Recent posts by Mona

The Challenges of Algorithmic Trading

Automated quantitative trading systems are sophisticated, but they face a myriad of challenges that can disrupt their performance. From issues with data and models to internal operations, there’s a lot that can go wrong. 

One of the biggest challenges is the reliance on third-party data sources. These sources can change without notice, causing disruptions that ripple through your entire system. Moreover, when you’re using different models and versions across various markets and asset classes, it becomes incredibly difficult to detect performance degradations (caused by a variety of things, e.g., data drift). The sheer volume and complexity of the data make identifying problems early like, “finding a needle in a haystack.”

Market behavior is inherently volatile, further complicating the process of distinguishing between real issues and noise. Unfortunately, many issues are only detected after they’ve already caused a decline in returns—by then, it’s too late. While most teams have some form of monitoring in place, it’s often standard, semi-manual, and reactive.

Efforts to deeply and automatically monitor these systems are hampered by a range of problems: big data challenges, organizational constraints, and most notably, the issue of false alarms, which can lead to alert fatigue within the team.

Monitoring is critical to the success of machine learning models deployed in production systems. Because ML models are not static pieces of code but, rather, dynamic predictors which depend on data, hyperparameters, evaluation metrics, and many other variables, it is vital to have insight into the training, validation, deployment, and inference processes in order to prevent model drift and predictive stasis, and a host of additional issues. However, not all monitoring solutions are created equal. In this post, I highlight three must-haves for machine learning monitoring, which hopefully serve you well whether you are deciding to build or buy a solution.

Before you launch a project to build an artificial intelligence monitoring system from scratch, consider whether or not this would be a good use of your resources. When does it make sense to buy instead? Let’s discuss. This post explores the advantages and disadvantages of both alternatives so that you can make an informed decision about what’s best for your organization.

We hope that everyone had a fantastic holiday season and is now ready to tackle the 2022 New Year! Looking back to where we started in 2018 to where we are now, we have grown so much overall as a company. From three (⅓ balding) guys with a crazy idea nobody understood, through assembling a team of passionate trailblazers, and to building advanced features for Mona’s platform - now leveraged by incredible AI/ML teams at industry leaders, and even recognized by Gartner, we are continuing to strengthen our position as the leading monitoring solution for AI,  - providing the most flexible and comprehensive insight engine.