Metadata-Version: 2.1
Name: bt
Version: 0.2.9
Summary: A flexible backtesting framework for Python
Home-page: https://github.com/pmorissette/bt
Author: Philippe Morissette
Author-email: morissette.philippe@gmail.com
License: MIT
Keywords: python finance quant backtesting strategies
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Software Development :: Libraries
Classifier: Programming Language :: Python
Requires-Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*
License-File: LICENSE.txt
Requires-Dist: ffn (>=0.3.5)
Requires-Dist: pyprind (>=2.11)
Provides-Extra: dev
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Requires-Dist: coverage ; extra == 'dev'
Requires-Dist: cython (>=0.25) ; extra == 'dev'
Requires-Dist: flake8 ; extra == 'dev'
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Requires-Dist: ffn (>=0.3.5) ; extra == 'dev'
Requires-Dist: pyprind (>=2.11) ; extra == 'dev'

.. image:: http://pmorissette.github.io/bt/_static/logo.png



.. image:: https://github.com/pmorissette/bt/workflows/Build%20Status/badge.svg

    :target: https://github.com/pmorissette/bt/actions/



.. image:: https://codecov.io/gh/pmorissette/bt/branch/master/graph/badge.svg

    :target: https://codecov.io/pmorissette/bt



bt - Flexible Backtesting for Python 

====================================



bt is currently in alpha stage - if you find a bug, please submit an issue.



Read the docs here: http://pmorissette.github.io/bt.



What is bt?

-----------



**bt** is a flexible backtesting framework for Python used to test quantitative

trading strategies. **Backtesting** is the process of testing a strategy over a given 

data set. This framework allows you to easily create strategies that mix and match 

different `Algos <http://pmorissette.github.io/bt/bt.html#bt.core.Algo>`_. It aims to foster the creation of easily testable, re-usable and 

flexible blocks of strategy logic to facilitate the rapid development of complex 

trading strategies. 



The goal: to save **quants** from re-inventing the wheel and let them focus on the 

important part of the job - strategy development.



**bt** is coded in **Python** and joins a vibrant and rich ecosystem for data analysis. 

Numerous libraries exist for machine learning, signal processing and statistics and can be leveraged to avoid

re-inventing the wheel - something that happens all too often when using other

languages that don't have the same wealth of high-quality, open-source projects.



bt is built atop `ffn <https://github.com/pmorissette/ffn>`_ - a financial function library for Python. Check it out!



Features

---------



* **Tree Structure**

    `The tree structure <http://pmorissette.github.io/bt/tree.html>`_ facilitates the construction and composition of complex algorithmic trading 

    strategies that are modular and re-usable. Furthermore, each tree `Node

    <http://pmorissette.github.io/bt/bt.html#bt.core.Node>`_

    has its own price index that can be

    used by Algos to determine a Node's allocation. 



* **Algorithm Stacks**

    `Algos <http://pmorissette.github.io/bt/bt.html#bt.core.Algo>`_ and `AlgoStacks <http://pmorissette.github.io/bt/bt.html#bt.core.AlgoStack>`_ are

    another core feature that facilitate the creation of modular and re-usable strategy

    logic. Due to their modularity, these logic blocks are also easier to test -

    an important step in building robust financial solutions.



* **Charting and Reporting**

    bt also provides many useful charting functions that help visualize backtest

    results. We also plan to add more charts, tables and report formats in the future, 

    such as automatically generated PDF reports.



* **Detailed Statistics**

    Furthermore, bt calculates a bunch of stats relating to a backtest and offers a quick way to compare

    these various statistics across many different backtests via `Results'

    <http://pmorissette.github.io/bt/bt.html#bt.backtest.Result>`_ display methods.





Roadmap

--------



Future development efforts will focus on:



* **Speed**

    Due to the flexible nature of bt, a trade-off had to be made between

    usability and performance. Usability will always be the priority, but we do

    wish to enhance the performance as much as possible.



* **Algos**

    We will also be developing more algorithms as time goes on. We also

    encourage anyone to contribute their own algos as well.



* **Charting and Reporting**

    This is another area we wish to constantly improve on

    as reporting is an important aspect of the job. Charting and reporting also

    facilitate finding bugs in strategy logic.



Installing bt

-------------



The easiest way to install ``bt`` is from the `Python Package Index <https://pypi.python.org/pypi/bt/>`_

using ``pip`` or ``easy_insatll``:



.. code-block:: bash



    $ pip install bt 



Since bt has many dependencies, we strongly recommend installing the `Anaconda Scientific Python

Distribution <https://store.continuum.io/cshop/anaconda/>`_, especially on Windows. This distribution 

comes with many of the required packages pre-installed, including pip. Once Anaconda is installed, the above 

command should complete the installation. 



bt should be compatible with Python 2.7 and Python 3 thanks to the contributions

made by fellow users.



Recommended Setup

-----------------



We believe the best environment to develop with bt is the `IPython Notebook

<http://ipython.org/notebook.html>`__. From their homepage, the IPython Notebook

is:



    "[...] a web-based interactive computational environment

    where you can combine code execution, text, mathematics, plots and rich

    media into a single document [...]"



This environment allows you to plot your charts in-line and also allows you to

easily add surrounding text with Markdown. You can easily create Notebooks that

you can share with colleagues and you can also save them as PDFs. If you are not

yet convinced, head over to their website.



Special Thanks

--------------



A special thanks to the following contributors for their involvement with the project:



* Vladimir Filimonov `@vfilimonov <https://github.com/vfilimonov>`_ 

* Jordan Platts `@JordanPlatts <https://github.com/JordanPlatts>`_ 

* Pascal Tomecek `@ptomecek <https://github.com/ptomecek>`_ 





License

-------



MIT



