AI Analysis
The package shows low risks in terms of network calls, shell execution, and obfuscation. However, the lack of a GitHub repository and sparse maintainer information raises some concerns about its legitimacy.
- Sparse maintainer information
- No associated GitHub repository
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires online services.
- Shell: No shell execution patterns detected, indicating no immediate risk from command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package has no associated GitHub repository and the maintainer information is sparse, raising suspicion but not conclusive evidence of malice.
Package Quality Overall: Low (4.4/10)
Test suite present — 3 test file(s) found
3 test file(s) detected (e.g. test_AlphaPurifier.py)
Some documentation present
Detailed PyPI description (6853 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
82 type-annotated function signatures detected in source
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: gmail.com>
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a financial data analysis tool using Python's 'alphapurify' library, which is designed for fast quantitative factor cleaning and backtesting, leveraging the efficiency of Polars. Your task is to develop a mini-application that can import historical stock price data from an external API (such as Alpha Vantage or Yahoo Finance), clean the data using 'alphapurify', and perform basic backtesting on a simple trading strategy. Steps to follow: 1. Import historical stock price data for a specific company (e.g., Apple Inc.) over the last 5 years from an external API. 2. Use 'alphapurify' to clean the imported data by removing any anomalies or missing values, ensuring the dataset is ready for analysis. 3. Implement a simple moving average crossover strategy where you buy when the short-term moving average crosses above the long-term moving average, and sell when it crosses below. This will serve as your trading strategy. 4. Backtest this strategy using the cleaned dataset, calculating metrics such as total return, win rate, and average trade duration. 5. Visualize the performance of the strategy using matplotlib or seaborn, showing both the price movement and the signals generated by your strategy. Suggested Features: - User input for specifying the stock symbol and timeframe. - Ability to adjust the lengths of the short-term and long-term moving averages. - A summary report at the end of the backtest displaying key performance indicators. - Interactive plots allowing users to zoom in/out and inspect individual trades.
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