Introduction

Numerai is a data science tournament that powers the Numerai hedge fund. Watch the meta model video to understand how it works at a high level.

The long term vision of Numerai is to manage all the money in the world with a decentralized network of autonomous AI agents. Read our master plan to learn more.

This document is a brief overview of the tournament structure and rules. If you are new, start here!

Data

To make good predictions, you need good data. But production grade financial data is not easy to find. Hedge funds spend millions buying and managing this data, so they keep it secret.

Numerai provides this production grade financial data for free. Our data is obfuscated to keep the actual assets and features secret while preserving underlying structure.

This is what our training_data looks like. Each id represents an asset with some abstractfeatures and each era is a unit period of time in history. The target is an abstract measure of performance.

Modeling

Your task is to train a model to make predictions on the out-of-sample tournament_data. This dataset includes validation and test hold out sets, as well as live features of current stock market.

Here is a basic example model.

import pandas as pd
from xgboost import XGBRegressor
​
# training data contains features and targets
training_data = pd.read_csv("numerai_training_data.csv").set_index("id")
​
# tournament data contains features only
tournament_data = pd.read_csv("numerai_tournament_data.csv").set_index("id")
feature_names = [f for f in training_data.columns if "feature" in f]
​
# train a model to make predictions on tournament data
model = XGBRegressor(max_depth=5, learning_rate=0.01, \
                     n_estimators=2000, colsample_bytree=0.1)
model.fit(training_data[feature_names], training_data["target"])
​
# submit predictions to numer.ai
predictions = model.predict(tournament_data[feature_names])
predictions.to_csv("predictions.csv")

To help you get started, we have also written two detailed walkthroughs of the problem in Python and R. These guides cover key concepts such as feature importance, cross validation, consistency, overfitting, and how to use eras. Whether you are a novice or master level data scientist, we highly recommend that you go through these guides!

If you want to learn more about why we have setup the problem this way, check out the book Advances in Financial Machine Learning by Marcos Lopez de Prado, who is our scientific advisor.

Submissions

Every weekend, new tournament_data is released and a new round begins. To participate in the round, run the new tournament_data through your model and submit your predictions back to Numerai.

Submission files look like this. The id column must match the one in tournament_data exactly. The prediction can be any number between 0 and 1 (exclusive).

You can upload your submission at any time before the next round opens. However, only submissions made before Monday 14:30 UTC are considered on-time. Late submissions will not count towards your score and will not be eligible for payouts or bonuses.

You can upload your submission to our website or api. You can also use the Python and R client libraries to do this programatically.

For advanced users, check out Numerai Compute - a framework to help you automate your submission workflow.

Scoring

Numerai measures performance based on the rank_correlation between your predictions and the true targets.

# method='first' breaks ties based on order in array
ranked_predictions = predictions.rank(pct=True, method="first")
correlation = np.corrcoef(labels, ranked_predictions)[0, 1]

Each day (for 4 weeks) the submission gets an updated correlation score showing how well it has done so far.

If you upload new submissions each week, you will get overlapping scores of multiple submissions as shown below. Notice that there are no scores on Sundays or Mondays. These gaps correspond to the weekends when markets are closed.

Here is how the example model performed over 10 weeks. Each colored line represents the correlation of a different submission. Notice how they are staggered.

We combine these overlapping scores into a single continuous score by taking the daily marginal change in correlation score of each submission, and averaging it across all overlapping submissions. We call this average_daily_correlation, and is the primary score that all payouts and bonuses are based.

Here is a graph of the daily marginal changes in correlationshown above in colored dots and the average_daily_correlation in solid black.

Staking

You can stake on your model to start earning daily payouts.

Staking requires you to lock up NMR in an Erasure smart contract agreement. This gives Numerai the ability to grief (aka burn) your stake if your performance is poor. This also known as having "skin in the game".

Your daily payout is a function of your stake_value and average_daily_correlation. For example, if your stake_value is 100 NMR, and your average_daily_correlation is 0.1, your payout will be +50% and so you will earn 50 NMR. If instead your average_daily_correlation is -0.1, then your payout will be -50% and so you will lose 50 NMR.

Payouts occur every day scores are updated, and the payout curve is applied to each average_daily_correlation score independently. All payouts are rolled into your stake balance, but they don't effect your stake_value used for payout calculation until the following Thursday. For example, the payouts computed from the 11th to 17th use the initial stake_value of 100 but from the 18th forward until the next command, payouts will use 150 as the stake_value.

You can create and manage your stake on the website or directly on the Ethereum blockchain. Below is an example of staking on the website. We started off with 100 NMR in our wallet, and we stake 40 NMR so we have 60 NMR left.

If you increase your stake beforeMonday 14:30 UTC, then it will apply the next Thursday, otherwise it will apply the following Thursday. Decreasing works the same way except it always takes an additional 4 weeks.

At the beginning of each Thursday, up to 100K NMR in stakes will be selected and eligible for payouts. If the total amount staked exceeds this, then all stakes will be selected pro rata.

If you don't already have NMR, you can acquire it on the open market. The easiest way is through ETH on Uniswap or through BTC on Changelly, Upbit, Bittrex, Poloniex, and HitBTC.

Leaderboard

Maintaining a high average_daily_correlation over time earns you a place on the leaderboard and a large daily bonus.

Your rank on the leaderboard depends on your reputation, which is the sum of your average_daily_correlationover the past 100 days.

Any days with a missing average_daily_correlation score will be filled with a -0.005. We call this adjusted score average_daily_correlation_penalized and will use this to compute your reputation instead. This means that new users start with reputation of -0.5. This also means that if you have been submitting weekly, you would need to miss 4 submissions in a row to be penalized.

Your bonus is a function of your rank amongst all staked models (otherwise known as staked_rank) and your stake_value at the beginning of the 100 day window. For example, if your stake_value was 100 NMR at the beginning of the window and your staked_rank is 1, then you will get a 5 NMR bonus.

Like payouts, bonuses are paid into your stake balance. The max bonus paid out per day is 250 NMR across all models. If the total bonus amount amount exceeds this, then all bonuses will be paid pro rata.

We reserve the right to refund your stake and void all earnings and burns if we believe that you are actively abusing or exploiting the payout rules.

Support

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