atoti

v0.9.15 safe
2.0
Low Risk

Explore metrics across hundreds of dimensions, analyze live data at its most granular level and perform what-if simulations at unparalleled speed

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity with very low risks across all categories checked. The maintainer having only one package slightly raises metadata risk but is not indicative of malicious intent.

  • No network calls
  • No shell execution
  • No obfuscation
  • No credential harvesting
  • Single package from maintainer
Per-check LLM notes
  • Network: No network calls detected, which is normal for most non-network dependent packages.
  • Shell: No shell execution patterns detected, indicating the package does not execute external commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, which could indicate a new or less active account, but no other red flags were identified.

📦 Package Quality Overall: Low (4.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.activeviam.com/products/atoti/python-sdk/0.9.15
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in atoti/atoti
  • Two distinct contributors found

🔬 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: activeviam.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository atoti/atoti appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "ActiveViam" 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 atoti 
Your task is to develop a financial analysis tool using the Python package 'atoti'. This tool will help financial analysts and investors to explore and analyze stock market data in real-time. Here's a step-by-step guide on how to build this tool:

1. **Setup**: Start by installing the necessary packages including 'atoti', pandas, and any other required libraries.
2. **Data Collection**: Use an API like IEX Cloud or Alpha Vantage to collect historical stock price data for a set of selected companies. Ensure you store this data in a pandas DataFrame.
3. **Data Transformation**: Prepare the collected data for analysis by cleaning and transforming it into a format suitable for 'atoti'. This might include calculating daily returns, moving averages, and other relevant financial metrics.
4. **Building the Analysis Engine**: Utilize 'atoti' to create a session and load your transformed data into it. Leverage 'atoti's capabilities to dynamically slice and dice the data across different dimensions such as time, company, and financial metrics. Implement features that allow users to perform what-if analyses, such as changing parameters like interest rates or inflation.
5. **Visualization**: Integrate visualization tools like Plotly or Matplotlib to display key insights from the analysis in real-time. Create interactive dashboards that reflect changes made through the what-if scenarios.
6. **User Interface**: Develop a simple web-based user interface using Flask or Django where users can input their queries or what-if scenarios directly. The interface should provide options to select different stocks, view performance over time, and compare multiple stocks side by side.
7. **Performance Optimization**: Optimize the application for performance, ensuring that it can handle large datasets efficiently and respond quickly to user inputs.
8. **Testing and Deployment**: Test the application thoroughly under various conditions to ensure reliability and accuracy. Once satisfied, deploy the application either locally or on a cloud service provider like AWS or Azure.

This project aims to showcase 'atoti's ability to handle complex financial data analysis tasks with ease and speed. By following these steps, you'll create a valuable tool for anyone interested in deep financial analysis.

💬 Discussion Feed

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