Numerai
A data science tournament where participants build machine learning models to predict the stock market and power a hedge fund.
Overview
Numerai is a unique data science competition designed for developers and data scientists to build machine learning models that predict stock market returns. Participants receive a free, obfuscated dataset representing weekly stock data with quantitative features and a target variable indicating future stock performance. This obfuscation ensures the data can be freely shared and modeled without requiring financial domain expertise, while also preventing direct use for personal trading. Developers can use any programming language or machine learning framework to train models and submit daily predictions based on live market data released Tuesday through Saturday.
The platform scores submissions primarily on correlation to actual stock returns and contribution to a combined meta model. Participants can optionally stake Numerai's native cryptocurrency, Numeraire (NMR), on their models. Staking aligns incentives by rewarding accurate predictions with additional NMR and burning tokens for poor performance, enhancing trust and improving the meta model powering Numerai's hedge fund. This staking mechanism introduces a financial incentive layer uncommon in typical data science competitions.
Numerai's ecosystem includes benchmark models released by the platform and a community marketplace for buying and selling models. The combined meta model aggregates all participant predictions to inform trading decisions executed by Numerai's hedge fund. Developers can get started quickly by accessing comprehensive documentation, example Python scripts, and tutorials. The platform also supports community engagement through Discord and forums, fostering collaboration and support. Numerai offers a novel intersection of AI, finance, and blockchain incentives for data scientists aiming to contribute to real-world hedge fund strategies.
The Problem
Predicting stock market returns accurately is challenging due to noisy data, complex market dynamics, and the need for high-quality, unbiased datasets. Traditional financial data often requires domain expertise and can be costly or restricted, limiting participation from diverse data scientists. Additionally, aligning incentives for model quality and trustworthiness in collaborative prediction environments is difficult.
The Solution
Key Features
Daily live data and submissions
Participants receive fresh market data multiple times per week to generate and submit predictions.
Pricing
Free | |
|---|---|
| Price (Monthly) | Free |
| Price (Annual) | Free |
| Messaging | N/A |
| Support | Community support via Discord and forums |
| Analytics |
Start Building Now
Reliable RPC, powerful APIs, and zero hassle.
Resources
Numerai provides extensive documentation, example scripts, and community resources to help developers quickly start building and submitting models. The docs cover data structure, modeling examples, submission processes, scoring, and staking mechanics.