two-sigma-ml-at-scale

What problem does it solve?

This Skill enables the development of sophisticated machine learning trading systems by adopting the principles and practices pioneered by Two Sigma, focusing on large-scale data infrastructure, rigorous feature engineering, and robust ML deployment.

Core Features & Use Cases

• Feature Store Implementation: Centralize, version, and share features for reproducibility and efficiency.
• Distributed Backtesting: Scale backtesting across clusters to test numerous strategies and parameters rapidly.
• Alternative Data Pipelines: Ingest and process diverse data sources (e.g., satellite imagery) into actionable trading features.
• Model Monitoring: Continuously track model performance and detect drift in production.
• Use Case: Develop a new alpha research strategy by leveraging a feature store for historical data, running a distributed backtest to optimize parameters, and setting up continuous monitoring for production deployment.

Quick Start

Use the two-sigma-ml-at-scale skill to register a new feature named 'retail_parking_traffic' with the provided computation logic and dependencies.