Pipeline running — data updated daily

Institutional-Grade
On-Chain Order Flow

Name: Hyperliquid & Polymarket Tick-Level Orderbook Data
Creator: L1 Ticks

High-fidelity tick-level data for Hyperliquid and Polymarket. Captured via proprietary Layer 1 node infrastructure. Delivered in Zstd-compressed Parquet.

Get Free Sample → View Data Catalog

10 Markets

~400ms Resolution

128M+ Orders / Day

1.9B+ Rows Delivered

99.7% Uptime

100+ Kaggle DLs

Daily Data Updates

What You Can Build

Real use cases. Not hypothetical demos.

Smart Money Detection

Track wallet-level order flow to identify informed traders. Cluster wallets by behavior, detect accumulation/distribution patterns before price moves.

⇄

Execution Quality Analysis

Maker/taker attribution with per-fill fees. Model slippage, measure fill rates, and benchmark execution quality across venues.

ML Signal Engineering

Block-sequenced features with causal ordering guaranteed. Build order flow imbalance, cancel ratios, and toxicity signals for price prediction models.

◈

Market Microstructure Research

20-level orderbook snapshots + funding rate dynamics across 10 assets. Study liquidity provision, OBI signals, and cross-asset correlations on a fully on-chain exchange.

Event Probability Forecasting

Train supervised models on 8,700+ resolved markets with ground-truth YES/NO outcomes. Beat crowd-implied odds using microstructure features.

⇌

Market Making & Spread Analysis

Tick-level L2 depth for liquidity profiling. Analyze bid-ask dynamics, depth imbalance, and optimal quote placement across prediction markets.

∑

Academic Research

1.9B+ rows of prediction market microstructure — one of the largest publicly available datasets. Ideal for papers on information aggregation and market efficiency.

⚙

Feature Engineering Starter Kit

Pre-computed ML features — volatility, OBI, flow imbalance, spread metrics. Skip months of data wrangling and start modeling on day one.

Data Catalog

Every field verified. Every schema documented.

BTC ETH SOL HYPE SPX PAXG XRP LINK AAVE DOGE

Orders

Per-block ~400ms

block_height Int64 timestamp_ms Int64 wallet String coin String action Enum is_buy Boolean price Float64 size Float64 order_type String tif String reduce_only Boolean order_id Int64 (Cancels only) cloid String

128M+ events/day (BTC alone)

Fills (L1)

Per-trade

block_height Int64 timestamp_ms Int64 wallet String coin String side String role Maker / Taker price Float64 size Float64 fee Float64 is_liquidation Boolean tid Int64 oid Int64

68K+ fills/day • wallet-level tracking

L2 Orderbook

60s snapshots

timestamp_ms Int64 coin String level 1–20 bid_price Float64 bid_size Float64 ask_price Float64 ask_size Float64 obi Float64

20 levels bid + ask • OBI pre-computed

Funding + OI

5 min

timestamp_ms Int64 coin String open_interest Float64 funding_rate Float64 mark_price Float64 oracle_price Float64 premium Float64 day_volume Float64

1.4K+ readings/day • all 10 assets

Tick-Level L2 Book

~40ms latency

timestamp_received DateTime timestamp_created_at DateTime market_id String update_type String data JSON (YES/NO)

1.9+ Billion rows • 250+ GB • expanding daily

Depth Snapshots

1-min intervals

Aggregated L2 orderbook state
at 1-minute resolution.

Processed from raw tick data
for reduced storage + faster queries.

1,046+ Parquet files

Resolved Targets

Ground truth

Ground-truth YES/NO outcome labels
for resolved prediction markets.

Ready for supervised ML training.

8,700+ resolved markets with labels

Trade Executions

Historical fills

Complete trade history for
resolved markets. Includes
price, size, side, timestamp.

Joinable with target labels.

2.7M+ historical trades

ML Features

Pre-computed

bar_volatility
depth_imbalance (OBI)
close_spread
volume_1m
+ more engineered features

Skip the feature engineering phase

Load in One Line

No parsing. No preprocessing. Just Parquet.

orders_sample.py

import polars as pl

# Load 128M+ order events in seconds
orders = pl.read_parquet("orders/*.parquet")
btc = orders.filter(pl.col("coin") == "BTC")

print(btc.head(5))

shape: (5, 13)

block_height	timestamp_ms	wallet	coin	action	is_buy	price	size	order_type	tif	reduce_only	order_id	cloid
i64	i64	str	str	str	bool	f64	f64	str	str	bool	i64	str
915340000	1773044107480	0x2754f0..baf3	BTC	PlaceOrder	true	68127.0	1.0	Limit	Ioc	false	null	0x33d1..
915340001	1773044107880	0x8a32c1..e4d2	BTC	PlaceOrder	false	68133.0	0.5	Limit	Gtc	false	null	null
915340002	1773044108290	0x5f19b2..c7a8	BTC	Cancel	true	67950.0	0.25	Limit	Gtc	true	22158..	null

polymarket_ml.py

import polars as pl

# Load ground-truth targets & trades
targets = pl.read_parquet("targets/*.parquet")
trades = pl.read_parquet("trades/*.parquet")

# Join 2.7M+ trades with resolution outcomes
df = trades.join(targets, on="market_id", how="left")
print(df.head(5))

shape: (5, 8)

timestamp	market_id	side	price	size	market	outcome	resolved
datetime[ms, UTC]	str	str	f64	f64	str	str	bool
2026-02-28 14:22:01	0x4a8f..c2e1	Buy	0.72	150	BTC >70K	YES	true
2026-02-28 14:22:03	0x4a8f..c2e1	Sell	0.71	80	BTC >70K	YES	true
2026-02-28 14:22:05	0x7b2d..a9f3	Buy	0.45	200	ETH >4K	NO	true

Why This Data

Built for quants, not dashboards.

◉

Wallet-Level Order Flow

Track individual accounts across executions. See who's placing, canceling, and getting filled.

⇋

Maker / Taker Attribution

Every fill tagged with execution role. Know who initiated each trade at the protocol level.

⌗

Block-Height Sequenced

Causal ordering guaranteed by L1 block sequence. No timestamp drift, no exchange clock skew.

⊞

Parquet-Native

Zstd-compressed, columnar storage. One line of Python to load. No JSON parsing, no CSV headers.

◎

Cross-Asset Coverage

Crypto majors, DeFi protocols, memecoins, and index perps — all on decentralized order books. Incredibly rare dataset.

∞

No License Restrictions

Public blockchain data. Use it for research, backtesting, ML training, or academic papers. No NDA required.

Frequently Asked Questions

Everything you need to know about the data.

What is L1 Ticks?

L1 Ticks provides institutional-grade on-chain order flow data from Hyperliquid and Polymarket. Data is captured directly from Layer 1 blockchain infrastructure at ~400ms block resolution — not scraped from public APIs. Every order placement, cancellation, fill, and book state change is recorded per-block and delivered in Zstd-compressed Apache Parquet format.

How is the data captured?

We operate proprietary L1 node infrastructure that parses raw ABCI block data in real-time. A Hyperliquid validator node streams block events through our pipeline, which extracts orders, fills, book state, and funding data. No public API scraping — data comes directly from the blockchain with causal ordering guaranteed by block height.

What format is the data in?

All data ships as Apache Parquet files with Zstd level 3 compression. One line of Python loads the entire dataset: polars.read_parquet("orders/*.parquet"). Columnar storage means you can scan billions of rows in seconds. No JSON parsing, no CSV header issues, no data type guessing.

What markets are covered?

Hyperliquid: BTC, ETH, SOL, HYPE, SPX, PAXG, XRP, LINK, AAVE, and DOGE perpetual futures — spanning crypto majors, DeFi, TradFi-bridge, and meme sectors.

Polymarket: Full L2 orderbook for prediction markets with 1.9B+ tick-level rows across thousands of resolved markets.

Is there a free sample?

Yes. A free 7-day BTC sample with orders, fills, book, and funding data is available on Kaggle. Polymarket starter data is also included. No sign-up required — download and explore immediately.

Pricing

Start free. Scale when ready.

Free

7-day BTC sample with all 4 data streams. Request via Telegram — instant delivery.

7 days of BTC orders, fills, book, funding
All 13 columns per stream
Delivered via Google Drive
Just message @l1ticks on Telegram

Request Free Sample →

Starter

$69 / month

Daily Parquet delivery for all 10 Hyperliquid perps. 30-day rolling access from your start date.

Daily Parquet delivery for 30 days
All 10 perpetual markets
Orders, fills, book, funding streams
Auto-renews for continuous access

Get Started →

Institutional-GradeOn-Chain Order Flow