QuantSoftwareToolkit/Examples/Basic/tutorial1.py at master · testmana2/QuantSoftwareToolkit · GitHub

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'''

This source code is released under the New BSD license. Please see

http://wiki.quantsoftware.org/index.php?title=QSTK_License

for license details.

Created on January, 24, 2013

@author: Sourabh Bajaj

@contact: sourabhbajaj@gatech.edu

@summary: Example tutorial code.

'''

# QSTK Imports

import QSTK.qstkutil.qsdateutil as du

import QSTK.qstkutil.tsutil as tsu

import QSTK.qstkutil.DataAccess as da

# Third Party Imports

import datetime as dt

import matplotlib.pyplot as plt

import pandas as pd

print "Pandas Version", pd.__version__

def main():

''' Main Function'''

# List of symbols

ls_symbols = ["AAPL", "GLD", "GOOG", "$SPX", "XOM"]

# Start and End date of the charts

dt_start = dt.datetime(2006, 1, 1)

dt_end = dt.datetime(2010, 12, 31)

# We need closing prices so the timestamp should be hours=16.

dt_timeofday = dt.timedelta(hours=16)

# Get a list of trading days between the start and the end.

ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)

# Creating an object of the dataaccess class with Yahoo as the source.

c_dataobj = da.DataAccess('Yahoo')

# Keys to be read from the data, it is good to read everything in one go.

ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']

# Reading the data, now d_data is a dictionary with the keys above.

# Timestamps and symbols are the ones that were specified before.

ldf_data = c_dataobj.get_data(ldt_timestamps, ls_symbols, ls_keys)

d_data = dict(zip(ls_keys, ldf_data))

# Filling the data for NAN

for s_key in ls_keys:

d_data[s_key] = d_data[s_key].fillna(method='ffill')

d_data[s_key] = d_data[s_key].fillna(method='bfill')

d_data[s_key] = d_data[s_key].fillna(1.0)

# Getting the numpy ndarray of close prices.

na_price = d_data['close'].values

# Plotting the prices with x-axis=timestamps

plt.clf()

plt.plot(ldt_timestamps, na_price)

plt.legend(ls_symbols)

plt.ylabel('Adjusted Close')

plt.xlabel('Date')

plt.savefig('adjustedclose.pdf', format='pdf')

# Normalizing the prices to start at 1 and see relative returns

na_normalized_price = na_price / na_price[0, :]

# Plotting the prices with x-axis=timestamps

plt.clf()

plt.plot(ldt_timestamps, na_normalized_price)

plt.legend(ls_symbols)

plt.ylabel('Normalized Close')

plt.xlabel('Date')

plt.savefig('normalized.pdf', format='pdf')

# Copy the normalized prices to a new ndarry to find returns.

na_rets = na_normalized_price.copy()

# Calculate the daily returns of the prices. (Inplace calculation)

# returnize0 works on ndarray and not dataframes.

tsu.returnize0(na_rets)

# Plotting the plot of daily returns

plt.clf()

plt.plot(ldt_timestamps[0:50], na_rets[0:50, 3]) # $SPX 50 days

plt.plot(ldt_timestamps[0:50], na_rets[0:50, 4]) # XOM 50 days

plt.axhline(y=0, color='r')

plt.legend(['$SPX', 'XOM'])

plt.ylabel('Daily Returns')

plt.xlabel('Date')

plt.savefig('rets.pdf', format='pdf')

# Plotting the scatter plot of daily returns between XOM VS $SPX

plt.clf()

plt.scatter(na_rets[:, 3], na_rets[:, 4], c='blue')

plt.ylabel('XOM')

plt.xlabel('$SPX')

plt.savefig('scatterSPXvXOM.pdf', format='pdf')

# Plotting the scatter plot of daily returns between $SPX VS GLD

plt.clf()

plt.scatter(na_rets[:, 3], na_rets[:, 1], c='blue') # $SPX v GLD

plt.ylabel('GLD')

plt.xlabel('$SPX')

plt.savefig('scatterSPXvGLD.pdf', format='pdf')

if __name__ == '__main__':

main()

QuantSoftwareToolkit/Examples/Basic/tutorial1.py at master · testmana2/QuantSoftwareToolkit · GitHub

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