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Getting Started ​

Numerai is a data science tournament. Numerai provides a free, high-quality dataset. You build machine learning models on it. Numerai scores the performance of these predictions over time. If you stake NMR on your predictions, you earn (or burn) NMR based on your scores.

Numerai also operates a hedge fund. Only staked predictions are combined into an ensemble, called the "Meta Model", that the hedge fund uses to make trades. Numerai only makes money from these predictions when the predictions are correct, so we have to align our incentives via staking.

Sign up and go through our official onboarding for a full suite of tutorials.

Data ​

Numerai buys expensive hedge-fund-grade financial data. The data is obfuscated so it can be given out for free and modeled without any financial domain knowledge. This also means that models you build on this data cannot be used outside of the Numerai tournament.

Here is an example of the general structure of our dataset:

Numerai's obfuscated dataset

Each row in the dataset corresponds to a specific stock at a specific point in time. The point in time is noted by the era - each represents a week. The IDs are unique in each era such that you cannot match stocks across eras - this is necessary for the obfuscation. The features are quantitative attributes known about the stock at the time (e.g P/E ratio, ADV, etc.). The features are also obfuscated so it's impossible to determine which feature is which specific stock attribute. The target is a measure of stock market returns 20 days into the future where low means bad performance and high means good performance.

Here is an example of how to get our dataset:

python
from numerapi import NumerAPI
import pandas as pd

VERSION = "v5.3"

napi = NumerAPI()
napi.download_dataset(f"{VERSION}/train.parquet")
training_data = pd.read_parquet(f"{VERSION}/train.parquet")

See the Data section for more details.

Modeling ​

Your objective is to build machine learning models to predict the target given the features. You can use any language or framework that you like.

Here is an example model in Python using LightGBM:

python
import lightgbm as lgb

features = [f for f in training_data.columns if "feature" in f]

model = lgb.LGBMRegressor(
      n_estimators=2000,
      learning_rate=0.01,
      max_depth=5,
      num_leaves=2 ** 5,
      colsample_bytree=0.1
)
model.fit(
      training_data[features],
      training_data["target"]
)

See the Models section for more examples.

Submissions ​

Each day (Tuesday through Saturday), new live data is released. This represents the current state of the stock market. You must generate live predictions and submit them to Numerai. You are asked to submit a prediction value for each id in the live data.

This is what a submission might look like:

A sample submission

A sample submission

Here is an example of how you generate and upload live predictions in Python. First, create an API key and set its environment variables.

# Authenticate with NUMERAI_PUBLIC_ID and NUMERAI_SECRET_KEY
napi = NumerAPI()

VERSION = "v5.3"

# Download latest live features
napi.download_dataset(f"{VERSION}/live.parquet")
live_data = pd.read_parquet(f"{VERSION}/live.parquet")
features = [f for f in live_data.columns if "feature" in f]
live_features = live_data[features]

# Generate live predictions
live_predictions = model.predict(live_features)

# Format and save submission
submission = pd.Series(
    live_predictions, index=live_features.index
).to_frame("prediction")
submission.to_csv(f"submission.csv")

# Upload submission
napi.upload_predictions(f"submission.csv", model_id="your-model-id")

Behind the scenes, Numerai combines the predictions of all models into the Stake-Weighted Meta Model, which in turn is fed into the Numerai Hedge Fund for trading.

See the Submissions section for more details and examples.

Scoring ​

Submissions are scored against two primary metrics:

Since the target is a measure of 20 business days of stock market returns, it takes about 1 month for each submission to be fully scored.

See the Scoring section for more details.

Staking ​

Generally, you should start the tournaments without staking. You can predict and get scored for free. When you are ready and confident in your model's performance, you are allowed to stake on it with NMR - Numerai's utility token. Each round, you stake on a submission. After the 20 days of scoring for that submission, models with positive scores are rewarded with more NMR, while those with negative scores have a portion of their staked NMR burned (destroyed such that no one, not even Numerai, can access it).

Staking serves two important functions:

  1. "Skin in the game" allows Numerai to trust the quality of staked predictions. Otherwise, users could submit trash and never be punished.
  2. Payouts and burns continuously improve the weights of the Meta Model. Just like the weights of a neural network can be tuned with training data.

See the full Staking section for more details.

FAQ ​

See the FAQ for answers to the questions new users ask most often:

Support ​

Find us on Discord for questions, support, and feedback!

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