154 lines
6.8 KiB
Markdown
154 lines
6.8 KiB
Markdown
# XVARY Methodology (Public Framework)
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This document is the **public framework** for XVARY Research.
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It is intentionally the **menu, not the recipe**: stage names, logic flow, and decision philosophy are published; internal prompts, thresholds, and convergence algorithms are not.
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Full narrative: [xvary.com/methodology](https://xvary.com/methodology)
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## Research Philosophy
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XVARY is built around five principles:
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1. **Variant perception first**: value comes from being directionally right where consensus is wrong.
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2. **Evidence before narrative**: facts constrain the story, not the other way around.
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3. **Conviction is earned**: scores reflect cross-validated support, not tone or confidence theater.
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4. **Adversarial challenge is mandatory**: every thesis gets attacked before publication.
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5. **Kill-file discipline**: each call includes explicit thesis-invalidating conditions.
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## 22-Stage Operational DAG (21-Stage Research Spine + Finalize)
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```mermaid
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flowchart TD
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s1[directive_selection] --> s2[phase_a]
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s2 --> s3[data_quality_gate]
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s3 --> s4[evidence_gap_analysis]
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s4 --> s5[kvd_hypothesis]
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s4 --> s6[pane_selection]
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s6 --> s7[quant_foundation]
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s7 --> s8[model_quality_gate]
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s6 --> s9[phase_b]
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s5 --> s9
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s9 --> s10[triangulation]
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s10 --> s11[pillar_discovery]
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s11 --> s12[phase_c]
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s11 --> s13[why_tree]
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s12 --> s14[quality_gate]
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s13 --> s14
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s14 --> s15[challenge]
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s15 --> s16[synthesis]
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s16 --> s17[audit]
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s17 --> s18[report_json]
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s18 --> s19[audience_calibration]
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s18 --> s20[compliance_audit]
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s19 --> s21[completion_loop]
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s20 --> s21
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s21 --> s22[finalize]
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```
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> The operational DAG has 22 nodes in code (`finalize` included). Publicly we refer to the core research spine as the 21-stage methodology and treat finalization as release control.
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### Stage Intent (One-Line)
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1. `directive_selection`: choose sector/style evidence directives.
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2. `phase_a`: collect baseline facts, filings, market context, and broad evidence.
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3. `data_quality_gate`: block low-integrity factual inputs.
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4. `evidence_gap_analysis`: detect missing evidence and open targeted searches.
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5. `kvd_hypothesis`: identify candidate key value drivers.
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6. `pane_selection`: choose report panes for company profile.
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7. `quant_foundation`: build model scaffolding (valuation/risk context).
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8. `model_quality_gate`: sanity-check model outputs before synthesis.
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9. `phase_b`: run enrichment search and deeper context collection.
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10. `triangulation`: compare evidence across independent reasoning vectors.
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11. `pillar_discovery`: derive weighted thesis pillars.
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12. `phase_c`: execute module-level synthesis in parallel.
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13. `why_tree`: decompose causal claims and dependency chains.
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14. `quality_gate`: run structured quality tests and consistency checks.
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15. `challenge`: adversarially test each pillar and assumptions.
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16. `synthesis`: assemble conviction, variant view, and scenario posture.
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17. `audit`: multi-role verification with follow-up rounds.
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18. `report_json`: build structured report payload.
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19. `audience_calibration`: ensure readability + decision-usefulness.
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20. `compliance_audit`: verify methodology and policy compliance.
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21. `completion_loop`: repair sparse or inconsistent sections.
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22. `finalize`: release gating and artifact finalization.
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## Quality Gates (Public Names + What They Check)
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- **Data Quality Gate**: missingness, stale fields, broken units, filing coherence.
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- **Model Quality Gate**: sanity bounds, impossible outputs, assumption integrity.
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- **Quality Gate**: cross-module consistency, contradiction flags, evidence sufficiency.
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- **Audience Calibration**: clarity, thesis readability, decision speed under time pressure.
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- **Compliance Audit**: methodology adherence, sourcing hygiene, output policy checks.
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- **Finalize Gate**: final validation + publication readiness.
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## 23 Research Modules
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1. `kvd`: key value-driver identification and trajectory framing.
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2. `core_facts`: baseline thesis framing and variant setup.
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3. `operations`: revenue engine, segment economics, moat mechanics.
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4. `financials`: profitability, balance-sheet quality, cash conversion.
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5. `valuation`: intrinsic range, scenario math, and expectation gap.
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6. `management`: leadership quality, incentives, and execution credibility.
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7. `competition`: market structure, rival dynamics, strategic pressure.
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8. `risk`: kill criteria, thesis breakers, and downside maps.
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9. `capital_allocation`: buybacks/dividends/M&A capital discipline.
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10. `governance`: board structure, oversight quality, shareholder alignment.
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11. `catalysts`: event map and timing-sensitive thesis triggers.
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12. `product_tech`: product moat, roadmap durability, and innovation path.
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13. `supply_chain`: supplier dependency, resilience, and bottleneck exposure.
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14. `tam`: market size realism, penetration runway, and saturation risk.
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15. `street`: consensus expectations vs. internal thesis.
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16. `macro_sensitivity`: rates/FX/cycle sensitivity mapping.
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17. `value_framework`: investment framework fit + decision rubric.
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18. `quant_profile`: factor, drawdown, and liquidity behavior profile.
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19. `signals`: alternative/leading indicators and signal dashboard.
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20. `derivs`: options/short-interest positioning context.
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21. `earnings_track`: beat/miss quality and guidance reliability.
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22. `history`: strategic timeline and historical analog framing.
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23. `executive_summary`: cross-module synthesis for fast decisioning.
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## Conviction Scoring (Concept)
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Conviction is built from weighted pillars rather than a single-model output:
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- Pillar strength (how well each core claim is supported)
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- Pillar dependency risk (how fragile each claim is)
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- Cross-module consistency (do independent modules agree?)
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- Adversarial challenge survival (did core claims hold up?)
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- Downside asymmetry under identified kill criteria
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Weights are dynamic by business model and evidence reliability. Exact calibration is proprietary.
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## Kill-File Risks (Concept)
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Every thesis is paired with explicit conditions that invalidate it. A kill file is not a downside list; it is the shortest set of assumptions that, if broken, forces re-underwriting.
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Typical kill-file categories:
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- Structural demand break
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- Unit-economics deterioration
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- Balance-sheet fragility
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- Regulatory/regime shock
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- Management credibility failure
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## Five-Vector Triangulation (Concept)
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Each ticker is evaluated through five independent vectors before synthesis:
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1. **Accounting reality**
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2. **Market-implied expectations**
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3. **Operational execution**
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4. **Strategic position / industry structure**
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5. **Macro-regime sensitivity**
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The goal is convergence testing: where vectors agree, conviction rises; where they diverge, uncertainty is made explicit.
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## Intentionally Not Published
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- Module prompt templates
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- Prompt routing logic and fallback trees
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- Threshold matrices and gating cutoffs
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- Internal convergence scoring mechanics
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- Sector-specific directive libraries
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