arbi

v1.27.0 suspicious
4.0
Medium Risk

Python client for the ARBI API

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package is suspicious due to potential typosquatting targeting 'arq' and low maintainer activity. However, it does not exhibit high-risk behaviors such as network, shell, or obfuscation risks.

  • Potential typosquatting targeting 'arq'
  • Low maintainer activity and poor metadata quality
Per-check LLM notes
  • Network: Network calls are expected for packages that interact with external services or APIs.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of low maintainer activity and poor metadata quality, which could indicate potential risk.
  • ⚠ Typosquatting target: arq

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2619 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

  • 333 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 7.5

Found 5 network call pattern(s)

  • e: self._client = httpx.Client( base_url=self._base_url, co
  • context manager for internal httpx.Client (see httpx docs)""" self.get_httpx_client().__exit__(
  • self._async_client = httpx.AsyncClient( base_url=self._base_url, co
  • ontext manager for underlying httpx.AsyncClient (see httpx docs)""" await self.get_async_httpx_client
  • en self._client = httpx.Client( base_url=self._base_url, co
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

⚠ Typosquatting score 3.0

Possible typosquat of: arq

  • "arbi" is 2 edit(s) from "arq"
✓ Registered Email Domain

Email domain looks legitimate: arbi.city>

✓ 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 arbi 
Create a Python-based stock market analysis tool named 'ArbiTrader' using the 'arbi' package, which serves as a Python client for the ARBI API. This tool will allow users to perform real-time financial analysis, including fetching live stock prices, calculating moving averages, and identifying potential trading opportunities based on specific user-defined criteria. Here’s a detailed breakdown of the project steps and features:

1. **Setup Environment**: Begin by setting up your Python environment, ensuring you have the 'arbi' package installed along with other necessary libraries such as pandas and matplotlib for data manipulation and visualization.

2. **API Integration**: Utilize the 'arbi' package to integrate with the ARBI API. Configure your API key securely and set up functions to fetch real-time stock price data.

3. **Data Processing**: Implement functionality within 'ArbiTrader' to process incoming data. Calculate various technical indicators like simple moving average (SMA), exponential moving average (EMA), and relative strength index (RSI).

4. **Visualization**: Use matplotlib to visualize the fetched data alongside calculated indicators. Provide options for users to customize their charts, such as choosing the time frame and adjusting parameters for technical indicators.

5. **Alert System**: Develop an alert system that notifies users when certain conditions are met, such as crossing over of moving averages or reaching a specific RSI value. Users should be able to define these conditions through a simple configuration file.

6. **User Interface**: While not mandatory, consider developing a basic command-line interface (CLI) or even a web-based frontend using Flask or Django if you're comfortable with web development. This will enhance usability and accessibility.

7. **Documentation & Testing**: Write comprehensive documentation explaining how to use 'ArbiTrader', including setup instructions and examples. Ensure robust testing of all components to guarantee reliability.

By completing this project, you'll gain hands-on experience with integrating third-party APIs, processing real-time financial data, and building useful tools that can aid in making informed investment decisions.

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