---
language:
  - en
license: mit
pretty_name: BTCUSDT Microbar v2
tags:
  - finance
  - cryptocurrency
  - market-microstructure
  - binance
  - futures
  - btcusdt
size_categories:
  - 10M<n<100M
---

# BTCUSDT Microbar v2

Sub-candle microstructure data for Binance USD-M Futures BTCUSDT, collected continuously over six WebSocket streams. Successor to [`Torch-Trade/btcusdt-microbar`](https://huggingface.co/datasets/Torch-Trade/btcusdt-microbar).

A standard OHLCV candle compresses thousands of trades into 6 numbers. This dataset preserves the raw event-level data — every individual trade, every best bid/ask change, every depth snapshot — so the underlying microstructure features can be reconstructed at any timeframe.

## Why v2?

In April 2026 we discovered that the v1 collector had a silent routing bug. Binance USD-M Futures split market data across two WebSocket routing paths (`/public` and `/market`) — the legacy unified endpoint silently delivered streams that mapped to `/public` and dropped the rest **without error**.

Concretely, in v1:

| Stream         | v1 status                              | v2 status |
|----------------|----------------------------------------|-----------|
| `trade`        | working                                | working   |
| `bookTicker`   | working                                | working   |
| `depth5@500ms` | partial (missing during routing migration) | working   |
| `markPrice`    | **broken** (subscriptions silently dropped) | **fixed** |
| `miniTicker`   | **broken** (subscriptions silently dropped) | **fixed** |
| `forceOrder`   | **broken** (subscriptions silently dropped) | **fixed** |

If you only need `trades` and `book_ticks`, the v1 dataset remains usable. If you need funding rate, mark price, 24h stats, liquidations, or full depth — use v2. We renamed the repo (rather than appending) so consumers get a clean discontinuity instead of a silent quality jump.

The fix is in `binance-microbar` collector commit `f88a52a` (group streams by routing path and open one WebSocket per group).

## Layout

```
data/btcusdt/
├── trades/<YYYY-MM-DD>/<HHMMSS>.parquet          # individual trades
├── book_ticks/<YYYY-MM-DD>/<HHMMSS>.parquet      # best bid/ask updates
├── depth/<YYYY-MM-DD>/<HHMMSS>.parquet           # top-5 order book snapshots
├── liquidations/<YYYY-MM-DD>/<HHMMSS>.parquet    # forced liquidations
├── mark_price/<YYYY-MM-DD>/<HHMMSS>.parquet      # mark price + funding
└── mini_ticker/<YYYY-MM-DD>/<HHMMSS>.parquet     # 24h rolling stats
```

Files are flushed to disk every 60 seconds. Each row carries a `timestamp_ms` (exchange clock, UTC) which is the only safe key for joins — wall-clock arrival times are not preserved.

## Schemas

### `trades/`
| column            | type    | description                                   |
|-------------------|---------|-----------------------------------------------|
| `timestamp_ms`    | int64   | exchange trade time, ms since epoch UTC       |
| `price`           | float64 | trade price (USDT)                            |
| `quantity`        | float64 | trade quantity (BTC)                          |
| `is_buyer_maker`  | bool    | True = aggressive sell, False = aggressive buy |

Volume: ~50 trades/sec → ~4M rows/day.

### `book_ticks/`
| column         | type    | description                              |
|----------------|---------|------------------------------------------|
| `timestamp_ms` | int64   | exchange event time                      |
| `bid_price`    | float64 | best bid price                           |
| `bid_qty`      | float64 | best bid quantity                        |
| `ask_price`    | float64 | best ask price                           |
| `ask_qty`      | float64 | best ask quantity                        |

Volume: ~100 updates/sec → ~8M rows/day.

### `depth/`
Top 5 order book levels. Columns: `timestamp_ms`, `bid_price_0..4`, `bid_qty_0..4`, `ask_price_0..4`, `ask_qty_0..4`. Snapshots arrive every 500 ms → ~170k rows/day.

### `liquidations/`
| column         | type    | description                                                |
|----------------|---------|------------------------------------------------------------|
| `timestamp_ms` | int64   | liquidation time                                           |
| `price`        | float64 | average fill price                                         |
| `quantity`     | float64 | liquidated size                                            |
| `side`         | string  | `"BUY"` = short squeezed (bullish), `"SELL"` = long liquidated (bearish) |

Sparse — bursts during volatile moves.

### `mark_price/`
Updates every 3 seconds. Columns: `timestamp_ms`, `mark_price`, `index_price`, `funding_rate`, `next_funding_time_ms`. Funding rate is the most predictive macro signal in crypto futures.

### `mini_ticker/`
Updates every second. Columns: `timestamp_ms`, `open_24h`, `high_24h`, `low_24h`, `close`, `volume_24h`, `quote_volume_24h`. Distance from 24h high/low is a support/resistance signal.

## Loading

```python
from huggingface_hub import snapshot_download
import pandas as pd
from pathlib import Path

local = snapshot_download(
    "Torch-Trade/btcusdt-microbar-v2",
    repo_type="dataset",
    allow_patterns=["trades/2026-04-29/*.parquet"],
)

trades = pd.concat(
    pd.read_parquet(f) for f in sorted(Path(local, "trades/2026-04-29").glob("*.parquet"))
)
```

To compute aggregated microstructure features over arbitrary timeframes, use the [`binance-microbar`](https://github.com/TorchTrade/binance-microbar) library — `examples/build_feature_dataset.py` rebuilds 54-feature ML-ready datasets directly from these raw streams.

## Collection

- **Source**: Binance USD-M Futures public WebSocket streams (no auth required)
- **Collector**: [`binance-microbar`](https://github.com/TorchTrade/binance-microbar) at commit `f88a52a` or later
- **Host**: continuous collection on a Raspberry Pi 5 (`colony1`), uploads daily at 03:00 UTC
- **Coverage**: starts 2026-04-29; the immediate prior period (2026-04-28 → 2026-04-29) is missing because the collector was offline for the routing-fix deployment

## License

MIT. Market data is sourced from Binance's public WebSocket streams and is provided as-is. No financial advice; not affiliated with Binance.