Skip to content

Latest commit

 

History

History
2023 lines (1678 loc) · 48.9 KB

README.md

File metadata and controls

2023 lines (1678 loc) · 48.9 KB

Stock Analysis

In this project we will focus in exploratory data analysis of stock prices.Keep in mind, this project is just meant to practice visualizations and pandas skills, it is not meant to be a robust financial analysis or be taken as financial advice.

We will focus on some US bank stocks and see how they progressed thoughtout the financial crisis of 2016. Then do a quick comparison with last 10 years data.

Import your important libraries:

import numpy as np
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import datetime as dt
import yfinance as yf

Import plotly and cufflinks for interactive graphs

cufflinks connects pandas dataframe with plotly library and helps to run the visualizations directly

!pip install plotly
!pip install cufflinks
import plotly 
import cufflinks as cf
cf.go_offline()
sns.set_style('whitegrid')

Data

We need to get data using yahoo finance.We will get stock information for the following banks:

  • Bank of America
  • CitiGroup
  • GoldmanSachs
  • JPMorgan Chase
  • Morgan Stanley
  • Wells Fargo

Pull th stock data from Jan 1st 2006 to Jan 1st 2016 for each of these banks. Set each bank to be a separate dataframe, with variable name for that bank being its ticker symbol. This will involve few steps:**

  1. Use datetime to set start and end datetime objects.
  2. Figure out ticker symbol for each bank.
  3. Figure out how to use yfinance to grab info on the stock
start = dt.date(year = 2006, month = 1 , day = 1)
end = dt.date(2016,1,1)
#pull the stock information from yfinanc
#Process: create a df(BAC)->pull data using yf library

BAC = yf.download('BAC', start, end)          #Bank of America
C = yf.download('C', start, end)              #Citigroup
GS = yf.download('GS', start, end)            #Goldmansachs
JPM = yf.download('JPM', start, end)          #JPMorgan Chase
MS = yf.download('MS', start, end)            #MorganStanley
WFC = yf.download('WFC', start, end)          #WellsFargo
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed

WFC
Open High Low Close Adj Close Volume
Date
2006-01-03 31.600000 31.975000 31.195000 31.900000 18.979551 11016400
2006-01-04 31.799999 31.820000 31.365000 31.530001 18.759415 10870000
2006-01-05 31.500000 31.555000 31.309999 31.495001 18.738600 10158000
2006-01-06 31.580000 31.775000 31.385000 31.680000 18.848658 8403800
2006-01-09 31.674999 31.825001 31.555000 31.674999 18.845688 5619600
... ... ... ... ... ... ...
2015-12-24 54.970001 55.090000 54.709999 54.820000 42.477665 4999400
2015-12-28 54.549999 54.779999 54.169998 54.680000 42.369175 8288800
2015-12-29 55.110001 55.349998 54.990002 55.290001 42.841831 7894900
2015-12-30 55.270000 55.310001 54.790001 54.889999 42.531898 8016900
2015-12-31 54.509998 54.950001 54.220001 54.360001 42.121220 10929800

2517 rows × 6 columns

Create a list of ticker symbols(as strings) in alphabetical order. Call this list: tickers.

tickers = 'BAC C GS JPM MS WFC'.split()
tickers
['BAC', 'C', 'GS', 'JPM', 'MS', 'WFC']

Use pd.concat to concatenate the bank dataframes together to a single dataframe called bank_stocks. Set the key argument equal to the tickers list.

bank_stocks = pd.concat([BAC, C, GS, JPM, MS, WFC], axis = 1,keys=tickers)
bank_stocks
BAC C ... MS WFC
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 31.544905 16296700 490.000000 493.799988 481.100006 492.899994 ... 56.740002 58.310001 32.661312 5377000 31.600000 31.975000 31.195000 31.900000 18.979551 11016400
2006-01-04 47.000000 47.240002 46.450001 46.580002 31.209898 17757900 488.600006 491.000000 483.500000 483.799988 ... 58.349998 58.349998 32.683727 7977800 31.799999 31.820000 31.365000 31.530001 18.759415 10870000
2006-01-05 46.580002 46.830002 46.320000 46.639999 31.250097 14970700 484.399994 487.799988 484.000000 486.200012 ... 58.020000 58.509998 32.773338 5778000 31.500000 31.555000 31.309999 31.495001 18.738600 10158000
2006-01-06 46.799999 46.910000 46.349998 46.570000 31.203197 12599800 488.799988 489.000000 482.000000 486.200012 ... 58.049999 58.570000 32.806950 6889800 31.580000 31.775000 31.385000 31.680000 18.848658 8403800
2006-01-09 46.720001 46.970001 46.360001 46.599998 31.223280 15619400 486.000000 487.399994 483.000000 483.899994 ... 58.619999 59.189999 33.154228 4144500 31.674999 31.825001 31.555000 31.674999 18.845688 5619600
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2015-12-24 17.320000 17.379999 17.219999 17.270000 14.271372 29369400 52.480000 52.970001 52.450001 52.709999 ... 32.439999 32.480000 25.353054 2798200 54.970001 55.090000 54.709999 54.820000 42.477665 4999400
2015-12-28 17.219999 17.230000 16.980000 17.129999 14.155680 41777500 52.570000 52.570000 51.959999 52.380001 ... 31.950001 32.169998 25.111073 5420300 54.549999 54.779999 54.169998 54.680000 42.369175 8288800
2015-12-29 17.250000 17.350000 17.160000 17.280001 14.279634 45670400 52.759998 53.220001 52.740002 52.980000 ... 32.330002 32.549999 25.407690 6388200 55.110001 55.349998 54.990002 55.290001 42.841831 7894900
2015-12-30 17.200001 17.240000 17.040001 17.049999 14.089570 35066400 52.840000 52.939999 52.250000 52.299999 ... 32.200001 32.230000 25.157906 5057200 55.270000 55.310001 54.790001 54.889999 42.531898 8016900
2015-12-31 17.010000 17.070000 16.830000 16.830000 13.907768 47153000 52.070000 52.389999 51.750000 51.750000 ... 31.770000 31.809999 24.830067 8154300 54.509998 54.950001 54.220001 54.360001 42.121220 10929800

2517 rows × 36 columns

Set the column name levels 'Bank Tickers' and 'Stock info':

bank_stocks.columns.names =['Bank Tickers', 'Stock Info']
bank_stocks.columns.names
FrozenList(['Bank Tickers', 'Stock Info'])

Check the head of the bank_stocks dataframe

bank_stocks.head()
Bank Tickers 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 31.544905 16296700 490.000000 493.799988 481.100006 492.899994 ... 56.740002 58.310001 32.661312 5377000 31.600000 31.975000 31.195000 31.900000 18.979551 11016400
2006-01-04 47.000000 47.240002 46.450001 46.580002 31.209898 17757900 488.600006 491.000000 483.500000 483.799988 ... 58.349998 58.349998 32.683727 7977800 31.799999 31.820000 31.365000 31.530001 18.759415 10870000
2006-01-05 46.580002 46.830002 46.320000 46.639999 31.250097 14970700 484.399994 487.799988 484.000000 486.200012 ... 58.020000 58.509998 32.773338 5778000 31.500000 31.555000 31.309999 31.495001 18.738600 10158000
2006-01-06 46.799999 46.910000 46.349998 46.570000 31.203197 12599800 488.799988 489.000000 482.000000 486.200012 ... 58.049999 58.570000 32.806950 6889800 31.580000 31.775000 31.385000 31.680000 18.848658 8403800
2006-01-09 46.720001 46.970001 46.360001 46.599998 31.223280 15619400 486.000000 487.399994 483.000000 483.899994 ... 58.619999 59.189999 33.154228 4144500 31.674999 31.825001 31.555000 31.674999 18.845688 5619600

5 rows × 36 columns

Exploratory Data Analysis

Let's explore the data a bit! What is the max Close price for each bank's stock throughout the time period

# use cross section for multiindex data frames

bank_stocks.xs('Close',axis=1,level=1) # Returns the dataframe with Close columns
Bank Tickers BAC C GS JPM MS WFC
Date
2006-01-03 47.080002 492.899994 128.869995 40.189999 58.310001 31.900000
2006-01-04 46.580002 483.799988 127.089996 39.619999 58.349998 31.530001
2006-01-05 46.639999 486.200012 127.040001 39.740002 58.509998 31.495001
2006-01-06 46.570000 486.200012 128.839996 40.020000 58.570000 31.680000
2006-01-09 46.599998 483.899994 130.389999 40.669998 59.189999 31.674999
... ... ... ... ... ... ...
2015-12-24 17.270000 52.709999 182.470001 66.599998 32.480000 54.820000
2015-12-28 17.129999 52.380001 181.619995 66.379997 32.169998 54.680000
2015-12-29 17.280001 52.980000 183.529999 67.070000 32.549999 55.290001
2015-12-30 17.049999 52.299999 182.009995 66.589996 32.230000 54.889999
2015-12-31 16.830000 51.750000 180.229996 66.029999 31.809999 54.360001

2517 rows × 6 columns

bank_stocks.xs('Close',axis=1,level=1).max() #Returns the max value of each column
Bank Tickers
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. The 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()
print(returns)
Empty DataFrame
Columns: []
Index: []

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.

for i in tickers: 
    returns[i + ' Returns'] = bank_stocks.xs('Close', axis = 1, level = 1)[i].pct_change() 
    
returns
BAC Returns C Returns GS Returns JPM Returns MS Returns WFC Returns
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
... ... ... ... ... ... ...
2015-12-24 -0.004037 0.001520 -0.002624 -0.001948 -0.003681 -0.003997
2015-12-28 -0.008107 -0.006261 -0.004658 -0.003303 -0.009544 -0.002554
2015-12-29 0.008757 0.011455 0.010516 0.010395 0.011812 0.011156
2015-12-30 -0.013310 -0.012835 -0.008282 -0.007157 -0.009831 -0.007235
2015-12-31 -0.012903 -0.010516 -0.009780 -0.008410 -0.013031 -0.009656

2517 rows × 6 columns

Observations

  • What pct_change() does here is it computes the fractional change from the immidiately previous row by default. It is useful for comparing the fraction of change in a time series of elements. Remeber here it is a fraction if yoou want to change it to percentage u want to multiply it with 100
  • So we have got the columns for each of the stock tickers for each trading day
  • For the first day the return is obiviously Nan as there was no prior day element to calculate it
  • From the second row we can see the positive and negative returns , negatives are the days where it went down and positives are the days when it went up

Create a pair plot using seaborn of the returns dataframe. Do any stocks stands out ? Why or why not?

sns.pairplot(returns)
<seaborn.axisgrid.PairGrid at 0x3392183a0>

png

Observations

  • Diagonal elements are univariant data i.e, just for that particular column
  • Off diagonal elements are bivariant data
  • What pairplot does here is it creates a group of scatter plots for each pair of the numerical data in the data frame,For exit will create a 'BAC' vs 'C' scatterplot, 'BAC' vs 'GS' scatter plot and so on.
  • So we have all of these tickers/banks on X-axis and the same banks as well as on the Y-axis.
  • So if you compare 'WFC Returns' to the 'WFC Returns' it is a univariant data so therefore we have a Histogram.
  • If you compare 'WFC Returns' to the other banks we have the scatter plots to analyse the data.
  • Its very important to check the scales for it , almost everything are in decimals on both X and Y axis.
  • Sometimes based on the data you can get different scales for one of the columns and that graph will look very different.
  • Lets look at the 'MS Returns' graph is going from -0.25 to 0.75 and there is only one outlier point that is there at 0.75 which makes the slope is much higher but if you ignore the outlier the slope will be much equal as the distance from zero on both sides is almost equal for rest of the datapoints.
  • Same with the 'Citi Group' except few(countable) points most of them lies in the range of -0.25 to 0.25.
  • So if you analyse this for 'BAC Returns' compared to other banks, there is a very strong correlation between these banks or the returns of these banks as the X-axis goes up Y-axis goes up as well and that pattern is prevalent for all combinations of the all pairs of the banks here.
returns.corr()
BAC Returns C Returns GS Returns JPM Returns MS Returns WFC Returns
BAC Returns 1.000000 0.802752 0.685271 0.815410 0.643840 0.828337
C Returns 0.802752 1.000000 0.668989 0.739045 0.634352 0.717386
GS Returns 0.685271 0.668989 1.000000 0.738671 0.805155 0.662847
JPM Returns 0.815410 0.739045 0.738671 1.000000 0.646711 0.826276
MS Returns 0.643840 0.634352 0.805155 0.646711 1.000000 0.598681
WFC Returns 0.828337 0.717386 0.662847 0.826276 0.598681 1.000000

Observations

  • We can see the correlation in the matrix format.
  • We can use these later to create a heat map.
  • 'BAC Returns have strong relationship with the 'City Bank', 'JPMorgan' and 'WellsFargo' compared to the 'GoldmanSachs' and 'Morgan Stanley' banks.

Using this returns DataFrame, figure out on what dates each bank stock had the best and worst single day returns. Did anything significant happen on those days?

#Best single day returns
returns.max()  #Returns the max value of each column 
BAC Returns    0.352691
C Returns      0.578249
GS Returns     0.264678
JPM Returns    0.250967
MS Returns     0.869835
WFC Returns    0.327645
dtype: float64

#Best single day returns
returns.idxmax() #Returns the index of the max value
BAC Returns   2009-04-09
C Returns     2008-11-24
GS Returns    2008-11-24
JPM Returns   2009-01-21
MS Returns    2008-10-13
WFC Returns   2008-07-16
dtype: datetime64[ns]

#Worst single day returns
returns.idxmin() #Returns the index of the min value
BAC Returns   2009-01-20
C Returns     2009-02-27
GS Returns    2009-01-20
JPM Returns   2009-01-20
MS Returns    2008-10-09
WFC Returns   2009-01-20
dtype: datetime64[ns]

Did anything significant happen on those days?

Best days

  • If you notice the JPMorgan returns the best performance day(2009-01-21) was exactly after the worst performance day(2009-01-20) and the reason for that was it received a 700 billion dollar bailout from the federal reserve.

Worst days

  • If you look carefully four of the banks Bank of America, Goldman Sachs, JPMorgan and Wells Fargo had the worst return on the same day.

    What happened exactly?

  • On the particular day 2009-01-20, The Icelands banks collapsed.

  • InShort, About Iceland banks, During 2000-2007 the Icelandic banks were privatised and saw quiet a bit of Boom years so much so that the country's entire economy and GDP became dependent on the performance of the banks, and when the economy collapsed theirs banks and government collapsed . That might have created a widespread of panic in the market which might be one of the reasons for the decline on the particular day [ 1 ][2 . Finacncial crisis 2009].

  • For the City Bank,On 27th February 2009,The US government has reached a deal to take a stake of 30 to 40 percent , which raised concerns among the Investors regarding the nationalisation of the banks and thats why it has the biggest decline of its stock worldwide on that particular day.

More Details

Take a look at the standard deviation of the returns, which stock would you classify as the riskiest over the entire period? Which would u classify as the riskiest for the year 2015?

#For the entire 10 years period (More riskier higher the std.deviation)
returns.std()
BAC Returns    0.036647
C Returns      0.038672
GS Returns     0.025390
JPM Returns    0.027667
MS Returns     0.037819
WFC Returns    0.030238
dtype: float64

Observations

  • There isnt much to differentiate between these stocks. Statistically, you can say Citi bank is the most riskiest but its only by the bearest of margin really insignificant. Practically they all had similiar risk.
#For year 2015
returns.loc['2015-01-01':'2015-12-31'].std()
BAC Returns    0.016163
C Returns      0.015289
GS Returns     0.014046
JPM Returns    0.014017
MS Returns     0.016249
WFC Returns    0.012591
dtype: float64

Obseravtion

  • There isnt much difference among these stocks they all posses similiar risk.

Create a histplot using seaborn of the 2015 returns for the Morgan Stanley

sns.histplot(returns.loc['2015-01-01':'2015-12-31']['MS Returns'], color ='g', bins =50, kde = True)
<Axes: xlabel='MS Returns', ylabel='Count'>

png

Observations

  • If you see the data most of the data is in between -0.02 and 0.02. If you imagine this in percentage by multiplying the numbers with 100,so most of the daily return within plus or minus 2% of the previous day number.

Create a histplot using seaborn of the 2008 returns for the CitiGroup

sns.histplot(returns.loc['2008-01-01':'2008-12-31']['C Returns'], color = 'r', bins = 50, kde= True)
<Axes: xlabel='C Returns', ylabel='Count'>

png

Observations

  • If you notice the Kde graph there is a long tail on the right side that is because of some point 0.6 . Basically it says that they have a day where there is very high return approx. 6 percent but its only few days maybe only one or two days.

Create a line plot showing Close price for each bank for the entire index of time.

bank_stocks.xs('Close', axis = 1 ,level = 1).iplot(kind = 'line')

Moving Averages

Lets analyze the moving average for these stocks in the year 2008

BAC.loc['2008-01-01':'2008-12-31'].rolling(window=30).std().head(30)
Open High Low Close Adj Close Volume
Date
2008-01-02 NaN NaN NaN NaN NaN NaN
2008-01-03 NaN NaN NaN NaN NaN NaN
2008-01-04 NaN NaN NaN NaN NaN NaN
2008-01-07 NaN NaN NaN NaN NaN NaN
2008-01-08 NaN NaN NaN NaN NaN NaN
2008-01-09 NaN NaN NaN NaN NaN NaN
2008-01-10 NaN NaN NaN NaN NaN NaN
2008-01-11 NaN NaN NaN NaN NaN NaN
2008-01-14 NaN NaN NaN NaN NaN NaN
2008-01-15 NaN NaN NaN NaN NaN NaN
2008-01-16 NaN NaN NaN NaN NaN NaN
2008-01-17 NaN NaN NaN NaN NaN NaN
2008-01-18 NaN NaN NaN NaN NaN NaN
2008-01-22 NaN NaN NaN NaN NaN NaN
2008-01-23 NaN NaN NaN NaN NaN NaN
2008-01-24 NaN NaN NaN NaN NaN NaN
2008-01-25 NaN NaN NaN NaN NaN NaN
2008-01-28 NaN NaN NaN NaN NaN NaN
2008-01-29 NaN NaN NaN NaN NaN NaN
2008-01-30 NaN NaN NaN NaN NaN NaN
2008-01-31 NaN NaN NaN NaN NaN NaN
2008-02-01 NaN NaN NaN NaN NaN NaN
2008-02-04 NaN NaN NaN NaN NaN NaN
2008-02-05 NaN NaN NaN NaN NaN NaN
2008-02-06 NaN NaN NaN NaN NaN NaN
2008-02-07 NaN NaN NaN NaN NaN NaN
2008-02-08 NaN NaN NaN NaN NaN NaN
2008-02-11 NaN NaN NaN NaN NaN NaN
2008-02-12 NaN NaN NaN NaN NaN NaN
2008-02-13 2.490761 2.092302 2.555124 2.313858 1.698958 2.104923e+07

Plot the rolling 30 day average against the Close Price for Bank Of America's stock for the year 2008

BAC.loc['2008-01-01':'2008-12-31']['Close'].rolling(window=30).mean().plot(figsize = (10,4), label = '30 Day Average of Close Price')
BAC.loc['2008-01-01':'2008-12-31']['Close'].plot(label = 'Close Price')
plt.legend()

png

Observations

  • If you see the difference between rolling average and actual closing stock prices, the real stock prices can have sharp declines and inclines and what the 30 day average does is it ignores those sharp edges and smoothens the data based on average of the last 30 days at each point. This is basically used to analyse the stock data so the sudden falls can be ignored and its not the part of the stock analysis unless it is sustained over a longer period of time.
  • If u see the data between 2008-05 to 2008-07 the stock price started to fall but the average price was still high and because the fall sustained over a period of time the avg value came down as well. The avg values has much more roundish edge and the actual closing price has a sharp edge and the same thing happens between the period 2008-07 to 2008-11 as well.

Create a heatmap of the correlation between the stocks Close price.

bank_stocks.xs('Close', axis=1, level=1).corr()
Bank Tickers BAC C GS JPM MS WFC
Bank Tickers
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('Close', axis=1, level=1).corr(),cmap = 'crest', annot = True)

png

Observations:

  • If you see correlation between bank of america to bank of america is same .
  • Correlation basically says if the stock price for the bank of america/any bank increases what is the movement of the stock price for the other banks. So it basically denotes the linear relationship between those banks.
  • +ve relationship means if price of one bank increases, price of the other bank increases as well, -ve relation will show movement on the negative direction compared to the stock.
  • Correlation always varies between -1 to 1.
  • +ve 1 could mean perfect correlation which means both the stock goes up or down at the same time.
  • -ve 1 means everytime the stock goes up the other stock goes down.
  • Correlation of zero(0) means that they dont have correlation among themselves. The movement of one stock has no relationship with the movement of the other stock.
  • These are used by the hedgefunds and other guys who take lot of risk to hedge their risk based on the movement of the stock price. So if the are betting on BAC to go up it has a -ve correlation with some other stock and they can make some position in the other stock as well. So just in case if BAC goes down the other stock might go up there by they might not be losing as much money.
  • You can clearly see the BAC has a strong correlationship with the City Bank and Morgan Stanley Banks where as a very weak correlationship with WellsFargo. Even with JPMorgan it is 0.1 which is a weak correlation and close to zero .

Use seaborn's clustermap to cluster the correlations together:

sns.clustermap(bank_stocks.xs('Close', axis=1, level=1).corr(),figsize=(6,6),cmap = 'crest',annot= True)

png

Observations

  • Basically what clustermap does is that it creates a cluster based on the closed data.
  • If you see on the both X and the Y axis, the map BAC lies between MS and C whereas in the heatmap its had very strong relationship with the City bank and Morgan Stanley and Wells Fargo and JPMorgan were close to eachother.So in the cluster map WFC and JPM has put together.

USING CUFFLINKS

Use .plot(kind='candle') to create a candle plot of Bank of americas stock from Jans 1st 2015 to Jan 1st 2016.

BAC.loc['2015-01-01':'2016-01-01'].iplot(kind='candle')

png

Use .ta_plot(study='sma') to create a Simple Moving Average plot of Morgan Stanley for the year 2015.

MS.loc['2015-01-01':'2016-01-01']['Close'].ta_plot(study='sma',period=[14,21,55])

png

Observations

  • Simple moving average smoothens the edges of the stock.
  • period=[14,21,55] -> This gives the simple moving averages of 14th 21 and 55th day.
MS.loc['2015-01-01':'2016-01-01']['Close'].ta_plot(study='rsi')

png

Use .ta_plot(study='boll') to create a Bollinger Band plot for Bank of Americs for the year 2015.

BAC.loc['2015-01-01':'2015-12-31']['Close'].ta_plot(study='boll')

png

Observations

  • What the bollinger does is it creates a simple moving average and has a upper line and a lower line aswell.

Just update the start and end dates, and return the codes to analyze the last 10 years performance.

Findings

  • The maximum closing price for each bank's stock throughout the time period was calculated using the max() function.
  • The returns for each bank's stock were calculated using the pct_change() function and stored in a new dataframe called returns.
  • A pairplot was created using seaborn to visualize the returns dataframe. The plot showed that there was a strong correlation between the returns of the banks.
  • The best and worst single day returns for each bank were identified using the idxmax() and idxmin() functions. The worst single day returns for four of the banks occurred on the same day, which was the day that the Icelandic banks collapsed.
  • The standard deviation of the returns was calculated to determine which stocks were the riskiest over the entire period and in the year 2015. The results showed that there was not much difference in the riskiness of the stocks.
  • Histograms were created using seaborn to visualize the returns for Morgan Stanley in 2015 and for CitiGroup in 2008. The histograms showed that most of the daily returns were within plus or minus 2% of the previous day's number.
  • A line plot was created to show the close price for each bank for the entire index of time.
  • Moving averages were calculated for the stocks in the year 2008 using the rolling() function.
  • A line plot was created to show the rolling 30 day average against the close price for Bank of America's stock for the year 2008.
  • A heatmap was created to show the correlation between the stocks' close price using the corr() function and seaborn's heatmap() function. The heatmap showed that Bank of America had a strong correlation with CitiGroup and Morgan Stanley, while it had a weak correlation with Wells Fargo.
  • A clustermap was created to cluster the correlations together using seaborn's clustermap() function. The clustermap showed that Bank of America's stock was clustered with Morgan Stanley and CitiGroup, while Wells Fargo's stock was clustered with JPMorgan Chase.

Reports

  • The maximum closing price for each bank's stock throughout the time period was analyzed. Bank of America had the highest maximum closing price, while CitiGroup had the lowest.
  • The returns for each bank's stock were analyzed using a pairplot. The plot showed that there was a strong correlation between the returns of the banks.
  • The best and worst single day returns for each bank were identified. The worst single day returns for four of the banks occurred on the same day, which was the day that the Icelandic banks collapsed.
  • The standard deviation of the returns was calculated to determine which stocks were the riskiest over the entire period and in the year 2015. The results showed that there was not much difference in the riskiness of the stocks.
  • Histograms were created to visualize the returns for Morgan Stanley in 2015 and for CitiGroup in 2008. The histograms showed that most of the daily returns were within plus or minus 2% of the previous day's number.
  • A line plot was created to show the close price for each bank for the entire index of time. The plot showed that the close prices for the banks were generally trending upwards over the time period.
  • Moving averages were calculated for the stocks in the year 2008. The moving averages were used to smooth out the noise in the data and to identify trends.
  • A line plot was created to show the rolling 30 day average against the close price for Bank of America's stock for the year 2008. The plot showed that the rolling 30 day average followed the overall trend of the close price, but with less volatility.
  • A heatmap was created to show the correlation between the stocks' close price. The heatmap showed that Bank of America had a strong correlation with CitiGroup and Morgan Stanley, while it had a weak correlation with Wells Fargo.
  • A clustermap was created to cluster the correlations together. The clustermap showed that Bank of America's stock was clustered with Morgan Stanley and CitiGroup, while Wells Fargo's stock was clustered with JPMorgan Chase.
  • Overall, the analysis showed that there was a strong correlation between the returns of the banks, and that Bank of America had a strong correlation with CitiGroup and Morgan Stanley, while it had a weak correlation with Wells Fargo. The histograms and moving averages were used to smooth out the noise in the data and to identify trends. The clustermap was used to group the stocks based on their correlations.

Conclusions

This project demonstrates my skills as a data analyst by utilizing Python and various libraries pandas,numpy,matplotlib,seaborn and plotly to analyze stock data. Through exploratory data analysis, we identified patterns, trends, and correlations in the stock prices of major US banks over a 10-year period. By visualizing the data using interactive graphs and statistical analysis, we gained insights into the risk and return profiles of these stocks. This project showcases my ability to handle data, perform statistical analysis, and communicate findings effectively.

Author - Thanveer Ahmed Shaik

This project is part of my portfolio, showcasing the Python skills essential for data analyst roles. If you have any questions, feedback, or would like to collaborate, feel free to get in touch!