RRepoGEO

REPOGEO REPORT · LITE

cbailes/awesome-deep-trading

Default branch master · commit 91eee433 · scanned 6/25/2026, 6:48:16 AM

GitHub: 1,997 stars · 351 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /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
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 cbailes/awesome-deep-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
    Explicitly state 'awesome list' in the README's opening sentence

    Why:

    CURRENT
    List of code, papers, and resources for AI/deep learning/machine learning/neural networks applied to algorithmic trading.
    COPY-PASTE FIX
    An awesome list of curated code, papers, and resources for AI/deep learning/machine learning/neural networks applied to algorithmic trading.
  • highabout#2
    Add the repository URL as the homepage in the About section

    Why:

    COPY-PASTE FIX
    https://github.com/cbailes/awesome-deep-trading
  • mediumreadme#3
    Add a concise differentiator statement to the README's introduction

    Why:

    COPY-PASTE FIX
    Unlike general machine learning libraries or comprehensive textbooks, this repository specifically curates the most impactful and relevant resources for deep learning in financial trading, saving researchers and practitioners time in discovery.

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 cbailes/awesome-deep-trading
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Advances in Financial Machine Learning
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Advances in Financial Machine Learning · recommended 1×
  2. Quantopian Lecture Series · recommended 1×
  3. Machine Learning for Algorithmic Trading · recommended 1×
  4. Kaggle Competitions · recommended 1×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    Seeking curated resources for applying deep learning to quantitative trading strategies and analysis.
    you: not recommended
    AI recommended (in order):
    1. Advances in Financial Machine Learning
    2. Quantopian Lecture Series
    3. Machine Learning for Algorithmic Trading
    4. Kaggle Competitions
    5. PyTorch
    6. TensorFlow
    7. Papers With Code
    8. Algorithmic Trading with Python

    AI recommended 8 alternatives but never named cbailes/awesome-deep-trading. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best machine learning tools for developing automated cryptocurrency trading systems?
    you: not recommended
    AI recommended (in order):
    1. Python
    2. NumPy (numpy/numpy)
    3. Pandas (pandas-dev/pandas)
    4. Scikit-learn (scikit-learn/scikit-learn)
    5. TensorFlow (tensorflow/tensorflow)
    6. Keras (keras-team/keras)
    7. PyTorch (pytorch/pytorch)
    8. QuantConnect (Lean Engine) (QuantConnect/Lean)
    9. MetaTrader 5
    10. MetaTrader5 package (MetaQuotes/MetaTrader5)
    11. R
    12. quantmod (quantmod/quantmod)
    13. caret (topepo/caret)
    14. forecast (robjhyndman/forecast)
    15. TTR (joshuaulrich/TTR)
    16. Google Cloud AI Platform
    17. AWS SageMaker
    18. Azure Machine Learning
    19. Jupyter Notebooks
    20. JupyterLab

    AI recommended 20 alternatives but never named cbailes/awesome-deep-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
    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 cbailes/awesome-deep-trading?
    pass
    AI did not name cbailes/awesome-deep-trading — 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?

  • If a team adopts cbailes/awesome-deep-trading in production, what risks or prerequisites should they evaluate first?
    pass
    AI named cbailes/awesome-deep-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 cbailes/awesome-deep-trading solve, and who is the primary audience?
    pass
    AI named cbailes/awesome-deep-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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cbailes/awesome-deep-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