RRepoGEO

REPOGEO REPORT · LITE

PandaAI-Tech/panda_factor

Default branch main · commit a783e697 · scanned 6/23/2026, 7:58:05 AM

GitHub: 2,726 stars · 432 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
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 PandaAI-Tech/panda_factor, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    A high-performance quantitative factor library providing operators for financial data analysis, technical indicator calculation, factor construction, and visualization.
  • mediumreadme#2
    Move the core value proposition to the top of the README

    Why:

    CURRENT
    The current README starts with '# PandaFactor - PandaAI量化因子库' followed by contest details and quotes, before reaching the '概述' section.
    COPY-PASTE FIX
    # PandaFactor - PandaAI量化因子库

PandaFactor is a high-performance quantitative factor library providing operators for financial data analysis, technical indicator calculation, factor construction, and visualization. It supports both Python and formula-based factor creation, making it accessible for various skill levels.

## PandaAI首届因子大赛已启动，你的Alpha值得被看见
“没有一个alpha，一开始就是alpha”

“开始量化，最好是十年前，其次是现在”

“如果没有天赋，那就一直重复”

“看似不起眼的数学，会在将来的某一天，突然让你看到坚持的意义”

“一切都很好，我听到自己，向上的声音”

“市场会惩罚，模糊的愿望，奖励清晰的请求”

“你正在寻找的因子，此刻也在寻找你”

点击报名
## 概述

PandaFactor 提供了一系列高性能的量化算子，用于金融数据分析、技术指标计算和因子构建，并且提供了一系列的可视化图表.

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 PandaAI-Tech/panda_factor
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TA-Lib/ta-lib
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TA-Lib/ta-lib · recommended 2×
  2. quantopian/zipline · recommended 1×
  3. quantopian/pyfolio · recommended 1×
  4. quantopian/alphalens · recommended 1×
  5. mementum/backtrader · recommended 1×
  • CATEGORY QUERY
    Seeking a robust library to build and analyze quantitative financial factors.
    you: not recommended
    AI recommended (in order):
    1. Zipline (quantopian/zipline)
    2. Pyfolio (quantopian/pyfolio)
    3. Alphalens (quantopian/alphalens)
    4. Backtrader (mementum/backtrader)
    5. Pandas (pandas-dev/pandas)
    6. NumPy (numpy/numpy)
    7. SciPy (scipy/scipy)
    8. TA-Lib (TA-Lib/ta-lib)

    AI recommended 8 alternatives but never named PandaAI-Tech/panda_factor. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are high-performance tools for calculating technical indicators and market analysis?
    you: not recommended
    AI recommended (in order):
    1. TA-Lib (TA-Lib/ta-lib)
    2. Pandas TA (twopirllc/pandas-ta)
    3. VectorBT (polakowo/vectorbt)
    4. NumPy/SciPy
    5. Lean Engine (QuantConnect/Lean)
    6. TradingView Pine Script

    AI recommended 6 alternatives but never named PandaAI-Tech/panda_factor. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 PandaAI-Tech/panda_factor?
    pass
    AI named PandaAI-Tech/panda_factor explicitly

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

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

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

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MARKDOWN (README)
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PandaAI-Tech/panda_factor — 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