The Strategy Report

Definitions, in plain language, for every term the rest of the document uses. Skim it now; refer back as needed.

Quality vs. price — two different questions

The framework keeps two questions strictly apart. “Is this a good business?” is about quality — profits, debt, growth durability. “Is the share a good price right now?” is about valuation. A wonderful business can be a poor buy if it’s expensive, and a weak business can look cheap and still be a trap. Mixing the two is the classic mistake; we never do.

Deep Score (0–100)

Our single quality grade for a business, built from a 16-point value-investing checklist: profitability (return on equity/capital, margins), growth (revenue and earnings over years), balance-sheet strength (debt, free-cash-flow consistency), valuation discipline, and a moat/quality factor. It deliberately ignores the share price — it grades the company, not the stock chart — so price and momentum can sharpen a decision without contaminating the quality read. Bands: ≥85 exceptional, 70–84 high quality, 55–69 acceptable, 40–54 middling, below 40 “avoid.”

Fair value — a range, not a number

Our estimate of what a share is actually worth, produced by blending several independent valuation models (see §6). We never publish a single fake-precise figure; we publish a low–high range and a confidence level (e.g. “C$240–C$320, medium confidence”). A price inside the range is fairly valued; above the top is genuinely expensive; below the bottom is a genuine discount.

Concentration, weight & “look-through”

Weight is how big a position is. Per-account weight is its size inside one account; aggregate (look-through) weight is its total size across every account you own. The aggregate is the number that matters — the same stock held in a TFSA and an RRSP is one bet, and per-account numbers hide that. Concentration is having too much in one stock, one industry, or one currency.

Market regime

A plain label for “what kind of market are we in right now,” read from interest rates, the yield curve, credit spreads, volatility (VIX), and whether the market is above its long-term trend line. The four regimes are expansion (calm, risk-on), recovery (climbing out of trouble), late-cycle (getting toppy, caution), and contraction (stress/downturn). We never predict a price; we just describe the weather. If any of those five readings could not be fetched, the regime reads Unknown: we name the missing reading, list no regime actions, and fill nothing in with an assumed value.

Canadian tax words you need

• Capital gain, 50% inclusion — if you sell something for more than you paid in a taxable account, half of the profit is added to your income and taxed. Selling a winner is therefore a real, immediate cost — not free.
• ACB (adjusted cost base) — your true average cost per share, used to work out the gain.
• Registered vs. taxable accounts — inside a TFSA, RRSP, or FHSA, gains aren’t taxed as you trade; in a non-registered/joint account they are. So we prefer to do any necessary trimming inside registered accounts first.
• US dividend withholding (15%) & the treaty — the US withholds 15% of dividends paid to Canadians — except in an RRSP/RRIF, where the Canada-US treaty waives it. In a TFSA or FHSA the 15% is simply lost (the treaty covers retirement plans only, not the TFSA or FHSA); in a taxable account you can usually recover it via the foreign tax credit.
• Tax-loss harvesting & the superficial-loss rule — deliberately selling a loser to offset gains — but if you, your spouse, or one of your own registered accounts buys the identical security in the 30 days before or the 30 days after the sale, the CRA denies the loss (the “superficial loss” rule). The safe move is to wait a full 30 clear days before rebuying.

A few more terms you’ll meet

• Beta — how much a stock tends to move relative to the whole market. Beta 1 moves with the market; above 1 is more volatile (amplifies both up and down); below 1 is steadier.
• Sequence-of-returns risk — the danger that a big loss arrives at the worst possible time — right before or early in retirement, or just before a goal — when there’s little time left to recover. The same average return can hurt far more if the bad years come first.
• Dispersion / confidence — how much our valuation models disagree with each other. Tight agreement = high confidence; wide disagreement = we say “unsure” instead of guessing.

A single generation pass executes a deterministic pipeline; the LLM is invoked once, late, with a fully-resolved context object.

1. Ingestion & enrichment. Holdings are joined to live prices, the latest fundamentals snapshot, account-type registry (capital-gains treatment, US-withholding flags), and the investor profile / IPS inputs. Per-lot FX conversion yields both price_native and price_cad; aggregate single-name weights are computed across accounts.
2. Quality & valuation. Each name carries a fundamental Deep Score and an ensemble fair-value point + range + confidence tier (§3–4).
3. Signal engine. ~28 deterministic rules evaluate every holding, emitting typed signals with strength scores; de-duplicated and precedence-ranked (§7). Concentration ceilings are tightened in hostile regimes (§8).
4. Portfolio analytics. Concentration (single-name, GICS sector, industry cluster, currency), beta-adjusted drawdown stress tests, and contribution-aware goal solve (§8–7, §11).
5. LLM narration. The resolved context (slimmed holdings, concentration block, macro, grouped signals, goal, profile, engine flags) is passed to the model, which returns structured JSON: verdict, ranked priority actions, diversifiers, simulation, scenario notes (§15).
6. Post-processing & render. Engine post-checks (look-through fund overlap; backfill of the contribution-dependency note) run on the model output; the designed multi-page artifact is assembled and served as printable HTML/PDF.

A 0–100 composite of profitability, balance-sheet, growth-durability and capital-efficiency factors, descended from a 16-point value-investing checklist. It is deliberately price-blind — it grades the business, not the security — so valuation and momentum can sharpen it without contaminating it.

A cross-sectional rank of the entire universe on the traits that recurred in history’s long-run compounders. Where the Deep Score grades quality today, Winner Odds asks which names have the makings of a business that keeps compounding and does not fail — and answers with a rank and a tier, never a probability. It is not a multi-bagger finder: on the point-in-time population test, top-tier names rose fourfold in five years about as often as any other name.

The factor set

Eight curated factors in two families, ranked against every other name using the same peer-relative robust-z statistics core as the Deep Score:

• Core quality — return on invested capital, gross profit over assets (Novy-Marx), the nine-signal Piotroski F-score, low accruals (Sloan), and margin stability.
• Core growth — growth acceleration, the Rule of 40, and reinvestment runway.

Value and momentum are computed as a side overlay only — never blended into the quality-and-growth rank, so a name cannot climb on cheapness or price action alone.

Percentile → tier, with hard gates

Two hard gates override the tier regardless of the factor score: financial distress (a weak Altman-Z) caps a name at Unlikely, and a cyclical business at a likely earnings peak caps it at Watch.

A forward-looking companion to the Deep Score. It reads the rate of change — is the business getting better, faster — to catch businesses that are turning a corner before the trailing averages the Deep Score relies on can show it.

The inflection signals (engine 3.0.0, 2026-09-28)

Eleven signals, each measured as a trend (recent vs. earlier), not a level. Six carry weight: each is the name’s percentile among its sector peers that night, and the score is the weight-averaged percentile, re-based to the published bands. The weights are each signal’s measured rank correlation with the three-year outcome on every US 10-K filer FY2009–2022, scaled to sum to 100. The other five are shown as facts with no weight.

Guards

• Cyclicality guard — commodity / cyclical names whose “acceleration” is just the up-leg of a cycle are capped at 50, so a rebound isn’t mistaken for a structural inflection. Heavy dilution caps a name at 50 the same way.
• Confidence band — names with less financial history score fewer signals; the band discloses how much data stood behind the number, and fewer than four of the six weighted signals gets no number at all.

Intrinsic value is estimated by a multi-model ensemble (two DCF variants, EV/EBITDA, Graham-style, dividend models, etc.), blended with dispersion-aware confidence. The output is a point estimate, a low–high range, a coefficient-of-variation, and a confidence tier — never a single fake-precise figure.

1. Applicability filter. Keep models with a positive fair value applicable to the sector (e.g. exclude FCF/EBITDA constructs for financials via sector policy).
2. Price-anchored sanity gate. Drop any model outside [0.33× , 3×] of the current price — these reflect broken inputs (e.g. a normal multiple on peak-cycle EPS).
3. Median outlier trim. Drop values outside [0.40× , 2.50×] of the median.
4. Family reduction. Average within each model family → one representative per family (prevents a single methodology from dominating).
5. Blend & dispersion. Point = mean of family representatives; range = their min/max; dispersion = coefficient of variation (how widely the models disagree, σ/μ).
6. Confidence tiering. CoV < 0.15 → HIGH, < 0.30 → MEDIUM, < 0.50 → LOW, > 0.50 → NOISE (suppressed — no trustworthy estimate; caller skips the name rather than print a fantasy number). Requires ≥ 2 corroborating families.

Acknowledged bias: the ensemble is conservative on steady mega-caps; in a genuine melt-up it will correctly flag names well above range — that is signal, not error.

Rules evaluate per holding and emit typed signals with 0–100 strength. Precedence: SELL / ROTATE (exits) → TRIM → HOLD → ADD; TAX fires independently. De-duplication keeps the strongest signal per (ticker, account, type); a TRIM rounding to < 1 share is suppressed as a no-op.

NEW The allocation policy, and the trade a breach implies

Where a client has declared an investment policy (target weights for equity, fixed income and cash, plus a drift band), that policy is the first lens and a breach is the first action in the plan — ahead of any single-name idea, enforced by the engine after the narrative is written rather than requested of the model.

When a breach has already been shown, the report asks about the policy. CFA Standard III(C) says an investment policy is reviewed at least annually, and a client holding an off-policy book is discussed with: the trade is made or the policy updated. The report asks that question when the book was outside the same policy, in the same class and direction, in the report before (and every report back to the first such one), and the policy has not been saved since; or when the policy was last saved more than 365 days ago. Re-saving the policy restarts the clock. A report inside the band, or one whose lens failed, ends the chain. An unknown save date triggers nothing. The question never proposes moving the policy to match the book.

A declared cash reserve is outside the policy. Liquidity needs are the first constraint a policy is written under (CFA Standard III(C)). The client declares a reserve in dollars; the mix removes min(reserve, cash) from the cash class and from the base every percentage is a share of, so drift, the policy trade and the contribution plan are computed on deployable money only. A reserve larger than the cash on file is reported as a shortfall and is never made up from a GIC or a holding; new contributions rebuild it first. With no reserve declared the value is unknown, not zero: no number moves, and where a cash-over breach draws on savings the client entered, the report states how much of the cash that is and asks. The reserve is never inferred from an item's name or from months of expenses.

• Every breached class, not only the largest — the over-weighted class and the under-weighted one are a single trade. Naming only the bigger side leaves half an instruction: a book 24pp over in cash and 20pp under in fixed income was previously told “reduce cash toward 5%”, and the destination was the second breach.
• In dollars, and the legs balance — every class moves to the target the client declared, so what is added equals what is reduced by construction. A class inside its band still appears, because it is where money comes from or goes; a rebalance naming only breached classes does not add up.
• The funding source, because it decides the cost — money moved out of cash is not a disposition and realises nothing; selling equity or bonds realises a gain or loss in a non-registered account. The split is reported, and new contributions are offered first because they move the mix without a sale.
• The tax itself is not estimated. It depends on which lots in which accounts are sold, and the trade builder is handed neither — so it reports how much requires a disposition and stops there rather than publishing a plausible figure.
• Unclassified money is named, never absorbed — holdings the look-through cannot split into an asset class are excluded from the trade and disclosed, so the class figures are not quietly inflated.

Concentration (the first lens)

• Single-name — measured on the aggregated weight across accounts (the same issuer in a TFSA and an RRSP is one position); honours a client-specified max_single_position_pct, else a 25% ceiling, trimming toward ~70% of the ceiling (18% at 25%).
• Sector & industry — a GICS sector ceiling (see regime-aware tightening below); a finer industry cluster lens (e.g. semiconductors as one cyclical complex) is surfaced because it is often the real risk even when the “Technology” sector merely looks large.
• Currency — a USD-exposure ceiling; trimming is registered-account-only and never applied to a taxable winner for a soft FX reason.

NEW Regime-aware concentration ceilings

Concentration hurts more when markets are fragile, because in a downturn correlated names fall together. So the sector and currency ceilings are now a function of the market regime rather than fixed:

The tightening is deliberately modest (≤7 points) and the rationale string names the regime and the active ceiling, so it is transparent. When the macro feed is unavailable, the ceiling falls back to the base 45% — missing data can never tighten anything.

Drawdown & horizon

• Beta-adjusted stress tests — dollar-denominated downside under semiconductor-downcycle, broad-bear and severe-recession scenarios, framed against the client’s stated max-drawdown tolerance. Each holding falls by its own beta (a CIBC CDR by its US company’s); a holding with no beta on file is left out and named, never given one, and the book’s value after a market-wide fall is then not stated (the Goal Risk line then says the shortfall cannot be stated). With no beta anywhere nothing can be modelled, and the rows state no loss and no value after.
• Dual horizon — the engine reconciles risk capacity from the retirement horizon with the nearest funded goal: where a major drawdown landing near the goal year would cause a shortfall, it surfaces an explicit Goal Risk and a light glide-path — without forcing the whole book conservative. Sequence-of-returns risk dominates inside ~7 years.
• Deterioration sells fire BEFORE price — note that several SELL rules (dangerous leverage, shrinking revenue, an AVOID-rated score the business itself confirms) trigger on the business deteriorating, independent of any price drawdown. The 20%-drawdown “Crash Watch” (§9) is a separate, last-line protective trim, not the only defence.

The governing policy — chosen for an after-tax, buy-and-hold personal book — is that valuation alone does not trigger a SELL of a quality compounder: a rich price routes to HOLD (stop adding), preserving compounding and avoiding a realised capital gain. SELL is reserved for genuine deterioration (§7). Extreme valuation is handled not by a valuation-SELL but by the concentration ceiling (no name runs unbounded as a share of the book, §8) and the goal-return lens (which can redeploy on forward-return grounds, §11). We accept the trade-off openly: a pure valuation discipline would occasionally sell a very expensive great business; we weigh that against tax friction and the cost of interrupting compounding.

The single sanctioned price-driven protective action is the crash-risk overlay: the engine does not trim a winner for being up or richly valued, but when a quality holding has rolled over — drawdown ≤ −20% from its 52-week high plus a confirmed trend break (sub-200-DMA, a sharp 3-month decline, or a realized-volatility expansion) — it raises a “Crash Watch” and proposes a ~25% protective trim or a stop, the client’s discretion. Sizing is tax-tiered (realise in registered first; quantify the gain in non-registered). A normal pullback with the trend intact is tolerated and produces no signal.

NEW Tax-neutral rebalancing — fund changes with new money first

The framework’s standing preference is to reach a target allocation without realising a taxable gain wherever possible. Two mechanisms enforce this:

• Contributions as the primary de-risking lever — when the contribution stream can pull an overweight industry to target within ~3 years, the engine computes that path and prefers directing new cash over selling winners; it states the dependency (and a fallback floor if contributions fall short). This logic predates this revision and lives in the Strategy report’s action plan.
• Tax-Neutral Check in the manual Rebalance tool — when a rebalance plan would realise a capital-gains tax, the preview now shows a Tax-Neutral Check: it quantifies the taxable gain the plan triggers and, where the increases could instead be funded from idle cash or upcoming contributions, says so explicitly — and confirms when the buys fit entirely within idle cash and need no sale at all. The reflex becomes “add with new money before trimming a taxable winner.”

A separate Tax & Structure Audit report handles full contribution-room / placement modelling; the Strategy report keeps tax to a single room-nudge and per-action consequence.

Regime & probability-weighted outlook

Current regime is classified from the rate/cycle environment. A forward outlook ensemble — yield-curve slope (10Y–2Y), HY credit-spread trajectory, index vs 200-DMA, VIX trend, and CPI direction — produces an outlook_pressure, a heading (late-cycle / contraction), a transition probability and a confidence. Deteriorating readings bias defensive timing toward act-now / within-30-days, and (per §8) tighten concentration ceilings. This is a probability-weighted regime trajectory, not a price or index-level forecast.

Goal-based framing

• Contribution-aware required return — the required CAGR is solved (binary search) including a level annual contribution, since the balance-only figure materially overstates difficulty when contributions are large; on-track is benchmarked at ≤ 8%.
• Goal-return lens — idle cash and any holding whose realistic forward return sits below the required rate are framed as opportunity-cost drags — candidates to redeploy into higher-expected-return diversifiers (tax-aware), not a reason to sell a quality compounder.

• Objective-aware — growth / aggressive or 12+ yr horizon keeps a growth tilt (quality compounders in different growth industries; broad/growth funds), explicitly not defensive sectors or bonds; income / capital-preservation / < 7 yr tilts to dividend, low-beta, broad-market or fixed income.
• Grounded single-name candidates — sourced from the client’s own scored universe via a GARP screen (deep-score ≥ 70, D/E < 1.0, ROE ≥ 15%, earnings/revenue CAGR ≥ 12%, valuation gate), with multi-year downtrends and currently-held names excluded; the LLM may not invent a ticker.
• Phase-in vs deploy-now — each candidate carries an engine-decided deploy_mode: at/below fair value → deploy idle cash; above fair value → accumulate via contributions / on a pullback toward the 200-DMA.
• Currency-matched — deploy each account’s idle cash in its own currency to avoid an FX spread and a taxable conversion event.
• Look-through overlap — a recommended broad fund is checked against the client’s overweight names; material single-name/mega-cap overlap is disclosed rather than silently recommended.
• NEW Where the next contribution goes — for a client still saving, what they are about to add outweighs what they hold. The report splits the annual contribution across asset classes in dollars: after a contribution the book is its present value plus that amount, each class’s target is its policy weight times that, and the contribution is shared in proportion to each class’s shortfall. A class already above its target receives nothing; nothing is sold; growth is ignored, so every horizon is conservative and no return is assumed. Where a year of contributions reaches the policy the report says so beside the rebalancing trade; where it would take longer it says how many years. It also states what the contribution does to a concentrated sector. It needs a declared policy, and it does not say which account the money goes into.
• NEW Funds are screened and measured, not recalled — a recommended fund comes from the scanned fund universe, never from the model’s memory. To be offered it must carry known sector weights, classify as equity, sit in the fund score’s top tier, and hold no more than 40% in any one sector — the same threshold at which this report calls a client’s own book concentrated. Funds are ranked by their weight in the client’s overweight sector, and each recommendation states that weight and what the book’s sector weight becomes if new money equal to a tenth of the book goes into the fund. That is arithmetic on holdings, not a forecast of return. Where a sector exceeds 40% of the book and the recommendations name no screened fund, the engine places the first one at the top of the list, with no position size. The quality floor inherits the fund score’s trailing-return component, so it leans toward funds that have done well; sector weights are on file for 182 of 245 scanned funds, and the other 63 cannot be offered.
• NEW What “diversifies” rests on — the screen selects candidates outside the client’s overweight sectors, and until now the word “diversifies” stood on that alone: the candidate record carries ticker, name, sector, Deep Score, the growth-pillar fields and valuation fields, and nothing about how the name moves with the book. A different sector is a real basis and a weak one — two GICS sectors can move together closely, which is precisely why the Risk X-Ray measures effective bets rather than counting names. Each single name now states that basis, and where price history allows it carries the measured change in effective bets, computed through the same risk_data / risk_concentration pair the X-Ray uses so the two cannot disagree about one book. Coverage is a minority and is named as such: measured on 2026-09-24, 4 of 10 live candidates carry the ≥60 days of history a correlation needs, and the other six read NOT measured rather than staying silent — silence beside a diversification claim reads as assent. A candidate that measurably does not diversify is disclosed as such, with the recommendation left standing and the contradiction stated beside it.
• NEW The screen can suspend itself — if graded cohorts persistently underperform (three, a recorded decision rather than a fitted number), the report stops recommending single names entirely and reverts to allocation-level advice; ETF recommendations continue, because allocation rests on a declared policy, a tax rule and an arithmetic identity and is not what failed. The rule cannot fire yet — the grader is network-bound and not in CI, so no cohort verdict is stored where the report can read one — and it returns a state with its basis (“no graded cohort yet”) rather than a boolean, because “not suspended” and “not yet judgeable” are different facts and a boolean collapses them into the reassuring one.
• NEW A single name is research, never sized — an individual company arrives labelled Research with no suggested position size, and states how much graded forward evidence stands behind the screen. A size is the sentence that turns research into advice: “consider this name” and “put 3–5% into this name” are different acts, and the second implies a basis this screen does not have — no pick has a graded forward return yet. Measured before the change: 180 of 180 single-name recommendations across 48 stored reports carried a weight. An ETF keeps its weight, because an allocation decision rests on the client’s declared policy, a tax rule or an arithmetic identity. Stripped at the engine choke point rather than requested of the model (the prompt already asked and produced 180 of 180 the other way), and guarded in both renderers so reports written earlier stop showing a size too.
• NEW Growth-pillar dependence, disclosed by the engine — where a recommended single name owes more of its Deep Score to the Growth pillar than an evenly-scoring name would — above the pillar’s own weight share of 25 of 100 points — that share is stated in the recommendation itself. Both of the pillar’s inputs (revenue_growth, earnings_growth) are historical, and the literature is against extrapolating them: Chan, Karceski & Lakonishok (Journal of Finance, 2003) find no persistence in long-term earnings growth beyond chance, and Lakonishok, Shleifer & Vishny (Journal of Finance, 1994) find the fastest-growing names underperform over five years. Measured on the 2026-09-24 production scan of 2,577 scored names: the rank correlation between the shipped score and the score with the pillar removed is 0.97, yet 45 of the top 50 names are in that set only because of it, and 6 of the 8 names the live screen surfaced were Growth-lifted (mean 20.6 of 25 against a universe mean of 12.5). The pillar is disclosed, not re-weighted — whether its sign is wrong on this book is not testable yet. The price-history gap that used to be the reason is closed (2,786 of 2,792 scored names; 988 of the 1,059 scored on 8 June 2026), so what remains is time — that group's 126-day window closes 12 October 2026, and until it is graded, re-weighting on the literature alone would trade one unvalidated prior for another. The note says screen, not forecast: no forward claim is made, because none has been validated (CFA Standard V.A).
• NEW Single-name concentration, stated against a budget — the share of the book held as individual securities rather than funds, measured by value and shown whenever it exceeds the 20% default budget, with its basis: only 42.6% of roughly 26,000 US stocks since 1926 beat one-month Treasury bills over their lifetime and the best 4% of firms account for all net wealth creation (Bessembinder, Journal of Financial Economics, 2018), and removing 90% of diversifiable risk takes 40–50 names rather than 15–20 (Statman 1987; Domian, Louton & Racine 2007). Disclosed, not enforced — no trade is blocked and no plan rejected for exceeding it. Measured 2026-09-24: all six live books exceed it, from 62.9% to 100.0%.
• The engine writes these, not the model — both notes are appended after the narrative has been written, at one choke point (_apply_engine_disclosures). A field placed in a prompt is a request; the model writes its own reasons and is free to leave the inconvenient half out. Neither pass changes a score, a rank, a pick or a position size.
• Contributions as the primary de-risking lever — see §10; directing new cash is preferred to realising gains, with a computed dependency and fallback floor.

The best frameworks make room for genuine human knowledge without letting it quietly corrupt a deterministic engine. The conviction override is that bridge.

• Structured override — on any holding, the investor can overrule the engine’s recommendation (set it to ADD / HOLD / TRIM / SELL). The engine’s own verdict always remains visible beside the override, so the disagreement is explicit, never hidden.
• Documentation is compelled on a contrarian call — when the override softens a protective verdict — the engine said SELL or TRIM and the investor overrides to HOLD or ADD — the system blocks the save until a written thesis is entered (“why, and what would prove you wrong?”). Agreeing with the engine, going more cautious, or clearing the override needs no thesis. So conviction is allowed, but never undocumented.
• Append-only audit trail — every change is written to a conviction log — a dated, append-only history showing the stance change (e.g. Auto → ADD) and the thesis. Nothing is overwritten; the record of why a position was held against the engine survives for later review. This mirrors the Sector Lab conviction journal already used for sector theses.

A good framework keeps score of itself — honestly — so its parameters can be improved by human judgement over time. It does not secretly rewrite its own weights.

What is measured

• Fair-value accuracy — stored fair values are graded forward against the realized price weeks later: a directional hit-rate (did the under/overvalued call match the later move?) and a median absolute % error, broken out by confidence tier and sector.
• Verdict forward grade — because every scheduled scan appends a dated snapshot of each name’s quality verdict (STRONG BUY … AVOID), the engine can grade its own calls as they age: for each verdict it measures the realized forward return ~6 and ~12 months later, bucketed by band, and checks the basic monotonicity that STRONG BUY/BUY should beat AVOID. The report states how many name-dates had enough forward history, so coverage is never silently overstated. This is a forward grade, not a back-test, and nothing has been graded yet: no cohort has matured (the first reaches 126 days on 2026-10-12), so no hit rate is published and the labels have not been tested against returns. It reads stored closing prices, which a split or a delisting between scans can distort. A separate point-in-time population test (2026-10-06, US filers FY2009–2022) found the Deep Score’s fundamentals ranked later peer-relative returns modestly well; its label cut-offs, valuation inputs and the sizing bands were not tested. Even when graded, this measures signal accuracy on accrued history — not the investor’s realized portfolio return.

PRINCIPLE Measurement informs people — it never auto-tunes the engine

This track record is read by a human to decide whether to recalibrate — exactly as the sector multiples and normalized-earnings model were deliberately re-tuned in June 2026. No code path adjusts the Deep Score weights or fair-value parameters automatically from outcomes. We reject self-tuning on principle (§16): on a single portfolio of a few dozen names over a couple of years it would overfit noise, chase recent performance — the very behaviour value investing exists to resist — and quietly convert a transparent, deterministic calculator into a self-modifying advice algorithm, which is precisely the NI 31-103 line the product guards.

• Constrained role — the model (Claude, structured-JSON output) validates signals, ranks ~8 priority actions, adds timing/framework/tax narration and a simulation. It cannot fabricate a price, fair value, SELL, or out-of-universe ticker.
• Flag demotion — deterministic decisions (deploy-mode, contribution-dependency, fund-overlap) are computed in the engine and merely narrated, with engine-side backfill so a flag cannot be silently dropped under prompt load.
• Audit trail — wherever the model’s final action differs from the deterministic rule signal for a name, the override is disclosed with its rationale (“rule said TRIM → advisor chose HOLD: …”).
• Test & ops — the rule base and renderers are covered by an automated suite in which every signal rule carries at least one threshold test; the data layer self-heals its schema across deploys; the report degrades gracefully on missing inputs.

Common questions a reviewer asks, and the deliberate reasoning behind each answer.

Why a semiconductor-cluster heuristic instead of a live correlation matrix?

For a personal book of ~10–30 names, a rolling correlation matrix is fragile: correlations are noisiest exactly when you would act on them (in a crash everything correlates toward 1), so it would generate the most sell pressure at the worst time. The honest cluster heuristic (treating, e.g., semiconductors as one cyclical complex) captures the concentration that actually matters, and the new regime-aware ceilings (§8) supply the “tighten when fragile” behaviour without the instability or the false precision.

Is the tax handling reactive or projective?

Both, by design. Per-action tax consequences are reactive (they price the cost of a trade you’re considering). But the plan is projective: contributions are modelled as the primary de-risking lever, and the Rebalance tool’s Tax-Neutral Check (§10) steers you to reach targets with new money before triggering a taxable disposition. Full contribution-room/placement projection lives in the separate Tax & Structure Audit, so the Strategy report stays focused.

How does human intuition enter without introducing model bias?

Through the conviction override (§13): a structured, documented, append-only override that sits beside the deterministic engine rather than inside it. The investor may overrule a rating, but overruling a caution compels a written thesis, and every change is logged with a date. The engine stays deterministic; the human judgement is explicit and on the record.

The 20%-drawdown trigger is “late by design” — isn’t that dangerous?

The Crash Watch is intentionally late because it protects a winner you want to keep after a tolerated pullback — trimming early at every top would forfeit the compounding that is the whole point. Crucially it is not the only defence: fundamental-deterioration SELLs (dangerous leverage, structural revenue decline, an AVOID-rated score the business itself confirms — §7/§8) fire on the business breaking, ahead of price. So catastrophic-failure protection comes from the fundamentals path; Crash Watch only guards a still-good name whose chart has clearly rolled over.

Why not let the engine self-optimise its weights from outcomes?

Because a self-tuning loop on one small portfolio over a short history overfits noise and chases recent performance — the opposite of disciplined value investing — and it would turn a transparent, auditable calculator into an opaque, self-modifying advice algorithm (an NI 31-103 concern). We build the measurement (§14) and let a human recalibrate deliberately, which is both safer and more honest.

• Rules-based signals, not personalized advice. The report applies published rules to the investor’s own inputs; its SELL/TRIM/HOLD/ADD outputs — some with a suggested share count — are general information and starting points for the investor’s own judgement, not personalized advice from a registered adviser. The investor makes every decision, and nothing executes automatically.
• No price/level forecasts. Forward statements are probability-weighted regime trajectories, never targets.
• Fair-value conservatism. The ensemble is conservative on steady non-mania names; the range + confidence tier is the explicit mitigant against false precision, and NOISE-tier names are suppressed.
• Crash Watch is late by design. The trend-rollover overlay protects the residual after a tolerated drawdown, not the peak; the fundamental-deterioration SELLs are the earlier line of defence.
• No company-news trigger. Confirmation is price-and-fundamentals based; idiosyncratic event risk (earnings, litigation, M&A) is not modelled in the deterministic engine.
• Validation is measurement, not control. The track record informs human recalibration; it never auto-tunes engine parameters.
• Data dependency. Outputs are only as current as the latest scan; the forward track record accrues forward history over time and states its own coverage.