quantitative-screening

SkillCommerce & finance

Quantitative stock screening, forward-looking valuation outlier detection, backward-looking financial statement validation, PEG ratio analysis, earnings growth profile assessment, turnaround vs value trap discrimination, data mining bias prevention

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the quantitative-screening skill

What this skill tells your AI

The instructions your AI receives, as published by agentii-ai/agentii-investment-intelligence in plugins/vertical-plugins/idea-generation/skills/agentii/quantitative-screening/SKILL.md and read by ahel’s review.

Methodology fused from professional trading and investment frameworks; all text is an original paraphrase.

Defaults

ParameterDefault ValueRationale
screening_universeS&P 500 + Russell 1000 liquidBroad enough for diversity, liquid enough for execution
historical_years5Minimum years of financial data for trend analysis
peg_threshold1.0PEG < 1.0 suggests undervaluation relative to growth
fcf_conversion_min70%FCF/Net Income below 70% flags earnings quality issues
earnings_beat_threshold70%Beat frequency above 70% suggests conservative guidance

Preflight

Run canonical pre-flight per contracts/preflight.md. Propagate X-Agentii-Trace per contracts/x-agentii-trace-header.md.

Data Source Priority

  1. Quantitative methodology — references/quant-methodology.md (bundled screening framework)
  2. Financial data — SEC XBRL facts via agentii MCP for historical financials
  3. Market data — ~~market_data placeholder for real-time valuation multiples
  4. Strategy frameworks — search_investment_strategies(domain=fundamental, kind=screening)

Methodology

Retrieval Scope

structured_only

Retrieval Strategy

Ownership & insider signals: search_institutional_holdings (top-10 holders + whale portfolios, direction=accumulating|reducing|new|exited) and search_insider_trades (Form-4 transactions with SEC URLs) are available as signal inputs.

Branch (a) Structured Data Query from contracts/retrieval.md: primary retrieval via XBRL facts for financial statement data. Supplement with search_investment_strategies for screening methodology validation. Detailed methodology in references/quant-methodology.md.

Temporal Scope

See frontmatter temporal_scope block.

Tool Allowlist

See frontmatter allowed_tools.

Protocol

This skill implements a two-directional screening process: forward-looking valuation discovery and backward-looking financial statement validation. Core principle: the market is mostly efficient. An outlier exists because either the market is wrong (your edge) or you are missing something. Non-participation is always an option.

Detailed methodology: peer selection protocol, turnaround financial scorecard, 7-step sector cleaning, and data mining bias catalog are in references/quant-methodology.md.

Foundational principle: P/E measures what the market is willing to pay for forward earnings — it is a market psychology metric, not intrinsic value. "Cheap" and "expensive" are not analytical conclusions. The question is: why has the market assigned this multiple? PEG < 1.0 is not a universal buy signal — calibrate sector-relatively, growth-rate-adjust, and cross-check with EV/EBITDA-to-Growth. This skill uses PEG as a screening filter only; for a standalone PEG-based valuation, defer to the peg-valuation skill.

Steps
  1. Universe and Macro Filter: Apply portfolio bias from orchestrator. Long → $3B-$10B mid-caps. Short → $20B+ large caps. Neutral → both, emphasize pairs. Weight sectors by macro regime preferences.

  2. Forward-Looking Valuation Scan: Screen using four-pillar framework (PE1, PE2; EG1, EG2; PEG1, PEG2; revenue multiples). Rank by deviation from sector median. Top/bottom decile advance. Calibrate PEG sector-relatively. Use EV/EBITDA-to-Growth as cross-check; prefer EBIT over EBITDA for capital-intensive sectors.

  3. Backward-Looking Financial Validation (execute in this order):

    • Revenue: growth trajectory, organic vs. acquisition quality, concentration risk
    • Earnings quality: GAAP vs. non-GAAP (> 20% gap = investigate), SBC > 10% revenue = red flag, "non-recurring" in 3+ of 4 quarters = recurring
    • Margin: gross margin trend, incremental margins (> 50% strong, < 20% weak)
    • Cash flow: FCF/Net Income conversion. > 80% excellent, 70-80% acceptable, 50-70% explain, < 50% hard stop for longs. DSO + inventory both rising = channel stuffing risk.
  4. Peer Selection (dual-path): Sector path (GICS → 10-K competition → sell-side → merger docs) + Fundamentals path (cluster by growth, margins, ROIC). Must converge on 4-6 names. Divergence = classification error. Use median. For a formal benchmarked peer set, hand off to peer-bench; for full multiple spreading and calendarization, hand off to comps — do not rebuild either here.

  5. Growth Profile and Trap Detection: EPS CAGR 3-5yr (consistency > magnitude). Estimate trajectory: rising + rising = aligned; falling + rising = danger. Beat/raise = strongest signal. Decompose growth source (revenue vs. cost-cutting vs. buybacks). Turnaround scorecard (0-10): 7-10 investigate long, 0-3 avoid/short. Exclude revenue-growth stories from turnaround classification. Scan for data mining biases.

  6. Sector Cleaning (when data errors suspected): Apply 7-step protocol from reference. Only clean < 20 candidates that pass initial screen.

  7. Output: Score each candidate (valuation × validation × growth). Flag GREEN/AMBER/RED. Handoff: ranked list, peer data, turnaround scores, data quality flags.

Output File

{ticker}/{YYYY-MM-DD_HHMM}_quantitative-screening_{affix}.md

Output Structure

  1. Executive Summary — Universe scanned, outliers found, top 5 candidates ranked
  2. Screening Parameters — Universe, macro filter, metrics used, thresholds
  3. Outlier Results — Ranked list with valuation metrics, sector comparisons
  4. Financial Validation — Revenue, earnings quality, margin, cash flow analysis per candidate
  5. Growth Assessment — EPS trajectory, estimates trend, earnings surprise history
  6. Trap Detection — Turnaround/value trap flags per candidate
  7. Data Quality Report — Bias checks, data freshness, caveats
  8. Handoff Summary — GREEN/AMBER/RED classification with recommended next steps
  9. Coverage Gaps — Data limitations, missing data points, degraded-mode flags

Error Handling

ErrorFallback
No XBRL data for candidateUse market data estimates; flag as lower confidence
Sector comparison data insufficientUse broad market medians; flag sector gap
search_investment_strategies unreachableProceed with manual methodology; flag

Memory Load

See contracts/memory-load.md.

Snapshot

See contracts/snapshot-synthesis.md.

Final Summary (TUI)

Include ### Key Citations block with 0-10 clickable /v/ URLs.

References

  • references/quant-methodology.md
  • contracts/citation-and-memory.md
  • contracts/output-frontmatter-schema.md
  • contracts/memory-load.md
  • contracts/snapshot-synthesis.md
  • contracts/preflight.md
  • contracts/retrieval.md

Signals

GitHub stars
204
Forks
16
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
quantitative-screening
Source
github.com/agentii-ai/agentii-investment-intelligence