crowdcent-challenge 0.1.22

CrowdCent Challenge Python Client

CrowdCent Challenge

Open data science competitions for ML engineers and data scientists

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The CrowdCent Challenge is an open data science competition designed for machine learning engineers, data scientists, AI agents, and other technical professionals to hone their skills in a real-world setting.

What is CrowdCent?

CrowdCent is on a mission to decentralize investment management by changing the way investment funds make decisions and allocate capital. We are the machine learning and coordination layer for online investment communities looking to turn their data into actionable, investable portfolios.

📦 Installation

Using uv (Recommended)

uv add crowdcent-challenge

Using pip

pip install crowdcent-challenge

🚀 Quick Start

1. Get an API Key: Generate your key from your profile page
2. Set up authentication:

   export CROWDCENT_API_KEY=your_api_key_here
   # or create a .env file with: CROWDCENT_API_KEY=your_api_key_here
   

3. Start competing:

   from crowdcent_challenge import ChallengeClient
   
   # Initialize client for a challenge
   client = ChallengeClient(challenge_slug="hyperliquid-ranking")
   
   # Download training data
   client.download_training_dataset("latest", "training_data.parquet")
   
   # Download inference data
   client.download_inference_data("current", "inference_data.parquet")
   
   # Submit predictions
   client.submit_predictions(file_path="predictions.parquet")
   
   # Or test a new model with an experimental submission + private note
   # (requires another slot to have a non-experimental submission)
   client.submit_predictions(
       file_path="predictions.parquet",
       slot=2,
       is_experimental=True,
       notes="2-layer transformer w/ sector embeddings",
   )
   

🏆 Available Challenges

• Hyperliquid Ranking: Rank crypto assets on Hyperliquid by expected relative returns

• Equity NLP: Coming soon!

💻 CLI Usage

The package includes a command-line interface:

# List all challenges
crowdcent list-challenges

# Set default challenge
crowdcent set-default-challenge hyperliquid-ranking

# Download data
crowdcent download-training-data latest -o training.parquet
crowdcent download-inference-data current -o inference.parquet

# Submit predictions
crowdcent submit predictions.parquet

# Submit an experimental prediction with a private note
crowdcent submit predictions.parquet --slot 2 --experimental \
    --notes "2-layer transformer w/ sector embeddings"

Documentation: docs.crowdcent.com

🤖 AI Agents Integration

CrowdCent provides a Model Context Protocol (MCP) server that enables direct interaction with the Challenge API from AI agents like Cursor or Claude Desktop using natural language.

MCP Server: github.com/crowdcent/crowdcent-mcp

🤝 Contributing

Contributions are welcome! The crowdcent-challenge client library and documentation are open source.

See our contributing guidelines for details on:

• Forking and cloning the repository
• Setting up development environment
• Making changes and submitting PRs

📬 Have Questions?