pip install pandas-datareader
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Successfully installed pandas-datareader-0.10.0
Note: you may need to restart the kernel to use updated packages.
from pandas_datareader import data, wb
import pandas as pd
import numpy as np
import datetime
%matplotlib inline
Using the pandas datareader, will retrieve stock information for the specified banks.
pip install yfinance
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start = datetime.datetime(2006, 1, 1)
end = datetime.datetime(2016, 1, 1)
import yfinance as yf
# Fetch Bank of America stock data from Yahoo Finance using yfinance
BAC = yf.download("BAC", start=start, end=end)
C = yf.download("C", start=start, end=end)
GS = yf.download("GS", start=start, end=end)
JPM = yf.download("JPM", start=start, end=end)
MS = yf.download("MS", start=start, end=end)
WFC = yf.download("WFC", start=start, end=end)
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List of the ticker symbols (as strings) in alphabetical order.
import yfinance as yf
# Fetch data for multiple financial institutions from Yahoo Finance using yfinance
tickers = ['BAC', 'C', 'GS', 'JPM', 'MS', 'WFC']
df = yf.download(tickers, start=start, end=end)
# Display the first few rows of the DataFrame
df.tail()
Use pd.concat to concatenate the bank dataframes together to a single data frame called bank_stocks. Set the keys argument equal to the tickers list.
bank_stocks = pd.concat([BAC, C, GS, JPM, MS, WFC],axis=1,keys=tickers)
bank_stocks.head()
bank_stocks.columns.names = ['Bank Ticker','Stock Info']
bank_stocks.head()
| Bank Ticker | BAC | C | ... | MS | WFC | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Stock Info | Open | High | Low | Close | Adj Close | Volume | Open | High | Low | Close | ... | Low | Close | Adj Close | Volume | Open | High | Low | Close | Adj Close | Volume |
| Date | |||||||||||||||||||||
| 2006-01-03 | 46.919998 | 47.180000 | 46.150002 | 47.080002 | 32.168701 | 16296700 | 490.000000 | 493.799988 | 481.100006 | 492.899994 | ... | 56.740002 | 58.310001 | 33.585461 | 5377000 | 31.600000 | 31.975000 | 31.195000 | 31.900000 | 19.369587 | 11016400 |
| 2006-01-04 | 47.000000 | 47.240002 | 46.450001 | 46.580002 | 31.827045 | 17757900 | 488.600006 | 491.000000 | 483.500000 | 483.799988 | ... | 58.349998 | 58.349998 | 33.608505 | 7977800 | 31.799999 | 31.820000 | 31.365000 | 31.530001 | 19.144922 | 10870000 |
| 2006-01-05 | 46.580002 | 46.830002 | 46.320000 | 46.639999 | 31.868053 | 14970700 | 484.399994 | 487.799988 | 484.000000 | 486.200012 | ... | 58.020000 | 58.509998 | 33.700642 | 5778000 | 31.500000 | 31.555000 | 31.309999 | 31.495001 | 19.123680 | 10158000 |
| 2006-01-06 | 46.799999 | 46.910000 | 46.349998 | 46.570000 | 31.820234 | 12599800 | 488.799988 | 489.000000 | 482.000000 | 486.200012 | ... | 58.049999 | 58.570000 | 33.735214 | 6889800 | 31.580000 | 31.775000 | 31.385000 | 31.680000 | 19.236012 | 8403800 |
| 2006-01-09 | 46.720001 | 46.970001 | 46.360001 | 46.599998 | 31.840734 | 15619400 | 486.000000 | 487.399994 | 483.000000 | 483.899994 | ... | 58.619999 | 59.189999 | 34.092335 | 4144500 | 31.674999 | 31.825001 | 31.555000 | 31.674999 | 19.232977 | 5619600 |
5 rows × 36 columns
bank_stocks.xs(key='Close',axis=1,level='Stock Info').max()
Bank Ticker BAC 54.900002 C 564.099976 GS 247.919998 JPM 70.080002 MS 89.300003 WFC 58.520000 dtype: float64
Create a new empty DataFrame called returns. This dataframe will contain the returns for each bank's stock. returns are typically defined by:
$$r_t = \frac{p_t - p_{t-1}}{p_{t-1}} = \frac{p_t}{p_{t-1}} - 1$$returns = pd.DataFrame()
for tick in tickers:
returns[tick+' Return'] = bank_stocks[tick]['Close'].pct_change()
returns.head()
| BAC Return | C Return | GS Return | JPM Return | MS Return | WFC Return | |
|---|---|---|---|---|---|---|
| Date | ||||||
| 2006-01-03 | NaN | NaN | NaN | NaN | NaN | NaN |
| 2006-01-04 | -0.010620 | -0.018462 | -0.013812 | -0.014183 | 0.000686 | -0.011599 |
| 2006-01-05 | 0.001288 | 0.004961 | -0.000393 | 0.003029 | 0.002742 | -0.001110 |
| 2006-01-06 | -0.001501 | 0.000000 | 0.014169 | 0.007046 | 0.001025 | 0.005874 |
| 2006-01-09 | 0.000644 | -0.004731 | 0.012030 | 0.016242 | 0.010586 | -0.000158 |
Pairplot using seaborn of the returns dataframe.
import seaborn as sns
sns.pairplot(returns[1:])
C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight self._figure.tight_layout(*args, **kwargs)
<seaborn.axisgrid.PairGrid at 0x2002738ebd0>
# Worst Drop (4 of them on Inauguration day)
returns.idxmin()
BAC Return 2009-01-20 C Return 2009-02-27 GS Return 2009-01-20 JPM Return 2009-01-20 MS Return 2008-10-09 WFC Return 2009-01-20 dtype: datetime64[ns]
# Best Single Day Gain
# citigroup stock split in May 2011, but also JPM day after inauguration.
returns.idxmax()
BAC Return 2009-04-09 C Return 2008-11-24 GS Return 2008-11-24 JPM Return 2009-01-21 MS Return 2008-10-13 WFC Return 2008-07-16 dtype: datetime64[ns]
returns.std() # Citigroup riskiest
BAC Return 0.036647 C Return 0.038672 GS Return 0.025390 JPM Return 0.027667 MS Return 0.037819 WFC Return 0.030238 dtype: float64
returns.loc['2015-01-01':'2015-12-31'].std()
BAC Return 0.016163 C Return 0.015289 GS Return 0.014046 JPM Return 0.014017 MS Return 0.016249 WFC Return 0.012591 dtype: float64
Distplot using seaborn of the 2015 returns for Morgan Stanley
sns.distplot(returns.loc['2015-01-01':'2015-12-31']['MS Return'],color='green',bins=100)
C:\Users\ACER\AppData\Local\Temp\ipykernel_14776\1960598734.py:1: UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751 sns.distplot(returns.loc['2015-01-01':'2015-12-31']['MS Return'],color='green',bins=100)
<Axes: xlabel='MS Return', ylabel='Density'>
Distplot using seaborn of the 2008 returns for CitiGroup
sns.set_style('whitegrid')
sns.distplot(returns.loc['2008-01-01':'2008-12-31']['C Return'],color='red',bins=100)
C:\Users\ACER\AppData\Local\Temp\ipykernel_14776\1612079653.py:1: UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751 sns.distplot(returns.loc['2008-01-01':'2008-12-31']['C Return'],color='red',bins=100)
<Axes: xlabel='C Return', ylabel='Density'>
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()
Line plot showing Close price for each bank for the entire index of time.
for tick in tickers:
bank_stocks[tick]['Close'].plot(label = tick , figsize = (12,4))
plt.legend()
<matplotlib.legend.Legend at 0x2002f4e9cd0>
bank_stocks.xs(key='Close',axis=1,level='Stock Info').plot()
<Axes: xlabel='Date'>
bank_stocks.xs(key='Close',axis=1,level='Stock Info').iplot()
Analyze the moving averages for these stocks in the year 2008.
The rolling 30 day average against the Close Price for Bank Of America's stock for the year 2008
plt.figure(figsize=(12,6))
BAC['Close'].loc['2008-01-01':'2009-01-01'].rolling(window=30).mean().plot(label='30 Day Avg')
BAC['Close'].loc['2008-01-01':'2009-01-01'].plot(label='BAC CLOSE')
plt.legend()
<matplotlib.legend.Legend at 0x200317e3650>
# Assuming 'Stock Info' is a level in your multi-index DataFrame
correlation_matrix = bank_stocks.xs('Close', level='Stock Info', axis=1).corr()
correlation_matrix
| Bank Ticker | BAC | C | GS | JPM | MS | WFC |
|---|---|---|---|---|---|---|
| Bank Ticker | ||||||
| BAC | 1.000000 | 0.971516 | 0.550898 | 0.103874 | 0.944218 | 0.008542 |
| C | 0.971516 | 1.000000 | 0.434123 | 0.003515 | 0.933609 | -0.068536 |
| GS | 0.550898 | 0.434123 | 1.000000 | 0.685286 | 0.683792 | 0.499897 |
| JPM | 0.103874 | 0.003515 | 0.685286 | 1.000000 | 0.250427 | 0.940269 |
| MS | 0.944218 | 0.933609 | 0.683792 | 0.250427 | 1.000000 | 0.131835 |
| WFC | 0.008542 | -0.068536 | 0.499897 | 0.940269 | 0.131835 | 1.000000 |
sns.heatmap(bank_stocks.xs(key='Close',axis=1,level='Stock Info').corr(),annot=True)
<Axes: xlabel='Bank Ticker', ylabel='Bank Ticker'>
sns.clustermap(bank_stocks.xs(key='Close',axis=1,level='Stock Info').corr(),annot=True)
<seaborn.matrix.ClusterGrid at 0x20032bbdad0>
close_corr = bank_stocks.xs('Close',axis=1,level='Stock Info').corr()
close_corr.iplot(kind='heatmap',colorscale='rdylbu')
BAC[['Open', 'High', 'Low', 'Close']].loc['2015-01-01':'2016-01-01'].iplot(kind='candle')
Simple Moving Averages plot of Morgan Stanley for the year 2015.
MS['Close'].loc['2015-01-01':'2016-01-01'].ta_plot(study='sma',periods=[13,21,55],title='Simple Moving Averages')
Bollinger Band Plot for Bank of America for the year 2015.
BAC['Close'].loc['2015-01-01':'2016-01-01'].ta_plot(study='boll')