RRepoGEO

REPOGEO REPORT · LITE

philipperemy/deep-learning-bitcoin

Default branch master · commit b04f1157 · scanned 6/5/2026, 7:07:45 PM

GitHub: 530 stars · 130 forks

AI VISIBILITY SCORE
22 /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
1 / 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 philipperemy/deep-learning-bitcoin, 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 opening to highlight unique image-based approach

    Why:

    CURRENT
    # When Bitcoin meets Artificial Intelligence
    
    Exploiting Bitcoin prices patterns with Deep Learning. Like OpenAI, we train our models on raw pixel data. Exactly how an experienced human would see the curves and takes an action.
    COPY-PASTE FIX
    # Deep Learning for Bitcoin Price Prediction: Training on Raw Pixel Data
    
    This project explores exploiting Bitcoin price patterns using Deep Learning, uniquely training models on raw pixel data of price charts—mimicking how an experienced human visually interprets curves to make trading decisions.
  • mediumreadme#2
    Add a 'Key Differentiator' section to the README

    Why:

    COPY-PASTE FIX
    ## Key Differentiator
    
    Unlike many approaches, this project trains deep learning models directly on raw pixel data of Bitcoin price charts, similar to how OpenAI trains models on visual inputs. This method aims to capture patterns an experienced human would identify visually, rather than relying solely on engineered features.
  • lowhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/philipperemy/deep-learning-bitcoin

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 philipperemy/deep-learning-bitcoin
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LSTM Networks
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LSTM Networks · recommended 1×
  2. Transformer Networks · recommended 1×
  3. CNNs · recommended 1×
  4. GRUs · recommended 1×
  5. Deep Reinforcement Learning · recommended 1×
  • CATEGORY QUERY
    How can deep learning predict future movements in cryptocurrency markets?
    you: not recommended
    AI recommended (in order):
    1. LSTM Networks
    2. Transformer Networks
    3. CNNs
    4. GRUs
    5. Deep Reinforcement Learning
    6. A2C
    7. PPO
    8. DQN
    9. Autoencoders
    10. Variational Autoencoders
    11. GNNs

    AI recommended 11 alternatives but never named philipperemy/deep-learning-bitcoin. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for analyzing Bitcoin price charts with convolutional neural networks?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow / Keras
    2. PyTorch
    3. Pandas
    4. NumPy
    5. Matplotlib / Seaborn
    6. Scikit-learn
    7. TA-Lib

    AI recommended 7 alternatives but never named philipperemy/deep-learning-bitcoin. 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 philipperemy/deep-learning-bitcoin?
    pass
    AI did not name philipperemy/deep-learning-bitcoin — 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 philipperemy/deep-learning-bitcoin in production, what risks or prerequisites should they evaluate first?
    pass
    AI named philipperemy/deep-learning-bitcoin 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 philipperemy/deep-learning-bitcoin solve, and who is the primary audience?
    pass
    AI did not name philipperemy/deep-learning-bitcoin — 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?

Embed your GEO score

Drop this badge into the README of philipperemy/deep-learning-bitcoin. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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philipperemy/deep-learning-bitcoin — 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