Most stock tools give you a number and ask you to trust it. Stoxa shows the work: valuations, screens, rankings and backtests built on point-in-time SEC filings, with every assumption editable and every figure traced to its source.
| Company | Price | Price impliesgrowth/yr | 5-yr historygrowth/yr | Strict value | Street value | Data check |
|---|---|---|---|---|---|---|
| AAPLApple Inc. | $344.6330 Oct 2026 | 32.3% | 8.7% | $144−58% | $173−50% | passed |
Company pages, the valuation workbench, a screener, watchlists and saved screens, macro context, and the API and Python SDK.
Before showing a fair value, Stoxa runs the model backwards: holding everything else fixed, how fast would revenue have to grow to justify today's price? (Analysts call this a reverse DCF.)
Compare that with what the company has actually done and you already know a lot.
See what NVIDIA's price assumes →What the price assumes
32% revenue growth a year
for the next 10 years. Apple's last five years: 8.7% a year.
The model opens with assumptions taken from the company's own filings: how fast revenue has grown, what margin it earns, how much it reinvests, what its debt costs. Each one is labelled with where it came from.
Two ready-made cases: a strict one in the academic tradition, and a street case closer to how brokers model. Or edit any input. The value, the sensitivity table and the simulations all update together.
Try both cases on Apple →Company filings are messy. Before showing a value, Stoxa checks the inputs against each other: the earnings per share we derive against the one the company reported, margins that are impossible, debt counted twice, tags a company stopped filing years ago.
If something is off, you see a warning, not a confident number.
See a valuation with data warnings: Johnson & Johnson →Revenue, margins, debt, share count: open any line and see the filing it came from, with the filing date and a link to EDGAR. Where a figure was restated, the original is shown next to it.
Company pages carry ten years of statements, charts and filings.
Open Apple's company page →| Input | Value | Source |
|---|---|---|
| Revenue growth | 8.7% | 5y PIT CAGR |
| Operating margin | 33.2% | TTM |
| WACC | 9.3% | Rf 4.98% |
| Terminal growth | 2.5% | min(Rf, 2.5%) |
Every input in the workbench carries a source label like these. The filing behind each statement line is one click away on the company page.
Filter US stocks on fundamentals, multiples and how they compare with their industry, as of any date, and see how many survive each rule. Order them with your own ranking system and check that higher ranks really earned more.
Backtest the screen with dividends and trading costs, run it as a portfolio with sell rules, stops and a hedge, or follow one of eight ready strategies whose rules are shown in full and tracked live every night.
Open the screener →Rankings →Backtest →Simulate →Strategies →| Watchlist | Company |
|---|---|
| AAPL | Apple |
| MSFT | Microsoft |
| NVDA | NVIDIA |
The same data and engine through a REST API and Python SDK. The same numbers in your own code.
For developers and agents →
# pip install stoxa
from datetime import date
from stoxa import equities
d = date(2026, 10, 30)
# the same engine the workbench uses
val = equities.value_company("AAPL", as_of=d)
# the reverse solve: growth the price implies
g = equities.implied_growth("AAPL", as_of=d)Revenue, margins, debt, share count: open any line and see the filing it came from, with the filing date and a link to EDGAR. Where a figure was restated, the original is shown next to it.
Company filings are messy, so Stoxa checks the inputs against each other before showing a value. If something is off, you see a warning, not a confident number.
The method is the standard discounted cash flow framework taught in finance courses and used by analysts. You can read every step.
| Feature | Stoxa | GuruFocus | Simply Wall St | TIKR | ValueInvesting.io |
|---|---|---|---|---|---|
| Shows the assumptions behind the value and lets you change them | Yes | Partial | No | Partial | — |
| Shows what the current price assumes | Yes | No | No | No | — |
| Links every number to its SEC filing | Yes | No | No | No | — |
| Checks the data and warns you | Yes | No | No | No | No |
| Strict and street cases side by side | Yes | No | No | No | No |
| API and Python SDK included | Yes | Add-on | No | No | No |
| Coverage | US listed | Global | Global | Global | Global |
| Price | $29/mo | ~$35/mo | ~$11–21/mo | ~$55/mo | $15–43/mo |
As published on each company's public site in October 2026; check each for current terms.
New to intrinsic valuation? Learn how it works, from free cash flow to the reverse DCF, with every formula the engine uses.
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Screens, ranking systems, backtests and a portfolio simulator on point-in-time data, plus eight ready strategies tracked live.
The same data and engine through a REST API and Python SDK.
$29 a month or $290 a year. The full workbench, the API and the Python SDK. No data add-ons. Cancel any time.
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