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Experimental Design and Common Pitfalls of Machine Learning in Finance

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 the seven sins of backtesting. By implementing the research protocol outlined in these articles, an investment manager can avoid making the seven common mistakes when backtesting and building quant models.

December 13, 2022 by

QuantConnect Integration with MlFinLab

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 have a strong community, historical simulations, data feeds, and live execution. QuantConnect was a natural choice!

October 17, 2022 /1 Comment/by
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How to Build a World-Class Quant Team

What is the best environment and culture for a quant team? This question may have different answers depending on who you ask. Fortunately, there are some glimpses and statements from the top quantitative research groups that afford us a window into their work environment behind the scenes. For this short article, we have scraped the internet for some fascinating insights into the structure and culture of some of the best performing quant hedge funds in the world.

January 23, 2022 by
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Best Research Practices for Your Quantitative Finance Research Group

How do you perform research? And what are some of the best-recommended practices that you should follow?

In this article, I will describe some of the main aspects of the scientific research process and also recommend some best research practices. The topics that I will cover are literature reviews, writing a research proposal, performing research, writing a paper for publication, useful tools, and, finally, hosting reading groups.

January 14, 2022 /3 Comments/by
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Introducing: Arbitragelab Tear Sheets

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 two methods – utilizing the Engle-Granger approach or the Johansen approach. To truly have the complete picture of the pairs suitability, with the Engle-Granger approach, the researcher should perform the test(and further analysis) for both possible combinations, A/B or B/A, in a pair since it is sensitive to which asset we choose to be the “dependent” one.
The Johansen test, in turn, provides multiple cointegration vectors, which also should be examined separately and taken into account. Not to mention the possible analysis of the residuals, auto-correlation tests, etc., brings even more data to the table for you to make your judgement.

And now, we have two options: memorize everything or constantly switch between numerous parameters and plots to check, contrast and compare. It results in loading your brain with tons of ‘noise’ that distracts from focusing on the evaluation itself. But it doesn’t have to be this way. Data analysis thrives when there is order, accessibility and clarity. And what embodies these three qualities better than combining everything into an interactive well-rounded tear sheet?

June 24, 2021 by
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The Definitive Guide to Pairs Trading

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 strategy, often taught in advanced MSc Financial Engineering programs.

April 7, 2021 by
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Minimum Profit Optimization: Optimal Boundaries for Mean-reversion Trading

It is time to get down to the nitty-gritty of the implementation of a mean-reversion strategy.

The crux of implementing a mean-reversion trading strategy is to pinpoint the trade location. Apparently, we want to initiate a trade when the spread value has deviated considerably from its long-term mean. However, “a considerable deviation” is a rather vague description and needs to be quantified when it comes to trade execution. For the sake of convenience and clarity, I will use “boundary” to refer to the trade location and “spread” to both the spread of the long-short asset pairs and the value of the multi-asset portfolio in the remainder of this article.

March 29, 2021 /1 Comment/by
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Advanced Pairs Trading Lecture Videos

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 compete with the world’s best hedge funds!

February 15, 2021 by

10 Learnings from Open Source

Daniel Pink wrote that the things that motivate people are autonomy, mastery, and purpose. These are the 3 golden pillars of open-source and it highlights what we believe to be the strongest motivation for people to contribute. I add the extra theme: It’s all about reputation and building one.

This provides you with purpose, you’re making a difference in the world and people are noticing you. It takes time to plan out the tools that you want to work on (autonomy) and in order to do so – you will need to stretch your skills and learn how to write production-level code (mastery) that thousands of quants will use every day (reputation).

August 8, 2020 by