Using Python, and perhaps Pandas? This webinar explains how you can provide production data workloads to your Python code and Jupyter Notebooks, adding 8x to 100x more data power to your models and reducing the time needed to take research into production.
This webinar explores how to:
• build a data ingestion pipeline
• dramatically transform Python time series workflows and analytics (asof joins; xbar for time series formatting)
• augment and improve your data science in Jupyter Notebook using KX’s Python Interface
Watch this webinar to see a demonstration of a computational finance workflow and to understand the same rigor and capabilities that can be deployed to any use case that utilizes Python and highly granular and high-frequency datasets.