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Intro to Python for Financial Data Analysis

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Intro to Python for Financial Data Analysis

  1. 1. Intro to Python for Financial Data Analysis General Assembly, 6/18/2012
  2. 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. 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. 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. 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. 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. 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. 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

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