algochains

v1.0b2 suspicious
4.0
Medium Risk

AlgoChains - Quant backtesting library for algorithmic trading

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package is flagged as potentially suspicious due to its use of obfuscation techniques and reliance on network calls to an external API. These factors raise concerns about the legitimacy and intentions behind the package.

  • Use of obfuscation techniques that could be employed for malicious purposes
  • Makes network calls to an API endpoint
Per-check LLM notes
  • Network: The package makes network calls to an API endpoint, which could be legitimate for data retrieval or service interaction, but should be reviewed for the necessity and scope of data being sent.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The code appears to be using obfuscation techniques that could be used for malicious purposes, but without more context, it's hard to determine if it's legitimate or not.
  • Credentials: No clear signs of credential harvesting detected.
  • Metadata: The maintainer has only one package, which might indicate a new or less active account, but no other suspicious activities are observed.

📦 Package Quality Overall: Low (3.2/10)

○ Low Test Suite 1.0

No test suite detected

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

Some documentation present

  • Detailed PyPI description (3482 chars)
○ 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

  • 12 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • data from our api resp = requests.post( f"{ALGOCHAINS_API_URL}/research", # FAST-API endpoi
  • AlgoChains API …") resp = requests.post( f"{ALGOCHAINS_API_URL}/research", json={
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • e = None # _tick_cache_lock = __import__("threading").Lock() # def _get_tick_cache(api_key: str | None = None)
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ 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

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "AlgoChains 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 algochains 
Create a fully-functional mini-application for backtesting algorithmic trading strategies using the 'algochains' Python package. This application will serve as a tool for traders and developers to test various trading algorithms before deploying them in real-world markets. Here’s a detailed breakdown of the steps and features you need to implement:

1. **Setup Environment**: Ensure your development environment is set up with Python and the 'algochains' package installed.
2. **Data Fetching Module**: Implement a module that fetches historical financial data from popular sources such as Yahoo Finance or Alpha Vantage. Use 'algochains' to integrate this data into your backtesting framework.
3. **Strategy Development**: Develop a simple moving average crossover strategy as a starting point. The user should be able to input parameters like short-term and long-term moving averages.
4. **Backtesting Engine**: Utilize 'algochains' to create a robust backtesting engine capable of simulating trades based on the chosen strategy. The engine should account for market conditions, transaction costs, and slippage.
5. **Performance Metrics**: Implement performance metrics such as Sharpe ratio, maximum drawdown, and annualized return. Use 'algochains' to calculate these metrics efficiently.
6. **Visualization**: Include a feature that visualizes the backtest results, showing price charts, trade signals, and equity curves.
7. **User Interface**: Develop a simple command-line interface (CLI) for users to interact with the application. Allow them to select data, choose a strategy, run the backtest, and view the results.
8. **Documentation**: Provide comprehensive documentation explaining how to install the application, use its features, and interpret the results.

This project aims to demonstrate the capabilities of 'algochains' while providing a practical tool for anyone interested in algorithmic trading.

💬 Discussion Feed

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