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Bitget Historical Market Data

Download Bitget historical tick trades, L2 order books, liquidations, open interest, and funding data for spot and futures markets.

SpotFutures
View Bitget datasetsRead access docs

Why Bitget on CryptoHFTData?

Use separate Bitget spot and futures identifiers with a consistent output schema. Each linked dataset page states its own coverage window because some feeds began later than the venue integration.

  • Full-depth order book streams start with a snapshot and continue as updates
  • Spot and futures events are normalized without collapsing market identity
  • Trade and derivatives datasets are stored as hourly Parquet/Zstd files

Coverage

Bitget 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 BookSpot · Futuresbitget_futures · bitget_spot2025-07-15bitget_futures/2025-08-01/20/BTCUSDT_orderbook.parquetHourly Parquet/Zstd
Tick TradesSpot · Futuresbitget_futures · bitget_spot2025-07-15bitget_futures/2025-08-01/20/BTCUSDT_trades.parquetHourly Parquet/Zstd
Ticker StatisticsSpot · Futuresbitget_futures · bitget_spot2025-07-15bitget_futures/2025-08-01/20/BTCUSDT_ticker.parquetHourly Parquet/Zstd
LiquidationsFuturesbitget_futures2026-02-01bitget_futures/2026-02-01/20/BTCUSDT_liquidations.parquetHourly Parquet/Zstd
Open InterestFuturesbitget_futures2025-07-15bitget_futures/2025-08-01/20/BTCUSDT_open_interest.parquetHourly Parquet/Zstd
Funding & Mark PriceFuturesbitget_futures2025-07-15bitget_futures/2025-08-01/20/BTCUSDT_mark_price.parquetHourly Parquet/Zstd

Before analysis

Bitget conventions that matter

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

01

Bitget uses distinct product categories for spot, USDT-, USDC-, and coin-margined futures. Quantity units can differ across those contract types.

02

The full-depth books feed is different from Bitget's one-, five-, and fifteen-level snapshot channels; use the dataset schema rather than assuming top-of-book data.

03

The historical liquidation series starts later than some Bitget datasets and contains only events published by the public feed.

Research questions this archive supports

  • Compare spot and futures liquidity using matched symbols
  • Track open-interest changes alongside funding observations
  • Measure trade flow and depth before public liquidation events

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 Bitget data

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

Download data

Choose an exchange ID, date, hour, symbol, and dataset in the download workspace.

Read the SDK docs

Review schemas and Python methods before integrating the files into a pipeline.

Check ingestion status

Confirm current collector health separately from historical file availability.

Compare nearby venues

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