GifariKemalandClaude Opus 4.6
e8355b3f62
- 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>
134 lines
3.8 KiB
Markdown
134 lines
3.8 KiB
Markdown
# XGBoost — *Signal Predictor*
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> **File:** `src/ml_model.py`
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> **Model:** `models/xgboost_model.pkl`
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> **Library:** `xgboost`
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---
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## Apa Itu XGBoost *Signal Predictor*?
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XGBoost (*Extreme Gradient Boosting*) adalah model *machine learning* yang memprediksi **sinyal *trading*** — BUY, SELL, atau HOLD — berdasarkan **37 fitur teknikal**. Model ini berfungsi sebagai **konfirmasi kedua** setelah analisis SMC.
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---
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## Alur Prediksi
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```mermaid
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graph LR
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A["37 Fitur Teknikal"] --> B["XGBoost Model"]
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B --> C["Probabilitas per Kelas"]
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C --> D["BUY: 72%"]
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C --> E["SELL: 18%"]
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C --> F["HOLD: 10%"]
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D --> G["Signal: BUY<br/>Confidence: 72%"]
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```
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---
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## 37 *Features* (Fitur *Input*)
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Model menerima 37 fitur yang dihitung oleh `FeatureEngineer`:
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| Grup | Fitur | Jumlah |
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|------|-------|--------|
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| **Momentum** | RSI 14, RSI 7, MACD, *MACD Signal*, *MACD Histogram* | 5 |
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| **Volatilitas** | ATR 14, *Bollinger Upper/Lower/Width*, *Keltner Channel* | 5 |
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| **Trend** | EMA 9/20/50, SMA 20/50, *EMA Crossover* | 6 |
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| **Volume** | *Volume Ratio*, *Volume MA*, *OBV*, *Volume Change* | 4 |
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| ***Price Action*** | *Body Size*, *Shadow Ratio*, *Candle Pattern*, Jarak dari EMA | 5 |
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| **Struktur** | *Higher High/Lower Low*, *Swing Detection*, BOS/CHoCH | 4 |
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| ***Derived*** | *Returns* (1/3/5 bar), *Volatility Ratio*, *Momentum Score* | 5 |
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| ***Lagged*** | Fitur-fitur di atas dengan *lag* 1-3 bar | 3 |
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---
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## *Output*: Prediksi
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```python
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@dataclass
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class MLPrediction:
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signal: str # "BUY", "SELL", atau "HOLD"
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confidence: float # 0.0 - 1.0
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probabilities: Dict # {"BUY": 0.72, "SELL": 0.18, "HOLD": 0.10}
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```
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---
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## Peran dalam Sistem
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XGBoost **bukan pembuat keputusan utama** — fungsinya adalah **konfirmasi dan filter**:
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```mermaid
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graph TD
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SMC["SMC Analyzer<br/>Sinyal Utama"] --> COMBINE["Kombinasi Sinyal"]
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ML["XGBoost<br/>Konfirmasi"] --> COMBINE
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COMBINE --> CHECK{"ML setuju?"}
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CHECK -->|"Ya (≥50%)"| PASS["✅ Lanjut ke filter berikutnya"]
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CHECK -->|"Sangat tidak setuju (>65%)"| VETO["🛑 VETO — blokir entry"]
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CHECK -->|"Ragu (<50%)"| SKIP["⚠️ Skip — confidence terlalu rendah"]
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```
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### Aturan Kombinasi:
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| Kondisi | Aksi |
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|---------|------|
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| SMC = BUY, ML = BUY (≥50%) | ✅ **Konfirmasi** — lanjut |
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| SMC = BUY, ML = HOLD | ⚠️ *Skip* — ML tidak yakin |
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| SMC = BUY, ML = SELL (≥65%) | 🛑 **VETO** — ML *strongly disagree* |
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| SMC = BUY, ML = SELL (<65%) | ✅ *Pass* — ML kurang yakin untuk veto |
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---
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## *Confidence Threshold*
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| Level | Nilai | Penggunaan |
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|-------|-------|------------|
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| **Minimum** | **0.50** | Batas paling rendah untuk diterima |
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| ***Entry*** | **0.65-0.70** | *Default* dari `DynamicConfidence` |
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| ***High*** | **0.75** | Sinyal kuat — *lot multiplier* aktif |
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| ***Very High*** | **0.80** | Sangat yakin — batas atas |
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*Threshold* disesuaikan secara dinamis oleh `DynamicConfidenceManager` berdasarkan kondisi pasar.
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---
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## *Training*
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```python
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# train_models.py
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model = XGBClassifier(
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n_estimators=500,
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max_depth=6,
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learning_rate=0.01,
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subsample=0.8,
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colsample_bytree=0.8,
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min_child_weight=3,
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reg_alpha=0.1, # L1 regularization
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reg_lambda=1.0, # L2 regularization
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)
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# Training data: 1000+ bar XAUUSD M15
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# Label: Pergerakan harga setelah N bar
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# Validasi: Walk-forward dengan 80/20 split
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```
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### ***Auto-Retrain***
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Model otomatis di-*retrain* oleh `AutoTrainer` setiap **7 hari** atau saat:
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- Akurasi prediksi turun signifikan
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- Distribusi pasar berubah
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- *Confidence calibration* menyimpang
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---
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## Penyimpanan Model
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| Properti | Nilai |
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|----------|-------|
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| **Format** | `.pkl` (*pickle*) via `xgboost` |
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| **Lokasi** | `models/xgboost_model.pkl` |
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| **Ukuran** | ~1-5 MB |
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| **Fitur** | 37 kolom (harus identik saat *training* dan *inference*) |
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| ***Retrain*** | Otomatis tiap 7 hari |
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