mission creates perpetual learning opportunities.
Purpose
The only constant is change.
Hitting moving targets requires innovation and continuous learning.
Langhavens is a platform for personal development based on this premise.
Features
The primary objective is to provide a problem-domain for continuous, deep learning.
Langhavens is building a comprehensive system to build, manage, and run algorithmic trading strategies.
While the emphasis of the project is placed on learning opportunities, the algorithmic trading system will be used to place real trades.
Anything affordable is being deployed to AWS. This deepens learning for cloud concepts.
Lessons learned and milestones achieved are published on the Langhavens blog.
Many of the tools in this project already exist, but we're re-inventing the wheel just for the educational benefit.
The projects in Langhavens use professional-grade development lifecycle tools for CI/CD, project management, and programming.
We have a deep interest in this problem domain and are having a blast exploring solutions.
Projects - In Development
An open-source Java client for the Schwab Market Data developer API. This implementation features a reactive, non-blocking client as well as a traditional, blocking client.
Get a daily recap on market events with real data and analysis from Generative AI.
Projects - In the Backlog
Corporate actions like stock splits and symbol name changes create inconsistencies when looking back in time to compare financial data. A securities master keeps track of all these changes and transforms the data into a normalized format with respect to time.
Past performance is no guarantee of future results, but back-tests attempt to forecast an algorithm's future returns using historical data. The backtest engine provides the environment for these tests to run.
Our pilot algorithm and a learning experiment to predict future price candles using machine learning.