akquant

v0.2.40 safe
3.0
Low Risk

High-performance quantitative trading framework based on Rust and Python

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks with no network calls and no signs of obfuscation or credential harvesting. The main concern is the use of subprocess.run which should be reviewed for secure script execution. The author's single package history slightly reduces trust but does not strongly suggest malicious intent.

  • Low network and obfuscation risk
  • Use of subprocess.run requires scrutiny
  • Single package history by author
Per-check LLM notes
  • Network: No network calls detected, which is not inherently risky but unusual if the package is expected to interact with external services.
  • Shell: Executing scripts via subprocess.run can be legitimate, but it may pose risks if the scripts are not securely sourced or controlled.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author has only one package, suggesting a new or less active account which could indicate lower trustworthiness.

📦 Package Quality Overall: Medium (5.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. 07_option_test.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://akquant.akfamily.xyz
  • Detailed PyPI description (10237 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

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

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in akfamily/akquant
  • Single author but highly active (100 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)

  • script_path.parent.parent subprocess.run( [sys.executable, str(script_path)], cwd=str
✓ 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

Repository akfamily/akquant appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "AKQuant Developers" 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 akquant 
Create a high-performance stock market simulation tool using the 'akquant' package, which is a quantitative trading framework based on Rust and Python. This tool will allow users to backtest trading strategies on historical stock data, simulate trades, and analyze performance metrics. The application should include the following features:

1. **Data Importation**: Allow users to import historical stock price data from CSV files or directly fetch it from financial APIs.
2. **Strategy Development**: Provide a user-friendly interface where users can define their trading strategies. Strategies should be able to include conditions based on technical indicators such as moving averages, RSI, MACD, etc.
3. **Backtesting Engine**: Implement a robust backtesting engine that simulates trades based on the defined strategies on imported historical data. The engine should support different types of orders (e.g., market, limit) and account for slippage and transaction costs.
4. **Performance Analysis**: Offer detailed performance analysis reports including profit/loss, Sharpe ratio, drawdown, and other relevant metrics. Visualize these metrics using charts and graphs.
5. **Real-Time Simulation**: Extend the application to allow real-time trading simulations against live market data. Users should be able to see how their strategies perform in real-time without risking real money.
6. **User Interface**: Develop a clean and intuitive graphical user interface using a modern web framework like Flask or Django for the front-end, and use 'akquant' for the back-end processing.

The 'akquant' package will be utilized extensively throughout the project for its high-performance capabilities, especially for handling large datasets and executing complex trading algorithms efficiently. Make sure to leverage its integration with Python to ensure seamless development and deployment.