VectorizedBacktester

Building a professional-grade trading backtester from scratch using Python. Developed a powerful tool that connects to real-time market data, tests multiple trading strategies, and performs sophisticated performance analysis. Using Python libraries such as yfinance, numpy and pandas.

The VectorizedBacktester class simplifies backtesting by automating data downloads, return calculations, and the evaluation of trading strategies.

Key Attributes:

• Symbol: The stock or asset being traded (e.g., AAPL for Apple stock).
• Start and End: The date range for the historical data.
• Amount: Initial capital invested.
• TC: Transaction costs, representing commissions or slippage in real-world trading.

The class uses the yfinance package to fetch historical price data, calculates daily log returns, and applies the chosen trading strategy.

Trading Strategies:

Moving Average (MA)

This strategy compares two Simple Moving Averages (SMAs):

• SMA1: Short-term moving average (e.g., 50 days).
• SMA2: Long-term moving average (e.g., 200 days).

A buy signal is generated when the short-term average crosses above the long-term average, and a sell signal occurs when it crosses below.

Relative Strength Index (RSI)

RSI measures price momentum to identify overbought or oversold conditions:

• Upper bound: Overbought signal (e.g., RSI > 70).
• Lower bound: Oversold signal (e.g., RSI < 30).

Buy and sell decisions are based on crossing these thresholds.

Strategy Metrics:

• Total Return:
  Total Return = (Final Value - Initial Value) / Initial Value
  Shows the overall performance of the strategy.

• Excess Return:
  The difference between the total return and the market return.

• Volatility:
  Measures the risk by showing how much the returns fluctuate.

• Sharpe Ratio:
  Sharpe Ratio = (Average Return - Risk-Free Rate) / Volatility
  This metric adjusts returns by risk, measuring the reward per unit of risk. A higher Sharpe ratio indicates better risk-adjusted returns.