Intro to Python for Financial Data Analysis

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Intro to Python for
Financial Data Analysis
General Assembly, 6/18/2012

about me
• MIT ’07
• AQR Capital: 2007 - 2010
• pandas: 2008 - Present
WES MCKINNEY • wes (at) lambdafoundry.com

• Twitter: @wesmckinn

Jun 18, 2012 2

Why Python?
• Easy to learn, but richly featured
• Readability
• Conciseness
• “Python gets out of my way” - Robert Kern
• Multi-paradigm: object-oriented, functional, procedural
• Easy integration with C / C++ / Fortran
• Mature scientific libraries and large community

Jun 18, 2012 3

Text

Source: “Python wraps its coils around the enterprise”
http://www.theregister.co.uk/2012/06/18/scripting_languages_in_the_enterprise/

Jun 18, 2012 4

Upcoming book
• To be ~400 pages
• NumPy + IPython
• pandas
• Case studies
• Python language
• Incomplete Early Release
available on oreilly.com, print
version in September or October

Jun 18, 2012 5

Lambda Foundry!
• RapidQuant: Python-based financial analytics libraries and research
environment
• Support and Training
• Consulting
• pandas, statsmodels, and related open source development

Jun 18, 2012 6

Core ﬁnancial stack
• IPython: rich interactive environment
• NumPy: multidimensional arrays, linear algebra
• pandas: high level, intelligent data structures
• SciPy: like MATLAB toolboxes
• statsmodels: statistics and econometrics
• Visualization: matplotlib, Chaco, mayavi

Jun 18, 2012 7

pandas
• Richly featured data handling tool built on NumPy
• Mature, well-tested codebase
• Intuitive API, well-suited for REPL and system development
• Powerful time series capabilities
• Widely used in the quant finance industry
• Large cross-disciplinary user base
• Upcoming major 0.8.0 release (this week hopefully)

Jun 18, 2012 8

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Intro to Python for Financial Data Analysis

1. Intro to Python for Financial Data Analysis General Assembly, 6/18/2012

2. about me • MIT ’07 • AQR Capital: 2007 - 2010 • pandas: 2008 - Present WES MCKINNEY • wes (at) lambdafoundry.com • Twitter: @wesmckinn Jun 18, 2012 2

3. Why Python? • Easy to learn, but richly featured • Readability • Conciseness • “Python gets out of my way” - Robert Kern • Multi-paradigm: object-oriented, functional, procedural • Easy integration with C / C++ / Fortran • Mature scientific libraries and large community Jun 18, 2012 3

4. Text Source: “Python wraps its coils around the enterprise” http://www.theregister.co.uk/2012/06/18/scripting_languages_in_the_enterprise/ Jun 18, 2012 4

5. Upcoming book • To be ~400 pages • NumPy + IPython • pandas • Case studies • Python language • Incomplete Early Release available on oreilly.com, print version in September or October Jun 18, 2012 5

6. Lambda Foundry! • RapidQuant: Python-based financial analytics libraries and research environment • Support and Training • Consulting • pandas, statsmodels, and related open source development Jun 18, 2012 6

7. Core ﬁnancial stack • IPython: rich interactive environment • NumPy: multidimensional arrays, linear algebra • pandas: high level, intelligent data structures • SciPy: like MATLAB toolboxes • statsmodels: statistics and econometrics • Visualization: matplotlib, Chaco, mayavi Jun 18, 2012 7

8. pandas • Richly featured data handling tool built on NumPy • Mature, well-tested codebase • Intuitive API, well-suited for REPL and system development • Powerful time series capabilities • Widely used in the quant finance industry • Large cross-disciplinary user base • Upcoming major 0.8.0 release (this week hopefully) Jun 18, 2012 8