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>
348 lines
9.4 KiB
Markdown
348 lines
9.4 KiB
Markdown
# *Dynamic Confidence* --- Penyesuaian *Threshold* Otomatis
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> **File:** `src/dynamic_confidence.py`
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> **Class:** `DynamicConfidenceManager`
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> **Digunakan di:** `main_live.py`, `backtest_live_sync.py`
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---
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## Apa Itu *Dynamic Confidence*?
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*Dynamic Confidence* adalah sistem yang **menyesuaikan confidence *threshold* ML secara otomatis** berdasarkan kondisi pasar saat ini. Saat kondisi ideal, *threshold* diturunkan agar lebih banyak peluang. Saat kondisi buruk, *threshold* dinaikkan untuk lebih selektif.
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**Analogi:** *Dynamic Confidence* seperti **termometer yang mengatur AC otomatis** --- saat cuaca panas (pasar bagus), AC diset dingin (*threshold* rendah, lebih banyak trade). Saat cuaca dingin (pasar buruk), AC dimatikan (*threshold* tinggi, kurangi trade).
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---
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## Flowchart
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```mermaid
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flowchart TD
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A["Kondisi Market Saat Ini"] --> B["6 Faktor Dianalisis"]
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B --> F1["1. Session<br/>+/- 20 poin"]
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B --> F2["2. Regime<br/>+/- 15 poin"]
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B --> F3["3. Volatility<br/>+/- 10 poin"]
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B --> F4["4. Trend Clarity<br/>+/- 10 poin"]
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B --> F5["5. SMC Confluence<br/>+/- 10 poin"]
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B --> F6["6. ML Alignment<br/>+/- 5 poin"]
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F1 --> S["Score (0 - 100)"]
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F2 --> S
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F3 --> S
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F4 --> S
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F5 --> S
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F6 --> S
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S --> Q{"Quality Level?"}
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Q -->|"Score >= 80"| E["EXCELLENT<br/>Threshold: 60%"]
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Q -->|"Score 65-79"| G["GOOD<br/>Threshold: 65%"]
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Q -->|"Score 50-64"| M["MODERATE<br/>Threshold: 70%"]
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Q -->|"Score 35-49"| P["POOR<br/>Threshold: 80%"]
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Q -->|"Score < 35"| AV["AVOID<br/>Threshold: 85%"]
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E --> D{"ML Confidence<br/>>= Threshold?"}
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G --> D
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M --> D
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P --> D
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AV --> SKIP["SKIP --- Jangan Trade"]
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D -->|"YES"| ENTRY["ENTRY Diizinkan"]
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D -->|"NO"| WAIT["TUNGGU --- Confidence Kurang"]
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style A fill:#4a90d9,color:#fff
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style S fill:#f5a623,color:#fff
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style Q fill:#7b68ee,color:#fff
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style E fill:#27ae60,color:#fff
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style G fill:#2ecc71,color:#fff
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style M fill:#f39c12,color:#fff
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style P fill:#e67e22,color:#fff
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style AV fill:#e74c3c,color:#fff
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style ENTRY fill:#27ae60,color:#fff
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style WAIT fill:#e67e22,color:#fff
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style SKIP fill:#e74c3c,color:#fff
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```
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---
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## Prinsip Dasar
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```
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Market BAGUS (trending, session bagus) --> Threshold RENDAH (60%) --> Lebih banyak trade
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Market BIASA (normal) --> Threshold SEDANG (70%) --> Trade normal
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Market JELEK (choppy, low liquidity) --> Threshold TINGGI (80%) --> Sangat selektif
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Market BERBAHAYA (crisis, weekend) --> Threshold MAXIMUM (85%) --> Hindari trading
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```
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---
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## Konfigurasi
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```python
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DynamicConfidenceManager(
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base_threshold=0.70, # Default threshold 70%
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min_threshold=0.60, # Minimum (kondisi terbaik): 60%
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max_threshold=0.85, # Maximum (kondisi terburuk): 85%
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)
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```
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---
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## 6 Faktor Penilaian
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Score dimulai dari **50** (tengah), lalu disesuaikan oleh 6 faktor:
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### Faktor 1: *Session* (+/- 20 poin)
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| *Session* | Poin | Alasan |
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|---------|------|--------|
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| London-NY Overlap / Golden | **+20** | Likuiditas tertinggi, spread rendah |
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| London | **+15** | Volume tinggi |
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| New York | **+10** | Volume tinggi |
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| Asia/Tokyo | **+0** | *Volatility* rendah |
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| Market Closed/Weekend | **-30** | Tidak ada likuiditas |
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| Lainnya | **+5** | Default |
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### Faktor 2: *Regime* (+/- 15 poin)
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| *Regime* | Poin | Alasan |
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|--------|------|--------|
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| Medium *Volatility* | **+15** | Kondisi ideal untuk trading |
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| Low *Volatility* | **+5** | Hati-hati *ranging* |
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| High *Volatility* | **-5** | Perlu lot kecil |
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| Crisis | **-25** | Hindari trading |
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### Faktor 3: *Volatility* (+/- 10 poin)
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| *Volatility* | Poin | Alasan |
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|-----------|------|--------|
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| Medium | **+10** | Pergerakan cukup, bisa diprediksi |
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| Low | **+0** | Pergerakan terlalu kecil |
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| High | **-5** | Sulit diprediksi |
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| Extreme | **-10** | Sangat berbahaya |
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### Faktor 4: Trend Clarity (+/- 10 poin)
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| Trend | Poin | Alasan |
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|-------|------|--------|
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| Uptrend / Downtrend (*trending*) | **+10** | Arah jelas, sinyal lebih akurat |
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| Neutral / *Ranging* | **-5** | Sinyal sering whipsaw |
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### Faktor 5: SMC *Confluence* (+/- 10 poin)
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| Kondisi | Poin | Alasan |
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|---------|------|--------|
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| Ada sinyal SMC (OB/FVG/BOS) | **+10** | Konfirmasi tambahan |
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| Tidak ada sinyal | **+0** | Tanpa konfirmasi |
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### Faktor 6: ML Alignment (+/- 5 poin)
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| ML Confidence | Poin | Alasan |
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|--------------|------|--------|
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| >= 70% | **+5** | ML sangat yakin |
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| >= 60% | **+2** | ML cukup yakin |
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| < 60% | **+0** | ML kurang yakin |
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---
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## Pemetaan Score ke *Market Quality*
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Score dihitung (0--100), lalu dipetakan ke **5 level kualitas**:
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```
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Score: 0 10 20 30 35 50 65 80 100
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|-----|-----|-----|-----|-----|-----|-----|-----|
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| AVOID |POOR | MODERATE |GOOD | EXCELLENT
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| (< 35) | | (50-64) | | (80+)
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| thresh: 85% |80% | 70% |65% | 60%
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```
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| Score | Quality | *Threshold* | Aksi |
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|-------|---------|-----------|------|
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| **80+** | EXCELLENT | 60% | Trade dengan percaya diri |
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| **65-79** | GOOD | 65% | Trade normal |
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| **50-64** | MODERATE | 70% | Trade hati-hati |
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| **35-49** | POOR | 80% | Sangat selektif |
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| **< 35** | AVOID | 85% | Jangan trade |
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---
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## Contoh Perhitungan
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### Contoh 1: Kondisi Ideal (Score: 95)
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```
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Base score: 50
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[+20] Session: London-NY Overlap --> 70
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[+15] Regime: Medium Volatility --> 85
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[+10] Volatility: Medium --> 95
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[+10] Trend: UPTREND --> 105 --> cap 100
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[+10] SMC: Ada FVG + BOS --> 100
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[+5] ML: 72% confidence --> 100
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Score: 100 --> EXCELLENT --> Threshold: 60%
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```
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**Artinya:** ML cukup confidence 60% saja untuk entry. Lebih banyak trade opportunity.
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### Contoh 2: Kondisi Jelek (Score: 50)
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```
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Base score: 50
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[+0] Session: Asia --> 50
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[+5] Regime: Low Volatility --> 55
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[+0] Volatility: Low --> 55
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[-5] Trend: RANGING --> 50
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[+0] SMC: Tidak ada signal --> 50
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[+0] ML: 58% confidence --> 50
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Score: 50 --> MODERATE --> Threshold: 70%
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```
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**Artinya:** ML harus confidence 70% untuk entry. Lebih selektif.
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### Contoh 3: Kondisi Berbahaya (Score: 0)
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```
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Base score: 50
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[-30] Session: Weekend --> 20
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[-25] Regime: Crisis --> -5 --> cap 0
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[-10] Volatility: Extreme --> 0
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[-5] Trend: Ranging --> 0
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[+0] SMC: Tidak ada --> 0
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[+0] ML: 55% --> 0
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Score: 0 --> AVOID --> Threshold: 85% (praktis tidak trade)
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```
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---
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## Integrasi di Entry Flow
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```python
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# main_live.py --- Step 6 dari 11 filter entry
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# 1. Analisis kondisi market
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market_analysis = dynamic_confidence.analyze_market(
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session=session_name, # "London-NY Overlap"
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regime=regime_name, # "medium_volatility"
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volatility=volatility_level, # "medium"
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trend_direction=trend, # "UPTREND"
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has_smc_signal=True, # Ada SMC signal
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ml_signal=ml_pred.signal, # "BUY"
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ml_confidence=ml_pred.confidence, # 0.68
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)
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# 2. Cek quality
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if market_analysis.quality == MarketQuality.AVOID:
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return # SKIP --- market tidak layak
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# 3. Cek apakah ML confidence memenuhi threshold dinamis
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can_entry, reason = dynamic_confidence.get_entry_decision(
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ml_confidence=0.68,
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analysis=market_analysis,
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)
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# can_entry = True (0.68 >= 0.60 threshold untuk EXCELLENT)
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# reason = "Entry OK: ML 68% >= threshold 60% (score=95)"
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```
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---
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## Integrasi di Backtest
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```python
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# backtest_live_sync.py --- identik dengan live
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market_analysis = self.dynamic_confidence.analyze_market(
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session=session_name,
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regime=regime,
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volatility="medium",
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trend_direction=regime,
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has_smc_signal=True,
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ml_signal=ml_pred.signal,
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ml_confidence=ml_pred.confidence,
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)
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if market_analysis.quality == MarketQuality.AVOID:
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continue # Skip bar ini
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```
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---
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## Method `get_entry_decision()`
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```python
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def get_entry_decision(ml_confidence, analysis) -> (bool, str):
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"""
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Keputusan final entry berdasarkan analisis.
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1. Quality == AVOID? --> False (jangan trade)
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2. ML confidence >= threshold? --> True (entry OK)
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3. ML confidence < threshold? --> False (tunggu)
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"""
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# Contoh output:
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# True, "Entry OK: ML 68% >= threshold 60% (score=95)"
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# False, "Wait: ML 55% < threshold 70% (need +15%)"
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# False, "Market quality: AVOID (score=20)"
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```
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---
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## Logging
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```python
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def get_threshold_summary(analysis) -> str:
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"""
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Output: "Market: EXCELLENT (score=95) --> Threshold: 60%"
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"""
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```
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Contoh log di main_live.py:
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```
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[14:30] Market: EXCELLENT (score=95) --> Threshold: 60%
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[14:35] Entry OK: ML 68% >= threshold 60% (score=95)
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[15:00] Market: MODERATE (score=55) --> Threshold: 70%
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[15:05] Wait: ML 62% < threshold 70% (need +8%)
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[04:00] Market: AVOID (score=15) --> Threshold: 85%
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```
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---
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## Ringkasan Visual
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```
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Kondisi Market Saat Ini
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6 Faktor Dianalisis:
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+-- Session +/- 20 poin
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+-- Regime +/- 15 poin
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+-- Volatility +/- 10 poin
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+-- Trend +/- 10 poin
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+-- SMC +/- 10 poin
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+-- ML +/- 5 poin
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v
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Score (0-100)
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Quality Level:
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+-- EXCELLENT (80+) --> Threshold 60%
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+-- GOOD (65-79) --> Threshold 65%
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+-- MODERATE (50-64) --> Threshold 70%
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+-- POOR (35-49) --> Threshold 80%
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+-- AVOID (<35) --> Threshold 85% / SKIP
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v
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ML Confidence >= Threshold?
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+-- YES --> ENTRY diizinkan
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+-- NO --> TUNGGU
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```
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