Acutic — Methodologyspecimen № 238

Fig. 01 — How the system thinks

Methodology paper · public

Research,
not signals.

Most platforms hide their scoring formulas. Acutic publishes the whole thing. This page documents every input, every weight, every agent1, every hard filter, and every known limitation. If you cannot reproduce the score from this page, that is a defect — please write to us.

The system never executes trades, never produces a personal directive, and never addresses an instrument decision to a specific user. It produces a structured research dossier. The investor does the rest.

01

First principles

Five rules the methodology never breaks.

i

No invented data

The model never fabricates a price, a fundamental, an earnings number, or a news article. Inputs come from market-data providers; missing inputs become explicit data-quality warnings.

ii

Schema-first output

Every agent output is validated against a Zod schema before persistence. Free-text narrative is constrained to specific fields with character limits.

iii

No instrument-level directives

The output is a research dossier, not a directive. Agents describe what the data shows; the investor draws the conclusion.

iv

Versioned and auditable

Every prompt has a version string. Every score is reproducible from stored inputs. Old versions remain accessible alongside the current one.

v

Transparency over polish

When the data is incomplete, the score is lower-confidence and says so. The system would rather show a tentative answer with warnings than a confident wrong one.

vi

Calm language

No hype, no urgency, no "signal" framing. The product reads like a research note, because it is one.

02

The seven sub-scores

Every candidate that passes hard filtering receives a composite score from 0 to 100, composed of seven sub-scores normalised to 0–100 and combined using strategy-specific weights.

total_score = Σ (sub_scorei × weighti)

A high score means "suitable for further research" — not that any particular outcome is implied. Status classification uses the full rule tree, not only the total.

§02.01

Fundamental quality

25% in balanced · 20% in growth · 25% in conservative

Measures the underlying business — profitability, financial health, and consistency.

For stocks, the score reflects revenue growth, EPS, profit margin, debt-to-equity, free cash flow, return on equity, and a market-cap stability proxy. For ETFs, the inputs collapse to expense ratio, AUM, and tracking quality. Each input is bucketed into discrete points (e.g. revenue growth above 10% earns more than 5–10%), then summed and normalised to 0–100.

Inputs

  • +Revenue growth (YoY)
  • +EPS (TTM)
  • +Profit margin
  • +Debt-to-equity ratio
  • +Free cash flow
  • +Return on equity
  • +Market cap stability proxy

How missing data behaves

Missing free cash flow or earnings data does not zero the score — it raises a data-quality warning instead.

§02.02

Growth

15% in balanced · up to 30% in AI/tech aggressive

Measures the rate and consistency of business expansion.

Growth combines historical revenue growth, EPS growth, forward revenue guidance, and the trend in analyst estimate revisions. The score is asymmetric: an estimate revision moving down subtracts points, while a revision moving up adds them. The cap of 100 ensures a single explosive metric cannot dominate.

Inputs

  • +Revenue growth (YoY)
  • +EPS growth (YoY)
  • +Forward revenue guidance
  • +Analyst estimate revision trend

How missing data behaves

Forward guidance is frequently unavailable. When missing, the input contributes zero points and is logged as a data warning — not silently treated as a negative signal.

§02.03

Valuation

15% in balanced · 20% in conservative · 5% in AI/tech aggressive

Measures whether the current price offers a reasonable entry relative to fundamentals.

A high valuation does not automatically remove a candidate from research. It lowers the valuation score and lifts the risk score; a high-quality compounder with a stretched P/E may still rank well overall. Inputs include trailing P/E, forward P/E, price/sales, PEG, sector median P/E, and dividend yield. ETFs use expense ratio as the primary valuation signal.

Inputs

  • +P/E ratio (TTM)
  • +Forward P/E
  • +Price-to-sales
  • +PEG ratio
  • +Sector median P/E
  • +Dividend yield

How missing data behaves

Companies with negative earnings cannot be P/E-scored; in those cases the agent falls back to price/sales and PEG, with a confidence reduction noted.

§02.04

Technical setup

15% in balanced · up to 25% in AI/tech aggressive · 5% in conservative

Measures whether current price action and trend support a reasonable entry.

Technical setup uses moving-average alignment (price vs SMA 50 / SMA 200 / golden cross), RSI in a healthy 45–65 band, MACD direction, volume trend, and distance from the 52-week high. Extreme RSI readings score poorly in either direction — overbought conditions earn fewer points than balanced trends.

Inputs

  • +Price vs SMA 200
  • +Price vs SMA 50
  • +SMA 50 vs SMA 200 (golden / death cross)
  • +RSI (14-day)
  • +MACD signal
  • +Volume trend
  • +Distance from 52-week high

How missing data behaves

A candidate within 2% of its 52-week high loses the “not overextended” bonus. The score discourages chasing — it does not forbid it.

§02.05

Sentiment & news

10% in balanced · 0% in conservative · 0% for most ETFs

Measures recent news tone and proximity to material event risk.

The v1 scorer uses a simplified bucket — predominantly positive recent news (80), mixed or neutral (50), predominantly negative (25). Subtractions apply for upcoming earnings within 48 hours (−20) and within seven days (−10). Regulatory or legal flags subtract 15. The final value is clamped to 0–100.

Inputs

  • +Recent news articles (last 14 days)
  • +Per-article sentiment
  • +Next earnings date
  • +Regulatory / legal flags from provider metadata

How missing data behaves

No news in the window does not punish the score. It defaults to a neutral 50 with a “missing news” data-quality warning.

§02.06

Risk

10% in balanced · 25% in conservative · 25% in ETF-first

Measures downside risk. Higher score = lower risk.

The risk score blends 30-day annualised volatility, 90-day max drawdown, debt-to-equity, earnings event proximity, average daily liquidity, asset type (ETFs receive a diversification bonus), and a data-quality completeness bonus. It is the dominant input for conservative and dividend-focused weight presets.

Inputs

  • +30-day historical volatility
  • +Max drawdown (90 days)
  • +Debt-to-equity
  • +Earnings event proximity
  • +Sector cyclicality
  • +Liquidity (average daily volume)
  • +Data quality completeness

How missing data behaves

Risk is the only sub-score where missing data directly costs points — incomplete inputs reduce the +10 “all required data present” bonus.

§02.07

Portfolio fit

10% in balanced · 30% in ETF-first · 0% in AI/tech aggressive

Measures how the candidate complements the active strategy template, not the user as a person.

Portfolio fit checks whether the asset type matches the strategy need (e.g. an ETF for an ETF-first template), whether the candidate fills an uncovered sector gap, whether it duplicates an already-high-scoring candidate, and whether it lines up with the core/satellite role described by the strategy. The score is clamped at zero when speculative assets appear in templates that exclude them.

Inputs

  • +Active strategy template
  • +Existing candidate list
  • +Sector exposure of existing candidates
  • +Region exposure
  • +User-excluded sectors and symbols

How missing data behaves

Per the compliance posture, this score never expresses a “match” between a user and an instrument. It evaluates the strategy template, not the person.

Reading the score

  • 80–100 — Strong across most dimensions, low number of data warnings.
  • 65–79 — Reasonable research candidate with minor concerns flagged.
  • 50–64 — Mixed picture, some dimensions strong and others weak.
  • 35–49 — Multiple criteria fail; better treated as a watch entry than a research focus.
  • 0–34 — Composite is poor in most regimes; classification is likely "below screening criteria".

These bands are descriptive, not directive. They do not constitute personal guidance on any specific instrument.

03

Weight presets per strategy

Different strategies prioritise different sub-scores. Acutic ships five canonical weight presets. Each preset's weights sum to exactly 1.0.

Balanced core-satellite

Default starting point. Emphasises business quality with even attention to growth, valuation, and trend.

Used by self-directed investors with an ETF-core and 5–15 satellite stocks.

Sub-scoreWeight
Fundamental quality0.25
Growth0.15
Valuation0.15
Technical setup0.15
Sentiment / news0.10
Risk0.10
Portfolio fit0.10

Aggressive growth

Skews toward growth, momentum, and quality. Less patient about valuation.

Used by investors who accept higher volatility in exchange for upside.

Sub-scoreWeight
Growth0.25
Technical setup0.20
Fundamental quality0.20
Valuation0.10
Sentiment / news0.10
Risk0.10
Portfolio fit0.05

Conservative / dividend-focused

Quality and risk dominate. Sentiment is muted because dividend payers should not chase headlines.

Used by investors prioritising capital preservation, low drawdowns, and dividend income.

Sub-scoreWeight
Fundamental quality0.25
Risk0.25
Valuation0.20
Portfolio fit0.15
Growth0.10
Technical setup0.05
Sentiment / news0.00

ETF-first

Diversification quality and cost dominate. Single-stock growth and sentiment are zero-weighted.

Used by investors building a primarily ETF portfolio with optional satellite stocks.

Sub-scoreWeight
Portfolio fit (diversification)0.30
Risk (expense ratio, volatility)0.25
Fundamental quality (tracking, AUM)0.20
Technical setup0.15
Valuation0.10
Growth0.00
Sentiment0.00

AI / tech aggressive

Momentum and growth bias. Portfolio fit drops to zero — the template is intentionally concentrated.

Used by investors taking a thematic, single-sector position alongside a diversified core elsewhere.

Sub-scoreWeight
Growth0.30
Technical setup0.25
Fundamental quality0.20
Sentiment / news0.10
Risk0.10
Valuation0.05
Portfolio fit0.00

What this is not

Selecting a preset is a strategy choice, not a personal-fit endorsement. Acutic never tells a user a preset matches them personally. The user picks a preset; the system shows the resulting candidate ordering on the same data.

04

Hard filters

Candidates that fail any hard filter are excluded from scoring entirely. The list is short, conservative, and explicit.

UniverseFilterThresholdWhy
UniversalMissing price dataNo market snapshot foundWithout a price, no score can be computed and no risk can be quantified.
StocksMarket capBelow US$ 500MSub-cap names are excluded from research-grade analysis to keep liquidity, coverage, and data quality acceptable.
StocksAverage daily volumeFewer than 100,000 shares per dayThinly traded names introduce execution and disclosure risk that the score cannot model.
StocksPrice historyFewer than one year of daily candlesSMA 200 and 90-day drawdown cannot be calculated reliably without sufficient history.
StocksRecent extreme newsSevere negative event flag in provider metadataMaterial litigation, fraud, or restatement events are excluded until the situation stabilises.
ETFsAssets under managementBelow US$ 100MSmall ETFs face closure risk and tracking-error risk that no scoring method should obscure.
ETFsExpense ratioAbove 0.75% per yearAfter fees, high-cost ETFs lag their cheaper peers structurally. They are filtered out unless data is unavailable.
ETFsTrading volumeFewer than 50,000 shares per dayLiquidity below this band creates spread and execution issues for everyday investors.
UniversalUser-excluded sectorsProfile excludes the sectorA user can hide entire sectors from their universe (e.g. tobacco, defense). The score never overrides the exclusion.
UniversalUser-excluded symbolsProfile excludes the symbolSymbol-level exclusions take precedence over every signal — the candidate is dropped before scoring.

The hard-filter list is intentionally short. Most decisions belong in scoring, not in pre-screening. A filter is added only when the underlying data is structurally missing or the candidate creates a known reliability risk for the rest of the pipeline.

05

The five LLM agents

Five analysts examine each candidate from a different angle. Their outputs are consolidated into a single research dossier in code — no synthesis verb, no directive, no personal pick.

Agent §01

ETF Core Analyst

Evaluates whether an ETF is appropriate as a long-term diversified core position.

Reads the ETF in context of the strategy and asks whether it can carry the backbone of the portfolio.

Input

  • Symbol, name, AUM, expense ratio, average daily volume
  • Composite score and the Fundamental, Portfolio Fit, Risk sub-scores
  • Price-vs-SMA-200 and RSI
  • Strategy style, preferred regions, max sector allocation, ETF core preference
  • Data-quality warnings

Output (Zod-validated)

  • Diversification quality (broad / moderate / narrow / unknown)
  • Core-role rating (excellent / good / acceptable / not suitable as core)
  • Overlap risk vs other ETF candidates
  • Cost and liquidity assessments
  • Per-output confidence (low / medium / high)
Agent §02

Stock Quality Analyst

Evaluates the fundamental business quality of an individual company.

Looks at the business — durability, growth, margins, debt — and writes a candid summary, weaknesses included.

Input

  • Symbol, name, sector, industry
  • Revenue growth, EPS, profit margin, debt/equity, P/E, forward P/E, free cash flow
  • Composite score plus Fundamental, Growth, Valuation sub-scores
  • User strategy style and risk tolerance
  • Data-quality warnings

Output (Zod-validated)

  • Business quality (excellent / good / average / poor / unknown)
  • Growth quality (strong / moderate / weak / declining / unknown)
  • Profitability and balance-sheet assessments
  • Long-term suitability label (highly suitable / suitable / marginal / not suitable)
  • Key strengths and key weaknesses
Agent §03

Technical Setup Analyst

Evaluates whether the current price action supports a reasonable entry.

Reads the trend, momentum, and overbought / oversold posture — and is honest when the data does not support a clear read.

Input

  • Price, SMA 20 / 50 / 200, EMA 20
  • RSI (14), MACD signal
  • Distance from 52-week high and low
  • 30-day annualised volatility
  • Technical Setup sub-score

Output (Zod-validated)

  • Trend label (strong uptrend / uptrend / ranging / downtrend / strong downtrend / unknown)
  • Momentum (positive / neutral / negative / unknown)
  • Overbought / oversold posture
  • Entry risk (low / moderate / high / very high)
  • Entry posture (good entry / acceptable entry / wait for pullback / wait for trend confirmation / avoid entry now)
Agent §04

Portfolio Fit Analyst

Evaluates how the candidate fits the user-selected strategy template.

Frames the candidate against the active strategy — role, allocation band, duplication risk — without ever framing the candidate as personally matched to the user.

Input

  • Asset type, sector, indicated role
  • Composite score and Portfolio Fit sub-score
  • Strategy style, max single-stock and max sector limits
  • ETF-core preference and speculative-allowance flag
  • List of other top candidates already on the radar

Output (Zod-validated)

  • Indicated role (global core ETF, US core ETF, growth compounder, dividend income, defensive, cyclical, AI / semiconductor, speculative growth, cash equivalent)
  • Indicated allocation min and max (whole-percent band)
  • Duplication and concentration risk
  • Overall fit (strong / good / marginal / poor) with rationale
Agent §05

Candidate Risk Manager

The skeptic. Challenges the bull case and surfaces conditions under which the thesis breaks.

Takes the opposing view. Required to cite specific data, never to invent generic risks.

Input

  • Asset type, sector, candidate status
  • Risk sub-score and composite score
  • P/E, forward P/E, debt/equity, volatility, next earnings date
  • RSI, price vs SMA 200, distance from 52-week high
  • Recent news sentiment
  • Prior agent summaries (Quality, Technical, Portfolio Fit)

Output (Zod-validated)

  • Main risks — concrete, grounded in the data
  • Data problems that reduce confidence
  • Conditions under which entering the position would be unwise
  • Conditions under which exiting is warranted if already held
  • Overall risk level (low / moderate / high / very high)

Common configuration

  • Temperature 0.2 — low, for consistent structured output.
  • Max output tokens 1500 — fits the schema without truncation.
  • JSON response mode where the provider supports it.
  • One automatic retry on Zod validation failure.
  • Outputs containing forbidden language are rejected at validation time and surface as a data-quality warning.
06

Caveats & failure modes

The honest section. What the methodology is not good at, where it can go wrong, and how often it changes.

Can the methodology predict short-term price moves?

No. Acutic models long-horizon research signals — quality, growth, valuation, risk. The technical sub-score reads trend and momentum, not direction. There is no claim about where a price will be next week, next month, or next quarter.

What kinds of stocks are systematically harder for the model?

Companies with negative earnings, very recent IPOs (under one year of price history), pre-revenue businesses, micro-caps below the US$ 500M filter, and names with thin analyst coverage. The score reflects this honestly through reduced confidence and explicit data warnings rather than silent guesses.

What are the known failure modes?

Stale data is the largest one — a fundamentals snapshot more than a few days old can produce a misleading score. Sector concentration in news sources can skew sentiment. Forward P/E numbers from secondary sources occasionally disagree with the issuer. Each of these triggers a data-quality warning instead of a clean score.

Is the score personalised to my situation?

No. The score is computed against a strategy template that you select. Your profile may exclude sectors or symbols, but the methodology never highlights or ranks instruments “for you” specifically. Acutic operates as non-personalised research under EU MAR Art. 20 and equivalent rules elsewhere.

How often is the methodology updated?

The scoring weights and prompt versions are versioned. Each agent prompt has an explicit version string in `llm_prompt_versions`. We retain old prompt versions for audit. Material changes to the scoring weights or hard filters are announced and dated; the previous version remains accessible alongside the new one.

Has the system been tested through a full bear market?

No. The current model has been live since 2026 and has not yet been tested through a complete bear cycle. We are explicit about this. As more data accumulates we will publish backtests, including across negative regimes, on this page.

How do you guard against LLM errors?

Every agent output is validated against a Zod schema. Outputs containing forbidden language are rejected. The system retries once on validation failure; persistent failures surface to the user with a data-quality warning rather than a fabricated answer. The LLM never invents prices, fundamentals, or news — it works only with data passed in the prompt context.

Does the methodology adapt to changing market regimes?

Indirectly. The technical sub-score reflects current trend and momentum, the risk sub-score reflects realised volatility, and the sentiment sub-score reads the most recent news window. The structural weights, however, do not auto-adjust. Switching strategy templates is the explicit way to shift weights toward conservative or aggressive postures.

Honest limitations

The system has not been tested through a full bear cycle. Backtests are limited. Several sub-scores rely on third-party fundamentals that occasionally disagree with primary filings. We document failures publicly here as we learn about them.

07

Data sources

Where every input comes from. Acutic does not crawl issuer websites or scrape brokerage feeds — every input has a provider and a vintage.

Market data

Real-time and end-of-day quotes, fundamentals, daily candles.

Provider abstraction supports OpenBB, Finnhub, and a deterministic mock for development. Data is cached server-side with TTLs that vary by data type.

Fundamentals

Income statement, balance sheet, free cash flow, EPS, profit margin, debt ratios.

Sourced through the same market-data providers. Vendors disagree at the margin; the methodology favours the issuer’s most recent filing where available.

Technical indicators

Computed on stored daily candles, not vendor-supplied.

SMA 20 / 50 / 200, EMA 20, RSI 14, MACD, 30-day annualised volatility, 52-week high / low distance. Computation is reproducible from the candle history.

News

Provider-supplied news articles tagged by symbol.

Articles are scored individually for sentiment, then aggregated into a 14-day rolling read. The full list of articles is shown alongside the score so the user can audit it.

Reference data

Sector and industry classification, market cap, ETF metadata.

Used for hard filters and for computing sector-median P/E. Reference data is refreshed less frequently than market data and is logged with a vintage timestamp.

Data provenance

Every score the system displays records the input vintage — when the price was fetched, when the fundamentals were last refreshed, when the news window was computed. If anything is stale, the user sees the warning before they see the score.

08

Human-in-the-loop

The most important constraint. Acutic does not act on instruments. The investor does.

  • Acutic never executes trades. It does not connect to a brokerage. It does not place, modify, or cancel orders.
  • Acutic does not produce instrument-level directives for a specific user. The product surfaces evidence; the investor weighs it.
  • Acutic does not match instruments to users. Profile data may hide instruments (excluded sectors / symbols). It never highlights, ranks, or promotes an instrument as "right" for any individual.
  • Every AI-generated output carries a transparency badge stating that the content is AI-generated, that errors are possible, and that the user must verify before acting.
  • The investor remains the sole decision-maker for every action taken in their brokerage account.

Comments and corrections

If something on this page is wrong, unclear, or could be more rigorous, write to hello@acutic.io. We treat methodology corrections with the same gravity as bug reports.

Registration open

Acutic is open to everyone and free to use. Create a free account to use the system the methodology describes.

Sourced from docs/scoring-model.md, docs/prompt-design.md, and docs/product-spec.md §8 and §10. Last revised 2026-05-05. — The Acutic Research Team