🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks as it does not engage in network calls, shell executions, or obfuscation techniques. However, the incomplete author information slightly increases its metadata risk.

No network calls or shell executions detected.
Incomplete author information.

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell executions detected, indicating the package does not attempt to run external commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author information is incomplete, suggesting potential low credibility.

🔬 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: outlook.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository BolunHan/PyAlgoEngine appears legitimate

⚠ 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 PyAlgoEngine

Create a fully-functional mini-trading app using the PyAlgoEngine package in Python. This application will serve as a simple backtesting tool for trading algorithms, allowing users to simulate different trading strategies on historical market data. Here’s a detailed breakdown of the project steps and features:

1. **Project Setup**: Begin by setting up your Python environment and installing PyAlgoEngine along with other necessary packages such as pandas for data manipulation.
2. **Data Integration**: Integrate historical financial market data into your application. This data can be sourced from APIs like Alpha Vantage, Yahoo Finance, or any other reliable source.
3. **Strategy Development**: Utilize PyAlgoEngine’s core functionalities to develop a basic trading strategy. For example, you could implement a Moving Average Crossover strategy where buy/sell signals are generated based on the crossing of short-term and long-term moving averages.
4. **Backtesting Mechanism**: Implement a backtesting feature within the application. This allows users to test their trading strategies against historical data to evaluate performance metrics such as return on investment (ROI), Sharpe ratio, and maximum drawdown.
5. **Visualization**: Incorporate visualization tools to display the results of backtests. Use libraries like matplotlib or plotly to create charts showing price movements, signal execution, and performance metrics over time.
6. **User Interface**: Develop a user-friendly interface for the application. This could be a simple command-line interface or a more advanced graphical user interface (GUI) using Tkinter or PyQt.
7. **Documentation and Testing**: Ensure thorough documentation of your code and conduct rigorous testing to ensure the reliability and accuracy of the application.

By following these steps, you will have developed a robust mini-trading app that leverages the capabilities of PyAlgoEngine to facilitate algorithmic trading strategy development and evaluation.