This is the second blog in our series on factor modeling. In our first factor modeling blog, we developed a framework to mine quickly new factors using Amazon Bedrock, AWS Batch, and Step Functions from alternative data. Read more about that approach. After discovering effective factors, the natural next step is to incorporate these factors into systematic trading strategies.
1. Introduction
Hedge fund quantitative researchers and developers face significant challenges when developing and backtesting systematic trading strategies. These include processing massive volumes of historical market data, meeting intensive computational requirements, and managing complex job orchestration.
This blog shows how cloud-native AWS solutions transform the strategy development process for quants, making it scalable and extensible. We’ll show you how to implement, backtest, and analyze a long-short equity strategy using factors identified through our previous factor…
