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🚀 Low-Latency Algorithmic Cryptocurrency Trading System (Go + Python)

A high-performance cryptocurrency trading platform with a Go-based backend and a Python Streamlit frontend. Designed for backtesting and executing algorithmic strategies using historical and live data with low latency.

📐 System Design

  • Procedure Diagram:
    Procedure Diagram
  • Sequence Diagram:
    Sequence Diagram
  • System Architecture:
    Architecture
  • Class Diagram:
    Class Diagram

📁 Repository Structure

trading-system/
├── backend/           # Go backend for strategy execution and APIs
├── frontend/          # Python Streamlit frontend for user interface
├── dependencies/      # go.mod and go.sum for module management
├── docs/              # Design documents and PDFs
├── logs/              # Log files for trades
├── README.md          # Project documentation

🔧 Requirements

Backend (Go)

  • Go 1.18+
  • Key packages:
    • github.com/adshao/go-binance/v2
    • github.com/gin-gonic/gin
    • gonum.org/v1/plot

Frontend (Python)

  • Python 3.8+
  • Key packages:
    • streamlit
    • requests
    • pandas

⚙️ Installation & Setup

Backend Setup

# Install Go from https://golang.org/dl/
git clone https://github.com/yourusername/trading-system.git
cd trading-system/backend
go mod tidy
go run main.go

Frontend Setup

# Install Python from https://python.org
cd frontend
pip install -r requirements.txt
streamlit run frontend.py

🚀 Running the Application

  • Backend runs on localhost:8080 with routes like:

    • POST /trade
    • GET /exchanges
    • GET /symbols

  • Frontend launches a local Streamlit web UI where users can:

    • Select exchange/symbol
    • Input strategy parameters
    • Run backtests
    • View & download results

📊 Strategy & Usage

  • Backend:

    • Supports custom strategies like KAGE, KITSUNE, ZEN, TENSHI, etc.
    • Reads historical data from CSV or Binance API.
    • Logs and visualizes trades.

  • Frontend:

    • User-friendly tabs for input and visualization.
    • Trade results displayed with metrics and graphs.
    • CSV export option for trade data.

📦 Backend Code Highlights

  • main.go: Core logic, strategy handlers, API endpoints.
  • Custom indicators: RSI, Stochastic, SMA/EMA, Novel Oscillator.
  • Multiple strategy modules, each independently testable.

💻 Frontend Highlights

  • Streamlit dashboard with real-time UI components.
  • Sidebar input for parameters.
  • Tabs for strategy logic, documentation, and results.

🤝 Contributing

  1. Fork the repository.
  2. Create your feature branch (git checkout -b feature-name).
  3. Commit your changes.
  4. Push to the branch (git push origin feature-name).
  5. Open a pull request!

📄 License

This project is licensed under the MIT License. See LICENSE for details.

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License

Distributed under the MIT License. See LICENSE for more information.