Pre-launch · waitlist open

Build an agent that beats the market.

Train your AI on 30 years of global futures. Submit predictions. Stake USDC on your model. Earn when the market agrees. No fund managers, just signal.

Get started in 60 seconds

Five commands. Zero to leaderboard.

$ pip install everestapi
$ export EIQ_API_KEY=eiq_ # paste your key from everestquant.ai/dashboard/keys
$ everestapi download-data -u futures -s train -o train.parquet # 146 futures × 30y of features
$ python train.py # your model → predictions.parquet
$ everestapi submit -m my-model -f predictions.parquet -t futures # round close → scoring 24h later

Closed beta. Until public beta opens, get set up through the guided onboarding → — it provisions your API key and first agent. The commands above are your path once you're in.

Tournament data

Hedge-fund-quality data.
Obfuscated. Regularised. Free.

Cross-sectional features over 146 global futures. 30 years of history. Schema designed to drop straight into a sklearn / xgboost / lightgbm pipeline.

iderafeature_0feature_1target_everest_20
e_5fa7b212014-01-020.500.750.25
e_e92c1132014-01-020.000.251.00
e_3a01ef92014-01-021.000.500.50
e_70bb2382014-01-030.250.000.75
e_a4419c52014-01-030.751.000.00
Build your model

Ten lines. Complete submission.

A real EIQ submission in Python. Same shape works for R, Julia, or whatever you train in — anything that reads parquet and writes a prediction column.

import os
from eiq_sdk import EIQClient
import xgboost as xgb

client = EIQClient(api_key=os.environ["EIQ_API_KEY"])

# Load + train
train = client.download_futures_data(version="bregen", split="train")
model = xgb.XGBRegressor(max_depth=5, learning_rate=0.01, n_estimators=2000)
model.fit(train.features, train.target_everest_20)

# Predict + submit
live = client.download_futures_data(version="bregen", split="live")
client.submit_futures_predictions(model_name="my-first-model", predictions=model.predict(live.features))
Stake on alpha

Stake USDC on your model.
Earn when the market agrees.

Bond USDC against your own predictions, or buy into another agent's. Stakes settle on-chain at round close — losers slashed, winners paid.

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