AutoTrader is no longer maintained

This project is no longer actively maintained. I've decided to archive it because I no longer have the time to support or update it.

---

AutoTrader

AutoTrader is Python-based platform intended to help in the development, optimisation and deployment of automated trading systems. A basic level of experience with Python is recommended for using AutoTrader, but the docs aim to make using it as easy as possible with detailed tutorials and documentation.

Latest News

• CryptoBots has been released along with version 1.0.0, offering ready-to-trade crypto strategies from the command line
• Version 0.7 has been released, adding integrations with CCXT crypto exchanges. Many more powerful upgrades too.
• AutoTrader has been featured in GitClone's recent article, Top Crypto Trader Open-Source Projects on Github.

Features

• A feature-rich trading simulator, supporting backtesting and

papertrading. The 'virtual broker' allows you to test your strategies in a risk-free, simulated environment before going live. Capable of simulating multiple order types, stop-losses and take-profits, cross-exchange arbitrage and portfolio strategies, AutoTrader has more than enough to build a profitable trading system.

• Integrated data feeds, making OHLC data retrieval as easy as possible.
• Automated interactive visualisation using Bokeh
• Library of custom indicators.
• Live trading supported for multiple venues.
• Detailed documenation and tutorials
• Repository of example strategies

[back to top]

Supported Brokers and Exchanges

| Broker | Asset classes | Integration status | | -------- | ------------- | ------------------ | | Oanda | Forex CFDs | Complete | | Interactive Brokers | Many | In progress | | CCXT | Cryptocurrencies | In progress |

[back to top]

Installation

AutoTrader can be installed using pip:

pip install autotrader

Updating

AutoTrader can be updated by appending the --upgrade flag to the install command:

pip install autotrader --upgrade

[back to top]

Documentation

AutoTrader is very well documented in-code and on Read the Docs. There is also a detailed walthrough, covering everything from strategy concept to livetrading.

Example Strategies

Example strategies can be found in the demo repository.

[back to top]

Backtest Demo

The chart below is produced by a backtest of the MACD trend strategy documented in the tutorials (and available in the demo repository). Entry signals are defined by MACD crossovers, with exit targets defined by a 1.5 risk-to-reward ratio. Stop-losses are automatically placed using the custom swing detection indicator, and position sizes are dynamically calculated based on risk percentages defined in the strategy configuration.

Running this strategy with AutoTrader in backtest mode will produce the following interactive chart.

Note that stop loss and take profit levels are shown for each trade taken. This allows you to see how effective your exit strategy is - are you being stopped out too early by placing your stop losses too tight? Are you missing out on otherwise profitable trades becuase your take profits are too far away? AutoTrader helps you visualise your strategy and answer these questions.

[back to top]

Contributing

To contribute to autotrader, please read the instructions below, and stick to the styling of the code.

Setting up for Development

• Create a new Python virtual environment to isolate the package. You

can do so using venv or anaconda.

• Install the code in editable mode using the command below (run from

inside the autotrader root directory). Also install all dependencies using the [all] command, which includes the developer dependencies.

pip install -e .[all]

• Install the pre-commit hooks.

pre-commit install

• Start developing! After following the steps above, you are ready

to start developing the code. Make sure to follow the guidelines below.

Developing AutoTrader

• Fork the repository and clone to your local machine for development.

• Run black on any

code you modify. This formats it according to PEP8 standards.

• Document as you go: use

numpy style docstrings, and add to the docs where relevant.

• Write unit tests for the code you add, and include them in tests/.

This project uses pytest.

• Commit code regularly to avoid large commits with many changes.

• Write meaningful commit messages, following the

Conventional Commits standard. The python package commitizen is a great tool to help with this, and is already configured for this repo. Simply stage changed code, then use the cz c command to make a commit.

• Open a Pull Request

when your code is complete and ready to be merged.

Building the Docs

To build the documentation, run the commands below.

cd docs/
make html
xdg-open build/html/index.html

If you are actively developing the docs, consider using sphinx-autobuild. This will continuosly update the docs for you to see any changes live, rather than re-building repeatadly. From the docs/ directory, run the following command:

sphinx-autobuild source/ build/html --open-browser

[back to top]

Legal

License

AutoTrader is licensed under the GNU General Public License v3.0.

Disclaimer

Never risk money you cannot afford to lose. Always test your strategies on a paper trading account before taking it live.

[back to top]