RRepoGEO

REPOGEO REPORT · LITE

ArvinLovegood/go-stock

Default branch dev · commit f3a89bc6 · scanned 5/16/2026, 11:41:15 PM

GitHub: 5,722 stars · 1,023 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 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 ArvinLovegood/go-stock, 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
    Reposition README opening to clarify it's an actively maintained AI-powered desktop application

    Why:

    CURRENT
    # go-stock : 基于大语言模型的AI赋能股票分析工具
    COPY-PASTE FIX
    # go-stock : 基于大语言模型的AI赋能股票分析工具
## 🚀 这是一个积极维护的跨平台桌面应用程序，利用AI大模型提供智能股票分析、选股和市场情绪洞察。
  • mediumtopics#2
    Add more specific topics to improve AI categorization

    Why:

    CURRENT
    ai-tools, deepseek, golang, lmstudio, naiveui, ollama, openai, stock, wails
    COPY-PASTE FIX
    ai-tools, deepseek, golang, lmstudio, naiveui, ollama, openai, stock, wails, stock-analysis, llm-application, desktop-app, financial-analysis, investment-tools
  • lowreadme#3
    Add explicit disclaimer that it is not an automated trading bot

    Why:

    CURRENT
    本项目仅供娱乐，不喜勿喷，AI分析股票结果仅供学习研究，投资有风险，请谨慎使用。
    COPY-PASTE FIX
    本项目仅供娱乐，不喜勿喷，AI分析股票结果仅供学习研究，投资有风险，请谨慎使用。**请注意，本项目是一个股票分析和选股工具，不提供任何自动化交易功能。**

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 ArvinLovegood/go-stock
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
github.com/gonum/gonum
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. github.com/gonum/gonum · recommended 2×
  2. TrendSpider · recommended 1×
  3. Stock Rover · recommended 1×
  4. Simply Wall St · recommended 1×
  5. Trade Ideas · recommended 1×
  • CATEGORY QUERY
    Looking for an AI-driven tool to analyze market trends and assist with stock selection.
    you: not recommended
    AI recommended (in order):
    1. TrendSpider
    2. Stock Rover
    3. Simply Wall St
    4. Trade Ideas
    5. Koyfin
    6. AlphaSense

    AI recommended 6 alternatives but never named ArvinLovegood/go-stock. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are some Go-based applications for stock market analysis using large language models?
    you: not recommended
    AI recommended (in order):
    1. go-openai (github.com/sashabaranov/go-openai)
    2. resty/v2 (github.com/go-resty/resty/v2)
    3. gonum/stat (github.com/gonum/gonum)
    4. gonum/floats (github.com/gonum/gonum)
    5. colly (github.com/gocolly/colly)
    6. gorm (gorm.io/gorm)
    7. influxdb-client-go/v2 (github.com/influxdata/influxdb-client-go/v2)
    8. alpaca-trade-api-go/v2 (github.com/alpacahq/alpaca-trade-api-go/v2)
    9. go-quote (github.com/markcheno/go-quote)
    10. techan (github.com/sdcoffey/techan)
    11. kafka-go (github.com/segmentio/kafka-go)
    12. nats.go (github.com/nats-io/nats.go)
    13. cron (github.com/robfig/cron)
    14. cobra (github.com/spf13/cobra)
    15. cli (github.com/urfave/cli)
    16. gin (github.com/gin-gonic/gin)
    17. echo (github.com/labstack/echo)
    18. mux (github.com/gorilla/mux)

    AI recommended 18 alternatives but never named ArvinLovegood/go-stock. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 ArvinLovegood/go-stock?
    pass
    AI named ArvinLovegood/go-stock explicitly

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

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

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

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ArvinLovegood/go-stock — 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