Plotting-Price-Data-Using-Matplotlib.ipynb

Kernel: Python 3

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Visualizing Data with Matplotlib

Line Plots, Histograms, Scatter plots, and Heat Maps

Import libraries

Let's start by importing the libraries we need.

import matplotlib.pyplot as plt
import numpy as np  # used later for OLS

Visualizing SPY

We will visualize SPY using a time-series line plot and a histogram.

Gathering Data

To begin, we retrieve daily close data for SPY

qb = QuantBook()
spy = qb.AddEquity("SPY")
history = qb.History(qb.Securities.Keys, 360, Resolution.Daily)
spy_hist = history.loc['SPY']['close']
spy_hist

Here, we visualize SPY with a time-series line plot. Note that pandas has built-in support for matplotlib, making it easier and quicker for us to create plots.

fig, ax = plt.subplots()
spy_hist.plot(ax=ax) 
plt.title('SPY Daily Close')

Next, we want to visualize the returns distribution of SPY. We call pct_change() on our SPY Series to get the daily returns.

spy_daily_ret = spy_hist.pct_change()
plt.hist(spy_daily_ret, bins=50)
plt.title('Are normally distributed returns a hoax?')
plt.xlabel('Daily Return')
plt.ylabel('Count')

Visualizing the Correlation Between GLD and BAR Returns

GLD and BAR are two different Gold-tracking ETFs, and we would like to visualize their correlation. To do this, we will employ a scatter plot of their returns.

Gathering Data

First, we get daily close returns data for GLD and BAR

qb.AddEquity('GLD')
qb.AddEquity('BAR') 

gold_etf_hist = qb.History(['GLD', 'BAR'], 300, Resolution.Daily)

gld_ret = gold_etf_hist.loc['GLD']['close'].pct_change().dropna()
bar_ret = gold_etf_hist.loc['BAR']['close'].pct_change().dropna()

Since we would like to include a trend line (OLS) in our scatter plot, we will need to employ numpy to calculate our OLS line.

# get coefficients of the OLS Line (trend line)
m, b = np.polyfit(gld_ret, bar_ret, deg=1)

x = np.linspace(-.045, .06)
plt.plot(x, x * m + b, color='red')

plt.scatter(gld_ret, bar_ret)

plt.title('GLD vs BAR daily returns Scatter Plot')
plt.xlabel('GLD')
plt.ylabel('BAR')
plt.show()

Visualizing Correlations Between Banking Stocks

Correlations between stocks in the same sector are commonly applied to develop pairs trading strategies. We will visualize the correlations between a few stocks in the banking sector using a heat map. Note: we strongly advise using Seaborn instead for heat maps, as it needs only roughly 1/10th of the number of lines of code.

Gathering Data

First, we get historical closing prices for four banking sector stocks.

tickers = [
    "COF", # Capital One Financial Corporation
    "JPM", # J P Morgan Chase & Co
    "STI", # SunTrust Banks, Inc.
    "WFC" # Wells Fargo & Company
]
symbols = [qb.AddEquity(ticker, Resolution.Daily).Symbol for ticker in tickers]

history = qb.History(tickers, 
                     datetime(2020, 2, 1), 
                     datetime(2020, 7, 1), 
                     Resolution.Daily)

df = history['close'].unstack(level=0)

Before we create the heat map, we must create a correlation matrix, which has all the correlations between all possible pairs of symbols. This can be easily done with pandas DataFrame's built-in corr() method.

corr_matrix = df.corr()
corr_matrix

With the correlation matrix, we are ready to create a heat map.

fig, ax = plt.subplots()
im = ax.imshow(corr_matrix)

# full symbol data will clutter the heat map
tickers_ordered = [symbol.split()[0] for symbol in corr_matrix.columns]

# matplotlib has a bug where the first tick doesn't appear
# so we've put in a place holder
# we STRONGLY advise using Seaborn for heat maps
ax.set_xticklabels(['-'] + tickers_ordered)
ax.set_yticklabels(['-'] + tickers_ordered)

# make sure x and y-axes don't have too many ticks
ax.locator_params(axis='x', nbins=len(tickers_ordered))
ax.locator_params(axis='y', nbins=len(tickers_ordered))

# Rotate the x tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
         rotation_mode="anchor")

# Loop over data dimensions and create text annotations.
for i in range(len(corr_matrix.index)):
    for c in range(len(corr_matrix.columns)):
        label = round(corr_matrix.iloc[i][corr_matrix.columns[c]], 2)
        text = ax.text(c, i, label,
                       ha="center", va="center", color="w")

ax.set_title("Banking Stocks Correlation Heat Map")