PyAlgoMate

Important Note: The strategies located in the pyalgomate/strategies directory may not be fully up-to-date with recent framework changes, such as asynchronous operations and broker modifications. We recommend using these strategies as reference examples to develop your own custom trading strategies that align with the latest framework updates.

Note on Finvasia Websocket: The websocket subscription for Finvasia has been disassociated from the strategy and now uses a local feed via ZMQ. For paper or live trading modes, you need to run python pyalgomate/brokers/finvasia/wsclient.py in a separate process or terminal to receive both market feed and order updates.

PyAlgoMate is a Python library for event-driven algorithmic trading, developed as an extension of PyAlgoTrade (https://github.com/gbeced/pyalgotrade).

With PyAlgoMate, you can seamlessly perform backtesting, paper trading, and live trading with popular brokers such as Finvasia and Zerodha. It offers a comprehensive set of tools and functionalities to support various stages of the trading process.

Getting Started

To get started with PyAlgoMate, follow the steps below:

1. Clone/Download the Repository

First, clone or download the PyAlgoMate repository to your local machine. You can do this by clicking on the "Code" button on the repository's GitHub page and selecting your preferred method (e.g., cloning with Git or downloading as a ZIP file).

git clone https://github.com/NagarajuGunda/PyAlgoMate.git

2. Set Up a Python Virtual Environment

It is highly recommended to create a Python virtual environment to isolate PyAlgoMate's dependencies from your system's Python installation. This ensures that the library and its dependencies do not conflict with other Python projects on your machine.

Navigate to the directory of the cloned/downloaded repository using the command line and create a new virtual environment.

cd PyAlgoMate
python3 -m venv .venv

3. Activate the Virtual Environment

Activate the virtual environment using the appropriate command for your operating system.

On Linux

source .venv/bin/activate

On Windows

.venv\Scripts\activate

4. Install the Dependencies

Once the virtual environment is activated, you can install the required dependencies for PyAlgoMate using pip.

pip3 install -r requirements.txt

This command will install all the necessary packages specified in the requirements.txt file.

Congratulations! You have successfully set up PyAlgoMate and installed all the necessary dependencies. Now you're fully equipped to explore the wide range of features offered by PyAlgoMate. It is designed to assist you in writing effective trading strategies, conducting thorough backtesting, and executing trades. PyAlgoMate provides a powerful framework to develop and implement your own trading strategies in Python. Let's dive in and leverage the capabilities of PyAlgoMate to enhance your trading experience!

PyAlgoMate Usage

PyAlgoMate offers two flexible methods for running trading strategies: Command-Line Interface (CLI) and User Interface (UI). Both methods support various trading modes, including backtesting, paper trading, and live trading. Choose the method that best suits your workflow and preferences.

1. Running a Strategy using Command-Line Interface (CLI)

To perform a backtest using PyAlgoMate, it is recommended to use the command-line interface (CLI) method. Prior to executing the command, users need to set an environment variable called PYTHONPATH to ensure Python can locate the PyAlgoMate module. Follow the instructions below based on the operating system:

On Unix/Linux

export PYTHONPATH=path_to_pyalgomate_directory

On Windows

set PYTHONPATH=path_to_pyalgomate_directory

After setting the PYTHONPATH variable, users can run a strategy file with CLI capability using the following command:

python pyalgomate/strategies/strategy.py backtest --data "path_to_parquet_file" --underlying BANKNIFTY

Replace path_to_parquet_file with the actual path to the Parquet file containing the relevant data. Alternatively, you can find sample data under the repository NSEIndexOptionsData.

Specify the --underlying parameter with the appropriate underlying asset for the backtest.

To explore the available options and parameters supported by the CLI, use the --help flag with the strategy file, as shown below:

python pyalgomate/strategies/strategy.py --help
python pyalgomate/strategies/strategy.py backtest --help

These commands will provide information on the available options and their usage.

2. Running a Strategy using User Interface (UI)

For users who prefer a graphical interface, PyAlgoMate provides a user-friendly UI option. Follow these steps to utilize the UI:

1. Locate the file named strategies.yaml.sample in the PyAlgoMate directory.

2. Create a copy of this file and rename it to strategies.yaml.

3. Open strategies.yaml in a text editor and update its contents with your specific configuration:

   • Specify the broker (e.g., Backtest, Finvasia)
   • Define the underlying assets (e.g., BANKNIFTY, NSE|NIFTY BANK)
   • List the strategies you want to use, along with their required arguments

4. Once you've configured the strategies.yaml file, launch the UI by running the following command in your terminal:

python flet/PyAlgoMate.py

Please note that the README will be updated as the project progresses, including additional instructions and improvements. We apologize for any inconvenience caused and appreciate your understanding as we enhance the usability of the library.

For further assistance or any questions, please reach out. Happy exploring and trading with PyAlgoMate!

Issues

On Windows

If you are having trouble with MTM graph generation on Windows 11 try to downgrade kaleido library from latest to 0.1.0

pip install --upgrade "kaleido==0.1.*"

Analysing back test results

All backtest results are stored in csv file with file name as a strategy name. You can analyise the results with backtest analalyzer using below command

streamlit run backtestanalyzer.py

Contributing

If you find any issues or have suggestions for improvements, contributions to PyAlgoMate are welcome. Please open a GitHub issue or submit a pull request with your proposed changes.