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.