Update README.md

README.md

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-# Hello 👋
+# <center>CapiPort V2</center>
+
+# Portfolio Management for Indian Equity Markets
+[![Build Status](https://github.com/bhanuprasanna527/CapiPortV2/actions/workflows/HF_sync_space.yml/badge.sv)](https://github.com/bhanuprasanna527/CapiPortV2/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://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 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)**