/autotrader_system_1

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

AutoTrader_System1 is a Python-based platform designed for building, testing, and deploying automated trading strategies. It is independent of MetaTrader and can be integrated with various trading platforms like Alpaca, Coinbase, Binance, or Interactive Brokers through their APIs. This bot is suitable for both beginners and experienced traders looking to automate their trading strategies.

Features

  • Platform Independence: Works with multiple trading platforms via their APIs.
  • Data Processing: Fetches and preprocesses real-time and historical market data.
  • Trading Strategies: Supports custom trading strategies, including technical indicators and machine learning models.
  • Backtesting: Evaluates trading strategies using historical data.
  • Execution: Executes trades programmatically through the trading platform's API.
  • Monitoring: Monitors performance and sends alerts based on predefined conditions.
  • MLOps: Manages data and model versioning, deployment pipelines, and model monitoring.
  • Utils: Provides utility functions for database connections, caching, and performance optimization.
  • Tests: Includes unit tests, integration tests, and end-to-end tests.
  • Configs: Manages configuration files for different environments and API keys.
  • Docs: Offers comprehensive documentation, including installation instructions, usage guides, and API references.
  • GitHub: Utilizes CI/CD pipelines for automated testing, building, and deploying the trading bot.
  • Deployment: Supports deployment to cloud platforms using Docker or Kubernetes.

Requirements

  • Python 3.6 or above
  • An IDE (e.g., Visual Studio Code, PyCharm, or Jupyter Notebook)
  • Basic knowledge of Python, such as functions and variables
  • Access to a trading platform’s API (e.g., Alpaca, Coinbase, Binance, Interactive Brokers)

Getting Started

  1. Clone the repository to your local machine.
  2. Create a new Python virtual environment.
  3. Install the required libraries using pip install -r requirements.txt.
  4. Set up your trading platform's API key and secret in the configuration files.
  5. Implement your trading strategy in the strategies module.
  6. Backtest your strategy using historical data.
  7. Deploy your bot and monitor its performance.

Development Guidelines

  • Follow PEP8 standards for code formatting.
  • Document your code using numpy style docstrings.
  • Write unit tests for new features and bug fixes.
  • Commit code regularly and write meaningful commit messages.
  • Use commitizen to help with commit messages.

License

MIT License

Copyright (c) 2023 Agitronics, LLC

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.