AlphEast is an event-driven backtesting engine designed for developing and evaluating quantitative trading strategies. While still in its early stages of development, it provides a robust, extensible framework for simulating and evaluating multi-symbol trading strategies with realistic considerations like transaction costs and slippage.
Our goal is to offer a flexible tool for researchers and traders to quickly prototype and test their ideas, without getting bogged down in boilerplate.
For complete usage instructions, API reference, detailed examples, and guides, please visit our official documentation website:
https://alpheast.readthedocs.io/en/latest/
- Event-Driven Architecture: Simulates market conditions and trade executions with a clear, sequential event flow.
- Multi-Symbol Backtesting: Simultaneously test strategies across multiple financial instruments.
- Pluggable Strategies: Easily define and integrate your own custom trading strategies.
- Customizable Position Sizing: Implement various position sizing methods to manage risk and allocate capital.
- Realistic Simulations: Accounts for transaction costs (commissions) and slippage to provide more accurate results.
- Performance Metrics & Visualization: Generates standard trading performance metrics and plots equity curves for quick analysis.
- Flexible Data Ingestion: Designed to integrate with various data sources, from direct in-memory data to custom database repositories.
Get your first backtest running in minutes!
pip install alpheastfrom datetime import datetime
from typing import Dict, List
from alpheast.config.data_source import DataSource, DataSourceType
from alpheast.engine import BacktestingEngine
from alpheast.config.backtest_config import BacktestingOptions
from alpheast.models.interval import Interval
from alpheast.models.price_bar import PriceBar
from examples.basic.example_strategy import ExampleStrategy
if __name__ == "__main__":
symbol = "AAPL"
options = BacktestingOptions(
symbols=[symbol],
start_date=datetime(2021, 1, 1),
end_date=datetime(2025, 1, 1),
interval=Interval.DAILY,
initial_cash=100_000.0
)
price_bar_data: Dict[str, List[PriceBar]] = {
symbol: [] # Provide your data
}
data_source = DataSource(
type=DataSourceType.DIRECT,
price_bar_data=price_bar_data,
)
engine = BacktestingEngine(
options=options,
data_source=data_source,
strategies=[ExampleStrategy(symbol)],
)
results = engine.run()
if results:
results.print_summary()
results.plot_equity_curve()
AlphEast is designed for efficiency and demonstrates strong scaling characteristics with both the number of symbols and the backtesting duration. Below are some example execution times measured on a standard setup:
| Symbols \ Duration | 1 Year | 5 Years |
|---|---|---|
| 1 | 0.2124 | 0.9258 |
| 2 | 0.3027 | 1.2424 |
| 10 | 0.9525 | 3.6243 |
In mid stages of development.
All contributions are warmly welcomed. Head over to CONTRIBUTING.md for details.