Plotting-Price-Data-Using-Seaborn.ipynb

Kernel: Python 3

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Visualizing Data With Seaborn

An example of visualizing data with Seaborn's plots and heatmap.

Import Library

Let's start by importing Seaborn

import seaborn as sns
sns.set(rc={'figure.figsize':(16,8)})

Gather & Prepare Data

Make a History request to fetch daily prices of several securities.

qb = QuantBook()

# Make symbols for each plot
line_tickers = ['SPY']
line_symbols = [qb.Symbol(ticker) for ticker in line_tickers]

scatter_tickers = ["BAR", "GLD"]
scatter_symbols = [qb.Symbol(ticker) for ticker in scatter_tickers]

heatmap_tickers = [
    "BAC", # Bank of America Corporation
    "COF", # Capital One Financial Corporation
    "C",   # Citigroup Inc.
    "JPM", # J P Morgan Chase & Co
    "WFC", # Wells Fargo & Company
]
heatmap_symbols = [qb.Symbol(ticker) for ticker in heatmap_tickers]

# Make history request
history = qb.History(line_symbols + scatter_symbols + heatmap_symbols, 
                     datetime(2019, 1, 1), 
                     datetime(2020, 7, 1), 
                     Resolution.Daily).close.unstack(level=0)

# Seperate the history for each plot
line_history = history[[str(symbol.ID) for symbol in line_symbols]]
scatter_history = history[[str(symbol.ID) for symbol in scatter_symbols]]
heatmap_history = history[[str(symbol.ID) for symbol in heatmap_symbols]]

Line Plot

Plot the price data using line plots for a quick visualization.

plot = sns.lineplot(x='time', 
                    y=str(line_symbols[0].ID), 
                    data=line_history.reset_index())
plot.set(ylabel="price", title="SPY Price Over Time");

Scatter Plot

Use scatterplot to study the price cointegration.

returns = scatter_history.pct_change()[1:]
plot = sns.regplot(x=returns.columns[0], y=returns.columns[1], data=returns)
plot.set(xlabel='GLD % Returns', ylabel='BAR % Returns', title='GLD vs BAR Daily % Returns');

Heatmap

Use heatmap to visualize the correlation between security returns.

sns.heatmap(heatmap_history.corr());