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Aster Historical Market Data

Download Aster historical perpetual order books, tick trades, liquidations, and funding or mark-price data in hourly Parquet files.

Perpetuals

Why Aster on CryptoHFTData?

Analyze Aster Pro's order-book perpetual market with the same core schema used for other derivatives venues. Coverage is limited to the dataset types linked below.

  • Perpetual order book snapshots and updates are packaged for replay
  • Tick trades and public liquidation events use normalized fields
  • Funding and mark-price observations are available in one dataset family

Coverage

Aster historical datasets

Each row is a separate landing page with its own exchange IDs, schema, sample path, caveats, and documented history start. A listed market applies only to that dataset row.

DatasetStored marketsArchive IDHistory startExample object path
Order BookPerpetualsaster_futures2025-09-29aster_futures/2025-09-29/20/BTCUSDT_orderbook.parquetHourly Parquet/Zstd
Tick TradesPerpetualsaster_futures2025-09-29aster_futures/2025-09-29/20/BTCUSDT_trades.parquetHourly Parquet/Zstd
Ticker StatisticsPerpetualsaster_futures2025-09-29aster_futures/2025-09-29/20/BTCUSDT_ticker.parquetHourly Parquet/Zstd
LiquidationsPerpetualsaster_futures2025-09-29aster_futures/2025-09-29/20/BTCUSDT_liquidations.parquetHourly Parquet/Zstd
Funding & Mark PricePerpetualsaster_futures2025-09-29aster_futures/2025-09-29/20/BTCUSDT_mark_price.parquetHourly Parquet/Zstd

Before analysis

Aster conventions that matter

Normalization makes columns consistent; it does not erase contract design, symbol mapping, or limits in the exchange's public feed.

01

This archive covers Aster Pro order-book perpetuals, not every Aster product or chain activity.

02

Symbols use compact identifiers such as BTCUSDT and the depth stream follows Binance-style update IDs; preserve sequence order during replay.

03

Aster's public liquidation stream publishes at most the latest liquidation per symbol within its documented interval, so aggregate totals are incomplete by design.

Research questions this archive supports

  • Reconstruct perpetual depth around high-volatility periods
  • Compare public taker flow with sampled liquidation messages
  • Benchmark Aster spreads against another perpetual venue

A safer first analysis

  1. 1. Open the relevant dataset page and confirm the market ID.
  2. 2. Download one known hour and inspect types, timestamps, and units.
  3. 3. Validate gaps and book sequence rules before scaling the date range.
  4. 4. Keep venue-specific contract metadata with every cross-venue join.

Next steps

Start working with Aster data

Use the dataset pages for exact semantics, then download a small interval and validate it against your research assumptions.

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