A comprehensive web application for managing and analyzing multi-asset investment portfolios. Built with Python and Flask, this project demonstrates how to use different data structures to organize financial data as covered in Chapter 5 of Practical Python for Effective Algorithmic Trading.
- Beautiful Dashboard: Modern, responsive design with Bootstrap 5
- Multi-Asset Support: Track stocks, ETFs, bonds and other asset classes
- Portfolio Analysis: View allocation by asset class and sector with interactive charts
- Performance Metrics: Identify best/worst performers and positions exceeding thresholds
- Watchlist Management: Find complementary securities to improve diversification
- Rebalancing Suggestions: Get actionable recommendations to maintain target allocations
Try the application live on Replit: Multi-Asset Portfolio Management System
- Python 3.7+
- Flask 2.3.3+
- NumPy 1.25.2+
- pandas 2.1.0+
- Plotly 5.16.1+
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Clone the repository
git clone https://github.com/thekuldeepsingh/Multi_Asset_Portfolio_Management_System.git cd Multi_Asset_Portfolio_Management_System -
Create and activate a virtual environment (optional but recommended)
python -m venv venv # Windows venv\Scripts\activate # macOS/Linux source venv/bin/activate
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Install dependencies
pip install -r requirements.txt
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Run the application
python main.py
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Open your browser Navigate to
http://localhost:5001
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Dashboard
- View portfolio summary with key metrics
- Analyze asset allocation and sector diversification
- Track performance with interactive charts
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Portfolio Management
- See detailed information on all holdings
- Identify best and worst performing assets
- Get rebalancing suggestions based on target allocations
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Watchlist Analysis
- Manage potential investments
- Find complementary securities for better diversification
- Compare securities in similar sectors
The system is built on several core concepts from Chapter 5 of Practical Python for Effective Algorithmic Trading:
- Dictionaries: Nested dictionaries for storing portfolio and security data
- Lists: For tracking purchase history and sorted performance metrics
- Tuples: For representing immutable data like purchase records
- Sets: For efficient lookups and finding relationships between securities
The web application extends these concepts with:
- Flask Backend: Provides routes and handles data processing
- Plotly Visualizations: Creates interactive financial charts
- Bootstrap Frontend: Delivers a responsive, modern user interface
Join The Quantitative Elite community to:
- Share your implementations and improvements
- Get help with customization and advanced portfolio strategies
- Connect with other algorithmic traders
- Learn advanced Python trading techniques
Subscribe to our YouTube Channel for video tutorials and live coding sessions.
The portfolio management system can be extended in several ways:
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Connect to Real Market Data
- Integrate with Yahoo Finance, Alpha Vantage, or other data providers
- Auto-update prices and portfolio metrics
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Add Database Storage
- Implement SQL or NoSQL database for persistent data
- Track historical portfolio performance
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Implement Authentication
- Add user accounts for personal portfolios
- Enable sharing and collaboration features
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Enhanced Analytics
- Calculate risk metrics (alpha, beta, Sharpe ratio)
- Implement portfolio optimization algorithms
- Add tax-lot tracking and tax-loss harvesting
For more advanced techniques and a deeper understanding of financial data structures with Python, refer to Practical Python for Effective Algorithmic Trading.
This project is licensed under the MIT License - see the LICENSE file for details.
- Based on concepts from Practical Python for Effective Algorithmic Trading
- Portfolio management strategies inspired by modern finance theory
- Web application designed to make portfolio analysis accessible to all investors
Happy investing and coding! 📈