badal39 / Portfolio-Analysis-and-Optimization-with-Sharpe-Ratio

Portfolio optimization system that maximizes returns while effectively managing risk.

Investment Portfolio Analysis & Optimization

The project aims to develop a portfolio optimization system that maximizes returns while effectively managing risk. It involves collecting historical data on various assets from Yahoo Finance, conducting a comprehensive analysis of the portfolio, and optimizing asset allocation based on risk-adjusted metrics. The implementation will be done using Python, utilizing libraries such as Scipy and Streamlit.

Demo Images

Here are some demo images for the project:

Problem Statement

The goal is to develop a system that automatically allocates assets in a portfolio based on historical data and risk-adjusted metrics. By doing so, investors can make informed decisions to achieve higher returns while considering the associated risks.

Data Collection Method

• yfinance Python: Historical data on various assets will be collected from Yahoo Finance.

Model Development

A portfolio optimization model can be developed using Modern Portfolio Theory. The model will aim to maximize returns while managing risk based on the selected features and risk-adjusted metrics.

Model Evaluation

The developed model will be evaluated using Sharpe Ratio and compared to evaluate the effectiveness of the portfolio optimization system. Additionally, backtesting is employed to assess the performance of the optimized portfolios against historical data.

Installation

To run the Portfolio-Analysis-and-Optimization-with-Sharpe-Ratio, follow these steps:

1. Clone the repository:

   git clone https://github.com/badal39/Portfolio-Analysis-and-Optimization-with-Sharpe-Ratio.git

2. Create a virtual environment:

   python -m venv env

3. Activate the virtual environment:

For Windows

env\Scripts\activate

For Linux/Mac

source env/bin/activate

4. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Run the Streamlit app:

   streamlit run FinApp.py

2. Open your web browser and go to http://localhost:8501 to access the application.

3. Follow the instructions on the web interface to use the Baroque-inspired Art Recommendation System.