docs: add architecture documentation for remaining 8 components (16-23)
New documentation files: - 16-MT5-Connector: Broker bridge with auto-reconnect & Polars native - 17-Configuration: 6 sub-configs with capital mode auto-adjustment - 18-Trade-Logger: Dual storage (PostgreSQL + CSV), thread-safe - 19-Position-Manager: 7 action conditions, trailing SL, market close handler - 20-Risk-Engine: Kelly Criterion sizing, circuit breaker, order validation - 21-Database: PostgreSQL integration with 6 repositories - 22-Train-Models: Initial training script (HMM + XGBoost) - 23-Main-Live-Orchestrator: Main loop coordinating 15+ components Updated README.md with complete index of all 23 components. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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GifariKemal
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Claude Opus 4.6
Claude Opus 4.6
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# Database Module — PostgreSQL Integration
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> **File:** `src/db/connection.py`, `src/db/repository.py`
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> **Database:** PostgreSQL
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> **Library:** psycopg2 (connection pooling)
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---
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## Apa Itu Database Module?
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Database Module menyediakan **penyimpanan persisten** untuk semua data trading — trade history, training log, sinyal, snapshot pasar, dan status bot. Menggunakan PostgreSQL dengan connection pooling untuk performa tinggi.
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**Analogi:** Database Module seperti **arsip perpustakaan** — menyimpan semua catatan trading secara terorganisir, bisa dicari kapan saja, dan tidak hilang meski bot di-restart.
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---
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## Arsitektur
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```
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Bot Components
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├── TradeLogger → TradeRepository, SignalRepository, MarketSnapshotRepository
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├── AutoTrainer → TrainingRepository
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├── main_live.py → BotStatusRepository, DailySummaryRepository
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└── Dashboard → Semua repository (READ)
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v
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DatabaseConnection (Singleton)
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v
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ThreadedConnectionPool (1-10 koneksi)
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v
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PostgreSQL Server
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```
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---
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## Connection (Singleton + Pooling)
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```python
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class DatabaseConnection:
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"""
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Thread-safe singleton dengan connection pooling.
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- Hanya 1 instance (singleton pattern)
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- Pool: 1-10 koneksi (ThreadedConnectionPool)
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- Auto-reconnect jika putus
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- Context manager support
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"""
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```
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### Konfigurasi
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```
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DB_HOST=localhost
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DB_PORT=5432
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DB_NAME=trading_db
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DB_USER=trading_bot
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DB_PASSWORD=trading_bot_2026
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```
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### Penggunaan
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```python
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from src.db import get_db, init_db
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# Initialize
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if init_db():
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db = get_db()
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# Query
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with db.get_cursor() as cur:
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cur.execute("SELECT * FROM trades WHERE profit_usd > 0")
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rows = cur.fetchall()
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# Simple execute
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result = db.execute("SELECT count(*) FROM trades", fetch=True)
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```
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---
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## 6 Repository
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### 1. TradeRepository
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| Method | Fungsi |
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|--------|--------|
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| `insert_trade()` | Insert trade baru (saat open) |
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| `update_trade_close()` | Update exit data (saat close) |
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| `get_trade_by_ticket()` | Cari trade per ticket |
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| `get_open_trades()` | Trade yang belum ditutup |
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| `get_recent_trades(100)` | 100 trade terakhir |
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| `get_trades_for_training(30)` | Trade 30 hari untuk ML |
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| `get_daily_stats(date)` | Statistik per hari |
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| `get_session_stats("London", 30)` | Statistik per sesi |
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| `get_smc_pattern_stats(30)` | Performa per pola SMC |
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### 2. TrainingRepository
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| Method | Fungsi |
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|--------|--------|
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| `insert_training_run()` | Catat mulai training |
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| `update_training_complete()` | Update hasil training |
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| `mark_rollback()` | Tandai model di-rollback |
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| `get_latest_successful()` | Training sukses terakhir |
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| `get_training_history(20)` | 20 training terakhir |
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### 3. SignalRepository
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| Method | Fungsi |
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|--------|--------|
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| `insert_signal()` | Catat sinyal yang dihasilkan |
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| `mark_executed()` | Tandai sinyal yang dieksekusi |
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| `get_recent_signals(100)` | 100 sinyal terakhir |
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| `get_signal_stats(24)` | Statistik 24 jam |
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### 4. MarketSnapshotRepository
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| Method | Fungsi |
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|--------|--------|
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| `insert_snapshot()` | Simpan snapshot pasar |
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| `get_recent_snapshots(60)` | Snapshot 60 menit terakhir |
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### 5. BotStatusRepository
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| Method | Fungsi |
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|--------|--------|
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| `insert_status()` | Catat status bot |
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| `get_latest_status()` | Status terbaru |
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### 6. DailySummaryRepository
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| Method | Fungsi |
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|--------|--------|
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| `upsert_summary()` | Insert/update ringkasan harian |
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| `get_summary(date)` | Ringkasan per tanggal |
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| `get_recent_summaries(30)` | 30 hari terakhir |
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---
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## Tabel Database
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### trades
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```sql
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├── ticket, symbol, direction
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├── entry_price, exit_price, stop_loss, take_profit
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├── lot_size, profit_usd, profit_pips
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├── opened_at, closed_at, duration_seconds
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├── entry_regime, entry_volatility, entry_session
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├── smc_signal, smc_confidence, smc_reason
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├── smc_fvg_detected, smc_ob_detected, smc_bos_detected, smc_choch_detected
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├── ml_signal, ml_confidence
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├── market_quality, market_score, dynamic_threshold
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├── exit_reason, exit_regime, exit_ml_signal
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├── balance_before, balance_after, equity_at_entry
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├── features_entry (JSON), features_exit (JSON)
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└── bot_version, trade_mode
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```
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### training_runs
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```sql
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├── training_type, bars_used, num_boost_rounds
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├── hmm_trained, hmm_n_regimes
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├── xgb_trained, train_auc, test_auc
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├── train_accuracy, test_accuracy
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├── model_path, backup_path
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├── success, error_message
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├── started_at, completed_at, duration_seconds
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└── rolled_back, rollback_reason, rollback_at
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```
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### signals
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```sql
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├── signal_time, symbol, price
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├── signal_type, signal_source, combined_confidence
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├── smc_*, ml_*
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├── regime, session, volatility, market_score
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└── executed, execution_reason, trade_ticket
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```
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### market_snapshots
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```sql
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├── snapshot_time, symbol, price, OHLC
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├── regime, volatility, session, ATR, spread
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├── ml_signal, ml_confidence, smc_signal, smc_confidence
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└── open_positions, floating_pnl, features (JSON)
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```
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### bot_status
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```sql
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├── status_time, is_running, status
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├── loop_count, avg_execution_ms, uptime_seconds
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├── balance, equity, margin_used
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├── open_positions, floating_pnl, daily_pnl
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└── risk_mode, current_session, is_golden_time
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```
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### daily_summaries
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```sql
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├── summary_date
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├── total/winning/losing/breakeven_trades
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├── gross_profit, gross_loss, net_profit
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├── start_balance, end_balance
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├── win_rate, profit_factor, avg win/loss
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├── trades per session (sydney/tokyo/london/ny/golden)
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└── SMC pattern stats (fvg/ob trades & wins)
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```
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---
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## Graceful Degradation
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```
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PostgreSQL tersedia?
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├── Ya → Gunakan DB + CSV backup
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└── Tidak → CSV saja (semua tetap berjalan)
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Bot TIDAK pernah crash karena database.
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```
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