38 lines
1.6 KiB
Markdown
38 lines
1.6 KiB
Markdown
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# EURUSD H1 — ONNX action model (full MT5 history)
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Same methodology as `ai/yt/train_article_split.py`:
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- **24 features** + **5 softmax classes** (`ai/xauusd_h1/features.py`, `labeling.py`).
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- **MinMaxScaler** is fit on **every** valid feature row MT5 returns (no 2010–2020 cut).
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- **Training sequences**: all but a **chronological tail** (default **12%**) used only for `val_loss` / EarlyStopping (does not remove data from the scaler).
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- Optional **KMeans** on forward-return fingerprints + class-balanced `sample_weight` on the train split.
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## Run
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```bash
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cd ai/eurusd1h
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pip install -r requirements.txt
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python main.py
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```
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Requires MetaTrader 5 with **EURUSD H1** history downloaded (Tools → History Center).
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## Environment overrides
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| Variable | Default | Meaning |
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|-----------------|-----------|----------------------------------------------|
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| `EUR_SYMBOL` | `EURUSD` | MT5 symbol |
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| `EUR_LOOKBACK` | `48` | Sequence length |
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| `EUR_EPOCHS` | `40` | Max epochs |
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| `EUR_BATCH` | `64` | Batch size |
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| `EUR_CLUSTERS` | `12` | KMeans clusters (`0` = off) |
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| `EUR_VAL_FRAC` | `0.12` | Fraction of sequences at **end** for val |
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## Outputs
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- `models/EURUSD_H1_action.onnx`
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- `models/EURUSD_H1_action_meta.json`
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- `models/EURUSD_H1_action_scaler.pkl`
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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.
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