Notebook (c) 2011, 2012 Georgia Tech Research Corporation 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. Basic tutorial 1 Ipython notebook and code refactoring by Yaser Martinez

Basic Tutorial 1¶

Import required libraries¶

In [1]:

# 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 pandas as pd

# Set golden ratio for image size
figsize(10*1.62, 10)

Import data¶

In [2]:

# 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))

# Getting the close prices in a dataframe.
closing_prices = d_data['close']

closing_prices.head()

Out[2]:

	AAPL	GLD	GOOG	$SPX	XOM
2006-01-03 16:00:00	74.43	53.12	435.23	1268.80	50.47
2006-01-04 16:00:00	74.65	53.30	445.24	1273.46	50.56
2006-01-05 16:00:00	74.06	52.34	451.24	1273.48	50.31
2006-01-06 16:00:00	75.97	53.72	465.66	1285.45	51.30
2006-01-09 16:00:00	75.72	54.60	466.90	1290.15	51.28

Plotting the prices¶

In [3]:

closing_prices.plot()                       # closing_prices.plot(y:'Adjusted Close') doesn't work in 0.7.3
ylabel('Adjusted close');

Normalizing the prices to start at 1 and plot relative returns¶

In [5]:

normalized_prices = closing_prices / closing_prices.ix[0]

normalized_prices.plot()
ylabel('Normalized Close');

Plot daily returns¶

In [7]:

returns = normalized_prices.shift(-1) / normalized_prices - 1

# Plotting the plot of daily returns
returns[['$SPX','XOM']][0:50].plot()
axhline(y=0, color='r')
ylabel('Daily Returns');

Scatter plot of daily returns XOM vs $SPX¶

In [8]:

scatter(returns['$SPX'], returns['XOM'], c='blue')
ylabel('XOM')
xlabel('$SPX');

Scatter plot of daily returns $SPX vs GLD¶

In [9]:

# Plotting the scatter plot of daily returns between $SPX VS GLD
scatter(returns['$SPX'], returns['GLD'], c='blue')  # $SPX v GLD
ylabel('GLD')
xlabel('$SPX');