RRepoGEO

REPOGEO REPORT · LITE

Y-Research-SBU/QuantAgent

Default branch main · commit 92519f80 · scanned 6/18/2026, 6:28:11 AM

GitHub: 2,745 stars · 596 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 Y-Research-SBU/QuantAgent, 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
  • highabout#1
    Update the repository description to specify its niche

    Why:

    CURRENT
    Official Repository for QuantAgent
    COPY-PASTE FIX
    QuantAgent: A multi-agent LLM framework for price-driven high-frequency trading and financial market analysis.
  • hightopics#2
    Add specific topics related to finance and trading

    Why:

    CURRENT
    agentic-ai, large-language-models
    COPY-PASTE FIX
    agentic-ai, large-language-models, high-frequency-trading, algorithmic-trading, quantitative-finance, financial-llm, multi-agent-system
  • mediumreadme#3
    Strengthen the README's opening sentence to reinforce the specific domain

    Why:

    CURRENT
    A sophisticated multi-agent tradin
    COPY-PASTE FIX
    QuantAgent is a sophisticated multi-agent LLM framework designed specifically for price-driven high-frequency trading and advanced financial market analysis.

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 Y-Research-SBU/QuantAgent
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. AutoGen · recommended 2×
  4. CrewAI · recommended 2×
  5. Confluent Kafka · recommended 1×
  • CATEGORY QUERY
    How to use multi-agent large language models for high-frequency algorithmic trading?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGen
    4. CrewAI
    5. Confluent Kafka
    6. Apache Flink
    7. Kdb+
    8. OpenAI GPT-4
    9. GPT-3.5 Turbo
    10. Llama 3
    11. Mixtral
    12. Google Gemini
    13. FIX Protocol
    14. Alpaca Markets API
    15. Interactive Brokers API
    16. Prometheus
    17. Grafana
    18. Elasticsearch
    19. Logstash
    20. Kibana
    21. Datadog
    22. New Relic

    AI recommended 22 alternatives but never named Y-Research-SBU/QuantAgent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks exist for building LLM-powered agentic systems for financial market analysis?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGen
    4. Haystack
    5. CrewAI
    6. OpenAI Assistants API
    7. DSPy

    AI recommended 7 alternatives but never named Y-Research-SBU/QuantAgent. 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 Y-Research-SBU/QuantAgent?
    pass
    AI named Y-Research-SBU/QuantAgent explicitly

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

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

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

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Y-Research-SBU/QuantAgent — 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