RRepoGEO

REPOGEO REPORT · LITE

AI4Finance-Foundation/FinRL-Meta

Default branch master · commit 15405db8 · scanned 6/28/2026, 3:37:38 AM

GitHub: 1,898 stars · 749 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 AI4Finance-Foundation/FinRL-Meta, 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 README's opening to lead with unique value proposition

    Why:

    CURRENT
    > Part of the **FinRL ecosystem**. For the next-generation production-oriented trading stack, see **FinRL-X / FinRL-Trading**.
...
**FinRL®-Meta** is the **market environment and benchmark layer** of the FinRL ecosystem. It builds a universe of market environments for data-driven financial reinforcement learning and helps users easily develop, evaluate, and compare trading agents across diverse financial tasks.
    COPY-PASTE FIX
    FinRL®-Meta is the dedicated market environment and benchmark layer for data-driven financial reinforcement learning, providing a comprehensive universe of simulated markets to easily develop, evaluate, and compare trading agents. It is part of the broader FinRL ecosystem; for the next-generation production-oriented trading stack, see **FinRL-X / FinRL-Trading**.
  • mediumabout#2
    Expand the repository description to include 'benchmarks' and 'evaluation'

    Why:

    CURRENT
    FinRL®-Meta: Dynamic datasets and market environments for FinRL.
    COPY-PASTE FIX
    FinRL®-Meta: Dynamic datasets, market environments, and benchmarks for financial reinforcement learning, enabling evaluation and comparison of trading agents.
  • lowtopics#3
    Add more specific topics related to financial simulation and RL benchmarks

    Why:

    CURRENT
    deep-reinforcement-learning, drl-trading-agents, finance, finrl-library, fintech, openai, openai-gym-environments
    COPY-PASTE FIX
    deep-reinforcement-learning, drl-trading-agents, finance, finrl-library, fintech, openai, openai-gym-environments, financial-simulation, trading-environments, rl-benchmarks, quant-finance-environments

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 AI4Finance-Foundation/FinRL-Meta
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI Gym
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI Gym · recommended 2×
  2. FinRL · recommended 2×
  3. Backtrader · recommended 2×
  4. Ray RLlib · recommended 1×
  5. TensorFlow · recommended 1×
  • CATEGORY QUERY
    What tools are available for developing and evaluating AI trading agents using deep reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym
    2. FinRL
    3. Ray RLlib
    4. TensorFlow
    5. PyTorch
    6. Stable Baselines3
    7. Backtrader
    8. QuantConnect
    9. Quantopian
    10. Lean Engine

    AI recommended 10 alternatives but never named AI4Finance-Foundation/FinRL-Meta. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for simulated market environments and benchmarks to train financial deep reinforcement learning models.
    you: not recommended
    AI recommended (in order):
    1. FinRL
    2. OpenAI Gym
    3. QuantConnect (Lean)
    4. TensorTrade
    5. Gym-Trading-Env
    6. Backtrader
    7. Pandas
    8. NumPy
    9. Yahoo Finance API
    10. Alpha Vantage

    AI recommended 10 alternatives but never named AI4Finance-Foundation/FinRL-Meta. 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 AI4Finance-Foundation/FinRL-Meta?
    pass
    AI named AI4Finance-Foundation/FinRL-Meta explicitly

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

  • If a team adopts AI4Finance-Foundation/FinRL-Meta in production, what risks or prerequisites should they evaluate first?
    pass
    AI named AI4Finance-Foundation/FinRL-Meta 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 AI4Finance-Foundation/FinRL-Meta solve, and who is the primary audience?
    pass
    AI named AI4Finance-Foundation/FinRL-Meta explicitly

    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
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AI4Finance-Foundation/FinRL-Meta — RepoGEO report