By Christopher van Hoecke, Max Margenot, and Delaney Mackenzie
https://www.quantopian.com/lectures/plotting-data
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
In [2]:
data = get_pricing('SPY', fields='price', start_date='2010-01-01', end_date='2017-01-01')
returns = data.pct_change()[1:]
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');
In [4]:
plt.hist(returns, bins = 30, cumulative='true');
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')
In [6]:
plt.scatter(SPY, SBUX);
plt.title('Scatter plot of spy and sbux');
plt.xlabel('SPY Price');
plt.ylabel('SBUX Price');
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
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]:
In [9]:
data.columns = [e.symbol for e in data.columns]
data['SBUX'].head()
Out[9]:
In [10]:
plt.plot(data['SBUX']);
plt.xlabel('Time');
plt.ylabel('Price');
plt.title('Price vs Time');
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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