About StockQuantix
A financial data platform built on primary sources, transparent methodology, and models you can take apart.
StockQuantix was built out of a simple need: to have one place where the data required for serious financial analysis is already assembled, verified, and ready to use — without spending hours pulling from multiple sources, reconciling formats, or questioning the origin of a number.
Everything on this platform starts from primary, publicly available sources:
SEC EDGAR for company filings (accessed directly via XBRL),
FRED (Federal Reserve Economic Data) for macroeconomic and market indicators,
and US Treasury data for yield curve and auction results.
We collect the raw data ourselves, process it through our own pipelines, and build every output from the ground up.
There is no dependency on third-party data aggregators.
From filing to valuation
A company filing is not a dataset. Every issuer tags its own XBRL facts, names its own line items,
and changes them between years. Our pipeline downloads the filings, reads the raw as-reported figures,
and standardizes them into one consistent statement structure — the same definitions across companies
and across years — while keeping the as-reported original alongside it. Everything above that layer is
built on the standardized data: historical analysis, adjusted operating profit and NOPLAT, invested
capital, ROIC, cost of capital, and finally a full enterprise valuation. That is the loop this platform
closes: from a raw filing, through a comparable statement, to a valuation whose every input can be
traced back to the document it came from.
What a valuation is — and what it is not
A discounted cash flow model does not tell you what a company is worth. It tells you what a company
would be worth if a specific set of assumptions turned out to be true. That distinction is the whole point.
Most published valuations stop at the market-level story — share, pricing, margins, growth. A model is
only as useful as the questions it lets you ask about the things that actually determine value: how long
an economic moat can hold returns above the cost of capital; how long the fade period should run before
returns converge to it; what terminal growth is defensible against the long bond; and, above all, the
relationship between return on invested capital and the cost of capital — because when the two are equal,
growth creates no value at all.
SQ Research Lab
is built so those questions can be asked and answered in seconds. Every assumption is an input you can
move, and the model recalculates the whole chain in front of you — so you can see not only what the value
becomes, but where in the forecast the value is actually created: in the explicit forecast years, in the
fade, or in the terminal value.
The market as a mirror
The reverse DCF turns the model around. Instead of producing a value and comparing it with the price,
it takes the price as given — the one observed fact in the exercise — and solves for what the market must
be assuming: the growth rate, the cost of capital, the length of the competitive advantage period. Those
implied figures can then be held against what the company has actually delivered, against analyst
consensus, and against its own industry. This is where a valuation becomes useful. The question is no
longer whether something is cheap or expensive, but what has to be true for this price to make sense —
and how likely that is.
Why data is shown as of T-1
Market data, the implied equity risk premium, the yield curve and credit spreads are shown as of the
previous trading day. This is deliberate. A full valuation run rebuilds a company's entire history and
forward model from the filings; the index-level work recalculates the implied equity risk premium across
the whole S&P 500. This is computation measured in hours, not seconds. Running it on a settled, complete
trading day produces a consistent, reproducible dataset — the same numbers today as tomorrow — rather
than a moving target that shifts with every intraday tick. For valuation work, a stable T-1 dataset is
more useful than a live one.
StockQuantix is the result of years of work — building, testing, and refining data pipelines and valuation models.
It is not finished, and it is not trying to be everything at once.
What it is committed to is accuracy, transparency, and steady improvement.
New data pipelines, broader company coverage, and additional analytical tools are added on an ongoing basis.
SQ Research Lab is currently in beta; company coverage is limited while we expand it.
StockQuantix is free to use. Data sources: SEC EDGAR, FRED, US Treasury.