Metadata-Version: 1.1
Name: FinPy
Version: 0.1.003
Summary: Financial Python. Using python to do stock analysis.
Home-page: http://pypi.python.org/pypi/FinPy/
Author: Tsung-Han Yang
Author-email: blacksburg98@yahoo.com
License: LICENSE.txt
Description: =====
        finpy
        Financial Python
        =====
        This is mainly inspired by QSTK and Professor Tucker's Computational Investing I
         at Coursera. I plan to expand the capabilities. Please let me know if you have 
        any suggestions.
        You can reach me at blacksburg98 (at) yahoo dot com
        
        I've tried to use docstring as much as possible, so you can try these commands
         in python shell to get more information.
        
        ::
            from finpy.utils import get_tickdata
            from finpy.equity import Equity
            help(Equity)
            from finpy.portfolio import Portfolio
            help(Portfolio)
        
        Please go to https://bitbucket.org/blacksburg98/finpy to file a issue if you have
         any problems.
        
        Recommend:
        Copy stock_data to a separate area.
            cp -R stock_data ~/stock_data
            setenv FINPYDATA ~/stock_data
        
        =====
        Tutorial 1
        =====
            """
            Tutorial 1 
            Load stock data and print 
            """
            import matplotlib
            matplotlib.use('Agg') # fix for matplotlib under multiprocessing
            import matplotlib.pyplot as plt
            import matplotlib.dates as mdates 
            import datetime as dt
            from finpy.utils import get_tickdata
        
            import finpy.fpdateutil as du
            if __name__ == '__main__':
                dt_timeofday = dt.timedelta(hours=16)
                dt_start = dt.datetime(2010, 1, 1)
                dt_end = dt.datetime(2010, 12, 31)
                ls_symbols = ['AAPL','GOOG', 'IBM', 'MSFT']
                ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
                all_stocks = get_tickdata(ls_symbols=ls_symbols, ldt_timestamps=ldt_timestamps)
                fig = plt.figure()
                ax = fig.add_subplot(111)
                for tick in ls_symbols:
                    ax.plot(ldt_timestamps, all_stocks[tick].normalized())
                legend = ls_symbols
                ax.legend(legend, loc=2)
                fig.autofmt_xdate()
                svg_file = 'tutorial1.pdf'
                fig.savefig(svg_file)
        
        
Platform: UNKNOWN
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Topic :: Office/Business :: Financial :: Investment
Classifier: Programming Language :: Python :: 2.7
