RRepoGEO

REPOGEO REPORT · LITE

0voice/Awesome-QuantDev-Learn

Default branch main · commit 9257ab4f · scanned 5/30/2026, 5:23:18 AM

GitHub: 849 stars · 120 forks

AI VISIBILITY SCORE
22 /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
1 / 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 0voice/Awesome-QuantDev-Learn, 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
  • hightopics#1
    Add 'awesome-list' and 'learning-path' topics

    Why:

    CURRENT
    c, cpp, python, quant, quantdev
    COPY-PASTE FIX
    c, cpp, python, quant, quantdev, awesome-list, learning-path, quant-learning-path
  • mediumhomepage#2
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/0voice/Awesome-QuantDev-Learn
  • lowreadme#3
    Clarify the license in the README

    Why:

    COPY-PASTE FIX
    This project is licensed under the terms specified in the [LICENSE.md](LICENSE.md) file.

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 0voice/Awesome-QuantDev-Learn
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Zipline
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Zipline · recommended 2×
  2. Backtrader · recommended 2×
  3. Quantopian Lecture Series · recommended 1×
  4. Python for Finance · recommended 1×
  5. Quantitative Trading: How to Build Your Own Algorithmic Trading Business · recommended 1×
  • CATEGORY QUERY
    Seeking a comprehensive learning path for quantitative trading development, covering Python and C++.
    you: not recommended
    AI recommended (in order):
    1. Quantopian Lecture Series
    2. Python for Finance
    3. Quantitative Trading: How to Build Your Own Algorithmic Trading Business
    4. C++ for Quantitative Finance
    5. Boost.Python
    6. Pybind11
    7. High-Performance Python
    8. Zipline
    9. Backtrader
    10. Lean (QuantConnect)

    AI recommended 10 alternatives but never named 0voice/Awesome-QuantDev-Learn. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find resources to learn building automated trading strategies and backtesting platforms?
    you: not recommended
    AI recommended (in order):
    1. Quantopian
    2. QuantRocket
    3. QuantConnect
    4. Backtrader
    5. Alpaca Markets
    6. Zipline
    7. Udemy
    8. Coursera

    AI recommended 8 alternatives but never named 0voice/Awesome-QuantDev-Learn. 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 0voice/Awesome-QuantDev-Learn?
    pass
    AI did not name 0voice/Awesome-QuantDev-Learn — 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 0voice/Awesome-QuantDev-Learn in production, what risks or prerequisites should they evaluate first?
    pass
    AI named 0voice/Awesome-QuantDev-Learn 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 0voice/Awesome-QuantDev-Learn solve, and who is the primary audience?
    pass
    AI did not name 0voice/Awesome-QuantDev-Learn — 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?

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MARKDOWN (README)
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0voice/Awesome-QuantDev-Learn — 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