https://github.com/shreyasdankhade/portfolio_optimatization_project
The Portfolio Optimization Project uses optimization techniques to balance risk and return, helping investors make efficient asset allocation decisions.
https://github.com/shreyasdankhade/portfolio_optimatization_project
flask flask-application matplotlib numpy pandas pandas-python porfolio-optimization portfolio python
Last synced: about 1 month ago
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The Portfolio Optimization Project uses optimization techniques to balance risk and return, helping investors make efficient asset allocation decisions.
- Host: GitHub
- URL: https://github.com/shreyasdankhade/portfolio_optimatization_project
- Owner: ShreyasDankhade
- Created: 2024-04-08T10:15:19.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-03-21T12:56:12.000Z (11 months ago)
- Last Synced: 2025-03-21T13:55:28.389Z (11 months ago)
- Topics: flask, flask-application, matplotlib, numpy, pandas, pandas-python, porfolio-optimization, portfolio, python
- Language: CSS
- Homepage:
- Size: 203 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🚀 Portfolio Optimization Project
## 📌 Overview
The **Portfolio Optimization Project** is designed to help investors allocate their assets efficiently by minimizing risk and maximizing returns using various optimization techniques. This project utilizes statistical and mathematical models to determine the best asset allocation strategies.
## 🎯 Features
- **Efficient Frontier Calculation**: Identifies the optimal portfolio combination.
- **Mean-Variance Optimization**: Uses Modern Portfolio Theory (MPT) to allocate assets.
- **Monte Carlo Simulation**: Evaluates different portfolio allocations using random sampling.
- **Sharpe Ratio Optimization**: Maximizes the risk-adjusted return.
- **Data Visualization**: Displays portfolio performance metrics using interactive charts.
## 🛠️ Technologies Used
- **Python**
- NumPy
- Pandas
- Matplotlib
- CVXPY
- Flask
## 📥 Installation
1. Clone the repository:
```sh
git clone https://github.com/ShreyasDankhade/Portfolio_Optimatization_Project.git
```
2. Navigate to the project directory:
```sh
cd Portfolio_Optimatization_Project
```
3. Install dependencies:
```sh
pip install -r requirements.txt
```
## 🚀 Usage
1. Run the provided Python scripts to analyze and optimize a sample portfolio.
2. Modify the dataset to test with different asset allocations.
## 📂 Project Structure
```md
Portfolio_Optimatization_Project/
│── static/
│ │── css/
│ │ │── main.css # CSS file
│ │── img/
│ │ │── logo.png # Logo image
│── templates/
│ │── base.html
│── main.py # Python scripts for optimization models
│── requirements.txt # Dependencies
│── README.md # Project documentation
```
## 📊 Examples
### Efficient Frontier Plot
- Generates an efficient frontier showing risk vs. return trade-offs.
### Monte Carlo Simulation
- Runs random portfolio allocations and visualizes the results.
### Sharpe Ratio Optimization
- Finds the portfolio with the highest Sharpe ratio to maximize returns per unit of risk.
## 👥 Contributors
- **Shreyas Dankhade** (Repository Owner)
- Contributions are welcome! Feel free to fork and submit pull requests.
## 🙌 Acknowledgments
- Inspired by **Markowitz’s Modern Portfolio Theory (MPT)**
- Uses concepts from quantitative finance and investment strategy development.
## 📧 Contact
For questions or support, contact Shreyas Dankhade at shreyasdankhade75@gmail.com.