amfipy

v0.3.0 suspicious
5.0
Medium Risk

Python client for AMFI India — NAV, TER, Fund Performance, Tracking Error, Risk Parameters and more

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to network interactions and suspicious maintainer history, suggesting potential supply-chain risks.

  • moderate network risk
  • suspicious maintainer history
Per-check LLM notes
  • Network: The presence of HTTP client initialization suggests the package interacts with external services, which is common but should be reviewed to ensure it aligns with the package's intended functionality.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: Suspicious maintainer history and git repository activity suggest potential risk.

📦 Package Quality Overall: Low (4.6/10)

✦ High Test Suite 9.0

Test suite present — 12 test file(s) found

  • Test runner config found: conftest.py
  • 12 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (25321 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

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

Single-author or unverifiable project

  • 1 unique contributor(s) across 13 commits in skamalj/amfipy
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • ) -> httpx.Client: return httpx.Client( base_url=BASE_URL, headers=_headers(referer
  • httpx.AsyncClient: return httpx.AsyncClient( base_url=BASE_URL, headers=_headers(referer
  • ) -> httpx.Client: return httpx.Client( base_url=POLLING_BASE, headers=_headers(ref
  • httpx.AsyncClient: return httpx.AsyncClient( base_url=POLLING_BASE, headers=_headers(ref
  • ) -> httpx.Client: return httpx.Client( base_url=PORTAL_URL, headers=_DEFAULT_HEADE
  • httpx.AsyncClient: return httpx.AsyncClient( base_url=PORTAL_URL, headers=_DEFAULT_HEADE
✓ 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 score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://myproxy:8080
⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • 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 amfipy 
Create a comprehensive financial analysis tool using the 'amfipy' Python package which provides access to various investment fund data from AMFI India. This tool will allow users to explore and analyze Mutual Funds by fetching and displaying key performance indicators such as Net Asset Value (NAV), Total Expense Ratio (TER), Fund Performance, Tracking Error, and Risk Parameters.

Step 1: Set up the project environment.
- Install necessary packages including 'amfipy', 'pandas', and 'matplotlib'.
- Initialize a virtual environment and install these packages.

Step 2: Design the user interface.
- Create a simple command-line interface (CLI) or a basic web interface using Flask for user interaction.
- Implement functions to fetch data based on user input (e.g., fund code).

Step 3: Implement core functionalities.
- Write a function to fetch NAV history of a given mutual fund over a specified period.
- Develop a feature to calculate and display the average TER of selected funds.
- Add functionality to compare fund performance against benchmark indices.
- Include an option to visualize risk parameters using graphs and charts.

Step 4: Enhance the application.
- Allow users to save their analysis results into CSV files for future reference.
- Integrate a feature to automatically update the data periodically.
- Consider adding documentation and comments to make the codebase easy to understand and maintain.

Throughout the development process, utilize 'amfipy' to interact with AMFI India's API, ensuring accurate and up-to-date data retrieval for all calculations and visualizations.

💬 Discussion Feed

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