ENKS / clustering article — notes → training in this repo

Summary of the methodology described in the article (MQL5 / ENKS trader clusters):

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.
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).
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).
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.
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.

Run training

From ai/yt (MetaTrader 5 must be installed and history available for the symbol):

pip install -r requirements.txt
python train_article_split.py

Environment overrides:

Outputs: ai/yt/models/<SYMBOL>_H1_article_split.onnx, scaler .pkl, *_meta.json.