RRepoGEO

REPOGEO REPORT · LITE

kh-kim/stock_market_reinforcement_learning

Default branch master · commit d5d2592d · scanned 6/9/2026, 9:03:21 AM

GitHub: 793 stars · 314 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 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 kh-kim/stock_market_reinforcement_learning, 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 to improve categorization

    Why:

    COPY-PASTE FIX
    reinforcement-learning, deep-q-learning, policy-gradient, openai-gym, stock-trading, financial-modeling, quantitative-finance, keras, machine-learning-environment
  • highlicense#2
    Add a LICENSE file to clarify usage rights

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • mediumreadme#3
    Clarify the project's research/framework purpose in the README overview

    Why:

    CURRENT
    This project provides a general environment for stock market trading simulation using OpenAI Gym. Training data is a close price of each day, which is downloaded from Google Finance, but you can apply any data if you want. Also, it contains simple Deep Q-learning and Policy Gradient from Karpathy's post.
    COPY-PASTE FIX
    This project offers a customizable OpenAI Gym environment for stock market trading simulations, designed as a general framework for deep reinforcement learning research. It includes implementations of Deep Q-learning and Policy Gradient, providing a foundation for developing and testing novel trading strategies.

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 kh-kim/stock_market_reinforcement_learning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pandas-dev/pandas
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. pandas-dev/pandas · recommended 2×
  2. QuantConnect/Lean · recommended 2×
  3. Farama-Foundation/Gymnasium · recommended 1×
  4. DLR-RM/stable-baselines3 · recommended 1×
  5. mementum/backtrader · recommended 1×
  • CATEGORY QUERY
    How to build a stock market trading simulation environment using reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. Gymnasium (Farama-Foundation/Gymnasium)
    2. Stable Baselines3 (DLR-RM/stable-baselines3)
    3. Backtrader (mementum/backtrader)
    4. Pandas (pandas-dev/pandas)
    5. TA-Lib (TA-Lib/ta-lib)
    6. TensorFlow (tensorflow/tensorflow)
    7. PyTorch (pytorch/pytorch)
    8. QuantConnect (QuantConnect/Lean)

    AI recommended 8 alternatives but never named kh-kim/stock_market_reinforcement_learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good OpenAI Gym environments for financial trading strategy development?
    you: not recommended
    AI recommended (in order):
    1. FinRL-Meta (AI4Finance-LLC/FinRL-Meta)
    2. Gym-AnyTrading (AminHP/gym-anytrading)
    3. gym-trading-env (AminHP/gym-trading-env)
    4. QuantConnect (QuantConnect/Lean)
    5. pandas (pandas-dev/pandas)
    6. numpy (numpy/numpy)

    AI recommended 6 alternatives but never named kh-kim/stock_market_reinforcement_learning. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 kh-kim/stock_market_reinforcement_learning?
    pass
    AI named kh-kim/stock_market_reinforcement_learning explicitly

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

  • If a team adopts kh-kim/stock_market_reinforcement_learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named kh-kim/stock_market_reinforcement_learning 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 kh-kim/stock_market_reinforcement_learning solve, and who is the primary audience?
    pass
    AI did not name kh-kim/stock_market_reinforcement_learning — 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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