simple-backtest 0.1.0

Simple backtesting framework for trading strategies

Simple Backtest

Simple, high-performance backtesting framework for trading strategies. I created this library so anyone can implement their own trading strategy with simple code, just create a new class that inherits from the Strategy class and implement the predict method and run the backtest on your data.

Features

• 🚀 Parallel strategy execution
• 📊 20+ performance metrics (Sharpe, Sortino, Calmar, etc.)
• 📈 Interactive Plotly visualizations
• 🎯 Clean Strategy Pattern architecture
• 💰 Flexible commission models

Installation

# Using uv (recommended)
uv pip install simple-backtest

# Or with pip
pip install simple-backtest

Quick Start

import pandas as pd
from simple_backtest import BacktestConfig, Backtest
from simple_backtest.strategy.moving_average import MovingAverageStrategy
from simple_backtest.visualization.plotter import plot_equity_curve

# Load OHLCV data
data = pd.read_csv("data.csv", index_col=0, parse_dates=True)

# Configure backtest
config = BacktestConfig(
    initial_capital=10000.0,
    lookback_period=50,
    commission_type="percentage",
    commission_value=0.001,
)

# Run backtest
strategy = MovingAverageStrategy(short_window=10, long_window=30, shares=100)
backtest = Backtest(data=data, config=config)
results = backtest.run([strategy])

# Visualize
plot_equity_curve(results).show()

# Print metrics
print(results[strategy.get_name()]['metrics'])

Create Custom Strategy

from simple_backtest import Strategy

class MyStrategy(Strategy):
    def predict(self, data, trade_history):
        """
        :param data: OHLCV DataFrame
        :param trade_history: List of past trades
        :return: Dict with signal, size, order_ids
        """
        if data['Close'].iloc[-1] < 100:
            return {"signal": "buy", "size": 10, "order_ids": None}
        elif data['Close'].iloc[-1] > 120:
            return {"signal": "sell", "size": 10, "order_ids": None}
        return {"signal": "hold", "size": 0, "order_ids": None}

Notebooks

Check out the notebooks/ folder for interactive examples:

• 01_basic_usage.ipynb: Introduction to the framework
• 02_rsi_strategy.ipynb: RSI momentum strategy
• 03_bollinger_bands_strategy.ipynb: Mean reversion with Bollinger Bands
• 04_strategy_comparison.ipynb: Compare multiple strategies

To run the notebooks:

# Install with dev dependencies
uv sync --all-extras

# Start Jupyter
jupyter notebook

Development

# Clone repo
git clone <repo-url>
cd simple-backtest

# Install with uv
uv sync --all-extras

# Run tests
uv run pytest

# Lint
uv run ruff check simple_backtest

Metrics

• Returns: Total Return, CAGR
• Risk: Volatility, Sharpe, Sortino, Calmar, Max Drawdown
• Trades: Win Rate, Profit Factor, Expectancy
• Benchmark: Alpha, Beta, Information Ratio

Built-in Strategies

• Buy and Hold: Simple baseline strategy
• Moving Average Crossover: Trade on MA crossovers

See notebooks/ for more strategy examples (RSI, Bollinger Bands, etc.).

License

MIT