Backtesting Engine

A modular and extensible Python engine for simulating, visualizing, and evaluating trading strategies using historical financial data. Designed for rapid research, testing, and strategy iteration.

Features

• Simulates trades on historical OHLCV data
• Modular strategy design (plug-and-play)
• Visualizes equity curves, buy/sell signals, and holding periods
• Computes key performance metrics:
  • Total Return
  • Max Drawdown
  • Win Rate
  • Sharpe Ratio
• Outputs results to structured folders with CSVs and plots
• Supports batch testing across multiple tickers and strategies

Project Structure

backtesting-engine/ core/ -> Core engine components backtester.py -> Main backtesting logic data_loader.py -> Loads and filters historical data portfolio.py -> Manages trades, cash, and holdings

strategies/ -> Trading strategy definitions sample_strategy.py bollinger_band_strategy.py frequent_trading_strategy.py momentum_strategy.py rsi_strategy.py

utils/ -> Utility modules metrics.py -> Performance metric calculations plotter.py -> Equity curve and metrics plot generator

data/ -> Input CSV data AAPL_data.csv, etc.

results/ -> Output directory (auto-generated) [ticker]/[strategy]/ -> Equity curve, trades, metrics, plots

main.py -> Main CLI runner requirements.txt -> Dependency list README.md -> This file

Installation

Python 3.8+ is required.

Install all dependencies: pip install -r requirements.txt

Contents of requirements.txt: pandas>=1.3.0 numpy>=1.21.0 matplotlib>=3.4.0

Getting Started

1. Place historical CSV files in the data/ directory. Each file should include:

   • Date, Open, High, Low, Close, Volume

2. Run the backtesting engine: python main.py

3. Select:

   • A ticker (e.g., AAPL or ALL)
   • A strategy (e.g., RSIStrategy or ALL)

4. Results will be saved in the results/ folder with plots, metrics, and summary CSVs.

Built-In Strategies

SampleStrategy - Simple moving average crossover RSIStrategy - Uses RSI thresholds to generate signals BollingerBandStrategy - Trades on breakouts from Bollinger Bands FrequentTradingStrategy - EMA crossovers + volatility filter MomentumStrategy - Momentum via Rate of Change (ROC)

To add your own strategy:

• Define generate_signal(self, index, row, data)
• Optionally add prepare(self, df) to precompute indicators

Output Example

Final output includes:

• equity_curve.png
• equity_curve.csv
• trades.csv
• metrics.txt
• summary.csv (aggregates all runs)

Example metrics: Total Return: 15.43% Max Drawdown: -10.15% Win Rate: 57.1% Sharpe Ratio: 1.25

Future Improvements

• Support for slippage and transaction costs
• Position sizing / risk management
• GUI

License

MIT License.