🤖 AI Analysis

Final verdict: SAFE

The package does not exhibit any significant red flags such as network calls, shell executions, or credential harvesting. However, there are some low-effort signs noted in the metadata, which slightly increase its risk score.

No network calls detected.
Low-effort signs in metadata.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
Shell: No shell execution patterns detected, reducing the likelihood of malicious activities like backdoors or data exfiltration.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows some low-effort signs but lacks clear malicious indicators.

🔬 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 "Sandeep Kumar" 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 advanced-distributions

Create a mini-application named 'RiskAnalyzer' that leverages the 'advanced-distributions' Python package to help financial analysts assess risk in investment portfolios. This application will enable users to input various asset allocation scenarios and analyze potential outcomes using advanced probability distributions provided by the package.

Steps:
1. Design a user-friendly interface where users can input details about their portfolio, such as the types of assets, their weights, and historical performance data.
2. Utilize the 'advanced-distributions' package to model the future returns of each asset in the portfolio under different market conditions. Consider incorporating features like volatility clustering, regime-switching models, and fat-tailed distributions.
3. Implement Monte Carlo simulations to generate thousands of possible future paths for the portfolio returns based on the modeled distributions.
4. Calculate key risk metrics such as Value at Risk (VaR), Conditional Value at Risk (CVaR), and Sharpe Ratio from the simulation results.
5. Visualize the results through graphs showing the distribution of potential portfolio returns, the risk profile over time, and comparison of different portfolio allocations.
6. Provide a report summarizing the risk assessment, including sensitivity analysis to show how changes in input assumptions affect the risk metrics.

Features:
- User Input Interface: Allows users to enter asset types, weights, and historical return data.
- Distribution Modeling: Uses advanced distributions to model future returns considering market volatility and other factors.
- Monte Carlo Simulations: Generates a large number of potential future outcomes for the portfolio.
- Risk Metrics Calculation: Computes VaR, CVaR, Sharpe Ratio, and other relevant statistics.
- Visualization Tools: Graphical representations of portfolio returns and risk profiles.
- Sensitivity Analysis: Reports on how different inputs impact the risk metrics.

The 'advanced-distributions' package is utilized throughout the project to model complex and realistic distributions of asset returns, which form the basis for the Monte Carlo simulations and subsequent risk analyses.