THE ULTIMATE TABLEAU PORTFOLIO PROJECT: From Pandas to an Amazing Interactive Stock Market Dashboard

Summary

This tutorial offers a step-by-step guide to creating an interactive stock market analysis dashboard using Tableau. It covers the manipulation of stock data for six major companies with Python's pandas library to generate key metrics like moving averages and percent changes. The video demonstrates building various visualizations, including line charts, histograms, and detailed tables, and assembling them into a cohesive dashboard, providing a comprehensive tool for stock performance analysis.

Takeaways

• 📊 The tutorial focuses on creating an interactive stock market analysis dashboard using Tableau.
• 📈 The dataset used contains information on approximately 6000 companies and is available online.
• 🔢 Six specific companies are selected for the dashboard: Apple, Facebook, Google, Nvidia, Tesla, and Twitter.
• 🐍 Python's pandas library is utilized to manipulate data, creating new columns like moving average and percent change.
• 📈 KPIs (Key Performance Indicators) and highlighters for different companies are part of the dashboard features.
• 📊 Multiple visualizations are created, including a line chart for trading volumes, a table for close prices and volumes, a line chart for moving averages and open prices, and a histogram for price percent changes.
• 📚 The tutorial provides guidance on how to filter data for the last five years and how to create parameters for custom date ranges.
• 📝 The script explains the process of saving modified dataframes as CSV files for use in Tableau.
• 🖥️ Detailed steps for creating each visualization component in Tableau are provided, including setting up filters, arranging sheets, and formatting the dashboard.
• 🔑 The importance of understanding pandas for data manipulation before using it in Tableau is highlighted for those unfamiliar with the library.
• 🔗 Links to resources such as the dataset, GitHub repository, and company logos are mentioned for further reference.

Q & A

What is the main topic of the video tutorial?

The main topic of the video tutorial is creating an interactive dashboard for stock market analysis using Tableau.

How many companies' data is included in the dataset provided for the tutorial?

The dataset provided includes data for approximately 6000 companies.

Which six companies' data are specifically used to create the dashboard in the tutorial?

The tutorial uses data for Apple, Facebook, Google, Nvidia, Tesla, and Twitter.

What programming library is mentioned to be used alongside Tableau for data manipulation?

The programming library mentioned for data manipulation is pandas.

What are some of the new columns created using pandas for the analysis?

Some of the new columns created using pandas include moving average and percent change, which are used for compilation.

What types of visualizations are created in the tutorial for the stock market dashboard?

The tutorial creates a multiple line chart for different volumes, a detailed table for close price and volume, a multiplying chart for moving average and open price, and a histogram for percent change in price.

What is the source of the original dataset used in the tutorial?

The original dataset is sourced from Google and contains historical data prices of NASDAQ trading stocks and ETFs.

How can viewers access the dataset or the individual files for the six companies?

Viewers can access the dataset through a link provided in the description or find the links to the GitHub repository where the six individual files are available.

What are the main columns contained in the dataset?

The main columns in the dataset include date, opening price, maximum price, minimum price, close price, adjusted for splits and dividends, and the value representing the number of shares traded.

How does the tutorial handle data for dates beyond April 1st, 2020?

For more up-to-date data beyond April 1st, 2020, the tutorial suggests forking and rerunning a data collection script that is available from the extractor.

What is the purpose of creating a list of dataframes in the tutorial?

The purpose of creating a list of dataframes is to streamline the process by applying the same function to all dataframes using a for loop, rather than applying the function individually six times.

What is the significance of creating 'previous day close price' and 'change in price' columns?

The 'previous day close price' and 'change in price' columns are created to calculate the daily change regarding the close price, which is essential for analyzing stock performance over time.

How are the moving average columns created in the tutorial?

The moving average columns are created by using the rolling built-in function in pandas with a specified number like 50 or 200 to calculate the average of the last 50 or 200 days' closing prices.

What is the role of the 'percent change in price' column in the analysis?

The 'percent change in price' column represents the return on investment and is calculated by dividing the change in price by the previous day's close price.

What are the steps taken to save the modified dataframes as CSV files for use in Tableau?

The steps include running a for loop to apply the necessary calculations to each dataframe, and then using the 'to_csv' method to save each dataframe as a CSV file with an appropriate name.

How does the tutorial handle the creation of the dashboard in Tableau?

The tutorial guides through importing the CSV files into Tableau, converting them into a union, applying filters, creating parameters for date ranges, and then using these to build various visualizations such as line charts, histograms, and detailed tables.

What are the filters applied to the dataset to simplify the analysis?

The tutorial applies a data source filter to include only the last five years of data and creates parameters for start and end dates to further refine the data displayed in the visualizations.

How is the 'study period' calculated field used in the tutorial?

The 'study period' calculated field is used to filter the data based on the selected start and end dates. It returns a value of one for dates within the specified range and zero for dates outside the range.

What is the purpose of creating parameters for 'start date' and 'end date' in Tableau?

The parameters for 'start date' and 'end date' allow users to dynamically control the date range displayed in the visualizations, making the dashboard interactive and adaptable to different time periods of analysis.

How are the detailed tables for close price and volume created in the tutorial?

The detailed tables are created by dragging and dropping the relevant fields such as company, close price, previous day close price, change in price, and percent change in price into the Tableau worksheet. Calculations for the last day's data are also added to display the most recent information.

What customization options are available for the visualizations in Tableau as shown in the tutorial?

The tutorial demonstrates customizing colors for different companies, editing axis titles, adjusting the number of bins in histograms, and formatting numbers to display as currency with specific decimal places.

How are the arrows indicating price movement direction added to the detailed price table?

The tutorial creates calculated fields for 'upper price' and 'down price' based on the percent change in price. These fields are then dragged into the text shelf in Tableau, where the arrows are applied to indicate the direction of price movement, with green for positive changes and red for negative changes.

What is the final step in creating the dashboard after setting up all the visualizations?

The final step is to arrange the visualizations on the dashboard, add a title, format the dashboard for aesthetics, and then enter presentation mode to view the completed stock market dashboard.

Outlines

00:00

📈 Introduction to Stock Market Analysis Dashboard

The script introduces a new Tableau tutorial focused on creating an interactive stock market analysis dashboard. It mentions the use of a dataset from Google containing information on 6000 companies and outlines the plan to select six specific companies (Apple, Facebook, Google, Nvidia, Tesla, and Twitter) for the analysis. The tutorial will utilize pandas for data manipulation, creating new columns such as moving averages and percent changes, which are essential for compilation. The goal is to create various KPIs, highlighters for company comparison, and different visualizations including a multiple line chart for trading volumes, a detailed table for close price and volume, a line chart for moving averages and open prices, and a histogram representing price changes.

05:03

🔍 Exploring the NASDAQ Data Set

This section delves into the specifics of the NASDAQ data set, which includes historical prices for stocks and ETFs traded on the platform. The data set is extensive, over 500 megabytes, and contains prices up to April 1st, 2020. Viewers are directed to the tutorial's description for a download link or to the GitHub repository for the six specific company files needed for the dashboard. The data set's structure is explained, with seven main columns: date, opening price, max price, min price, close price (adjusted for splits and dividends), and volume (number of shares traded). The script emphasizes the use of pandas for preliminary data manipulation before importing it into Tableau.

10:03

🔧 Data Manipulation with Pandas

The tutorial proceeds with instructions on how to manipulate the data using pandas in a Jupyter notebook. It guides through the process of importing CSV files for the six selected companies and creating a list of dataframes. The script details how to calculate moving averages (50 and 200 days) and other new columns such as 'previous day close price', 'change in price', and 'percent change in price'. These calculations are done in a loop to apply the same functions across all dataframes. The section also explains how to create additional columns for volume changes and save the modified dataframes as new CSV files for use in Tableau.

15:04

📊 Creating Interactive Visualizations in Tableau

After saving the modified CSV files, the script shifts to Tableau for creating interactive visualizations. It describes how to import the CSV files into Tableau, combine them into a single table, and add a 'company' column for differentiation. The tutorial then guides through adding data source filters to focus on the last five years of data. It proceeds to create parameters for start and end dates, a calculated field for the study period, and demonstrates how to visualize trading volume across companies based on the selected date range. The section also covers customizing the appearance of the visualizations, including colors and chart titles.

20:09

📈 Advanced Visualization Techniques

This part of the script focuses on creating more complex visualizations, such as a histogram for price percent change and a multiple line chart for moving averages and open prices. It explains how to set up the bins for the histogram, customize axis titles, and format the visualizations. The tutorial also details the creation of a detailed price table, showcasing how to use calculated fields to display the last day's data and format the table for clarity. The section includes instructions on adding arrows to indicate price changes and customizing the table's appearance with borders and currency formatting.

25:11

📊 Detailed Volume Analysis

The script continues with the creation of a detailed volume table, explaining how to organize the data in rows and columns, format the numbers, and add arrows to indicate volume changes. It covers the process of creating calculated fields for 'down volume' and 'upper volume' and how to apply conditional coloring to reflect changes. The tutorial also includes steps for formatting the table with borders and centering the text for a polished presentation.

30:12

📋 Creating Text Sheets for Key Metrics

The tutorial moves on to creating text sheets to display key metrics such as the last day's trading details, total volume, lowest and highest prices within the study period. It explains how to create calculated fields for these metrics and format them for clarity. The script details the process of adding these text sheets to the dashboard, hiding unnecessary titles, and ensuring the information is prominently displayed and easy to read.

35:13

🏢 Adding Company Logos to the Dashboard

In this section, the script describes how to enhance the dashboard by adding company logos for visual identification. It provides instructions on importing images and positioning them on the dashboard. The logos are added as floating objects and placed in designated areas to correspond with the companies' data in the visualizations.

40:19

🎨 Finalizing the Dashboard Design

The tutorial concludes with final touches to the dashboard design. It covers arranging the layout, adjusting the size of the visualizations, and formatting the dashboard background and padding. The script also explains how to add a title to the dashboard, center it, and set the appropriate font size. The goal is to create a cohesive and visually appealing dashboard that is easy to navigate and understand.

45:24

📑 Organizing the Dashboard Layout

This part of the script focuses on the final organization of the dashboard layout. It details the process of arranging the various sheets and KPIs within the dashboard, including adjusting their size and position for optimal viewing. The tutorial also explains how to apply highlighters to the tables and charts to enable interactive comparison between companies. The section concludes with adding logos for the companies and setting the dashboard to presentation mode to showcase the completed stock market analysis dashboard.

Keywords

💡Tableau

Tableau is a data visualization software platform used for interactive data analysis and sharing. In the context of the video, it is the primary tool used to create an interactive dashboard for stock market analysis, demonstrating how to manipulate and visualize data to gain insights into stock performance.

💡Dashboard

A dashboard in this video refers to a user interface that organizes and presents information in a way that is easy to read and understand at a glance. The tutorial focuses on creating a stock market analysis dashboard using Tableau, which includes various charts and tables to display key performance indicators (KPIs) of different companies.

💡Stock Market Analysis

Stock market analysis involves examining financial data to assess the performance of stocks and make informed investment decisions. The video's theme revolves around creating a dashboard for this purpose, using data from various companies to visualize trends, changes, and other relevant metrics.

💡Pandas

Pandas is a Python library used for data manipulation and analysis. In the script, it is mentioned as a tool to preprocess the data by creating new columns such as moving averages and percent changes, which are essential for the subsequent analysis in Tableau.

💡Data Manipulation

Data manipulation refers to the process of transforming and organizing data into a format suitable for analysis. The video describes using pandas to manipulate stock market data by creating new columns that will be used in the analysis, such as moving averages and percent changes.

💡Moving Average

A moving average is a technical analysis indicator that helps smooth out price data by creating a constantly updated average price. The script explains how to calculate a 50-day and a 200-day moving average using pandas, which are later visualized in the Tableau dashboard to identify trends.

💡Percent Change

Percent change is a measure of the relative change in a variable over time. In the context of the video, it is used to calculate the daily change in stock prices and is visualized in a histogram to show the distribution of price changes across different companies.

💡KPIs (Key Performance Indicators)

KPIs are quantifiable measures used to track the performance of a company or stock. The video mentions creating different KPIs such as volume of different companies and price changes, which are displayed in the dashboard to provide a quick overview of the companies' performance.

💡Jupyter Notebook

A Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations, and narrative text. In the script, it is suggested as the environment where pandas is used to preprocess the data before it is visualized in Tableau.

💡Data Visualization

Data visualization is the graphical representation of information and data. The video script describes the creation of various charts and tables in Tableau, such as line charts for volumes, histograms for percent changes, and detailed tables for close prices and volumes, to visually represent the analyzed data.

💡NASDAQ

NASDAQ is a stock exchange platform for technology companies and is known for its electronic trading platform. The script mentions that the dataset used in the tutorial contains historical data prices for all tickers currently trading on NASDAQ, which is the source of the data for the stock market analysis.

Highlights

Introduction to a new Tableau tutorial for creating an interactive stock market analysis dashboard.

Use of a dataset containing information on approximately 6000 companies available on Google.

Utilization of six specific company files: Apple, Facebook, Google, Nvidia, Tesla, and Twitter for dashboard creation.

Employment of pandas for data manipulation to generate new columns such as moving average and percent change.

Explanation of creating Key Performance Indicators (KPIs) for different companies with interactive highlighters.

Demonstration of creating a multiple line chart for different company trading volumes over time.

Inclusion of a detailed table showcasing close price and volume data.

Introduction of a multiple line chart illustrating moving averages and open prices.

Use of a histogram to represent the percent change in price for different companies.

Guidance on exploring and downloading the NASDAQ historical stock market dataset.

Instructions on importing CSV files into a Jupyter notebook for data processing with pandas.

Tutorial on creating a list of dataframes to streamline data manipulation processes.

Step-by-step guide on calculating moving averages and previous day's close price using pandas.

Method for calculating daily price changes and percent changes in price with pandas.

Process of creating new columns for volume changes and percent changes in volume.

Direction on saving manipulated dataframes as CSV files for use in Tableau dashboard creation.

Technique for combining multiple CSV files into a single union table in Tableau.

Use of data source filters to focus on the most recent five years of stock data.

Creation of parameters for start and end dates to dynamically filter the data visualization.

Design and layout of the final interactive stock market dashboard in Tableau.

Transcripts

hello guys and welcome to this new

Tableau tutorial in this video I will

show you how to create the following

interactive dashboard which is about

stock market analysis the data set that

you are going to use is available on

Google and it contains data for about

6000 companies to create our dashboard

we are going to pick six files for six

different companies which are

Apple Facebook Google Nvidia Tesla and

Twitter and also we are going to use

pandas with its built-in functions to

manipulate our data and create new

columns that you will use in our

analysis like for example moving average

and percent change which are mainly used

for compilation as you can see here we

are going to create different kpis

we'll have also highlighter for

different companies as you can see here

for example if we choose the company we

can see the different values we're going

to create a multiple line chart for

different volumes of different companies

same thing we are going to create a

detailed table for close price and

volume

another multiplying chart for moving

average and the open price and also a

histogram for different companies which

represent percent change in price so now

let's explore our data set so here we

have stock market data set which

represent historical data prices of

NASDAQ trading stocks and ETFs we can

find this link in the description and as

you can see here it is a huge data set

which is more than 500 megabytes

so you can either choose to download

this data set or you can find also the

links to my GitHub repository where you

can download the six files that you are

going to use to create our dashboard as

we have said this data set contains

historical data prices for all tickers

currently trading on NASDAQ

and it contains prices for up to April

1st 2020 and if you need more up-to-date

data you can fork and rerun data

collection script also available from

that extractor we have mainly seven

columns right so we have the date

specifies trading date we have opening

price

the maximum price during the day the

minimum price during the day close price

adjusted for splits

for both dividends and splits and also

we have the value which represents the

number of shares that change the hands

during a given day

but as I said we are not going to use

directly these files and upward them to

Tableau but to again first to use pandas

in order to create new calendars that do

I want to use in our analysis

so you open your jupyter notebook and if

you are not familiar with pandas then

you can find the links to the resulting

files that you are going to create using

pandas and you can use them directly to

create the dashboard

so first we import

pandas aspd right

next we are going to import our CSV

files so the first one is Apple

PD Dot

read CSV right

and its name is aapl.csv

next we have Facebook

so it is PD Dot edit CSV is FB dot CSV

next we have Google

PD dot read CSV and it is Google

to CSV

next we have Nvidia right

PPT

nvda dot CSV

next we have Tesla

tsla.csv

and the last one is Twitter

the same thing PD dot read CSV

and it is twtr.csv

execute that

if we write Apple dot the head

so get the first file

same thing for Facebook

Google dot head

Nvidia

we have Tesla

and finally we have Twitter

okay so like that we have all the files

that we need

and the second thing that we are going

to do is to create a list of data frames

so we call it DFS

which will contain all data frames so we

have apple

then we have Facebook

Google

Nvidia

[Music]

Tesla and Twitter

the reason to create our list is rather

than applying the same function six

times we are going to use a for Loop and

apply the same function to our list

the first thing that we are going to do

is to create the moving average

so to do that we'll write for DF in our

list

DFS

so for each data frame I'm going to add

a calendar which is within average 50.

which is equal to DF dot take the close

price and we use rolling built in

function

50.

dot mean

right

so we copy that

and you do the same thing for moving

average 200.

so 200 and here same thing 200.

so if we run that

and do for example we select Apple dot

head

we get our two new columns as you can

see here we have not a number for the

first rows because for the first one it

starts from 50 for the second one it

starts from 200 so if you write 50

run

to get our value starting from 50.

same thing if we write 200

all right

as you can see here it starts from this

row okay

next we are going to create another

calendar which is named previous day

close price so I'm going to use this

column in order to calculate the daily

change regarding the close price okay so

to do that right for the F in DFS

so the f

we name it previous

day

close price

is equal to

DF dot we set close price and we are

going to use it shift build infection

one

run that

same thing if we take apple dot head

so we have previous day close price this

one it is not a number because it is the

first one but for the second one for

example if we take the date which is

this one

the previous close price is this one and

it is the same in here

same thing for 17th before we have this

price and it is the same in here okay

now I'm going to create another color

that will name change in price okay so

same thing for TF in DFS

the F we said change

in price

it is equal to

DF close price

minus the f

the column that we have created so

previous

day

close price

so if we run that same thing Apple dot

head

we get our change in price okay so if it

is negative it means it decreased if it

is positive it increased

so we add new sales

next to calculate the percent change or

the return

and to do that we are going to use PCT

change or person change built-in

function

same thing right for DF in DFS

DF we call it

percent

change

in price right

it is equal to

DF Dot close

dot PCT change or percent change

execute that same thing right Apple dot

head

get our percent change so it is the

close price minus previous day close

price so it is changing price divided by

previous day close price okay so this is

the return

now I'm going to create three more

columns and this time I can create that

using the value so write for DF in DFS

the f

so we set previous

day

value right

it is equal to DF Dot volume dot shift

and right one

so if we write Apple dot head

we get previous day volume as you can

see here we have the following volume

the previous day volume is this one and

it is equal to this value

same thing we'll calculate change in

volume right

so for DF in DFS

DF

change

in volume

it is equal to DF

volume right

minus

DF

previous day value

okay same thing Apple dot head

and here we have the change okay same

thing it can be negative or it can be

positive

and the last column is percent change in

value okay so four

DF in DFS

so DF

percent

change in volume

it is equal to DF dot volume

dot percent change

okay

and volume v it is capital case right

so Apple dot head

and we get our percent change in volume

okay

now what we are going to do is that we

are going to save our data frames as CSV

files that we are going to use to create

our dashboard

to do that right for the first one apple

dot to CSV

and we give it a name so write Apple

dot CSV

okay so by default it will be stored or

it will be it will be saved in the same

place where I have the Jupiter notebook

but you can choose any location you want

okay

next one we have Facebook

dot to CSV and we'll write the same

thing Facebook

dot CSV

next we have google.csv

it will be

Google dot CSV

same thing for NVIDIA right

to CSV

dot to CSV right and this is the same

name so Nvidia

dot CSV

next we have Tesla right dot to CSV it

is Tesla

dot CSV and the last one it will be

Twitter

dot to CSV and same thing to write

Twitter

dot CSV

if we execute that

so get our files

so here we have our list of the CSV

files and when we drag apple and drop it

in here

we can see the following table now what

we are going to do since all the tables

have the same data structure and all the

columns have the same data types so

click on the down icon in here

convert to Union

would drag and drop all the files into

our unit right

Facebook Google

Nvidia

Tesla and Twitter

apply

okay

and as you can see here a new current

would be appeared which is table name

so here we have one single table and we

can distinguish between data from one

table to another using this column which

is table name

so we are going to change its name into

company so you click on the icon in here

rename and you name it company

okay

same thing you click one more time

aliasis and we remove dot CSV for all

these files

okay same thing for Facebook

for Google

Nvidia

Tesla and Twitter

okay

so like that we get the name of the

companies

next to make our analysis much sampler

we're going to add what we call data

source filter so we click on ADD

add

date okay

years next and here we select the last

five years so 2020 19 18 17 and 16. okay

okay

so like that we have only in the last

five years in the data set

so you click on your worksheet

and we are going to create two

parameters so you click on the down icon

in here

create parameter

we name it start date

data type it will be a date

range fixed add values from date okay

same thing create another parameter

and date

data type it will be date

range fixed add values from date okay

next we create a calculated field so you

click on down icon in here create

calculated field and we name it study

period

so we say if date

is greater or equal

then start date

and

date

is less or equal then and date

give me one

else zero

and

apply okay and we convert it to a

dimension so we click convert to

Dimension so you click in here

show parameter same thing for the end

date show parameter

so you can decrease

to the minimum right for start date and

for maximum four and date what you are

going to do is that we are going to

visualize the volume for different

companies depending on the date

so with your control update into column

and we select exact date

same thing whichever control Up the

Volume into the rows

control company into color

and as you can see here we have the

volume for different companies

into the filter we select one

apply okay

we double click on this axis

we remove the title

same thing for the date

we remove the title

and as you can see here if we change the

date we can see our charts are changing

we can also modify the colors so edit

colors for Apple which is gray Facebook

blue Google yellow Nvidia green Tesla

red and Twitter

choose this color

apply okay

so we name the sheet volume

okay and we click on format workbook and

we format our workbook so for worksheets

we select bold

like

it

for the titles we select bold black

11.

grid lines

of

zero lines same thing off

and access sticks in Black axis rulers

in Black

okay we double click on the title and we

put it in the center apply ok

so like that we get our multiple line

chart for the volume of different

companies depending on the date

next we are going to create a histogram

for Price percent change so you click on

new worksheet

we name it price

percent change

all right

double click on person change in price

we click on show more histogram

we'll remove this one

would you like a drop company into color

study period into filters we select one

apply ok

we click on the bins

edit

and we select 0.0025

okay

whether we click on the axis

to remove bin

and we select fixed

so we select minus

zero point

one

two zero point one

okay so like that we get our histogram

for Price percent change

double click on the axis we remove the

title

we double click on the title I will put

it in the center apply ok

and like that we get our histogram for

percent change in price

next we are going to create multiplying

chart for moving average and open price

Sony worksheet

all right moving

average

and open price

names into the filters

none

we select ma200 ma50 and open

apply okay

whichever control up the date into the

columns we select the exact date

same thing with drag and drop media

values into the rows

we press on Ctrl key and we drag and

drop usernames into the color we can

edit the colors for example we click in

here edit colors

you can select for example winter right

for ma200 we can select this color ma50

discolor and for open we can select this

color

apply okay

job company into filters we select one

apply okay

we show the filter and we select custom

remove all

and single value drop down double click

on the access we remove the title

same thing for the date

whether we click on the title I will put

it in the center apply ok

right click on the axis format

and we select numbers currency custom

and we select zero decimal places

okay

so like that we get our multiplying

chart for moving average and open price

next we are going to create detailed

price table

worksheet

so we name it detailed

price

table

so we have company

then we have close price

it will be

discrete

next we have previous day close price

Samsung it will be discrete

then we have change in price

I'm saying discrete

and percent change in price we put it in

text

same thing it will be discrete

but what we want really to do is to

display the close price of the last day

and we want to control this last day

so to do that we are going to create

calculated field

we name it last day only

and we say if date

equals to Max

since you want to control this last day

we are going to put end date

then

one else

zero and

but as you can see here we have an error

it says that cannot mix Aggregate and no

aggregate arguments with this function

so what we are going to do is that we

are going to add LOD right

so

right fixed

Max and date then one

else zero

so here we have last day only apply okay

we convert it to dimension

and would drag and drop it into the

filters select one

apply ok so as you can see here we have

last day only and we can control it

using the end date so we can

show the start date

show end date right

and like that

it is changing right

but as you can see here we don't have a

name for this column since we put it in

text

what we are going to do is that would

drag and drop usernames into columns

right click

edit aliases and we put change

in percentage

okay

we click on it format Pane and it will

be percentage okay

same thing for the remaining so right

click format header numbers it will be

custom

number custom to decimal places

same thing for this one

number custom and for close

number custom okay so like that we get

our table we can also add study period

to the filters you select one

apply okay

we can increase the width

all right now we need to create the

arrows right so you click on down icon

in here

create calculated field the first one we

call it upper

price

so we say if

percent change in price

is positive right

then

so we copy and paste the upper row so

same thing you can find the link in the

description to this file right so we

copy the upper row we put it

in here

and

so this is Upper price apply okay

we'll create another calculated field

we name it down

price

same thing if

percent change in price

is negative right

then

so same thing we copy and paste the down

arrow

and

apply okay

so drag and drop Upper price into text

same thing down price into text

we click on text we click on Three Dots

and we rearrange that right so

we get put it in here same thing for

down price

we put it in here

so Upper price we put it in

green and down price we put it in red

put it in the center apply ok

so like that we have our rows so if we

select another date

where we have both positive and negative

so as you can see here if it is positive

then it will be green if it is negative

it will be red

now we are going to format our sheet

double click put it in the center apply

okay

for the price we are going to add the so

format

it will be currency custom

same thing for this one currency

system

change in price the same thing currency

custom

okay

it will be

in the center same thing for the header

in the center

and we add our borders

put everything in Black

so just for a change in price we have to

put it as currency standard right so

like that to get our detail table for

the price next we are going to add

another detailed table for the volume

so click on your worksheet

we name it detailed

volume

table

okay

sudra can drop the company into the rows

next we have value

it will be discrete

next we have previous day volume

same thing it will be discrete

then we have change in volume

discrete

and percent change in volume we put it

in text

same thing it will be discrete

so increase the size

and we put visual names into the colors

now we select last day only

one

apply okay

so show

parameter

show parameter

study period we put it as one

apply okay

convert it into a percentage

format

and it will be percentage

so right click format

we put everything in the center right

and now we'll add the arrows

so for down price we duplicate this one

and edit

we name it down volume right

and same thing percent change in value

apply okay

same thing for upper price

so duplicate

edit

and it will be other

volume

and same thing we change the price into

volume

apply okay

drop upper volume into text same thing

for down volume

click on text Three Dots and we'll

rearrange

so we get paste same thing cut

paste so upper we put it

in

green

and down we put it in right

in the center apply okay

now we can format that so format add the

borders and put everything in Black

you can increase the width

like that you get our detailed volume

table

we just put the title in the center

apply okay and now I'm going to create

our text sheets

so click on your sheet the first one we

name it last day

to create one calculated field and we

name it last day text

oh

okay

and it would be

last day

apply okay

we create another one

create calculated field

we name it last day

and it would be Max

end date

apply okay

so we drag and drop

last day text into text

same thing last day into text

so we hide title

and we are going to format that

we select Pane and we select this format

so like that we get a clear idea about

the day that we are selecting as last

day because as you know on Saturday and

Sunday we don't have data all right so

here we know exactly which day of the

week we are selecting

we're going to format that so we put it

in the center

we click on text

so the first one we put it in

then and the second one we put it in

nine

apply okay

so here we have our last day

so duplicate this

we'll create another one we call it last

day

total volume right

so we copy

create calculated field

we name it last day total volume and we

add our text

apply

okay

so remove this we remove this

we drag and drop last day total volume

into text

would you like Angela plus day only into

the filters and we select one

apply okay and with drag and drop the

volume in two texts

so here we have last day total volume

Samsung click on text

we'll put the first one

in 10 and the second one in nine

apply okay

same thing could duplicate this one

and we name it lost

price independent

okay

so you remove everything

would you like a job study period into

the filters we select one apply okay

we create calculated field we name it

lowest price in the period same thing we

add our text

apply okay

drag and drop lowest price into text

and same thing we have here the low

price in two text

we select

minimum

you can also format that

and it will be currency custom

all right

same thing click on text

push the first one

in 10 and the second one in nine

apply okay

increase a little bit all right

duplicate this one

so now we select the highest

price

in the period right

and as you know the period depends on

the start date and end date so we keep

the study period

and we remove that

we create another calculated field

we name it highest price in the period

same thing string highest price in the

period apply okay drag and drop it into

text

and also would you like interoper high

into text

and we select the maximum

same thing we format that

and it will be custom currency

we click on text

we put this as 10 and this one as nine

apply okay

so duplicate this one

will have

total volume

in the period right

we create another regulated field we

name it total volume in the period and

the string it will be the same

apply okay

so remove the two

drag and drop total volume into text

and same thing would you like and drop

the volume into text

we'll click on it

so the first one it will be 10 and the

second one it will be

nine

apply okay

now create our title okay

so new worksheet we name it title okay

and we create calculated field we name

it title

and it's going to be

stock

market right dashboard

apply okay

so you put it in text

we hide this title

right click format and we put it as 48

and it will be in the center

so like that you have created all the

text sheets next we are going to create

our dashboard so you click on new

dashboard

we Define our size so we have 1850

and

1050 okay

now I'm going to add blacks right so

first one

have your second one

third one

Force

fifth

okay

another one in here

so like that we have for the four

okay

we add another one on the top

like that

and we add the remaining for the kpi so

one

blank

another blank

another blank

another blank

and another blank

okay

next you click on dashboard format

we select the following color right

next for the first one which is for the

title background we select the third one

seventy percent

okay

we add the outer padding right put it as

15.

for the remaining we are going to add

the background as white

so for this one it will be white

and then same thing 15.

same thing for the others so here 15

and it will be white

same thing for this one

put it to White

background white

15

or there's none and background white

same thing for this one

background to White

15.

White

15.

same thing

15.

right

15.

okay and the last one put it same thing

as 15.

and background white all right

so now we have to rearrange the sizes

okay

and here same thing increase little bit

like that

okay so now we have our design next we

are going to drag and drop our sheets

so we select floating right and you

start by volume right

so here

we have the end dates

put it like that

and edit title we put it in the center

okay

same thing start date

put it in here

and edit title in the center right

we add our highlighters so company

we put it in here right

edit title

remove highlight to put it in the center

okay

next here we remove the title

and we put it

in here right

now we increase our size

okay so we have our volume

next we have the price percent change

right to put it in here same thing

so put it like that

decrease

put it in here

you can also add the legend but it's the

same

okay

so remove the title and put it like that

okay

next we add the moving average and the

open price right

foreign

so here we have

our Legend remove the title put it

in here like that

for the company

same thing remove

the title

I'll put it in here

so you can change the company

okay let's create

the highlighter also it is applied on

the histogram

next we are going to add our tables so

detailed price

so we hide the title

same thing for the volume

we hide the title

and also we hide the header right

foreign

same thing for this one has entire View

and we are going to rearrange that

so for the first one

put it in here like that

and we add this one

so decrease decrease

same thing for this one

in degrees

okay

all right so like that we have

our detail table

okay

for both close price and volume okay and

as you can see here when we select the

highlighter it is applied on the tables

and also the histogram with the multiple

line chart

next we are going to add our kpis right

so we have last day

we hide the title

and you put it in here right

okay

next we have last day total volume same

thing

we'll hide the title

put it like that

lowest price

High title

put it inside

foreign

highest price same thing I at all

inside like that

and last one is total volume

High title

same thing

put it inside

okay

now we'll add our title

same thing High title

quiz it like that like that

format for the shading we select none

and we put it inside

okay

just put it in the center

and now add the logos for the companies

so same thing floating we click on

double click on image

first one is Apple logo okay

so put it like that

all right

same thing double click

Facebook

all right

so I have Google

just like that

next we have Nvidia

seems like we put it in here

then we have Tesla

so the links to download these logos are

available in the description

okay

and now add the last one which is

Twitter

so now if we select the presentation

mode and like that we get our final

stock market dashboard

so that's it for this tutorial I hope

that you have learned new things thanks

for watching and see you in the next

tutorial