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btc-dca-trading-system

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Smart, hedged BTC investing made simple.

Table of Contents

  1. Introduction
  2. Features
  3. Installation
  4. Usage
  5. Project Structure
  6. Configuration
  7. Contributing
  8. License

Introduction

DCAlytics is an interactive cryptocurrency dashboard and simulation tool that combines dynamic dollar-cost averaging (DCA) with risk-managed hedging strategies. The platform allows users to:

  • Visualize portfolio performance
  • Simulate BTC trades
  • Optimize investments while managing market volatility

It features a sleek, responsive interface built for both desktop and mobile.

Features

  • Dynamic DCA Engine: Configure recurring BTC purchases at user-defined intervals.
  • Hedging Strategies: Reduce exposure to market volatility with adjustable hedging percentages.
  • Portfolio Analytics: Real-time charts comparing DCA strategies vs HODL.
  • Trade Simulation: Backtest strategies using historical and live BTC data.
  • Responsive Interface: TailwindCSS + Chart.js for interactive dashboards.

Installation

Prerequisites

  • Python 3.11+
  • Node.js 18+ (for frontend if running locally)
  • Git

Clone the Repository

git clone https://github.com/canstralian/dcalytics.git
cd dcalytics

Backend Setup (FastAPI)

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate     # Windows

# Install dependencies
pip install -r requirements.txt

# Run FastAPI server
uvicorn backend.main:app --reload

Frontend Setup (Static Dashboard / Gradio / Streamlit)

  • Option 1: Static Frontend Simply open frontend/index.html in a browser or deploy to Hugging Face Spaces.

  • Option 2: Streamlit (Optional)

pip install streamlit
streamlit run frontend/app.py

Usage

  1. Open the dashboard in a browser.

  2. Configure your DCA strategy:

    • Investment Amount
    • DCA Frequency
    • Hedge Percentage
    • Time Period

  3. Click Run Simulation.

  4. Visualize results: portfolio value, BTC price evolution, risk metrics, and performance comparison charts.

  5. Export reports if needed.

Project Structure

dcalytics/
├── backend/                 # FastAPI backend
│   ├── main.py              # API routes
│   ├── models.py            # Data models
│   ├── trading_engine.py    # Algorithmic trading logic
│   └── requirements.txt
├── frontend/                # Dashboard UI
│   ├── index.html
│   ├── app.py               # Streamlit frontend (optional)
│   ├── styles.css
│   └── scripts.js
├── data/                    # Sample datasets or cached BTC prices
├── docs/                    # Documentation and diagrams
└── README.md

Configuration

  • Backend: config.yaml or .env for API keys (if using live BTC feeds).
  • Frontend: TailwindCSS variables for theming; Chart.js for charts.
  • Simulation Parameters: Set via dashboard sliders and dropdowns.

Contributing

  1. Fork the repo
  2. Create a feature branch (git checkout -b feature-name)
  3. Commit your changes (git commit -m 'Add feature')
  4. Push to branch (git push origin feature-name)
  5. Open a Pull Request

License

This project is licensed under Apache 2.0 — see the LICENSE file for details.