README.md

---
language:
  - en
license: mit
tags:
  - finance
  - trading
  - cryptocurrency
  - bitcoin
  - time-series
  - OHLCV
  - binance
  - futures
  - quantitative-finance
  - differentiable-trading
pretty_name: BTCUSDT 1-Min Futures — 5-Year Research Dataset (2021–2025)
size_categories:
  - 1M<n<10M
task_categories:
  - time-series-forecasting
---

# 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, normalization, and temporal splitting belong to the downstream
[DiffQuant](https://github.com/YuriyKolesnikov/diffquant) pipeline, described below
for reproducibility.

Full codebase: [GitHub Repository](https://github.com/YuriyKolesnikov/diffquant)

---

## 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 |

---

## 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

```python
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

```python
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.

```python
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 | `log(volume_t / rolling_mean(volume, window) + eps)` — relative intensity |
| num_trades | `log(num_trades_t / rolling_mean(num_trades, window) + eps)` — 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

```python
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

The full dataset supports arbitrary split boundaries via `SplitConfig`.
The primary DiffQuant experiment used the following non-overlapping splits:

```
Train    : 2024-01-01 → 2025-03-31  (15 months — intentionally recent)
Val      : 2025-04-01 → 2025-06-30  (3 months)
Test     : 2025-07-01 → 2025-09-30  (3 months, out-of-sample)
Backtest : 2025-10-01 → 2025-12-31  (3 months, final hold-out)
```

The training window is deliberately limited to 15 months rather than the full
historical record. This keeps the training regime close to the evaluation periods
and minimizes distribution shift. Extending to earlier data is the recommended
first ablation and is straightforward via `SplitConfig.train_start`.

### Step 5 — Full pipeline one-liner

```python
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).

---

## Project context

This dataset is the data foundation for **DiffQuant**, a research framework
studying direct optimization of trading objectives.

Most ML trading systems face a structural misalignment: models are trained
on proxy losses — MSE, cross-entropy, TD-error — while performance is measured in
realized PnL, Sharpe ratio, and drawdown. DiffQuant studies what happens when
this proxy is removed entirely: the full pipeline from raw features through a
differentiable mark-to-market simulator to the Sharpe ratio becomes a single
computation graph. `loss.backward()` optimizes what the strategy actually earns,
with transaction costs and slippage accounted for in every gradient update.

**Key references:**

- Buehler, H., Gonon, L., Teichmann, J., Wood, B. (2019). *Deep Hedging.*
  Quantitative Finance, 19(8). [`arXiv:1802.03042`](https://arxiv.org/abs/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 optimization as a training objective.

- Khubiev, K., Semenov, M., Podlipnova, I., Khubieva, D. (2026).
  *Finance-Grounded Optimization For Algorithmic Trading.*
  [`arXiv:2509.04541`](https://arxiv.org/abs/2509.04541)
  — closest parallel work on financial loss functions for return prediction.

<p>
<strong>Research article (English · Medium):</strong><br>
<a href="https://medium.com/@YuriKolesnikovAI/diffquant-end-to-end-sharpe-optimization-through-a-differentiable-trading-simulator-a64d428f0fd4">DiffQuant: End-to-End Sharpe Optimization Through a Differentiable Trading Simulator</a>
</p>
<p>
<strong>Статья (Русский · Habr):</strong><br>
<a href="https://habr.com/ru/articles/1022254/">DiffQuant: прямая оптимизация коэффициента Шарпа через дифференцируемый торговый симулятор</a>
</p>
<p>
<strong>DiffQuant pipeline:</strong>
<a href="https://github.com/YuriyKolesnikov/diffquant">github.com/YuriyKolesnikov/diffquant</a>
</p>

---

## Citation

```bibtex
@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},
}
```