The Alphanume Strategy Lab contains production-ready quantitative trading research powered by Alphanume market data APIs.
This repository demonstrates how Alphanume datasets can be integrated into real-world systematic trading workflows — from cross-sectional strategies to event-driven research and risk regime modeling.
Each folder contains:
- Research context and assumptions
- Fully reproducible Python code
- API integration examples
- Backtest or modeling workflows
The goal is not to publish theoretical notebooks, but to provide production-oriented examples of how practitioners can build directly on Alphanume endpoints.
- A demonstration of real quantitative trading use cases
- A reference implementation for API-based research pipelines
- A foundation for systematic strategy development
- Investment advice
- A signal service
- A black-box strategy vault
All research is fully transparent and reproducible.
All datasets used in this repository are sourced from:
Alphanume — Market Data APIs for Quantitative Trading
Available datasets include:
- Historical market capitalization
- Optionable stock history
- Dilution events (historical + live)
- Risk regime signals
- Event-driven datasets
Access to the API is required to reproduce research in this repository.
Learn more: Alphanume — Market Data APIs for Quantitative Trading
- Obtain an API key
- Install required dependencies
- Run the example workflows inside each strategy folder
Each folder is self-contained and includes instructions specific to that research subject.
We built Alphanume because we needed datasets that did not exist in structured, accessible API form.
Strategy Lab reflects how we actually use these datasets in quantitative trading research.