RRepoGEO

REPOGEO REPORT · LITE

LuckyOne7777/LLM-Trading-Lab

Default branch main · commit 1cdd2ee9 · scanned 5/30/2026, 8:28:07 AM

GitHub: 7,459 stars · 1,569 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 LuckyOne7777/LLM-Trading-Lab, 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 specific topics for LLM-driven trading and research

    Why:

    COPY-PASTE FIX
    llm-trading, algorithmic-trading, quantitative-finance, portfolio-management, ai-research, financial-llm, micro-cap-equities, trading-experiments
  • highlicense#2
    Add a LICENSE file to clarify usage rights

    Why:

    CURRENT
    (no LICENSE file detected)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the content of the MIT License.
  • mediumreadme#3
    Reposition the README's opening to emphasize its framework aspect

    Why:

    CURRENT
    # LLM Trading Lab
This repository started as a **6-month live micro-cap trading experiment** in which a large language model (ChatGPT) manages a real-money portfolio under strict, predefined rules.
What began as a single experiment has evolved into a **baseline framework** for studying how large language models behave as portfolio decision-makers.
    COPY-PASTE FIX
    # LLM Trading Lab: A Framework for AI-Driven Portfolio Experiments
This repository provides a **baseline framework** for studying how large language models behave as portfolio decision-makers, initially developed for a **6-month live micro-cap trading experiment** where ChatGPT managed a real-money portfolio.

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 LuckyOne7777/LLM-Trading-Lab
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 2×
  2. Backtrader · recommended 2×
  3. MLflow · recommended 2×
  4. Quandl (Nasdaq Data Link) · recommended 1×
  5. Alpha Vantage · recommended 1×
  • CATEGORY QUERY
    How to set up an experimental framework for AI-driven stock portfolio management?
    you: not recommended
    AI recommended (in order):
    1. Quandl (Nasdaq Data Link)
    2. Alpha Vantage
    3. PostgreSQL
    4. Pandas
    5. NumPy
    6. scikit-learn
    7. PyTorch
    8. TensorFlow/Keras
    9. Zipline
    10. Backtrader
    11. QuantConnect (Lean)
    12. MLflow
    13. Weights & Biases (W&B)

    AI recommended 13 alternatives but never named LuckyOne7777/LLM-Trading-Lab. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help evaluate large language models as financial market decision-makers?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Backtrader
    4. QuantConnect
    5. Quantopian
    6. OpenBB Terminal
    7. MLflow
    8. TensorFlow
    9. PyTorch

    AI recommended 9 alternatives but never named LuckyOne7777/LLM-Trading-Lab. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 LuckyOne7777/LLM-Trading-Lab?
    pass
    AI named LuckyOne7777/LLM-Trading-Lab explicitly

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

  • If a team adopts LuckyOne7777/LLM-Trading-Lab in production, what risks or prerequisites should they evaluate first?
    pass
    AI named LuckyOne7777/LLM-Trading-Lab 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 LuckyOne7777/LLM-Trading-Lab solve, and who is the primary audience?
    pass
    AI did not name LuckyOne7777/LLM-Trading-Lab — 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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LuckyOne7777/LLM-Trading-Lab — 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