| --- |
| 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. |
|
|
