Algorithmic Trading of the Future

How We Built a Platform for Stat Edge Investments

Stat Edge Investments tasked us with creating a multi-user platform for the development, management, and testing of algorithmic strategies. The key requirements were:

Scalability and support for various asset classes
Flexibility in backtesting strategies on historical data
An intuitive interface for analysts and quant researchers
Access and resource-management capabilities for individual researchers

We were also asked to showcase the system’s potential by developing a real-world, mid-term algorithmic trading strategy on NASDAQ futures.

Up to 80%

of analysts’ time was spent debugging strategies
20 days

were required to adapt algorithms to a new asset class due to a lack of standardized tools
3

Separate systems with incompatible data formats required manual processing

Project Focus and Challenges

How We Did It

We concentrated on ensuring the platform was both flexible and user-friendly, so analysts could quickly adapt their models to different markets. Our main challenges included:

Diverse Exchange Infrastructure: Each exchange operates differently
Computation Optimization: Large data volumes demand efficient algorithms
Stability and Reliability: The system must handle complex scenarios without failure

The project’s core difficulties stemmed from raw open-source tools (JupyterHub), plus integrating multiple external systems each with its own data formats (data providers, exchanges, and the model itself).

How We Addressed These Challenges

Interactive Strategy Builder: Implemented a user-friendly environment with Python support and an integrated backtesting system
Distributed Computations: Accelerated model testing using parallel processing
Automated Logging and Analysis: Simplified strategy debugging through comprehensive result tracking
Optimized Data Management: Developed a flexible caching system to handle large datasets efficiently

Results and Future Outlook

34%

Profitability of strategies developed by AI
70%

Reduction in strategy testing time
Over 500

Parallel test simulations can be run weekly, speeding up time-to-market for strategies

We not only met the client’s requirements but also laid a foundation for future scalability. The platform we created can:

Expand to additional markets and new asset classes
Integrate with ML models for adaptive trading strategies
Automate not only trading but also risk analysis

Such innovations represent the future of the financial industry, empowering companies to operate faster, more accurately, and more efficiently.