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/optuna_dart.py

@@ -0,0 +1,104 @@
+#!/usr/bin/env python3
+"""kis_trader/backtest/optuna_dart.py — DART Optuna TPE."""
+from __future__ import annotations
+
+import logging
+from datetime import datetime
+from typing import Any, Dict, Optional
+
+import optuna
+from optuna.samplers import TPESampler
+
+from database import TradeDB
+from kis_trader.backtest.optuna_common import (
+    announce_optuna_json_path,
+    resolve_optuna_storage_url,
+)
+from kis_trader.backtest.param_search_dart import (
+    _dart_grids,
+    apply_params_to_db,
+    evaluate_dart_param_combo,
+)
+from kis_trader.engine import dart_engine as de
+
+logger = logging.getLogger("param_search_optuna")
+_FAIL = -1e18
+
+
+def prepare_dart_search_context(start: str, end: str, mode: str) -> Optional[Dict[str, Any]]:
+    grids = _dart_grids()
+    if mode not in grids:
+        logger.error("DART mode: %s (fast/coarse/fine)", mode)
+        return None
+    db = TradeDB()
+    try:
+        env_row = db.get_merged_env_snapshot() or {}
+    finally:
+        db.close()
+    return {
+        "start": start,
+        "end": end,
+        "mode": mode,
+        "grid": grids[mode],
+        "env_row": env_row,
+        "base": de.get_dart_defaults_from_db(env_row=env_row),
+    }
+
+
+def run_dart_optuna(
+    ctx: Dict[str, Any],
+    *,
+    n_trials: int,
+    storage_url: Optional[str] = None,
+    study_name: Optional[str] = None,
+    min_trades: int = 1,
+    sampler_name: str = "tpe",
+    seed: int = 42,
+    show_progress: bool = False,
+    **_kwargs: Any,
+) -> optuna.Study:
+    grid: Dict[str, Any] = ctx["grid"]
+    storage = storage_url or resolve_optuna_storage_url()
+    name = study_name or "dart_%s_%s" % (
+        ctx["mode"], datetime.now().strftime("%Y%m%d_%H%M%S"),
+    )
+    sampler = TPESampler(seed=seed) if sampler_name != "random" else optuna.samplers.RandomSampler(seed=seed)
+    study = optuna.create_study(
+        study_name=name,
+        storage=storage,
+        direction="maximize",
+        sampler=sampler,
+        load_if_exists=True,
+    )
+
+    def objective(trial: optuna.Trial) -> float:
+        params = dict(ctx["base"])
+        for axis, values in grid.items():
+            params[axis] = trial.suggest_categorical(axis, list(values))
+        res = evaluate_dart_param_combo(
+            params, start=ctx["start"], end=ctx["end"], env_row=ctx["env_row"],
+        )
+        n = int(res.get("trade_count") or 0)
+        pnl = float(res.get("total_pnl") or 0)
+        trial.set_user_attr("trade_count", n)
+        trial.set_user_attr("total_pnl", pnl)
+        trial.set_user_attr("win_rate", float(res.get("win_rate") or 0))
+        if n < min_trades:
+            return _FAIL
+        return pnl
+
+    study.optimize(objective, n_trials=n_trials, show_progress_bar=show_progress)
+    announce_optuna_json_path("dart", study)
+    return study
+
+
+def apply_best_dart_trial(study: optuna.Study) -> bool:
+    try:
+        best = study.best_trial
+    except Exception:
+        return False
+    if float(best.value or _FAIL) <= 0:
+        logger.warning("DART best PnL<=0 — DB 미적용")
+        return False
+    apply_params_to_db(dict(best.params))
+    return True