profitable-expert-advisor/ai/eurusd1h/here.md

EURUSD H1 — ONNX action model (full MT5 history)

Same methodology as ai/yt/train_article_split.py:

• 24 features + 5 softmax classes (ai/xauusd_h1/features.py, labeling.py).
• MinMaxScaler is fit on every valid feature row MT5 returns (no 2010–2020 cut).
• Training sequences: all but a chronological tail (default 12%) used only for val_loss / EarlyStopping (does not remove data from the scaler).
• Optional KMeans on forward-return fingerprints + class-balanced sample_weight on the train split.

Run

cd ai/eurusd1h
pip install -r requirements.txt
python main.py

Requires MetaTrader 5 with EURUSD H1 history downloaded (Tools → History Center).

Environment overrides

Variable	Default	Meaning
EUR_SYMBOL	EURUSD	MT5 symbol
EUR_LOOKBACK	48	Sequence length
EUR_EPOCHS	40	Max epochs
EUR_BATCH	64	Batch size
EUR_CLUSTERS	12	KMeans clusters (0 = off)
EUR_VAL_FRAC	0.12	Fraction of sequences at end for val

Outputs

• models/EURUSD_H1_action.onnx
• models/EURUSD_H1_action_meta.json
• models/EURUSD_H1_action_scaler.pkl

Deploy like ai/yt/US500_H1_ArticleEA.mq5: embed ONNX, set InpLookback, paste scaler_feature_min / max from the meta JSON into the EA inputs.