RRepoGEO

REPOGEO REPORT · LITE

firmai/financial-machine-learning

Default branch master · commit a0fda6ac · scanned 5/12/2026, 2:18:24 AM

GitHub: 8,548 stars · 1,394 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 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 project's purpose

    Why:

    CURRENT
    The README currently starts with badges, followed by '🌟 We Are Growing!' and '🚀 About Sov.ai'.
    COPY-PASTE FIX
    Add the following sentence as the very first content in the README (after any badges): 'This repository is a curated list of practical financial machine learning tools and applications, designed for quantitative finance practitioners, researchers, and data scientists.'
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the root of the repository with the text of a permissive open-source license, such as the MIT License. For example:
    
    ```
    MIT License
    
    Copyright (c) [YEAR] [COPYRIGHT HOLDER]
    
    Permission is hereby granted, free of charge, to any person obtaining a copy
    of this software and associated documentation files (the "Software"), to deal
    in the Software without restriction, including without limitation the rights
    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
    copies of the Software, and to permit persons to whom the Software is
    furnished to do so, subject to the following conditions:
    
    The above copyright notice and this permission notice shall be included in all
    copies or substantial portions of the Software.
    
    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
    SOFTWARE.
    ```
  • mediumhomepage#3
    Update the repository homepage URL

    Why:

    CURRENT
    https://www.sov.ai/
    COPY-PASTE FIX
    Update the `Homepage` URL in the repository settings to `https://github.com/firmai/financial-machine-learning` to directly link to the project itself.

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
Zipline
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Zipline · recommended 2×
  2. Python · recommended 1×
  3. scikit-learn · recommended 1×
  4. pandas · recommended 1×
  5. matplotlib · recommended 1×
  • CATEGORY QUERY
    How can I apply machine learning to develop profitable stock market trading strategies?
    you: not recommended
    AI recommended (in order):
    1. Python
    2. scikit-learn
    3. pandas
    4. matplotlib
    5. seaborn
    6. numpy
    7. QuantConnect
    8. Zipline
    9. Quantopian
    10. TensorFlow
    11. PyTorch
    12. Keras
    13. Alpaca Markets
    14. Interactive Brokers
    15. TD Ameritrade
    16. Charles Schwab
    17. Optuna
    18. Hyperopt

    AI recommended 18 alternatives but never named firmai/financial-machine-learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best open-source tools for financial machine learning and algorithmic trading?
    you: not recommended
    AI recommended (in order):
    1. Zipline
    2. Backtrader
    3. Lean Engine
    4. PyAlgoTrade
    5. Catalyst
    6. TA-Lib
    7. Scikit-learn

    AI recommended 7 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 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?

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite