> For the complete documentation index, see [llms.txt](https://docs.everesteer.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.everesteer.ai/data/datasets.md).

# Datasets

<table data-view="cards"><thead><tr><th></th><th></th></tr></thead><tbody><tr><td><strong>Tenzing</strong></td><td>Tier 1, production. The served dataset. Public version name `tenzing`.</td></tr><tr><td><strong>Alps</strong></td><td>Tier 2, beta. Obfuscated factor returns. Coming soon.</td></tr><tr><td><strong>Benchmark models</strong></td><td>Reference, weekly refresh. Pre-trained models on the Tenzing tree.</td></tr><tr><td><strong>Live predictions</strong></td><td>Live, read-only stream. Top model predictions broadcast on a delay.</td></tr></tbody></table>

## Tenzing

Tenzing is a single obfuscated feature matrix plus rank-normalised targets. You never see the raw inputs. Every feature is cross-sectionally ranked, quintile-binned, and renamed. Targets are rank-normalised. The primary payout target is `target_everest_20`. Auxiliary targets are named after Himalayan peaks and are useful for ensembling. The live target list ships in `eiq_features.json`.

### Splits

| Split        | Targets | What it is for                                                                                                                       |
| ------------ | ------- | ------------------------------------------------------------------------------------------------------------------------------------ |
| `train`      | Labeled | Labeled history and the bulk of the data. Fit here. Carve a hold-out from it to score yourself offline before spending a submission. |
| `validation` | Blanked | The day-0 practice set: same features, targets withheld. Scored server-side. Predict on it only before round 1 opens.                |
| `live`       | Blanked | The currently open round. Same features, targets withheld until the round matures.                                                   |

Your predictions on the two blanked splits are what the boards score, against an answer key you never receive. Predicting on the wrong one of `validation` / `live` gives a frame whose ids do not join the open round.

### How the matrix is organised

Features are grouped by the domain they are derived from. Feature sets are `small` / `medium` / `all`. Do not copy a static group list into your code. Read `eiq_features.json` or `get_dataset_schema`. The feature space is versioned with the dataset.

Time is `exped_id`. Scores are computed per exped, then averaged. Join rows on `id`.

### What is not in the file

Raw institutional data is never exposed. Dataset scale (how many rows, names, or features) is not published. Validation and live target columns are present but blank. Do not look for a calendar field or a stable instrument ticker. The identifier you see on a row is unique to that instrument on that exped.

### Artifacts that travel with Tenzing

Alongside the three core splits you can download benchmark-model predictions, example predictions, `eiq_features.json` (feature sets and the target list), and `eiq_metadata.json` (build version, timestamp, git commit). Live artifacts update with the open round. See All dataset files.

## Pull data from your terminal

{% tabs %}
{% tab title="Python" %}

```python
import os
from everestapi import EverestAPI

client = EverestAPI(api_key=os.environ["EIQ_API_KEY"])
train      = client.download_dataset(split="train")       # labeled: features + target_*
validation = client.download_dataset(split="validation")  # target_* columns BLANKED
live       = client.download_dataset(split="live")        # current open round, targets blanked
```

{% endtab %}

{% tab title="curl" %}

```bash
curl -H "Authorization: Bearer $EIQ_API_KEY" \
  https://api.everesteer.ai/api/v1/data/download/train \
  -o train.parquet
```

{% endtab %}
{% endtabs %}


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