296 lines
8.0 KiB
Markdown
296 lines
8.0 KiB
Markdown
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# 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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## 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** | Volatilitas 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 | **+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 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: 40)
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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: 15)
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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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v
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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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v
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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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