The hardest data science tournament on the planet.
$1000000 paid out.

Start with hedge fund quality data.
It is clean and regularized, designed to be usable right away. Obfuscated, so it can be given out for free.

/download-dataset

id era feature1 . . . feature310 target
n2b2e3dd163cb422 era1 0.75 . . . 0.00 0.25
n177021a571c94c8 era1 1.00 . . . 0.25 0.75
n7830fa4c0cd8466 era1 0.25 . . . 1.00 0.00
nc584a184cee941b era1 0.25 . . . 0.00 1.00
nc5ab8667901946a era1 0.75 . . . 0.25 0.25
n84e624e4714a7ca era1 0.00 . . . 0.75 1.00

Apply machine learning to predict the stock market.
Build a model using the example Python and R scripts. Everything you need to get started in one package.

/clone-example-scripts

#!/usr/bin/env python
""" Example classifier on Numerai data using a xgboost regression. """

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")
id prediction
n60dffdaceb7e467 0.25
nadaeef0214b84a8 1.00
nb13883520a4344f 0.25
n423766c5a4fa42a 0.75
n252b14301e46a31 0.25
n75a5baf93a624cc 0.00
n2ff91086716e413 1.00

Submit your predictions to control the capital of the Numerai hedge fund.
Build reputation to claim your place on the leaderboard. Stake on your model to earn cryptocurrency.

/rules-and-payouts

Join the network and build the world’s last hedge fund.
Backed by Union Square Ventures, the cofounder of Renaissance, and the cofounder of Coinbase.