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REPOGEO REPORT · LITE

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

Default branch master · commit 8e96ade5 · scanned 6/23/2026, 3:42:20 PM

GitHub: 2,882 stars · 680 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
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 to improve categorization

    Why:

    COPY-PASTE FIX
    high-frequency-trading, hft, algorithmic-trading, pairs-trading, mean-reversion, interactive-brokers, ib-api, python, trading-model
  • highreadme#2
    Strengthen README opening to clarify repo's identity as a trading model

    Why:

    CURRENT
    Purpose
A basic trading model on Interactive Brokers' API dealing with high-frequency data studies.
    COPY-PASTE FIX
    This repository provides a concrete, open-source **high-frequency trading model implementation** for Interactive Brokers' API, focusing on **pairs trading** and **mean-reversion strategies** in Python. It's designed for algorithmic traders and developers seeking a practical, ready-to-adapt example rather than a generic framework or library.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    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
Interactive Brokers API
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Interactive Brokers API · recommended 1×
  2. Alpaca API · recommended 1×
  3. QuantConnect · recommended 1×
  4. TradeStation API · recommended 1×
  5. TD Ameritrade API · recommended 1×
  • CATEGORY QUERY
    How to implement a high-frequency pairs trading strategy with a broker API?
    you: not recommended
    AI recommended (in order):
    1. Interactive Brokers API
    2. Alpaca API
    3. QuantConnect
    4. TradeStation API
    5. TD Ameritrade API
    6. OANDA API

    AI recommended 6 alternatives but never named jamesmawm/High-Frequency-Trading-Model-with-IB. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking Python framework for automated mean-reversion trading strategies using a brokerage API.
    you: not recommended
    AI recommended (in order):
    1. QuantConnect (Lean Engine) (QuantConnect/Lean)
    2. Backtrader (mementum/backtrader)
    3. Zipline (quantopian/zipline)
    4. Alpaca-py (alpacahq/alpaca-py)
    5. ib_insync (erdewit/ib_insync)
    6. PyAlgoTrade (gbeced/pyalgotrade)
    7. Catalyst (quantopian/catalyst)

    AI recommended 7 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

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