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

---

## 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 | `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

```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 (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

```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 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`](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 optimisation 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.

🔗 **DiffQuant pipeline:** code release planned.

---

## 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},
}
```