ampion

v0.5.4 suspicious
6.0
Medium Risk

Python client for the Ampion Trading Platform — Numerai-style tournament for European day-ahead power prices.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network, shell, obfuscation, and credential activities. However, the metadata risk is significant due to missing repository and suspicious author details, which raises concerns about potential supply-chain compromise.

  • High metadata risk due to missing repository and suspicious author details.
  • Normal HTTP request behavior with no other red flags.
Per-check LLM notes
  • Network: The observed network call pattern suggests normal HTTP request behavior, possibly for API interaction or web scraping.
  • Shell: No shell execution patterns detected, indicating no immediate risk from shell command execution.
  • Obfuscation: No obfuscation patterns detected, suggesting low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The repository not found and the author details are suspicious, indicating potential risk.

📦 Package Quality Overall: Low (3.6/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/RathishanM/Ampion/tree/main/ampion-api
  • Detailed PyPI description (19280 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 26 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 1.5

Found 1 network call pattern(s)

  • p("/") self.session = requests.Session() self.session.headers.update({"Accept": "applicatio
✓ 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

Email domain looks legitimate: ampiontrading.com>

✓ 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 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 ampion 
Create a Python-based mini-app that integrates with the Ampion Trading Platform to predict European day-ahead power prices. This app will serve as a tool for traders and energy analysts to make informed decisions based on machine learning models. Your task is to develop a user-friendly interface that allows users to upload their datasets, train models using Ampion's API, and visualize the predicted power prices against actual market data.

Step 1: Set up your development environment with Python and install the 'ampion' package along with other necessary libraries such as pandas, numpy, and matplotlib for data manipulation and visualization.

Step 2: Design a simple command-line interface (CLI) where users can interact with the app. The CLI should support commands like 'upload', 'train', 'predict', and 'visualize'.

Step 3: Implement the 'ampion' package to connect to Ampion's API. Use the package to authenticate users, retrieve historical power price data, and submit predictions.

Step 4: Develop a feature within the app that allows users to upload their own datasets. These datasets should include relevant features that could influence power prices, such as weather conditions, time of year, and economic indicators.

Step 5: Integrate a model training functionality that uses the uploaded datasets. Users should be able to choose from a variety of machine learning algorithms provided by scikit-learn or TensorFlow/Keras.

Step 6: Once a model is trained, implement a prediction function that leverages Ampion's API to forecast future power prices. Ensure that these predictions are submitted back to Ampion for evaluation.

Step 7: Create a visualization component that displays both the predicted power prices and the actual market data over time. This will help users understand the accuracy of their models and adjust their strategies accordingly.

Suggested Features:
- User authentication and authorization to secure access to personal predictions and models.
- A leaderboard that ranks users based on the accuracy of their predictions.
- Notifications via email or SMS when new data is available or when predictions are due.
- An option to compare multiple models side-by-side for better analysis.

The goal is to create a comprehensive tool that not only predicts power prices but also educates users on the factors influencing these prices and helps them improve their predictive models over time.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!