INVESTMENT �ADVISIOR

HELLO!

We are Investor advisor

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TEAM PRESENTATION

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Abhishek Rajan�Senior Investment advisor

Beyond we go with

investment

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Deepak Verma�Senior Research Analyst

Any problem is a challenge

We analyse it

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Amith B J�Senior Dash board Presentor

Bring me data , simpling and dashboard . See it becoming great

Pawan R R

Senior Representor

Advising for Future

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Objectives

• Access BSE stock, clients’ income, and expenditure data.

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• Extract insight from data analysis.

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• Developed dashboards and a report in google Sheets using python.
• And Why?

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Libraries Required, Set up connections

• LIBRARIES used

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• Gspread
• Authtoclient
• Numpy
• Pandas
• Matplotlib

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• CREATING CONNECTION BETWEEN PYTHON AND SPREADSHEET:

«scopes=['https://www.googleapis.com/auth/spreadsheets','https://www.googleapis.com/auth/drive.file','https://www.googleapis.com/auth/drive’]»

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• CREATING CREDENTIALS:

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“credentials1 = ServiceAccountCredentials.from_json_keyfile_name('service_account.json', scopes=scopes)

gc = gspread.authorize(credentials1)”

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• Opening all the spread sheets Files
• Putting data in list of dictionaries.
• Creating Sumif() function.
• Finding:
• Net Income
• Net Expense
• Available investment amount
• Incurred amount of each sub-category
• Populate results to the spreadsheet.

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SUB TASK

Let’s start with the slides

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• Creating a column name delta.
• Applying given filters.
• Finding the top 5 companies for below given conditions
• Tags:
• High Risk
• Risk
• Moderate Risk
• Low Risk
• Populate results to the spreadsheet.

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SUB TASK

Let’s start with the slides

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PART-1

Comparing the median of column Enterprise Value(Cr) across different Sectors.

• Reading 'Sector' & 'Enterprise Value(Cr)' columns from BSE500 data table as we will use these column only.
•  Replacing blank cells with 'NaN' to remove  the errors.
• Grouping 'Sector' columns and finding median of 'Enterprise Value(Cr)' values.
• Populate results to the CSV.
• Drawing graph for the following.

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PART-2

Finding a relation between dividend per share with market cap(cr)

• Reading 'Market Cap(Cr)' & 'Dividend Per Share' columns from BSE500 data table.
• Replacing blank cells with 'NaN' to remove  the errors.
• Finding Correlation between 'Market Cap(Cr)', 'Dividend Per Share' columns.
• Populate results to the CSV.
• Drawing graph for the following.

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PART-3

Count the companies in different Industry with positive and negative 3-Year Return and deciding which industry would be recommended to someone to invest if the same return is followed.

•  Reading 'Industry' & '3-Year Return' columns from BSE500 data table.
•   Replacing blank cells with 'NaN' to remove  the errors.
•   Finding negative & positive 3 years return from the column '3-Year Return’.
• Grouping 'Industry' columns and counting companies having negative '3-Year Return' value & positive '3-Year Return' value.
•  Converting dataframe into dictionary by taking industry column as key
•  fetching distinct value of industry.
• Reading above created dictionaries and storing result in the form of list of list (industry,negative_count,positive_count).
•    we are sorting result on the basis of positive 3 year return.
• Populate results to the CSV.
• Drawing graph for the following.

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PART-4

The best company stock considering one of the KPI.

• Reading 'Sector', 'Company' & 'Price to Earnings' columns from BSE500 data table and sorting the values of the column 'Sector', 'Price to Earnings’.
•    Replacing blank cells with 'NaN' to remove  the errors.
•   grouping by sector and picking best stock from each sector.
• Populate results to the CSV.
• Drawing graph for the following.

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BIG CONCEPT

Can we add concept ?

Ans: yes then which one should be it of python or calculation

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THANKS!

For bearing

Any questions?

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