Market Data & Research Dashboard

Market levels, macroeconomic releases and model-based valuation in one structured view.

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Market Overview

Market quotes are delayed up to 15 minutes. Fair value and ERP are calculated from T-1 data.

Delayed / T-1

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Fair Value (T-1) — — Previous model value: —

Valuation Gap (T-1) — — Fair value vs. T-1 index close

Implicit ERP (T-1) — — Model date: —

US 10Y Yield — Loading... Previous close: —

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Economic Indicator History

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Precomputed public-source history.

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S&P 500 Fair Value Analysis

Price, model fair value and valuation band from the precomputed daily snapshot.

T-1

Fair value is estimated from a shareholder-yield DCF framework combining dividends, net buybacks and a terminal growth rate anchored to sustainable ROE. The implicit ERP is the market-implied return premium that equates model fair value to the observed index level; it is a time-varying signal of demanded return, not a normative forecast.

Last 10 Trading Days

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US Treasury Yield Curve

Latest curve and historical comparisons; click a legend item to show or hide a series.

T-1

Last 10 Trading Days

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Credit Spreads

Investment-grade and high-yield spreads with recent context.

T-1 / T-2

Latest Snapshot

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Reference series Interpolated Extrapolated

US Treasury Auctions

Latest completed auction by tenor: yield, bid-to-cover and bidder participation.

Latest available

Latest by Tenor

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BTC (Bid-to-Cover) is total bids divided by the amount offered. Values above 2.0x generally indicate healthy demand; the 6-auction and 2-year averages provide tenor-specific context.

High / Average / Low Yield show the accepted-yield range. The difference between high and average yield is the auction tail: a larger tail can indicate less confident or more dispersed bidding.

Direct, Indirect and Primary Dealer amounts show who received the securities. Higher indirect participation often signals stronger institutional or international demand, while a high primary-dealer share can indicate weaker end-investor demand.

Dedicated research workspace

SQ Research Lab

Charting, statistical analysis and company valuation in one focused workspace.

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Technical chart with indicators and drawing tools — **Technical charting**Indicators, drawing tools and long daily histories.

Multi-panel market chart workspace — **Multi-panel analysis**Several linked views in one focused workspace.

Backtest trades and report table — **Backtest reporting**Transparent trades, settings and result inspection.

Performance and drawdown analytics — **Statistics & risk**Returns, volatility, drawdowns and distributions.

Standardized financial statements with adjustments and NOPLAT — **Standardized financials**From the filing to NOPLAT, with every adjustment shown.

Enterprise discounted cash flow model with forecast and terminal value — **Enterprise DCF**Value split between forecast, fade and terminal value.

Sensitivity table and tornado chart of valuation assumptions — **Sensitivity**What actually moves the value, and by how much.

Capital structure analysis across debt ratios — **Capital structure**The cost of capital at every debt ratio.

About StockQuantix

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.

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.

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 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.

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.

Market Data & Research Dashboard

Market Overview

Economic Indicator History

S&P 500 Fair Value Analysis

Last 10 Trading Days

US Treasury Yield Curve

Last 10 Trading Days

Credit Spreads

Latest Snapshot

US Treasury Auctions

Latest by Tenor

SQ Research Lab

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About StockQuantix

From filing to valuation

What a valuation is — and what it is not

The market as a mirror

Why data is shown as of T-1

SQ Research Lab