analysis-poly

v0.1.8 suspicious
5.0
Medium Risk

Polymarket market PnL web analyzer

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risks due to network calls and low maintainer activity, but no clear malicious activities have been detected.

  • Network risk due to potential unauthorized data fetching
  • Low maintainer activity and poor metadata quality
Per-check LLM notes
  • Network: The package makes network calls which could be for legitimate purposes like fetching updates or external resources, but requires further investigation to confirm benign intent.
  • Shell: No shell execution patterns detected, suggesting low risk for direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of low maintainer activity and poor metadata quality, but lacks clear indicators of malicious intent.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 15 test file(s) found

  • Test runner config found: pyproject.toml
  • 15 test file(s) detected (e.g. test_activity_page_cache.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2652 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 166 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • try: with urllib.request.urlopen(probe_url, timeout=0.8): webbrowser.
  • t.com" self._client = httpx.AsyncClient(timeout=timeout_sec) self._activity_page_cache = Use
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with analysis-poly 
Your task is to develop a fully-functional mini-application named 'PolyPnLAnalyzer' using the Python package 'analysis-poly'. This application will serve as a web-based tool to analyze the Profit and Loss (PnL) of trades made on Polymarket, a prediction market platform. The application should be user-friendly, allowing users to input their trade data and receive insightful analysis about their trading performance. Here’s a step-by-step guide on what your application should accomplish:

1. **User Interface Design**: Create a clean and intuitive UI where users can easily input their trade data. Ensure that the form fields include all necessary information such as trade ID, asset type, quantity, entry price, exit price, and timestamp.
2. **Data Handling**: Implement functionality to handle the inputted data efficiently. The application should validate the data before processing it to ensure accuracy and completeness.
3. **Integration with 'analysis-poly' Package**: Utilize the 'analysis-poly' package to perform complex calculations related to PnL analysis. This includes calculating net profit/loss, average entry price, exit price, and other relevant metrics. Ensure that the integration is seamless and the package is correctly utilized to enhance the analytical capabilities of the app.
4. **Visualization**: Develop visual representations of the analysis results. Use charts and graphs to display trends over time, profitability of different assets, and overall trading performance. Libraries like Matplotlib or Plotly can be useful here.
5. **Reporting**: Provide users with the option to generate detailed reports based on their analysis. These reports should include comprehensive insights derived from the trade data, summarized statistics, and visual aids.
6. **Security Considerations**: Ensure that the application adheres to best practices in data security. Sensitive data should be encrypted, and access controls should be implemented to prevent unauthorized access.
7. **Testing and Validation**: Rigorously test the application to identify and fix any bugs or issues. Perform unit tests, integration tests, and end-to-end testing to ensure reliability.
8. **Deployment**: Deploy the application on a cloud platform like AWS, Google Cloud, or Heroku, making it accessible to users via a URL.

Some additional features to consider adding:
- Real-time data updates
- Comparison of performance across different assets or time periods
- Alerts for significant changes in PnL
- User authentication and authorization

Remember, the key is to leverage the 'analysis-poly' package effectively to provide valuable insights into Polymarket trading activities.

💬 Discussion Feed

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