qlib

What problem does it solve?

Automates the process of quantitative research for finance professionals, allowing for efficient backtesting, model training, and evaluation of financial models.

Core Features & Use Cases

• Factor Definition & Backtesting: Automates the creation of factors and the running of backtests to validate hypotheses.
• Model Training & Evaluation: Trains financial models and evaluates them against predefined metrics.
• Data Integration: Integrates various datasets required for financial analysis and machine learning tasks.
• Use Case: Ideal for creating a quantitative trading strategy where backtesting of various strategies against a dataset is necessary.

Quick Start

Create a new research environment using Qlib, then execute a simple backtest using the Qlib API.