CrowdCent Challenge
Open data science competitions for ML engineers and data scientists
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
- Get an API Key: Generate your key from your profile settings
- Set up authentication:
export CROWDCENT_API_KEY=your_api_key_here # or create a .env file with: CROWDCENT_API_KEY=your_api_key_here
- 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 / MCP Server
This package ships with a built-in Model Context Protocol (MCP) server, so assistants like Claude and Cursor can download data, submit predictions, backtest strategies on the meta-model, and check your performance in natural language. Connect the hosted server at https://mcp.crowdcent.com/mcp with your API key, or run it locally with one line:
{"mcpServers": {"crowdcent": {
"command": "uvx",
"args": ["--from", "crowdcent-challenge[mcp]", "crowdcent-mcp"],
"env": {"CROWDCENT_API_KEY": "your_api_key_here"}
}}}
See the AI Agents guide for hosted setup, the simulation tools, and live trading.
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?
1 maintainer
Release files for crowdcent-challenge 0.2.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| crowdcent_challenge-0.2.4.tar.gz | 468.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| crowdcent_challenge-0.2.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 516.1 kB
Release files / crowdcent_challenge-0.2.4.tar.gz
| Download URL | crowdcent_challenge-0.2.4.tar.gz |
|---|---|
| Size | 468.6 kB |
| Tags | Source |
|
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Release files / crowdcent_challenge-0.2.4-py3-none-any.whl
| Download URL | crowdcent_challenge-0.2.4-py3-none-any.whl |
|---|---|
| Size | 47.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
uv/0.7.13
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