Exploring Mean Reversion and Momentum Strategies in Arbitrage Trading Our recent reading group examined mean reversion and momentum strategies, drawing insights from the article, “Dynamically combining mean reversion and momentum investment strategies” by James Velissaris. The aim of the paper

Executing a live trading strategy can be a daunting task. From analyzing market data and identifying trading signals to deploying and monitoring trades in real-time, the process requires precision, speed, and accuracy. Fortunately, advancements in technology have paved the way for innovative

Welcome back, fellow traders and machine learning enthusiasts! We hope you’ve been enjoying our journey towards building a successful machine learning trading strategy. If you missed Part 1 of our series, don’t fret – you can always catch up on our exploration of various financial data

Trading in financial markets can be a challenging and complex endeavour, with ever-changing conditions and numerous factors to consider. With markets becoming increasingly competitive all the time, it is a never ending struggle to stay ahead of the curve. Machine learning (ML) has made several

The first lecture from the Experimental Design and Common Pitfalls of Machine Learning in Finance series addresses the four horsemen that present a barrier to adopting the scientific approach to machine learning in finance. The second lecture focuses on a protocol for backtesting and how to avoid

Announcing that MlFinLab is fully integrated into the powerful backtesting and execution platform of QuantConnect! At the start of 2022, we set out to improve the user experience across all of our products and to improve the accessibility of our libraries. This meant integrations into platforms that

Building on our last article regarding best practices for quantitative finance research groups, this article asks the question: What is the best setup and culture for a quant team? This question may have different answers depending on who you ask. Fortunately, there are some glimpses and statements

It’s early in the morning and the markets are about to open. As an individual trader/investor, or perhaps the manager of a group of traders/investors, you are intensely studying the latest news feed that you think may have an impact on your portfolio. Amongst the other plethora of tools at your

A group of strategies, named statistical arbitrage or pairs trading strategies are well-known for being market-neutral gained their popularity among institutional and individual investors. In general, to develop a pairs trading strategy, one needs to figure out two aspects, the first is how to

Traditional pairs trading strategies are prone to failures when fundamental or economic reasons cause a structural break and the pair of assets that were expected to move together are no longer having a strong relationship. Such a break may result in asset price spread having abnormally high

Pairs trading or statistical arbitrage is a famous strategy among institutional and individual investors since the 1990s. The concept behind this kind of strategy is straightforward. If the prices of assets move together historically, this tendency is likely to continue in the future. When the

The hedge ratio estimation problem is one of the most important issues for portfolio managers. The key concept of the hedging problem can be posed as the following equation: S_{t}=P_{1, t}+\sum_{n=2}^{N} \omega_{n} P_{n, t} where P_1 represents the market value at observation t of a portfolio we

This is a series where we aim to cover in detail various aspects of the classic Ornstein-Uhlenbeck (OU) model and the Ornstein-Uhlenbeck Jump (OUJ) model, with applications focusing on mean-reverting spread modeling under the context of pairs trading or statistical arbitrage. Given the universality

In our previous article, we’ve discussed a couple of trading strategies exploiting arbitrage between similar stocks using stochastic optimal control methods. A major shortcoming of those approaches is that we restricted ourselves to constructing delta-neutral portfolios. Along with this, the ratio

We have discussed Basic Distance Approach in the previous blog post. In this post, we’ll look into one of the advanced methods in the Distance Approach and its differences to the Basic Distance Approach. If you haven’t read the previous blog post, we recommend reading it before you read this

Ordinary least squares (OLS) regression is probably the most commonly used statistical method in quantitative finance (and likely in other quantitative fields). It is very fast to compute, and the results are often quite interpretable. Due to its simplicity, it serves as the cornerstone for many

There are many types of approaches you can use in pairs trading, but the Distance Approach is one of the most widely used because of its simplicity. The basic concept is as follows: Using Euclidean squared distance on the normalized price time series, n closest pairs of assets are chosen as pairs. S

Pairs selection is the first crucial step to building a pairs trading strategy. And it is no surprise, to perform it correctly, one must diligently examine, compare and contrast numerous test results, graphs and characteristics. For example, cointegration analysis alone can be performed in one of

The concept of pairs trading is pretty straightforward. As described in [Gatev et al. (2006)], we first find two stocks that have moved together historically and then monitor the spread between these stocks. If the prices of the two stocks diverge, we short the winner and go long on the loser,

This is the sixth article of the copula-based statistical arbitrage series. You can read all the articles in chronological order below. In this series, we dedicate articles 1-3 to pairs-trading using bivariate copulas and 4-6 to multi-assets statistical arbitrage using vine copulas. Copula for Pairs

This is the fifth article of the copula-based statistical arbitrage series. You can read the previous four articles with the first three focusing on pairs-trading: Copula for Pairs Trading: A Detailed, But Practical Introduction. Copula for Pairs Trading: Sampling and Fitting to Data. Copula for

Copula is a great statistical tool to study the relation among multiple random variables: By focusing on the joint cumulative density of quantiles of marginals, we can bypass the idiosyncratic features of marginal distributions and directly look at how they are “related”. Indeed, traders and

Born at Morgan Stanley in the late 1980s, under the wing of Nunzio Tartaglia and his team, who later split up to start several of the world’s best hedge funds, namely PDT Partners and D.E. Shaw (which then lead to Two Sigma). Pairs trading has proven to be a popular and sophisticated trading

In my previous articles, I introduced how to construct long-short asset pairs according to the concept of cointegration and how to build a sparse mean-reverting multi-asset portfolio. Now that we are able to answer the question “what to trade” with confidence, it is time to get down to the

“Buy low, sell high.” One cannot find a more succinct summary of a mean-reversion trading strategy; however, single assets that show stable mean-reversion over a significant period of time such that a mean-reversion trading strategy can readily become profitable are rare to find in markets

This is the third article of the copula-based statistical arbitrage series. You can read the previous two articles: Copula for Pairs Trading: A Detailed, But Practical Introduction. Copula for Pairs Trading: Sampling and Fitting to Data. Introduction Systematic approaches of pairs trading gained

ArbitrageLab is a python library filled with algorithms from the best academic journals and graduate-level textbooks, which focuses on the branch of statistical arbitrage known as pairs trading. This playlist is a series of lecture videos that explore advanced topics and highlight how your team can

This is the second article of the copula-based statistical arbitrage series. You can read the first article: Copula for Pairs Trading: A Detailed, But Practical Introduction. Overview Whether it is for pairs trading or risk management, two natural questions to ask before putting copula for use are:

Whilst backtesting architectures is a topic on its own, this article dives into how to correctly backtest a pairs trading investment strategy using a vectorized (quick methodology) rather than the more robust event-driven architecture. This is a technique that is very common amongst analysts and is

In this post, we will investigate and showcase a machine learning selection framework that will aid traders in finding mean-reverting opportunities. This framework is based on the book: “A Machine Learning based Pairs Trading Investment Strategy” by Sarmento and Horta. A time series is known to

Suppose that you encountered a promising pair of stocks that move closely together, the spread zig-zagged around 0 like some fine needle stitching that sure looks like a nice candidate for mean-reversion bets. What’s more, you find out that the two stocks’ prices for the past 2 years are all

Cointegration, a concept that helped Clive W.J. Granger win the Nobel Prize in Economics in 2003 (see Footnote 1), is a cornerstone of pairs and multi-asset trading strategies. Anecdotally, forty years have passed since Granger coined the term “cointegration” in his seminal paper “Some

The Black-Litterman (BL) model is one of the many successfully used portfolio allocation models out there. Developed by Fischer Black and Robert Litterman at Goldman Sachs, it combines Capital Asset Pricing Theory (CAPM) with Bayesian statistics and Markowitz’s modern portfolio theory

ArbitrageLab is a python library that helps traders who want to exploit mean-reverting portfolios by providing a complete set of algorithms from the best academic journals. How to Get Access Recently there has been a lot of interest in the development of our most recent library which focuses

Discriminating random variables on time-series on both their distribution and dependence information is motivated by the study of financial assets returns. For example, given two assets where their returns are perfectly correlated, are these returns always similar from a risk perspective? According

Pozzi, Di Matteo, and Aste (2013) conclude that it is “better to invest in the peripheries” of the Planar Maximally Filtered Graph (PMFG), as investing in the peripheries lead to better returns, and reduced risk. This blog post explores the impacts of Covid-19 by simulating two investment

Nothing makes a situation better like good timing. Whether it’s getting a promotion, catching the last train after a night out, meeting the love of your life, or joining a quant community – it is many of small consequential gambles of stopping decisions that get us to that triumphant “Yes!”

Network analysis can provide interesting insights into the dynamics of the market, and the continually changing behaviour. A Minimum Spanning Tree (MST) is a useful method of analyzing complex networks, for aspects such as risk management, portfolio design, and trading strategies. For example,

There are 6 properties that empirical correlation matrices exhibit that no synthetic generation method has been able to replicate, until now. Enabling researchers to backtest strategies on an abundance of data would make our algorithms and strategies more robust, accurate, and efficient. Since

For a long while, investors worked under the assumption that the risk and return relationship of a portfolio was linear, meaning that if an investor wanted higher returns, they would have to take on a higher level of risk. This assumption changed when in 1952, Harry Markowitz introduced Modern

Risk has always played a very large role in the world of finance with the performance of a large number of investment and trading strategies being dependent on the efficient estimation of underlying market risk. With regards to this, one of the most popular and commonly used representation of risk

As many of you will know by now, Hudson & Thames is pivoting towards an open-core business model and away from our dreams of pure open source and the “unlocking the commons”. What follows is a very brief history of our learnings with open-source. Starting Out MlFinLab started as an ambitious

Harry Markowitz’s Modern Portfolio Theory (MPT) was seen as an amazing accomplishment in portfolio optimization, earning him a Nobel Prize for his work. it is based on the hypothesis that investors can optimize their portfolios based on a given level of risk. While this theory works very well

Traditionally, correlation matrices have always played a large role in finance. They have been used in tasks ranging from portfolio management to risk management and are calculated based on historical empirical observations. Although they are used so frequently, these correlation matrices often have

As diversification is the only free lunch in finance, the Hierarchical Equal Risk Contribution Portfolio (HERC) aims at diversifying capital allocation and risk allocation. Briefly, the principle is to retain the correlations that really matter and once the assets are hierarchically clustered, a

In 2016, Dr. Marcos Lopez de Prado introduced the Hierarchical Risk Parity (HRP) algorithm for portfolio optimization. Prior to this, Harry Markowitz’s Modern Portfolio Theory (MPT) was used as an industry-wide benchmark for portfolio optimization. MPT was an amazing accomplishment in the field of

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. In the following blog post, we

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

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

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