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| title: CapiPort | |
| emoji: 🤗 | |
| sdk: streamlit | |
| sdk_version: 1.32.0 | |
| app_file: main.py | |
| pinned: false | |
| license: mit | |
| # Portfolio Management for Indian Equity Markets | |
| [](https://github.com/bhanuprasanna527/CapiPort/actions) | |
| ## Overview | |
| Welcome to our project on portfolio management for Indian equity markets! This project aims to help individuals efficiently allocate their money between different equities, optimizing returns while managing risk. | |
| ## Features | |
| - **Dynamic Allocation:** Our technique dynamically allocates funds among various equities based on a robust methodology. | |
| - **Risk Management:** The project incorporates risk management strategies to enhance overall portfolio stability. | |
| - **User-Friendly Interface:** Access the tool through our user-friendly web interface [here](https://capiport.streamlit.app/). | |
| ## Getting Started | |
| Follow these steps to get started with the project: | |
| 1. Clone the repository: | |
| ```bash | |
| git clone https://github.com/bhanuprasanna527/CapiPort/ | |
| 2. Install dependencies: | |
| ```bash | |
| pip install -r requirements.txt | |
| 3. Run the project: | |
| ```bash | |
| python main.py | |
| ## Technique used (Version 1) | |
| ### Mean-Variance Portfolio Optimization | |
| Overview | |
| Mean-Variance Portfolio Optimization is a widely used method in finance for constructing an investment portfolio that maximizes expected return for a given level of risk, or equivalently minimizes risk for a given level of expected return. This approach was pioneered by Harry Markowitz and forms the foundation of Modern Portfolio Theory (MPT). | |
| Methodology | |
| 1. Basic Concepts | |
| Expected Return: The anticipated gain or loss from an investment, based on historical data or other factors. | |
| Risk (Variance): A measure of the dispersion of returns. In portfolio optimization, we seek to minimize the variance of the portfolio returns. | |
| 3. Optimization Algorithm | |
| Our implementation utilizes the following steps: | |
| Input Data: Historical returns for each asset in the portfolio. | |
| Objective Function: Construct an objective function that combines the expected return and variance. | |
| Optimization Algorithm: We employ a mean-variance optimization algorithm that iteratively adjusts the weights to find the optimal combination. | |
| Convergence Criteria: The algorithm iterates over a specified number of iterations (e.g., 5000) or until convergence is achieved. | |
| 4. Implementation | |
| In our project, we have implemented the Mean-Variance Portfolio Optimization method with 5000 iterations. The process involves: | |
| Input: Historical return data for each equity in the Indian market. | |
| Objective: Maximize expected return while minimizing portfolio variance. | |
| Optimization: Utilize an iterative approach, adjusting weights to find the optimal allocation. | |
| Output: The final set of weights that represent the optimal portfolio allocation. | |
| #### Contributing | |
| We welcome contributions! If you have any ideas for improvements, open an issue or submit a pull request. | |
| License | |
| This project is licensed under the MIT License. | |
| ## Links | |
| 1. **[Streamlit Deployment](https://capiport.streamlit.app/)** | |
| 2. **[HuggingFace Spaces](https://huggingface.co/spaces/sankhyikii/CapiPort)** | |