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README.md

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----
-license: mit
----
+---
+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},
+}
+```