algorithm-stat

v0.1.2 safe
4.0
Medium Risk

Unified quant-finance toolkit: market data, metrics, charts, Monte Carlo, backtesting, ML, macro, options, mortgage, Alpaca.

🤖 AI Analysis

Final verdict: SAFE

The package is likely safe for use with some minor concerns about metadata integrity.

  • Obfuscation risk is low
  • No credential harvesting detected
  • Presence of non-HTTPS link and possibly private repo
Per-check LLM notes
  • Obfuscation: The code snippet appears to be using obfuscation for stylistic or code protection purposes rather than malicious intent.
  • Credentials: No credentials or secrets harvesting patterns detected in the provided code snippet.
  • Metadata: The presence of a non-HTTPS link and a potentially private repository raises some concerns, but there's no clear evidence of malicious intent.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (3301 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

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

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • rame]: try: req = urllib.request.Request(url, headers={"User-Agent": _UA}) with urlli
  • er-Agent": _UA}) with urllib.request.urlopen(req, timeout=30) as r: html = r.read().d
  • key, **params} resp = requests.get(_BASE, params=full_params, timeout=30) resp.raise_fo
  • try: resp = requests.get(url, params=params, timeout=30) if resp.stat
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • else __import__("numpy").linspace(d.min(), d.max(), 200) ys = kd
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • with_name("dashboard.py") subprocess.run([sys.executable, "-m", "streamlit", "run", str(p),
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: squidconsultancygroup.com>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://127.0.0.1:8000/docs
⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Squid Consultancy Group Ltd" 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 algorithm-stat 
Create a financial analysis tool using Python's 'algorithm-stat' package. This tool will serve as a comprehensive dashboard for traders and analysts to evaluate investment opportunities. The application should include the following functionalities:

1. **Market Data Integration**: Utilize 'algorithm-stat' to fetch real-time and historical market data from various sources. Ensure that users can select specific stocks or indices they are interested in.
2. **Performance Metrics Calculation**: Implement a feature to calculate key performance metrics such as Sharpe ratio, volatility, and alpha for selected securities over different time periods. Use 'algorithm-stat' to handle the complex calculations required.
3. **Monte Carlo Simulations**: Provide an interface where users can input parameters like initial investment, expected return, and risk level. Use 'algorithm-stat' to run Monte Carlo simulations to predict potential future outcomes of their investments.
4. **Backtesting Tool**: Incorporate a backtesting module within the application. Users should be able to upload their own trading strategies (in the form of rules or scripts), and the app will use 'algorithm-stat' to simulate the performance of these strategies on historical data.
5. **Machine Learning Integration**: Offer a feature that applies machine learning models to predict stock prices or market trends. Use 'algorithm-stat' to preprocess the data, train the models, and generate predictions.
6. **Options Pricing**: Include a section dedicated to options pricing. Users should be able to input details about an option contract (such as strike price, expiration date, etc.), and the app will use 'algorithm-stat' to calculate the theoretical value of the option.
7. **User Interface**: Develop a user-friendly web interface for the application. It should allow users to interact with all the above features seamlessly. Consider using frameworks like Flask or Django for the backend, and Bootstrap for styling.
8. **Documentation and Help**: Provide detailed documentation for each feature, explaining how it works and how to use it effectively. Also, include a FAQ section addressing common questions related to financial analysis and the use of 'algorithm-stat'.

By completing this project, you'll create a valuable tool for anyone involved in financial analysis, offering both educational insights and practical applications.

💬 Discussion Feed

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