CME vs Hyperliquid Positioning Divergence
Do regulated futures desks and the on-chain crowd agree on BTC and ETH? Weekly, for each: the z-score of CME non-commercial net positioning (from the CFTC Commitments of Traders, 3-year baseline), the z-score of Hyperliquid annualised funding (6-month baseline), and the divergence between them. A wide gap means CME speculators are leaning one way while on-chain funding leans the other - a cross-venue disagreement signal you can't get from either feed alone. Cross-source: CFTC COT (CME) × Hyperliquid on-chain funding.
- Source
- derived
- Licence
- Redistributable open data
- Updates
- Weekly
- Data through
- 2026-07-21
- Quality
- QC clean · 0 failures
- Format
- Apache Parquet + dictionary
Safe to train on
Built only from a named, redistributable official source under a documented open licence - not scraped web data. No copyright grey zone, no personal data. Ships machine-readable Croissant metadata (ML Commons - loads in Hugging Face / Kaggle / Google), an AI training-licence manifest documenting source, licence and provenance for your model's data governance, and a machine-readable data dictionary (drop it into an agent / RAG prompt so the model knows every column) - all inspectable before you buy.
What's included
- cme_net_spec_z - CME non-commercial net positioning z-score (CFTC COT, 3-yr baseline)
- hl_funding_z - Hyperliquid annualised-funding z-score (6-mo baseline)
- divergence_z - the gap between them (regulated futures vs on-chain crowd)
- BTC & ETH, weekly (COT cadence), over the HL history 2023-05 → present
- Cross-venue signal combining a TradFi and an on-chain source - no wallets, no CEX order data
- Sources: CFTC Commitments of Traders + Hyperliquid public asset-context data - derived
Schema
| Column | Type | Description | Filled | Distinct |
|---|---|---|---|---|
| zone | str | BTC or ETH | 100.0% | 2 |
| ts | timestamp (UTC) | COT report week | 100.0% | 157 |
| metric | str | cme_net_spec_z, hl_funding_z or divergence_z | 100.0% | 3 |
| value | float | Z-score (or z-score gap for divergence_z) | 100.0% | 936 |
| unit | str | z-score | 100.0% | 1 |
| res_min | int | Native resolution (weekly = 10080) | 100.0% | 1 |
Sample & preview
Every purchase ships as Apache Parquet with a data dictionary and the full QC report. A free sample (first rows + schema) is downloadable here - confirm fit before you buy. Source: derived (redistributable open data; attribution passes through - see our Licence).
Don't trust screenshots - drop the sample into your notebook right now.
import pandas as pd df = pd.read_parquet("hyperliquid-cme-dex-divergence_sample.parquet") df.info() # typed columns, gap-aware, ready to join⬇ Download free Parquet sample or CSV for Excel
Coverage: 2 zones
BTC · ETH
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