MarkoLabs - Portfolio Analytics Dashboard

A modern, interactive dashboard for portfolio analysis built with Plotly Dash. This application allows users to upload portfolio data, visualize performance metrics, and dynamically adjust portfolio weights.

Features

• Interactive data visualization with Plotly charts
• Dynamic portfolio weight adjustments
• Real-time performance metrics
• CSV file upload functionality
• Responsive design with Bootstrap components

Project Structure

MarkoLabs/
├── src/
│   ├── components/
│   │   ├── __init__.py
│   │   ├── charts.py      # Chart creation components
│   │   └── metrics.py     # Portfolio metrics calculations
│   ├── data/
│   │   └── loader.py      # Data loading and processing
│   ├── layouts/
│   │   └── dashboard.py   # Dashboard layout components
│   └── config.py          # Configuration settings
├── dashboard.py           # Main application file
├── requirements.txt       # Project dependencies
└── README.md             # Project documentation

Installation

Local Installation

1. Clone the repository:

git clone https://github.com/yourusername/MarkoLabs.git
cd MarkoLabs

2. Create and activate a virtual environment:

python -m venv venv
venv\Scripts\activate  # Windows
source venv/bin/activate  # Unix/MacOS

3. Install dependencies:

pip install -r requirements.txt

Docker Installation

1. Build and run using Docker Compose (recommended):

docker-compose up -d

2. Or build and run using Docker directly:

# Build the Docker image
docker build -t markolabs .

# Run the container
docker run -d -p 8051:8051 markolabs

3. Access the dashboard at http://localhost:8051

Environment Variables

The application can be configured using the following environment variables:

• HOST: Server host (default: 0.0.0.0)
• PORT: Server port (default: 8051)
• DASH_DEBUG_MODE: Enable debug mode (default: False)

These can be set in the docker-compose.yml file or passed directly to docker run:

docker run -d -p 8051:8051 -e PORT=8051 markolabs

Usage

1. Start the dashboard:

python dashboard.py

2. Open your browser and navigate to http://127.0.0.1:8051/

3. Upload your portfolio data CSV file or use the default data

4. Adjust portfolio weights using the input fields and click "Update Portfolio"

Data Format Requirements

CSV File Structure

The CSV file should follow this format:

Date,Asset1,Asset2,Asset3,...
20240101,100.0,150.0,200.0,...
20240102,101.0,151.0,202.0,...
20240103,99.0,152.0,198.0,...

Requirements:

1. Date Column:

   • Must be named exactly "Date"
   • Format: YYYYMMDD (e.g., 20240101)
   • Must be in chronological order

2. Asset Columns:

   • Each column represents one asset
   • Column names should be the asset identifiers
   • Values must be numeric (prices or returns)
   • No missing values allowed

3. File Format:

   • Must be a CSV file
   • UTF-8 encoding recommended
   • No special characters in column names

Example Data:

Date,AAPL,GOOGL,MSFT
20240101,185.50,140.20,375.80
20240102,186.20,141.50,377.90
20240103,184.90,139.80,374.60

Features and Functionality

Portfolio Metrics

• Cumulative Returns
• Rolling Statistics
• Drawdown Analysis
• Risk Metrics (VaR, CVaR)
• Sharpe Ratio
• Maximum Drawdown

Interactive Features

1. Data Upload:

   • Drag and drop CSV files
   • Automatic validation
   • Error messaging for invalid files

2. Weight Adjustment:

   • Individual asset weight inputs
   • Real-time validation
   • Automatic equal weight option

3. Visualizations:

   • Interactive charts
   • Hover tooltips
   • Zoom functionality
   • Download options

Contributing

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

Dependencies

• dash
• dash-bootstrap-components
• plotly
• pandas
• numpy
• quantstats
• python-dateutil

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built with Plotly Dash
• Styled with Dash Bootstrap Components
• Portfolio analytics powered by QuantStats