axionquant-sdk

v1.1.6 suspicious
5.0
Medium Risk

Market data optimized for quantitative research, fundamental analysis, and algorithmic trading.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risks due to low maintainer activity and poor metadata quality, which raises concerns about its reliability and safety.

  • Metadata risk score of 4 out of 10
  • Low maintainer activity
Per-check LLM notes
  • Network: The presence of network calls is expected for an SDK that might interact with a service, but specific headers should be reviewed for any unusual behavior.
  • Shell: No shell execution patterns detected, which is normal and safe.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
  • Metadata: The package shows low maintainer activity and poor metadata quality, which could indicate potential risk.

📦 Package Quality Overall: Low (3.0/10)

○ 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 (10651 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

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

Single-author or unverifiable project

  • 1 unique contributor(s) across 8 commits in axionquant/python-sdk
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • SE_URL self.session = requests.Session() self.session.headers.update({"Content-Type": "appl
✓ 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: axionquant.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ 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 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 axionquant-sdk 
Create a stock market analysis tool using the 'axionquant-sdk' Python package. This tool will provide users with real-time stock market data, historical data analysis, and predictive analytics based on quantitative models. Here’s a detailed breakdown of the steps and features required for this project:

1. **Setup Environment**: Begin by setting up your Python environment with the necessary libraries including 'axionquant-sdk'. Ensure you have an API key from AxionQuant to access their services.

2. **Real-Time Data Retrieval**: Implement functionality to fetch real-time stock prices for a given set of symbols. Use 'axionquant-sdk' to query the current market prices and display them in a user-friendly format.

3. **Historical Data Analysis**: Allow users to request historical data for specific stocks over a defined period. Analyze this data to calculate important metrics such as moving averages, volatility, and relative strength index (RSI).

4. **Predictive Analytics**: Develop machine learning models using the historical data provided by 'axionquant-sdk'. These models should predict future price movements based on past trends. Consider implementing both linear regression and decision tree algorithms for comparison.

5. **User Interface**: Design a simple web interface using Flask or Django where users can input stock symbols and select time periods for analysis. Display the results of real-time data, historical analysis, and predictions in a visually appealing manner.

6. **Notifications**: Integrate a feature that sends email notifications to users when certain conditions are met (e.g., when a stock reaches a specified price level). Use the smtplib library for sending emails.

7. **Documentation & Testing**: Provide comprehensive documentation explaining how to install the app, use its features, and troubleshoot common issues. Conduct thorough testing to ensure accuracy and reliability of all calculations and predictions.

Throughout the development process, make sure to utilize 'axionquant-sdk' efficiently to handle data retrieval and processing tasks. This project aims to showcase the power of 'axionquant-sdk' in providing robust tools for financial analysis and trading.

💬 Discussion Feed

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