TuShare

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.. image:: https://badge.fury.io/py/tushare.png :target: http://badge.fury.io/py/tushare

easy to use as most of the data returned are pandas DataFrame objects
can be easily saved as csv, excel or json files
can be inserted into MySQL or Mongodb

Target Users

financial market analyst of China
learners of financial data analysis with pandas/NumPy
people who are interested in China financial data

Installation

pip install tushare

Upgrade

pip install tushare --upgrade

Quick Start

::

import tushare as ts

ts.get_hist_data('600848')

return::

            open    high   close     low     volume    p_change  ma5     
date
2012-01-11   6.880   7.380   7.060   6.880   14129.96     2.62   7.060
2012-01-12   7.050   7.100   6.980   6.900    7895.19    -1.13   7.020
2012-01-13   6.950   7.000   6.700   6.690    6611.87    -4.01   6.913
2012-01-16   6.680   6.750   6.510   6.480    2941.63    -2.84   6.813
2012-01-17   6.660   6.880   6.860   6.460    8642.57     5.38   6.822
2012-01-18   7.000   7.300   6.890   6.880   13075.40     0.44   6.788
2012-01-19   6.690   6.950   6.890   6.680    6117.32     0.00   6.770
2012-01-20   6.870   7.080   7.010   6.870    6813.09     1.74   6.832

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