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.
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.
We delve into the challenge of making an asset price series stationary (for reasons discussed below) and preserving as much memory/signal from the original series.
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’.
This blog post investigates the idea of Meta Labeling and tries to help build an intuition for what is taking place.
MLFinLab is a “living and breathing” project in the sense that it is continually enhanced with new code from the chapters in the Advanced Financial Machine Learning book.
This weeks research was consumed by the concept of Meta-Labeling, how it works, and does it work out-of-sample? We have published a research report as well as an accompanying slide show.
First of all we want to thank everyone who has reached out to us with ideas and contributions to our package. Without all of your help, none of this would be possible.