Exercises: Plotting - Answer Key

By Christopher van Hoecke, Max Margenot, and Delaney Mackenzie

Lecture Link:

https://www.quantopian.com/lectures/plotting-data

IMPORTANT NOTE:

This lecture corresponds to the Plotting Data lecture, which is part of the Quantopian lecture series. This homework expects you to rely heavily on the code presented in the corresponding lecture. Please copy and paste regularly from that lecture when starting to work on the problems, as trying to do them from scratch will likely be too difficult.

Part of the Quantopian Lecture Series:

In [1]:
# Useful Functions
import numpy as np
import matplotlib.pyplot as plt

Exercise 1: Histograms

a. Returns

Find the daily returns for SPY over a 7-year window.

In [2]:
data = get_pricing('SPY', fields='price', start_date='2010-01-01', end_date='2017-01-01')
returns = data.pct_change()[1:]

b. Graphing

Using the techniques laid out in lecture, plot a histogram of the returns

In [3]:
plt.hist(returns, bins = 30);
plt.xlabel('Random Numbers');
plt.ylabel('Number of Times Observed');
plt.title('Frequency Distribution of randomly generated number');

c. Cumulative distribution

Plot the cumulative distribution histogram for your returns

In [4]:
plt.hist(returns, bins = 30, cumulative='true');

Exercise 2 : Scatter Plots

a. Data

Start by collecting the close prices of McDonalds Corp. (MCD) and Starbucks (SBUX) for the last 5 years with daily frequency.

In [1]:
SPY  = get_pricing('SPY', fields='close_price', start_date='2013-06-19', end_date='2018-06-19', frequency='daily')
SBUX = get_pricing('SBUX', fields='close_price', start_date='2013-06-19', end_date='2018-06-19', frequency='daily')

b. Plotting

Graph a scatter plot of SPY and Starbucks.

In [6]:
plt.scatter(SPY, SBUX);
plt.title('Scatter plot of spy and sbux');
plt.xlabel('SPY Price');
plt.ylabel('SBUX Price');

c. Plotting Returns

Graph a scatter plot of the returns of SPY and Starbucks.

In [7]:
SPY_R = SPY.pct_change()[1:]
SBUX_R = SBUX.pct_change()[1:]

plt.scatter(SPY_R, SBUX_R);
plt.title('Scatter plot of spy and starbucks returns');
plt.xlabel('SPY Return');
plt.ylabel('SBUX Return');

Remember a scatter plot must have the same number of values for each parameter. If spy and SBUX did not have the same number of data points, your graph will return an error

Exercise 3 : Linear Plots

a. Getting Data

Use the techniques laid out in lecture to find the open price over a 2-year period for Starbucks (SBUX) and Dunkin Brands Group (DNKN). Print them out in a table.

In [8]:
data = get_pricing(['SBUX', 'DNKN'], fields='open_price', start_date = '2015-01-01', end_date='2017-01-01') ## Your code goes here. 
data.head()
Out[8]:
Equity(6683 [SBUX]) Equity(41759 [DNKN])
2015-01-02 00:00:00+00:00 39.945 40.893
2015-01-05 00:00:00+00:00 38.977 40.721
2015-01-06 00:00:00+00:00 39.074 40.664
2015-01-07 00:00:00+00:00 38.787 40.616
2015-01-08 00:00:00+00:00 40.042 41.931

b. Data Structure

The data is returned to us as a pandas dataframe object. Index your data to convert them into simple strings.

In [9]:
data.columns = [e.symbol for e in data.columns] 
data['SBUX'].head()
Out[9]:
2015-01-02 00:00:00+00:00    39.945
2015-01-05 00:00:00+00:00    38.977
2015-01-06 00:00:00+00:00    39.074
2015-01-07 00:00:00+00:00    38.787
2015-01-08 00:00:00+00:00    40.042
Freq: C, Name: SBUX, dtype: float64

c. Plotting

Plot the data for SBUX stock price as a function of time. Remember to label your axis and title the graph.

In [10]:
plt.plot(data['SBUX']);
plt.xlabel('Time');
plt.ylabel('Price');
plt.title('Price vs Time');

Exercise 4 : Best fits plots

Here we have a scatter plot of two data sets. Vary the a and b parameter in the code to try to draw a line that 'fits' our data nicely. The line should seem as if it is describing a pattern in the data. While quantitative methods exist to do this automatically, we would like you to try to get an intuition for what this feels like.

In [11]:
data1 = get_pricing('SBUX', fields='open_price', start_date='2013-01-01', end_date='2014-01-01')
data2 = get_pricing('SPY', fields='open_price', start_date = '2013-01-01', end_date='2014-01-01')

rdata1= data1.pct_change()[1:]
rdata2= data2.pct_change()[1:]
plt.scatter(rdata2, rdata1);
In [12]:
plt.scatter(rdata2, rdata1)

# Answer
a = 0.0004
b = 1.14
#Answer

x = np.arange(-0.02, 0.03, 0.01)
y = a + (b*x)
plt.plot(x,y, color='r');

Congratulations on completing the answer key to the Plotting exercises!

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