This project involves the development of a Python program that consumes a list of stocks and generates the safest possible portfolio from the given stocks. The core objective is to design and implement an algorithm that evaluates the correlation between different stocks' historical data and selects the least correlated group of stocks to maximize portfolio diversification and minimize risk. Steps that were taken for the implementation of the project:
- Designed and implemented an algorithm to analyze and compare stock correlations based on historical data.
- Utilized Pandas for data manipulation and analysis, including data cleaning, transformation, and visualization.
- Leveraged NumPy for numerical operations and efficient handling of large datasets.
- Generated a diversified portfolio by selecting the least correlated stocks to minimize risk.
- Used Jupyter Notebook to display the results step by step.
- Stock List Consumption: The program accepts a list of stocks as input.
- Historical Data Analysis: It retrieves and analyzes historical data from Yahoo Finance for each stock in the list.
- Correlation Comparison: An algorithm compares the correlation between the historical data of different stocks.
- Optimal Portfolio Selection: The program selects the least correlated group of stocks to form a diversified and low-risk portfolio.