Tutorial at PyData Boston 2013 July 27, 2013 at 4:30 pm. http://pydata.org/bos2013/schedule/

You can view the video of the talk here.

Thomas Wiecki, Quantopian Inc. and Brown University

Thomas Wiecki is a Quantitative Researcher at Quantopian Inc -- a Boston based startup providing you with the first browser based algorithmic trading platform -- and a PhD student at Brown University where he studies Computational Cognitive Neuroscience. He specializes in Bayesian Inference, Machine Learning, Scientific Computing in Python, algorithmic trading and Computational Psychiatry.

This tutorial will provide hands-on experience of various data analysis tools relevant for financial analysis in Python. We will first see how financial data can be imported from various sources such as Yahoo! finance. Pandas, Matplotlib and statsmodels can be used for basic and more advanced time-series analysis. While rudimentary backtesting of investment strategies on historical data can be carried out using Pandas, a more realistic simulation that considers transaction costs, slippage and avoids look-ahead bias, introduces various complexities. We will then see how Zipline, an open-source streaming-based financial simulator written in Python, can make realistic backtesting much easier. After going through some simple example algorithms we will see how statistical Python libraries like scikits.learn can easily be incorporated with Zipline to build state-of-the art trading algorithms. Finally, I will briefly show how the same algorithm code can be run with minimal code changes on Quantopian -- a free, browser-based platform for developing algorithmic trading models.

The target audience for the tutorial includes all new Python users, though we recommend that users also attend the NumPy and IPython session in the introductory track.

• Timeseries analysis using Pandas
• Using Google Trends to predict market movements
• Build your own trading strategies using Zipline
• Common trading strategies:
  • Momentum trading
  • Mean-reversion

For students familiar with Git, you may simply clone this repository to obtain all the materials (IPython notebooks and data) for the tutorial. Alternatively, you may download a zip file containing the materials. A third option is to simply view static notebooks by clicking on the titles of each section below.

I strongly encourage you to set up the environment on your own computer so that you can follow along during the tutorial.

After you have the materials, from a command shell cd into the financial-analysis-in-python-tutorial directory and execute:

ipython notebook --pylab=inline

This should open a new browser window from where you can access the notebooks.

You can view the video of the talk here.

1. Pandas basics

• Creating/Loading time-series data
• Series and DataFrame: First steps
• Data alignment
• Plotting basics
• Common financial analyses (returns, correlations, ...)

2. Pandas replication of Google Trends paper

• Replication of recent paper: Quantifying Trading Behavior in Financial Markets Using Google Trends
• Uses Google search trends to predict market movements

3. Backtesting using Zipline

• What gets modeled? Why?
• Stream-based computing
• My first algorithm
• Example momentum trade algorithm
• Example mean-reversion algorithm

4. Quantopian: Community, Data, Infrastructure, Live Trading

• Quick intro
• Example algorithm

• Python 2.7 (Python 3 is not supported at this point!)
• pandas >= 0.11.1
• NumPy >= 1.6.1
• SciPy >= 0.7.0
• Matplotlib >= 1.0.0
• Zipline == 0.5.10

The easiest way to install all the necessary packages (except Zipline) is to use Continuum Analytics' Anaconda.

Zipline can then be installed via pip:

pip install zipline