--- title: CapiPort emoji: 📈 sdk: streamlit sdk_version: 1.32.0 app_file: main.py pinned: false license: mit --- #
CapiPort V2
## 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://huggingface.co/spaces/bhanuprasanna527/CapiPort). ## 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 2) 1) Efficient Frontier - Parameters used: 1.1) Maximum Sharpe Ratio\ 1.2) Efficient Risk\ 1.3) Efficient Return\ 1.4) Minimum Volatility 2) Hierarchical Risk Parity # Overview 1. 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://capiport2.streamlit.app/)** 2. **[HuggingFace Spaces](https://huggingface.co/spaces/bhanuprasanna527/CapiPort)**