Margot makes it super easy to backtest trading elgorithms. Firstly, Margot makes it super easy tocreate neat and tidy Pandas dataframes for time-series analysis.
Margot manages data collection, caching, cleaning, feature generation, management and persistence using a clean, declarative API. If you've ever used Django you will find this approach similar to the Django ORM.
Margot also provides a simple framework for writing and backtesting systematic trading algorithms.
Results from margot's trading algorithms can be analysed using pyfolio.
pip install margot
Next you need to make sure you have a couple of important environment variables set::
export ALPHAVANTAGE_API_KEY=YOUR_API_KEY
export DATA_CACHE=PATH_TO_FOLDER_TO_STORE_HDF5_FILES
Once you've done that, try running the code in the notebook.
This is still an early stage software project, and should not be used for live trading just yet.
The documentation is at readthedocs.
Feel free to make a pull request or chat about your idea first using issues.
Dependencies are kept to a minimum. Generally if there's a way to do something in the standard library (or numpy / Pandas), let's do it that way rather than add another library.
Margot is licensed for use under Apache 2.0. For details see the License.
FAQs
An algorithmic trading framework for PyData.
We found that margot demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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