27 lines
1.7 KiB
Text
27 lines
1.7 KiB
Text
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=== POST-PURGE BASELINE (SB-01 + SB-02 fixed) ===
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Tanggal : 2026-08-21
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Command : python train_model.py XAUUSDc --out repaired\post_purge\SniperGold_ML_postpurge.mqh --tag v20260821_postpurge
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Hash kode baru (train_model.py, setelah edit) : (lihat provenance hash di bawah)
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Dataset (features_XAUUSDc.npz) : E60285F55AB5AF7B397653373F0EE5B8B814708DC178DD58691DAE0BE122946A
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Model POST : repaired\post_purge\SniperGold_ML_postpurge.mqh (SHA256 9D8906A89DB6580A01A1F1747E6E24D4C9D3F849910AEDCE2AFE873B6F1FE44E)
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Determinisme : dijalankan 2x -> hasil IDENTIK (reproducible).
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Hasil (kode baru; SB-01 metrik validasi per-head; SB-02 purge gap=H_LABEL):
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SB-02 purged split: split_bar=44972 train=35652 purge=13 test=11872
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max_train_outcome_end=44995 < min_test_outcome_start=44997 OK
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AUC validasi (SB-01 corrected): LONG=0.6796 SHORT=0.6796 mean=0.6796 (epoch terbaik 55)
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LONG : TRAIN AUC 0.7483 | VAL 0.6796 | TEST 0.6582
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SHORT: TEST AUC 0.6582
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CALIBRASI LONG A=0.6913 B=0.1935 | SHORT A=0.6913 B=-0.1935
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n_tr=30304 n_purge=13 n_te=11872 split_bar=44972 tag=v20260821_postpurge
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PERBANDINGAN PRE vs POST:
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PRE (kode lama, metrik rusak, split tanpa purge): TEST 0.6270 / 0.6207 ; best_epoch=0
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POST (kode baru, metrik benar, purge gap=24) : TEST 0.6582 / 0.6582 ; best_epoch=55
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Interpretasi: KOREKSI METODOLOGI (bukan optimasi). Freeze lama = model ~1 epoch
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(early stopping memilih epoch 0 karena metrik validasi rusak ~0.5 konstan).
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CATATAN:
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- angka ini adalah ESTIMASI terkoreksi pada cache features_XAUUSDc.npz (feed XAUUSDc,
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split 75/25 dengan purge). Bukan klaim edge; belum divalidasi lintas feed.
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- freeze Include\SniperGold_ML.mqh TIDAK diubah.
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