feat: Enhance Optuna integration and logging for backtesting framework

Changes:
- Added new API endpoints for continuing and confirming Optuna jobs, allowing for better management of ongoing studies.
- Introduced detailed logging for tick feed tracking and order book processing, improving traceability of vendor performance during backtests.
- Updated database schema to include new fields for managing Optuna study results, enhancing the ability to track study progress and outcomes.
- Refactored existing functions to utilize the new logging and tracking features, ensuring consistency across the backtesting framework.

Impact:
- These enhancements improve the robustness and transparency of the Optuna backtesting process, facilitating better analysis and optimization of trading strategies.

kis_trader/backtest/scalping_backtest_common.py

@@ -173,21 +173,15 @@ def load_scalp_candles_by_code(
         )
         return candles_by_code, total_candles
 
-    from kis_trader.backtest.bt_candle_source import (
-        fetch_ws_candles_for_code,
-        list_ws_candle_codes,
+    from kis_trader.backtest.bt_candle_source import fetch_ws_candles_by_code_bulk
+
+    loaded = fetch_ws_candles_by_code_bulk(
+        db, 1, start_key, end_key,
+        confirmed_only=True,
     )
-
-    codes = list_ws_candle_codes(db, 1, start_key, end_key)
-
     candles_by_code: Dict[str, List[Dict]] = {}
     total_candles = 0
-
-    for code in codes:
-        rows = fetch_ws_candles_for_code(
-            db, code, 1, start_key, end_key,
-            confirmed_only=True,
-        )
+    for code, rows in loaded.items():
         if len(rows) < min_bars:
             continue
         candles_by_code[code] = rows