analogtrader-sdk

v0.1.0 suspicious
4.0
Medium Risk

AnalogTrader Risk API — Python SDK (typed, sync + async, retry-aware)

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate network risk due to potential external service calls and concerns over its metadata, including its recent creation and lack of a public git repository.

  • moderate network risk
  • concerning metadata
Per-check LLM notes
  • Network: The network call patterns suggest the package may be making HTTP requests to external services, which could be normal for a trading SDK but warrants further investigation into the purpose and destinations of these calls.
  • Shell: No shell execution patterns were detected, indicating that the package does not appear to execute system commands directly from the provided code snippets.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of being newly created with limited history and a missing git repository, which raises some suspicion.

📦 Package Quality Overall: Medium (5.2/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

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

Some documentation present

  • Detailed PyPI description (3082 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 32 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • off_max self._http = httpx.Client( base_url=self._base_url, timeout=
  • i_key}" self._http = httpx.AsyncClient( base_url=self._base_url, timeout=
  • "at_live_test") c._http = httpx.Client(base_url="http://test", transport=transport, headers=c._defa
  • "at_live_test") c._http = httpx.AsyncClient( base_url="http://test", transport=httpx.Moc
✓ 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 score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "AnalogTrader" 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 analogtrader-sdk 
Create a financial risk assessment tool using the AnalogTrader Risk API Python SDK (analogtrader-sdk). This tool will allow users to input various financial data points such as asset types, market conditions, and historical performance metrics. The app will then use the SDK to assess potential risks associated with these inputs and provide a detailed report on the likelihood of losses under different scenarios. Here’s how you can structure your project:

1. **Setup Environment**: Ensure Python 3.8+ is installed along with the necessary packages including `analogtrader-sdk`. Initialize a new Python virtual environment for this project.

2. **Data Input Module**: Develop a user-friendly interface where users can input their financial data. Consider allowing multiple assets, varying market conditions, and customizable timeframes.

3. **Risk Assessment Engine**: Utilize the `analogtrader-sdk` package to integrate with the AnalogTrader Risk API. Use its core functionalities to process the input data and perform risk assessments. Leverage the SDK’s typed, synchronous/async capabilities to handle large datasets efficiently.

4. **Report Generation**: Once the risk assessment is complete, generate a comprehensive report detailing the findings. Include visual aids like graphs and charts to make the data more accessible.

5. **User Feedback Loop**: Implement a feature that allows users to refine their inputs based on the initial report. This could include adjusting asset allocations, modifying market assumptions, etc., and re-running the risk assessment.

6. **Documentation and Testing**: Thoroughly document the codebase and write unit tests for all major components of the application. Pay special attention to error handling and retries provided by the SDK to ensure robustness.

Optional Features:
- Real-time data updates from external APIs.
- Integration with a database to store user inputs and reports.
- Multi-language support for the UI.
- A machine learning model that predicts future risks based on past data.

💬 Discussion Feed

Leave a comment

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