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

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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.

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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.

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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

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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.

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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

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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.

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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

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License

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

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