Backtesting stands as a cornerstone technique in the development of systematic investment strategies, but its successful use is often compromised by methodological pitfalls and common biases. These shortcomings can lead to false discoveries and strategies that fail to perform out of sample. This article provides practitioners with guidance on adopting more reliable backtesting techniques by reviewing the three principal types of backtests (walk-forward testing, the resampling method, and Monte Carlo simulations), detailing their unique challenges and benefits. Additionally, it discusses methods to enhance the quality of simulations and presents approaches to Sharpe ratio calculations that mitigate the negative consequences of running multiple trials. Thus, it aims to equip practitioners with the necessary tools to generate more accurate and dependable investment strategies.
Separating the side and size of a position allows for sophisticated strategy structures to be developed. Modeling the size component can be done through a meta-labeling approach. This video establishes several heterogeneous architectures to account for key aspects of meta-labeling. They serve as a guide for practitioners in the model development process, as well as for researchers to further build on these ideas.
MlFinlab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
mlfinlab is a “living and breathing” project in the sense that it is continually enhanced with new code from the chapters in the Advances Financial Machine Learning book. We have built this on lean principles with the goal of providing the greatest value to the quantitative community.
