RRepoGEO

REPOGEO REPORT · LITE

AI4Finance-Foundation/FinRL-Trading

Default branch master · commit e65d6f04 · scanned 6/22/2026, 5:47:06 AM

GitHub: 3,337 stars · 1,020 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-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 FinRL-X as the successor to FinRL in the README's opening

    Why:

    CURRENT
    ### An AI-Native Modular Infrastructure for Quantitative Trading
    COPY-PASTE FIX
    ### An AI-Native Modular Infrastructure for Quantitative Trading

FinRL-X is the next-generation successor to the original FinRL framework, designed for the LLM and agentic AI era.
  • highreadme#2
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison

Unlike general-purpose reinforcement learning libraries (e.g., Stable Baselines3, Ray RLlib), FinRL-X provides a domain-specific, full-stack solution tailored for quantitative trading. Compared to broad algorithmic trading platforms (e.g., QuantConnect, Zipline), FinRL-X is uniquely designed as an AI-native infrastructure, deeply integrating deep reinforcement learning and agentic AI for strategy development and deployment.
  • mediumtopics#3
    Add topics emphasizing full-stack and production-readiness

    Why:

    CURRENT
    a2c-algorithm, automated-stock-trading, ddpg, deep-reinforcement-learning, ensemble-strategy, finrl, finrl-x, portfolio, portfolio-allocation, ppo, sharpe-ratio, stock-selection, stock-trading, stock-trading-strategy
    COPY-PASTE FIX
    a2c-algorithm, automated-stock-trading, ddpg, deep-reinforcement-learning, ensemble-strategy, finrl, finrl-x, portfolio, portfolio-allocation, ppo, sharpe-ratio, stock-selection, stock-trading, stock-trading-strategy, algorithmic-trading-platform, production-ready, full-stack, quantitative-finance-platform

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-Trading
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Stable Baselines3
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Stable Baselines3 · recommended 1×
  2. FinRL · recommended 1×
  3. Ray RLlib · recommended 1×
  4. OpenAI Gym · recommended 1×
  5. Gymnasium · recommended 1×
  • CATEGORY QUERY
    How to build and backtest deep reinforcement learning strategies for automated stock trading?
    you: not recommended
    AI recommended (in order):
    1. Stable Baselines3
    2. FinRL
    3. Ray RLlib
    4. OpenAI Gym
    5. Gymnasium
    6. Backtrader
    7. TensorFlow
    8. PyTorch
    9. QuantConnect
    10. Quantopian

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

    Show full AI answer
  • CATEGORY QUERY
    What are robust, modular platforms for developing and deploying AI-driven algorithmic trading systems?
    you: not recommended
    AI recommended (in order):
    1. QuantConnect (Lean Engine) (QuantConnect/Lean)
    2. Zipline (quantopian/zipline)
    3. Alpaca Trade API
    4. MetaTrader 5
    5. Interactive Brokers API
    6. Backtrader (mementum/backtrader)

    AI recommended 6 alternatives but never named AI4Finance-Foundation/FinRL-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 AI4Finance-Foundation/FinRL-Trading?
    pass
    AI named AI4Finance-Foundation/FinRL-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 AI4Finance-Foundation/FinRL-Trading in production, what risks or prerequisites should they evaluate first?
    pass
    AI named AI4Finance-Foundation/FinRL-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 AI4Finance-Foundation/FinRL-Trading solve, and who is the primary audience?
    pass
    AI named AI4Finance-Foundation/FinRL-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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AI4Finance-Foundation/FinRL-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