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https://github.com/pawelkn/btester
Python framework optimized for running backtests on multiple asset portfolios
https://github.com/pawelkn/btester
algorithmics analysis backtesting multi-asset multi-assets parallel portfolio python python3 quantitative trading
Last synced: about 2 months ago
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Python framework optimized for running backtests on multiple asset portfolios
- Host: GitHub
- URL: https://github.com/pawelkn/btester
- Owner: pawelkn
- License: mit
- Created: 2024-01-12T13:37:33.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2024-02-03T11:19:00.000Z (12 months ago)
- Last Synced: 2024-11-12T09:12:32.759Z (2 months ago)
- Topics: algorithmics, analysis, backtesting, multi-asset, multi-assets, parallel, portfolio, python, python3, quantitative, trading
- Language: Python
- Homepage:
- Size: 4.51 MB
- Stars: 14
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# btester - Multi-Assets Backtesting Framework
[](https://github.com/pawelkn/btester/actions/workflows/test-btester.yml) [](https://pypi.python.org/pypi/btester/) [](https://pypi.python.org/pypi/btester/) [](https://codecov.io/gh/pawelkn/btester/)
`btester` is a Python framework optimized for running backtests on multiple asset portfolios.
It provides tools for backtesting trading strategies based on historical market data. The framework includes classes for managing financial positions, completed trades, and a flexible abstract base class for implementing custom trading strategies.
## Installation
You can install `btester` using pip. Simply run the following command:
```bash
pip install btester
```
## Usage
1. Define your custom trading strategy by creating a class that inherits from the `Strategy` abstract class.
2. Implement the required methods in your custom strategy: `init` for initialization and `next` for the core strategy logic.
3. Instantiate the `Backtest` class with your custom strategy, historical market data, and other parameters.
4. Run the backtest using the `run` method, which returns a `Result` object containing backtest results.
## Example Usage
```python
# Example usage of the btester
from btester import Strategy, Backtest
import pandas as pd
# Define a custom strategy by inheriting from the abstract Strategy class
class MyStrategy(Strategy):
def init(self):
# Custom initialization logic for the strategy
pass
def next(self, i: int, record: Dict[Hashable, Any]):
# Custom strategy logic for each time step
pass
# Load historical market data
data = pd.read_csv('historical_data.csv', parse_dates=['Date'])
data.set_index('Date', inplace=True)
# Initialize and run the backtest
backtest = Backtest(strategy=MyStrategy, data=data, cash=10000, commission=0.01)
result = backtest.run()
# Access backtest results
returns_series = result.returns
completed_trades = result.trades
remaining_positions = result.open_positions
```
## Examples
Check out the examples in the `examples` directory for additional use cases and demonstrations. The examples cover various scenarios and strategies to help you understand the versatility of the `btester`.
- [Example 1: Multi-Assets Moving Average Crossover Strategy](https://colab.research.google.com/github/pawelkn/btester/blob/master/examples/multi-assets-ma-crossover.ipynb)
- [Example 2: Multi-Assets Breakout Strategy](https://colab.research.google.com/github/pawelkn/btester/blob/master/examples/multi-assets-brakeout.ipynb)
- [Example 3: Single Asset Moving Average Crossover Strategy](https://colab.research.google.com/github/pawelkn/btester/blob/master/examples/single-asset-ma-crossover.ipynb)
- [Example 4: Single Asset Breakout Strategy](https://colab.research.google.com/github/pawelkn/btester/blob/master/examples/single-asset-brakeout.ipynb)
Feel free to explore and adapt these examples to suit your specific needs and trading strategies.