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In this new post on Python for Finance, we are going to build an amazing Python tool to retrieve company fundamentals. It will be super simple to use. We wil

Retrieve Company Fundamentals with Python

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Image credit: Benjamin Child

In this new post on Python for Finance, we are going to build an amazing Python tool to retrieve company fundamentals. It will be super simple to use.

We will pass the ticker of the company that we are interested in and Python will do its magic to show us below Pandas DataFrame converted into an Excel file.

Photo by Pixabay on Pexels

Below is just a sample for Apple. All main Apple fundamentals are basically presented to us thanks to this powerful script:

The main goal of the code is to get company financials from the last 5 years. Then, calculate some growth metrics and export to an Excel file. I would state below the main steps to achieve this:

  • Request data from the financialmodelingprep.

  • Load into a Python variable the main financial data requested from the API. That is, IS, BS, CF and main financial ratios.

  • Create an empty dictionary in order to store the values using the respective year as the key.

  • Transform the dictionary into a Pandas DataFrameCalculate a few growth measures as new columns

  • Export to Excel

And here is the code:

import pandas as pd

pd.options.display.float_format = '{:,.2f}'.format

#pass ticker of the company

company = 'AAPL'

api = 'your api key'

# Request Financial Data from API and load to variables

IS = requests.get(f'https://financialmodelingprep.com/api/v3/income-statement/{company}?apikey={api}').json()

BS = requests.get(f'https://financialmodelingprep.com/api/v3/balance-sheet-statement/{company}?apikey={api}').json()

CF = requests.get(f'https://financialmodelingprep.com/api/v3/cash-flow-statement/{company}?apikey={api}').json()

Ratios = requests.get(f'https://financialmodelingprep.com/api/v3/ratios/{company}?apikey={api}').json()

key_Metrics = requests.get(f'https://financialmodelingprep.com/api/v3/key-metrics/{company}?apikey={api}').json()

profile = requests.get(f'https://financialmodelingprep.com/api/v3/profile/{company}?apikey={api}').json()

millions = 1000000

#Create empty dictionary and add the financials to it

financials = {}

dates = [2021,2020,2019,2018,2017]

for item in range(5):

financials[dates[item]] ={}

#Key Metrics

financials[dates[item]]['Mkt Cap'] = key_Metrics[item]['marketCap'] /millions

financials[dates[item]]['Debt to Equity'] = key_Metrics[item]['debtToEquity']

financials[dates[item]]['Debt to Assets'] = key_Metrics[item]['debtToAssets']

financials[dates[item]]['Revenue per Share'] = key_Metrics[item]['revenuePerShare']

financials[dates[item]]['NI per Share'] = key_Metrics[item]['netIncomePerShare']

financials[dates[item]]['Revenue'] = IS[item]['revenue'] / millions

financials[dates[item]]['Gross Profit'] = IS[item]['grossProfit'] / millions

financials[dates[item]]['R&D Expenses'] = IS[item]['researchAndDevelopmentExpenses']/ millions

financials[dates[item]]['Op Expenses'] = IS[item]['operatingExpenses'] / millions

financials[dates[item]]['Op Income'] = IS[item]['operatingIncome'] / millions

financials[dates[item]]['Net Income'] = IS[item]['netIncome'] / millions

financials[dates[item]]['Cash'] = BS[item]['cashAndCashEquivalents'] / millions

financials[dates[item]]['Inventory'] = BS[item]['inventory'] / millions

financials[dates[item]]['Cur Assets'] = BS[item]['totalCurrentAssets'] / millions

financials[dates[item]]['LT Assets'] = BS[item]['totalNonCurrentAssets'] / millions

financials[dates[item]]['Int Assets'] = BS[item]['intangibleAssets'] / millions

financials[dates[item]]['Total Assets'] = BS[item]['totalAssets'] / millions

financials[dates[item]]['Cur Liab'] = BS[item]['totalCurrentLiabilities'] / millions

financials[dates[item]]['LT Debt'] = BS[item]['longTermDebt'] / millions

financials[dates[item]]['LT Liab'] = BS[item]['totalNonCurrentLiabilities'] / millions

financials[dates[item]]['Total Liab'] = BS[item]['totalLiabilities'] / millions

financials[dates[item]]['SH Equity'] = BS[item]['totalStockholdersEquity'] / millions

financials[dates[item]]['CF Operations'] = CF[item]['netCashProvidedByOperatingActivities'] / millions

financials[dates[item]]['CF Investing'] = CF[item]['netCashUsedForInvestingActivites'] / millions

financials[dates[item]]['CF Financing'] = CF[item]['netCashUsedProvidedByFinancingActivities'] / millions

financials[dates[item]]['CAPEX'] = CF[item]['capitalExpenditure'] / millions

financials[dates[item]]['FCF'] = CF[item]['freeCashFlow'] / millions

financials[dates[item]]['Dividends Paid'] = CF[item]['dividendsPaid'] / millions

#Income Statement Ratios

financials[dates[item]]['Gross Profit Margin'] = Ratios[item]['grossProfitMargin']

financials[dates[item]]['Op Margin'] = Ratios[item]['operatingProfitMargin']

financials[dates[item]]['Int Coverage'] = Ratios[item]['interestCoverage']

financials[dates[item]]['Net Profit Margin'] = Ratios[item]['netProfitMargin']

financials[dates[item]]['Dividend Yield'] = Ratios[item]['dividendYield']

#BS Ratios

financials[dates[item]]['Current Ratio'] = Ratios[item]['currentRatio']

financials[dates[item]]['Operating Cycle'] = Ratios[item]['operatingCycle']

financials[dates[item]]['Days of AP Outstanding'] = Ratios[item]['daysOfPayablesOutstanding']

financials[dates[item]]['Cash Conversion Cycle'] = Ratios[item]['cashConversionCycle']

#Return Ratios

financials[dates[item]]['ROA'] = Ratios[item]['returnOnAssets']

financials[dates[item]]['ROE'] = Ratios[item]['returnOnEquity']

financials[dates[item]]['ROCE'] = Ratios[item]['returnOnCapitalEmployed']

financials[dates[item]]['Dividend Yield'] = Ratios[item]['dividendYield']

#Price Ratios

financials[dates[item]]['PE'] = Ratios[item]['priceEarningsRatio']

financials[dates[item]]['PS'] = Ratios[item]['priceToSalesRatio']

financials[dates[item]]['PB'] = Ratios[item]['priceToBookRatio']

financials[dates[item]]['Price To FCF'] = Ratios[item]['priceToFreeCashFlowsRatio']

financials[dates[item]]['PEG'] = Ratios[item]['priceEarningsToGrowthRatio']

financials[dates[item]]['EPS'] = IS[item]['eps']

financials[dates[item]]['EPS'] = IS[item]['eps']

#Transform the dictionary into a Pandas

fundamentals = pd.DataFrame.from_dict(financials,orient='columns')

#Calculate Growth measures

fundamentals['CAGR'] = ((fundamentals[2021]/fundamentals[2017])**(1/5) - 1

fundamentals['2021 growth'] = (fundamentals[2021] - fundamentals[2020] )/ fundamentals[2020]

fundamentals['2020 growth'] = (fundamentals[2020] - fundamentals[2019] )/ fundamentals[2019]

fundamentals['2019 growth'] = (fundamentals[2019] - fundamentals[2018] )/ fundamentals[2018]

fundamentals['2018 growth'] = (fundamentals[2018] - fundamentals[2017] )/ fundamentals[2017]

#Export to Excel

fundamentals.to_excel('fundamentals.xlsx')

print(fundamentals)

Once the script runs, Python should create an Excel file including all company fundamentals.

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