alphalens-core

v0.3.2 suspicious
5.0
Medium Risk

AlphaLens — an event-driven backtesting & walk-forward engine for systematic strategies

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential misuse, particularly concerning network and shell execution risks, despite no clear evidence of malicious intent. Further scrutiny is advised.

  • moderate network risk
  • potential shell execution misuse
Per-check LLM notes
  • Network: The network calls appear to be related to authentication and might be legitimate if the package requires API interactions.
  • Shell: The shell execution is likely intended for version control operations but could pose a risk if misused for unintended purposes.
  • Obfuscation: No obfuscation patterns detected.
  • Credentials: The code prompts for a password input which may indicate interaction with a database or service requiring authentication, but does not inherently suggest malicious intent.
  • Metadata: The repository is not found and the maintainer has a single package, suggesting potential unreliability.

📦 Package Quality Overall: Medium (5.4/10)

✦ High Test Suite 9.0

Test suite present — 15 test file(s) found

  • Test runner config found: pyproject.toml
  • 15 test file(s) detected (e.g. test_audit_fixes.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2398 chars)
◈ Medium Contributing Guide 7.0

Some contribution signals present

  • Governance file: security.py
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 265 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 3.0

Found 2 network call pattern(s)

  • try: resp = httpx.post( endpoint, headers={
  • try: resp = httpx.post( f"{self.url}/auth/v1/token",
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • ng.""" try: out = subprocess.run( ["git", "rev-parse", "--short", "HEAD"],
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • password = args.password or getpass.getpass("Supabase password: ") try: sess = Supab
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "AlphaLens LLC" 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 alphalens-core 
Create a financial analysis tool using the 'alphalens-core' Python package. This tool will serve as a simplified version of a professional quantitative trading system, focusing on backtesting and walk-forward analysis of trading strategies based on alpha signals.

Step 1: Define the Application Scope
- The application will allow users to input historical stock data and alpha signals.
- It will support multiple stocks and different time periods for backtesting.

Step 2: Set Up the Project Environment
- Use Python 3.8+ and install necessary packages including 'alphalens-core', pandas, and matplotlib.
- Ensure all dependencies are managed via a requirements.txt file.

Step 3: Data Input and Preprocessing
- Develop a user-friendly interface (CLI or GUI) for importing CSV files containing historical stock prices and alpha signals.
- Preprocess the data to ensure it's clean and ready for analysis (handling missing values, normalization, etc.).

Step 4: Implement Core Functionality
- Utilize 'alphalens-core' to backtest the performance of given alpha signals over historical data.
- Implement walk-forward analysis to evaluate the robustness of trading strategies over different market conditions.
- Provide visualizations of backtest results using matplotlib or similar libraries.

Step 5: Enhance User Experience
- Include a feature to automatically generate summary statistics from backtest results.
- Allow users to compare multiple strategies side-by-side.
- Offer insights into the effectiveness of different alpha signals over various time frames.

Step 6: Testing and Documentation
- Write comprehensive tests to validate the correctness of your implementation.
- Document your code thoroughly and provide usage instructions for other developers and end-users.

The goal is to create a tool that not only leverages the power of 'alphalens-core' but also makes complex financial analysis accessible and understandable for non-experts.

💬 Discussion Feed

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