RRepoGEO

REPOGEO REPORT · LITE

khscience/OSkhQuant

Default branch main · commit 7228f557 · scanned 6/19/2026, 7:36:58 AM

GitHub: 1,302 stars · 386 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
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 khscience/OSkhQuant, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    quantitative-trading, backtesting, a-share, stock-market, python, trading-strategy, quant-framework
  • mediumreadme#2
    Clarify the project's license(s) directly in the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example:

## 📄 许可协议

本项目的源代码遵循 [在此处填写具体许可证名称或条款，例如：自定义许可协议，允许自由使用、修改和分发，但需保留原作者信息。请查阅 `LICENSE` 文件获取完整详情。]

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 khscience/OSkhQuant
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
quantopian/zipline
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. quantopian/zipline · recommended 2×
  2. mementum/backtrader · recommended 2×
  3. QuantConnect/Lean · recommended 2×
  4. quantopian/pyfolio · recommended 1×
  5. vnpy/vnpy · recommended 1×
  • CATEGORY QUERY
    Looking for an open-source quantitative backtesting framework for A-share market analysis.
    you: not recommended
    AI recommended (in order):
    1. Zipline (quantopian/zipline)
    2. pyfolio (quantopian/pyfolio)
    3. Backtrader (mementum/backtrader)
    4. vn.py (vnpy/vnpy)
    5. LEAN Engine (QuantConnect/Lean)
    6. Catalyst (enigmampc/catalyst)

    AI recommended 6 alternatives but never named khscience/OSkhQuant. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good free tools for developing and visualizing stock trading strategies?
    you: not recommended
    AI recommended (in order):
    1. QuantConnect (Lean) (QuantConnect/Lean)
    2. Backtrader (mementum/backtrader)
    3. TradingView (Free Plan)
    4. MetaTrader 5 (MT5)
    5. Zipline (quantopian/zipline)
    6. PyAlgoTrade (gbeced/pyalgotrade)

    AI recommended 6 alternatives but never named khscience/OSkhQuant. 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 khscience/OSkhQuant?
    pass
    AI did not name khscience/OSkhQuant — 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 khscience/OSkhQuant in production, what risks or prerequisites should they evaluate first?
    pass
    AI named khscience/OSkhQuant 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 khscience/OSkhQuant solve, and who is the primary audience?
    pass
    AI named khscience/OSkhQuant explicitly

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

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khscience/OSkhQuant — 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