🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network usage, shell execution, and code obfuscation. However, the incomplete maintainer's information and lack of a GitHub repository increase suspicion, though it does not conclusively indicate malicious intent.

Incomplete maintainer's information
No associated GitHub repository

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access to function properly.
Shell: No shell execution patterns detected, indicating the package does not execute system commands, reducing the risk of potential malicious activities.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating secure handling of sensitive information.
Metadata: The package has no associated GitHub repository and the maintainer's information is incomplete, raising some suspicion but not definitive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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: gmail.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 MultiFactor

Create a financial analysis tool named 'StockAnalyzer' using Python, which leverages the 'MultiFactor' package to collect and analyze stock data based on multiple factors. The application should allow users to input a list of stock symbols and specify the time range for data collection. Once the data is collected, the app will perform various analyses such as calculating moving averages, volatility, and correlation coefficients between different stocks. Additionally, it should provide visualizations like line charts and scatter plots to help users better understand the data.

Key Features:
1. Data Collection: Utilize 'MultiFactor' to gather historical stock prices and other relevant financial data.
2. Analysis Tools: Implement functions to calculate technical indicators such as Simple Moving Average (SMA), Exponential Moving Average (EMA), and Bollinger Bands.
3. Statistical Analysis: Compute statistical measures including mean, median, standard deviation, and correlation coefficients.
4. Visualization: Display collected and analyzed data through interactive graphs and charts using libraries like Matplotlib or Plotly.
5. User Interface: Develop a simple command-line interface or, if possible, a web-based interface using Flask or Django for easier interaction.

How 'MultiFactor' is Utilized:
- 'MultiFactor' will be the primary source for fetching raw financial data, which includes daily closing prices, volume traded, and other economic indicators that might influence stock performance.
- Users will input their desired stocks and date range via the command-line or web form. This information will be passed to 'MultiFactor' to retrieve the corresponding dataset.
- After retrieving the data, 'StockAnalyzer' will process it according to the selected analysis tools and display the results in a user-friendly format.