zhutoutoutousan
31 lines
1.9 KiB
Markdown
31 lines
1.9 KiB
Markdown
# ENKS / clustering article — notes → training in this repo
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Summary of the methodology described in the article (MQL5 / ENKS trader clusters):
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- Models are trained in **Python**, then converted to **ENKS** for the MetaTrader include/bot stack. This repository does **not** ship an ENKS encoder; training here exports **ONNX + JSON meta + scaler** like `ai/xauusd_h1/`. Convert ENKS with the author’s tool or workflow from the article.
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- **Clustering** (article: Cayley / trade matching): use **forward-return fingerprints** per bar and **KMeans** on the in-sample window only, then optional **per-cluster balancing** of sample weights during training (see `train_article_split.py`).
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- **Windows**: train **2010-01-01 → 2019-12-31**; out-of-sample / forward **2020-01-01 → 2024-12-31**. Scaler is fit **only** on the train window (no leakage).
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- **Capital / Capodon-style US H1**: default symbol `US500` on **H1**; override with `YT_SYMBOL`. The article notes models can be attached on other timeframes; EA SL/TP and filters are tuned separately.
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- **Includes (`tendq`, etc.)**: not present in this repo; wire your ONNX EA to the exported `*_meta.json` and scaler like the existing XAUUSD H1 action EA.
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## Run training
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From `ai/yt` (MetaTrader 5 must be installed and history available for the symbol):
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```bash
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pip install -r requirements.txt
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python train_article_split.py
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```
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Environment overrides:
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| Variable | Default | Meaning |
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|----------------|----------------|----------------------------------|
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| `YT_SYMBOL` | `US500` | MT5 symbol |
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| `YT_LOOKBACK` | `48` | Sequence length (bars) |
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| `YT_EPOCHS` | `40` | Max epochs |
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| `YT_BATCH` | `64` | Batch size |
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| `YT_CLUSTERS` | `12` | KMeans clusters (0 = disable) |
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Outputs: `ai/yt/models/<SYMBOL>_H1_article_split.onnx`, scaler `.pkl`, `*_meta.json`.
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