MonteCarloPortfolioOptimization

Objective

Implementing Monte Carlo Simulation for Portfolio Optimization by assigning random weights to each Sector SPDR ETF. Iterating n-times to increase the accuracy for portfolio optimization. Calculating portfolio's returns and standard deviation to calculate Portfolio's Sharpe-Ratio

Metric

Sharpe Ratio (risk adjusted return): the ratio of Portolfio's Returns to Portfolio's Volatility (risk), annualized.
The Sharpe Ratio gives market-participants the confidence to apply leverage.

Mathematics

Output

Returns        0.445189
Volatility     0.197381
Sharpe Ratio   2.835657

SPY,0.15537764950322155
XLB,0.14840094378559168
XLE,0.11230647154471428
XLF,0.07320378035724731
XLI,0.00922948739999223
XLK,0.13076865641677185
XLP,0.13864787179631605
XLU,0.13783414102104255
XLV,0.06801761827474444
XLY,0.02621337990035807

Repository File Structure

├── src          
│   ├── main.py              # Monte Carlo Simulation and calculating the max annualized sharpe-ratio and returns
│   ├── data.py              # Extract the Adjusted-Closing Price for each SPDR Sector ETF
│   └── config.py            # Define path as global variable
├── inputs
│   ├── sector_weights.csv   # Output for optimal sector weights
│   └── train.csv            # Adj-Closing Price
├── requierments.txt         # Packages used for project
└── README.md

Data: Sector SPDR ETFs

Collecting Sector SPDR ETFs

SPY: S&P 500 Index Fund
XLF: Financial
XLY: Consumer Discretionary
XLK: Technology
XLB: Materials
XLI: Industrials
XLE: Energy
XLU: Utilites
XLV: Health Care
XLP: Consumer Staples