RRepoGEO

REPOGEO REPORT · LITE

goldspanlabs/optopsy

Default branch main · commit 40bb8b2a · scanned 6/25/2026, 6:07:01 AM

GitHub: 1,402 stars · 209 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 goldspanlabs/optopsy, 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
    Clarify the core purpose of `optopsy` vs. `optopsy-mcp` in the README

    Why:

    CURRENT
    ## 🤖 Looking for AI/LLM integration?

optopsy-mcp provides a high-performance MCP server for strategy screening and backtesting. Powered by a complete Rust rewrite of the Optopsy engine, it is specifically built for seamless interaction with Large Language Models.
    COPY-PASTE FIX
    Add the following sentence directly before the '## 🤖 Looking for AI/LLM integration?' heading: 'This repository, `goldspanlabs/optopsy`, focuses on the Python options backtesting library. For its companion project providing AI/LLM integration, see `optopsy-mcp`.'
  • mediumreadme#2
    Enhance the README's opening statement for immediate clarity and impact

    Why:

    CURRENT
    A nimble backtesting and statistics library for options strategies.

Optopsy is a Python backtesting engine that lets you go from "How do 45-DTE iron condors on SPX perform with a 50% profit target and 2x stop loss vs holding to expiration?" to detailed performance statistics in seconds, not spreadsheets.
    COPY-PASTE FIX
    Replace the current opening paragraph with: 'Optopsy is a **nimble Python library for options research and backtesting**, designed for quantitative traders and sophisticated investors. It provides a high-performance engine to simulate complex multi-leg options strategies, analyze their performance, and track portfolio equity with detailed statistics, eliminating the need for spreadsheets.'
  • lowabout#3
    Refine the repository's 'About' description for conciseness and keyword density

    Why:

    CURRENT
    A nimble options research and backtesting library for Python
    COPY-PASTE FIX
    A nimble Python library for options research, backtesting, and strategy analysis for quantitative traders.

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 goldspanlabs/optopsy
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Backtrader
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Backtrader · recommended 2×
  2. Pandas · recommended 2×
  3. NumPy · recommended 2×
  4. QuantConnect (Lean Engine) · recommended 1×
  5. Pyfolio · recommended 1×
  • CATEGORY QUERY
    What Python library helps backtest options trading strategies and analyze their performance?
    you: not recommended
    AI recommended (in order):
    1. QuantConnect (Lean Engine)
    2. Pyfolio
    3. Backtrader
    4. Zipline
    5. Opyen
    6. Pandas
    7. NumPy

    AI recommended 7 alternatives but never named goldspanlabs/optopsy. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I simulate complex multi-leg options strategies and track portfolio equity in Python?
    you: not recommended
    AI recommended (in order):
    1. QuantLib
    2. Py_vollib
    3. OptionPrice
    4. Pandas
    5. NumPy
    6. Matplotlib
    7. Seaborn
    8. Backtrader
    9. QuantConnect
    10. Lean Engine

    AI recommended 10 alternatives but never named goldspanlabs/optopsy. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 goldspanlabs/optopsy?
    pass
    AI named goldspanlabs/optopsy explicitly

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

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

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

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goldspanlabs/optopsy — 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