Crypto Market Data Library
Institutional-grade datasets stored as Hive-partitioned Apache Parquet files. Free download via R2 or stream through the API.
Event Streams
Append-only transaction-level data. Every trade, swap, bridge transfer, and staking event captured as it happens.
📊 HLP Perpetual Trades
Tick-level trade executions from HyperLiquid perpetual futures
🔄 DEX Swaps
On-chain DEX swap events from Uniswap, Aerodrome, Jupiter, and more
💧 LP Mint/Burn Events
Concentrated liquidity LP events from Uniswap V3 and Aerodrome
🌉 Bridge Transfer Events
Cross-chain bridge transfers from Across Protocol and Wormhole
🥩 Liquid Staking Events
Lido stETH deposits, withdrawals, and claims
⚡ Flashbots Builder Bids
Per-block Flashbots relay builder bids on Ethereum
🎯 Intent-Based Order Flow
UniswapX and CowSwap intent-based orders with solver competition
💵 Stablecoin On-Chain Flows
Cross-chain USDC, USDT, and DAI mint/burn/transfer events
State Snapshots
Point-in-time snapshots of market state. Orderbook depth, funding rates, gamma exposure, and network metrics.
📖 L2 Order Book Snapshots
20-level L2 orderbook snapshots from HyperLiquid at ~100ms resolution
💰 Perpetual Funding Rates
Funding rates, mark prices, and open interest from HyperLiquid perps
📐 Gamma Exposure (GEX) Profile
Options-derived gamma exposure from Deribit for BTC and ETH
⚖️ Cross-Venue Orderbook Imbalance
Aggregated buy/sell pressure imbalance across venues for BTC, ETH, SOL
🚦 Network Congestion
Per-block gas and fee metrics for Ethereum and Solana
🌐 Macro Sentiment Indicators
Pyth oracle prices and Polymarket prediction market odds
🏦 DeFi Lending Yields
Minute-by-minute Aave V3 lending and borrowing rates
Quick Start
# Read any dataset with DuckDB (no download needed) import duckdb df = duckdb.sql(""" SELECT * FROM read_parquet( 's3://algotick-data-lake/events/trades/exchange=hyperliquid/year=2026/month=03/day=14/node=eu-central/data.parquet' ) LIMIT 100 """).df() print(df)
Architecture
Dual-Citadel Ingest: Data is independently collected by two geographically separated nodes (Frankfurt, EU and Canada, NA). This enables cross-validation and geographic arbitrage analysis.
Hive Partitioning: Every file follows the pattern
{category}/{dataset}/{partition}/year=YYYY/month=MM/day=DD/node={region}/data.parquet
for efficient date-range and region-specific queries.
Dual Timestamps: Every row has time_chain (on-chain/exchange event time)
and time_local (ingestion time) for microsecond-accuracy latency analysis.
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