u/Either_Door_5500

Hey,

I've just released an entirely new and broad API for US fundamentals for stocks!

After trying many of the existing data providers, I was struck by how bad data quality sometimes was!

But not only that. Its also very evident that most providers just cover up the fact that many numbers are actually amended a few times.
If you want to get your hands on a full auditable trail of amendments, you are a bit out of luck with all these black-box normalization layers.

The next problem I see is the steep pricing cliff whenever you go from private use to commercial use. That immediately prices out a large segment of developers!

I used this as motivation to create an entirely different and low cost API. The goal is to provide the best fundamentals with the broadest coverage and a full per fact amendment trail. Perfect for anyone who wants to know the details, and not just headline numbers!

Highlights for the Stock Coverage:

• Free covers the basics: financial statements (income, balance sheet, cash flow), EPS and dividend history, plus the standard charts.
• Derived analytics layer: growth rates, sector-aware key metrics, Piotroski and Altman health scores, earnings snapshot.
• Single-name equity research: insider transactions (5 different cuts), ownership data (9 different cuts including institutional, beneficial, share classes, portfolio history), executive comp, IPO, earnings dates, splits, governance, etc.
• Audit-grade economic models: AI-classified business structure with verbatim SEC citations on every claim.
• Stock splits, earnings trends, the full earnings calendar, and upcoming earnings.
• ETF coverage with 19 fund-specific capabilities (holdings, flows, overlap, exposure model, fee analysis, performance).
• Filing timeline, all stock capabilities and all ETF capabilities combined for those who straddle equities and funds.

Of course the hardest part is getting more eyes on the product, and that is why I'm writing here. You are the reason this project exists and I want to get in touch with you. But don't worry, evaluating a product fit is always included in the free tier with high RPM limits!

I'm with you every step of the way! Unlike many competitors, I really care about every subscriber and will personally help anyone all along the way.

Hey,

I've just released an entirely new and broad API for US fundamentals for stocks!

After trying many of the existing data providers, I was struck by how bad data quality sometimes was!

But not only that. Its also very evident that most providers just cover up the fact that many numbers are actually amended a few times.
If you want to get your hands on a full auditable trail of amendments, you are a bit out of luck with all these black-box normalization layers.

The next problem I see is the steep pricing cliff whenever you go from private use to commercial use. That immediately prices out a large segment of developers!

I used this as motivation to create an entirely different and low cost API. The goal is to provide the best fundamentals with the broadest coverage and a full per fact amendment trail. Perfect for anyone who wants to know the details, and not just headline numbers!

Highlights for the Stock Coverage:

• Free covers the basics: financial statements (income, balance sheet, cash flow), EPS and dividend history, plus the standard charts.
• Derived analytics layer: growth rates, sector-aware key metrics, Piotroski and Altman health scores, earnings snapshot.
• Single-name equity research: insider transactions (5 different cuts), ownership data (9 different cuts including institutional, beneficial, share classes, portfolio history), executive comp, IPO, earnings dates, splits, governance, etc.
• Audit-grade economic models: AI-classified business structure with verbatim SEC citations on every claim.
• Stock splits, earnings trends, the full earnings calendar, and upcoming earnings.
• ETF coverage with 19 fund-specific capabilities (holdings, flows, overlap, exposure model, fee analysis, performance).
• Filing timeline, all stock capabilities and all ETF capabilities combined for those who straddle equities and funds.

Of course the hardest part is getting more eyes on the product, and that is why I'm writing here. You are the reason this project exists and I want to get in touch with you. But don't worry, evaluating a product fit is always included in the free tier with high RPM limits!

I'm with you every step of the way! Unlike many competitors, I really care about every subscriber and will personally help anyone all along the way.

Hey,

I've just released an entirely new and broad API for US fundamentals for stocks!
After trying many of the existing data providers, I was struck by how bad data quality sometimes was! But not only that. Its also very evident that most providers just cover up the fact that many numbers are actually amended a few times.
If you want to get your hands on a full auditable trail of amendments, you are a bit out of luck with all these black-box normalization layers.

The next problem I see is the steep pricing cliff whenever you go from private use to commercial use. That immediately prices out a large segment of developers!

I used this as motivation to create an entirely different and low cost API. The goal is to provide the best fundamentals with the broadest coverage and a full per fact amendment trail. Perfect for anyone who wants to know the details, and not just headline numbers!

Highlights for the Stock Coverage:

• Free covers the basics: financial statements (income, balance sheet, cash flow), EPS and dividend history, plus the standard charts.
• Derived analytics layer: growth rates, sector-aware key metrics, Piotroski and Altman health scores, earnings snapshot.
• Single-name equity research: insider transactions (5 different cuts), ownership data (9 different cuts including institutional, beneficial, share classes, portfolio history), executive comp and governance.
• Audit-grade economic models: AI-classified business structure with verbatim SEC citations on every claim.
• Stock splits, earnings trends, the full earnings calendar, and upcoming earnings.
• ETF coverage with 19 fund-specific capabilities (holdings, flows, overlap, exposure model, fee analysis, performance).
• Filing timeline, all stock capabilities and all ETF capabilities combined for those who straddle equities and funds.

Of course the hardest part is getting more eyes on the product, and that is why I'm writing here. You are the reason this project exists and I want to get in touch with you. But don't worry, evaluating a product fit is always included in the free tier with high RPM limits!

I'm with you every step of the way! Unlike many competitors, I really care about every subscriber and will personally help anyone all along the way.

I've been working on a small fintech side project and I've been going back and forth on whether the angle I'm betting on is actually solving something, or just something I've convinced myself is a problem.

The idea is a financial data API where the commercial-use license is just included in every paid tier (no sales calls), the free tier is actually generous enough to ship a working app, and the entry paid tier sits in indie-dev range, not "contact us for a quote."

The motivation came from realizing how brutal the pricing structure of this space is for anyone not at a fund or a funded fintech. Most providers split into two camps: the cheap or free APIs that quietly forbid commercial use unless you upgrade to a hidden enterprise plan ("book a call with sales"), or the legit commercial APIs that start at $300 to $500 per month per provider and assume you've already raised. If you're an indie shipping a small investing dashboard or a niche analytics tool, you spend more on data per year than you'll make on the whole product before you've even validated the idea.

Where I'm stuck is whether this is actually still a real pain point or if I'm rediscovering something Polygon, FMP, or Tiingo already solved for most people at $20 to $50 per month. I also can't tell if the people who genuinely care about this are indie devs shipping side-project investing apps, microsaas operators serving narrow niches like REIT investors or dividend hunters, course and educator types who need data they're allowed to redistribute, or hobbyists who were never going to pay anyway.

So I wanted to ask people here who've actually shipped something against a financial data API:

Did the licensing or pricing cliff actually stop you from building something, or did you find a workaround (scraping SEC EDGAR yourself, free-tier abuse, etc.) and ship anyway? And if you did pay, what tier did you actually need before the data became usable for production?

And more generally, when you've built in a space where the existing pricing seems indefensible but the incumbents clearly still have customers, how did you figure out whether the gap was real or just imagined?

Would really appreciate honest thoughts, even if it's criticism.

I've been working on a small fintech side project and I've been going back and forth on whether the angle I'm taking is actually solving something, or just something I've convinced myself is a problem.

The idea is a financial data API where every single fact (revenue, net income, EPS, etc.) carries a full audit trail of what changed and when, including every amendment and restatement. So when a company files a 10-K, then files a 10-K/A two months later that nudges revenue up by 0.5%, the response returns both values with their original filing dates and accession numbers. You can mathematically rewind the data to any historical date.

The motivation came from realizing how silently most, if not all fundamentals APIs overwrite the original numbers when amendments land. The pre-amendment value is just gone. For anyone running a backtest, this is look-ahead bias dressed up as clean data. Your simulation ends up trading on the corrected revenue weeks or months before the market actually saw it, and the strategy looks better than it really is.

Where I'm stuck is whether this is actually a real pain point or if I'm rediscovering something the serious players already solved 20 years ago. I also can't tell if the people who really care about this are quant funds (who probably already pay $50k+/yr for Compustat PIT and aren't shopping for another vendor), solo quants and retail backtesters (who might not even know amendment leakage is baked into their results), academic researchers, or fintech teams building backtesting products on top.

So I wanted to ask people here who've actually built or worked on quant systems, fintech products, or data businesses:

Is per-fact, per-amendment audit-trail data an actual problem developers run into, or is your current solution already good enough so that you wouldn't care?
And does anyone here actually correct for amendment leakage in their own backtests today, or is it just an accepted source of overstated returns?

And more generally, when you've built something in a space where the customers are sophisticated enough to know the problem exists but small in number, how did you figure out whether there was real demand before going too far?

Would really appreciate honest thoughts, even if it's criticism.
I already have all of this working, so if anyone is interested to take a closer look, I'm open to that.

I've been working on a small fintech side project and I've been going back and forth on whether the angle I'm taking is actually solving something, or just something I've convinced myself is a problem.

The idea is a financial data API where every single fact (revenue, net income, EPS, etc.) carries a full audit trail of what changed and when, including every amendment and restatement. So when a company files a 10-K, then files a 10-K/A two months later that nudges revenue up by 0.5%, the response returns both values with their original filing dates and accession numbers. You can mathematically rewind the data to any historical date.

The motivation came from realizing how silently most, if not all fundamentals APIs overwrite the original numbers when amendments land. The pre-amendment value is just gone. For anyone running a backtest, this is look-ahead bias dressed up as clean data. Your simulation ends up trading on the corrected revenue weeks or months before the market actually saw it, and the strategy looks better than it really is.

Where I'm stuck is whether this is actually a real pain point or if I'm rediscovering something the serious players already solved 20 years ago. I also can't tell if the people who really care about this are quant funds (who probably already pay $50k+/yr for Compustat PIT and aren't shopping for another vendor), solo quants and retail backtesters (who might not even know amendment leakage is baked into their results), academic researchers, or fintech teams building backtesting products on top.

So I wanted to ask people here who've actually built or worked on quant systems, fintech products, or data businesses:

Is per-fact, per-amendment audit-trail data an actual problem developers run into, or is your current solution already good enough so that you wouldn't care?
And does anyone here actually correct for amendment leakage in their own backtests today, or is it just an accepted source of overstated returns?

And more generally, when you've built something in a space where the customers are sophisticated enough to know the problem exists but small in number, how did you figure out whether there was real demand before going too far?

Would really appreciate honest thoughts, even if it's criticism.
I already have all of this working, so if anyone is interested to take a closer look, I'm open to that.

Hello,

5 years ago, I have developed a long-term investment related app for the US Stock Market. I've been in the indie developer shoes myself and know a good chunk more developers who are in that same boat.

One of the biggest obstacles is how you can get your hands on financial data (real-time and fundamentals) that is commercially usable and fits a budget of said developers.

APIs in that market today either don't easily allow commercial use at all until you talk to their sales team, or have such a brutal $$$ cliff that these developers are completely priced out of the market.

The second problem is black-box normalized fundamentals. After testing many different providers, I can only conclude that many of them seem to do some metric adjustments in some cases that are entirely opaque and not documented at all. If you are really interested to get a clear picture of what exactly a company filed with the SEC, you are sometimes really left alone.

It was around that time when the idea started to grow that I want to make a change in this market eventually.

Fast forward, I started to work on a completely new API for fundamentals one year ago. My goal is to provide data (raw and normalized) with a complete and 100% auditable trail.
Any number I would serve, would have a linkage to the exact SEC filing it comes from. But that is not enough.
To put this to the extreme, I also serve a complete amendment/restatement trail for every single extracted fact. You would know exactly that an originally filed revenue from a 10-Q was amended once after 3 months from value X to Y, then restated in a later 10-K from Y to Z.

It is this level of transparency that builds the foundation and trust layer that I want everything else to be built on.

This month I've launched my API, covering every stock/etf/mutual fund of the US Stock Market.

Here are the facts of where I stand:
100 million+ raw XBRL facts
86 million+ institutional holdings ingested
6.2 million filings ingested (new ones updated daily)
17,921 tickers tracked (stocks/etfs/mf)
88 API endpoints to get everything for any company or ETF from simple details, fundamentals, sector/industry aware metrics, executives, earnings dates, IPOs, filings, insider transactions, beneficial owners, institutional holders, fund reverse lookups, fund compositions, fund fee analysis, performance, overlap, flows, and much more.
Everything shipped with a complete documentation for all endpoints - arguably one of the most underrated features!
Modern MCP server support to allow AI Agent integrations.

The foundation I've been able to build is fully automated and cost effective from its infrastructure and design. It is fully designed to be expanded to more markets for an eventual worldwide coverage.

There is much more to to this project, but I will leave it at that for the moment.

Instead, I want to go deeper on the launch itself!

As mentioned above, I launched the project as a solo founder completely bootstrapped 2 weeks ago.

Since then, I've seen a good amount of traction, both from paid advertisement (Google Ads optimized campaign) and from manual engagement in various communities (reddit, X, LinkedIn, Discord).

>One recent permission for a testimonial I got reads like this:
StockFit is the only financial API I’ve found that provides a true Point-In-Time (PIT) Temporal Rollback Ledger out of the box. Exposing the exact SEC amendment trail allowed me to mathematically eliminate Lookahead Bias from my quantitative execution engine in a single weekend. It is an absolute game-changer for deterministic backtesting. Marcus M. Simmons, Architect, LogicFortress

Some stats for the 2 weeks mark:
- 100 signups
- 4 subscriptions (2 annual, 2 monthly) Net Volume $803, MRR $191

The subscribers also span multiple different use cases. One subscriber is from a Quant Trading Platform, another one is from an AI Unit of a software development company. Then we have the entire backtesting use-case I spoke about.

The hardest problem that I'm trying to solve is actually marketing. If you know marketing, chances are you also know that the finance niche is one of the most expensive ones there is. This is why I'm combining it with a lot of manual outreach. But it remains the biggest challenge.

After having so many detailed discussions about the state of the market, I'm convinced that this is a path worth following!
And I'm fully committed to do just that!

What are your thoughts on this project after reading all this?

Do you have any tips for me on how else I can reach potential customers in a more cost effective way?