Built with Modern Tools
Tech Stack
Our technology stack combines industry-standard tools with cutting-edge AI frameworks to deliver reliable, scalable, and secure memecoin trading solutions.
Technology Overview
Comprehensive view of all technologies powering Vibe Trading
Performance Metrics
Key performance indicators and system capabilities
Technology Categories
Programming Languages
Core languages powering our platform
Python
Primary language for ML, data processing, and backend services
TypeScript
Frontend development with type safety and modern tooling
SQL
Database queries and data manipulation
Machine Learning
AI and ML frameworks for intelligent analysis
Scikit-learn
Machine learning algorithms and model training
Hugging Face
NLP models and transformer architectures
TensorFlow
Deep learning and neural network frameworks
Data Storage
High-performance databases and caching systems
PostgreSQL
Primary relational database for structured data
TimescaleDB
Time-series database for market data and metrics
Redis
In-memory cache and session storage
Infrastructure
Containerization and orchestration platforms
Docker
Containerization for consistent deployments
Kubernetes
Container orchestration and scaling
Helm
Package management for Kubernetes
Web3 & Blockchain
Blockchain integration and cryptocurrency handling
Web3.py
Python library for Ethereum blockchain interaction
Hardware Wallets
Secure key management and transaction signing
DEX APIs
Decentralized exchange integration
APIs & Communication
HTTP clients and real-time communication
aiohttp
Asynchronous HTTP client/server framework
requests
Synchronous HTTP library for API calls
Socket.io
Real-time bidirectional communication
Why This Stack?
The rationale behind our technology choices
Performance
Optimized for speed with async processing, caching, and efficient algorithms
Scalability
Microservices architecture with container orchestration for horizontal scaling
Security
Enterprise-grade security with hardware wallets and encrypted communications
AI-First
Built around machine learning with state-of-the-art NLP and pattern recognition
Open Source
Leveraging proven open-source tools for transparency and community support
Developer Experience
Modern tooling and frameworks for rapid development and easy maintenance
Integration Examples
How our technologies work together in real scenarios
Real-time Data Processing Pipeline
GMGN WebSocket feeds are processed by Python async handlers, cached in Redis for sub-millisecond access, and distributed to multiple AI processing workers.
AI Model Training & Inference
Sentiment analysis using Hugging Face transformers, pattern recognition with Scikit-learn, and time-series data stored in TimescaleDB for model training.
Secure Execution Layer
Trading decisions are executed through Web3.py with hardware wallet integration, secured by AWS KMS, and deployed in isolated Docker containers.