AWS in Trading

AWS can be used to build scalable research and execution systems, especially for data intensive workloads. Cloud infrastructure provides elastic compute and managed services that reduce operational overhead.

Common Services

Compute services support backtesting and model training. Object storage holds large historical datasets. Databases store time series and metadata. Streaming services support real time data pipelines. Monitoring services capture metrics, logs, and alerts.

Typical Architectures

Many teams use batch pipelines for research and event driven systems for live trading. A common approach separates research environments from production execution to reduce operational risk. Data lakes and feature stores can support large scale experimentation.

Benefits and Tradeoffs

Cloud resources scale quickly and reduce capital expense. However, latency can be higher than on-premise systems, and costs can grow if not managed carefully. Security and compliance controls must be configured correctly for sensitive data.

Conclusion

AWS can accelerate development and scale research, but careful design is needed to control cost, latency, and security risk.