As a data scientist, I’ve had my fair share of experience with MLFlow, but I recently stumbled upon an innovative use case that really caught my attention. It’s not about managing ML runs, but rather using MLFlow to track and manage algo trading backtests. And the best part? It’s not even for machine learning runs, but for testing a momentum strategy.
I have to say, I was impressed by the potential of experiment tracking in this context. It’s a great way to organize and keep track of different backtest iterations, making it easier to identify what works and what doesn’t. And let’s be honest, who doesn’t love the idea of streamlining their trading strategy development process?
What I find particularly interesting is that this approach can be applied to a wide range of trading strategies, not just momentum. It’s all about experimenting, tracking, and refining – and MLFlow seems like a great tool to facilitate that process.
Have you ever used experiment tracking for backtesting? What are your thoughts on this approach? Share your experiences in the comments below!