Macro-aware, price-led tactical allocation.

The system is built for one decision: where should capital go for the next four-week tactical window? It uses macro as a current, not a prophecy. Price and breadth lead, macro confirms or de-risks, and the final allocation follows fixed rules. The long-term reasons for this universe live on the Theses page; this page is the operating manual.

Portfolio Construction

In normal mode, the top two category representatives receive 30% each and the other eight receive 5% each. If Defensive, Bitcoin, or AltSeason is required, the overlay receives exactly 50%; the category sleeve still uses top-two structure with 13%, 13%, and 3% across the remaining eight representatives.

The investable portfolio is now measured as a rolling four-week ladder. Each Friday report creates a 25% tranche, bought at the next Monday open and held for four weeks. The newest tranche replaces the tranche from four weeks earlier. This is why the Portfolio and Performance pages emphasize four-week completed-tranche results instead of one-week noise.

Weekly Decision Sequence

Every run follows the same order. First, the macro engine classifies the backdrop and decides whether a Defensive warning applies. Second, the crypto engine determines NoCrypto, ValueBTC, TrendBTC, or AltSeason using deterministic Bitcoin and macro-risk rules. Third, the overlay priority is resolved: confirmed crypto-cycle exposure comes first, then cause-matched defense if crypto is NoCrypto, then normal category allocation. Fourth, every category is scored through its active macro-condition method. Fifth, one representative ticker is selected inside each category. Sixth, the top two eligible categories receive the overweight sleeves. The AI/reasoning layer explains those decisions, but it does not alter the state machine or override hard risk rules.

Crypto Cycle Overlay

Bitcoin and altcoin exposure are rules-based. ValueBTC and TrendBTC require Bitcoin trend repair and confirmation. AltSeason is stricter: crypto trend, liquidity, sentiment, dollar, credit, and risk appetite must all cooperate. If broad macro risk is elevated while crypto is confirmed, the model can block AltSeason or force the overlay to stay in Bitcoin. The slow Defensive trigger does not replace confirmed Bitcoin-cycle exposure.

Defensive Overlay

Defensive is a 50% overlay, not a permanent asset class and not a discretionary opinion. The edge is that the system asks what benefits from the same macro force that is hurting broad risk assets. A liquidity panic is not the same as an inflation drawdown; a stagflation scare is not the same as a disinflationary slowdown. When active, the remaining 50% still follows the category engine with 13%, 13%, and 3% x 8 across category representatives.

The trigger is broad-market defense: crisis-level macro risk or at least three of five broad-market checks, covering SPY trend damage, QQQ trend damage, HYG/SPY credit breakdown, rising dollar pressure, and weak risk appetite. This is meant to catch persistent bear markets and extended drawdowns. Defensive only takes the overlay when crypto is NoCrypto; confirmed Bitcoin-cycle exposure wins over slow macro deterioration.

The payload is chosen by a cause selector, not by the broad regime label alone. Liquidity stress uses SGOV because cash-like collateral usually wins when markets are forced to de-risk. Inflation/scarcity stress uses XLE because energy cash flows can benefit from the same pressure hurting broad equities. Monetary or disinflation stress uses GLD because falling real-yield pressure and currency hedging matter more than cyclical growth. Stagflation scarcity splits GLD and XLE. Unclear transition stress uses SGOV, GLD, and XLU so the system avoids pretending one cause is obvious when the tape is mixed.

This means Defensive is cause-matched, not label-matched. The same Stagflation Risk headline can come from monetary stress, inflation scarcity, or mixed transition pressure. The payload is selected from the stress cause detected by the macro and market-implied checks, then the remaining 50% of capital still follows the category system.

Weekly Operating Instructions

1. Read the new Friday report after it is published and treat it as the instruction set for the next Monday open.
2. On Monday, sell the tranche created by the report five Fridays earlier. That tranche has completed its four-week Monday-open-to-Monday-open holding window.
3. Allocate that freed 25% tranche into the new report's allocation table at the Monday open.
4. Leave the three newer tranches unchanged. The live portfolio is always the blend of the newest four report tranches.
5. Repeat every week unless the report is marked unreliable or a data-quality warning says the allocation should not be changed automatically.

Example: a Friday report dated June 12 becomes the Monday June 15 buy. At that same Monday open, the tranche from the Friday report five weeks earlier is sold and replaced by the new June 12 allocation. This is why the public scorecard measures four-week completed tranches rather than one-week noise.

How Categories Are Chosen

Category selection is not supposed to be a single-ticker contest. Each category is now scored from exactly three liquid ETFs chosen for pure category fit, strong liquidity, broad internal coverage, and minimal redundancy. The first category evidence point is a 3/2/1 weighted ETF basket: first place receives three parts, second receives two parts, and third receives one part. The final category score is a proof-burden score: 44% weighted ETF basket, 20% relative-strength leadership, 16% volume-price sponsorship, 10% persistence, 5% tactical timing, 3% risk/reward, and 2% setup quality, plus explicit stance adjustments and caps.

This is the most important control in the system. A category favored by macro still needs at least two of its three ETFs confirming with adequate score, volume-price behavior, and SPY-relative evidence. A category fighting the macro backdrop can still win, but only with exceptional volume and relative strength across the basket. Commodity and cyclical categories such as Oil, Natural Gas, Industrial Metals, and Agriculture receive extra penalties when attractive support or upside to resistance is not backed by sponsorship. The model is designed to avoid rewarding cheap-looking charts that are still being distributed.

Macro Methods

The practical macro framework is intentionally compact. The model does not need dozens of investable macro states. It uses a small set of regimes: risk-on liquidity expansion or Goldilocks, reflation or late-cycle reflation, disinflation or slowdown, stagflation risk, risk-off/high stress, and mixed transition. Those regimes change what the model emphasizes. Risk-on tapes reward leadership, momentum, and volume-backed breakouts. Reflation rewards commodity breadth, cyclical relative strength, and volume-confirmed breakouts. Disinflation and slowdown reward quality pullbacks, support defense, falling-rate beneficiaries, and risk/reward. Risk-off regimes reward defensive relative strength and can trigger the 50% Defensive overlay. Defensive is intentionally rare: it requires crisis-level macro risk or a bear-defense confirmation where at least three of five checks fire: SPY below its 40-week average or down more than 8% over 13 weeks, QQQ below its 40-week average or down more than 10% over 13 weeks, HYG/SPY credit breakdown, rising dollar pressure, and risk appetite below 45. Once active, the cause selector chooses the defensive payload. Defensive does not override confirmed crypto-cycle exposure.

Macro is factored at two levels. At the category level, it selects the method the category must pass. At the asset level, it changes the execution playbook, so the same indicator does not mean the same thing in AI, gold, natural gas, utilities, and defense. Macro is not allowed to invent strength: the category still needs price, breadth, and volume confirmation.

The macro stance is intentionally strict. Favored means the macro and narrative backdrop are aligned, but the category still needs confirmation. Neutral means the category gets no story credit and must win on price, volume, breadth, and relative strength. Headwind means the macro backdrop is working against the category; in that case the score is capped unless the category shows exceptional sponsorship. This is why the method is not a generic momentum screen and not a subjective macro opinion.

The Categories page lists the explicit matrix: Category, Macro Condition, Formula/Method, and Reasoning. That table is the plain-English rulebook. It keeps the system from feeling discretionary while still acknowledging that a good setup in AI, gold, natural gas, utilities, or defense should not be graded by the exact same playbook.

How Asset Winners Are Chosen

The execution ticker inside a category is chosen with a professional technical-analysis playbook. The model looks for price behavior that is actually being sponsored by buyers: persistent 13-week and 26-week relative strength, strength versus SPY, strength versus category peers, price above important moving averages, MACD confirmation, constructive stochastic RSI, clean support/resistance, and volume that confirms the price move. Thin-volume rallies, distribution weeks, unsupported bottom-fishing, bearish MACD, and overextended rollovers are penalized.

The active macro regime changes the asset formula. In risk-on liquidity regimes, the model emphasizes leadership, momentum, and volume-backed breakouts. In reflation regimes, it emphasizes volume-backed commodity/cyclical breakouts and persistent category-relative strength. In disinflation or slowdown regimes, it emphasizes quality pullbacks, support defense, and risk/reward. In risk-off regimes, it emphasizes defensive relative strength and lower failure risk. The report identifies the active playbook in each category so the reader can see why the winner was selected.

The category itself now changes the asset formula too. AI and Technology reward leadership, sponsorship, and volume-backed momentum. Utilities and Defense reward defensive relative strength, trend persistence, and failure avoidance. Oil, Industrial Metals, Precious Metals, Natural Gas, Uranium, and Agriculture require more respect for cyclicality, commodity noise, extension, and support/retest behavior. In those sleeves, the model gives more weight to volume in relation to price, MACD/stochastic confirmation, risk/reward, and whether a breakout is actually confirmed rather than merely exciting. This is intentional: the same chart setup should not mean the same thing in a uranium developer, a gold bullion ETF, a mega-cap software stock, and a regulated utility.

Volume in relation to price is a primary audit item. A rally on improving volume, constructive MACD, and rising relative strength is treated very differently from a bounce into resistance on thin participation. A pullback into support can be attractive, but only when the broader basket and the selected ETF show evidence of sponsorship. Support by itself is not a reason to allocate a 30% sleeve.

How The Audits Should Be Read

Category Audit asks whether the two overweighted category representatives beat the other eight representatives. Asset Audit asks whether the selected category winner beat its own three-ETF basket and the other two ETFs inside its category. The main scorecard is the four-week result: whether the same Friday decision worked for an investor holding from the Monday after the report to the Monday open four weeks later.