☰

The hardest data science tournament on the planet.

Build the world's last hedge fund by predicting the stock market.

Staked Models

control the Numerai hedge fund

-

NMR at Stake

$ -

-

Average 3 Month Return

on NMR staked

-

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. $1000000 paid out to data scientists.

Rules and Payouts

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

7,500 Faceless Coders Paid in Bitcoin Built a Hedge Fund's Brain

This Is The World's First Cryptocurrency Issued By A Hedge Fund

Artificial intelligence-focused Numerai raises $1.5m

Contact

Note: Numerai runs a quantitative global equity market neutral hedge fund which is unsuitable for most investors. The fund is designed for institutional investors though some high net worth accredited individuals may qualify.

Receive updates on Numerai

☰

The hardest data science tournament on the planet. Build the world's last hedge fund by predicting the stock market. Staked Models control the Numerai hedge fund - NMR at Stake $ - - Average 3 Month Return on NMR staked -

SIGN UP LEARN ›

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

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

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. $1000000 paid out to data scientists.

Rules and Payouts

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

SIGN UP