Tag: FSI

This blog post will explore the technical attributes of computationally demanding high performance computing (HPC) workloads within the financial services sector. By examining the key characteristics of your workloads, we will guide you through a decision tree approach to help determine the most suitable HPC platform for the cloud – whether it be a commercial vendor solution, open-source option, or a fully cloud-native implementation.

Developing robust investment strategies requires thorough testing, but relying solely on historical data can introduce biases and limit your insights. Learn how synthetic data from agent-based models can provide an unbiased testbed to systematically evaluate your strategies and prepare for future market scenarios. Part 2 covers implementation details and results.

Developing robust investment strategies requires thorough testing, but relying solely on historical data can introduce biases and limit your insights. Learn how synthetic data from agent-based models can provide an unbiased testbed to systematically evaluate your strategies and prepare for future market scenarios. Part 1 of 2 covers the theoretical foundations of the approach.

Balyasny Asset Management (BAM), a $22B global investment firm, faced a unique challenge: how to empower 160 investment teams to conduct cutting-edge research across six strategies. Discover how they leveraged AWS Batch and HAQM EKS to supercharge their research capabilities.

Financial professionals: Simulate realistic market conditions with Simudyne’s agent-based modeling on AWS and Red Hat OpenShift. Learn how HKEX leverages these insights.

You can boost the performance of your compute grids by strategically distributing your binaries. Our experts looked at lots of strategies for fast & efficient compute grid operations – to save you some work.

Financial services firms: we stress-tested YellowDog’s HPC environment to see if it could handle a 10m task batch at 3,000 tasks per second. Check out the results.

Struggling with long compute times for numerical simulations in finance? See how AWS makes it simple to leverage the cloud for large-scale financial modeling. We walk through a real example using QuantLib and Monte Carlo methods.

In this post, we’ll walk you through how banks and other financial services firms migrate or burst their grid workloads onto AWS using AWS ParallelCluster and the Slurm scheduler.

In this post, we’ll show you an open-source solution for a real-time quant trading system that you can deploy on AWS. We’ll go over the challenges brought on by monitoring portfolios, the solution, and its components. We’ll finish with the installation and configuration process and show you how to use it.