feat(param-search): Add new evaluation functions for breakout, momentum, and tail parameter combinations

Changes:
- Added `apply_params_to_db` function to streamline parameter application to the database.
- Introduced `evaluate_breakout_param_combo`, `evaluate_momentum_param_combo`, and `evaluate_tail_param_combo` functions to enhance the evaluation of parameter combinations for respective strategies.
- Updated `requirements.txt` to include `optuna==4.2.1` for improved optimization capabilities.

Impact:
- These additions improve the modularity and efficiency of parameter evaluations across different trading strategies, facilitating better optimization and backtesting processes.

kis_trader/backtest/optuna_search_space.py

@@ -0,0 +1,71 @@
+#!/usr/bin/env python3
+"""
+kis_trader/backtest/optuna_search_space.py — Optuna 탐색 공간 (Grid 축 재사용)
+==============================================================================
+각 전략 Grid 와 동일한 이산 축을 trial.suggest_categorical 로 샘플링.
+"""
+from __future__ import annotations
+
+from typing import Any, Dict, List
+
+import optuna
+
+from kis_trader.backtest.param_search_breakout import _breakout_grids
+from kis_trader.backtest.param_search_momentum import (
+    _momentum_combo_grid_valid,
+    _momentum_grids,
+)
+from kis_trader.backtest.tail_param_search import _tail_grids
+
+
+def _dedupe_preserve_order(values: List[Any]) -> List[Any]:
+    seen = set()
+    out: List[Any] = []
+    for v in values:
+        key = v if isinstance(v, (int, float, str, bool)) else repr(v)
+        if key in seen:
+            continue
+        seen.add(key)
+        out.append(v)
+    return out
+
+
+def _suggest_from_grid(trial: optuna.Trial, grid: Dict[str, List[Any]]) -> Dict[str, Any]:
+    combo: Dict[str, Any] = {}
+    for key, values in grid.items():
+        if not values:
+            continue
+        choices = _dedupe_preserve_order(list(values))
+        combo[key] = trial.suggest_categorical(key, choices)
+    return combo
+
+
+def suggest_tail_params(trial: optuna.Trial, mode: str) -> Dict[str, Any]:
+    return _suggest_from_grid(trial, _tail_grids(mode))
+
+
+def suggest_momentum_params(trial: optuna.Trial, mode: str) -> Dict[str, Any]:
+    combo = _suggest_from_grid(trial, _momentum_grids()[mode])
+    if not _momentum_combo_grid_valid(combo):
+        raise optuna.TrialPruned("momentum invalid combo")
+    return combo
+
+
+def suggest_breakout_params(trial: optuna.Trial, mode: str) -> Dict[str, Any]:
+    combo = _suggest_from_grid(trial, _breakout_grids()[mode])
+    if "prev_chg_min" in combo and "prev_chg_max" in combo:
+        if float(combo["prev_chg_min"]) >= float(combo["prev_chg_max"]):
+            raise optuna.TrialPruned("breakout prev_chg invalid")
+    return combo
+
+
+def tail_grid_axis_keys(mode: str) -> List[str]:
+    return list(_tail_grids(mode).keys())
+
+
+def momentum_grid_axis_keys(mode: str) -> List[str]:
+    return list(_momentum_grids()[mode].keys())
+
+
+def breakout_grid_axis_keys(mode: str) -> List[str]:
+    return list(_breakout_grids()[mode].keys())