alpha-common

v0.1.9 suspicious
4.0
Medium Risk

Alpha 策略研究基础库：交易日历、存储引擎、并发框架、ClickHouse 驱动

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate level of risk due to potential untrusted network calls and incomplete metadata. Further investigation is required to confirm its legitimacy.

  • Network risk due to potential untrusted file downloads
  • Incomplete metadata with missing maintainer history and git repository link
Per-check LLM notes
  • Network: The detected network call pattern suggests the package may download files from a specified URL during runtime, which could be legitimate but should be reviewed to ensure the source is trusted and secure.
  • Shell: No shell execution patterns were detected in the provided code snippet.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret or credential theft.
  • Metadata: The package shows some red flags such as lack of maintainer history and a git repository link, but no clear signs of malicious intent.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

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

Some documentation present

  • Detailed PyPI description (4476 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

  • 96 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 1.5

Found 1 network call pattern(s)

  • ents=True, exist_ok=True) urllib.request.urlretrieve(_constants.FILE_URL, str(dest)) return dest
✓ 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: outlook.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ 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 alpha-common 
Your task is to develop a financial analysis tool using the Python package 'alpha-common'. This tool will help investors and analysts to evaluate historical market data more efficiently. The application should integrate several key functionalities provided by 'alpha-common', including transaction calendars, storage engines, concurrent frameworks, and ClickHouse drivers. Here’s a detailed breakdown of the requirements and steps for building this tool:

1. **Project Setup**: Begin by setting up your Python environment and installing 'alpha-common'. Ensure you have ClickHouse installed and configured as well.
2. **Data Collection**: Utilize the transaction calendar feature from 'alpha-common' to fetch historical trading dates for a specified stock market. This data will serve as the backbone for your analysis.
3. **Storage Mechanism**: Implement a robust storage solution using the storage engine provided by 'alpha-common'. Store both the collected trading date information and any additional financial data you might gather (e.g., stock prices).
4. **Concurrency Handling**: To improve performance, especially when dealing with large datasets, implement a concurrency framework using 'alpha-common'. This will allow multiple data fetching and processing tasks to run simultaneously without overwhelming the system resources.
5. **Database Interaction**: Use the ClickHouse driver included in 'alpha-common' to interact with your ClickHouse database. Design efficient queries to retrieve and manipulate stored data.
6. **Analysis Tools**: Develop analytical tools within your application that leverage the stored data. These could include trend analysis, volatility calculation, or any other relevant metrics.
7. **User Interface**: While not strictly necessary, consider adding a simple user interface to make your tool more accessible. This could be a command-line interface or a web-based frontend.
8. **Documentation & Testing**: Finally, ensure your project is well-documented and thoroughly tested. Include examples of how to use each feature of your application effectively.

By completing these steps, you'll create a powerful yet user-friendly financial analysis tool that leverages the capabilities of 'alpha-common'.

💬 Discussion Feed

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