RRepoGEO

REPOGEO REPORT · LITE

AlphaFin-proj/AlphaFin

Default branch main · commit 068d93ea · scanned 6/16/2026, 10:38:08 PM

GitHub: 809 stars · 80 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface AlphaFin-proj/AlphaFin, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise description to the repository's About section

    Why:

    COPY-PASTE FIX
    AlphaFin provides a retrieval-augmented Stock-Chain framework, AlphaFin dataset, and StockGPT chat models for financial analysis, stock trend prediction, and financial Q&A research.
  • mediumreadme#2
    Add a concise summary statement immediately after the main title in the README

    Why:

    COPY-PASTE FIX
    AlphaFin provides an open-source retrieval-augmented framework (Stock-Chain), a financial dataset, and StockGPT models for advanced financial analysis research, focusing on stock trend prediction and Q&A.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface AlphaFin-proj/AlphaFin
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers Library
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers Library · recommended 1×
  2. BERT · recommended 1×
  3. RoBERTa · recommended 1×
  4. DistilBERT · recommended 1×
  5. XLNet · recommended 1×
  • CATEGORY QUERY
    What AI models and datasets are available for financial market analysis research?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. BERT
    3. RoBERTa
    4. DistilBERT
    5. XLNet
    6. T5
    7. FinBERT
    8. Prophet
    9. PyTorch
    10. TensorFlow
    11. Quandl
    12. Nasdaq Data Link
    13. EOD Stock Prices
    14. Zacks Fundamental Data
    15. Yahoo Finance
    16. Kaggle Datasets
    17. FRED (Federal Reserve Economic Data)
    18. SEC EDGAR Database
    19. Twitter API
    20. Google News
    21. Reuters
    22. Bloomberg

    AI recommended 22 alternatives but never named AlphaFin-proj/AlphaFin. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I build a retrieval-augmented chat system for financial data tasks?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Hugging Face Transformers
    4. Hugging Face Datasets
    5. OpenAI API
    6. Azure OpenAI Service
    7. Pinecone
    8. Weaviate
    9. Chroma
    10. Faiss
    11. Streamlit
    12. Gradio

    AI recommended 12 alternatives but never named AlphaFin-proj/AlphaFin. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of AlphaFin-proj/AlphaFin?
    pass
    AI named AlphaFin-proj/AlphaFin explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts AlphaFin-proj/AlphaFin in production, what risks or prerequisites should they evaluate first?
    pass
    AI named AlphaFin-proj/AlphaFin explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo AlphaFin-proj/AlphaFin solve, and who is the primary audience?
    pass
    AI named AlphaFin-proj/AlphaFin explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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AlphaFin-proj/AlphaFin — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite