[](https://app.codacy.com/manual/pymargot/margot?utm_source=github.com&utm_medium=referral&utm_content=pymargot/margot&utm_campaign=Badge_Grade_Dashboard)
[](https://travis-ci.org/github/pymargot/margot)
[](https://margot.readthedocs.io/en/latest/?badge=latest)
[](https://codecov.io/gh/pymargot/margot)
[](https://github.com/pymargot/margot/blob/master/LICENSE)
[](https://pypi.org/project/margot/)
# What is margot?
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.
# Getting Started
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](notebook.margot.data).
# Status
This is still an early stage software project, and should not be used for live
trading just yet.
# Documentation
The documentation is at [readthedocs](https://margot.readthedocs.io/en/latest/).
# Contributing
Feel free to make a pull request or chat about your idea first using [issues](https://github.com/atkinson/margot/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.
# License
Margot is licensed for use under Apache 2.0. For details see [the License](https://github.com/atkinson/margot/blob/master/LICENSE).
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