Specialty Industries

Sales of big tractors and combines fall 36% to 42% year over year at the cycle bottom. Historically, pent-up replacement demand plus steadier crop prices lift equipment sales 18 to 36 months off that trough. This pattern reads the depth of the sales decline as the trough signal: the deeper the drop, the more replacement demand is building behind it. It applies only to pure-play farm-equipment makers, not diversified industrials with some farm exposure, because the cycle needs to dominate the business to read cleanly.

Deep year-over-year sales declines, dealer inventories normalizing, and crop prices stabilizing. A weak signal is large-equipment sales down at least 20% year over year. It firms up at 30%-plus with dealer inventories returning to normal — an early sign the destocking that deepens a downturn is ending. The strongest read adds stabilizing crop prices, per USDA data, to confirm the demand side is bottoming alongside the inventory side.

Weak (M1) is large-equipment sales down at least 20% year over year. Medium (M2) is sales down at least 30% plus dealer inventories returning to normal. Strong (M3) is sales down at least 35%, plus crop prices stabilizing per USDA data, plus dealer inventory below its historical norm. The strong read stacks a deep decline with confirmation from both the dealer channel and the farm-economics side.

The historical lift runs 18 to 36 months off the trough — a multi-year cyclical recovery, not a snap-back. That's the window the pattern is built around: it flags the bottom while sales are still deeply negative, before the replacement wave shows up in the numbers. The framework surfaces the trough and its strength; the investing decision is the user's.

When airlines grow capacity — seats flown — slower than demand — passengers carried — and planes fly more than 82% full, that discipline supports fares. It's the opposite of the 2010s overcapacity glut that kept fares and margins depressed. Full planes plus restrained seat growth mean carriers hold pricing power. The pattern applies only to US-listed scheduled passenger airlines, not freight carriers or aircraft-leasing companies, whose economics don't run on this load-factor-versus-capacity dynamic.

High load factors and seat growth trailing demand growth. A weak signal is capacity discipline being talked about, or planes flying more than 80% full. It firms up when planes are more than 82% full with seat capacity growing slower than passenger demand in the current period. The strongest read is seat-capacity growth running below half of demand growth and holding, with planes more than 84% full and capacity-restraint language in the filings.

Weak (M1) is discipline being discussed or load factor above 80%. Medium (M2) is load factor above 82% with capacity growing slower than demand. Strong (M3) is seat-capacity growth below half of demand growth and holding, load factor above 84%, and capacity-restraint language in the filings. The grading walks from talk toward hard capacity-versus-demand data with management on record.

It holds as long as the discipline does — the strong read requires the capacity-versus-demand gap to persist, not just print for one quarter. Fares respond over the following quarters as full planes keep pricing firm. The framework flags whether the discipline is genuine and durable and how strong it is; whether that supports a position is the investor's call.

A Bitcoin miner's economics come down to hashprice — the revenue earned per unit of computing power, set by the Bitcoin price, transaction fees, and how much total mining power is competing — measured against the miner's power cost. Roughly every four years the block reward halves (the April 2024 halving dropped it from 6.25 to 3.125 BTC). From about six months before a halving to 6 to 12 months after, hashprice mechanically compresses around 50% as the reward halves while total mining power keeps growing. High-cost, debt-funded miners get squeezed first — the bearish case.

About 18 to 24 months after a halving, hashprice typically recovers as Bitcoin's price climbs — it rose 4x to 6x within 12 to 18 months in each of the three prior cycles. The best-run miners, with low power cost and growing their own Bitcoin holdings, gain the most. So the pattern reads both legs: bearish into the post-halving squeeze on high-cost miners, bullish into the recovery on low-cost ones. Where a miner sits on the power-cost curve turns a general cycle call into a company-specific one.

Weak (M1) is the cycle stage alone: bearish if a halving is within six months or the last was within twelve, and hashprice is down 30%-plus over six months; bullish if it's 18 to 24 months post-halving with hashprice and Bitcoin both up. Medium (M2) adds power cost versus peers — bearish above the peer median, bullish below. Strong (M3) adds a structural amplifier: bearish on debt-funded expansion or 10%-plus share growth over four quarters; bullish on growing self-mined holdings plus rising Bitcoin-ETF inflows.

They're distinct patterns. A crypto-treasury vehicle — a company like MicroStrategy — is about a stock trading at a premium to the Bitcoin it holds, and the risk of that premium collapsing. This pattern is about the mining companies themselves and the mechanical squeeze-and-recovery of their hashprice-versus-cost economics around the halving. The framework tracks both separately so a miner isn't read through a treasury-vehicle lens or vice versa.

When ocean freight rates swing hard — tracked by benchmarks like the Shanghai Containerized Freight Index and the Baltic Dry Index — shipping-company earnings follow about one to two quarters later. Which way it cuts depends on where the rate cycle sits: bullish when rates are climbing and carriers hold capacity in check, bearish when rates collapse into a glut of ships. The pattern applies only to container and dry-bulk shipping lines, where rates are the dominant earnings driver.

Rate moves in the filings, share-price moves in the group, and management's capacity language, lined up in the same direction. A weak signal is a filing-disclosed rate move of at least 5% year over year in a quarter, or the shipping group's stocks moving at least 15% up or down over 30 days — the cycle showing in the share price even when a filing didn't spell out the rate. It confirms when two straight quarters of 10%-plus moves run the same way with management talking capacity to match.

Weak (M1) is the 5% filing move or the 15% 30-day group stock move. Medium (M2) is two straight quarters of 10%-plus rate moves in one direction plus matching management capacity talk, or a 10%-plus filing move with the group's stocks moving 15% the same way over 30 days. Strong (M3) is a confirmed multi-quarter rate cycle with capacity-discipline language (bullish) or overcapacity language (bearish) and a 30%-plus year-over-year rate change, or the medium setup plus the group's stocks moving 25%-plus over 30 days — an extreme move all pointing one way.

No — it's bidirectional, so it flags the direction the cycle is turning and how strong the confirmation is, not an action. Rates lead earnings by a quarter or two, so the value is early sight of the turn before it hits the income statement. Bullish and bearish both fire on the same machinery, just with opposite rate direction and capacity language. The framework surfaces the inflection; the investing decision stays with the user.

Oilfield-service revenue is downstream of upstream producer budgets. When the exploration-and-production cohort's aggregate reported capex contracts, service revenue follows with a one-to-two-quarter lag — the producers cut spending first, and the service firms feel it a quarter or two later. The pattern reads aggregate reported capex across a curated pure-upstream registry and flags the transmission before it fully shows in service-company results. A guidance-aggregation variant is the approved upgrade path once the guidance-series table lands.

Falling aggregate upstream capex, and the service member's own revenue already flattening. A weak signal is the upstream registry's aggregate trailing-two-quarter capex down 10%-plus year over year, read as bearish on oilfield-service registry members. The confirmation is deeper aggregate cuts plus the service member's own revenue growth at or below zero — the transmission from producer budgets to service sales is visibly underway.

Weak (M1) is upstream aggregate capex down 10%-plus year over year. Medium (M2) is aggregate down 15%-plus and the service member's own revenue growth at or below zero — transmission visible. Strong (M3) isn't reachable in the current version; it's a medium ceiling pending production observation. The grading moves from "producers are cutting" to "the cut is showing up in the service company's revenue."

One to two quarters from the upstream capex cut to the service-revenue impact. That lead is what the pattern surfaces — a bearish read on the service company before the producer-budget squeeze fully lands on its income statement. The framework flags the transmission and whether it's confirmed yet; the investing decision stays with the user.

A defense contractor's growing order backlog only matters if it turns into cash. This pattern reads that conversion: it fires when the book-to-bill ratio — new orders versus revenue billed — stays above 1.2 for two or more years and free cash flow runs at least 85% of net income. That combination is proof the backlog is becoming cash, not just piling up as paper awards. It separates high-quality growth from growth stuck in working capital, which is the difference retail coverage of "record backlog" headlines usually misses.

Defense primes, their subsystem suppliers, and defense-services firms. It excludes diversified industrials that have some defense work but earn most of their revenue elsewhere, because the pattern requires a backlog dominated by defense. A conglomerate with a small defense segment doesn't qualify — the backlog signal is only clean when defense is the majority of the business.

Weak (M1) is backlog growing 5%-plus year over year, or a book-to-bill above 1.0 — early signs of demand. Medium (M2) has two paths: backlog up 20%-plus with book-to-bill above 1.2 in a single year (Path A), or backlog up 10%-plus with book-to-bill above 1.0 (Path B). Strong (M3) is the full quality stack: backlog up 20%-plus sustained for two years, book-to-bill above 1.2, and free cash flow at least 85% of net income. The strong read is where growth and cash conversion both hold.

It's a multi-year read by construction — the strong signal requires two years of sustained book-to-bill above 1.2. That's the opposite of a quick trade: it's designed to identify contractors whose backlog is durably converting to cash. The framework flags the conversion quality and its magnitude; the investing decision is the user's. Free registration shows which defense names are firing this pattern today.

When two or more gas-weighted producers announce production curtailments, shut-ins, or deferred completions in the same window, supply discipline is breaking the glut — historically the capitulation that precedes the gas-price bottom, as in the 2020 and 2024 curtailment rounds. It's a news-cluster trigger across the gas-producer registry. But shut-ins only signal a bottom when prices are genuinely low: a price-trough gate requires Henry Hub spot below the 40th percentile of its trailing-24-month range, so curtailment at mid or high prices fails the premise and stays silent.

A cluster of curtailment announcements plus a confirmed low-price backdrop. A weak signal is at least two distinct registry members using curtailment vocabulary in the trailing 90 days with Henry Hub below its 40th-percentile trough — fired on registry members benefiting from the discipline. The stronger read is the member itself announcing curtailments with Henry Hub in a deep trough below the 20th percentile — capitulation it controls, at a confirmed price bottom.

Weak (M1) is at least two members with curtailment vocabulary in 90 days and Henry Hub below the 40th percentile. Medium (M2) is the member itself announcing curtailments and Henry Hub in a deep trough below the 20th percentile — capitulation it controls, at a confirmed bottom. Strong (M3) isn't reachable in the current version; it's a medium ceiling pending production observation. The price gate is robust to single-day spikes, and an EIA data outage fails open to the pre-gate behavior.

Historically the capitulation precedes the gas-price bottom — the curtailment wave is the supply response that ends the glut, and the price recovery follows. The gate is what makes the read precise: shut-ins at genuinely low prices are a bottom signal, shut-ins at healthy prices aren't. The framework flags the capitulation and confirms the price backdrop; the investing decision stays with the user.

A traditional automaker — Ford, GM, Stellantis, Toyota, Honda — is bleeding cash on its electric-vehicle business while its gas-engine business pays for the transition. The squeeze has four parts: EV development and battery-plant spending runs $10 to $20 billion a year for a major automaker; the EVs themselves lose money (Ford lost roughly $50,000 on every F-150 Lightning sold in 2023, with GM and Stellantis in similar shape); the gas-engine business is cyclically shrinking and losing share to Tesla and Chinese makers like BYD, NIO, and XPeng; and the capital is trapped — the automaker can't abandon EVs but can't fund them without draining the gas business.

Only traditional gas-engine automakers. Pure-EV makers like Tesla, Rivian, and Lucid are excluded — they don't have a gas business funding EV losses, so the transmission mechanism doesn't exist for them. Commercial-truck builders are also out. The pattern is specifically about the trapped-capital dynamic where an internal-combustion business is bankrolling an unprofitable EV transition, which only exists at legacy passenger-car makers.

Weak (M1) is any meaningful EV-segment loss disclosed in the annual report — no dollar floor yet. Medium (M2) is an EV loss of at least $500 million over trailing twelve months plus one of: gas-engine market share down at least 1 point with loss to Tesla or Chinese rivals cited, or a disclosed EV launch delay. Strong (M3) is the medium setup with EV losses of at least $2 billion, plus a sign of capital strain — EV spending cut over four quarters, layoffs of at least 5%, or a board-level strategic review (like the late-2024 ousting of Stellantis CEO Carlos Tavares, an admission the EV strategy failed).

The competitive pressure is live: Chinese makers with structural cost advantages keep taking share, and the losses on each EV sold have forced several legacy makers to cut or delay EV spending — which the strong read treats as an admission the cash burn is unsustainable. The pattern flags where the burden is deepest and whether capital strain is showing yet. It doesn't tell you to sell; it surfaces the squeeze and its magnitude, and the investor decides.

Each new US LNG export terminal that reaches first production adds feedgas demand to the domestic gas balance — gas that used to stay in the domestic market now gets liquefied and shipped. A single terminal ramps from first LNG to full capacity over roughly 12–24 months, so the demand arrives gradually. When several terminals ramp at once, the cumulative pull is a structural tailwind for the Henry Hub benchmark price. That benefits gas producers through realized prices and benefits the pure-play export operators through throughput and earnings ramp.

The terminal-startup schedule is public and dated — first-production announcements, commissioning cargoes, and ramp progress are disclosed facts, not forecasts. The framework tracks each terminal's incremental feedgas demand only during its ramp window (first LNG through about 24 months after), because once a terminal runs at capacity its demand is baked into the market balance and stops being a fresh catalyst. The sign to watch is overlap: multiple terminals inside their ramp windows simultaneously, stacking billions of cubic feet per day of new pull on the same supply base.

The pattern sums the in-ramp-window feedgas demand across a ratified registry of terminal startups. It fires weak (M1) when the cumulative in-window demand sits between 2.0 and 5.0 billion cubic feet per day. It fires medium (M2) at 5.0 Bcf/d or more — a major structural pull, the scale you get when large projects like Plaquemines, Corpus Christi Stage 3, and Golden Pass ramp concurrently. The strong level is deliberately not reachable yet: the framework wants to observe a realized firming in Henry Hub attributable to the demand add before allowing the top rating.

The mechanism runs on the 12–24 month ramp window per terminal, so a wave with several overlapping ramps can support the gas balance for two years or more. But it decays by design — as each terminal completes its ramp, its demand leaves the fresh-catalyst column. The pattern also has a supply-side mirror in the framework (the production-curtailment bottom); both tighten the balance and can fire at the same time, which is a stronger composite read than either alone. Users decide what to do with that; the framework's job is to show when the window is open and when it has closed.

Luxury runs in multi-year cycles driven by global wealth creation, currency swings between the dollar, yuan, and euro, the behavior of aspirational shoppers (China's middle class is the swing buyer at roughly 30% of global demand), and supply discipline by the top houses protecting their pricing power. The 2020–2022 boom — post-COVID stimulus and lockdown savings — sent demand soaring, with LVMH's organic growth peaking at +23% in fiscal 2022. Then 2023–2024 cooled as China weakened and aspirational shoppers pulled back, and LVMH growth fell to mid-single digits.

By the cycle stage. Bullish is the bottom: growth bottoming, a currency tailwind, aspirational shoppers returning, supply discipline holding. Bearish is the peak: growth at a multi-year high, Chinese shoppers fatigued, inventory building, management talking normalization. The confirmation is geography and persistence — China or Asia-Pacific organic growth moving the same way as the cycle, and the shift holding for more than one quarter. The pattern applies only to luxury brand houses, not mass-market apparel or sportswear.

Weak (M1) is LVMH or a sector benchmark showing organic growth shifting at least 3 points year over year in one direction from a single quarter — possibly a blip. Medium (M2) adds geography or persistence: China or Asia-Pacific growth moving 5-plus points the cycle's way, or the shift holding two straight quarters. Strong (M3) adds an earnings call explicitly calling the turn — "demand normalization," "soft luxury," "aspirational consumer fatigue," "China recovery," "supply discipline" — or management flagging a material currency tailwind or headwind.

These are multi-year cycles, so a confirmed stage is a multi-quarter read rather than a trade. The strong signal requires management to have built the cycle view into forward guidance, which is why it stacks geography, persistence, and explicit call-language. The framework flags the stage and direction — bullish bottom or bearish peak — and how well confirmed it is; the investing decision stays with the user.

Memory and storage is the largest cost line in a computer maker's bill of materials — around 35% this cycle versus about 18% historically. So a memory pricing upswing, read from the same cohort regime that drives the memory-cycle pattern, compresses OEM gross margins with a lag unless the maker passes the cost through. The fire gate is the OEM's own realized margin compression, which means AI-server mix dilution gets captured rather than masked — the pattern reads what actually happened to the margin, not just the input cost.

The memory regime armed and the OEM's own gross margin falling. A weak signal is the memory pricing regime armed — two or more producers in a cohort upswing — and the OEM's own gross margin down 50 to 100bps year over year. The confirmation is deeper margin compression: the regime armed and the OEM's own gross margin down more than 100bps. Because it gates on realized margin, it won't fire on a rising input cost that the maker successfully passed through.

Weak (M1) is the memory regime armed and OEM gross margin down 50 to 100bps year over year. Medium (M2) is the regime armed and OEM gross margin down 100bps-plus year over year. Strong (M3) isn't reachable in the current version; it's a medium ceiling pending production observation. The grading tracks how deep the realized margin hit is while the memory upswing is on.

Memory costs have run elevated as AI-driven demand tightens DRAM and NAND supply, and computer makers carrying a heavier memory content this cycle feel that in their margins. The pattern is the downstream bearish counterpart to the memory-cycle upswing: the same regime that lifts memory producers squeezes the OEMs buying their chips. The framework flags the squeeze and its depth; whether it matters for a position is the investor's call.

Memory is the commodity sub-cycle inside semiconductors. DRAM and NAND contract-price regimes reset every producer's margin together, so a pricing upswing shows up as cohort-wide sequential gross-margin expansion with tightening inventory — a single name's margin move is mix or execution noise, not the cycle. The peak announces itself when a member re-accelerates capex into record margins: the supply response that has ended every memory upswing. Reading the cohort together is what separates a real regime from one company's quarter.

The upswing is cohort-wide margin expansion with inventory days falling. The peak is a member pushing capex up hard into near-record margins — the tell that the industry is about to add supply and end the run. So the same pattern carries a bullish upswing leg and a bearish peak-warning leg, distinguished by whether margins are still expanding on tight inventory or whether capex is re-accelerating into the top.

There's no weak (M1) level — the cohort gate requiring two members in regime is the base requirement, so the weakest firing is medium by construction. Medium (M2) is at least two cohort members with sequential gross-margin expansion of 300bps-plus in each of the last two quarter-pairs and inventory days declining year over year (expansion without tightening inventory is a mix artifact and doesn't count), with the evaluated member in regime; the bearish peak leg is regime active plus the member's trailing-twelve-month capex up 50%-plus at margins within 100bps of the two-year high. Strong (M3) is the upswing leg only: the member's own expansion averaging 500bps-plus per quarter with inventory days down 15%-plus year over year.

Very — memory demand tied to the AI data-center buildout has been a live driver of this cycle's pricing regime, and the pattern reads that through the same cohort margin-and-inventory machinery whatever the demand source. The bearish peak leg is the counterweight: it watches for the capex re-acceleration that historically ends every upswing. The framework flags the stage and direction; the investing decision is the user's.

The biggest miners slashed spending after a 2013 peak and have kept it lean for years. Sustained restraint plants the seeds of future supply shortages — the opposite of the 2010s overbuilding binge that crushed metals prices. When a miner shows real capital discipline and says so in its filings, the setup is bullish: today's underinvestment tightens tomorrow's supply. The pattern applies only to major miners and metals producers, where the capex cycle drives the commodity price years out.

Capex low as a share of sales, plus management explicitly committing to restraint and, at the strongest, flat-to-declining production guidance. A weak signal is capital spending at most 8% of sales over the trailing twelve months. It firms up at 5% of sales — a multi-cycle low — or at 8% paired with stated discipline. The point is to distinguish miners deliberately holding back from ones cutting because they're distressed.

Weak (M1) is capex at most 8% of sales trailing twelve months. Medium (M2) is capex at most 5% of sales (a multi-cycle low), or at most 8% together with management spelling out capital discipline. Strong (M3) is capex at most 5% of sales, plus stated discipline, plus flat-to-declining production guidance — with growth-focused spending at most 30% of the budget where disclosed. The strong read is a miner deliberately not adding supply.

Mining supply responds on multi-year lags — new capacity takes years to build, so a capex drought tightens the market well down the road. That's why this is a slow-burn setup, not a quick trade: it flags miners whose restraint today sets up supply scarcity later. The framework surfaces the discipline and its strength; when and whether that translates to a position is the investor's call.

A signed contract, not an announcement. A bitcoin miner's AI pivot stays narrative until a definitive multi-year colocation or hosting agreement with a substantial counterparty is signed — the contract converts pivot talk into contracted revenue. This is the 2025–26 conversion class whose graduates now form the AI-infrastructure cohort. Letters of intent and MOUs don't count: the pattern requires binding, definitive language, because non-binding deals evaporate.

Definitive contract language in the miner's own news, and a real scale figure. A weak signal is signed or definitive colocation-hosting agreement vocabulary in the member's own news within 120 days — non-binding language disqualifies. The stronger read is that the matched announcement carries a contracted scale figure of 100 megawatts or more, which is what turns a signed deal into a materially large one.

Weak (M1) is signed or definitive colocation-hosting agreement vocabulary in the member's news within 120 days, with non-binding language disqualifying. Medium (M2) is the matched announcement carrying a contracted scale figure of 100 megawatts or more. Strong (M3) isn't reachable in the current version; it's a medium ceiling, and an 8-K Item 1.01 heuristic-extractor variant is the upgrade path once wired.

Because the AI data-center buildout gave bitcoin miners — who already run large power-connected sites — a route to repurpose that infrastructure for AI compute, and 2025–26 saw a wave of such conversions. The discipline is separating real, contracted pivots from press-release talk: the pattern fires only on a definitive signed agreement, so it flags conversions that have actually landed contracted revenue. The framework surfaces the validated pivot and its scale; the investing decision is the user's.

For a company that earns a large share of revenue from government and institutional customers, a named public defection — a government terminating, banning, or discontinuing the product — is a real revenue-at-risk event, not just reputational noise. It also tends to cascade: allied governments watch each other's procurement decisions, and one confirmed exit lowers the political cost of the next. The framework treats the confirmed, named walk-away as the trigger, because it converts controversy from an opinion into a booked commercial fact.

Criticism alone is not the pattern. Defense and surveillance-adjacent names get protested constantly without losing a single contract — social-license controversy is background radiation in those sectors. The framework requires a NAMED major customer confirming an exit before treating it as more than atmosphere. The distinction matters in both directions: investors who sell on every controversy overreact, and investors who dismiss a confirmed government ban as "just politics" miss that concentrated government revenue depends entirely on continued acceptance by a small number of decision-makers.

Weak (M1) is the pre-defection state: the company is government-revenue-concentrated and controversy is escalating — a genuine cluster of human-rights, surveillance, or sovereignty criticism — but no customer has actually left. That is a watch condition. Medium (M2) fires when at least one named major government or institutional customer confirms terminating, banning, or discontinuing the product. Strong (M3) fires on two or more named defections — the allied-government cascade — or one defection plus concurrent domestic US scrutiny. The escalation logic mirrors how the risk actually compounds: each confirmed exit makes the next one easier.

Yes, and this is the point of running patterns independently. The same company can legitimately fire a bullish share-capture pattern on its growth numbers while firing this bearish pattern on its customer concentration. Growth and fragility are different axes. The framework surfaces both rather than netting them into a single opinion, because the composite view — a fast-growing business whose largest customers can exit by press release — is exactly what a single-thesis narrative hides. The Live Tape shows both directions firing on the same ticker when that is the honest state.

Property-and-casualty pricing runs in roughly nine-year cycles. A "hard market" is a stretch of strong pricing power — when insurers push through rate increases and underwriting results keep improving. The gauge is the combined ratio: claims plus expenses as a share of premiums, where under 100 means an underwriting profit. This pattern fires when carriers are pushing rate increases of at least 5% and their combined ratios keep improving, signaling the hard market is holding rather than rolling over.

Commercial-lines, specialty, and reinsurance carriers only. Personal auto and home insurers run a different cycle — regulated rate filings chasing claims inflation, where strong results plus trailing rate increases mark the top of that cycle rather than persistence. Title and mortgage insurers price off housing volumes and credit risk, not this pricing cycle at all. Mapping the pattern to the wrong insurer type reads the cycle backwards, which is why the scope is deliberately narrow.

Weak (M1) is a hard market being talked about, or a single period with a combined ratio under 100 (an underwriting profit). Medium (M2) is at least two quarters with a combined ratio under 100 plus rate increases of at least 5%. Strong (M3) is at least four straight quarters with a combined ratio under 97 plus rate increases of at least 8% — sustained underwriting profitability with real pricing power, the signature of a hard market that's persisting.

The underlying cycle runs multiple years — roughly nine years peak to peak — so a confirmed hard market is a multi-quarter-to-multi-year condition, not a trade. The strong read requires four straight quarters of profitable underwriting and rising rates precisely because persistence is the whole point. The framework flags whether the hard market is holding and how strong it is; the investing decision is the user's.

When two or more platform peers announce billion-scale competing subsidy programs in the same window — the 2025 instant-commerce war is the class case — the discretionary spending war compresses every participant's margins for the following two to four quarters. Each player pours money into subsidies to hold share, and the collective effect is a margin hit across the group. The pattern fires on announced participants only: a peer that stays out of the war isn't penalized, because it isn't spending into the battle.

A cluster of billion-scale subsidy announcements among peers, with the evaluated member among the announcers. There's no weak (M1) level — the two-participant cluster gate is the base requirement, so the weakest firing is medium by construction. It fires when at least two distinct cohort members use billion-scale subsidy vocabulary in the trailing 90 days and the member being evaluated is one of the announced participants.

Medium (M2) is the base and only reachable level: at least two distinct cohort members with billion-scale subsidy vocabulary in the trailing 90 days, with the member as one of the announced participants. Strong (M3) isn't reachable in the current version; it's a medium ceiling pending production observation. The participant gate is the discipline — it keeps the pattern pointed at companies actually spending into the war, not bystanders in the same sector.

Two to four quarters after the subsidy war escalates — the spending flows through each participant's margins over the following quarters. That's the window the pattern surfaces: a bearish read on the participants while the war is on. The framework flags who's in the war and that the margin compression is coming; whether it matters for a position is the investor's call.

A building corporate refinancing wall — a bulge of debt coming due — shows up first as debt-wall stress flags across many issuers, then converts into ratings-agency transaction revenue (issuance and surveillance fees) two to three quarters later as that debt refinances. The engine reads its own debt-wall firings as the leading indicator. It's the vendor-side bullish mirror of the issuer-side bearish pattern: the same wall that pressures the borrowers hands the rating agencies a fee windfall as the wall gets refinanced.

A rising count of issuers flagged with debt walls, and the rater's own revenue starting to accelerate. A weak signal is a trailing-90-day count of at least 20 distinct tickers carrying debt-wall flags, running at least 1.5x the prior 90-day count — the wall is building — read as bullish on the ratings-agency registry. The confirmation is the wall ratio climbing further and the rating agency's revenue growth already accelerating, meaning the wall is converting to fees rather than just looming.

Weak (M1) is the debt-wall count at 20-plus and at least 1.5x the prior period. Medium (M2) is the wall ratio at 2x or more and the rater's own revenue growth already accelerating — conversion visible. Strong (M3) isn't reachable in the current version; it's a medium ceiling pending production observation. The grading moves from "the wall is building" to "the wall is actually converting to revenue."

Two to three quarters — the debt-wall flags appear before the refinancing activity that generates fees. That lead is the value the pattern adds: early sight of a demand tailwind for the rating agencies before it prints in their results. Because it's a cross-cohort read, the Live Tape can show the issuer debt-wall firings and the ratings-agency firing together. The framework surfaces the setup; the investing decision is the user's.

It means operations are not paying for the buildout. Miners publish monthly production updates disclosing three numbers: coins produced, coins sold, and coins held. When sold exceeds produced for consecutive months — and the treasury has been drawn down materially from its peak — the company is liquidating its balance sheet to fund expansion. The mining margin plus any external financing is not covering capital spending, so the coin stack is the funding source of last resort. That is a structural cash-flow statement hiding inside a routine press release, and it is readable months before it shows up in quarterly financials.

No, and the framework keeps them separate. The crypto-treasury pattern covers vehicles whose main asset is a coin pile and whose stock trades at a premium or discount to it — the collapse mechanism there runs through the premium and at-the-market share issuance. This pattern covers operating miners: companies with real mining operations whose monthly disclosures reveal that production economics cannot carry the expansion. A miner can fire this pattern while trading below asset value; the mechanism is the funding gap, not the premium.

The pattern reads the produced/sold/held triplets straight from the monthly 8-K production-update exhibits. It fires weak (M1) when coins sold exceed coins produced in both of the two most recent monthly updates — a two-month streak, not a single anomalous month. It escalates to medium (M2) when, on top of that streak, the latest disclosed holdings are down 10% or more from their trailing peak — the treasury drawdown confirms the sales are cumulative, not timing noise. The strong level is currently not reachable: the amplifier the specification calls for (a sustained decline in mining economics) needs a data feed that is not yet wired, so the pattern deliberately caps at medium.

Not by itself — the framework flags conditions; it does not issue instructions. Selling production can be rational treasury management for a quarter or two. What the pattern isolates is persistence plus drawdown: consecutive months of net selling with holdings well off their peak. That combination narrows the benign explanations. The educational read is about runway — how long can the treasury fund the gap, and what happens to the equity if external financing is the only remaining source. The Live Tape shows which miners are firing this pattern today and what else is firing alongside.

Chip stocks tend to turn one to two quarters before their profits do, and the tell is in the inventory. When a chipmaker's stockpile of unsold parts starts shrinking from its peak and new orders begin outrunning shipments — book-to-bill above 1.0 — the cycle is bottoming. Waiting for earnings to confirm means missing the inflection, so the pattern reads the inventory and order data instead. It applies only to pure-play semiconductor makers: chip designers, foundries, and chip-equipment suppliers, not software or consumer-hardware names.

Falling inventory days of supply and rising book-to-bill, together. A turn being talked about, or any early sign inventory is coming down, is the weakest hint. It firms up when inventory days are down at least 10% year over year and orders are outrunning shipments. The strongest read is inventory days down 15%-plus, book-to-bill clearly above 1.2, and the inventory drawdown confirmed — all pointing to a cycle that has bottomed rather than one that just paused.

Weak (M1) is a turn being discussed or inventory just starting to fall. Medium (M2) is inventory days down at least 10% year over year with book-to-bill above 1.0. Strong (M3) is inventory days down at least 15%, book-to-bill above 1.2, and a confirmed drawdown. The grading walks from anecdote toward hard, corroborated inventory and order data — the stronger the read, the less it depends on management talk.

It can be — the pattern reads pure-play chip names, and demand tied to the AI data-center buildout flows through the same inventory and order data it tracks. But the pattern is cycle-mechanical, not thematic: it fires on inventory drawdown and book-to-bill turning, whatever the demand source. So it captures an AI-driven inflection the same way it captures any other, without assuming the narrative. The framework flags the inflection and its strength; the investor decides.

Specialty-chemical formulators buy the commodity-chemical cohort's output as their raw material. When upstream margins collapse on oversupply — the structural post-2020 ethylene buildout regime — that input deflation flows into the specialty makers' cost of goods. Members whose own pricing holds capture the spread over the following two to three quarters: their input costs fall while their selling prices don't, widening margins. The pattern reads the upstream glut as the leading signal for the specialty maker's spread windfall.

Collapsing commodity-chemical margins upstream and the specialty member's own pricing holding. A weak signal is the commodity-chemical cohort's median gross margin down 300bps-plus year over year at both of the two most recent readings — glut confirmed — and the specialty member's own gross margin flat or better year over year, meaning its pricing is holding. The confirmation is the specialty member's gross margin already up 100bps-plus — the spread capture is printing, not just set up.

Weak (M1) is the glut confirmed (commodity cohort median gross margin down 300bps-plus at both recent readings) and the specialty member's gross margin flat or better. Medium (M2) is the glut confirmed and the member's own gross margin already up 100bps-plus year over year — capture visible. Strong (M3) isn't reachable in the current version; it's a medium ceiling pending production observation. The grading moves from "inputs are cheap and pricing holds" to "the margin is actually expanding."

Two to three quarters — the input deflation flows through the specialty maker's cost of goods on a lag. That lead is what the pattern surfaces: a bullish read on the formulator while the upstream glut is still the story. The framework flags the setup and whether capture is printing yet; the investing decision is the user's.

Used-car prices move on new-vehicle supply, financing rates, the wave of cars coming off lease, and consumer demand. The 2020–2022 supply shock — COVID plus the chip shortage — choked new-car production and drove used prices up more than 50%, with the Manheim Used Vehicle Value Index peaking around 235 in January 2022. As chip supply and production recovered through 2022–2024, used prices fell back about 30%, with Manheim near 200 by mid-2024. The pattern fires both ways depending on where the cycle sits.

By the cycle stage plus the dealer's own numbers. Bullish is the bottom: write-downs absorbed, the index stabilizing, profit per car recovering, management saying normalization is done. Bearish is the peak or a lease-return wave: rising new-car supply, the index falling, profit per car compressing, write-down risk visible. The confirmation is the dealer's gross profit per unit and days of inventory moving in the cycle's direction — bearish needs profit per car falling and inventory days rising; bullish needs the reverse.

Weak (M1) is the Manheim index moving at least 10% year over year in one direction — on its own possibly just a swing. Medium (M2) adds the dealer's own numbers: profit per car changing 10%-plus in the cycle's direction, or a management-noted change in inventory days. Strong (M3) adds an amplifier — bearish needs an active inventory write-down in the past four quarters ("inventory reserve," "lower of cost or market," a "vehicle inventory adjustment"); bullish needs management explicitly saying "normalization complete" or "inventory cycle is resolved."

Inventory-heavy dealers like Carvana and CarMax feel the swings hardest — Carvana's stock ran from $360 in August 2021 to $4 in December 2022 to $80 by August 2024 across one full cycle — while diversified dealers feel it less. That's why the dealer's own profit-per-car and inventory data are the confirmation: the same index move hits different balance sheets very differently. The framework flags the cycle stage and direction; the investing decision is the user's.