RRepoGEO

REPOGEO REPORT · LITE

HKUDS/Vibe-Trading

Default branch main · commit 13236a4c · scanned 6/17/2026, 11:21:17 PM

GitHub: 12,466 stars · 2,399 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 HKUDS/Vibe-Trading, 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 the README's opening to clarify Vibe-Trading as a development framework

    Why:

    CURRENT
    <h1 align="center">Vibe-Trading: Your Personal Trading Agent</h1>
<p align="center">
  <b>One Command to Empower Your Agent with Comprehensive Trading Capabilities</b>
</p>
    COPY-PASTE FIX
    <h1 align="center">Vibe-Trading: An Open-Source Framework for Multi-Agent AI Trading Systems</h1>
<p align="center">
  <b>Empower your trading strategies with a comprehensive, customizable platform for building and backtesting AI-driven agents.</b>
</p>
  • mediumreadme#2
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    ## 💡 Comparison with Alternatives

Vibe-Trading is a specialized open-source framework for developing and backtesting multi-agent AI trading systems.

-   **Vs. General ML Libraries (e.g., TensorFlow, PyTorch, OpenAI Gym):** While these provide foundational AI tools, Vibe-Trading is purpose-built for financial markets, integrating trading-specific functionalities like data handling, backtesting environments, and agent orchestration.
-   **Vs. General Algorithmic Trading Platforms (e.g., QuantConnect Lean, Backtrader, Zipline):** These platforms offer robust backtesting and execution. Vibe-Trading differentiates itself by focusing on an agent-centric, LLM-powered approach, enabling more intelligent, adaptive, and multi-agent driven strategies, rather than just script-based automation.
  • lowabout#3
    Refine the repository description to emphasize 'framework' or 'platform'

    Why:

    CURRENT
    Vibe-Trading: Your Personal Trading Agent
    COPY-PASTE FIX
    Vibe-Trading: An open-source framework for building and backtesting multi-agent AI trading systems.

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 HKUDS/Vibe-Trading
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
QuantConnect/Lean
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. QuantConnect/Lean · recommended 2×
  2. Alpaca Markets (API) · recommended 1×
  3. tensorflow/tensorflow · recommended 1×
  4. pytorch/pytorch · recommended 1×
  5. openai/gym · recommended 1×
  • CATEGORY QUERY
    How can I build an automated trading system using AI agents?
    you: not recommended
    AI recommended (in order):
    1. QuantConnect (Lean Engine) (QuantConnect/Lean)
    2. Alpaca Markets (API)
    3. TensorFlow (tensorflow/tensorflow)
    4. PyTorch (pytorch/pytorch)
    5. OpenAI Gym (openai/gym)
    6. Stable Baselines3 (DLR-RM/stable-baselines3)
    7. Pandas (pandas-dev/pandas)
    8. NumPy (numpy/numpy)
    9. Scikit-learn (scikit-learn/scikit-learn)
    10. Apache Kafka (apache/kafka)
    11. RabbitMQ (rabbitmq/rabbitmq-server)

    AI recommended 11 alternatives but never named HKUDS/Vibe-Trading. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a Python framework to backtest multi-agent quantitative trading strategies.
    you: not recommended
    AI recommended (in order):
    1. Lean Engine (QuantConnect/Lean)
    2. Backtrader (mementum/backtrader)
    3. Zipline (quantopian/zipline)
    4. PyAlgoTrade (gbeced/pyalgotrade)
    5. Catalyst (enigmampc/catalyst)
    6. Fynance (ArthurFDLR/fynance)

    AI recommended 6 alternatives but never named HKUDS/Vibe-Trading. 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 HKUDS/Vibe-Trading?
    pass
    AI named HKUDS/Vibe-Trading explicitly

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

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

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

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HKUDS/Vibe-Trading — 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