backtester-machine 0.1.2

A foundational trading strategy framework for simulating and managing financial trades with customizable profit tracking and balance management.

Backtester Machine

A framework for backtesting financial strategies.

Table of Contents

• Installation
• Examples
• Features
• Contributing

Installation

To install backtester_machine, clone the repository and install the package using pip:

git clone https://github.com/yourusername/backtester_machine.git
cd backtester_machine
pip install .   

Or install directly from PyPI (if available):

pip install backtester_machine

For development purposes, install the extra dependencies:

pip install backtester_machine[dev]

Examples

Example 1: Simple Backtest

from backtester_machine import Backtester, Strategy

# Define a simple strategy
class SimpleStrategy(Strategy):
    def generate_signals(self, data):
        return data['price'].rolling(3).mean() > data['price']

# Create backtester instance
bt = Backtester(SimpleStrategy())

# Run backtest on data
results = bt.run(data)
print(results)

Example 2: Advanced Backtest

from backtester_machine import Backtester, Strategy

# Define more advanced strategy
class AdvancedStrategy(Strategy):
    def generate_signals(self, data):
        return data['price'].rolling(5).mean() < data['price']

# Run with different parameters
bt = Backtester(AdvancedStrategy(), start_cash=10000, commission=0.001)
results = bt.run(data, from_date='2020-01-01', to_date='2023-01-01')
print(results)

Features

• Customizable strategies: Define your own trading strategies with ease.
• Multiple asset support: Backtest on various asset classes like stocks, crypto, etc.
• Performance metrics: Built-in evaluation metrics such as returns, volatility, and drawdowns.
• Modular design: Easily extend or modify the framework to suit your needs.

Contributions

Contributions are welcome! Please check out the contribution guidelines for more details.