arbitrix-core

v0.1.3 suspicious
5.0
Medium Risk

MIT-licensed open-source backtest engine and cost model from the Arbitrix trading toolkit.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential risk, particularly concerning shell execution and metadata indicators. However, without concrete evidence of malicious activity, it cannot be conclusively labeled as dangerous.

  • shell risk due to potential code injection or privilege escalation
  • low activity and new maintainer account metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external communications.
  • Shell: The shell execution pattern observed may be legitimate if the package involves executing scripts or other Python files, but it could also indicate potential risks like code injection or privilege escalation.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting secure handling of sensitive information.
  • Metadata: The low activity and new maintainer account suggest potential risk, but there's no clear evidence of malice.

📦 Package Quality Overall: Medium (5.6/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

  • Test runner config found: pyproject.toml
  • Test runner config found: conftest.py
  • 5 test file(s) detected (e.g. test_bar_observer_hook.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://g14mb0.github.io/arbitrix-core/
  • Detailed PyPI description (2682 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 202 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 82 commits in G14MB0/arbitrix-core
  • Single author but highly active (82 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • THONPATH", "")]) result = subprocess.run( [sys.executable, str(target)], capture_outp
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Arbitrix Team" 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 arbitrix-core 
Your task is to develop a fully-functional mini-application that leverages the 'arbitrix-core' package to perform historical trading strategy backtesting. This application will serve as a tool for traders to evaluate the performance of their strategies using real historical market data. Here's a detailed outline of what your application should include:

1. **Setup**: Begin by installing the 'arbitrix-core' package via pip or by cloning its repository from GitHub.
2. **Data Integration**: Integrate a source of historical financial market data. This could be CSV files, a database, or an API like Alpha Vantage or Yahoo Finance.
3. **Strategy Definition**: Allow users to define trading strategies programmatically within the app. Strategies should be able to specify entry and exit conditions based on various indicators (e.g., Moving Averages, RSI).
4. **Backtesting Engine**: Use the 'arbitrix-core' package to implement a backtesting engine that simulates the execution of these trading strategies against historical data. Ensure that the engine can handle different types of orders (market, limit) and supports various timeframes.
5. **Cost Model Simulation**: Incorporate the cost model from 'arbitrix-core' to accurately simulate transaction costs, including bid-ask spreads and broker fees.
6. **Performance Metrics**: After running a backtest, calculate and display key performance metrics such as Sharpe Ratio, Maximum Drawdown, and Annualized Return.
7. **Visualization**: Implement a simple visualization feature to plot the equity curve over time and highlight trades made during the backtest period.
8. **User Interface**: Develop a basic command-line interface (CLI) or a simple web-based interface where users can input parameters for their strategies and view backtest results.
9. **Documentation**: Provide clear documentation explaining how to use the application, including examples of how to define strategies and interpret the backtest results.

This project aims to provide a practical understanding of how to utilize 'arbitrix-core' for backtesting trading strategies, making it a valuable tool for both educational purposes and real-world trading applications.

💬 Discussion Feed

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