feat: add 5 dashboard features — dark mode, trade history, backtests, model insights, alerts
- Dark mode: class-based theme toggle with localStorage persistence and flash prevention - Trade History (/trades): paginated table, stats cards, equity curve chart with DB API endpoints - Backtest Viewer (/backtests): log parser for 35 backtest results, sidebar + detail + comparison tabs - Model Insights: dashboard card + dialog showing feature importance, regime distribution, training history - Alert/Signal Log (/alerts): signal stats, filterable table with execution tracking - API: 8 new endpoints with psycopg2 DB connection pool - Dark mode sweep across books page, about dialog, and all dashboard components - Architecture docs rewritten with Mermaid diagrams (23 docs) - README and FEATURES.md rewritten bilingual (Indonesian + English) - main_live.py: write model_metrics.json on startup and retrain Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
GifariKemal
co-authored by
Claude Opus 4.6
Claude Opus 4.6
parent
b2dc2dacd7
commit
e8355b3f62
@@ -1,4 +1,4 @@
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# Auto Trainer — Sistem Retraining Otomatis
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# *Auto Trainer* --- Sistem *Retraining* Otomatis
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> **File:** `src/auto_trainer.py`
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> **Class:** `AutoTrainer`
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@@ -6,21 +6,21 @@
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---
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## Apa Itu Auto Trainer?
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## Apa Itu *Auto Trainer*?
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Auto Trainer adalah sistem yang **melatih ulang model AI secara otomatis** agar tetap up-to-date dengan kondisi pasar terbaru. Retraining dilakukan saat market tutup (05:00 WIB) untuk menghindari gangguan saat trading aktif.
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*Auto Trainer* adalah sistem yang **melatih ulang model AI secara otomatis** agar tetap up-to-date dengan kondisi pasar terbaru. *Retraining* dilakukan saat market tutup (05:00 WIB) untuk menghindari gangguan saat trading aktif.
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**Analogi:** Auto Trainer seperti **pelatih yang membuat atlet berlatih setiap malam** — setelah pertandingan selesai, atlet (model AI) dilatih dengan data terbaru agar siap menghadapi tantangan esok hari.
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**Analogi:** *Auto Trainer* seperti **pelatih yang membuat atlet berlatih setiap malam** --- setelah pertandingan selesai, atlet (model AI) dilatih dengan data terbaru agar siap menghadapi tantangan esok hari.
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---
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## Jadwal Retraining
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## Jadwal *Retraining*
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| Tipe | Waktu | Data | Boost Rounds | Kondisi |
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|------|-------|------|-------------|---------|
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| **Daily** | 05:00 WIB (market close) | 8.000 bar | 50 | Senin–Jumat |
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| **Weekend** | 05:00 WIB Sabtu/Minggu | 15.000 bar | 80 | Deep training |
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| **Emergency** | Kapan saja | 8.000 bar | 50 | AUC < 0.65 |
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| **Daily** | 05:00 WIB (market close) | 8.000 bar | 50 | Senin--Jumat |
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| **Weekend** | 05:00 WIB Sabtu/Minggu | 15.000 bar | 80 | *Deep training* |
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| **Emergency** | Kapan saja | 8.000 bar | 50 | *AUC* < 0.65 |
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| **Initial** | Pertama kali | 8.000 bar | 50 | Belum pernah training |
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```
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@@ -53,91 +53,137 @@ AutoTrainer(
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---
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## Proses Retraining (Step-by-Step)
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## Proses *Retraining* (Step-by-Step)
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### Flowchart Keputusan *Retraining*
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```mermaid
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flowchart TD
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A[Cek should_retrain] --> B{Sudah >= 20 jam\nsejak retrain terakhir?}
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B -- Tidak --> Z[Skip retrain]
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B -- Ya --> C{Jam 05:00 WIB\natau AUC < 0.65?}
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C -- Tidak --> Z
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C -- Ya --> D{Weekend?}
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D -- Ya --> E[Deep training:\n15K bar, 80 rounds]
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D -- Tidak --> F[Daily training:\n8K bar, 50 rounds]
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E --> G[Backup model lama]
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F --> G
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G --> H[Fetch data dari MT5]
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H --> I[Feature Engineering\n+ SMC Analysis]
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I --> J[Train HMM + XGBoost]
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J --> K[Validasi AUC]
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K --> L{Test AUC >= 0.60?}
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L -- Ya --> M[Simpan model baru]
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L -- Tidak --> N[Rollback ke model lama]
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M --> O[Record hasil ke DB]
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N --> O
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O --> P[Selesai]
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```
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### Detail Langkah-Langkah
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```
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1. SHOULD RETRAIN CHECK
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├ Sudah >= 20 jam sejak retrain terakhir?
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├ Sekarang jam 05:00 WIB (±30 menit)?
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├ Weekend? → Deep training (15K bar)
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└ AUC < 0.65? → Emergency retrain
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+-- Sudah >= 20 jam sejak retrain terakhir?
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+-- Sekarang jam 05:00 WIB (+-30 menit)?
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+-- Weekend? -> Deep training (15K bar)
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+-- AUC < 0.65? -> Emergency retrain
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2. BACKUP MODEL LAMA
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├ Copy xgboost_model.pkl → backups/YYYYMMDD_HHMMSS/
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├ Copy hmm_regime.pkl → backups/YYYYMMDD_HHMMSS/
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└ Bersihkan backup lama (simpan 5 terakhir)
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+-- Copy xgboost_model.pkl -> backups/YYYYMMDD_HHMMSS/
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+-- Copy hmm_regime.pkl -> backups/YYYYMMDD_HHMMSS/
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+-- Bersihkan backup lama (simpan 5 terakhir)
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3. FETCH DATA TERBARU
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├ Ambil 8K bar (daily) atau 15K bar (weekend) dari MT5
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├ Symbol: XAUUSD, Timeframe: M15
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└ Validasi: minimal 1000 bar
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+-- Ambil 8K bar (daily) atau 15K bar (weekend) dari MT5
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+-- Symbol: XAUUSD, Timeframe: M15
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+-- Validasi: minimal 1000 bar
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4. FEATURE ENGINEERING
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├ FeatureEngineer.calculate_all() → 40+ fitur
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├ SMCAnalyzer.calculate_all() → struktur pasar
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└ create_target(lookahead=1) → label UP/DOWN
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+-- FeatureEngineer.calculate_all() -> 40+ fitur
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+-- SMCAnalyzer.calculate_all() -> struktur pasar
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+-- create_target(lookahead=1) -> label UP/DOWN
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5. TRAINING HMM
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├ MarketRegimeDetector(n_regimes=3, lookback=500)
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├ hmm.fit(df)
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└ Save → models/hmm_regime.pkl
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+-- MarketRegimeDetector(n_regimes=3, lookback=500)
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+-- hmm.fit(df)
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+-- Save -> models/hmm_regime.pkl
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6. TRAINING XGBOOST
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├ TradingModel(confidence_threshold=0.60)
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├ xgb.fit(train_ratio=0.7, num_boost_round=50/80)
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├ Early stopping: 5 rounds
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└ Save → models/xgboost_model.pkl
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+-- TradingModel(confidence_threshold=0.60)
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+-- xgb.fit(train_ratio=0.7, num_boost_round=50/80)
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+-- Early stopping: 5 rounds
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+-- Save -> models/xgboost_model.pkl
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7. VALIDASI
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├ Cek Train AUC & Test AUC
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├ Test AUC < 0.60? → ROLLBACK ke model lama (v4: dinaikkan dari 0.52)
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├ Test AUC < 0.65? → WARNING (alert)
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└ Test AUC >= 0.65? → SUCCESS
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+-- Cek Train AUC & Test AUC
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+-- Test AUC < 0.60? -> ROLLBACK ke model lama (v4: dinaikkan dari 0.52)
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+-- Test AUC < 0.65? -> WARNING (alert)
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+-- Test AUC >= 0.65? -> SUCCESS
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8. RECORD HASIL
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├ Simpan ke PostgreSQL (training_runs table)
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├ Backup ke file (retrain_history.txt)
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└ Log: durasi, AUC, accuracy, status
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+-- Simpan ke PostgreSQL (training_runs table)
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+-- Backup ke file (retrain_history.txt)
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+-- Log: durasi, AUC, accuracy, status
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```
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---
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## Backup & Rollback
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## *Backup* & *Rollback*
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### Sistem Backup
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### Sistem *Backup*
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```
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models/
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├── xgboost_model.pkl # Model aktif
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├── hmm_regime.pkl # Model aktif
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└── backups/
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├── 20250206_050015/ # Backup terbaru
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│ ├── xgboost_model.pkl
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│ └── hmm_regime.pkl
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├── 20250205_050012/ # Backup kemarin
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│ ├── xgboost_model.pkl
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│ └── hmm_regime.pkl
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└── ... (max 5 backup)
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+-- xgboost_model.pkl # Model aktif
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+-- hmm_regime.pkl # Model aktif
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+-- backups/
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+-- 20250206_050015/ # Backup terbaru
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| +-- xgboost_model.pkl
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| +-- hmm_regime.pkl
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+-- 20250205_050012/ # Backup kemarin
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| +-- xgboost_model.pkl
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| +-- hmm_regime.pkl
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+-- ... (max 5 backup)
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```
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### Kapan Rollback?
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### Kapan *Rollback*?
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```
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Model baru di-training
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v
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Cek Test AUC
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├── AUC >= 0.65 ──> KEEP model baru ✅
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├── AUC 0.60-0.65 ──> KEEP tapi WARNING ⚠️
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| (akan trigger emergency retrain nanti)
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└── AUC < 0.60 ──> ROLLBACK ke model lama 🔄
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(v4: dinaikkan dari 0.52, karena 0.52 hampir = acak)
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#### Diagram Validasi *AUC*
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```mermaid
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flowchart TD
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A[Model baru selesai di-training] --> B[Hitung Test AUC]
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B --> C{Test AUC >= 0.65?}
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C -- Ya --> D[KEEP model baru]
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D --> D1[Status: SUCCESS]
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C -- Tidak --> E{Test AUC >= 0.60?}
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E -- Ya --> F[KEEP model baru\ndengan WARNING]
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F --> F1[Status: WARNING]
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F1 --> F2[Akan trigger\nemergency retrain nanti]
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E -- Tidak --> G[ROLLBACK ke model lama]
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G --> G1[Status: ROLLBACK]
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G1 --> G2[v4: threshold dinaikkan\ndari 0.52 ke 0.60]
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style D fill:#22c55e,color:#fff
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style D1 fill:#22c55e,color:#fff
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style F fill:#eab308,color:#000
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style F1 fill:#eab308,color:#000
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style F2 fill:#eab308,color:#000
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style G fill:#ef4444,color:#fff
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style G1 fill:#ef4444,color:#fff
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style G2 fill:#ef4444,color:#fff
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```
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### Method Rollback
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#### Ringkasan Keputusan
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| Kondisi | Aksi | Status |
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|---------|------|--------|
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| *AUC* >= 0.65 | KEEP model baru | SUCCESS |
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| *AUC* 0.60--0.65 | KEEP tapi WARNING (akan trigger *emergency* retrain nanti) | WARNING |
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| *AUC* < 0.60 | *ROLLBACK* ke model lama (v4: dinaikkan dari 0.52, karena 0.52 hampir = acak) | ROLLBACK |
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### Method *Rollback*
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```python
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def rollback_models(reason="Manual rollback"):
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@@ -151,28 +197,28 @@ def rollback_models(reason="Manual rollback"):
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---
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## AUC Monitoring
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## *AUC* Monitoring
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### Apa Itu AUC?
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### Apa Itu *AUC*?
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AUC (Area Under Curve) mengukur **seberapa baik model membedakan sinyal BUY vs SELL**:
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*AUC* (*Area Under Curve*) mengukur **seberapa baik model membedakan sinyal BUY vs SELL**:
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| AUC | Arti | Aksi |
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| *AUC* | Arti | Aksi |
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|-----|------|------|
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| 0.80+ | Sangat bagus | Model dalam kondisi prima |
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| 0.65-0.80 | Bagus | Normal, lanjut trading |
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| 0.60-0.65 | Minimum | Warning, pertimbangkan retrain |
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| < 0.60 | Buruk | **ROLLBACK** + retrain segera (v4 threshold) |
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| 0.60-0.65 | Minimum | Warning, pertimbangkan *retraining* |
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| < 0.60 | Buruk | **ROLLBACK** + *retraining* segera (v4 threshold) |
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| 0.50 | Sama dengan tebak koin | Model tidak berguna |
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### Auto-Retrain on Low AUC
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### Auto-Retrain on Low *AUC*
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```python
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def should_retrain_due_to_low_auc():
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"""
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Cek AUC saat ini:
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AUC < 0.65? → Perlu retrain
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Tapi: sudah retrain < 4 jam lalu? → Tunggu
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AUC < 0.65? -> Perlu retrain
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Tapi: sudah retrain < 4 jam lalu? -> Tunggu
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(mencegah retrain loop)
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"""
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```
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```
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Table: training_runs
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├── id # Auto-increment
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├── training_type # "daily" / "weekend"
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├── bars_used # 8000 / 15000
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├── num_boost_rounds # 50 / 80
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├── started_at # Timestamp mulai
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├── completed_at # Timestamp selesai
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├── duration_seconds # Durasi training
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├── hmm_trained # Boolean
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├── xgb_trained # Boolean
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├── train_auc # AUC di data training
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├── test_auc # AUC di data test
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├── train_accuracy # Akurasi training
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├── test_accuracy # Akurasi test
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├── model_path # Path model disimpan
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├── backup_path # Path backup model lama
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├── success # Boolean
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└── error_message # Pesan error (jika gagal)
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+-- id # Auto-increment
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+-- training_type # "daily" / "weekend"
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+-- bars_used # 8000 / 15000
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+-- num_boost_rounds # 50 / 80
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+-- started_at # Timestamp mulai
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+-- completed_at # Timestamp selesai
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+-- duration_seconds # Durasi training
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+-- hmm_trained # Boolean
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+-- xgb_trained # Boolean
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+-- train_auc # AUC di data training
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+-- test_auc # AUC di data test
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+-- train_accuracy # Akurasi training
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+-- test_accuracy # Akurasi test
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+-- model_path # Path model disimpan
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+-- backup_path # Path backup model lama
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+-- success # Boolean
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+-- error_message # Pesan error (jika gagal)
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```
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### File Fallback
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@@ -209,9 +255,9 @@ Table: training_runs
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Jika PostgreSQL tidak tersedia:
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```
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data/retrain_history.txt
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├── 2025-02-06T05:00:15+07:00
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├── 2025-02-05T05:00:12+07:00
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└── ... (append per retrain)
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+-- 2025-02-06T05:00:15+07:00
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+-- 2025-02-05T05:00:12+07:00
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+-- ... (append per retrain)
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```
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---
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@@ -256,18 +302,18 @@ if candle_count % 20 == 0: # Setiap 20 candle baru
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| Data | 8.000 bar M15 (~83 hari) |
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| Train/Test Split | 70% / 30% |
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| XGBoost Rounds | 50 |
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| Early Stopping | 5 rounds |
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| *Early Stopping* | 5 rounds |
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| HMM Regimes | 3 |
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| HMM Lookback | 500 bar |
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### Weekend Deep Training (Sabtu-Minggu)
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### Weekend *Deep Training* (Sabtu-Minggu)
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| Parameter | Nilai |
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|-----------|-------|
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| Data | 15.000 bar M15 (~156 hari) |
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| Train/Test Split | 70% / 30% |
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| XGBoost Rounds | 80 |
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| Early Stopping | 5 rounds |
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| *Early Stopping* | 5 rounds |
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| HMM Regimes | 3 |
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| HMM Lookback | 500 bar |
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Reference in New Issue
Block a user