AWS Components for Algorithmic Trading Systems (Part 1)
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I’m exited to announce a new series of blog tutorials with the subject of building a scalable algorithmic trading system on the Amazon Web Services (AWS) cloud platform. Bear with me, as I’m learning AWS myself.
My plan is to introduce each AWS service by providing a Python tutorial on basic operations for the service and then move on to a series of tutorials on how to build an automated trading system by integrating the different service components.
The advantage of using AWS is that it offers dozens of powerful, integrated software services that can be used to build a scalable, high-performance, low-cost production trading system.
AWS offers dozens of services for different industries and use cases but not all make sense to use for trading systems. So in this first post, I’d like to cover the AWS components that I would use to build such a system.
This story is solely for general information purposes, and should not be relied upon for trading recommendations or financial advice. Source code and information is provided for educational purposes only, and should not be relied upon to make an investment decision. Please review my full cautionary guidance before continuing.
Computing Platform, VPS
AWS EC2 Private Servers
AWS EC2 is Amazon’s cloud hardware platform service for building scalable systems. There are a wide variety and combinations of operating systems, processors, memory- and storage options available. For example, EC2 images include Linus flavors, Mac and Windows OS or Intel, AMD and Arm processors.
EC2 can be used a s building blocks for building scalable automated trading systems. For example, one could spin up one or multiple EC2 instances to support each process of the trading development and production system such as data import, data preparation and aggregation, backtesting, machine learning, live data analysis, and running bot farms.
Learn more about the EC2 Computing Platform here.
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