azapy

v1.2.6 suspicious
6.0
Medium Risk

Financial Portfolio Optimization Algorithms

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to network and obfuscation concerns, with no immediate signs of credential theft or shell execution.

  • High obfuscation risk due to use of eval
  • Moderate network risk from HTTP GET requests
Per-check LLM notes
  • Network: The package makes HTTP GET requests to an external API, which could indicate legitimate functionality but also poses a risk for unauthorized data transmission.
  • Shell: No shell execution patterns were detected, indicating low risk of direct system command injection.
  • Obfuscation: The code uses eval to execute strings as Python code, which is a common technique for obfuscation and can be used for malicious purposes.
  • Credentials: No obvious patterns of credential harvesting were detected.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account.

📦 Package Quality Overall: Low (3.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://azapy.readthedocs.io/en/latest
  • Detailed PyPI description (4344 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 64 commits in Mircea-MMXXI/azapy
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • api_key) req = requests.get(url) if req.status_code != 200: if sel
  • set': 0} req = requests.get('http://api.marketstack.com/v1/tickers/'
  • t'] api_result = requests.get('http://api.marketstack.com/v1/tickers/'
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • ce = esprice.apply(lambda x: [eval(y) for y in x]) esprice.index = pd.to_datetime(
✓ 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: outlook.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Mircea-MMXXI/azapy appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Mircea Marinescu" 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 azapy 
Create a financial portfolio optimization tool using the 'azapy' package in Python. This tool will help users optimize their investment portfolios based on their risk tolerance and return expectations. Here’s a detailed plan for building this mini-application:

1. **Project Overview**: Develop a web-based application where users can input details of their current investment portfolio (stocks, bonds, etc.) along with expected returns and risks associated with each asset.
2. **User Input Form**: Design a user-friendly interface where users can enter the following information for each asset in their portfolio:
   - Asset name
   - Expected annual return (%)
   - Standard deviation (risk measure)
   - Correlation matrix (showing how each asset moves relative to others)
3. **Risk Tolerance Input**: Allow users to specify their risk tolerance level, which will be used to adjust the portfolio weights accordingly.
4. **Optimization Engine**: Utilize the 'azapy' package to perform the following optimizations:
   - Minimize portfolio variance (risk) while achieving a target return.
   - Maximize Sharpe Ratio (return per unit of risk).
5. **Results Display**: Present the optimized portfolio allocation to the user, showing how much of their money should be invested in each asset to meet their specified risk/reward goals.
6. **Additional Features**:
   - Provide historical performance charts for each asset.
   - Include a feature to simulate different market scenarios and see how the portfolio performs under stress conditions.
7. **Implementation Steps**:
   - Install necessary packages including 'azapy' via pip.
   - Set up a basic Flask or Django backend to handle form submissions and data processing.
   - Use Plotly or Matplotlib for generating visualizations.
   - Integrate 'azapy' functions to calculate optimal portfolio weights.
8. **Testing and Deployment**: Thoroughly test the application to ensure accuracy and reliability of results. Deploy it using Heroku or AWS services for public access.

This project aims to leverage 'azapy'’s financial algorithms to provide personalized investment advice, making complex portfolio optimization accessible to everyday investors.

💬 Discussion Feed

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