asset-allocation-runtime-common

v3.7.4 suspicious
5.0
Medium Risk

Shared transport-neutral runtime helpers for the Asset Allocation split repos.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to low maintainer activity and poor metadata quality, which raises concerns about its legitimacy and maintenance status.

  • Metadata risk indicates low maintainer activity and poor metadata quality.
  • No direct evidence of malicious activities like network exploitation, shell execution, obfuscation, or credential harvesting.
Per-check LLM notes
  • Network: The network call patterns suggest the package is likely making HTTP requests for legitimate purposes, such as fetching remote resources or communicating with an API.
  • Shell: No shell execution patterns were detected, indicating there is no evidence of potential shell command execution within the package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity.
  • Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising some suspicion but not definitive evidence of malice.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ 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

  • 372 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)

  • any threads session = requests.Session() adapter = HTTPAdapter(pool_connections=100, pool_m
  • self._http = http_client or httpx.Client(timeout=httpx.Timeout(config.timeout_seconds), trust_env=Fal
✓ 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 asset-allocation-runtime-common 
Create a financial dashboard application using Python that integrates with the 'asset-allocation-runtime-common' package. This application will help users visualize and manage their investment portfolios efficiently. Here are the steps and features to implement:

1. **Setup**: Install necessary packages including 'asset-alocation-runtime-common', pandas, matplotlib, and streamlit.
2. **Data Fetching**: Use 'asset-alocation-runtime-common' to fetch real-time stock market data from a chosen API or source. Ensure that the data fetching mechanism is efficient and can handle different data formats.
3. **Portfolio Management**: Allow users to input their current portfolio details (stocks, ETFs, etc.) and track changes over time. Implement functionality to add, delete, or modify assets within the portfolio.
4. **Performance Analysis**: Calculate key performance metrics such as total return, Sharpe ratio, and drawdown for each asset and the overall portfolio. Use 'asset-alocation-runtime-common' to ensure calculations are accurate and consistent across different types of assets.
5. **Visualization**: Display the portfolio's performance through interactive charts and graphs using matplotlib and streamlit. Visualizations should include historical price movements, returns distribution, and risk metrics.
6. **Optimization Tools**: Integrate basic optimization tools that suggest optimal allocation strategies based on user-defined risk tolerance levels. These suggestions should leverage the shared runtime helpers from 'asset-alocation-runtime-common' to ensure neutrality and efficiency.
7. **User Interface**: Develop a clean and intuitive UI with Streamlit, allowing users to easily navigate through different sections of the dashboard.
8. **Documentation**: Provide comprehensive documentation detailing how to install and use the application, including any dependencies and setup instructions.

By utilizing 'asset-alocation-runtime-common', the application will benefit from robust and transport-neutral runtime helpers, ensuring that all financial calculations and data manipulations are handled accurately and efficiently.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!