RRepoGEO

REPOGEO REPORT · LITE

pipiku915/FinMem-LLM-StockTrading

Default branch main · commit be814aa4 · scanned 5/30/2026, 9:03:12 AM

GitHub: 900 stars · 191 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 pipiku915/FinMem-LLM-StockTrading, 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 better categorization

    Why:

    COPY-PASTE FIX
    llm, stock-trading, algorithmic-trading, finance, ai-agent, memory-networks, large-language-models
  • highreadme#2
    Reposition README's opening to emphasize its function as an LLM trading agent

    Why:

    CURRENT
    # FINMEM: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design
    
    [](https://www.python.org/downloads/release/python-3100/) [](https://opensource.org/licenses/MIT) [](https://github.com/ambv/black) [](https://arxiv.org/abs/2311.13743)
    
    ```text
    "So we beat on, boats against the current, borne back ceaselessly into the past."
                                            -- F. Scott Fitzgerald: The Great Gatsby
    ```
    
    This repo provides the Python source code for the paper:
    FINMEM: A Performance-Enhanced Large Language Model Trading Agent with Layered Memory and Character Design [[PDF]](https://arxiv.org/pdf/2311.13743.pdf)
    COPY-PASTE FIX
    # FINMEM: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design
    
    [](https://www.python.org/downloads/release/python-3100/) [](https://opensource.org/licenses/MIT) [](https://github.com/ambv/black) [](https://arxiv.org/abs/2311.13743)
    
    ```text
    "So we beat on, boats against the current, borne back ceaselessly into the past."
                                            -- F. Scott Fitzgerald: The Great Gatsby
    ```
    
    This repository provides the Python source code for FinMem, an advanced LLM-powered stock trading agent. It implements layered memory and character design to enhance trading performance, as detailed in our paper: FINMEM: A Performance-Enhanced Large Language Model Trading Agent with Layered Memory and Character Design [[PDF]](https://arxiv.org/pdf/2311.13743.pdf)
  • mediumhomepage#3
    Add the arXiv paper link as the repository homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2311.13743

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 pipiku915/FinMem-LLM-StockTrading
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GPT-4
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GPT-4 · recommended 1×
  2. Claude 3 Opus · recommended 1×
  3. Llama 3 · recommended 1×
  4. Mixtral 8x7B · recommended 1×
  5. BloombergGPT · recommended 1×
  • CATEGORY QUERY
    How to build an intelligent stock trading agent using large language models?
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. Claude 3 Opus
    3. Llama 3
    4. Mixtral 8x7B
    5. BloombergGPT
    6. Google Gemini
    7. FinBERT

    AI recommended 7 alternatives but never named pipiku915/FinMem-LLM-StockTrading. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking enhanced LLM solutions for algorithmic stock trading with advanced memory features.
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. Redis (redis/redis)
    3. ChromaDB (chroma-core/chroma)
    4. LlamaIndex (run-llama/llama_index)
    5. Pinecone
    6. Weaviate (weaviate/weaviate)
    7. Haystack (deepset-ai/haystack)
    8. Elasticsearch (elastic/elasticsearch)
    9. Milvus (milvus-io/milvus)
    10. FAISS (facebookresearch/faiss)
    11. Annoy (spotify/annoy)
    12. Microsoft Semantic Kernel (microsoft/semantic-kernel)
    13. Azure Cosmos DB

    AI recommended 13 alternatives but never named pipiku915/FinMem-LLM-StockTrading. 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 pipiku915/FinMem-LLM-StockTrading?
    pass
    AI did not name pipiku915/FinMem-LLM-StockTrading — 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 pipiku915/FinMem-LLM-StockTrading in production, what risks or prerequisites should they evaluate first?
    pass
    AI named pipiku915/FinMem-LLM-StockTrading 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 pipiku915/FinMem-LLM-StockTrading solve, and who is the primary audience?
    pass
    AI did not name pipiku915/FinMem-LLM-StockTrading — 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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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite