Financial technical analysis in python

 

Do you know if there is any financial technical analysis module available for python ? I know Numpy has a little but I'm looking for classic technical indicators like RSI , Macd, EMA and so on. Was wondering if they existed as part of a module.

Here are a few thoughts... I have only used Numpy, Scipy, and Matplotlib for financial calculations.

  • py-fi - very basic financial functions
  • fin2py - financial tools
  • Numpy/Scipy - covers all of the statistics basics
  • Matplotlib - plotting financial functions
  • RPy - a Python interface to R allowing use of R libraries
  • ystockquote - Python API for Yahoo! Stock Data
  • QuantLib - Open source library (supposedly has Python Bindings)
  • PyFinancial - Docs in Spanish
  • PyMacLab - "Series of classes useful for conducting research in dynamic macroeconomics"
  • TSDB - for storing large volumes of time series data
  • PyVol - volatility estimation of financial time series

    • TA-Lib - Library of indicators. How to compile for Python

    There is also a Computational Finnance Course on Coursera.org.

    They use a Python Open Source Library called QSTK (QuantSoftware ToolKit). They have a bunch of tutorials on the wiki page and you can always take the course if you want to learn more.

    For convenience I copied the description from the wiki page below:

    QSToolKit (QSTK) is a Python-based open source software framework designed to support portfolio construction and management. We are building the QSToolKit primarily for finance students, computing students, and quantitative analysts with programming experience. You should not expect to use it as a desktop app trading platform. Instead, think of it as a software infrastructure to support a workflow of modeling, testing and trading. 

    Scroll through the Gallery to see the sorts of things you can do easily with QSTK.
If you are in a hurry, you can skip to the QSToolKit_Installation_Guide.

    Key components of QSTK are: 

    - Data: A data access package that enables fast reading of 
  historical data (qstkutil.DataAccess).
- Processing tools: Uses pandas, a Python package designed for time series 
  evaluation of equity data.
- Portfolio optimization: Using the CVXOPT library.
- Event studies: An efficient event analyzer, Event_Profiler.
- Simulation: A simple backtester, quicksim, 
  that includes transaction cost modeling.
    链接地址: http://www.djcxy.com/p/25358.html

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