Monte Carlo & SMA Crossover Backtest

This project explores two quantitative finance techniques using Python in QuantConnect:

• Monte Carlo Simulation: Models possible future price paths of Ralph Lauren (RL) stock.
• SMA Crossover Backtest: Tests a 10-day vs. 50-day Simple Moving Average (SMA) trading strategy to evaluate historical performance and risk.

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Project Overview

I built this project to strengthen my programming and quantitative finance skills:

• Learned how to implement Monte Carlo simulations in Python.
• Gained experience with backtesting trading strategies.
• Developed a better understanding of Python functions, syntax, and files.
• Important Note: This project will only be able to be run in QuantConnect. This code needs (Lean Engine) for backtesting and needs to be able to access QuantConnect's free API.

When I started, I had minimal Python experience — this project was a great interactive way to learn both coding and market modeling.

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Tools & Libraries

• QuantConnect (Lean Engine) for backtesting
• Python for coding simulations and strategies

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Acknowledgements

Special thanks to the YouTube channel QuantProgram for troubleshooting guidance on QuantConnect throughout the project.

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Repository Contents

• main.py → Algorithm code (Monte Carlo + SMA crossover)
• research.ipynb → Research script exploring RL with Bollinger Bands
• README.md → Project documentation
• results.json → Full written results downloaded from QuantConnect. The essential results are pictured below.
• Strategy Equity.png → Equity curve of the strategy
• Drawdown.png → Historical drawdown chart
• Assets Sales Volume.png → Trading activity and sales volume
• Portfolio Margin.png → Portfolio margin usage
• Overview 1.png, Overview 2.png → Sharpe ratio, win rate, average win/loss, total orders, net profit, and more statistics.