AI Analysis
Final verdict: SUSPICIOUS
The HedgeTech package has low technical risks but raises concerns due to incomplete maintainer information, suggesting potential lack of accountability or legitimacy.
- Incomplete maintainer's author information
- Low metadata risk score
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no immediate risk of command injection or privilege escalation.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer's author information is incomplete and may indicate a less established or potentially suspicious account.
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: hedgetech.ir>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository HedgeTech-ir/HedgeTech appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 HedgeTech
Create a fully-functional mini-trading app named 'HedgeSim' using the Python package 'HedgeTech'. This application will simulate trading activities for educational purposes, allowing users to practice trading strategies without risking real money. The goal is to provide an interactive platform where users can analyze historical data, place simulated trades, and review their performance metrics. Steps to Build 'HedgeSim': 1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python installed along with the HedgeTech package. You may also need additional packages like Pandas for data manipulation and Matplotlib for visualization. 2. **Data Integration**: Utilize HedgeTech's market data interface to fetch historical stock price data. Integrate this data into your application so users can view and analyze past trends. 3. **Trading Interface**: Develop a simple UI (using Tkinter or Streamlit) where users can input their desired trades (buy/sell orders), including quantity and price. Use HedgeTech's order management system to process these trades. 4. **Real-Time Simulation**: Implement a feature that simulates real-time trading based on live market data. Users should see their trades being executed as if they were in a real market environment. 5. **Performance Analysis**: After each trading session, allow users to review their performance through various metrics such as ROI, profit/loss, and trade accuracy. Visualize these metrics using graphs and charts. 6. **User Accounts and Strategies**: Optionally, allow users to create accounts and save their trading strategies. This way, they can come back to the app and continue practicing from where they left off. Features: - Historical Data Analysis - Real-Time Trading Simulation - Order Management - Performance Metrics Review - User Accounts & Saved Strategies How HedgeTech is Utilized: - For fetching market data, HedgeTech’s market data interface will be used to pull historical and real-time data. - The order management system within HedgeTech will handle all aspects of placing, canceling, and managing trades. - HedgeTech’s execution system will simulate the real-time aspect of trading, ensuring that trades are processed as they would be in a live market scenario. This project aims to leverage HedgeTech's powerful capabilities to create an engaging and educational tool for aspiring traders.