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VISUALISATION AND COMPARISON OF NIFTY50 STOCK DATASETS

Exploratory Data Analysis Of Datasets

Submitted By :- SNEHIL VUKKUSILA --- RVCE22BAI006

Department of Artifial Intelligence And Machine Learning, RV COLLEGE OF ENGINEERING®, BENGALURU – 560059 pg. 2 Outline

•Introduction •Problem Statement

•Objectives •Methodology •Results •Conclusion pg. 3 Introduction and Summary Problem Statement The Tata Group is one of the largest conglomerates in India with businesses ranging from steel, automobiles, and IT services to hotels, airlines, and consumer goods. Through analysis our task is performing an exploratory data analysis (EDA) on the stock prices of Tata Group companies over the last 10 years. We want to see how the stocks of a particular sector within a huge conglomerate as Tata, have grown along with the group as a whole, and the individual stock performances over a long period of growth. The goal of this project is to identify trends and patterns in the stock prices and the range of volatility of different Tata Group companies, and to provide insights that can help inform investment decisions, through visualisation and comparison of Volume, Price, Moving Averages, Market Capitalization. We will be utilizing past data of stocks present in the Nifty 50 Index, and by visualization and comparison we will be able to determine the risk , and future prospects for investment into those stock Overview The objective of this Exploratory Data Analysis is to apply a complete datadriven analytic approach to understand the trend in the price of a stock, and create comparisons with other stocks in same sector or group of companies. It involves data collection, data wrangling, EDA with data visualization, Building a Dashboard with Plotly and matplotlib, Seaborn. Utilising Data analysis libraries and modules such as NumPy , Pandas Scikitlearn . Visualization libraries and modules like matplotlib , Seaborn pg. 4 Understanding the Problem Statement Broadly, stock market analysis is divided into two parts – Fundamental Analysis and Technical Analysis. ● Fundamental Analysis involves analyzing the company’s future profitability on the basis of its current business environment and financial performance. ● Technical Analysis, on the other hand, includes reading the charts and using statistical figures to identify the trends in the stock market. To understand the risk associated with it there must be a proper analysis of stock before buying it. Objectives to find answers: 1)Utilising previous data predict of price, volume, volatility, market capitalization, we will try to analyse the patterns and the similarities of the stock price movement over a period of 15 years worth of data. 2)We will visualize the various parameters that influence stock analysis, which will affect the decision of an investor. 3)Plot and show how stocks have different volatility values and ROI , and why some stocks a more risky and some are consolidated in nature. 4)Analyse the moving averages of 50 day and 200 day price values, and plot these with respect to the daily values, and see how we can predict the future movements. pg. 5 Methodology •Data collection methodology: •The data was collected by sending get request to Kaggle API and by web scraping NSE INDIA and Nifty 50 data sets Records. •Perform data wrangling on the csv files which have been generated. •Dealing with missing values, creating new columns, dropping irrelevant columns and visualizing through Panda's data frames •Perform exploratory data analysis (EDA) using Pandas and Numpy •Perform visual analytics Plotly , Matplotlib •Perform comparative analysis using plots and graphs Including, Price Analysis, Volume Comparison, 50-day and 200-day Moving Average comparison, Volatility Histogram •Comparison analysis pg. 6 Data Collection How was the data collected? •The data is collected from the Kaggle dataset, and the API will give us data in form of a zipped file containing csv files of various stock data. •We also use NSEIndia website to extract csv files. •We extract the data set into the form of rows and columns which can be easily operated upon, by using pandas, which can easily access and parse through CSV files. Data Wrangling

pg. 7 Data description: •Open: The price of the stock when the market opens in the morning. •Close: The price of the stock when the market closed in the evening. •High: Highest price the stock reached during that day. •Low: Lowest price the stock is traded on that day. •Volume: The total amount of stocks traded on that day. What is Stock Market Analysis? Stock market analysis enables investors to identify the intrinsic worth of a security even before investing in it. All stock market tips are formulated after thorough research by experts. Stock analysts try to find out activity of an instrument/sector/market in future. By using stock analysis, investors and traders arrive at equity buying and selling decisions. Studying and evaluating past and current data helps investors and traders to gain an edge in the markets to make informed decisions. Fundamental Research and Technical Research are two types of research used to first analyse and then value a security. pg. 8 Comparison of Price According to the graph above, the price of TCS has skyrocketed significantly higher than that of Tata Steel and Tata Motors. TCS’s pricing trajectory has been generally upward from its beginning, whereas Tata Steel and Tata Motors have been more on a consolidation trend, this shows the innovative IT industry as a whole has developed vastly, as compared to a legacy generalised industreis such as steel ore and motor vehicles are more slow growth, but stable and guaranteed to be used for many generations. pg. 9 Comparison of Volume Though the price of TCS has risen more significantly as compared to Tata Steel and Tata Motors, we can notice from the above graph that TCS has the least volume signifying that the stock has been traded comparatively less as compared to Tata Steel and Tata Motors and is lesser liquid. Tata Motors on the other hand has been traded the most signifying higher liquidity, and better order execution. We can see that traders and investors who may be of a retail or FII level investment firms, have more confidence on a stable industry such as Tata Steel and Tata Motors and are wary about the IT industry as it is still growing at such a rapid pace without a proven track record. pg. 10 Moving Averages The above shows the price, along with the 50 day and 200 day moving average plots. As we know the stock prices are highly volatile and prices change quickly with time. To observe any trend or pattern we can take the help of a 50-day 200-day average pg. 11 Volatility Analysis It is clear from the graph that the percentage increase in stock price histogram for TCS is the widest which indicates the stock of TCS is the most volatile among the three companies compared. The width of the histogram shape is related to the standard deviation of the distribution. The higher it is the more variants we have, the variants of a certain distribution are directly related to the standard deviation. It’s actually the square value of the standard deviation, the higher the standard deviation is, the higher the variance is. The more volatility we have in terms of stock analysis. pg. 12 Data Collection and Wrangling pg. 13 pg. 14 Data Collection and Wrangling Once downloaded, extract the zip file. This data set consists of a number of companies’ stock data from 2000-2021 including Adani Ports, Bajaj Finance, Wipro, Infosys, and many more. But for this project, we will be analyzing three Tata stocks – Tata Motors, Tata Steel, and Tata Consultancy Services (TCS). pg. 15 The data in the data set of the csv file opened using excel consists of Date, Symbol, Prev Close, Open, High, Low, Last, Close, VWAP, Turnover, Trades, Deliverable Volume, and % Deliverable. We will be utilizing the Date, Open, and Volume.

pg. 16 Data viewing Here, we can notice the data type of “Date” is an ‘object’ in the Tata Motors dataset, hence we need to convert it into the ‘date’ datatype (Which we will do in the “Working on Data” section). You will see similar results for the datatypes for Tata Steel and TCS datasets after executing the tata_steel.info() and tcs.info() functions respectively. pg. 17 From the above table, we can view the first 5 rows of the Tata Motors dataset and get a brief overview of the data present.You will see the results of the dataset for Tata Steel and TCS by executing the tata_steel.head() and tcs.head() functions respectively.

pg. 18 Working on Data Converting the “Date” column dtype from object to date Once this code is executed, if you try executing the .info() function on any of the datasets, you will notice the datatype of the ‘Date’ column changed from ‘object’ to ‘datetime64[ns]’ for all 3 datasets Dropping columns Trades, Deliverable Volume, and %Deliverable Once this code is executed, if you try running the .head() or .tail() function on any of the datasets, you will notice all the 3 columns Trades, Deliverable Volume, and %Deliverable not present. pg. 19 Once this code is executed, if you try running the .head() or .tail() function on any of the datasets, you will notice 3 new columns ‘Day’, ‘Month’ and ‘Year’ present. We will be using the ‘Day’ column for our analysis

pg. 20 EDA with Data Visualization Visualization charts and reason for the choice of the chart •Bar Graph makes it easy to compare sets of data between different groups immediately. The graph represents categories on one axis and a discrete value in the other. The goal is to show the relationship between the two axes. Bar charts can also show big changes in data over time. Mean vs. Orbit •Line Graphs are useful in that they show data variables and trends very clearly and can help to make predictions about the results of data not yet recorded

pg. 21 Price Comparision The next segment of code creates a plot showing the relationship between the opening price of each company's stock over time. It plots the 'Open' column against the 'Date' column for each company, sets the title and axes labels, and adds a legend for clarity.

pg. 22 Volume Comparision The next plot shows the relationship between the volume of each company's stock over time. It plots the 'Volume' column against the 'Date' column for each company, sets the title and axes labels, and adds a legend for clarity.

pg. 23 Moving Averages The following segment of code creates a plot showing the rolling average of TCS's opening stock price over time. It calculates the rolling average using the 'rolling' function and plots the 'Open', 'MA50', and 'MA200' columns against the 'Date' column, sets the title, and adds a legend and gridlines for clarity.

pg. 24 Volatility Analysis The final segment of code creates a histogram showing the distribution of returns for each company's stock. It calculates the daily returns for each company by dividing the 'Close' column by the previous day's 'Close' value and subtracting 1. It then creates a histogram with 100 bins for each company's returns, sets the title, adds a legend, and displays the plot. pg. 25

Code

import pandas as pd import matplotlib.pyplot as plt tata_motors=pd.read_csv("C:/Users/svukk/PycharmProjects/TheoryEL/TA TAMOTORS.csv") tata_steel=pd.read_csv("C:/Users/svukk/PycharmProjects/TheoryEL/TAT ASTEEL.csv") tcs=pd.read_csv("C:/Users/svukk/PycharmProjects/TheoryEL/TCS.csv") tata_motors["Date"]=pd.to_datetime(tata_motors["Date"]) tata_steel["Date"]=pd.to_datetime(tata_steel["Date"]) tcs["Date"]=pd.to_datetime(tcs["Date"]) tata_motors=tata_motors.drop(['Trades','Deliverable Volume','%Deliverble'], axis=1) tata_steel=tata_steel.drop(['Trades','Deliverable Volume','%Deliverble'], axis=1) tcs=tcs.drop(['Trades','Deliverable Volume','%Deliverble'], axis=1) tata_motors['Month']=tata_motors["Date"].dt.month tata_motors['Year']=tata_motors["Date"].dt.year tata_motors['Day']=tata_motors["Date"].dt.day tata_steel['Month']=tata_steel["Date"].dt.month tata_steel['Year']=tata_steel["Date"].dt.year pg. 26 tata_steel['Day']=tata_steel["Date"].dt.day tcs['Day']=tcs['Date'].dt.day tcs['Year']=tcs['Date'].dt.year tcs['Month']=tcs['Date'].dt.month plt.figure(figsize=(20,7)) plt.plot(tata_motors['Date'],tata_motors['Open'],color='blue',label='Tata Motors') plt.plot(tata_steel['Date'],tata_steel['Open'],color='grey',label='Tata Steel') plt.plot(tcs['Date'],tcs['Open'],color='orange',label='TCS') plt.title("Relation between Tata Motors, Tata Steel and TCS Price") plt.xlabel("Year") plt.ylabel("Price") plt.legend(title="") #plt.show() plt.figure(figsize=(20,7)) plt.plot(tata_motors['Date'],tata_motors['Volume'],color='blue',label='Tata Motors') plt.plot(tata_steel['Date'],tata_steel['Volume'],color='grey',label='Tata Steel') plt.plot(tcs['Date'],tcs['Volume'],color='orange',label='TCS') plt.title("Relation between Tata Motors, Tata Steel and TCS Volume") plt.xlabel("Year") plt.ylabel("Volume") plt.legend(title="") #plt.show() pg. 27 fig = plt.figure() tcs['MA50'] = tcs['Open'].rolling(50).mean() tcs['MA200'] = tcs['Open'].rolling(200).mean() tcs['Open'].plot(figsize = (15,7)) tcs['MA50'].plot() tcs['MA200'].plot() plt.legend() plt.grid() plt.show() fig = plt.figure() tcs['returns'] = (tcs['Close']/tcs['Close'].shift(1)) -1 tata_motors['returns'] = (tata_motors['Close']/tata_motors['Close'].shift(1))-1 tata_steel['returns'] = (tata_steel['Close']/tata_steel['Close'].shift(1)) - 1 tcs['returns'].hist(bins = 100, label = 'TCS', alpha = 0.5, figsize = (15,7)) tata_motors['returns'].hist(bins = 100, label = 'tata_motors', alpha = 0.5) tata_steel['returns'].hist(bins = 100, label = 'tata_steel', alpha = 0.5) plt.legend() plt.show()