README.md

metadata

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.

Getting Started

Follow these steps to get started with the project:

1. Clone the repository:

   git clone https://github.com/bhanuprasanna527/CapiPort/
   

2. Install dependencies:

    pip install -r requirements.txt
   

3. Run the project:

    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
2. HuggingFace Spaces