Company Financial Trends

Get free financial reporting data on any public company from using an API. You can actually get time series trends for any line item (XBRL tag) in their financial reporting documents.

Scott Wied


November 6, 2022

1 Introduction

If you want to do fundamental research on a publicly traded company, then there are a lot of services that will charge you a lot of money for that data. What if you don’t have a lot of money? And, what if you don’t trust any data that doesn’t come directly from its source? In this article I will show you how to use the vast data resources provided by the US government… for free.

2 Intro to XBRL tagging

A public company is required, by the SEC, to use XBRL line item tags whenever it files its 10-Q (quarterly) and 10-K (annual) financial reports. There is an API that you can use to get data on the performance of a companies fundamental metrics over time. You will need the XBRL tag name for each metric that you wish to track.

Below is a quote from an XBRL Wikipedia article that gives a technical desciption of the XBRL standard:

XBRL (eXtensible Business Reporting Language) is a freely available and global framework for exchanging business information. XBRL allows the expression of semantic meaning commonly required in business reporting. The language is XML-based and uses the XML syntax and related XML technologies such as XML Schema, XLink, XPath, and Namespaces. One use of XBRL is to define and exchange financial information, such as a financial statement. The XBRL Specification is developed and published by XBRL International, Inc. (XII).

The SEC has a page that describes how they use XBRL. It even includes a link to a YouTube video that you can watch sometime if you are having trouble sleeping.

The best way to become familiar with XBRL is to go through an example. Let’s do that below.

2.1 Find the CIK for Apple

As and example, let’s use Apple (stock ticker: APPL). Every company that files with the SEC receives a central index key (CIK). The API that we will use requires this CIK code.

ticker = 'AAPL'

Start by doing a search for a company on EDGAR. Look for the company CIK code (circled in red below) as you type in the ticker symbol. Write this value down, and then click on the search result.

# Create a variable from the CIK code in the 
# above image.  Make sure that the CIK is
# uppercase, and that there is no space in the name.
cik = 'CIK0000320193'

2.2 Open a 10-K report

The next page (see below) will display the search results for the chosen company. Notice that the numeric part of the CIK code is included in the URL. Now, click on one of the 10-K or 10-Q reports listed under “Selected Filings”

There are several files shown on the Filing Detail page (see below). We want to view the top one, the one with the green iXBRL note beside it. Click on the link to open the filed report.

2.3 Find the finacial statements

These SEC filing reports can be quite wordy. Scroll down to the table of contents (see below) after opening the document. Find the section titled Financial Statements and click on the bookmark link. This will take you to the page where you will find several financial reports.

2.4 Identify the Net Income line item

Scroll down a little bit, until you see a table of numbers. These reports are all in a specific format that is mandated by the SEC. Each line item name is a registered and approved XBRL tag. If you want to find the name of the tag, then click on one of the numbers with red lines above and below it.

2.5 Find the XBRL tag name for Net Income

A pop-up window will appear (see below) showing the attributes of that line item value. Find the Tag attribute row, and copy the part that says, ‘NetincomeLoss’. This is our XBRL tag name! We now have all of the information that we need to use the API.

# Create a variable with the XBRL tag name.
# Note that it is case sensitive.
xbrl_tag = 'NetIncomeLoss'

3 API data pull

In our example, we will attempt to plot the quarterly trend for Apple’s Net Income line item. We will be pulling the data from the api.

3.1 Import python packages

These are the packages that we will need. All of them can be downloaded and installed with Anaconda.

import requests
import json
from pprint import pprint
import pandas as pd
import numpy as np
import re
import altair as alt
import pandasql

3.2 Make the API Call

# Build up the API request URL
api_url_path = ''
url = f"{api_url_path}/{cik}/us-gaap/{xbrl_tag}.json"

# Run the API request
# Note that it will not run without your email address
email_address = ''
request_tag_data = (
         headers={'User-Agent': email_address}))

# Print the status code (200 is good, 404 is bad)
print("Request status code:", request_tag_data.status_code)
print("Content Type:", request_tag_data.headers['Content-Type'])
print("Content Length:", 
      request_tag_data.headers['Content-Length'], 'bytes')
Request status code: 200
Content Type: application/json
Content Length: 3281 bytes

3.3 Take a look at the raw JSON data

tag_data = request_tag_data.json()
pprint(tag_data, depth=2)
{'cik': 320193,
 'description': 'The portion of profit or loss for the period, net of income '
                'taxes, which is attributable to the parent.',
 'entityName': 'Apple Inc.',
 'label': 'Net Income (Loss) Attributable to Parent',
 'tag': 'NetIncomeLoss',
 'taxonomy': 'us-gaap',
 'units': {'USD': [...]}}

3.4 Convert to a Pandas Dataframe

# Create dataframe from dictionary
df_tag_data = (

# Display the last 8 records in the dataframe
(   df_tag_data
start end val accn fy fp form filed frame
284 2021-09-26 2022-03-26 59640000000 0000320193-22-000059 2022 Q2 10-Q 2022-04-29 NaN
286 2021-09-26 2022-06-25 79082000000 0000320193-22-000070 2022 Q3 10-Q 2022-07-29 NaN
278 2020-09-27 2021-06-26 74129000000 0000320193-22-000070 2022 Q3 10-Q 2022-07-29 NaN
280 2021-03-28 2021-06-26 21744000000 0000320193-22-000070 2022 Q3 10-Q 2022-07-29 CY2021Q2
287 2022-03-27 2022-06-25 19442000000 0000320193-22-000070 2022 Q3 10-Q 2022-07-29 CY2022Q2
269 2019-09-29 2020-09-26 57411000000 0000320193-22-000108 2022 FY 10-K 2022-10-28 CY2020
282 2020-09-27 2021-09-25 94680000000 0000320193-22-000108 2022 FY 10-K 2022-10-28 CY2021
288 2021-09-26 2022-09-24 99803000000 0000320193-22-000108 2022 FY 10-K 2022-10-28 CY2022

Note a few things about the above table:

  1. There are multiple records for each quarter. This is because the financial report shows values for the current quarter, the previous quarter, and the current quarter from the previous fiscal year. It would be great if the API provided a field describing which time period was which. When we go to plot the graph we will only want to include the current quarters value for each quarter.
  2. The values for Q1, Q2, and Q3 come from the form 10-Q. There is no 10-Q for Q4, and the 10-K report only shows yearly totals. In order to find the values for Q4 of each year we’ll need to take the annual total and subtract the totals from Q1, Q2, and Q3.

4 Clean and filter data

Since the data set does not have a column indicating which quarter each value is associated with, we will have to use a brute force approach. We’ll have to use a window function to pick a single record for each fiscal year/quarter. Let’s use the sqldf method from the pandasql package. Note that sqldf is kind of slow on large dataframes.

4.1 Rank records over fiscal year and period

In our first SQL query (see below), we are going to rank records over partitions defined by fiscal year and fiscal period. We’ll calculate the number of days in between the dates start and end. And, finally, we’ll create a new val_10q column to indicate the values that came from 10-Q reports. We’ll need it later when we subtract the sum of the values for Q1-3 from the annual amount to calculate the Q4 value.

df_rank = pandasql.sqldf("""
    select fy, fp, end, start,
        (   julianday(date(end))
        ) as num_days_in_period,
        val, form,
        (   case 
            when form='10-Q'
            then val
            else 0
        ) as val_10q,
            partition by fy, fp
            order by 
                end desc, 
                start desc
        ) as rnk
    from df_tag_data
    where form in('10-Q','10-K')

# Show the first two records
fy fp end start num_days_in_period val form val_10q rnk
0 2009 FY 2009-09-26 2008-09-28 363.0 5704000000 10-K 0 1
1 2009 FY 2008-09-27 2007-09-30 363.0 4834000000 10-K 0 2

4.2 Filter the dataframe for rank=1 records

df_filtered = pandasql.sqldf("""
        fy, fp, 
        end, start,
        val, form,
        over(partition by fy
            ) as total_val_10q
    from df_rank
    where rnk=1

# Show the first two records
fy fp end start num_days_in_period val form total_val_10q
0 2009 FY 2009-09-26 2008-09-28 363.0 5704000000 10-K 1229000000
1 2009 Q3 2009-06-27 2009-03-29 90.0 1229000000 10-Q 1229000000

4.3 Finalize data for plotting

x_axis_name = 'End Date'
y_axis_name = f'{xbrl_tag} (USD in Billions)'

df_plot_data = pandasql.sqldf(f"""
    fy as `Fiscal Year`, 
    (   case 
        when fp='FY' then 'Q4'
        else fp
    ) as `Fiscal Quarter`,
    end as `{x_axis_name}`,
        case when form='10-K' 
            and num_days_in_period > 360
        then (val - total_val_10q)/1e9
        else val/1e9
    ) as `{y_axis_name}`
from df_filtered
order by `{x_axis_name}`

Fiscal Year Fiscal Quarter End Date NetIncomeLoss (USD in Billions)
49 2021 Q4 2021-09-25 20.55
50 2022 Q1 2021-12-25 34.63
51 2022 Q2 2022-03-26 25.01
52 2022 Q3 2022-06-25 19.44
53 2022 Q4 2022-09-24 20.72

5 Show trend as a bart chart

    x = alt.X(
        axis = alt.Axis(labelOverlap='greedy')),
    y = y_axis_name,
    tooltip=(x_axis_name, y_axis_name)
    title = f"{ticker} - {y_axis_name}",
    width = 500,
    height = 200
No matching items