Torch-Trade/btcusdt_perp_bookticker_features_1m_05_2023_to_03_2024

BTCUSDT Perpetual Futures L1 BookTicker Features (1m)

1-minute aggregated features derived from Binance UM Futures bookTicker tick data (best bid/ask price + quantity updates). Captures spread, top-of-book imbalance, microprice, and quote update intensity — the level-1 microstructure layer.

Coverage

• Range: 2023-05-16 11:49 → 2024-03-31 23:59 (UTC)
• Cadence: 1 minute
• Rows: 460,265
• Completeness vs 1-min grid: 99.726%

⚠️ Temporal gap — Binance stopped publishing bookTicker historical dumps

Binance publishes bookTicker as monthly files at https://data.binance.vision/data/futures/um/monthly/bookTicker/BTCUSDT/. Available files cover 2023-05 through 2024-03 only (11 months). The 2024-04 file is published but is 0 bytes (corrupt). All files 2024-05 onwards return HTTP 404. No daily bookTicker files are published.

If you need bookTicker features for 2024-04 onwards, you must collect them live yourself from the Binance WebSocket @bookTicker stream. This dataset cannot cover that period.

Columns

Column	Description
timestamp	1-min bar open time (UTC)
bt_spread_bps_close	Spread at last tick of bar, in basis points: (ask − bid)/mid × 10000
bt_spread_bps_twap	Mean spread across all ticks within the bar
bt_bid_qty_close	Best bid quantity at last tick
bt_ask_qty_close	Best ask quantity at last tick
bt_imbalance_close	(bid_qty − ask_qty) / (bid_qty + ask_qty) at last tick. Range [-1, 1]
bt_imbalance_twap	Mean imbalance across all ticks within the bar
bt_microprice_close	(ask × bid_qty + bid × ask_qty) / (bid_qty + ask_qty) at last tick
bt_microprice_premium_close	(microprice − mid) / mid — signed pressure proxy
bt_update_rate	Count of bookTicker events within the bar (quote activity intensity)

Feature meaning

• Imbalance > 0: more size on the bid; canonical short-term predictor of upward price drift (Cartea/Jaimungal/Penalva, Algorithmic and High-Frequency Trading, Cambridge 2015).
• TWAP vs close: TWAP averages over the whole bar; close is the latest snapshot. Large divergence between TWAP and close indicates persistent vs transient imbalance.
• Microprice premium: the microprice sits between bid and ask weighted by sizes. When positive, pressure favors upward moves; when negative, downward. Signed.
• Update rate: more quote churn correlates with information arrival / elevated volatility.

Source

Binance publishes per-tick bookTicker events as monthly zips. This dataset is built by streaming each monthly file, computing per-minute aggregations, discarding the raw, and concatenating. Build script: scripts/build_datasets/build_bookticker.py in the torchtrade-experiments repo.

Usage

from datasets import load_dataset
import pandas as pd

ds = load_dataset("Torch-Trade/btcusdt_perp_bookticker_features_1m_05_2023_to_03_2024", split="train")
bt = ds.to_pandas().sort_values("timestamp").reset_index(drop=True)
print(bt.head())

Joining with 1-minute OHLCV

from datasets import load_dataset
import pandas as pd

ohlcv = load_dataset(
    "Torch-Trade/btcusdt_perp_1m_05_2021_to_02_2026", split="train"
).to_pandas()
bt = load_dataset("Torch-Trade/btcusdt_perp_bookticker_features_1m_05_2023_to_03_2024", split="train").to_pandas()

ohlcv["timestamp"] = pd.to_datetime(ohlcv["timestamp"])
bt["timestamp"] = pd.to_datetime(bt["timestamp"])

# Both are 1-min native — direct inner join on timestamp
joined = ohlcv.merge(bt, on="timestamp", how="inner")

Expected joined row count ≈ 482,000 across the 11-month overlap window.

Derived features at training time

• imb_momentum = bt_imbalance_close.diff(5) — 5-min change in imbalance
• spread_zscore = (bt_spread_bps_close - rolling_mean_60) / rolling_std_60
• micro_prem_sign_persist — fraction of last N bars where microprice_premium had the same sign

License

Data © Binance. Redistributed under fair use for research purposes. See https://data.binance.vision/ for upstream licensing.