Python, Pandas and Matplotlib 1.1 : Analysis and Storage of Large Time-Series Data from the BTC-e API

First off I would like to thank Wes McKinney for his great work compiling together the pandas library over the past years. His book Python for Data Analysis (~$25 on Amazon) is fantastic and a great addition to your home library.

This post will be an introduction to help you better understand the resources and tools available to you in python and the pandas library. If you have been following the MATLAB data capture tutorials you will transition into these python tutorials fairly well.

Our main objective is to capture the JSON data from the BTC-e API and continually store and analyze it to our liking. To accomplish this we will be utilizing Data Frames (Not to be confused with the Data Frames found in “R”) and Hierarchical Indexing.

Now, I started off hacking away in Python 2.7 with the following Libraries/Packages/IDE:

  • IPython
  • Pandas
  • Matplotlib
  • Spyder
  • Numpy
  • SQLite3

I recommend downloading the following Python(x,y) Package.

It will provide you with the following:

  • IPython 2.4.1-10
  • Pandas 0.16.2-15
  • Matplotlib 1.4.3-7
  • Spyder 2.3.5.2-17
    • If you’re comfortable working in the MATLAB environment the Spyder IDE will feel like your home away from home.
  • Numpy 1.9.2-8

Our main working environments will be the Spyder IDE along with instances of IPython Notebooks. I have found that this is a great combination for testing out new strategies.

Once you have accumulated the necessary resources for this tutorial continue on to Part 1.2. If you have any trouble feel free to leave comments or contact embeddedthought directly.

if you have any questions or concerns feel free to ask away!

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