feat: Add DART strategy and related configurations

ㅇ
Changes:
- Introduced the DART strategy to the trading system, including its configuration and integration into the existing framework.
- Updated the database schema to include DART-specific tables for disclosures and watchlists.
- Enhanced the backtesting and parameter search functionalities to support the DART strategy.
- Implemented new rules for browser verification and API interactions to ensure compliance with the updated DART strategy.

Impact:
- These additions expand the trading capabilities of the system, allowing for more comprehensive analysis and execution of DART-related strategies, while maintaining system integrity and performance.

kis_trader/backtest/param_search_optuna.py

@@ -75,6 +75,11 @@ from kis_trader.backtest.optuna_scalping import (
     prepare_scalp_search_context,
     run_scalp_optuna,
 )
+from kis_trader.backtest.optuna_dart import (
+    apply_best_dart_trial,
+    prepare_dart_search_context,
+    run_dart_optuna,
+)
 from kis_trader.backtest.optuna_search_space import suggest_tail_params, tail_grid_axis_keys
 from kis_trader.backtest.param_search_cli_common import (
     add_portfolio_cli_args,
@@ -106,6 +111,7 @@ STRATEGY_MODES: Dict[str, List[str]] = {
     "momentum": ["fast", "exit", "rr", "coarse", "fine", "wide", "full"],
     "breakout": ["fast", "coarse", "fine", "wide", "full"],
     "scalp": ["fast", "trigger", "exit", "coarse", "fine", "full", "wide"],
+    "dart": ["fast", "coarse", "fine"],
 }
 
 
@@ -864,6 +870,23 @@ def main() -> None:
             if args.apply_best:
                 apply_best_scalp_trial(study)
 
+        elif strategy == "dart":
+            ctx_d = prepare_dart_search_context(args.start, args.end, mode)
+            if ctx_d is None:
+                sys.exit(1)
+            study = run_dart_optuna(
+                ctx_d,
+                n_trials=n_trials,
+                storage_url=storage_url,
+                study_name=study_name,
+                min_trades=args.min_trades,
+                sampler_name=sampler_name,
+                seed=args.seed,
+                show_progress=not args.no_progress,
+            )
+            if args.apply_best:
+                apply_best_dart_trial(study)
+
         else:
             ctx_b = prepare_breakout_search_context(
                 args.start, args.end, mode,