RRepoGEO

REPOGEO REPORT · LITE

jamesmawm/High-Frequency-Trading-Model-with-IB

Default branch master · commit 8e96ade5 · scanned 5/13/2026, 6:49:53 AM

GitHub: 2,866 stars · 682 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 jamesmawm/High-Frequency-Trading-Model-with-IB, 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 relevant topics for categorization

    Why:

    COPY-PASTE FIX
    high-frequency-trading, interactive-brokers, ib-api, pairs-trading, mean-reversion, algorithmic-trading, python, quant-trading
  • highreadme#2
    Strengthen README's opening statement and add disclaimer

    Why:

    CURRENT
    Purpose
A basic trading model on Interactive Brokers' API dealing with high-frequency data studies.
    COPY-PASTE FIX
    This repository provides a high-frequency trading model implemented in Python, leveraging the Interactive Brokers API for pairs and mean-reversion strategies. It is intended for educational purposes only and should not be considered financial advice.
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://github.com/jamesmawm/High-Frequency-Trading-Model-with-IB

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 jamesmawm/High-Frequency-Trading-Model-with-IB
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
QuantConnect/Lean
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. QuantConnect/Lean · recommended 1×
  2. Interactive Brokers · recommended 1×
  3. IBKR TWS API · recommended 1×
  4. erdewit/ib_insync · recommended 1×
  5. Alpaca Markets API · recommended 1×
  • CATEGORY QUERY
    How to implement a high-frequency trading strategy in Python using a broker API?
    you: not recommended
    AI recommended (in order):
    1. QuantConnect Lean Engine (QuantConnect/Lean)
    2. Interactive Brokers
    3. IBKR TWS API
    4. ib_insync (erdewit/ib_insync)
    5. Alpaca Markets API
    6. alpaca-py (alpacahq/alpaca-py)
    7. OANDA API
    8. oandapyV20 (oanda/oandapyV20)
    9. Binance API
    10. python-binance (sammchardy/python-binance)
    11. MetaTrader 5 (MT5)
    12. MetaTrader5 package
    13. Quantopian
    14. Zipline (quantopian/zipline)

    AI recommended 14 alternatives but never named jamesmawm/High-Frequency-Trading-Model-with-IB. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for Python examples of mean-reversion or pairs trading algorithms with broker connectivity.
    you: not recommended
    AI recommended (in order):
    1. QuantConnect
    2. Zipline
    3. Backtrader
    4. Alpaca-py
    5. ib_insync

    AI recommended 5 alternatives but never named jamesmawm/High-Frequency-Trading-Model-with-IB. 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 jamesmawm/High-Frequency-Trading-Model-with-IB?
    pass
    AI did not name jamesmawm/High-Frequency-Trading-Model-with-IB — 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 jamesmawm/High-Frequency-Trading-Model-with-IB in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jamesmawm/High-Frequency-Trading-Model-with-IB 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 jamesmawm/High-Frequency-Trading-Model-with-IB solve, and who is the primary audience?
    pass
    AI did not name jamesmawm/High-Frequency-Trading-Model-with-IB — 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 jamesmawm/High-Frequency-Trading-Model-with-IB. 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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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite