Add complete time series aggregation example and refactor OKXCollector for repository pattern

- Introduced `example_complete_series_aggregation.py` to demonstrate time series aggregation, emitting candles even when no trades occur.
- Implemented `CompleteSeriesProcessor` extending `RealTimeCandleProcessor` to handle time-based candle emission and empty candle creation.
- Refactored `OKXCollector` to utilize the new repository pattern for database operations, enhancing modularity and maintainability.
- Updated database operations to centralize data handling through `DatabaseOperations`, improving error handling and logging.
- Enhanced documentation to include details on the new aggregation example and repository pattern implementation, ensuring clarity for users.

docs/architecture/architecture.md

@@ -194,6 +194,70 @@ def calculate_performance_metrics(portfolio_values: List[float]) -> dict:
 6. **Virtual Trading**: Simulation-first approach with fee modeling
 7. **Simplified Architecture**: Monolithic design with clear component boundaries for future scaling
 
+## Repository Pattern for Database Operations
+
+### Database Abstraction Layer
+The system uses the **Repository Pattern** to abstract database operations from business logic, providing a clean, maintainable, and testable interface for all data access.
+
+```python
+# Centralized database operations
+from database.operations import get_database_operations
+
+class DataCollector:
+    def __init__(self):
+        # Use repository pattern instead of direct SQL
+        self.db = get_database_operations()
+        
+    def store_candle(self, candle: OHLCVCandle):
+        """Store candle using repository pattern"""
+        success = self.db.market_data.upsert_candle(candle, force_update=False)
+        
+    def store_raw_trade(self, data_point: MarketDataPoint):
+        """Store raw trade data using repository pattern"""
+        success = self.db.raw_trades.insert_market_data_point(data_point)
+```
+
+### Repository Structure
+```python
+# Clean API for database operations
+class DatabaseOperations:
+    def __init__(self):
+        self.market_data = MarketDataRepository()  # Candle operations
+        self.raw_trades = RawTradeRepository()     # Raw data operations
+        
+    def health_check(self) -> bool:
+        """Check database connection health"""
+        
+    def get_stats(self) -> dict:
+        """Get database statistics and metrics"""
+
+class MarketDataRepository:
+    def upsert_candle(self, candle: OHLCVCandle, force_update: bool = False) -> bool:
+        """Store or update candle with duplicate handling"""
+        
+    def get_candles(self, symbol: str, timeframe: str, start: datetime, end: datetime) -> List[dict]:
+        """Retrieve historical candle data"""
+        
+    def get_latest_candle(self, symbol: str, timeframe: str) -> Optional[dict]:
+        """Get most recent candle for symbol/timeframe"""
+
+class RawTradeRepository:
+    def insert_market_data_point(self, data_point: MarketDataPoint) -> bool:
+        """Store raw WebSocket data"""
+        
+    def get_raw_trades(self, symbol: str, data_type: str, start: datetime, end: datetime) -> List[dict]:
+        """Retrieve raw trade data for analysis"""
+```
+
+### Benefits of Repository Pattern
+- **No Raw SQL**: Business logic never contains direct SQL queries
+- **Centralized Operations**: All database interactions go through well-defined APIs
+- **Easy Testing**: Repository methods can be easily mocked for unit tests
+- **Database Agnostic**: Can change database implementations without affecting business logic
+- **Automatic Transaction Management**: Sessions, commits, and rollbacks handled automatically
+- **Consistent Error Handling**: Custom exceptions with proper context
+- **Type Safety**: Full type hints for better IDE support and error detection
+
 ## Database Architecture
 
 ### Core Tables