DEV Community: Matheus Coelho

The fy field in SEC XBRL data doesn't mean what you think

Matheus Coelho — Wed, 14 Oct 2026 17:04:08 +0000

The SEC gives you every US public company's financials for free through data.sec.gov. No key, no signup. It's one of the best free datasets in finance, and it's easy to get wrong.

Here's Apple's net income from the companyfacts endpoint, filtered the way most people would try first: fy == 2025 and form == "10-K".

start        end          value            fp   frame
2022-09-25   2023-09-30   96,995,000,000   FY   CY2023
2023-10-01   2024-09-28   93,736,000,000   FY   CY2024
2024-09-29   2025-09-27   112,010,000,000  FY   CY2025

Three rows, three different numbers, all tagged fy: 2025.

fy is the fiscal year of the filing, not of the number. Apple's FY2025 10-K reports three years of income statement data (2023, 2024 and 2025), and every one of those values gets fy: 2025. If your code takes the first match, you just reported Apple's 2023 profit as 2025's.

The other traps

Tags change over time. Apple reported revenue as Revenues until 2018 and as RevenueFromContractWithCustomerExcludingAssessedTax after the ASC 606 accounting change. Query one tag and you get a series that stops or starts in the middle. NVIDIA reports capex as PaymentsToAcquireProductiveAssets instead of the more common PaymentsToAcquirePropertyPlantAndEquipment.

Fiscal years aren't calendar years. Apple's fiscal 2023 ran 371 days (a 53-week year). Microsoft's ends in June, NVIDIA's in late January.

Annual and quarterly values live side by side. A 10-K also contains Q4-only values for some items, and a 10-Q contains year-to-date numbers. They all share the same tag.

The same period gets restated. A value first reported in 2023 can be refiled in 2024 with a correction. You want the latest one.

What actually works

Ignore fy. For each value, look at the period itself:

If it has a start, measure the duration. About 365 days (350 to 380, to cover 52/53-week years) is annual. About 90 days is a quarter. Anything else (year-to-date, 6 or 9 months) gets dropped.
If it has no start, it's a balance-sheet snapshot. Classify it by the form that reported it: 10-K means annual, 10-Q means quarterly.
Key each value by its period end date and keep the one with the latest filed date.
For each metric, try a list of tags in priority order and merge them by period, so a tag switch doesn't leave a hole.

Done that way, Apple's last five fiscal years come out right:

Fiscal year end	Revenue	Net income	Free cash flow	Net margin
2025-09-27	$416.2B	$112.0B	$98.8B	26.9%
2024-09-28	$391.0B	$93.7B	$108.8B	24.0%
2023-09-30	$383.3B	$97.0B	$99.6B	25.3%
2022-09-24	$394.3B	$99.8B	$111.4B	25.3%
2021-09-25	$365.8B	$94.7B	$93.0B	25.9%

And NVIDIA's last five years, which make a nicer chart:

Fiscal year end	Revenue	Net income
2026-01-25	$215.9B	$120.1B
2025-01-26	$130.5B	$72.9B
2024-01-28	$60.9B	$29.8B
2023-01-29	$27.0B	$4.4B
2022-01-30	$26.9B	$9.8B

The short version in Python

If you'd rather not maintain the tag lists and period logic yourself, I packaged all of the above into an Apify Actor: SEC EDGAR Filings & Financials. You pass tickers, it returns normalized annual and quarterly statements plus the company's recent filings with document links.

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("ninja.dev/sec-edgar-filings-financials").call(run_input={
    "companies": ["AAPL", "MSFT", "NVDA"],
    "maxFilingsPerCompany": 0,   # financials only
    "financialYears": 5,
    "financialQuarters": 4,
})

for row in client.dataset(run["defaultDatasetId"]).iterate_items():
    if row["recordType"] != "financials":
        continue
    for year in row["annual"]:
        print(row["ticker"], year["periodEnd"], year["revenue"], year["netIncome"], year.get("freeCashFlow"))

Each annual or quarterly row has revenue, gross profit, operating income, net income, EPS (basic and diluted), operating cash flow, capex, free cash flow, R&D, assets, liabilities, equity, cash, long-term debt and the three margins.

It also does full-text search across every filing since 2001 ("fullTextQuery": "\"going concern\""), which is handy for screening.

Doing it yourself

If you build your own pipeline instead, two practical notes from the SEC's side: send a User-Agent with a contact email, and stay under 10 requests per second. They block clients that don't.

And whatever you do, don't filter on fy.

I read 800 recent reviews of the top habit tracker apps. Paywalls are the #1 complaint.

Matheus Coelho — Wed, 14 Oct 2026 17:01:15 +0000

Every habit tracker in the top search results has a rating between 4.3 and 4.95 stars. If you're thinking about building one, that looks like a market with no room left.

The star rating is an all-time average, though. Some of these apps have millions of ratings collected over years. A rough few months barely moves that number, so I wanted to see what people are saying right now.

I pulled the 100 most recent reviews for the top 4 results for "habit tracker" on both the App Store and Google Play. That's 8 apps and 800 reviews.

The numbers

Store	App	Store rating	Avg of last 100 reviews	1–2 star share
App Store	Habit Tracker	4.79	4.03	22%
App Store	HabitKit	4.84	4.48	10%
App Store	Onrise	4.84	4.40	6%
App Store	Finch: Self-Care Pet	4.95	4.68	5%
Google Play	Loop Habit Tracker	4.72	4.84	1%
Google Play	HabitNow	4.74	4.60	5%
Google Play	Disciplined	4.29	4.26	11%
Google Play	HabitKit	4.55	3.85	20%

Two apps show a real gap. Habit Tracker on iOS sits at 4.79 overall but its recent reviews average 4.03, and more than one in five is a 1 or 2. HabitKit on Android drops from 4.55 to 3.85.

Loop is the opposite case. It's open source, completely free, and its recent reviews are better than its lifetime average.

What the angry reviews are about

Out of 800 reviews, 80 were 1 or 2 stars. I tagged them by topic (a review can hit more than one):

Complaint	Reviews
Subscription, paywall, paid features	22
Bugs and crashes	12
Sync problems or lost data	10
Widgets	9
Reminders and notifications	5
Ads	1

Ads barely show up. Money does. A few real examples:

"paywalled at 3 habits"
"must pay to track more than 4"
"i paid for the family premium but it didn't work"
"i bought the premium version a year ago and when they updated i lost everything"

The pattern is people who paid and then lost data or features, or who hit a wall at habit number 3 or 4 and felt tricked. The widget complaints are a smaller version of the same story: someone paid for Pro and the widget still shows "subscribe to pro".

So is there room for another habit tracker?

Based on this, I'd look at:

A generous free tier. Unlimited habits for free is the main thing people are mad about losing, and Loop shows it earns goodwill.
Boring reliability around sync and backups. Losing a 200-day streak is how you get a 1-star review that never gets edited.
Widgets that work on day one, since they're the main way people interact with these apps.

800 reviews is a small sample, so treat this as a starting point for your own research.

How I got the data

I used an Apify Actor I built that scrapes both stores into one format: App Store & Google Play Scraper. Apple data comes from Apple's official iTunes Search API and review feed; Google Play uses the open-source google-play-scraper library.

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("ninja.dev/app-store-google-play").call(run_input={
    "searchTerms": ["habit tracker"],
    "store": "both",
    "maxSearchResults": 4,
    "maxReviewsPerApp": 100,
})

rows = list(client.dataset(run["defaultDatasetId"]).iterate_items())
apps = [r for r in rows if r["recordType"] == "app"]
reviews = [r for r in rows if r["recordType"] == "review"]

for app in apps:
    s = app["reviewStats"]
    print(app["store"], app["title"], app["rating"], "->", s["averageRating"], f'{s["negativeShare"]:.0%}')

Every app row already includes reviewStats (average of the fetched reviews, star distribution and share of 1–2 star reviews), so the table above is one loop. For the complaint tagging I used a few regexes over the 1–2 star texts. An LLM would do a nicer job if you want to go deeper.

Swap "habit tracker" for whatever category you're eyeing and you'll get the same picture for your niche in a couple of minutes.