asycaus

v1.0.0 suspicious
6.0
Medium Risk

Asymmetric Granger-causality suite for Python (Hatemi-J, Bahmani-Oskooee, Fang et al., Nazlioglu et al.)

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score primarily due to suspicious metadata indicating potential issues with the git repository and maintainer history.

  • Metadata risk flagged due to suspicious git repository and maintainer history
  • No direct security risks identified in code execution or network calls
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution detected, indicating no immediate risk of command injection or similar attacks.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret or sensitive information being stolen.
  • Metadata: The package shows signs of being a potential threat due to suspicious git repository and maintainer history flags.

📦 Package Quality Overall: Low (4.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_basic.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (12163 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 23 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 3 commits in merwanroudane/asycaus
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 5.0

Git history flags: Single contributor with only 3 commit(s) — possibly throwaway account

  • Single contributor with only 3 commit(s) — possibly throwaway account
  • All 3 commits happened within 24 hours
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with asycaus 
Create a financial analysis tool using the 'asycaus' package that helps investors understand the causal relationships between different stock prices and economic indicators. The application should allow users to upload historical data of stocks and economic indicators, and then perform asymmetric Granger causality tests to determine if one time series can predict another. Here’s a detailed breakdown of the requirements:

1. **Data Input Module**: Design an interface where users can input or upload CSV files containing historical stock prices and economic indicator data. Ensure the data is properly parsed and stored for further analysis.
2. **Data Visualization Module**: Implement graphs and charts to visualize the uploaded data over time. This will help users to better understand the trends before performing any causality tests.
3. **Causality Testing Module**: Utilize the 'asycaus' package to conduct asymmetric Granger causality tests on the uploaded data. Allow users to select pairs of datasets to test for causality, and provide options for different types of causality tests supported by the package.
4. **Result Interpretation Module**: Present the results of the causality tests in an understandable format. Include statistical significance levels and confidence intervals. Provide explanations for what these results mean in terms of predictive power between the selected datasets.
5. **User Interface Enhancements**: Make sure the application has an intuitive user interface that guides users through each step of the process from data input to result interpretation. Consider adding tooltips or FAQs for each section to assist users.
6. **Documentation and Help Section**: Create comprehensive documentation for both developers and end-users. Include examples of how to use the application effectively, common issues, and troubleshooting tips.

The goal is to create a tool that not only performs complex statistical analyses but also makes these analyses accessible and actionable for non-expert users interested in financial market analysis.

💬 Discussion Feed

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