composite. Finally, the ensemble model was created based on the combination of the signals from all investment strategies for our two basis instruments. Results show that the portfolio obtains returns four (seven) times larger than the Buy & Hold strategy on S&P 500 (Nasdaq Composite) with a similar level of risk in the last 31 years.