RRepoGEO

REPOGEO REPORT · LITE

notadamking/RLTrader

Default branch master · commit c5c85db5 · scanned 6/20/2026, 1:11:51 PM

GitHub: 1,859 stars · 547 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
35 /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
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 notadamking/RLTrader, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening to clarify its value as an RL environment

    Why:

    CURRENT
    # RLTrader — The Predecessor to TensorTrade
    
    Development on this library has slowed down, in favor of working on TensorTrade - a framework for trading with RL: https://github.com/notadamking/tensortrade
    COPY-PASTE FIX
    # RLTrader: A Deep Reinforcement Learning Environment for Cryptocurrency Trading (Predecessor to TensorTrade)
    
    RLTrader offers a foundational OpenAI Gym-compatible environment for developing and evaluating deep reinforcement learning strategies in cryptocurrency trading. While development has transitioned to TensorTrade (https://github.com/notadamking/tensortrade), this repository remains a valuable resource for understanding initial concepts and implementations, as detailed in our Medium articles.
  • mediumreadme#2
    Add a section clarifying RLTrader's role compared to trading APIs and other RL frameworks

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example, after the initial description, titled 'RLTrader's Role and Differentiators'. Content: 'Unlike direct trading APIs (e.g., Binance API, Coinbase Pro API), RLTrader provides a complete, simulated OpenAI Gym environment specifically designed for developing and testing deep reinforcement learning agents for cryptocurrency trading. It offers a structured framework for experimentation, distinct from raw data feeds or execution platforms, and serves as a foundational precursor to more advanced frameworks like TensorTrade.'

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 notadamking/RLTrader
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Backtrader
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Backtrader · recommended 2×
  2. Binance API · recommended 1×
  3. Coinbase Pro API · recommended 1×
  4. KuCoin API · recommended 1×
  5. CryptoCompare API · recommended 1×
  • CATEGORY QUERY
    How can I build an automated cryptocurrency trading bot using machine learning?
    you: not recommended
    AI recommended (in order):
    1. Binance API
    2. Coinbase Pro API
    3. KuCoin API
    4. CryptoCompare API
    5. NewsAPI.org
    6. Google News API
    7. PostgreSQL
    8. InfluxDB
    9. Pandas
    10. NumPy
    11. TA-Lib
    12. Pandas-TA
    13. NLTK
    14. SpaCy
    15. Hugging Face Transformers
    16. Scikit-learn
    17. TensorFlow
    18. Keras
    19. PyTorch
    20. RLlib
    21. Stable Baselines3
    22. Backtrader
    23. Zipline
    24. QuantConnect (Lean Engine)
    25. CCXT (CryptoCurrency eXchange Trading Library)
    26. Freqtrade
    27. Hummingbot
    28. AWS
    29. Google Cloud Platform
    30. DigitalOcean
    31. Vultr
    32. Docker

    AI recommended 32 alternatives but never named notadamking/RLTrader. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good reinforcement learning environments for developing financial trading strategies?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym
    2. FinRL
    3. QuantConnect
    4. TensorTrade
    5. Gym-Forex
    6. Backtrader

    AI recommended 6 alternatives but never named notadamking/RLTrader. 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 notadamking/RLTrader?
    pass
    AI named notadamking/RLTrader explicitly

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

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

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

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notadamking/RLTrader — 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