arcticdb

v6.18.0 safe
3.0
Low Risk

ArcticDB DataFrame Database

🤖 AI Analysis

Final verdict: SAFE

The package shows very low risks across all categories except for metadata, where it has a minor concern due to a single package from the author and a non-HTTPS external link. Overall, there is no indication of a supply-chain attack.

  • No network calls detected.
  • No shell execution patterns.
  • No obfuscation patterns.
  • No credential harvesting patterns.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet connectivity.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
  • Metadata: The package has no typosquatting or email domain flags, but the author has only one package and there's a non-HTTPS external link.

📦 Package Quality Overall: Low (3.8/10)

○ Low Test Suite 1.0

No test suite detected

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

Some documentation present

  • Detailed PyPI description (9318 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 11 unique contributor(s) across 100 commits in man-group/arcticdb
  • Active community — 5 or more distinct contributors

🔬 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: arcticdb.io

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://arcticdb.io
✓ Git Repository History

Repository man-group/arcticdb appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Man Alpha Technology" 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 arcticdb 
Your task is to develop a fully functional mini-application that leverages the 'arcticdb' Python package to manage and analyze financial time series data. This application will serve as a basic stock market analysis tool for investors. Here’s a detailed plan on how to approach this project:

1. **Project Overview**: Create a stock market analysis tool named 'StockAnalyzer' using ArcticDB to store and retrieve financial time series data efficiently.
2. **Features**:
   - **Data Storage**: Users can upload CSV files containing historical stock price data (including Open, High, Low, Close, Volume).
   - **Data Retrieval**: Implement functionality to query stored data based on specific dates or date ranges.
   - **Data Analysis**: Provide tools to calculate simple moving averages, relative strength index (RSI), and other common technical indicators.
   - **Visualization**: Generate graphs showing the stock price trends and calculated indicators over time.
3. **Implementation Steps**:
   - **Setup Environment**: Install necessary packages including 'arcticdb', 'pandas', 'matplotlib', and 'ta-lib'.
   - **Database Initialization**: Use ArcticDB to initialize a library and create a collection for storing financial data.
   - **Data Upload**: Develop a function to read CSV files into pandas DataFrames and store them in the ArcticDB collection.
   - **Query Functionality**: Implement functions to query data from ArcticDB based on user-defined criteria.
   - **Analysis Tools**: Utilize 'ta-lib' to calculate various technical indicators and integrate these calculations into your app.
   - **Graphing**: Use matplotlib to visualize stock prices and technical indicators.
4. **Utilization of 'arcticdb' Package**: ArcticDB will be primarily used for efficient storage and retrieval of large volumes of time series data. It allows for quick access to historical data which is crucial for real-time analysis and backtesting of trading strategies.
5. **Testing and Validation**: Ensure all functionalities work as expected by testing with different sets of financial data. Validate the accuracy of your analysis tools against known benchmarks or existing financial software.
6. **Documentation**: Write clear documentation explaining how to use each feature of 'StockAnalyzer', including examples of queries and visualizations.

This project will not only demonstrate the power of ArcticDB but also provide valuable insights into financial market analysis.

💬 Discussion Feed

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