ResearchRL/diffquant-data

BTCUSDT 1-Min Futures — 5-Year Research Dataset (2021–2025)

A gap-free 1-minute OHLCV dataset for BTCUSDT Binance USDⓈ-M Perpetual Futures covering five full calendar years: 2021-01-01 through 2025-12-31 (UTC).

This repository contains raw market bars only. Feature engineering, aggregation, sample construction, normalisation, and temporal splitting belong to the downstream DiffQuant pipeline, described below for reproducibility.

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What this dataset is — and is not

Is:

• A clean, gap-free 1-minute futures bar dataset (2,629,440 bars)
• A reproducible research input for intraday quantitative studies
• The primary data source for the DiffQuant differentiable trading pipeline

Is not:

• A trading signal or strategy
• A labelled prediction dataset
• An RL environment with rewards or actions
• Order-book, trades, funding rates, open interest, or liquidation data

---

Dataset card

Asset	BTCUSDT Binance USDⓈ-M Perpetual Futures
Resolution	1-minute bars, close-time convention
Period	2021-01-01 00:00 UTC → 2025-12-31 23:59 UTC
Total bars	2,629,440
Coverage	100.00% — zero gaps
File	btcusdt_1min_2021_2025.npz (40.6 MB, NumPy compressed)
Price range	$15,502 → $126,087
OHLC violations	0 ✓
Duplicate timestamps	0 ✓
License	MIT

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Collection and quality assurance

Source: Binance USDⓈ-M Futures public API via internal database. All bars use close-time convention — each timestamp marks the end of the bar.

QA checks applied before release:

• Duplicate timestamp detection
• Full date-range gap scan (minute-level)
• OHLC consistency: low ≤ min(open, close) and high ≥ max(open, close)
• Negative price and volume checks
• Schema validation across all columns

Results for this release:

Check	Result
Duplicate timestamps	0 ✓
Missing minutes	0 ✓
OHLC violations	0 ✓
Negative prices	0 ✓
Zero-volume bars	213 (retained — valid observations)

---

File structure

import numpy as np

data = np.load("btcusdt_1min_2021_2025.npz", allow_pickle=True)

bars       = data["bars"]           # (2_629_440, 6)  float32  — raw exchange bars
timestamps = data["timestamps"]     # (2_629_440,)    int64    — Unix ms UTC, close-time
columns    = list(data["columns"])  # ['open', 'high', 'low', 'close', 'volume', 'num_trades']
meta       = str(data["meta"][0])   # provenance string

Channels (raw values)

Index	Name	Description
0	open	First trade price in the bar
1	high	Highest trade price in the bar
2	low	Lowest trade price in the bar
3	close	Last trade price in the bar
4	volume	Total base asset volume (BTC)
5	num_trades	Number of individual trades

All values are stored as raw floats with no pre-processing applied.

Summary statistics

Channel	Min	Max	Mean
open	15,502.00	126,086.70	54,382.59
high	15,532.20	126,208.50	54,406.74
low	15,443.20	126,030.00	54,358.47
close	15,502.00	126,086.80	54,382.60
volume	0.00	40,256.00	241.90
num_trades	0.00	263,775.00	2,551.55

Bars by year

2021:   525,600  ██████████████████████████████
2022:   525,600  ██████████████████████████████
2023:   525,600  ██████████████████████████████
2024:   527,040  ██████████████████████████████  (leap year)
2025:   525,600  ██████████████████████████████

Sample bars

First 5 bars (2021-01-01):

#	Datetime UTC	open	high	low	close	volume	num_trades
0	2021-01-01 00:00	28939.90	28981.55	28934.65	28951.68	126.0	929
1	2021-01-01 00:01	28948.19	28997.16	28935.30	28991.01	143.0	1120
2	2021-01-01 00:02	28992.98	29045.93	28991.01	29035.18	256.0	1967
3	2021-01-01 00:03	29036.41	29036.97	28993.19	29016.23	102.0	987
4	2021-01-01 00:04	29016.23	29023.87	28995.50	29002.92	85.0	832

Mid-dataset (2023-07-03):

#	Datetime UTC	open	high	low	close	volume	num_trades
1314720	2023-07-03 00:00	30611.70	30615.70	30611.70	30612.70	42.0	649
1314721	2023-07-03 00:01	30612.70	30624.40	30612.70	30613.90	150.0	1846
1314722	2023-07-03 00:02	30613.90	30614.00	30600.00	30600.00	241.0	1796

Last 5 bars (2025-12-31):

#	Datetime UTC	open	high	low	close	volume	num_trades
2629435	2025-12-31 23:55	87608.40	87608.40	87608.30	87608.30	10.0	182
2629436	2025-12-31 23:56	87608.40	87613.90	87608.30	87613.90	14.0	343
2629437	2025-12-31 23:57	87613.90	87621.70	87613.80	87621.70	7.0	231
2629438	2025-12-31 23:58	87621.60	87631.90	87603.90	87608.10	38.0	815
2629439	2025-12-31 23:59	87608.10	87608.20	87608.10	87608.20	11.0	206

---

Quick start

from huggingface_hub import hf_hub_download
import numpy as np
import pandas as pd

path = hf_hub_download(
    repo_id   = "ResearchRL/diffquant-data",
    filename  = "btcusdt_1min_2021_2025.npz",
    repo_type = "dataset",
)

data  = np.load(path, allow_pickle=True)
bars  = data["bars"]        # (2_629_440, 6) float32
ts    = data["timestamps"]  # Unix ms UTC

index = pd.to_datetime(ts, unit="ms", utc=True)
df    = pd.DataFrame(bars, columns=list(data["columns"]), index=index)
print(df.head())

---

Reference pipeline: DiffQuant

The dataset is designed to be used with the DiffQuant data pipeline. Below is a precise description of the transformations applied — included here so the dataset can be used reproducibly outside DiffQuant as well.

Step 1 — Aggregation

Resample from 1-min to any target resolution using clock-aligned buckets. origin="epoch" ensures bars always land on exact boundaries (:05, :10, …). Partial buckets at series edges are dropped.

from data.aggregator import aggregate
from configs.base_config import MasterConfig

cfg = MasterConfig()
cfg.data.timeframe_min = 5   # valid: {1, 2, 3, 4, 5, 6, 10, 12, 15, 20, 30, 60}

bars_5m, ts_5m = aggregate(bars_1m, timestamps, cfg)

Step 2 — Feature engineering

Applied channel-by-channel after aggregation. The first bar is always dropped (no prior close available for log-return computation).

Channel	Transformation
open, high, low, close	log(price_t / close_{t-1}) — log-return vs previous bar close
volume	volume_t / global_mean(volume) — ratio to mean of the full aggregated series
num_trades	num_trades_t / global_mean(num_trades) — same
typical_price (optional)	log(((H+L+C)/3)_t / close_{t-1})
time features (optional)	[sin_hour, cos_hour, sin_dow, cos_dow] — cyclic UTC encoding

Step 3 — Feature presets

cfg.data.preset = "ohlc"    # 4 channels
cfg.data.preset = "ohlcv"   # 5 channels  (default)
cfg.data.preset = "full"    # 6 channels

cfg.data.add_typical_price = True   # +1 channel
cfg.data.add_time_features  = True  # +4 channels

# Or fully custom:
cfg.data.preset          = "custom"
cfg.data.feature_columns = ["close", "volume"]

Step 4 — Temporal splits (DiffQuant defaults)

Train    : 2021-01-01 → 2025-03-31  (~4.25 years)
Val      : 2025-04-01 → 2025-06-30  (3 months)
Test     : 2025-07-01 → 2025-09-30  (3 months)
Backtest : 2025-10-01 → 2025-12-31  (3 months)

Boundaries are fully configurable via SplitConfig.

Step 5 — Full pipeline one-liner

from data.pipeline import load_or_build
from configs.base_config import MasterConfig

cfg    = MasterConfig()
splits = load_or_build("btcusdt_1min_2021_2025.npz", cfg, cache_dir="data_cache/")

# splits["train"]["full_sequences"]  — (N, ctx+hor, F) sliding windows for training
# splits["val"]["raw_features"]      — continuous array for walk-forward evaluation

Results are MD5-hashed and cached on disk. Cache is invalidated automatically when the config changes (timeframe, preset, split boundaries, feature flags).

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Project context

This dataset is the data foundation for DiffQuant, a research framework studying direct optimisation of trading objectives:

In standard ML trading pipelines, models are trained on proxy objectives — MSE for price prediction, TD-error for RL — evaluated indirectly through downstream trading logic. DiffQuant studies a tighter formulation: position generation, transaction costs, and portfolio path interact directly with the Sharpe ratio as the training objective through a differentiable simulator.

Key references:

• Buehler, H., Gonon, L., Teichmann, J., Wood, B. (2019). Deep Hedging. Quantitative Finance, 19(8). arXiv:1802.03042 — foundational framework for end-to-end differentiable financial objectives.

• Moody, J., Saffell, M. (2001). Learning to Trade via Direct Reinforcement. IEEE Transactions on Neural Networks, 12(4). — original formulation of direct PnL optimisation as a training objective.

• Khubiev, K., Semenov, M., Podlipnova, I., Khubieva, D. (2026). Finance-Grounded Optimization For Algorithmic Trading. arXiv:2509.04541 — closest parallel work on financial loss functions for return prediction.

🔗 DiffQuant pipeline: code release planned.

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Citation

@dataset{Kolesnikov2026diffquant_data,
  author    = {Kolesnikov, Yuriy},
  title     = {{BTCUSDT} 1-Min Futures — 5-Year Research Dataset (2021--2025)},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/ResearchRL/diffquant-data},
}