GoQuant — A Simple & Fast Backtesting Framework in Go

GoQuant is a lightweight, modular backtesting engine for quantitative trading strategies written in Go. It supports CSV-based historical data, built-in performance metrics (Sharpe, Max Drawdown, etc.), and is designed for easy extension with new strategies.

Perfect for learning quant development, testing ideas, or building a foundation for a live trading system.

• Go 1.16+
• Historical market data in CSV format (see Data Format below)

git clone https://github.com/techmagic/goquant.git
cd goquant

Place your OHLCV CSV files in the data/ directory:

mkdir -p data

# Example: download BTC-USD.csv from Yahoo Finance and put it here
cp /path/to/BTC-USD.csv data/

go run cmd/goquantlab/main.go --symbol BTC-USD --ma-fast 10 --ma-slow 30

You’ll see a summary like:

=== Summary ===
Symbol: BTC-USD
Bars: 4034
Initial capital: 10000.00
Final equity: 2544418.79
Total return: 25344.19%
Max Drawdown: 63.25%
Annualized Return: 52.93%
Annualized Volatility: 39.84%
Sharpe: 1.329
Trades:
2014-10-18 BUY qty=25.5466 price=391.44 cash=0.00
2014-10-28 SELL qty=25.5466 price=357.62 cash=9135.91
...

Your CSV files must follow this format:

• File location: data/.csv (e.g., data/BTC-USD.csv)
• Header row: Required (will be skipped)
• Columns (in any order):
  • Date (YYYY-MM-DD)
  • Open
  • High
  • Low
  • Close
  • Volume

✅ Example (data/BTC-USD.csv):

Date,Open,High,Low,Close,Volume
2014-09-17,465.86,468.17,452.42,457.33,21056800
2014-09-18,456.86,456.86,413.10,424.44,34483200
...

🔍 Note: The parser reads columns by name, not position — so column order doesn’t matter! 💡 Where to get data? Download free historical data from Yahoo Finance :

1. Search for a symbol (e.g., BTC-USD)
2. Go to Historical Data → Download

goquant/
├── cmd/goquantlab/      # CLI entry point
├── internal/
│   ├── data/            # CSV data loader
│   ├── strategy/        # Trading strategies (e.g., MA Crossover)
│   ├── backtest/        # Backtesting engine
│   ├── metrics/         # Performance metrics (Sharpe, Drawdown, etc.)
│   └── utils/           # Config loader
├── data/                # Place your CSV files here
├── configs/             # Example config
└── go.mod               # Go module

• Strategy Interface: Easy to add new strategies (implement OnBar)
• Modular Design: Swap data sources, metrics, or position sizing without touching core logic

Add a new strategy

• Create internal/strategy/mystategy.go
• Implement the Strategy interface:

type MyStrategy struct { ... }
func (s *MyStrategy) OnBar(bar data.Bar) int { ... }

• Register it in main.go

Add transaction costs Modify internal/backtest/backtest.go to deduct fees on trades.

MIT License — feel free to use, modify, and distribute.

Inspired by classic quant backtesting frameworks Uses standard Go tooling — no external dependencies

Happy backtesting! 📈 If you find this useful, consider giving it a ⭐ on GitHub!