README.md

metadata

license: mit
language:
  - en
tags:
  - finance
  - crypto
  - bitcoin
  - quantitative-finance
  - time-series
  - algorithmic-trading
size_categories:
  - 1M<n<10M

BTCUSDT Perpetual Futures — 5-Minute Feature Dataset

Complete historical dataset for Binance BTCUSDT USDT-Margined Perpetual Futures, covering 2020-09-10 → 2026-05-31 (~5.7 years, 601,920 five-minute bars).

Built for quantitative research and ML model training. All raw data is sourced from data.binance.vision (Binance's official public archive) and processed with a deterministic, event-driven feature pipeline.

---

Repository structure

features/
  BTCUSDT/
    2020-09-10.parquet   # 288 rows — one per 5-min bar
    2020-09-11.parquet
    ...
    2026-05-31.parquet

raw/
  klines_1m/BTCUSDT/    # 1-minute OHLCV bars
  klines_5m/BTCUSDT/    # 5-minute OHLCV bars
  bookDepth/BTCUSDT/    # L2 order-book depth snapshots (from 2023-01-01)
  metrics/BTCUSDT/      # Open interest, long/short ratios
  aggTrades/BTCUSDT/    # Tick-level aggregated trades (~17 GB, optional)

---

Quick start

import polars as pl
from huggingface_hub import snapshot_download

# Download only the feature files (120 MB) — skip raw data
local_dir = snapshot_download(
    repo_id="ibrahimdaud/btcusdt-futures-features",
    repo_type="dataset",
    ignore_patterns=["raw/*"],
)

# Load all feature bars into a single DataFrame
df = pl.read_parquet(f"{local_dir}/features/BTCUSDT/*.parquet")
print(df.shape)          # (~601920, 27)
print(df.dtypes)

Or load a single day:

df = pl.read_parquet(f"{local_dir}/features/BTCUSDT/2024-01-15.parquet")

---

Feature schema (27 columns)

Identity

Column	Type	Description
bar_time_ms	int64	Bar open time in milliseconds UTC
symbol	str	Always "BTCUSDT"

Price features

Column	Type	Formula / Notes
close	float64	5m bar close price (USDT)
log_ret_1m	float64	ln(close_1m[t] / close_1m[t-1]) — log return of the most recent 1m bar
log_ret_5m	float64	ln(close_5m[t] / close_5m[t-1]) — log return of this 5m bar
log_ret_15m	float64	ln(close_5m[t] / close_5m[t-3]) — log return over the last 3 × 5m bars
log_ret_60m	float64	ln(close_5m[t] / close_5m[t-12]) — log return over the last 12 × 5m bars
realized_vol_30m	float64	Sample std-dev of the last 30 one-minute log returns: sqrt( Var( ln(c[i]/c[i-1]) ) )
rsi_14	float64	Wilder RSI(14) on 5m close prices: 100 - 100/(1 + avg_gain/avg_loss) over the last 14 bars

Null policy: log_ret_15m / log_ret_60m are null for the first 3 / 12 bars of the dataset (insufficient history). All other price features are available from the first bar.

Volume / taker-flow features

Column	Type	Formula / Notes
vol_5m	float64	Total BTC volume traded in this 5m bar (from klines_5m)
taker_buy_ratio_5m	float64	taker_buy_volume / vol_5m ∈ [0, 1]. Values > 0.5 indicate net taker buying.
trade_count_5m	int64	Number of aggregated trades in this 5m bar
avg_trade_size_5m	float64	vol_5m / trade_count_5m — mean aggTrade size in BTC

Order book depth

Sourced from Binance bookDepth snapshots (available from 2023-01-01 onward). Each snapshot covers cumulative depth in percentage-price bands around mid.

Column	Type	Formula / Notes
depth_imbalance_1pct	float64	(bid_depth_1pct − ask_depth_1pct) / (bid_depth_1pct + ask_depth_1pct) ∈ [−1, 1]. Positive = more bid-side depth within 1% of mid. Null before 2023-01-01 (no bookDepth data in Binance bulk archive).

Note on 0.2% band: Binance's bulk bookDepth export does not populate the ±0.2% band (bid_02pct / ask_02pct are always null in the source files). Those columns were therefore excluded from this dataset entirely.

VPIN (Volume-Synchronized Probability of Informed Trading)

Implementation follows Easley et al. (2012). Trade-flow is classified using the bulk-volume method (no tick test needed).

Column	Type	Formula / Notes
vpin_50	float64	`(1/50) × Σ
vpin_bucket_imbalance	float64	Buy-volume fraction in the current open bucket: V_buy / (V_buy + V_sell) ∈ [0, 1]

Parameters used: bucket_btc = 100, window = 50 (≈65 minutes of flow at average volume).

Hawkes process intensities

Trades are modelled as a bivariate Hawkes process. Each buy or sell trade excites future arrivals of its own kind. The intensity at time t is:

λ_buy(t)  = μ  +  α × Σ_{t_i < t, buy}   exp(−β × (t − t_i))
λ_sell(t) = μ  +  α × Σ_{t_j < t, sell}  exp(−β × (t − t_j))

Parameters used: α = 1.0, β = 10.0 /s (decay half-life ≈ 70 ms), μ = 6.0 trades/s.

Column	Type	Description
hawkes_buy_intensity	float64	λ_buy(t) at bar close — buy-side arrival rate (trades/s)
hawkes_sell_intensity	float64	λ_sell(t) at bar close — sell-side arrival rate (trades/s)
hawkes_net	float64	(λ_buy − λ_sell) / (λ_buy + λ_sell) ∈ [−1, 1] — directional imbalance of trade flow

Market structure (from Binance futures metrics endpoint)

5-minute snapshots of open interest and long/short positioning. Small data gaps exist around 2022 Q1 (128 days for taker_ls_vol_ratio, 19 days for ls_count_ratio).

Column	Type	Description
oi_btc	float64	Open interest in BTC. Fully populated from 2020-09-10.
oi_change_1h	float64	Fractional OI change vs 60 minutes ago: (OI[t] − OI[t−12]) / OI[t−12]
ls_count_ratio	float64	Long-account count / short-account count (all accounts on the exchange)
taker_ls_vol_ratio	float64	Taker buy volume / taker sell volume over the last 5 minutes

Forward targets (ML labels)

Filled post-hoc from future bars. The last few bars of the dataset have null targets (no future data to look forward to).

Column	Type	Description
fwd_ret_5m	float64	ln(close[t+1] / close[t]) — log return of the NEXT 5m bar
fwd_ret_15m	float64	ln(close[t+3] / close[t]) — 15-minute forward return
fwd_ret_60m	float64	ln(close[t+12] / close[t]) — 60-minute forward return
fwd_direction_5m	int64	+1 if fwd_ret_5m > 0.05%, −1 if < −0.05%, 0 otherwise

---

Raw data schemas

raw/klines_1m/ and raw/klines_5m/

Standard Binance OHLCV kline format.

Column	Type
open_time_ms	int64
open, high, low, close	float64
volume	float64 (BTC)
close_time_ms	int64
quote_volume	float64 (USDT)
trade_count	int64
taker_buy_volume	float64 (BTC)
taker_buy_quote_volume	float64 (USDT)

raw/aggTrades/

Each row is one aggregated trade (all fills of a single taker order).

Column	Type
time_ms	int64
price	float64
quantity	float64 (BTC)
is_buyer_maker	bool — True means the buyer was the maker (i.e., a taker sell)

raw/bookDepth/

L2 depth snapshots at ±1%, ±2%, ±3%, ±4%, ±5% price bands from mid. Available from 2023-01-01.

Column	Type
snapshot_time_ms	int64
bid_1pct … bid_5pct	float64 — cumulative BTC depth on the bid side
ask_1pct … ask_5pct	float64 — cumulative BTC depth on the ask side

raw/metrics/

Binance futures 5-minute metrics snapshot.

Column	Type
create_time_ms	int64
oi_btc, oi_usd	float64
ls_count_ratio	float64
taker_ls_vol_ratio	float64
top_ls_count, top_ls_value	float64 — top-trader L/S ratios

---

Data coverage summary

Source	Coverage	Notes
klines (1m, 5m)	2020-09-10 → 2026-05-31	Complete, no gaps
aggTrades	2020-09-10 → 2026-05-31	Complete, ~17 GB
metrics (OI / L/S)	2020-09-10 → 2026-05-31	Two small gaps in 2022 Q1
bookDepth	2023-01-01 → 2026-05-31	Binance bulk archive starts here

---

Reproducing this dataset

All feature computation code is open-source:

git clone https://github.com/ibrahimdaud/quant-hack
cd quant-hack
uv sync

# 1. Download raw data from data.binance.vision
uv run intraday data download --start 2020-09-10 --end 2026-05-31

# 2. Compute features (16 parallel workers, ~20 min on 16-core machine)
uv run intraday features compute --start 2020-09-10 --end 2026-05-31 --workers 16

---

Citation

If you use this dataset in research, please cite:

@dataset{btcusdt_futures_features_2026,
  title        = {BTCUSDT Perpetual Futures 5-Minute Feature Dataset},
  author       = {ibrahimdaud},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/ibrahimdaud/btcusdt-futures-features},
  note         = {2020-09-10 to 2026-05-31, sourced from data.binance.vision}
}

---

License

MIT — free to use for research and commercial purposes. Data originally sourced from Binance's public archive (terms).