---
license: mit
task_categories:
  - time-series-forecasting
tags:
  - finance
  - cryptocurrency
  - order-book
  - market-microstructure
  - high-frequency
pretty_name: Binance BTC/ETH/SOL L2 Order Book & Trades
configs:
  - config_name: depth
    data_files: "depth/binance/*/*.parquet"
  - config_name: trades
    data_files: "trades/binance/*/*.parquet"
  - config_name: snapshots
    data_files: "snapshots/binance/*/*.parquet"
---

# Binance BTC/ETH/SOL - L2 Order Book, Trades & Snapshots

Continuous Level-2 order book (depth diffs), trades, and periodic full-book
snapshots for BTCUSDT, ETHUSDT, and SOLUSDT, captured from Binance's public
WebSocket feeds with [`crypto-lob-stream`](https://pypi.org/project/crypto-lob-stream/)
and published monthly as a contribution to open market-microstructure research.

Full depth-of-book data is paywalled or licensed for most asset classes; crypto
is the exception, where the raw feeds are genuinely public. This dataset exists
to lower that data barrier for independent researchers and students.

## Layout

```
depth/binance/{ASSET}/YYYY-MM.parquet        # order book diff events
trades/binance/{ASSET}/YYYY-MM.parquet       # executed trades
snapshots/binance/{ASSET}/*.parquet          # full-book anchors for replay
```

Files are Snappy-compressed Parquet, compacted to one file per month per asset,
and carry an explicit `exchange` column - i.e. the native schema of the
`crypto-lob-stream` package, so its reconstruction helper works directly.

## Reconstructing the order book

Depth rows are diffs, not a standing book. Replay a snapshot plus every
subsequent diff, pruning to your intended depth after each update, to rebuild
the book at any instant. The package does this for you, ghost-level-safe:

```python
pip install crypto-lob-stream

from crypto_lob_stream import reconstruct
book = reconstruct("./depth", exchange="binance", asset="BTCUSDT")
bids, asks = book.top(n=10)
```

## Coverage & limitations

- **Reconstructable from 2026-06-03 onward.** Every depth file here carries
  `first_update_id` / `last_update_id`, so the diff sequence can be replayed
  against the snapshots. Earlier data lacks sequence ids and is not published.
- **Known gap — July 2026:** capture paused ~2026-07-05 20:56 to ~21:39 UTC
  (~43 min) for all three symbols across depth and trades, due to a host
  restart. A fresh snapshot was written on reconnect, so books before and
  after the gap reconstruct cleanly; do not replay a diff sequence across it.
- Reconstruct each market on its own - never merge books across exchanges.
- Check the diff sequence for continuity before replaying across a boundary.