ai-prophet

v0.1.5 safe
3.0
Low Risk

AI Prophet ecosystem CLI and Prophet Arena trade benchmark runner

🤖 AI Analysis

Final verdict: SAFE

The package ai-prophet v0.1.5 is assessed as safe with a low risk score. While there are some concerns regarding metadata quality and maintainer activity, the lack of obfuscation, shell execution, and credential risks suggests it is not malicious.

  • Low risk of network, shell, obfuscation, and credential misuse
  • Metadata quality and maintainer activity are suboptimal
Per-check LLM notes
  • Network: The observed network calls are likely for legitimate API interactions or data fetching, but should be reviewed against the package's documentation and intended use.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows some signs of low maintainer activity and poor metadata quality, but there are no clear indicators of malicious intent.

📦 Package Quality Overall: Low (4.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://www.prophetarena.co
  • Detailed PyPI description (8063 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

  • 179 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 6.0

Found 4 network call pattern(s)

  • else: resp = requests.post(agent_url, json=event, timeout=timeout) resp
  • ay self.http_client = httpx.Client(timeout=120.0) def _convert_messages_to_gemini(self, me
  • rbosity self.client = httpx.Client( base_url=self.BASE_URL, headers={
  • ) self._session = aiohttp.ClientSession( headers=headers, timeout=timeout,
✓ Code Obfuscation

No obfuscation patterns detected

✓ 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 4.0

2 maintainer concern(s) found

  • Author "AI Prophet Team" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ai-prophet 
Create a financial trading simulation tool using the 'ai-prophet' Python package. This tool will allow users to simulate trading strategies on historical market data, evaluate their performance, and compare them against a benchmark. The application should have a user-friendly interface and provide visualizations of the results. Here are the key steps and features:

1. **Setup**: Install the required packages including 'ai-prophet'. Ensure that the environment supports running command-line interfaces and processing time-series data.
2. **Data Import**: Allow users to import historical market data in CSV format. Support multiple financial instruments such as stocks, cryptocurrencies, etc.
3. **Strategy Definition**: Provide a mechanism for users to define trading strategies. Strategies can be simple rules-based (e.g., buy when price crosses above a moving average) or more complex machine learning models.
4. **Backtesting**: Implement backtesting functionality to apply the defined strategies to historical data. Calculate metrics such as Sharpe ratio, maximum drawdown, and annual return.
5. **Benchmark Comparison**: Use the 'ai-prophet' package's Prophet Arena feature to run benchmarks and compare user-defined strategies against predefined ones or market indices.
6. **Visualization**: Integrate a plotting library (such as Matplotlib or Plotly) to visualize the performance of different strategies over time. Include charts showing equity curves, returns distributions, and other relevant metrics.
7. **Reporting**: Generate comprehensive reports summarizing the backtest results, including tables and graphs. Users should be able to export these reports in PDF or Excel formats.
8. **User Interface**: Develop a simple web-based UI using Flask or Django, allowing users to interact with the tool without needing to use the command line. Ensure the UI is responsive and accessible.

Utilize the 'ai-prophet' package primarily for its Prophet Arena benchmarking capabilities and possibly for any additional utilities it provides for financial analysis. Your goal is to create a versatile tool that can help both beginners and experienced traders evaluate and refine their trading strategies.

💬 Discussion Feed

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