A data science project exploring how to balance risk and return when investing in multiple stocks, using historical market data and portfolio optimization techniques.
Understand how diversification and asset allocation work in practice, and how data can be used to build a balanced investment portfolio while optimizing risk.
- Market Data Collection
- Return Calculation
- Risk Analysis
- Portfolio Optimization
- Visualization of Efficient Frontier
- Risk vs. return trade-off
- Diversification across assets
- Covariance between stocks
- Portfolio weight allocation
- Efficient frontier visualization
This project demonstrates how core data science concepts—such as statistics, optimization, and visualization—can be applied to real-world financial decision-making.
MOHIT