Multi-asset market exploration and risk comparison
This project analyzes seven large-cap assets across sectors to compare prices, returns, volatility, cumulative performance, and cross-asset relationships.
Challenge
- Multi-asset market behavior needs comparison across returns, volatility, and correlation rather than price charts alone.
- Assets from different sectors can show different risk and diversification behavior.
- Exploratory analysis needs a clean reproducible workflow before modeling.
System architecture
Data and inputs
Daily data from 2019 onward for AAPL, MSFT, AMZN, JPM, XOM, PFE, and TSLA, with engineered returns, moving averages, Bollinger Bands, cumulative returns, and normalized prices.
Technical approach
- Download historical market data with yfinance.
- Clean missing trading-day values and reshape into tidy format.
- Compute return, volatility, cumulative performance, and normalized metrics.
- Visualize distributions, risk, correlations, and cumulative performance.
Evaluation and results
7 large-cap assets
Daily data from 2019 onward
AAPL/MSFT return correlation ~0.69
- TSLA showed the highest daily-return volatility in the selected group.
- PFE showed the lowest volatility among the selected assets.
- AAPL and MSFT had a strong positive return correlation of about 0.69.
Implementation and code
Implementation focus
The implementation connects data preparation, modeling, evaluation, and interpretation in a structured workflow that makes the technical decisions clear.
Source code
The code is available for exploring the implementation details and extending the experiment when needed.
Scope and responsible use
The analysis is intended for modeling and evaluation, not investment advice. Real trading use would require risk controls, transaction-cost modeling, out-of-sample validation, and continuous monitoring.
Future development
- Add sector-level aggregation and rolling-correlation analysis.
- Add risk-adjusted metrics such as Sharpe and downside deviation.
- Use the EDA outputs as a foundation for later modeling experiments.
Technical contribution
The project builds a clean foundation for financial analysis by comparing risk, return, and correlation across assets.