Finance Data Project¶
Tyler Chia¶
In this data project we will focus on exploratory data analysis of stock prices for the following banks:
- Bank of America
- CitiGroup
- Goldman Sachs
- JPMorgan Chase
- Morgan Stanley
- Wells Fargo
We'll focus on bank stocks and see how they progressed throughout the financial crisis all the way to early 2016.
Question 1: Getting the Data¶
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# import libraries
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from pandas_datareader import data, wb
import pandas as pd
import numpy as np
import datetime
%matplotlib inline
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# create a start and end date
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start = datetime.datetime(2006,1,1)
end = datetime.datetime(2016,1,1)
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# read in the data using the pandas datareader library
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# Bank of America
BAC = data.DataReader('BAC', 'yahoo', start, end)
# CityGroup
C = data.DataReader('C','yahoo',start,end)
# Goldman Sachs
GS = data.DataReader('GS', 'yahoo', start, end)
# JPMorgan Chase
JPM = data.DataReader('JPM', 'yahoo', start, end)
# Morgan Stanley
MS = data.DataReader('MS', 'yahoo', start, end)
# Wells Fargo
WFC = data.DataReader('WFC', 'yahoo', start, end)
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MS
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# create a list of the ticker symbols in alphabetical order
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tickers = ['BAC','C','GS','JPM','MS','WFC']
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# concatenate the bank dataframes together to a single data frame and set the keys argument equal to the tickers list
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bank_stocks = pd.concat([BAC,C,GS,JPM,MS,WFC], axis = 1, keys = tickers)
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# set the column name levels
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bank_stocks.columns.names = ['Bank Ticker','Stock Info']
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bank_stocks.head()
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Question 2: Exploratory Data Analysis¶
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# finding the max Close price for each bank's stock throughout the time period
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for tick in tickers:
print(tick,bank_stocks[tick]['Close'].max())
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returns = pd.DataFrame()
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# use pandas pct_change() method on the Close column to create a column representing this return value.
# create a for loop that goes and for each Bank Stock Ticker creates this returns column and sets it as a
# column in the returns DataFrame.
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for tick in tickers:
returns[tick+' Return'] = bank_stocks[tick]['Close'].pct_change()
returns.head()
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# import visualization libraries
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import seaborn as sns
import matplotlib.pyplot as plt
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# create a pairplot using seaborn of the returns dataframe
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sns.pairplot(returns[1:])
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# use idxmin and max to find out on what dates each bank stock had the best and worst single day returns
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returns.idxmin()
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Note: January 20, 2009 was actually Obama's inauguration date.
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returns.idxmax()
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# take the standard deviation of the returns to find the riskiest stock over this time period
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returns.std()
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It looks like the riskiest would have to be CityGroup as their standard deviation was the highest.
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# check to see the riskiest stock for the year 2015
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returns.loc['2015-01-01':'2015-12-31'].std()
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# Morgan Stanley was the riskiest bank stock for 2015 as they had the highest standard deviation
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# create a distplot using seaborn of the 2015 returns for Morgan Stanley
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sns.distplot(returns.loc['2015-01-01':'2015-12-31']['MS Return'], color = 'green', bins = 50)
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# create a distplot using seaborn of the 2008 returns for CitiGroup
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sns.distplot(returns.loc['2008-01-01':'2008-12-31']['C Return'], color = 'red', bins = 50)
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Question 3: More Visualization¶
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# more imports
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import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
%matplotlib inline
# Optional Plotly Method Imports
import plotly
import cufflinks as cf
cf.go_offline()
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# create a line plot showing Close price for each bank for the entire index of time
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bank_stocks.xs(key='Close',axis=1,level='Stock Info').iplot()
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# analyze the moving averages for these stocks in the year 2008.
# plot the rolling 30 day average against the Close Price for Bank Of America's stock for the year 2008
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plt.figure(figsize=(12,4))
BAC['Close'].loc['2008-01-01':'2009-01-01'].rolling(window=30).mean().plot(label='30 Day Moving Avg')
BAC['Close'].loc['2008-01-01':'2009-01-01'].plot(label='BAC Close')
plt.legend()
plt.title('30 Day AVG & Close Price for BAC Stock in 2008')
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# create a heatmap of the correlation between the stocks Close Price
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sns.heatmap(bank_stocks.xs(key='Close', axis=1, level='Stock Info').corr(), annot=True)
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# create a clustermap to cluster the correlations together
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sns.clustermap(bank_stocks.xs(key='Close', axis=1, level='Stock Info').corr(), annot=True)
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