Throughout this post, we will explore the intuition behind Hierarchical Risk Parity and also learn to apply it using the MlFinLab library.
Research articles for the Hudson and Thames home page.
Pattern matching locates similarly acting historical market windows and makes future predictions based on the similarity. They combine the strengths of both momentum and mean reversion by exploiting the statistical correlations of the current market window to the past.
Mean Reversion is an effective quantitative strategy based on the theory that prices will revert back to its historical mean. A basic example of mean reversion follows the benchmark of Constant Rebalanced Portfolio.
By setting a predetermined allocation of weight to each asset, the portfolio shifts its weights from increasing to decreasing ones. This module will implement four types of mean reversion strategies:
Today we will be exploring the second chapter of our newest online portfolio selection module, momentum.
Momentum strategies have been a popular quantitative strategy in recent decades as the simple but powerful trend-following allows investors to exponentially increase their returns. This module will implement two types of momentum strategies with one following the best-performing assets in the last period and the other following the Best Constant Rebalanced Portfolio until the last period.
Online Portfolio Selection is an algorithmic trading strategy that sequentially allocates capital among a group of assets to maximize the final returns of the investment.
Traditional theories for portfolio selection, such as Markowitz’s Modern Portfolio Theory, optimize the balance between the portfolio’s risks and returns. However, OLPS is founded on the capital growth theory, which solely focuses on maximizing the returns of the current portfolio.
“The complexity of machine learning models presents a substantial barrier to their adoption for many investors. The algorithms that generate machine learning predictions are sometimes regarded as a black box and demand interpretation. Yimou Li, David Turkington, and Alireza Yazdani present a framework for demystifying the behavior of machine learning models. They decompose model predictions into linear, nonlinear, and interaction components and study a model’s predictive efficacy using the same components. Together, this forms a fingerprint to summarize key characteristics, similarities, and differences among different models.”
This article explores the intuition behind the development of Hierarchical Risk Parity, a detailed explanation of its working and how it compares to the other allocation algorithms.
The Hierarchical Risk Parity algorithm is fast, robust and flexible.
In this article we discuss how the problem of non IID samples, faced in financial machine learning can be solved by applying Sequential Bootstrapping.
In the summer of 2018 we attended a conference organized by Quantopian in which we heard Dr. Marcos Lopez de Prado outline the challenges of building successful quantitative investment platforms.
In this project we explore an example of applying meta labeling to high quality S&P500 EMini Futures data and create an open-source python package (mlfinlab) that is based on the work of Dr. Marcos Lopez de Prado in his book ‘Advances in Financial Machine Learning’.