HFT Engineering

HFT Engineering

HFT Engineering

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Chief Architect

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Startup

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Strategy Lab

Project Description

The Strategy Lab project focused on the development and optimization of high-frequency trading systems for the futures and options markets. This initiative leveraged Java, Python, and C++ to ensure low-latency performance and scalability. A key component of the project was the design and deployment of a custom suite for market analytics, system automation, and data visualization, utilizing the MEAN stack (MongoDB, Express.js, Angular.js, Node.js) with a Python and Docker-based backend.

Key Features:

  1. Graphical Flow-Based Domain-Specific Language (DSL):

    • Allowed traders to visually design trading strategies before writing code.
    • Facilitated the creation and manipulation of components and connections through a graphical interface.
    • Simplified strategy replication and ensured type safety during runtime.

  2. Market Analytics and Visualization:

    • Included tools for data visualization and inline sampling of market data.
    • Provided profiling components to log and analyze market activity.

  3. Backend Infrastructure:

    • Integrated disparate systems using RabbitMQ as a messaging middle layer, ensuring seamless communication between components.
    • Offered low-level system access through a C++ plugin model for high-performance operations.

  4. Python Integration:

    • Utilized Boost Python for scripting interfaces, enabling flexible command and control.
    • Fully interactive Python console for sandboxed runtime environments, allowing real-time adjustments to strategies.

  5. Component Library:

    • Centralized definitions in library.json for port and component metadata.
    • Enabled import/export of strategy configurations as JSON objects, facilitating easy sharing and deployment.

Architecture:

  • Frontend: Built with Angular.js for an interactive graphical user interface.
  • Backend: Python and Docker-based, integrated with MongoDB for data storage and RabbitMQ for messaging.
  • Messaging: Supported dynamic message types and a wide range of protocol integrations, including JSON, RMQ, and more.

Contributions:

  • Designed and implemented a flow-based DSL for trading strategy development.
  • Developed a high-performance backend infrastructure using Docker and Python for analytics and data processing.
  • Integrated RabbitMQ for robust inter-component messaging, enabling real-time system automation.
  • Created tools for market data visualization and strategy optimization, enhancing operational efficiency.

Outcomes:

  • Efficiency: Reduced code duplication and boilerplate through automated code generation and modular design.
  • Scalability: Improved platform scalability with support for dynamic message types and global strategy deployment.
  • Usability: Simplified trading strategy development and deployment with a user-friendly interface and real-time system control.

Strategy Lab serves as a groundbreaking tool for trading teams, combining cutting-edge technology with innovative design to streamline trading operations and enhance market responsiveness.