Algorithmic-Trading-Backtest-Engine

An independent project which delves into the mechanics of algorithmic trading and backtesting. Python-based engine designed to quantitatively assess trading strategies against a Buy & Hold benchmark using 20 years of S&P500 close price data. Calculation of key risk-return metrics utilised to investigate the real-world trade-off between risk mitigation and return maximisation.

🔍 Key Findings

Backtesting a 50/200-day SMA Crossover strategy revealed:

• Nearly Identical Returns: ~8.5% annualized returns for both SMA and Buy & Hold.
• Marginally Superior Risk-Adjusted Performance: Achieved a higher Sharpe Ratio (0.451 vs. 0.445).
• Significant Risk Reduction: Demonstrated a 6% improvement in Maximum Drawdown, highlighting effective capital preservation.

🔧Key Features

• Historical data processing using `yfinance' API
• Strategy simulation (50/200-day SMA Crossover & Buy & Hold)
• Performance metrics calculation (CAGR, Sharpe Ratio, Max Drawdown)
• Equity curve visualization with matplotlib

📊 Full Report & Analysis

For complete methodology and analysis, please read the full project report:

📄 Full Project Report (PDF)