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Financial Python. Using python to do stock analysis.

Project 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://github.com/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)

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