feat(옵투나): score·min_trades·후처리 재탐색 및 웹 job 개선

PnL/(MDD+ADD) score·legacy 정렬·거래일×min_trades 게이트를 공통화한다.
후처리 ob_modes·study store·4전략 TPE 순차 스크립트와 문서를 갱신한다.

Co-authored-by: Cursor <cursoragent@cursor.com>

kis_trader/backtest/optuna_scalping.py

@@ -366,15 +366,17 @@ def _make_sampler(name: str, seed: Optional[int]):
     return TPESampler(seed=seed, multivariate=True, warn_independent_sampling=False)
 
 
-def _scalp_objective_value(result: Dict[str, Any], sort_by: str) -> float:
-    pnl = float(result["total_pnl"])
-    if sort_by == "score":
-        mdd_floor = get_env_float("SCALP_SCORE_MDD_FLOOR", 5000.0)
-        mdd = float(result.get("mdd") or 0)
-        return pnl / max(mdd, mdd_floor)
-    if sort_by == "win_rate":
-        return float(result["win_rate"])
-    return pnl
+def _scalp_objective_value(
+    result: Dict[str, Any],
+    sort_by: str,
+    *,
+    start: Any = None,
+    end: Any = None,
+) -> float:
+    from kis_trader.backtest.optuna_common import optuna_objective_value
+    return float(optuna_objective_value(
+        result, sort_by, start=start, end=end, strategy="scalp",
+    ))
 
 
 def run_scalp_optuna(
@@ -386,7 +388,7 @@ def run_scalp_optuna(
     min_trades: int,
     min_win_rate: float,
     min_pf: float,
-    sort_by: str = "pnl",
+    sort_by: str = "score",
     sampler_name: str = "tpe",
     seed: Optional[int] = None,
     n_jobs: int = 1,
@@ -438,23 +440,21 @@ def run_scalp_optuna(
         if result is None:
             trial.set_user_attr("gates_ok", False)
             return _FAIL_OBJECTIVE
-        obj = _scalp_objective_value(result, sort_by)
         trial.set_user_attr("gates_ok", True)
         trial.set_user_attr("total_pnl", float(result["total_pnl"]))
         trial.set_user_attr("win_rate", float(result["win_rate"]))
         trial.set_user_attr("pf", float(result.get("pf") or 0))
         trial.set_user_attr("mdd", float(result.get("mdd") or 0))
-        trial.set_user_attr(
-            "score",
-            float(obj if sort_by == "score" else _scalp_objective_value(result, "score")),
-        )
         trial.set_user_attr("total_trades", int(result["total_trades"]))
         trial.set_user_attr(
             "merged_json",
             json.dumps(result.get("merged_params") or {}, ensure_ascii=False),
         )
+        from kis_trader.backtest.optuna_common import optuna_store_trial_score_user_attrs
         set_optuna_trial_stability_attrs(trial, result)
-        return float(obj)
+        return optuna_store_trial_score_user_attrs(
+            trial, result, sort_by, start=ctx.start, end=ctx.end, strategy="scalp",
+        )
 
     logger.info(
         "🔬 Optuna SCALP | study=%s | mode=%s | trials=%d | sort=%s | universe=%s | ticks=%s",
@@ -475,6 +475,7 @@ def run_scalp_optuna(
             )
         elapsed = time.time() - t0
 
+        from kis_trader.backtest.optuna_common import optuna_score_fields_from_trial
         passing: List[Dict[str, Any]] = []
         for trial in study.trials:
             if trial.state != optuna.trial.TrialState.COMPLETE:
@@ -494,18 +495,15 @@ def run_scalp_optuna(
                 "total_pnl": float(trial.user_attrs.get("total_pnl") or 0),
                 "pf": float(trial.user_attrs.get("pf") or 0),
                 "mdd": float(trial.user_attrs.get("mdd") or 0),
-                "score": float(trial.user_attrs.get("score") or 0),
+                **optuna_score_fields_from_trial(trial),
+                "period_daily_avg_pnl": float(trial.user_attrs.get("period_daily_avg_pnl") or 0),
                 "optuna_trial_number": trial.number,
             }
             row.update(stability_fields_from_trial_attrs(trial))
             passing.append(row)
 
-        if sort_by == "score":
-            passing.sort(key=lambda r: (-r["score"], -r["total_pnl"], -r["win_rate"]))
-        elif sort_by == "win_rate":
-            passing.sort(key=lambda r: (-r["win_rate"], -r["total_pnl"]))
-        else:
-            passing.sort(key=lambda r: (-r["total_pnl"], -r["win_rate"]))
+        from kis_trader.backtest.optuna_common import _sort_optuna_rows
+        passing = _sort_optuna_rows(passing, sort_by)
 
         tiers = build_optuna_result_tiers(passing, sort_by=sort_by)
 
@@ -540,6 +538,8 @@ def run_scalp_optuna(
             "tick_backtest": ctx.tick_backtest_meta,
             **tiers,
         }
+        from kis_trader.backtest.optuna_common import annotate_optuna_period_daily_avg
+        annotate_optuna_period_daily_avg(out_data)
 
         ts = datetime.now().strftime("%Y%m%d_%H%M%S")
         out_path = os.path.join(_results_dir_for_write(), f"optuna_scalp_{ctx.mode}_{ts}.json")