alpheast 0.1.1

An event-driven backtesting engine for trading strategies.

AlphEast Backtesting Engine

🌟 Overview

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.

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📚 Documentation

For complete usage instructions, API reference, detailed examples, and guides, please visit our official documentation website:

https://alpheast.readthedocs.io/en/latest/

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✨ Key Features

• 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.

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🚀 Quick Start

Get your first backtest running in minutes!

Installation

pip install alpheast

Basic Usage Example

from 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()

⚡️ Performance

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:

Execution Time (seconds)

Symbols \ Duration	1 Year	5 Years
1	0.2124	0.9258
2	0.3027	1.2424
10	0.9525	3.6243

Status

In mid stages of development.

Contributing

All contributions are warmly welcomed. Head over to CONTRIBUTING.md for details.