In today’s fast-paced financial markets, the need for speed and accuracy in trading decisions has never been greater. Quantitative traders rely heavily on real-time data and efficient processing to gain competitive advantage. This article explores how to build a low-latency, scalable infrastructure for real-time quant trading using AWS services, highlighting architectural patterns and deployment strategies.

Why AWS for Quant Trading?

AWS offers a robust environment tailored for high-performance computing (HPC), making it a natural fit for quantitative trading. With features like high-throughput data streaming, scalable compute, and integrated analytics, AWS enables traders to execute complex strategies at scale and with minimal latency.

Solution Overview

The proposed architecture combines real-time data ingestion, processing, model execution, and order generation—all within a highly available cloud infrastructure.


Figure 1 – This architecture describes the one-time installation process of the solution. It first details the use of the AWS Cloud Development Kit (AWS CDK) to deploy the solution into your AWS account. It then looks at the method of uploading certain values like API keys in AWS Secrets Manager. The diagram also shows the different application stacks that are created through AWS CloudFormation, like AWS Batch, different databases, as well as AWS Lambda. Keep in mind that for any specific customisations, stacks will need to be redeployed for application coding changes.

At the heart of the system lies a real-time data pipeline using Amazon Kinesis Data Streams. This ingests live market data, which is then processed by AWS Lambda functions. These functions interface with trained ML models hosted on Amazon SageMaker, which output trading signals to be executed via APIs or FIX protocols.

Deep Dive: Data Processing and Inference

To handle the massive volume of financial market data, the architecture employs a modular design. This ensures each component, from ingestion to decision logic, can scale independently and be maintained easily.


Figure 2 – The architecture shown here demonstrates the flow of real-time data through various stages. Market data is streamed into AWS using Amazon Kinesis, where it’s consumed by AWS Lambda functions. These components perform on-the-fly pre-processing before triggering model inference via Amazon SageMaker. Once a decision is made, trade signals are sent to execution endpoints.

Incoming data is validated and processed using AWS Lambda and Amazon EMR. This step includes pre-processing for ML models and storing key insights in Amazon S3 for historical analysis. The ML model inference happens in near-real time with low latency, using SageMaker endpoints.

Key Components in Action

AWS Glue and Amazon Athena are used for metadata management and ad-hoc querying respectively. Meanwhile, Amazon CloudWatch ensures visibility and monitoring of all events and processes in the system.

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Deploying the Real-Time Market Portfolio Solution on AWS

This section walks you through the steps needed to launch a real-time market portfolio application on AWS, using the AWS Cloud Development Kit (CDK). The deployed infrastructure includes a preconfigured example portfolio based on the S&P 500 Intraday Momentum strategy—a well-known approach which posits that the market’s first 30 minutes of returns often provide predictive power over its final 30 minutes. This tendency is more prominent on days marked by high volatility, elevated trading volumes, recessionary signals, or significant macroeconomic announcements.

Note: To use this solution in a fully functional setup, you will need access to a market data provider such as Bloomberg B-PIPE or IEX, and a valid API key.

Initial Setup with AWS Cloud9

To get started, AWS Cloud9 will serve as your integrated development environment (IDE), although you're free to use another IDE if you’re more comfortable with it.

  1. Log in to the AWS Cloud9 Console and select Create environment.
  2. Name your environment MarketPortfolioEnv.
  3. Choose the t2.micro instance type.
  4. Leave the remaining settings as default and click Create.
  5. Once the environment is ready, click Open to launch the Cloud9 IDE.
  6. In the terminal at the bottom, clone the repository with the following command:
git clone https://github.com/aws-samples/quant-trading.git

CDK Deployment

With your development environment prepared, you're now ready to deploy the application. AWS CDK will automate the provisioning of all necessary cloud resources.

Navigate to the cloned repository:

cd quant-trading

Then execute the deployment script, which will install dependencies, bootstrap your environment, and deploy the full application stack:

./deployment.sh

In some instances, you may encounter a Docker build error due to insufficient disk space. To resolve this, run the following commands to increase root volume space:

chmod +x aws-quant-infra/src/utils/resize_root.sh &&
aws-quant-infra/src/utils/resize_root.sh 50

Additionally, if you face errors related to creating a DynamoDB replica in the database stack, go to the DynamoDB Console, manually delete the problematic replica, remove the entire DB stack, and then re-deploy the CDK resources from scratch.

Adding Your API Key for Market Data Integration

Once your infrastructure is live, you’ll need to enable live data ingestion by registering your market data API key in AWS Secrets Manager. This step allows the Intraday Momentum algorithm to begin receiving and processing real-time signals.

  1. Head to the AWS Secrets Manager Console.
  2. Locate the two required secrets:
    • api_token_pk_sandbox
    • api_token_pk

These secrets securely store your credentials for connecting to either IEX Cloud or Bloomberg B-PIPE.

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Conclusion

AWS provides a secure, scalable, and low-latency environment for deploying real-time quant trading platforms. With integrated services tailored to HPC and ML workloads, it’s easier than ever to turn quantitative strategies into production-grade solutions.