RRepoGEO

REPOGEO REPORT · LITE

firmai/financial-machine-learning

Default branch master · commit a0fda6ac · scanned 6/22/2026, 8:33:07 AM

GitHub: 8,652 stars · 1,408 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
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 firmai/financial-machine-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
  • highreadme#1
    Reposition README opening to clearly state repo's purpose

    Why:

    CURRENT
    The current README starts with recruitment and company promotion before defining the repository's core offering.
    COPY-PASTE FIX
    Move the description 'A curated list of practical financial machine learning tools and applications.' to the very top of the README, perhaps as the main heading or immediately below it, before any recruitment or company information.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root. Consider a widely recognized open-source license such as MIT, Apache-2.0, or GPL-3.0 to ensure clarity for users and AI.
  • mediumreadme#3
    Integrate company information more clearly after repo's purpose

    Why:

    CURRENT
    The current README places 'About Sov.ai' and recruitment prominently at the beginning.
    COPY-PASTE FIX
    After clearly stating the repository's purpose (e.g., 'A curated list...'), introduce Sov.ai as the maintainer or sponsor, for example: 'This repository is a project by Sov.ai, a leader in integrating advanced machine learning with financial data analysis.'

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 firmai/financial-machine-learning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Python
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Python · recommended 2×
  2. R · recommended 2×
  3. scikit-learn/scikit-learn · recommended 1×
  4. numpy/numpy · recommended 1×
  5. pandas-dev/pandas · recommended 1×
  • CATEGORY QUERY
    What machine learning tools are best for developing quantitative trading strategies?
    you: not recommended
    AI recommended (in order):
    1. Python
    2. scikit-learn (scikit-learn/scikit-learn)
    3. NumPy (numpy/numpy)
    4. Pandas (pandas-dev/pandas)
    5. Matplotlib (matplotlib/matplotlib)
    6. Seaborn (mwaskom/seaborn)
    7. TensorFlow (tensorflow/tensorflow)
    8. Keras (keras-team/keras)
    9. PyTorch (pytorch/pytorch)
    10. R
    11. quantmod (joshuaulrich/quantmod)
    12. xts (joshuaulrich/xts)
    13. TTR (joshuaulrich/TTR)
    14. caret (topepo/caret)
    15. Julia
    16. Flux.jl (FluxML/Flux.jl)
    17. ScikitLearn.jl (cstjean/ScikitLearn.jl)
    18. MATLAB
    19. Statistics and Machine Learning Toolbox
    20. C++

    AI recommended 20 alternatives but never named firmai/financial-machine-learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I apply AI and ML techniques to analyze cryptocurrency and stock market data?
    you: not recommended
    AI recommended (in order):
    1. Python
    2. Pandas
    3. NumPy
    4. Scikit-learn
    5. Keras
    6. TensorFlow
    7. QuantConnect (Lean Engine)
    8. PyTorch
    9. MetaTrader 5
    10. MetaTrader5 package
    11. R
    12. quantmod
    13. TTR
    14. caret
    15. AWS SageMaker
    16. Google Cloud AI Platform
    17. Azure Machine Learning

    AI recommended 17 alternatives but never named firmai/financial-machine-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
    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 firmai/financial-machine-learning?
    pass
    AI did not name firmai/financial-machine-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?

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

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

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firmai/financial-machine-learning — RepoGEO report