d---
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)**