Managing Quant Experiments with Experiment Manager
Overview
Quantitative research involves running extensive backtests and stability tests to refine trading strategies. Managing these experiments efficiently—tracking tested parameters, handling combinatorial complexity, and ensuring reproducibility—can be challenging.
In this webinar, learn how to streamline quant research using Experiment Manager.
Highlights
We will demonstrate how to:
- Organize and track large-scale parameter scans and stability tests.
- Automate and parallelize backtesting workflows to improve efficiency.
- Evaluate strategy robustness by analyzing parameter sensitivity and asset dependence.
Using a mean-reversion trading strategy implemented in Python, we will walk through setting up experiments, computing performance metrics, and visualizing results. Experiment Manager provides a structured approach to managing quant research, making it easy to optimize hyperparameters, validate strategies, and ensure robustness within the same framework.
Who Should Attend
Quant researchers, traders, and financial engineers running systematic strategy tests.
About the Presenter
Dr. Yuchen Dong is a Senior Application Engineer at MathWorks focusing on customers in the financial services industry. His focus areas are financial instruments, portfolio optimization, and risk management. Before joining MathWorks, Yuchen worked as a derivative valuation analyst. He holds a Ph.D. in mathematical sciences and a master’s degree in financial mathematics.
Stuart Theakston is the European Financial Services Industry Manager at MathWorks, where he helps customers adopt model-led technology across trading and risk management. Stuart has over 20 years’ experience in quantitative investment management, high-frequency trading, and investment banking. He holds a master’s in finance from the London Business School and a degree in computer science from Cambridge University.
