🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to potential obfuscation techniques and incomplete metadata, despite showing no direct signs of malicious intent or network risks.

Moderate obfuscation risk
Incomplete maintainer metadata

Per-check LLM notes

Network: No network calls detected, indicating low risk.
Shell: Shell execution is present but seems to be used for file conversion tasks, suggesting it's part of the package's functionality rather than malicious activity.
Obfuscation: The presence of base64 decoding suggests some level of obfuscation, but it could also be part of legitimate functionality like data encoding.
Credentials: No patterns indicative of credential harvesting were detected.
Metadata: The maintainer's author name is missing and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

ms()): raw_size = len(base64.b64decode(b64)) total_raw += raw_size print(f" {rel}

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

' '.join(cmd)}") result = subprocess.run(cmd, capture_output=True, text=True, timeout=120) if res
try: subprocess.run( convert_cmd, check=
try: text_res = subprocess.run( [pdftotext_bin, str(pdf_path), "-"],

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: example.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 Khy-quant

Create a mini-application that analyzes rebar futures trading strategies using the Khy-quant package. This application should allow users to input specific parameters related to rebar futures and financial statements, then generate insights and recommendations based on quantitative analysis. Here’s a step-by-step guide on what the application should do:

1. **Setup**: Begin by setting up a Python environment with the necessary packages installed, including Khy-quant.
2. **Data Input**: Develop a user-friendly interface where users can input data related to rebar futures, such as historical price data, volume data, and financial statement information from companies involved in the rebar industry.
3. **Strategy Analysis**: Utilize Khy-quant to analyze different trading strategies based on the input data. This could include trend-following strategies, mean-reversion strategies, or more complex models that incorporate financial statement data.
4. **Visualization**: Implement visualizations to help users understand the analysis results. Graphs could show trends over time, performance of different strategies, and key metrics like Sharpe ratio or drawdown.
5. **Recommendations**: Based on the analysis, provide actionable recommendations to the user. This could involve suggesting the best trading strategy given the current market conditions or warning about potential risks.
6. **Documentation**: Ensure all code is well-documented and include a README file explaining how to use the application and what each part of the code does.

Some suggested features for the application include:
- An option to import data directly from a CSV file or API.
- Real-time data updates if possible.
- Customizable settings for different types of analyses.
- A feature to compare multiple strategies side by side.
- Detailed reports that summarize findings and recommendations.

By leveraging Khy-quant, the application will be able to perform sophisticated quantitative analysis that would otherwise require extensive coding and data processing knowledge. The goal is to create a tool that makes advanced trading strategy analysis accessible to a broader audience.