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Cryptocurrency Trading Process Optimization

Azati team helped the customer develop a reliable business utility to optimize the cryptocurrency trading process. The customer had previously created a utility to handle crypto trading but faced challenges with performance and scalability. Azati’s task was to optimize the trading scripts, improve the performance, revamp the features, and release a new version of the CLI tool.

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All Technologies Used

Ruby
Ruby
Docker
Docker
PostgreSQL
PostgreSQL
Redis
Redis
ElasticSearch
ElasticSearch

Motivation

The customer needed to optimize the performance of their existing cryptocurrency trading utility and revamp its features. The solution required improving the CLI tool, which lacked a visual interface and scalability, and developing new features to enhance the overall trading process.

Main Challenges

Challenge 1
Limited External Dependencies

The customer preferred using Ruby but wanted to minimize the use of third-party libraries and frameworks, which required Azati to re-create open-source modules and find alternative solutions.

Challenge 2
Performance Optimization

The original solution lacked the necessary performance to handle fast-paced cryptocurrency trading. The system needed optimization for speed and scalability.

Key Features

  • Optimized CLI Tool: Advanced filtering system to streamline command navigation, improved performance and scalability for cryptocurrency trading operations, and TCP server handling remote user requests for more efficient execution.
  • Integration with Trading Bots: Integrated trading bots to automate and optimize cryptocurrency trading operations, advanced business logic for handling algorithms, data transformations, and price calculations.
  • Docker Integration: Improved deployment and scalability through Docker containerization, streamlined infrastructure management for future scaling.

Our Approach

Optimizing the Codebase
Azati engineers minimized external dependencies and focused on optimizing the existing codebase, creating new modules where necessary.
Docker Implementation
The solution was moved to Docker for better scalability and ease of deployment.
Enhanced CLI Tool
The CLI tool was enhanced with a TCP server that handled user requests remotely, improving the overall performance and efficiency.
Filtering System Implementation
A filtering system was added to simplify navigation through the CLI commands.

Project Impact

Performance Boost: System optimizations resulted in a 300% improvement in performance, significantly reducing trading times and enabling faster execution of operations.

Enhanced Deployment and Scalability: The integration of Docker containers simplified deployment and improved system flexibility, allowing for scalable and maintainable infrastructure.

Automation and Efficiency: The implementation of an advanced filtering system and integration with trading bots reduced the need for manual intervention, making the trading process more automated and efficient.

Improved Project Management: The transition to GitHub Boards streamlined task tracking and project management, increasing team productivity.

Usability and Future Scalability: The newly released CLI tool with advanced features enhanced usability, while future plans include introducing a web interface for even more accessible user interaction.

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