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GifariKemal a240d974f6 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>

2026-02-06 09:17:20 +07:00

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Database Module — PostgreSQL Integration

File: src/db/connection.py, src/db/repository.py Database: PostgreSQL Library: psycopg2 (connection pooling)

Apa Itu Database Module?

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.

Analogi: Database Module seperti arsip perpustakaan — menyimpan semua catatan trading secara terorganisir, bisa dicari kapan saja, dan tidak hilang meski bot di-restart.

Arsitektur

Bot Components
├── TradeLogger    → TradeRepository, SignalRepository, MarketSnapshotRepository
├── AutoTrainer    → TrainingRepository
├── main_live.py   → BotStatusRepository, DailySummaryRepository
└── Dashboard      → Semua repository (READ)
         |
         v
    DatabaseConnection (Singleton)
         |
         v
    ThreadedConnectionPool (1-10 koneksi)
         |
         v
    PostgreSQL Server

Connection (Singleton + Pooling)

class DatabaseConnection:
    """
    Thread-safe singleton dengan connection pooling.

    - Hanya 1 instance (singleton pattern)
    - Pool: 1-10 koneksi (ThreadedConnectionPool)
    - Auto-reconnect jika putus
    - Context manager support
    """

Konfigurasi

DB_HOST=localhost
DB_PORT=5432
DB_NAME=trading_db
DB_USER=trading_bot
DB_PASSWORD=trading_bot_2026

Penggunaan

from src.db import get_db, init_db

# Initialize
if init_db():
    db = get_db()

    # Query
    with db.get_cursor() as cur:
        cur.execute("SELECT * FROM trades WHERE profit_usd > 0")
        rows = cur.fetchall()

    # Simple execute
    result = db.execute("SELECT count(*) FROM trades", fetch=True)

6 Repository

1. TradeRepository

Method	Fungsi
`insert_trade()`	Insert trade baru (saat open)
`update_trade_close()`	Update exit data (saat close)
`get_trade_by_ticket()`	Cari trade per ticket
`get_open_trades()`	Trade yang belum ditutup
`get_recent_trades(100)`	100 trade terakhir
`get_trades_for_training(30)`	Trade 30 hari untuk ML
`get_daily_stats(date)`	Statistik per hari
`get_session_stats("London", 30)`	Statistik per sesi
`get_smc_pattern_stats(30)`	Performa per pola SMC

2. TrainingRepository

Method	Fungsi
`insert_training_run()`	Catat mulai training
`update_training_complete()`	Update hasil training
`mark_rollback()`	Tandai model di-rollback
`get_latest_successful()`	Training sukses terakhir
`get_training_history(20)`	20 training terakhir

3. SignalRepository

Method	Fungsi
`insert_signal()`	Catat sinyal yang dihasilkan
`mark_executed()`	Tandai sinyal yang dieksekusi
`get_recent_signals(100)`	100 sinyal terakhir
`get_signal_stats(24)`	Statistik 24 jam

4. MarketSnapshotRepository

Method	Fungsi
`insert_snapshot()`	Simpan snapshot pasar
`get_recent_snapshots(60)`	Snapshot 60 menit terakhir

5. BotStatusRepository

Method	Fungsi
`insert_status()`	Catat status bot
`get_latest_status()`	Status terbaru

6. DailySummaryRepository

Method	Fungsi
`upsert_summary()`	Insert/update ringkasan harian
`get_summary(date)`	Ringkasan per tanggal
`get_recent_summaries(30)`	30 hari terakhir

Tabel Database

trades

├── ticket, symbol, direction
├── entry_price, exit_price, stop_loss, take_profit
├── lot_size, profit_usd, profit_pips
├── opened_at, closed_at, duration_seconds
├── entry_regime, entry_volatility, entry_session
├── smc_signal, smc_confidence, smc_reason
├── smc_fvg_detected, smc_ob_detected, smc_bos_detected, smc_choch_detected
├── ml_signal, ml_confidence
├── market_quality, market_score, dynamic_threshold
├── exit_reason, exit_regime, exit_ml_signal
├── balance_before, balance_after, equity_at_entry
├── features_entry (JSON), features_exit (JSON)
└── bot_version, trade_mode

training_runs

├── training_type, bars_used, num_boost_rounds
├── hmm_trained, hmm_n_regimes
├── xgb_trained, train_auc, test_auc
├── train_accuracy, test_accuracy
├── model_path, backup_path
├── success, error_message
├── started_at, completed_at, duration_seconds
└── rolled_back, rollback_reason, rollback_at

signals

├── signal_time, symbol, price
├── signal_type, signal_source, combined_confidence
├── smc_*, ml_*
├── regime, session, volatility, market_score
└── executed, execution_reason, trade_ticket

market_snapshots

├── snapshot_time, symbol, price, OHLC
├── regime, volatility, session, ATR, spread
├── ml_signal, ml_confidence, smc_signal, smc_confidence
└── open_positions, floating_pnl, features (JSON)

bot_status

├── status_time, is_running, status
├── loop_count, avg_execution_ms, uptime_seconds
├── balance, equity, margin_used
├── open_positions, floating_pnl, daily_pnl
└── risk_mode, current_session, is_golden_time

daily_summaries

├── summary_date
├── total/winning/losing/breakeven_trades
├── gross_profit, gross_loss, net_profit
├── start_balance, end_balance
├── win_rate, profit_factor, avg win/loss
├── trades per session (sydney/tokyo/london/ny/golden)
└── SMC pattern stats (fvg/ob trades & wins)

Graceful Degradation

PostgreSQL tersedia?
├── Ya → Gunakan DB + CSV backup
└── Tidak → CSV saja (semua tetap berjalan)

Bot TIDAK pernah crash karena database.

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