Forum Tournament

Numerai has gathered the best data scientists in the world and let them build machine learning models on the highest quality stock market data.

Numerai has gathered the best data scientists in the world and let them build machine learning models on the highest quality stock market data.

The result is a next-generation hedge fund which is outperforming market neutral indexes and traditional quant funds.

How we're different

Only a fraction of the world’s best data scientists are currently contributing intelligence to the problem of predicting the stock market. Numerai was founded to change that by gathering more data science talent than any other hedge fund.

Numerai Circles

Wall Street quants

World's best data scientists

Investment Process

1 Regularized Data

Numerai researches and combines a large number of data sources together and transforms the data into regularized features and targets to create a pure dataset for Numerai's data science community.

2 Modeling

Datasets are made available to our community of thousands of data scientists who compete to create the best predictive models in our data science tournament.

3 Stakes

Data scientists stake their best models with our cryptocurrency, NMR. Good models are rewarded with more NMR. Bad models have their stakes burned.

4 Stake-Weighted Meta Model Signal

Numerai creates the Stake-Weighted Meta Model by combining the latest predictions from the tournaments and outputs a signal for each stock in our investable universe.

5 Portfolio Construction

Convex optimization turns Meta Model signal into a portfolio by constraining risk factors such as country, sector and market risk.

6 Portfolio Returns

Numerai's returns are uncorrelated with the market and uncorrelated with traditional quant funds creating a differentiated and diversifying asset for institutional allocators.

Skin in the game incentivizes the crowd.

Numerai uses cryptocurrency staking to incentivize our global network of data scientists to submit the best possible machine learning models which power our global equity hedge fund. We never trade crypto. But we have created an institutional grade market neutral hedge fund using crypto incentives.


staked AI models power Numerai


at stake

Get in touch

If you're an institutional investor, mail us at [email protected]

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Frequently Asked Questions

What is Numerai?

Numerai is a quant hedge fund built on thousands of crowdsourced machine learning models.

What's in the Numerai dataset?

The Numerai dataset consists of standard financial market data for a universe of roughly 5,000 global equities. We assemble it from several market data providers and consistently research both traditional and unique features in data that's available over a 10-20 year timeframe.

Why give it away for free?

Numerai gives away data so that users around the world have free, hedge-fund quality data to build their machine learning models -- data they would otherwise not have access to. From inception, Numerai has believed that open participation, in the form of readily available data, is a core component in crowdsourcing solutions to significant data science problems.

Why is the data obfuscated for the tournament participants?

Finding a way to share data without revealing it, a solution that allows us to freely distribute a high-quality set of financial data, was also an early, core innovation that Numerai built. We needed a way to secure the value of the data set while simultaneously retaining its unique features for the purpose of building predictive models with machine learning.

How has the data set evolved over time?

Numerai data has significantly expanded over several iterations: the original data set contained just 40 features and a single target variable. In subsequent releases, the feature count expanded to 310, then to 1100 and now sits at more than 2,000 features. We also now offer multiple targets on which to train your models. More data means better model performance.

Why and how do users stake on the models that they submit?

The ability for Numerai tournament participants to stake on the predictive accuracy of their models is another core innovation we designed. "Skin in the game" both rewards accurate predictions and penalizes inaccurate submissions. Effectively, it is a way to align incentives and engender trust between Numerai and those submitting their models.

Staking is done with NMR, a crypto-token Numerai launched in 2017. NMR trades freely on several of the major crypto exchanges (Coinbase, Binance, etc.) and its utility is solely to stake on models in the Numerai tournament. Based on a scoring metric, users receive a fractional payout on their stake for performant models, with a portion of the stake being burned (destroyed) for poorly performing models. Stake payouts and burns occur on a blockchain with no direct financial benefit accruing to Numerai.

What are the basic guidelines of the tournament?

The Numerai data tournament occurs daily; Participants must submit ranked signals for all ~5,000 global equities in the Numerai data set, forecasting performance over a 20 day (four week) period. Only individual stock predictions, and not any actual models nor code are submitted.

While staking is not required, only staked submissions will be incorporated into Numerai's Meta Model which ultimately manages the trading for and the portfolios in the Numerai hedge fund.

As of July 2023, approximately 5,400 staked models have been submitted for the most recent data tournament, with a staked amount totaling $13 million.

What is the Numerai Meta Model?

The Numerai Meta Model is an ensemble of the individual models that are submitted as part of the Numerai data tournament. Crowdsourcing predictive models and then combining those signals at scale is another unique innovation that Numerai has developed.

The concept of a performant meta model is predicated on the idea that a broad and diverse selection of uncorrelated models will more accurately predict the outcome of a complex data science problem. Particularly, in the case of predicting financial markets where high accuracy and low variance (a high Sharpe ratio) matter more, this kind of ensembling exercise is doubly helpful.

Seven and half years of iterating on this concept of meta model supremacy (better than simple regression, better than an individual handful of correlated machine learning models) has proven out. Numerai's ability to design the right kind of targets in our data set and our ability to neutralize the impact of traditional risk factors have also contributed to the growth in performance of the Numerai hedge fund over the past several years.

What are the characteristics of the Numerai hedge fund portfolios?

The Numerai hedge fund consists of equally weighted long and short portfolios with approximately 750 individual equities in each portfolio. The beta of the combined long/short portfolio is close to zero, uncorrelated to broad market movements and also not exposed to traditional market risk factors such as country, sector or market cap groupings.

What accounts for the excellent fund performance during times of significant market dislocation, like in 2020?

Through several years of iterating on things like data, staking and meta model composition, Numerai has been able to consistently construct a market portfolio where 80%+ of the return is derived from true idiosyncratic (stock selection) risk, rather than the usual factors that drive returns. During times of severe market dislocation like we saw in 2020, a beta-neutral portfolio that's also not exposed to traditional factors will significantly outperform both the market and other purported "market neutral" funds.

Building the Meta Model

Learn about the technology behind the Numerai Meta Model
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