kissbt

kissbt, the keep it simple backtesting framework, is a lightweight and user-friendly Python framework for backtesting trading strategies. It focuses on simplicity, performance, and ease of extensibility while providing essential tools for effective backtesting.

Why kissbt?

• 🚀 Lightweight – Minimal dependencies ensure fast installation and execution.
• 📖 Simple API – Lowers the barrier for traders new to backtesting.
• 🔌 Extensible – Modular architecture enables easy customization.
• 📊 Essential Features – Includes tools for data handling, strategy implementation, and performance evaluation.

Features

✔️ Object-oriented design for intuitive strategy development ✔️ Fast execution, even for large universes ✔️ Supports long and short positions ✔️ Built-in trade execution, position tracking, and P&L calculation ✔️ Performance analysis with key trading metrics ✔️ Backtesting with historical market data ✔️ Modular components (Strategy, Broker, Engine, Analyzer)

Installation

You can install kissbt using either pip or conda.

Using pip

To install kissbt via pip, run the following command:

pip install kissbt

Using conda

To install kissbt via conda, run the following command:

conda install -c conda-forge kissbt

Usage

1. Define a Strategy

Create a custom strategy by extending the Strategy class and implementing the generate_orders method:

from kissbt.strategy import Strategy
from kissbt.entities import Order, OrderType

class MyStrategy(Strategy):
    def generate_orders(self, current_data, current_datetime):
        # Example: Buy if the close price is above the 128-day SMA
        for ticker in current_data.index:
            close_price = current_data.loc[ticker, "close"]
            sma_128 = current_data.loc[ticker, "sma_128"]
            if close_price > sma_128:
                order = Order(ticker=ticker, size=10, order_type=OrderType.OPEN)
                self._broker.place_order(order)

2. Set Up the Broker

Initialize the Broker with starting capital, fees, and other parameters:

from kissbt.broker import Broker

broker = Broker(start_capital=100000, fees=0.001)

3. Run the Backtest

Use the Engine to run the backtest with your strategy and market data:

from kissbt.engine import Engine
import pandas as pd

# Load market data
data = pd.read_csv('market_data.csv', parse_dates=['date'])

# Initialize strategy and engine
strategy = MyStrategy(broker)
engine = Engine(broker, strategy)

# Run the backtest
engine.run(data)

4. Analyze Performance

Use the Analyzer to calculate and display performance metrics:

from kissbt.analyzer import Analyzer

analyzer = Analyzer(broker)
metrics = analyzer.get_performance_metrics()
print(metrics)

Examples

Check out the examples directory for more detailed examples and use cases.

License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.

Contributing

We welcome contributions! If you have ideas, bug fixes, or feature requests, feel free to open an issue or submit a pull request.

Contact

For any questions or inquiries, please contact Adrian Hasse at adrian.hasse@finblobs.com.