Resources
=========

-  `Catalyst Whitepaper <https://www.enigma.co/enigma_catalyst.pdf>`_ 


Related 3rd Party APIs
^^^^^^^^^^^^^^^^^^^^^^

- `Zipline <http://www.zipline.io/appendix.html>`_ is a Pythonic Algorithmic 
  Trading Library, and the project Catalyst forked off in the spring of 2017.
- `Quantopian <https://www.quantopian.com/help>`_ provides a platform for 
  freelance quantitative analysts develop, test, and use trading algorithms to 
  buy and sell securities. They aim to create a crowd-sourced hedge fund by
  fostering their community of freelance traders. Quantopian's backtesting and 
  live-trading engine is powered by *Zipline*.
- `Pandas <https://pandas.pydata.org/pandas-docs/stable/api.html>`_ is a Python
  library providing high-performance, easy-to-use data structures and data 
  analysis tools. Catalyst relies heavily on pandas, and many API functions 
  return data as Pandas dataframes.
- `Numpy <https://docs.scipy.org/doc/numpy/reference/>`_ is the fundamental 
  package for scientific computing with Python. Some of the data computation 
  that your algorithms will need, will be optimized leveraging Numpy.
- `Matplotlib <https://matplotlib.org/1.5.3/api/index.html>`_ is a Python 2D 
  plotting library that many of examples rely on to plot the performance of 
  trading algorithms