Methodology & Sourcing

How the work is built, graded, and sourced — and where its limits are.

AtomProphet is anonymous by design, so the work has to stand on its method, not a résumé. This page is the audit trail: how every causal link in the Cascade Graph is graded, where the numbers come from, and an honest account of what the analysis does and does not claim.

The Cascade Graph in one paragraph

The Cascade Graph is a directed map of the physical economy: 405 nodes connected by 597 sourced cause-and-effect links and 41 feedback loops. Nodes are drivers (structural demand forces), chokepoints (the constraints that bind), jurisdictions and geographies (who controls the supply), substitutes (what could cap a price), and terminals (the publicly-traded tickers that express exposure). Edges are not decorative — each one is a specific mechanism, written in plain language, assigned a relationship type, and graded by how strong the evidence for it is.

How every link is graded

Every edge carries a basis tag that tells you, without spin, how much to trust it. We would rather show you a defensible inference labelled as an inference than dress it up as a measurement.

Edges also carry a relationship type — drives demand for, strains, is the bottleneck for, constrains supply of, controls production of, is an input to, transmits price to, substitutes for, accrues value to, exposes risk to, and the loop-closing amplifies — each with a defined direction of effect. Open any node's evidence file in the graph and you can read each link, its grade, its magnitude, and its time horizon.

Where the data comes from

Claims are built on primary and institutional sources, not aggregators. In practice that means energy and minerals projections from the IEA, the World Nuclear Association, and S&P Global; climate physics from peer-reviewed literature (with DOIs where cited); and official records such as NOAA report cards and government supply-chain reviews. Market performance shown in articles is computed directly from real historical price data — not copied from a screenshot — and reported with its honest drawdown, not just its upside.

Where the data contradicts the narrative, the data wins. When a claim cannot be sourced, it is either labelled as reasoned inference or it does not go in.

How tickers are selected — the screen we actually run

This is the most important section to read carefully, because it is where most financial sites overclaim. We do not just sprinkle plausible names under a theme. Every ticker that earns a place at a node has to clear a documented, repeatable screen — and we apply the same screen whether a name appears in a flagship article or sits on a node deep in the graph. The screen establishes thematic fit and tradeability; it deliberately stops short of telling you a name is cheap or a good buy today.

Each node aims to carry two to three names that clear gates 1–4, typically pairing a diversified ETF (the low-effort expression) with one or two purer single names (the higher-conviction expression), each with its own sourced exposure note, real liquidity figure, and honest flag.

What the screen is not: it is not a valuation or price-target call, not a solvency rating, not an entry-timing signal, and not a buy list. A documented structural deficit is a statement about physical supply and demand — it is not a claim that the equity is cheap, and markets are forward-looking and may already have priced some or all of the thesis in. The screen gets you a clean, tradeable starting universe with honest exposure and liquidity facts; the valuation work after that is yours.

Size & liquidity reference

Below is the live tradeability reference for every ticker named across the site, computed from real market data. It is reference only — it does not screen for solvency or valuation, and it is not a buy list.

Who is AtomProphet?

The author writes anonymously to keep the focus on the data, not the byline. But the background shapes the method, so it is worth being plain about it. AtomProphet is written by a former computational mathematician turned applied scientist and engineer, who has spent a career building large-scale machine learning and anomaly-detection systems—the kind of fraud and bot-detection work that means living in petabytes of messy data and learning, the hard way, how easy it is to fool yourself.

That work comes down to one stubborn habit: pulling a faint, real signal out of overwhelming noise, and staying honest about the difference between the two. For years now, quietly and as a serious second pursuit, the author has turned that same discipline toward portfolio construction and macro research. The Cascade Graph is nothing more exotic than that—an anomaly-detection mindset, patiently pointed at the physical economy.

The standing rule on honesty

Numbers shown to you are real and sourced, or they are explicitly labelled as illustrative or inferred. Drawdowns are shown next to returns. Counter-arguments and failure modes are kept in view, not buried. That discipline is the entire point of an anonymous signal — there is nothing to sell you except the reasoning itself.

Common questions