A simple guide how to use the Financial Modeling Prep API in Python

Financial Modeling Prep (FMP) is a website providing stock markets information and provide a powerful API that can be easily used in Python. To make the use of FMP’s API easier, this article provides an easy step-by-step tutorial for beginners.

Good data is crucial if you work in finance, economics or statistics. Finacial Modeling Prep or FMP provides an excellent API that is (according to their website) “the most accurate financial data API out there”. With this API, you can access historical stock and currency data, financial statements of many public companies and much more. The basic API is free and pricing for advanced plans range from $19 to $99 per month.

When you sign up, you will receive your personal API key that you can use for any project. If you have just started with Python, this article will be very useful to quickly use the FMP API.

#1: Set Up

The data of the FMP API is stored in JSON format that can be easily read with Python. In a first step, you have to set up a function that lets you access the data from your API. The function lets you insert the URL and returns data you can work with in Python.

Please note that you also have to import other libraries to work with the data further.

import urllib, json
import requests
from urllib.request import urlopen

# Function to get the Data

def get_jsonparsed_data(url):
    res = urlopen(url)
    data = res.read().decode("utf-8")
    return json.loads(data)

#2: Use it — Historical Stock data

In the following I want to show how to use this function and how to get the data you need. With this Link you can look for the specific links you need for your project. First, I want to show how to get historical stock data you can work with in Python.

df = get_jsonparsed_data(f"https://financialmodelingprep.com/api/v3/historical-chart/5min/AAPL?from=2023-10-10&to=2023-08-10&apikey=YOURKEY")
df1 = pd.DataFrame(df)
df1

If you enter your personal key at the end of the link you will get 5-min historical stock data between 8 and 10 October 2023 from Apple. You can also change the company, by changing the stock ticker in the URL (for instance KO for Coca-Cola). Also, you can have 1min or 15min stock price intervals (you just have to use “1min” instead of “5min” and by changeing the dates in the URL, you can change the period.

Df1 would be a dataframe with the dates in the first column besides the open, low, high and close price and the volume. It will be useful to set the first column as the index. Here is a quick guide how to do so.

#3: Financial Statements

With FMP you can also get the data from the financial statements of public companies. Here is an example how to get a public company’s net income via the FMP API. With the link below you can get the income statement of OMV (traded in Vienna, therefore the ticker OMV.VI).

inst = get_jsonparsed_data(f"https://financialmodelingprep.com/api/v3/income-statement/{'OMV.VI'}?period=quarter&limit=50&apikey=YOURKEY")

With this URL you can get the financial statement of OMV for the last 50 quarters. “period=quarter” gives you the the quarterly income statements.

You can copy the link into your browser to see all the information given in the income statement. It is also possible to type “inst” + press enter to see what data is stored in this URL.

If you want to retrieve the company’s historical net income data, you can use a code similar to this one:

df1 = pd.DataFrame(inst)

df1 = df1.set_index(['date'])

df1['netIncome']

The first line of code converts the data into a data frame.
In the second line, the date column is defined as an index, and in the third line you receive the data for the net income.

This article gives you a brief introduction to using the FMP API for Python. FMP also has a documentation website that explains how to access the information via the API.

I hope this article was helpful. If you have any questions or find any errors, please let me know in the comments.