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GEO Audit Report: TradingGoose

Audit Date: 2026-04-05 URL: https://tradinggoose.ai Business Type: SaaS (Open-Source + Hosted, Freemium) Pages Analyzed: 7 sitemap URLs + robots.txt + llms.txt + docs subdomain + changelog

Executive Summary

Overall GEO Score: 50/100 (Poor — borderline Fair)

TradingGoose has world-class technical GEO foundations (robots.txt, llms.txt, rich schema, SSR) but is essentially invisible to AI systems as an entity because it has almost no third-party brand authority, no content marketing surface, and no named human experts backing the product. The site is perfectly crawlable — there just isn't enough there for AI systems to cite, and there's a competing "TradingGoose" GitHub project that creates entity-disambiguation risk.

Score Breakdown

Category Score Weight Weighted Score
AI Citability 62/100 25% 15.5
Brand Authority 22/100 20% 4.4
Content E-E-A-T 35/100 20% 7.0
Technical GEO 85/100 15% 12.75
Schema & Structured Data 72/100 10% 7.2
Platform Optimization 30/100 10% 3.0
Overall GEO Score 49.85 → 50/100

Critical Issues (Fix Immediately)

None. No blocking problems — crawlers can reach the site and parse it.

High Priority Issues (Fix Within 1 Week)

  1. Sitemap is dangerously thin (7 URLs). The sitemap only lists homepage, signup, login, terms, privacy, licenses, changelog. The 500+ documentation pages on docs.tradinggoose.ai are not in any sitemap, meaning AI crawlers have to discover them via links. For a developer-tool brand, docs pages are the single highest-value surface for AI citations ("how do I do X with TradingGoose?").

    • Fix: Publish docs.tradinggoose.ai/sitemap.xml and cross-reference it from the apex robots.txt.

  2. No named humans / zero E-E-A-T signals. Organization is listed only as "TradingGoose Studio." No founder name, no team page, no author bios, no LinkedIn profiles, no credentials. AI systems rank content partially on verifiable human expertise — anonymous SaaS projects get cited less.

    • Fix: Add a /team or /about page with named founders, each with Person schema including sameAs links to LinkedIn / X / GitHub profiles.

  3. Entity disambiguation risk: competing "TradingGoose" project on GitHub. A search for "TradingGoose" surfaces github.com/TradingGoose/TradingGoose.github.io (a different multi-agent LLM trading framework) ahead of this one. AI models will conflate the two.

    • Fix: (a) Add a prominent disambiguator to Organization schema (alternateName: "TradingGoose Studio", description emphasizing "visual workflow platform, not the multi-agent framework"). (b) Reach out to the other project to add a cross-link / clarification. (c) Submit both to Wikidata with distinct Q-entities.

  4. No blog / no content marketing surface. Zero articles, zero comparison pages, zero tutorials on the apex domain. AI systems prefer to cite editorial/tutorial content over product marketing pages.

    • Fix: Launch /blog with 6-12 seed articles: "TradingGoose vs n8n for trading automation", "How to build an RSI-triggered LLM agent", "PineTS vs Pine Script: what's different".

  5. llms.txt served via 308 redirect (apex → www). Some AI crawlers may not follow redirects for llms.txt. File exists and has good content, but accessibility is degraded.

    • Fix: Serve llms.txt with a 200 directly on both tradinggoose.ai/llms.txt and www.tradinggoose.ai/llms.txt.

Medium Priority Issues (Fix Within 1 Month)

  1. FAQPage schema only on homepage. Docs pages with Q&A content lack FAQPage/QAPage markup.
  2. No HowTo schema on tutorial/docs pages. HowTo is one of the highest-leverage schema types for AI citation ("step-by-step how do I...").
  3. Changelog entries lack Article schema. Each release entry (e.g., v2026.04.04) should be marked up as an Article or TechArticle with datePublished and author.
  4. No llms-full.txt. The short llms.txt is good but a companion llms-full.txt with complete docs context would dramatically improve AI grounding.
  5. No Wikipedia / Wikidata entry. Entities without Wikidata IDs are systematically under-cited by AI.
  6. No Product Hunt launch, no Reddit presence. These are two of the highest-weighted third-party signals AI models use for developer tools.
  7. Docs subdomain Organization schema may not be linked to apex Organization via sameAs — verify entity consolidation.

Low Priority Issues

  1. Meta description is solid but could lead with the primary differentiator ("no-code" / "visual") in the first 120 chars.
  2. No Open Graph image verified (review og:image dimensions/alt).
  3. Consider SoftwareApplication.applicationCategory set to "FinanceApplication" for richer entity classification.
  4. Add speakable schema property for key homepage passages (voice/AI assistant readability).

Category Deep Dives

AI Citability (62/100)

Strengths:

  • Clear, declarative value proposition in meta description and H1.
  • FAQPage schema with direct Q&A format (highly quotable).
  • llms.txt is well-written: states what the product IS and what it IS NOT — ideal for AI grounding.
  • Changelog uses clean H2/H3 hierarchy with dates.

Weaknesses:

  • Only 7 crawlable marketing URLs; AI engines have almost nothing to quote.
  • No comparison pages ("TradingGoose vs X") — AI systems heavily cite comparison content.
  • No listicles, no "top 10", no tutorial walkthroughs on the apex domain.
  • Most quotable content lives on docs.tradinggoose.ai but isn't sitemapped.

Rewrite suggestion for homepage FAQ: Add at least 8 more Q&A pairs covering: pricing tiers, self-hosting, supported brokers, backtesting capabilities, PineTS vs Pine Script, team/collaboration features, data retention, security.

Brand Authority (22/100)

Platform presence map:

Platform Status Notes
GitHub ✓ Present tradinggoose/tradinggoose-studio — star count not verified
Discord ✓ Linked Community server linked in footer
X (Twitter) ✓ Linked Profile exists
Reddit ✗ Absent Zero mentions in r/algotrading, r/selfhosted, r/LocalLLaMA
Product Hunt ✗ Absent No launch found
YouTube ✗ Not discoverable No channel / tutorials found
Wikipedia ✗ Absent No article
Wikidata ✗ Absent No entity ID
LinkedIn (company) ? Unverified Not linked from homepage
Hacker News ? Unverified No Show HN found

Critical gap: AI models disproportionately cite entities that appear on Reddit, YouTube tutorials, Wikipedia, and Product Hunt. TradingGoose has none of these.

Content E-E-A-T (35/100)

  • Experience: Not demonstrated — no case studies, no "who's using us" logos.
  • Expertise: Implied via product quality, but no named experts credited.
  • Authoritativeness: Weak — no press mentions, no conference talks, no external citations.
  • Trustworthiness: OK — legal pages present (Privacy, Terms, Licenses), open-source code, clear disclaimer ("not a financial advisor").

Technical GEO (85/100) — Strongest category

  • robots.txt: exemplary — 22+ AI crawlers explicitly allowlisted.
  • llms.txt: present and well-written (minor 308 redirect issue).
  • Rendering: Next.js SSR, core content in initial HTML.
  • Sitemap: present but thin.
  • HTTPS + HTTP/2: ✓
  • Disallow rules are appropriate (/api/, /workspace/, /_next/).

Schema & Structured Data (72/100)

Present on homepage:

  • SoftwareApplication ✓
  • Organization ✓
  • WebSite ✓
  • WebPage ✓
  • BreadcrumbList ✓
  • FAQPage ✓
  • Offer (multiple, for pricing tiers) ✓

Missing:

  • Person schema (no named humans) ✗
  • HowTo schema (no tutorials marked up) ✗
  • Article schema on changelog entries ✗
  • VideoObject (no videos) ✗
  • Review / AggregateRating ✗

Platform Optimization (30/100)

Platform Readiness
Google AI Overviews Medium — schema-rich but thin content
ChatGPT (SearchGPT) Medium — llms.txt helps, but low citation surface
Perplexity Low — no comparison/review content to cite
Gemini Low — no Google entity footprint (no Wikipedia, no G Business)
Bing Copilot Medium — robots.txt allowlists Bingbot + Bytespider

Quick Wins (Implement This Week)

  1. Generate and publish docs subdomain sitemap — 500+ new citable pages unlocked.
  2. Fix llms.txt redirect — serve 200 directly on apex.
  3. Add /team page with 2+ Person schemas including sameAs to LinkedIn/X/GitHub.
  4. Ship llms-full.txt — auto-generated from docs markdown.
  5. Submit Wikidata entity — "TradingGoose Studio" with instance of: software, disambiguated from the other TradingGoose project.
  6. Launch on Product Hunt — single highest-ROI GEO signal for a developer SaaS launch.

30-Day Action Plan

Week 1: Technical GEO Hardening

  • Publish docs.tradinggoose.ai/sitemap.xml
  • Serve llms.txt with 200 on both apex and www
  • Write and publish llms-full.txt
  • Add HowTo schema to top 10 docs tutorial pages

Week 2: Entity & Authority Foundation

  • Create /team and /about pages with named humans + Person schema
  • Submit Wikidata entity with disambiguator
  • Claim LinkedIn company page, link from footer
  • Cross-link Organization schema via sameAs across apex + docs subdomain

Week 3: Content Surface Expansion

  • Launch /blog with 3 seed articles (comparison + tutorial + roadmap)
  • Add 8+ Q&A pairs to homepage FAQPage
  • Mark up each changelog release entry as TechArticle
  • Create a "vs. alternatives" comparison page (n8n, Zapier, TradingView)

Week 4: Third-Party Authority

  • Product Hunt launch
  • Show HN post
  • Post in r/algotrading and r/selfhosted (disclosure-compliant)
  • Record 2 YouTube walkthroughs (even unlisted is a citable VideoObject)
  • Reach out to the other github.com/TradingGoose for mutual disambiguation

Appendix: Pages Analyzed

URL Title GEO Issues
https://tradinggoose.ai TradingGoose — Visual Workflow Platform for LLM Trading 0 technical, strong schema
https://tradinggoose.ai/signup Signup Thin, auth-gated
https://tradinggoose.ai/login Login Thin, auth-gated
https://tradinggoose.ai/terms Terms of Service Legal — OK
https://tradinggoose.ai/privacy Privacy Policy Legal — OK
https://tradinggoose.ai/licenses Licenses Legal — OK
https://tradinggoose.ai/changelog Changelog Missing Article schema per entry
https://tradinggoose.ai/robots.txt — Exemplary
https://tradinggoose.ai/llms.txt — 308 redirect to www
https://docs.tradinggoose.ai TradingGoose Documentation Not in apex sitemap

Audit constraints: Sitemap contained only 7 URLs so the 50-page crawl limit was not reached. Docs subdomain was sampled (homepage only) but not fully crawled in this audit.