Numerai is a data science tournament where you build machine learning models on obfuscated financial data to predict the stock market. Models can be staked with the NMR cryptocurrency to earn rewards. Together, staked models across the tournament control Numerai's hedge fund.

The Numerai Tournament is a part the Numerai master plan to build the world's last hedge fund. Read the Medium Post and watch this short film to learn more about how it all fits together.

Summary

1. Sign up to Numerai.

2. Build a machine learning model on the obfuscated dataset and upload your predictions via the website or API.

3. Stake NMR on your model to earn or lose NMR based on your performance relative to Numerai's custom targets.

4. Automate the weekly upload of your predictions and grow the value of your stake over time.

Data

In the provided training_data, each id corresponds to a stock with a set of obfuscated features. Thetarget represents future performance. Rows are grouped into eras that represent different points in time.

Your goal is to train a machine learning model to predict the target given new features.

Modeling

Below is an example how to train a model on the training_data to make predictions on the tournament_data.

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")

Submissions

Every Saturday at 18:00 UTC, a new round begins and new tournament_data is released. Submit your predictions to Numerai to enter the tournament.

The submission deadline is Monday 14:30 UTC. Late submissions will not be eligible for payouts.

Scoring

Your submission is scored on the correlation between your predictions and the true targets. The higher the correlation the better.

# 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]

Your submission will also be scored on your metamodel contribution or mmc.

See the metamodel contribution section for details.

Staking and Payouts

You can stake on your submission to start earning payouts. You can either stake on correlation or corr plus mmc.

You can start participating in Numerai without staking to learn more about the tournament and about your model(s)' performance. When you feel confident in your model(s), you can attach some stake to it. The minimum stake value is 0.01 NMR.

You earn or burn a percentage of your stake based on the score you are staking on. For example, if you stake 100 NMR on correlation and your score was +0.05, then you will earn 5% of 100NMR = 5NMR. The maximum you can earn or burn is 25% of your stake each round.

corr_payout = stake * clip(corr, -0.25, 0.25)
​
mmc_payout = stake * clip(corr + mmc, -0.25, 0.25)

See the staking and payouts section for details.

Daily Updates

Each submission will receive daily updated scores starting from the first Thursday after the submission deadline to the Wednesday 4 weeks after. For example, if you made the blue submission on Sun 7th, you will receive your first score on Thur 11th and your final score on Wed 7th of the next month.

If you staked on your submission, you will also receive daily updates on your payouts. But only your final score and final payout will count.

Reputation and Leaderboard

Your rank on the leaderboard is based on your reputation, which is a weighted average of your correlation scores over the past 20 rounds.

See the reputation section for details.

Support

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