vichetkao/xauusd_dataset_2026

XAUUSD Cleaned H1 Training Dataset (2009-2026)

Gold (XAUUSD) hourly bars with scale-free features, a verified US news calendar, lagged macro data and buy / sell / no-trade labels, ready for walk-forward model training. All timestamps are real UTC. Everything is real recorded market or official data; nothing is simulated.

Generated 2026-09-24T13:25:03+00:00. Total size 183 MB.

Files

File	Rows	Content
data/training/xauusd_H1_train.parquet	98,634	Main training table: 60 features + target, 2009-09-04 to 2026-09-24
data/training/xauusd_H1_labeled.parquet	100,000	Full H1 table before feature engineering: MT5 OHLC, spread, news flags, FRED values, labels
data/training/feature_list.json	-	Feature names, target classes, build stats
data/training/xauusd_H1_walk_forward.json	-	Walk-forward folds (test 2018...2025) and the 2026 final holdout
data/events/high_impact_events.parquet (+ .csv)	637	CPI, NFP, FOMC 2009-2027 in UTC
data/bars_reference/xauusd_M1.parquet	5,942,606	UTC reference M1 bars (HistData + Pcitycrypto)
data/bars_reference/xauusd_M5.parquet	1,196,624	UTC reference M5 bars (HistData + Pcitycrypto)
data/bars_reference/xauusd_M15.parquet	399,965	UTC reference M15 bars (HistData + Pcitycrypto)
data/bars_reference/xauusd_M30.parquet	200,527	UTC reference M30 bars (HistData + Pcitycrypto)
data/bars_reference/xauusd_H1.parquet	100,861	UTC reference H1 bars (HistData + Pcitycrypto)
data/bars_reference/xauusd_H4.parquet	26,007	UTC reference H4 bars (HistData + Pcitycrypto)
data/bars_reference/xauusd_D1.parquet	4,355	UTC reference D1 bars (HistData + Pcitycrypto)
reports/	-	Data quality report for the reference bars
manifest.json	-	Row counts, date ranges and SHA-256 of every file

data/bars_reference/ is the UTC reference series built from two public Hugging Face datasets (HistData M1 and a broker M1 feed). It was used to verify the MT5 timestamps; the model trains on MT5 data.

Quick start

from datasets import load_dataset

train = load_dataset("<your-user>/<this-repo>", "h1_training", split="train").to_pandas()

or with pandas after downloading: pd.read_parquet("data/training/xauusd_H1_train.parquet").

Main training table

• Column time_utc: H1 bar open time, UTC (first column; D1 bars use session_date).
• Target target: 0 = sell, 1 = no trade, 2 = buy. Share: sell 28.9%, no trade 42.6%, buy 28.5%.
• Features (60): ret_1h, ret_3h, ret_6h, ret_12h, ret_24h, ret_120h, vol_24h, vol_120h, vol_ratio, atr_pct, atr_ratio_120, range_atr, body_atr, upper_wick_atr, lower_wick_atr, dist_ema20_atr, dist_ema50_atr, dist_ema200_atr, ema20_slope_atr, rsi_14, dist_high24_atr, dist_low24_atr, dist_high120_atr, dist_low120_atr, tick_volume_ratio, hour_sin, hour_cos, day_of_week, session_asia, session_london, session_london_ny_overlap, session_ny_late, news_any_window, news_cpi_window, news_nfp_window, news_fomc_window, hours_to_next_news, hours_since_prev_news, macro_DFII10, macro_DGS10, macro_T10YIE, macro_DFF, macro_VIXCLS, macro_UNRATE, macro_DFII10_chg7d, macro_DFII10_chg30d, macro_DGS10_chg7d, macro_DGS10_chg30d, macro_T10YIE_chg7d, macro_T10YIE_chg30d, macro_VIXCLS_chg7d, macro_DFF_chg90d, macro_UNRATE_chg90d, macro_DTWEXBGS_pct7d, macro_DTWEXBGS_pct30d, macro_DCOILBRENTEU_pct7d, macro_DCOILBRENTEU_pct30d, macro_CPIAUCSL_yoy, macro_CPILFESL_yoy, macro_PAYEMS_chg90d
• Backtest-only columns (never use as features): open, high, low, close, atr_14, spread, label, planned_entry_price, planned_stop_price, planned_target_price.

Label rule

Entry at the H1 close. Buy if price reaches +1.5 x ATR14 + $0.30 before -1.0 x ATR14 - $0.30 within 8 bars; sell is the mirror; otherwise no trade. When stop and target are both touched in one bar, the stop counts first. $0.30 covers spread and slippage (the MT5 bar spread is the minimum of the hour and too optimistic).

No-lookahead rules

• Every price feature uses only past bars (returns, EMA/ATR distances, RSI, rolling highs/lows).
• FRED values are joined only after publication: daily series +1 day, dollar index and Brent +7 days, monthly series +45 days.
• News windows (1 h before to 3 h after) come from official schedules published in advance.
• Walk-forward folds end 8 bars before each test year so no label window crosses into the test data.

Sources and processing

Data	Source	Processing
XAUUSD H1	MetaTrader 5 broker history (100,000 bars)	Broker server time is Eastern European time (UTC+2/+3, EU DST); converted to UTC and verified 0 h offset against the reference bars
Reference M1	fokan/xauusd-2009-2026 (HistData), Pcitycrypto/xauusd	Clock offsets verified on FOMC 14:00 NY price spikes; duplicates removed; merged; resampled to M5-D1 aligned to the 18:00 NY session open
Macro	FRED: DFII10, DGS10, T10YIE, DFF, VIXCLS, UNRATE, DTWEXBGS, DCOILBRENTEU, CPIAUCSL, CPILFESL, PAYEMS	Publication lags as above; changes instead of trending levels
CPI, NFP dates	BLS release archives	89% show a gold spike at exactly 08:30 NY
FOMC dates	Federal Reserve	94% show a spike at the statement minute; 2026-10 onward are scheduled, not yet held

Known limitations

• PCE releases and Fed speeches are not in the calendar.
• MT5 spread is the minimum spread per hour; real costs are higher.
• One broker's price feed; other brokers differ by a few cents.
• atr_14 is a simple 14-bar average of the true range.
• Gold's 2018-2025 uptrend means "always buy" was profitable (+0.022 R per trade after costs); compare models against that baseline.

Expected model performance

On 2018-2025, always-buy wins 45.0% of trades and always-no-trade scores 42.0% 3-class accuracy. A realistic good model reaches 47-52% win rate on the trades it takes. Accuracy above about 60% usually means lookahead.

License

See LICENSE.md. Mixed sources: check each provider's terms before public or commercial use. Not financial advice.

---

📌 Citation / Attribution Requirements

If you use this dataset, models trained on it, or any derivatives/code derived from it in your research, articles, open-source projects, or commercial applications, you must properly cite it.

BibTeX citation:

@dataset{kao_vichet_xauusd_2026,
  author       = {Kao Vichet},
  title        = {XAUUSD Cleaned H1 Training Dataset (2009-2026)},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/vichetkao/xauusd_dataset_2026}
}