Trump Tariffs 2026: Trade War Risk with China & Mexico and What Prediction Markets Are Pricing In

Tariffs are back on the board—and markets are treating them less like campaign rhetoric and more like a tradable macro catalyst. Across prediction venues, you can already see the tariff risk stack forming: binary markets on additional China tariff hikes (and how high “reciprocal” rates go), Mexico/border-linked tariffs (especially around migration/fentanyl triggers), auto and auto-parts tariffs under Section 232, and the sleeper variable—whether courts (ultimately the Supreme Court) constrain the executive’s tariff playbook under authorities like Section 301/232 and IEEPA.

Why does this matter to investors? Because tariffs don’t just move “trade” stocks. They hit the core macro inputs that drive everything else:

• Inflation: Empirically, the 2018–20 experience showed near-complete pass-through into U.S. import prices on many categories, with a modest but real boost to aggregate inflation. Bigger, broader tariffs push that impulse closer to a first-order inflation story.
• Growth: Most major-model exercises characterize renewed broad tariffs as stagflationary—a growth drag via higher input costs, disrupted supply chains, and retaliation.
• Earnings and factor leadership: Tariffs reshape margins (retail, autos, industrials), capex timing, and inventory behavior.
• FX and risk sentiment: Escalation risk matters for USD/CNH, MXN, EM credit, and overall volatility.

The scale of exposure is not abstract. In 2023, the U.S. imported roughly $427–430B of goods from China and $475–480B from Mexico—with Mexico heavily concentrated in vehicles and parts (~$150–160B) and China in electronics and machinery. In other words: tariff shocks transmit directly into the real economy’s biggest supply chains.

SimpleFunctions is built for exactly this kind of environment: synthesizing scattered headlines, legal mechanics, retaliation paths, and market-implied odds into clean, tradable probabilities—a complement to polls, bank notes, and what you infer from rates and equities.

In this playbook, we’ll walk through: (1) Trump 2.0’s tariff architecture, (2) lessons from 2018–20, (3) China and Mexico 2026 scenario trees, (4) macro and sector pricing, and (5) how to express tariff views with prediction markets.

SimpleFunctions’ Trump Tariffs 2026 dashboard works best when you treat it as a map of linked policy states, not a pile of unrelated yes/no bets. We organize the tape into five clusters—each with a crisp legal “trigger” for resolution—then read them together as a macro/sector regime grid.

1) China escalation (reciprocal/Section 301 stack). These markets answer: does the U.S. raise the effective China tariff stack again in 2026—especially around the post‑deal window? The key calendar anchor is the Nov. 10, 2026 date referenced in the 2025 U.S.–China trade deal framework as a suspension point for further reciprocal hikes. Resolution typically requires (a) a Presidential proclamation/EO or (b) a USTR Federal Register notice that increases rates or expands covered tariff lines, plus evidence the change is implemented (CBP guidance/collection).

2) Mexico / border‑linked tariffs. These markets aren’t about USMCA theory—they’re about whether the White House reaches for IEEPA/emergency framing tied to migration/fentanyl. Resolution is usually: a declared emergency + an EO/proclamation imposing a tariff rate on Mexican-origin imports (or on non‑USMCA‑qualifying goods) that is actually collected at entry.

3) Autos and auto parts (Section 232 beyond 25%). Auto markets focus on the marginal step: escalation above the current 25% level (e.g., 35%/50% or expanded scope to parts/content rules). Resolution: a Commerce 232 action followed by a Presidential proclamation changing the rate/scope, effective on a stated date.

4) Legal constraint markets (courts/Congress). These are the “volatility dampener.” Resolution is a Supreme Court decision (or enacted legislation) that materially limits the executive’s ability to impose/maintain broad tariffs under IEEPA and/or Section 301 (e.g., narrowing emergency tariff authority, requiring additional procedures, or striking a tariff program).

5) Retaliation (China/Mexico). Retaliation markets resolve on official counter‑tariff schedules, export‑control/licensing actions, or formal trade actions (WTO/USMCA cases) that are implemented—China’s 2018–20 playbook suggests rapid, politically targeted retaliation; Mexico’s tends to be calibrated toward U.S. agriculture and USMCA process.

The practical read: China escalation markets primarily price a global manufacturing/inflation impulse (China imports were ~$427–430B in 2023), while Mexico/border markets price a North American supply‑chain shock (Mexico imports ~$475–480B; vehicles/parts ~$150–160B). The dashboard is designed to show when traders are implicitly betting on one shock but not the other—and where legal risk breaks the chain.

Inside Trump’s 2025–26 Tariff Architecture: Baseline, China Stack, Mexico & Autos

For trading this theme, the key is to stop thinking in slogans (“10% on everything,” “60% on China”) and start thinking in modules—separate tariff regimes that can be turned on, paused, exempted, or stacked. That modularity is why prediction markets can look “calm” on one leg (Mexico) while pricing real tail risk on another (China parcels or autos).

1) From “flat 10% on all imports” to a reciprocal baseline

The campaign framing was a clean universal tariff. The implemented structure is messier—and more tradable:

• Baseline is variable by partner (“reciprocal”), not a single global number.
• Exemptions are policy tools, not afterthoughts. Some partners are effectively placed in a “green zone” (baseline exempt), while others sit in “yellow/red zones” (higher baseline rates, subject to periodic review).

In practice, this creates a watchlist dynamic for 2026: the market isn’t only pricing the level of baseline tariffs, but the probability that a specific partner loses an exemption at the next review window.

2) China is a stack, not a single rate (“60%+” as shorthand)

The “60%+” China number is best read as political shorthand for a layered structure that can deliver China-wide effective rates in that neighborhood without ever printing a single “60%” line in the tariff schedule.

Key layers traders should separate:

1. Legacy Section 301 China tariffs from the 2018–20 trade war era, still the backbone of the China regime. Researchers commonly estimate the post-trade-war average applied tariff on Chinese imports at roughly ~19–21%.

2. New reciprocal tariffs on China layered on top of the legacy 301 structure. Trade-law trackers and policy summaries describe a ~34% reciprocal base on many Chinese tariff lines in the 2025–26 framework (with additional deal-linked timing constraints).

3. De minimis / small-parcel surcharges aimed at the e-commerce channel, functionally a separate “tariff lane” with very high effective rates.

4. Sectoral “100%” tariffs where the administration wants a headline deterrent (one example already implemented: ship-to-shore cranes and certain maritime cargo handling equipment at 100%).

The important market implication: China escalation can happen via coverage expansion (more HS lines), rate hikes (higher reciprocal add-on), channel enforcement (parcels/de minimis), or sectoral shock events (a sudden 100% list). Each has different inflation and earnings transmission.

3) Mexico, border leverage, and the USMCA “exempt—for now” status

Mexico sits in the most politically sensitive spot: it’s both the largest goods supplier to the U.S. and deeply embedded in U.S. production—especially autos and electronics. That’s why current architecture has two seemingly contradictory features:

• Mexico is designated “reciprocal: exempt” for now under the baseline framework—reflecting USMCA duty-free trade and supply-chain integration.
• Mexico is still the main target of conditionality, with tariffs repeatedly framed as leverage tied to migration and fentanyl.

Mechanically, that means the tradable question for 2026 is less “Does the U.S. scrap USMCA?” and more “Does the White House use emergency authority to tariff Mexican-origin imports (or selectively tariff non-USMCA-qualifying goods) as a border enforcement tool?”

4) Autos: Section 232 (25%) meets USMCA rules of origin

Autos are where these regimes collide.

• Section 232 is the national-security lever that can set a 25% tariff on autos/auto parts, with scope/rate changes via presidential proclamation.
• USMCA is the rulebook that determines whether a given vehicle/part qualifies for preferential treatment based on regional value content and other requirements.

So the intersection is not theoretical: in a stress scenario, you can see (a) a 232 expansion that hits non-North American vehicles hard while (b) tightening interpretation/enforcement of USMCA content rules, raising compliance costs and creating “origin risk” for North American producers that run complex cross-border bills of material.

5) Legal authorities and what they imply for timing (why this matters for markets)

Tariff timing is not “calendar-driven”—it’s process-driven. Traders should map each authority to the real-world implementation path:

• Section 301 (Trade Act of 1974): USTR-centered. Typically involves an investigation record and Federal Register notices. Faster when modifying an existing 301 regime; slower when building a fresh one.
• Section 232 (Trade Expansion Act of 1962): Commerce-led national security investigations and reports, then presidential action. Statutes allow long windows, but politically prioritized sectors can move quickly once a report exists.
• IEEPA (International Emergency Economic Powers Act): The speed tool. Once an emergency is declared, tariffs can be imposed via EO/proclamation and operationalized rapidly through CBP guidance.

For 2026, the “tape” to watch isn’t just speeches. It’s:

• Federal Register (USTR notices changing covered HS lines or rates)
• Presidential proclamations/EOs (especially emergency-linked)
• CBP operational guidance (CSMS messages and collection start dates)
• Deal-linked dates (e.g., the framework that suspends further reciprocal China hikes until Nov. 10, 2026)

This is how a prediction market can reprice in hours: the moment a measure is not just announced, but implemented and collectible at the border.

How exposed are China and Mexico?

For tariff traders, “exposure” is mostly arithmetic: (1) how big the import base is, (2) what rate is already embedded, and (3) how much higher the stack could go if policy flips from “targeted” to “blanket.”

China and Mexico are still the two biggest levers in the U.S. import system. In 2023–24 totals, the U.S. imported roughly $427–430B of goods from China and $475–480B from Mexico—similar magnitudes, but very different compositions.

• China’s mix is consumer- and capex-heavy: electronics (HS 85), machinery (HS 84), furniture/toys, textiles/apparel/footwear, and metals.
• Mexico’s mix is production-chain heavy: autos & parts (~$150–160B), machinery, electronics, fuels, and agriculture/food.

That distinction matters because China tariffs tend to transmit into retail goods and electronics baskets, while Mexico tariffs transmit into North American manufacturing costs (autos, appliances, industrial assemblies) and food—often with less ability to “route around” the shock quickly.

Existing tariffs vs. proposed levels: why the marginal change is bigger for Mexico

China already carries a meaningful tariff load from the first trade war: researchers commonly estimate the value‑weighted average applied tariff rose from ~3% pre‑2018 to ~19–21% after the Section 301 rounds. That means new policy doesn’t start at zero; it stacks.

A Trump‑style 2026 outcome that pushes China’s effective average into the high‑30s/40s+ (with 60–100% on politically sensitive lines) would roughly double the tariff wedge on a ~$430B base—before you even consider “channel” actions like de minimis surcharges.

Mexico is the opposite. Under USMCA, most qualifying trade is still duty‑free, so a 10–25% across‑the‑board Mexico tariff is not “a hike”—it’s a regime change. Mechanically, a 10% tariff on ~$480B is a ~$48B/year gross tax at the border; 25% is ~$120B/year.

Revenue and household cost: anchoring to 2018–20, then scaling

At the peak of the 2018–20 tariff package, widely cited CBO/PIIE-style tallies put tariff revenue around ~$70–80B per year, with research estimating ~$800–1,000 per household per year in costs (via higher prices and reduced efficiency).

If 2026 policy raises effective rates on a similar or larger import base—especially if Mexico moves from ~0% to double digits—both revenue and consumer incidence can plausibly run meaningfully above the first trade war’s peak. The key caveat for traders: revenue doesn’t scale one‑for‑one because imports can contract and reroute, but the price shock often arrives fast because pass‑through to import prices has historically been high.

What this means for traders: map trade flows to CPI baskets and earnings sensitivity

Think of China and Mexico as two different “tariff betas”:

• China content = electronics + home goods + apparel. Watch categories where China is hard to replace in the short run (consumer electronics supply chains, furniture, low-to-mid price apparel). These are the lines that show up in core goods inflation, retailer margins, and electronics assemblers.
• Mexico content = autos + appliances + food. Mexico’s ~$150–160B autos/parts channel is the cleanest transmission to vehicle prices, parts inflation, and OEM/supplier margins. A blanket Mexico tariff also hits large-ticket durables assembled in North America (appliances, HVAC components, industrial machinery) and specific food categories.

Practically, this is why SimpleFunctions treats “China escalation” and “Mexico/border tariffs” as separate market clusters: a China-only shock is a global manufacturing/consumer goods impulse; a Mexico shock is a North American production-chain shock. When prediction markets price one but not the other, you can often see the mismatch in sector dispersion (retail vs autos/industrials) before it shows up in macro prints.

What the 2018–20 U.S.–China Trade War Taught Markets

The cleanest way to think about 2018–20 is not “one big tariff.” It was a sequence of legally implemented rate/coverage steps, punctuated by negotiation headlines that repeatedly yanked risk assets around. For 2026 traders, that history is useful because it tells you (1) which policy moments actually changed the cash-flow math and (2) how fast those changes showed up in hard data.

1) Timeline and escalation: the steps that mattered

The escalation came in rounds, and each round had two market-moving components: announcement (sentiment) and effective date (real incidence).

• 2018 (opening salvo): Section 232 steel/aluminum (global), then Section 301 China tariffs—starting with a 25% list on ~$50B (Lists 1–2).
• 2018–19 (expansion): The fight broadened to about $370–380B of Chinese imports under U.S. tariffs—ultimately spanning 10–25% rates across multiple lists.
• 2019 (consumer-facing turn): “List 4” was the inflection where tariffs moved decisively into consumer goods. List 4A took effect Sept. 1, 2019 (initially 15% on about $112B), with List 4B (the remaining ~$160B) used as negotiation leverage and then canceled.
• 2020 (partial rollback): The Phase One deal (signed Jan. 15, 2020) halved List 4A from 15% to 7.5% and canceled List 4B—while leaving earlier 25% tariffs on roughly $250B in place.

For markets, the “big” days were usually the ones that changed either (a) coverage (new HS lines), (b) the rate (10→25%), or (c) whether consumer categories were included. Negotiation “good vibes” without an actual implementation change tended to mean-revert fast.

2) Coverage and diversion: why the supply chain moved, but mostly didn’t “reshore”

By late 2019, more than two-thirds of U.S. imports from China by value were tariffed. That did what tariffs usually do: it changed routing. But the dominant pattern was trade diversion—not a clean reversal of globalization.

Empirical trade data from the period show U.S. imports of tariffed products fell from China, while rising from Vietnam, Mexico, Taiwan, and other suppliers in similar categories. That’s the key mechanism for 2026: higher China rates can reduce “Made in China” import share without meaningfully increasing “Made in USA,” especially when production can relocate within Asia (or into North America) faster than it can rebuild U.S. capacity.

For investors, diversion is a double-edged sword:

• It dampens the direct CPI hit over time (substitution away from tariffed source).
• It raises transition costs (new suppliers, compliance, inventory buffers), which shows up in margins and capex uncertainty.

3) Price and macro effects: what actually showed up in the data

The 2018–19 literature converged on a simple result: for many categories, tariffs were passed through nearly one-for-one into U.S. import prices, rather than being absorbed by foreign exporters.

At the macro level, the inflation impact was modest but real because tariffed goods are a slice of the overall consumption basket. Common syntheses put the boost to aggregate CPI around ~0.1–0.3 percentage points at peak, with much larger category-level moves in the targeted goods.

On growth and jobs, the sign was negative but the magnitude was smaller than the headlines: most estimates point to a few tenths of a percentage point drag on GDP (before considering broader uncertainty effects), while the pain was sharper locally—in regions/sectors directly exposed to tariffed inputs, export retaliation, or disrupted farm/industrial demand.

The practical implication for 2026 is timing: import price pass-through can show up quickly, while CPI and “macro growth” are slower and noisier—often overwhelmed by other forces unless the tariff base is broad (or Mexico/auto supply chains are hit).

4) Market behavior: headlines moved prices—but not always for the right reasons

If you traded 2018–19 live, you remember the pattern:

• Equities: sudden risk-off on escalation headlines; relief rallies on “talks going well.” The highest-volatility windows clustered around rate hikes (10→25%), consumer-list threats, and “truce” announcements.
• FX: USD/CNH became a real-time “stress gauge.” The August 2019 episode—when China’s currency weakened through the psychologically important 7-per-dollar level alongside tariff escalation—illustrated how quickly trade policy bleeds into financial conditions.
• Commodities: agricultural products were especially sensitive to retaliation risk (soybeans as a classic pressure point), while industrial commodities tracked global growth fears.

The important meta-lesson is that markets often overreacted to rhetoric and underreacted to incremental, legally binding steps. In other words: a tweet could move SPX for a day, but a Federal Register notice with a firm effective date changed earnings math for quarters.

5) Trading takeaway for 2026: a playbook built from 2018–20

Here are the rules that would have paid in the first trade war—and map directly to how we read prediction markets now:

1. Trade the implementable step, not the speech. The highest-signal events were the ones tied to a real implementation mechanism: published lists, rate changes, exemptions, CBP collection dates.

2. Coverage beats headline rate. A 10–15% tariff on a broad consumer basket can matter more for inflation and retail margins than a 25% tariff on a narrower intermediate list.

3. Pass-through arrives before CPI does. Watch the data in this order: import prices and PPI-sensitive inputs first; CPI follows with lags and substitution effects.

4. Assume diversion, not reshoring—at least initially. The first response is rerouting supply (Vietnam/Mexico/others), then renegotiating contracts, then capex. That ordering matters for which sectors win/lose.

5. Prediction markets can arbitrage “rhetoric vs process.” When markets price a high probability of “tough talk” but low probability of an implemented rate/coverage change by a deadline, that’s often the cleanest edge—because implementation is what hits cash flows.

Macro Scenarios for 2026: Growth, Inflation, and the Effective Tariff Rate

Once you stop treating “tariffs” as a single headline and start treating them as an effective tariff rate (ETR) path plus a retaliation path, the macro mapping gets clearer. Most credible modeling groups agree on direction—tariffs are a tax on imports that behaves like a negative supply shock—but they disagree on scale because outcomes hinge on (1) breadth of coverage (China-only vs. North America vs. global), (2) pass-through, and (3) whether the Fed “looks through” the initial price-level jump.

What the external models are really saying (in macro language)

1) Yale Budget Lab: “macro hit, fast price impulse.” In their simulation of the 2025–26 tariff package (including retaliation), the Budget Lab estimates 2025 real GDP growth ~1.1pp lower (Q4/Q4), the unemployment rate ~0.55pp higher by end‑2025, and the near‑term price level ~2–3% higher (they report +2.9% short run, +1.7% after substitution). Longer-run, the level of GDP is ~0.6% smaller.

2) Richmond Fed: “ETR is the regime variable.” Their scenario work reframes the conversation as: how high does the U.S. average effective tariff rate go? They anchor the 2024 baseline at ~2.2%, then show broad China + Mexico/Canada + autos/EU-type packages can push the ETR into the low teens and as high as ~17%—levels that begin to resemble a full macro regime shift, not a sector story.

3) Tax Policy Center (TPC): “revenue is big, welfare loss is bigger than you think.” TPC’s high-end package generates trillions in revenue over 2026–35 (about $3.3T in one aggressive scenario), but their 2026 distributional result is the key macro anchor: average real income lower by about $2,900–$3,100 per taxpayer in 2026. That’s consistent with the “stagflationary” characterization: you can collect revenue while still reducing real purchasing power.

4) IMF and major banks: “stagflationary impulse, Fed reaction is the swing factor.” Across IMF-style trade-tension simulations and bank strategy notes (Goldman/JPM/Citi), the common framing is weaker growth (roughly −0.5 to −1.5pp at peak) alongside higher inflation (+1.5 to +3pp) in the short run. The dispersion is mostly about whether tighter financial conditions compound the shock (Fed hikes into it) or dampen it (Fed looks through it).

Three stylized 2026 macro paths (and how to trade the divergence)

Below is a practical way to translate those studies into tradable 2026 states. The point is not to forecast CPI to a tenth; it’s to pin down ETR direction + retaliation + Fed reaction, then check whether prediction markets (policy odds) and macro markets (rates/inflation) are telling the same story.

Path A — Mild escalation (China elevated but stable; Mexico mostly exempt)

• Policy shape: China tariffs remain high but do not broaden meaningfully; Mexico stays largely protected under exemptions/USMCA treatment; autos don’t reprice dramatically.
• ETR intuition: rises modestly vs. baseline (think single-digit ETR, not teens).
• Macro: growth drag is present but manageable; inflation impulse looks more like a one-off level effect in goods than a multi-year spiral.
• Fed reaction: most likely to look through the near-term goods-price bump if services inflation is cooling; policy stays data-dependent.
• Asset narrative: “contained trade shock” usually favors quality/growth over heavy cyclicals; USD supported but not in crisis mode; EM performs idiosyncratically.

Path B — Full China shock + selective Mexico/auto tariffs (ETR into the low teens)

• Policy shape: coverage expansion and/or higher China rates show up in implemented schedules, plus selective Mexico/border-linked tariffs and/or autos/parts actions.
• ETR intuition: jumps into the low-teens, consistent with Richmond Fed mid/high scenarios.
• Macro: this is where the bank-language “stagflationary” call becomes real: inflation pops while growth undershoots. The 2018–20 lesson (fast pass-through into import prices) matters more because the base is broader.
• Fed reaction: the key question becomes credibility vs. optics. A common compromise outcome is: the Fed does not chase the first-round CPI spike, but keeps a tighter bias to prevent second-round effects—i.e., fewer cuts than the growth data alone would imply.
• Asset narrative: rotation toward value/defensives over long-duration growth; pressure on goods-sensitive margins (retail/consumer durables); USD/CNH and MXN become macro stress gauges; EM beta underperforms DM.

Path C — Maximalist trade war (China + Mexico + EU autos; broad retaliation)

• Policy shape: tariffs broaden across partners and sectors, with meaningful retaliation and non-tariff actions (export controls, regulatory pressure). This is the closest analogue to IMF-style “global trade shock.”
• ETR intuition: pushes toward the upper bound (~15–17%).
• Macro: higher inflation and lower growth at the same time, plus global confidence effects. IMF-style simulations of broad trade escalation often imply global GDP down ~1%+ vs baseline over several years, with concentrated hits to trade-intensive economies.
• Fed reaction: unpleasant choice set. If inflation expectations lift, the Fed may have to stay restrictive despite weaker activity (the worst case for risk assets). If it prioritizes growth, real rates fall but inflation risk premia rise.
• Asset narrative: risk-off regime: USD strong vs high-beta FX, higher cross-asset vol, and relative support for defensives and domestic “protected” producers (though broad tariffs can still hurt them through inputs).

Where prediction markets add edge: mapping policy odds to macro pricing

Prediction markets don’t need to forecast CPI prints to be useful. Their edge is earlier in the chain: they estimate the likelihood that the policy state flips from A → B → C. Your job is to convert those policy probabilities into a distribution over ETR and retaliation, then compare it to what’s implied in:

• Rates curves: Are front-end cuts priced as if Path A dominates, while tariff markets imply Path B/C odds are rising?
• Breakevens / inflation swaps: Are inflation risk premia complacent relative to a low-teens ETR scenario?
• FX: Is USD/CNH behaving like a “contained” shock even as escalation odds rise?
• Equity factor leadership: Are cyclicals/EM pricing Path A while policy odds drift toward Path B?

The actionable setup is a misalignment trade: when prediction markets are moving the policy probability mass (say, toward Mexico/auto implementation) but macro markets are still anchored to a benign path, you often get a cleaner risk/reward than trying to time the CPI release.

Below is a SimpleFunctions-friendly way to operationalize it: treat the scenario basket as a “meta market,” then keep a running comparison versus your preferred macro proxy (2y yields, 5y5y inflation, USD/CNH, MXN).

Note: The card above is a template for how we recommend structuring a composite “macro-path” view from multiple underlying policy markets (China escalation, Mexico/border tariffs, autos/232, retaliation, and court constraints). Plug in live venue odds and let the composite update automatically; the trade comes from the gap between this basket and what rates/FX/equities imply.

A practical rule: if your composite assigns meaningful probability to low-teens ETR (Path B) but the rates market still prices an aggressive easing cycle as if inflation risk is fading, you’re effectively seeing a market that’s long “disinflation certainty” while policy odds say “goods inflation shock is back.” That divergence is often where the best cross-asset expressions live (and where prediction markets add the earliest signal).

China 2026: Tariff ladders, retaliation playbook, and sector landmines

The clean way to trade China tariff risk in 2026 is to stop asking “60% or not?” and instead map the ladder—the sequence of implementable steps that can ratchet the effective China tariff stack higher even if there’s no single headline “60%” line item.

That ladder matters because it interacts with a second, equally tradable system: Beijing’s retaliation menu. In 2018–20 the market learned that tariff headlines move prices; in 2025–26 the market is learning the sharper lesson that tariff implementation + export controls + administrative pressure can create sector shocks that don’t look like “trade” at all until earnings start missing.

1) The U.S. “tariff ladder” on China (how escalation actually happens)

There are three practical escalation rails in 2026:

Rail A — Raise the reciprocal baseline on China. In the current architecture, many China lines carry a ~34% reciprocal add-on layered on top of legacy Section 301 tariffs (and other duties where applicable). The tradable move is not only “raise the rate,” but expand the set of tariff lines subject to the reciprocal rate (coverage is often more important than the headline number).

Rail B — Add new 100%+ sector tariffs (headline deterrence). The 2025 playbook already showed a willingness to go straight to triple‑digit tariffs for politically resonant categories. A clear precedent: 100% tariffs on Chinese ship‑to‑shore cranes and certain maritime cargo handling equipment (a national-security framing plus a visible U.S. infrastructure choke point). In 2026, similar lists could plausibly include ships, cranes, advanced manufacturing equipment, “strategic” industrial inputs, or defense-adjacent supply chain nodes.

Rail C — Tighten the “channel” (de minimis/parcels). The administration has already treated parcels as a separate tariff lane: an April 2025 action imposed 90% ad valorem or $75 per item (whichever is higher) on covered China-origin postal parcels, with the per‑item charge rising to $150 shortly thereafter. In practice, this is a lever to hit China-linked e‑commerce and low-value consumer goods without having to re-litigate every HS code.

2) The Nov. 10, 2026 clause: why China has a built-in event horizon

Markets love dates, because dates force resolution. The November 2025 U.S.–China framework created exactly that: a pause window that suspends some further reciprocal escalation until Nov. 10, 2026.

Translation for traders: China tariff markets can look “sleepy” for months—and then violently reprice as (a) the policy review calendar approaches and (b) negotiators start using the date as leverage. It’s the trade-policy equivalent of an option’s gamma zone: probability mass can move quickly as you near the deadline.

3) China’s retaliation playbook (what Beijing can do that actually hurts)

Beijing’s 2025 response to new U.S. tariffs is the most relevant template because it combines classic counter‑tariffs with a broader toolkit.

(1) Counter‑tariffs (broad and often symmetrical). Historically, China’s first move is to match the U.S. with wide coverage, focusing on U.S. exports with domestic political salience and available substitutes. Reported 2025 rounds included moves that escalated from targeted lines (energy, farm machinery, vehicles) to across‑the‑board additional tariffs, reaching levels (e.g., 34% and even 84% in later escalation) that can render trade uneconomic.

(2) Export controls (chokepoints, not broad bans). The high-leverage risk for 2026 is tightening export licensing/quotas for:

• Rare earths and magnet supply chains
• Critical minerals and battery materials (graphite, processed inputs)
• Solar and battery manufacturing inputs

Export controls are attractive to Beijing because they can be targeted, deniable, and reversible, while creating real operational uncertainty for U.S. firms.

(3) Regulatory harassment / administrative pressure. This is the “gray zone”:

• Customs delays, inspections, product holds
• Antitrust/cyber/data investigations
• Procurement guidance to SOEs
• Symbolic commercial freezes (e.g., pausing Boeing order activity during acute tension)

Unlike tariffs, these measures can hit specific companies without an obvious CPI footprint—making them easy for equity investors to miss until guidance changes.

4) Sector landmines: where the two-sided policy risk stacks

Tech & electronics. Investors often price this as a U.S. tariff story (higher landed cost of components), but the more dangerous tail is dual exposure: U.S. import barriers plus China export controls on inputs (materials, subcomponents) that are harder to dual-source quickly.

EVs, batteries, renewables. This complex is uniquely vulnerable because it sits at the intersection of (a) U.S. “industrial policy tariffs” on finished goods and equipment and (b) China’s ability to constrain upstream materials. Triple-digit U.S. sector tariffs are plausible; so are targeted China licensing actions.

Agriculture & energy. These remain the most “politically efficient” retaliation targets. Counter‑tariffs here transmit into commodity spreads, freight, and regional U.S. income (Midwest and Gulf-linked export corridors). Even when volumes reroute, the transition period can widen basis and raise volatility.

5) Mapping to prediction markets (how to isolate the China-specific bet)

A useful SimpleFunctions approach is to build a China escalation bundle and a China retaliation bundle, then compare them to broader “global growth” proxies.

Concrete market ideas (venue-dependent):

• U.S. adds new 100%+ China sector tariffs (ships/cranes/advanced manufacturing)
• U.S. tightens de minimis/parcels rules again (coverage expansion, higher per-item fees)
• China imposes rare-earth / critical-mineral export controls affecting U.S. buyers
• China announces broad counter‑tariffs on U.S. agriculture and/or energy

If your venue offers conditional markets, use them to separate regimes:

• “China retaliation occurs if the U.S. raises reciprocal baseline above X”
• “Rare-earth controls if new U.S. 100% sector tariff list is implemented”

That structure helps you isolate China policy-to-supply-chain transmission from the generic “global slowdown” narrative—which is often what macro markets price first.

Mexico, the Border, and Autos: Low‑Probability, High‑Impact Tariff Risk

After mapping China’s “tariff ladder,” the next risk to price is Mexico—because it behaves less like a normal trade dispute and more like a supply‑chain shock with a political trigger.

1) The policy baseline: USMCA integration is the starting point (and it’s still mostly duty‑free)

Mexico is structurally different from China in U.S. trade policy. Under USMCA, most qualifying U.S.–Mexico goods trade is duty‑free, and Mexico is currently treated as exempt from the reciprocal tariff baseline in the 2025–26 architecture. That exemption is not just “goodwill”; it reflects the reality that Mexico is embedded inside U.S. production, especially autos, electronics, and machinery.

Mexico has also been strengthening its “North America first” posture on the other side of the border. As of January 2026, Mexico implemented tariff increases of roughly 5%–50% on 1,463 tariff lines for non‑FTA partners, including China—covering products like auto parts, steel, textiles, plastics, appliances, furniture, and more. In practice, this is Mexico signaling: we want to remain the preferred platform for North American manufacturing—even as U.S. policy hardens toward China.

2) Why Trump’s Mexico tariff threats are politically “cheap” (even if economically expensive)

Mexico tariff risk is often framed as “unlikely because USMCA exists.” That misses the political logic: Trump’s most credible Mexico threats are not framed as trade renegotiation—they’re framed as border enforcement.

The template is familiar from the first term: threats to impose escalating tariffs on Mexican imports unless Mexico took specific actions on migration. The same logic now extends to fentanyl and broader “border emergency” messaging. This matters because it changes the legal rails and the timing.

3) IEEPA is the fast lane—and it has already been used in the North America context

For markets, the key is not whether USMCA is popular; it’s whether the White House can plausibly justify a rapid tariff action under emergency authority.

In early 2025, tariff actions tied to the “influx of illegal aliens and illicit drugs” were explicitly justified through IEEPA/emergency framing (as summarized in a Congressional Research Service review of presidential tariff actions). That precedent matters because it establishes a repeatable playbook for a 2026 “border or drugs crisis” moment:

1. declare/expand an emergency predicate,
2. issue an EO/proclamation imposing tariffs (possibly broad, possibly conditional),
3. operationalize through CBP guidance and collections.

This is why Mexico tariff markets can reprice violently: the implementation path is shorter than a full trade investigation.

4) Why Mexico tariffs are systemically dangerous: autos are the transmission mechanism

Mexico is the largest U.S. goods supplier by value (roughly $475–480B in 2023). But the real macro fragility is the composition: around $150–160B of U.S. imports from Mexico are vehicles and parts (HS 87)—roughly ~31–33% of the total.

Autos are uniquely vulnerable because North American production is cross‑border and just‑in‑time. A single vehicle’s bill of materials can cross the border multiple times as engines, transmissions, wire harnesses, and electronics move between plants. That creates two distinct “tariff shock” channels:

• Direct cost shock: even a “modest” 10% tariff is a large, immediate wedge on a massive base.
• Operational shock: inspections, compliance friction, and rerouting delays behave like a production constraint—inventory buffers get rebuilt, plants pause, and the earnings hit often arrives before final vehicle prices adjust.

The Richmond Fed’s scenario work on effective tariff rates makes the same point in macro language, warning that higher tariff regimes can “threaten widespread disruptions across key U.S. industries,” with North American manufacturing at the center of the blast radius.

5) Food and agriculture: the underappreciated CPI channel

Mexico is also a meaningful source of U.S. food and agricultural imports (roughly $35–45B in 2023, about ~7–9% of total Mexico imports). Tariffs here don’t just hit “trade volumes”—they show up as:

• fresh‑produce inflation (where substitution can be slow and seasonal),
• retail margin compression (grocers and restaurants),
• a broader “headline CPI optics” problem that can accelerate political escalation rather than dampen it.

6) Geography: the pain concentrates in specific U.S. clusters

A Mexico tariff episode is not evenly distributed across the U.S. Regions with dense auto, trucking, and border‑logistics exposure—Texas/border metros, the Midwest auto corridor, and supplier networks in surrounding states—carry outsized earnings and employment sensitivity. That’s why Mexico tariff risk often shows up as local industrial stress (and supplier guidance cuts) before it shows up as “macro.”

7) Mexico’s retaliation toolkit is real—even if it’s calibrated

Mexico is constrained (it sends ~80% of its exports to the U.S.), but it still has a meaningful response set:

• Counter‑tariffs aimed at politically sensitive U.S. exports—especially yellow corn, meat, and dairy, plus selected manufactured products where Mexico has alternative suppliers.
• SPS and regulatory tools (inspections, labeling, permits) that slow U.S. farm and industrial exports without needing a headline tariff schedule.
• USMCA dispute mechanisms (fast, partner‑specific) and WTO cases as parallel tracks (even if the WTO system is imperfect, filings are still useful for coalition signaling).

8) How to read this in prediction markets: Mexico risk is often underpriced

Prediction markets tend to overweight China because the story is familiar and the policy ladder is explicit. But Mexico is the classic tail‑risk mispricing: low probability, high impact.

The simple expected‑value math is why. A blanket Mexico tariff is a regime change from near‑zero:

• 10% on ~$480B of imports implies ~$48B/year in gross border taxes.
• 25% implies ~$120B/year.
• Concentrate that on autos/parts (~$150–160B) and you’re immediately talking about tens of billions in incremental costs—before considering delays and production downtime.

That’s why even “small odds” markets—10–25% Mexico tariff by date X—deserve attention in sector‑specific contracts tied to autos, USMCA supply‑chain enforcement, and U.S. agriculture retaliation.

Can Trump Keep Raising Tariffs? Legal and Political Constraints Traders Must Track

The Mexico/border and autos tail risks are scary precisely because they can move fast. But in 2026, the bigger question for traders is whether the fast lanes stay open—or whether courts, Congress, and allied-country blowback impose a hard ceiling on how far unilateral tariff escalation can go.

1) The three legal “rails” — and why IEEPA is the most fragile

Most Trump 2.0 tariff steps still route through three statutes, each with different constraint points:

• Section 301 (Trade Act of 1974): USTR’s unfair‑trade tool. It’s slower, record-driven, and therefore harder to block instantly—especially when the administration is modifying an existing China program. For markets, the constraint is usually process (notice, scope, effective date), not legality.

• Section 232 (Trade Expansion Act of 1962): Commerce’s national‑security lever (steel/aluminum precedent; autos is the obvious 2026 extension). The weak point is that “national security” has been stretched for years; expect renewed litigation arguing that 232 has become too open‑ended and that presidential modifications exceed what Congress intended.

• IEEPA (International Emergency Economic Powers Act): the speed tool. If tariffs are justified as an “emergency” tied to border security, fentanyl, or sanctions-adjacent objectives, they can be implemented quickly via executive action. That same speed is why IEEPA is also the highest‑beta legal risk: its text is not a blank check for permanent tariff regimes, and legal challengers increasingly frame expansive IEEPA tariffs as a separation‑of‑powers problem (major questions / nondelegation themes).

This matters for pricing because the market’s headline odds on “Mexico tariffs” or “autos >25%” are not just about politics—they’re also conditional on whether courts will tolerate emergency‑tariff logic or a broad “national security” definition.

2) Growing legal pushback: the Supreme Court is the real volatility catalyst

A second term changes the litigation landscape: more time for cases to mature, more industries with standing, and a Supreme Court that has been increasingly willing to police broad delegations.

Think of this as a regime modifier:

• If courts uphold expansive IEEPA/232 tariff use, traders should expect repeatable episodes (emergency → EO → CBP collection).
• If courts narrow those tools, tariff risk shifts back toward the slower, more bureaucratic 301 channel—reducing “sudden shock” odds even if the rhetoric stays hot.

Cato and other trade-law critics have argued that the most aggressive emergency-style tariff programs carry non-trivial Supreme Court risk—not because tariffs are illegal per se, but because the claimed authority can become “too big to be implied.”

3) Congress and allies: the political cost function can flip quickly

Even if courts don’t move, 2026 is a midterm year—and Congress dislikes tariffs when they:

• raise consumer prices (easy to message),
• hit allies (Canada/EU coordination amplifies the domestic business backlash), or
• create “industrial chaos” (autos and parts are the cleanest example).

That sets up a plausible bipartisan path: proposals to require congressional approval for large new tariff programs, to sunset emergency tariffs, or to tighten reporting/renewal requirements under 232/IEEPA. These efforts often stall—until inflation optics and constituent pain make them politically cheap.

On the external front, when autos or Mexico are targeted, allies and partners can coordinate WTO cases and retaliatory schedules designed to maximize U.S. political pressure. Even in a weakened WTO system, filings are useful coalition signals—and retaliation threat raises the U.S. domestic cost of escalation.

4) The 2026 domestic calculus: “tough” messaging vs. inflation reality

In midterm cycles, tariffs are attractive because they can be sold as:

• tough on China,
• tough on the border,
• pro‑manufacturing.

But they become politically toxic when the economy does the talking. If inflation is re‑accelerating into early 2026, the White House has incentives to pivot to exemptions, delays, or “deal” announcements (declare victory, avoid price spikes). If growth is holding up and inflation is behaving, escalation becomes easier to sustain.

5) What to trade in prediction markets: don’t just price tariffs—price the constraints

In SimpleFunctions terms, “tariff odds” should be read through a second dashboard:

• Court-constraint markets: Will a major IEEPA/232 tariff authority be materially narrowed by a court (up to SCOTUS) by date X?
• Congress-action markets: Will tariff-limiting legislation pass a chamber / reach the President / become law by date X?

These contracts are powerful because they are multipliers: they shift the probability of every fast‑implementation tariff market (Mexico/border, autos) without needing to predict the next headline.

Trading Tariff Headlines: From Policy Shock to CPI, Earnings, and Asset Prices

The prior section’s punchline was: constraint risk (courts/Congress) changes the odds that a tariff threat becomes collectible at the border. This section is the tactical follow‑through—how to translate collectible tariff news into (1) the first data series that will move, (2) the quarter when guidance risk becomes real, and (3) the cross‑asset expressions that typically reprice first.

1) From announcement → implementation → macro prints: the lag map

Traders routinely lose money on tariffs by treating them like an FOMC decision—instant macro impact. In reality, the transmission is fast in micro prices, slow and noisy in aggregates, and it comes in a fairly repeatable order.

Day 0–Day 3 (headline shock):

• Presidential proclamation / EO (IEEPA, 232) or USTR action (301) hits the tape.
• Markets react first to scope + enforceability: Which HS lines? Any exemptions? Is there a CBP collection start date?

Week 1–Week 4 (implementation becomes tradable):

• The “real” catalyst is usually the operational paper trail:
  • Federal Register publication and annexes (HS codes, rates, exclusions)
  • CBP guidance (CSMS messages) confirming collection mechanics
• This is also the window for front‑running behavior: importers pull forward shipments, rebuild inventories, and bid up freight—often boosting near‑term activity even as it worsens the medium‑term margin math.

Month 1–Month 2 (first clean data signal): Import prices → input costs

• For broad goods tariffs, the earliest hard-data tell tends to be BLS Import Price Index (or category import prices in private datasets).
• The 2018–20 literature repeatedly found near‑complete pass‑through into import prices on many tariffed categories—i.e., the tariff shows up as a higher landed price rather than a big exporter price concession.

Month 2–Month 4: PPI and goods CPI start to reflect it

• PPI often reacts before CPI because intermediate goods are closer to the border.
• CPI spillover is slower: retailers manage promos, substitute suppliers, or absorb some margin before repricing.
• In the first trade war, the aggregate CPI effect was modest but real (often summarized as roughly ~0.1–0.3pp at peak), even while category-level moves were large.

Quarter 1–Quarter 3: earnings guidance, then capex/inventory regime change

• Guidance risk typically clusters around the first earnings season after implementation, when companies have enough visibility on (a) tariff incidence, (b) supplier renegotiations, (c) substitution feasibility, and (d) demand elasticity.
• Capex and footprint changes are later-stage: relocation, dual sourcing, and compliance systems tend to show up two to three quarters after the tariff is collectible.

2) Asset‑class playbook: what usually reprices first

Equities: dispersion beats direction

Tariffs don’t move “the market” as cleanly as they move sector dispersion and factor leadership.

Typical winners (relative):

• Tariff‑protected domestic producers with low imported input share (select industrials/materials)
• Firms with pricing power in goods categories where substitutes are scarce

Typical losers:

• Import‑reliant manufacturers (electronics assembly, machinery using imported components)
• Retailers/consumer durables exposed to China consumer goods baskets
• Autos and suppliers in a Mexico/USMCA stress scenario (cross‑border bills of material, just‑in‑time fragility)

2018–20 pattern to remember: the biggest equity air pockets often came when policy shifted from “intermediate inputs” to consumer‑facing coverage (e.g., the 2019 consumer-list moment). The lesson for 2026 is to focus less on the headline rate and more on coverage and category mix.

Rates: inflation impulse vs growth drag—Fed reaction is the swing factor

Rates traders should treat tariffs as a stagflationary shock with an endogenous policy response:

• Near term: higher goods inflation risk can push front-end inflation compensation higher.
• Medium term: weaker growth can flatten curves—unless the Fed has to defend credibility.

This is why tariff odds matter even when CPI hasn’t moved yet: if your policy dashboard drifts toward a low‑teens effective tariff rate regime (Richmond Fed scenarios run from ~2.2% baseline to ~10%+, with an upper case near ~17%), the curve can reprice well ahead of realized inflation prints.

FX: two common regimes

• Risk‑off trade-war phase: USD strength vs high beta, with USD/CNH acting as a real‑time stress gauge (the 2019 “7 per dollar” episode is the classic reference point).
• Inflation/fiscal interpretation: if tariffs are framed as persistent, inflationary, and politically entrenched, USD strength can be less reliable—especially if markets believe real policy rates will fall later or deficits dominate.

For 2026, watch MXN separately: Mexico tariffs are often a tail risk that can gap FX on implementation headlines because the import base is large (Mexico is roughly $475–480B of U.S. imports in 2023) and supply chains are tight.

Commodities: retaliation is the volatility engine

Commodities tend to trade the retaliation matrix more than the U.S. CPI math:

• Ag: soybeans/corn/meat are the politically efficient targets.
• Energy: LNG and crude can see headline-driven volatility.
• Metals/critical minerals: China export controls (rare earths, battery materials) and U.S. 232 moves can shock specific links in the industrial chain.

3) Using prediction markets as hedge + early‑warning signal

Tariff event markets are most useful when you treat them as probability-weighted scenario inputs into your existing exposures.

A simple approach:

1. Build an “expected tariff shock” estimate by partner/sector:
   • Expected incidence ≈ Probability × (rate change) × import base × assumed pass‑through
2. Map that to what you actually own:
   • cyclicals vs defensives, retailers, autos, semis, EM FX, ag/metal inputs.
3. Hedge the policy state, not the CPI print:
   • If odds of Mexico tariffs rise, hedge autos/suppliers exposure before the first import price print.

Signal edge: prediction markets often move on process proof (draft lists, Federal Register cues, CBP language) before equities/rates fully internalize it. In practice, a rising probability of “Mexico tariff by date X” can be an early warning that the next two moves are (a) supply-chain disruption headlines and (b) margin/guidance cuts—well before CPI forces macro desks to react.

4) Practical checklist (and a dashboard you can actually run)

Policy documents (high signal):

• USTR notices and annexes (301 scope/rates)
• Federal Register entries (implementation details)
• White House fact sheets (useful for scope framing; verify against the Register)
• CBP CSMS messages (collection start date = tradable)
• WTO filings / USMCA dispute documents (retaliation and coalition signal)

Data releases (lagged confirmation):

• BLS Import Price Index (first macro confirmation)
• PPI (inputs) → CPI (core goods)
• BEA/Census trade volumes (diversion vs demand destruction)

Markets & earnings (where it shows up first):

• Sector indices: retail, autos, industrials, semis
• Freight rates and inventory commentary
• Earnings transcripts: explicit mentions of “tariff pass-through,” “exclusions,” “rerouting,” “USMCA qualification,” “customs delays”

Dashboard suggestion: combine three live lines:

1. Tariff market odds (China escalation, Mexico/border, autos/232, retaliation, court constraints)
2. A running effective tariff rate (ETR/AETR) estimate (scenario-weighted)
3. A macro “reality check”: inflation surprise indices and/or breakevens to spot misalignment

When (1) moves and (2) rises but (3) doesn’t, you often have the cleanest setups.

Chart Pack: Linking Tariffs, Prices, Trade Flows, and Sector Performance

If you want to turn “tariff odds” into a trade, you need a consistent set of time series that lets you (1) anchor what actually changed at the border, (2) observe where the price impulse shows up first, and (3) measure who won and lost in markets. Below is the SimpleFunctions chart pack we recommend—built to be model-friendly (daily/weekly market data, monthly macro data) and annotation-ready.

1) Tariff & policy timelines (the “implementation spine”)

Chart A — U.S. average applied tariff rate, 2017–2026 (overall and China-only).

• Plot two lines: U.S. AETR overall and AETR on China (value-weighted if available).
• Annotate effective dates, not just headlines: 232 metals, 301 List rounds, Phase One cut, 2025–26 reciprocal/China stack changes, and the Nov. 10, 2026 pause horizon.
• Add vertical markers for: USTR Federal Register notices, presidential proclamations/EOs, and CBP collection start dates (these are the moments that typically matter for earnings math).

Chart B — “branch timelines” (separate strips):

• Mexico/border-linked measures: any IEEPA/emergency actions, exemptions, and rollback language.
• Autos/parts Section 232: rate/scope changes and effective dates.
• Retaliation announcements: China and Mexico counter-tariff schedules, export-control steps, and WTO/USMCA filings.

The goal is to make your dataset answer a practical question: was this event “tradable talk,” or did it become collectible at the border?

2) Macro price data (where pass-through appears)

Chart C — CPI & core goods CPI vs tariff waves (monthly).

• Overlay shaded bands for major tariff waves (2018–19 rounds; 2025–26 implemented changes).
• Keep both headline CPI and core goods CPI: tariffs tend to show up in goods first, and aggregates can be noisy.

Chart D — Import prices and PPI for tariff-sensitive categories (monthly).

• Use BLS Import Price Index and PPI slices for: metals, machinery, electronics, apparel, furniture.
• Optional: add a “tariff exposure” weight per category (share sourced from China/Mexico) so the chart is interpretable as a beta.

3) Trade flows & reshoring/nearshoring indicators

Chart E — U.S. imports by partner and sector (China vs Mexico vs Vietnam, monthly/quarterly).

• Plot levels and shares for major HS buckets (e.g., HS 84/85/87) to visualize trade diversion (China → Mexico/Vietnam) and any partial reshoring.

Chart F — Investment/relocation proxies (monthly/quarterly).

• FDI into Mexico (nearshoring signal), U.S. manufacturing construction spending, and (if you track it) large manufacturing project announcements.

4) Sector & asset performance (who is the market blaming?)

Chart G — Sector equity performance around tariff episodes (event windows).

• Autos, semis, retailers, industrials, agriculture proxies.
• Use event windows around implementation dates; keep a second view around announcement dates to quantify “headline vs cash-flow.”

Chart H — EM vs DM equity and FX baskets (2018–20 spikes + 2025–26 announcements).

• Add USD/CNH and MXN overlays; these often behave like real-time tariff stress gauges.

5) Integrating prediction markets (lead/lag tests)

For every chart above, add a third layer: market-implied probability of the relevant policy state (China escalation, Mexico tariffs, autos >25%, retaliation, court constraints). Then run two simple diagnostics:

1. Does the probability move before the market/macro series? (prediction markets lead)
2. Does it only jump after prices move? (prediction markets lag / headline-chasing)

That overlay is where SimpleFunctions users typically find edge: policy odds can drift for weeks before CPI, trade data, or even sector dispersion makes the shift obvious.

2026 Outlook: Key Catalysts, Tail Risks, and How to Position in Prediction Markets

The prior sections built the “lag map” from implementation → prices → earnings. The last step is turning 2026 into a calendar of forced decision points—and then building a portfolio that doesn’t rely on guessing the next headline.

1) The 2025–26 catalyst calendar (what can actually flip the regime)

A. The Nov. 10, 2026 China “pause horizon” becomes a volatility magnet. The 2025 U.S.–China framework effectively creates a clean deadline for whether additional reciprocal China escalation remains suspended or reactivates. In practice, markets tend to reprice before the date as negotiators use it for leverage (think “option gamma” into late 2026).

B. Border/migration flare-ups can fast-track Mexico tariffs (IEEPA). Mexico markets are structurally “tail-y” because the trigger is political (migration/fentanyl) and the implementation pathway can be fast. The key tell isn’t rhetoric—it’s emergency framing language that looks like it can survive judicial scrutiny.

C. Courts and Congress can change the entire distribution. 2026 is when legal challenges have time to mature—and when Congress may find tariff-limiting reforms politically attractive if goods inflation reappears. In prediction-market terms, these are multipliers: a court-constraint shock reprices every “fast-lane” tariff contract (Mexico/IEEPA, autos/232).

D. Global events can abruptly raise (or lower) trade-war temperature. Taiwan/South China Sea incidents, China export-control escalations, or Mexico domestic political stress can all shift the administration from “tariffs as bargaining chip” to “tariffs as coercion.” These aren’t scheduled—but they’re exactly why you want convex exposure in at least one leg of your portfolio.

2) Tail risks and surprises (what’s not in the base case)

Upside tail (risk-on): a high-but-stable tariff deal. The market tends to underprice how positive “certainty” can be for planning. A negotiated outcome that locks in elevated but predictable tariffs (plus exemptions and clear compliance rules) can reduce escalation risk premia—even if the average effective tariff rate stays higher than pre-2018. That regime often benefits risk assets relative to an on/off tariff threat cycle.

Downside tail (risk-off): sudden broad Mexico and/or EU autos + China chokepoints. The ugly scenario isn’t just higher China rates; it’s a North American supply-chain shock (Mexico tariffs) layered on an auto action (232) plus Beijing tightening export controls on critical inputs.

The “legal cliff” surprise: a major ruling that materially constrains IEEPA/232 tariff tools can be disruptive even if you’re “anti-tariff,” because it forces policy improvisation (new authorities, new scopes, faster/rougher administrative moves). That kind of constraint can reduce some tariff probabilities while increasing short-term policy volatility.

3) Positioning in prediction markets: build coherent baskets, not single bets

A practical 2026 structure is a three-bucket book:

1. Base-case exposure (China path): contracts tied to China reciprocal escalation/coverage expansion around late-2026.
2. North America tail hedges (Mexico + autos): smaller notional, higher convexity; these are the “gap risk” positions.
3. Constraint overlay (courts/Congress): positions that hedge the probability of the fast lanes staying open.

Then diversify with macro companion markets:

• If you’re long tariff-escalation risk, pair it with markets linked to inflation persistence or fewer Fed cuts.
• If you’re long “deal/stability,” pair it with lower inflation / more Fed easing / sector-earnings relief contracts (retail, autos, industrials) where available.

The goal is not to predict CPI; it’s to ensure your portfolio is internally consistent: tariffs → inflation impulse → Fed reaction → earnings dispersion.

If you’re building a real trade‑war dashboard (rather than trading vibes), start with primary implementation documents, then layer in macro/sector research, and finally pull clean, model‑ready data.

Primary policy & legal documents (implementation spine): Use USTR’s 2025 Trade Policy Agenda and the White House/USTR tariff actions to anchor scope, authority (301/232/IEEPA), and effective dates. For “what counts as implemented,” rely on Federal Register notices and CBP collection guidance, then cross‑check with a structured timeline (CRS/Congress.gov trackers).

Analytical work (how big is the shock?): For macro and welfare estimates, the most cited, spreadsheet‑friendly references are the Yale Budget Lab, Richmond Fed (AETR framing), and the Tax Policy Center. For broader global spillovers and retaliation dynamics, use IMF/WTO work; for legal/political constraints and historical context, add PIIE, Brookings/AEI/Cato, and public bank research summaries.

Data for custom models: Pull exact HS‑level exposure from USITC DataWeb, then marry it to BEA/Census trade totals, BLS CPI/PPI/import price indexes, and FRED macro series for rates, inflation breakevens, and activity.

Finally, combine all of the above with SimpleFunctions prediction market probabilities (China escalation, Mexico/border tariffs, autos/232, retaliation, court constraints) to convert documents and data into a forward‑looking, scenario‑weighted trade‑war view.