RRepoGEO

REPOGEO REPORT · LITE

lc2panda/StockAnal_Sys

Default branch main · commit bf204334 · scanned 5/9/2026, 6:08:05 AM

GitHub: 834 stars · 196 forks

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 lc2panda/StockAnal_Sys, 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

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

OVERALL DIRECTION
  • highreadme#1
    Clarify the README's main heading to emphasize it's an AI-powered stock analysis system.

    Why:

    CURRENT
    # 智能分析系统
    COPY-PASTE FIX
    # AI驱动多Agent股票智能分析系统
  • hightopics#2
    Add specific topics to improve categorization and searchability.

    Why:

    COPY-PASTE FIX
    python, flask, langgraph, multi-agent-system, stock-analysis, financial-data, ai-powered, investment-decision, quantitative-finance, akshare, baostock
  • mediumreadme#3
    Enhance the '项目概述' (Project Overview) to immediately highlight the multi-agent AI as the core differentiator.

    Why:

    CURRENT
    智能分析系统是一个基于Python、Flask和LangGraph的Web应用，整合了多Agent协同分析能力和人工智能辅助决策功能。系统通过多数据源（AKShare/BaoStock）获取股票数据，结合13个专业Agent（技术分析、基本面、资金流、情绪分析、多空辩论、投资者人格、风险管理、智能决策），为投资者提供全方位的AI驱动投资决策支持。
    COPY-PASTE FIX
    智能分析系统是一个**创新的AI驱动多Agent协同股票分析Web应用**，基于Python、Flask和LangGraph构建。它整合了强大的多Agent协同分析能力和人工智能辅助决策功能，通过13个专业Agent（如技术分析、基本面、资金流、情绪分析、多空辩论、投资者人格、风险管理、智能决策）和多数据源（AKShare/BaoStock），为投资者提供全方位的AI驱动投资决策支持。

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 lc2panda/StockAnal_Sys
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Django
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Django · recommended 1×
  2. Django REST Framework · recommended 1×
  3. FastAPI · recommended 1×
  4. Flask · recommended 1×
  5. Flask-RESTful · recommended 1×
  • CATEGORY QUERY
    How to build an AI-powered web application for comprehensive stock market analysis and investment decision support?
    you: not recommended
    AI recommended (in order):
    1. Django
    2. Django REST Framework
    3. FastAPI
    4. Flask
    5. Flask-RESTful
    6. Flask-RESTX
    7. React
    8. Vue.js
    9. Angular
    10. Scikit-learn
    11. TensorFlow
    12. Keras
    13. PyTorch
    14. NLTK
    15. spaCy
    16. PostgreSQL
    17. Pandas
    18. YFinance
    19. Alpha Vantage API
    20. Finnhub API
    21. Plotly.js
    22. Dash
    23. Chart.js
    24. D3.js

    AI recommended 24 alternatives but never named lc2panda/StockAnal_Sys. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a Python-based multi-agent system for real-time financial data analysis and automated trading insights.
    you: not recommended
    AI recommended (in order):
    1. OpenBB Terminal (OpenBB-finance/OpenBBTerminal)
    2. FinRL (AI4Finance-LLC/FinRL)
    3. Mesa (projectmesa/mesa)
    4. Ray (ray-project/ray)
    5. Gymnasium (Farama-Foundation/Gymnasium)

    AI recommended 5 alternatives but never named lc2panda/StockAnal_Sys. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 lc2panda/StockAnal_Sys?
    pass
    AI did not name lc2panda/StockAnal_Sys — likely talking about a different project

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

  • If a team adopts lc2panda/StockAnal_Sys in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lc2panda/StockAnal_Sys 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 lc2panda/StockAnal_Sys solve, and who is the primary audience?
    pass
    AI named lc2panda/StockAnal_Sys explicitly

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

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lc2panda/StockAnal_Sys — 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