Continue with Step 4

src/calibration/optimization_inspector.py

@@ -1,60 +1,41 @@
 # src/calibration/strategies_inspector.py
 from __future__ import annotations
 
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Tuple
 
 import numpy as np
 import pandas as pd
 
-from src.data.storage import StorageManager
-from src.utils.logger import log
-
 from src.core.walk_forward import WalkForwardValidator
-
+from src.data.storage import StorageManager
+from src.risk.sizing.percent_risk import PercentRiskSizer
 from src.risk.stops.fixed_stop import FixedStop
 from src.risk.stops.trailing_stop import TrailingStop
 from src.risk.stops.atr_stop import ATRStop
+from src.strategies.registry import STRATEGY_REGISTRY
+from src.utils.logger import log
 
-from src.risk.sizing.percent_risk import PercentRiskSizer
-
-
-# --------------------------------------------------
-# Strategy registry (con metadata de parámetros)
-# --------------------------------------------------
-from src.strategies.ma_crossover import MovingAverageCrossover
-from src.strategies.rsi_reversion import RSIStrategy
-
-
-STRATEGY_REGISTRY = {
-    "moving_average": {
-        "class": MovingAverageCrossover,
-        "params": ["fast_period", "slow_period"],
-    },
-    "rsi": {
-        "class": RSIStrategy,
-        "params": ["rsi_period", "overbought", "oversold"],
-    },
-}
-
-
-# --------------------------------------------------
-# Helpers
-# --------------------------------------------------
 
 def list_available_strategies() -> List[Dict[str, Any]]:
-    """
-    Devuelve metadata completa para UI.
-    """
     out = []
-
+    for sid, strategy_class in STRATEGY_REGISTRY.items():
-    for sid, entry in STRATEGY_REGISTRY.items():
+        schema = strategy_class.parameters_schema()
         out.append({
             "strategy_id": sid,
-            "name": entry["class"].__name__,
+            "name": strategy_class.__name__,
-            "params": entry["params"],
+            "params": list(schema.keys()),
-            "tags": [],  # puedes rellenar más adelante
+            "parameters_meta": [
+                {
+                    "name": name,
+                    "type": meta.get("type"),
+                    "default_value": meta.get("default"),
+                    "choices": meta.get("choices"),
+                    "min": meta.get("min"),
+                    "max": meta.get("max"),
+                }
+                for name, meta in schema.items()
+            ],
         })
-
     return out
 
 
@@ -89,32 +70,163 @@ def _accumulate_equity(initial: float, returns_pct: List[float]) -> List[float]:
     return eq
 
 
-def _build_param_values(min_v: float, max_v: float, step: float) -> List[float]:
+def _coerce_like(value: Any, meta: Dict[str, Any]) -> Any:
-    min_v = float(min_v)
+    typ = (meta or {}).get("type")
-    max_v = float(max_v)
+    if typ == "int":
-    step = float(step)
+        return int(round(float(value)))
+    if typ == "float":
+        return float(value)
+    if typ == "bool":
+        if isinstance(value, bool):
+            return value
+        if isinstance(value, str):
+            return value.strip().lower() in {"1", "true", "yes", "on"}
+        return bool(value)
+    return value
 
-    # Valor único si min == max
-    if min_v == max_v:
-        return [min_v]
 
-    # Valor único si step <= 1
+def _build_numeric_grid(meta: Dict[str, Any], baseline_value: Any, spec: Dict[str, Any]) -> List[Any]:
-    if step <= 1:
+    min_v = float(spec.get("min", baseline_value))
-        return [min_v]
+    max_v = float(spec.get("max", baseline_value))
+    step = float(spec.get("step", 1))
 
-    values = []
+    if max_v < min_v:
-    v = min_v
+        min_v, max_v = max_v, min_v
-    while v <= max_v:
-        values.append(v)
-        v += step
 
-    return values
+    if step <= 0 or min_v == max_v:
+        values = [min_v]
+    else:
+        n_steps = int(np.floor((max_v - min_v) / step))
+        values = [min_v + i * step for i in range(n_steps + 1)]
+        if not np.isclose(values[-1], max_v):
+            values.append(max_v)
 
-# --------------------------------------------------
+    coerced = []
-# Main
+    for value in values:
-# --------------------------------------------------
+        coerced_value = _coerce_like(value, meta)
+        if coerced_value not in coerced:
+            coerced.append(coerced_value)
 
-def inspect_strategies_config(
+    baseline_coerced = _coerce_like(baseline_value, meta)
+    if baseline_coerced not in coerced:
+        coerced.append(baseline_coerced)
+
+    return coerced
+
+
+def _build_strategy_param_grid(strategy_class, baseline_parameters: Dict[str, Any], optimization_parameters: Dict[str, Any]) -> Dict[str, List[Any]]:
+    schema = strategy_class.parameters_schema()
+    grid: Dict[str, List[Any]] = {}
+
+    for param_name, meta in schema.items():
+        baseline_value = baseline_parameters.get(param_name, meta.get("default"))
+        spec = optimization_parameters.get(param_name, baseline_value)
+        ptype = meta.get("type")
+
+        if isinstance(spec, dict) and {"min", "max", "step"}.issubset(spec.keys()) and ptype in {"int", "float"}:
+            grid[param_name] = _build_numeric_grid(meta, baseline_value, spec)
+        else:
+            grid[param_name] = [_coerce_like(spec, meta)]
+
+    return grid
+
+
+def _summarize_wf_result(wf_res: Dict[str, Any], initial_capital: float) -> Tuple[Dict[str, Any], List[float], List[float]]:
+    win_df: pd.DataFrame = wf_res["windows"]
+    if win_df is None or win_df.empty:
+        summary = {
+            "n_windows": 0,
+            "oos_final_equity": float(initial_capital),
+            "oos_total_return_pct": 0.0,
+            "oos_max_dd_worst_pct": 0.0,
+            "oos_avg_sharpe": None,
+            "total_trades": 0,
+            "windows": [],
+        }
+        return summary, [], [float(initial_capital)]
+
+    windows_out = []
+    oos_returns = win_df["return_pct"].astype(float).tolist()
+    equity_curve = _accumulate_equity(float(initial_capital), oos_returns)
+
+    for _, r in win_df.iterrows():
+        windows_out.append({
+            "window": int(r["window"]),
+            "train_start": str(r["train_start"]),
+            "train_end": str(r["train_end"]),
+            "test_start": str(r["test_start"]),
+            "test_end": str(r["test_end"]),
+            "return_pct": float(r["return_pct"]),
+            "sharpe": float(r["sharpe"]),
+            "max_dd_pct": float(r["max_dd_pct"]),
+            "trades": int(r["trades"]),
+            "params": dict(r["params"]) if isinstance(r["params"], dict) else r["params"],
+        })
+
+    oos_final = float(equity_curve[-1]) if equity_curve else float(initial_capital)
+    summary = {
+        "n_windows": int(len(windows_out)),
+        "oos_final_equity": oos_final,
+        "oos_total_return_pct": float((oos_final / float(initial_capital) - 1.0) * 100.0),
+        "oos_max_dd_worst_pct": float(np.min(win_df["max_dd_pct"])) if not win_df.empty else 0.0,
+        "oos_avg_sharpe": float(win_df["sharpe"].mean()) if not win_df.empty else None,
+        "total_trades": int(win_df["trades"].sum()) if not win_df.empty else 0,
+        "windows": windows_out,
+    }
+    return summary, oos_returns, equity_curve
+
+
+def _decide_acceptance(baseline: Dict[str, Any], optimized: Dict[str, Any]) -> Dict[str, Any]:
+    return_delta = float(optimized["oos_total_return_pct"] - baseline["oos_total_return_pct"])
+    sharpe_delta = float((optimized.get("oos_avg_sharpe") or 0.0) - (baseline.get("oos_avg_sharpe") or 0.0))
+    dd_delta = float(optimized["oos_max_dd_worst_pct"] - baseline["oos_max_dd_worst_pct"])
+    trades_delta = int(optimized.get("total_trades", 0) - baseline.get("total_trades", 0))
+
+    reasons: List[str] = []
+    overfit_flag = False
+    accepted = True
+
+    if return_delta <= 0:
+        accepted = False
+        reasons.append("optimized_return_not_better")
+
+    if sharpe_delta < -0.15:
+        accepted = False
+        overfit_flag = True
+        reasons.append("sharpe_deterioration")
+
+    if dd_delta < -5.0:
+        accepted = False
+        overfit_flag = True
+        reasons.append("drawdown_worse_than_baseline")
+
+    baseline_trades = max(int(baseline.get("total_trades", 0)), 1)
+    optimized_trades = int(optimized.get("total_trades", 0))
+    if optimized_trades < max(3, int(round(baseline_trades * 0.6))):
+        accepted = False
+        overfit_flag = True
+        reasons.append("trade_count_collapse")
+
+    if return_delta > 0 and sharpe_delta >= -0.05 and dd_delta >= -2.5 and optimized_trades >= max(3, int(round(baseline_trades * 0.75))):
+        accepted = True
+        if reasons == ["optimized_return_not_better"]:
+            reasons = []
+
+    if accepted and not reasons:
+        reasons.append("improvement_accepted")
+
+    return {
+        "accepted": accepted,
+        "overfit_flag": overfit_flag,
+        "reasons": reasons,
+        "return_delta_pct": return_delta,
+        "sharpe_delta": sharpe_delta,
+        "max_dd_delta_pct": dd_delta,
+        "trades_delta": trades_delta,
+    }
+
+
+def inspect_optimization_config(
     *,
     storage: StorageManager,
     payload,
@@ -122,101 +234,30 @@ def inspect_strategies_config(
     include_series: bool,
     progress_callback=None,
 ) -> Dict[str, Any]:
-
     df = storage.load_ohlcv(symbol=payload.symbol, timeframe=payload.timeframe)
     if df is None or df.empty:
-        return {
+        return {"valid": False, "status": "fail", "checks": {}, "message": "No OHLCV data", "results": []}
-            "valid": False,
-            "status": "fail",
-            "checks": {},
-            "message": "No OHLCV data",
-            "results": [],
-        }
 
-    checks: Dict[str, Any] = {}
+    checks: Dict[str, Any] = {
-    checks["data_quality"] = {
+        "data_quality": {
             "status": data_quality.get("status", "unknown"),
             "message": data_quality.get("message", ""),
         }
+    }
 
     if data_quality.get("status") == "fail":
         return {
             "valid": False,
             "status": "fail",
             "checks": checks,
-            "message": "Step 1 data quality is FAIL. Strategies cannot be validated.",
+            "message": "Step 1 data quality is FAIL. Optimization cannot be executed.",
             "results": [],
-            "series": {} if include_series else None,
+            "series": {"strategies": {}} if include_series else None,
         }
 
     stop_loss = _build_stop_loss(payload.stop)
     base_sizer = _build_position_sizer(payload.risk)
 
-    train_td = pd.Timedelta(days=int(payload.wf.train_days))
-    test_td = pd.Timedelta(days=int(payload.wf.test_days))
-    step_td = pd.Timedelta(days=int(payload.wf.step_days or payload.wf.test_days))
-
-    overall_status = "ok"
-    results: List[Dict[str, Any]] = []
-    series: Dict[str, Any] = {"strategies": {}} if include_series else {}
-
-    for sel in payload.strategies:
-
-        sid = sel.strategy_id
-        entry = STRATEGY_REGISTRY.get(sid)
-
-        if entry is None:
-            results.append({
-                "strategy_id": sid,
-                "status": "fail",
-                "message": f"Unknown strategy_id: {sid}",
-                "n_windows": 0,
-                "oos_final_equity": payload.account_equity,
-                "oos_total_return_pct": 0.0,
-                "oos_max_dd_worst_pct": 0.0,
-                "degradation_sharpe": None,
-                "windows": [],
-            })
-            overall_status = "fail"
-            continue
-
-        strategy_class = entry["class"]
-        valid_params = set(entry["params"])
-        range_params = set(sel.parameters.keys())
-
-
-        # 🔒 Validación estricta de parámetros
-        if range_params != valid_params:
-            msg = f"Parameter keys {range_params} do not match expected {valid_params}"
-            
-            results.append({
-                "strategy_id": sid,
-                "status": "fail",
-                "message": msg,
-                "n_windows": 0,
-                "oos_final_equity": payload.account_equity,
-                "oos_total_return_pct": 0.0,
-                "oos_max_dd_worst_pct": 0.0,
-                "degradation_sharpe": None,
-                "windows": [],
-            })
-            overall_status = "fail"
-            continue
-
-        # --------------------------------------------------
-        # Convert ranges -> param_grid real
-        # --------------------------------------------------
-        param_grid = {}
-
-        for pname, prange in sel.parameters.items():
-            values = _build_param_values(
-                min_v=prange.min,
-                max_v=prange.max,
-                step=prange.step,
-            )
-            param_grid[pname] = values
-
-        # Wrapper sizer
     class _CappedSizer(type(base_sizer)):
         def __init__(self, inner):
             self.inner = inner
@@ -238,11 +279,85 @@ def inspect_strategies_config(
 
     capped_sizer = _CappedSizer(base_sizer)
 
-        log.info(f"🧠 Step3 | WF run | strategy={sid}")
+    train_td = pd.Timedelta(days=int(payload.wf.train_days))
+    test_td = pd.Timedelta(days=int(payload.wf.test_days))
+    step_td = pd.Timedelta(days=int(payload.wf.step_days or payload.wf.test_days))
+
+    results: List[Dict[str, Any]] = []
+    series: Dict[str, Any] = {"strategies": {}} if include_series else {}
+    overall_status = "ok"
+
+    progress_total = max(len(payload.strategies) * 2, 1)
+    progress_done = 0
+
+    def report_progress(strategy_id: str, phase: str, local_window: int, local_total: int):
+        nonlocal progress_done
+        if progress_callback is None:
+            return
+        effective_done = progress_done + (local_window / max(local_total, 1))
+        progress_callback(
+            current_strategy=strategy_id,
+            current_phase=phase,
+            completed_runs=effective_done,
+            total_runs=progress_total,
+        )
+
+    for sel in payload.strategies:
+        sid = sel.strategy_id
+        strategy_class = STRATEGY_REGISTRY.get(sid)
+        if strategy_class is None:
+            results.append({
+                "strategy_id": sid,
+                "status": "fail",
+                "message": f"Unknown strategy_id: {sid}",
+                "promotion_status": sel.promotion_status,
+                "promotion_score": sel.promotion_score,
+                "selection_source": sel.selection_source,
+                "diversity_blocked_by": sel.diversity_blocked_by,
+                "diversity_correlation": sel.diversity_correlation,
+                "baseline_parameters": sel.baseline_parameters,
+                "optimized_parameters": sel.baseline_parameters,
+                "optimization_parameters": sel.optimization_parameters,
+                "baseline": _summarize_wf_result({"windows": pd.DataFrame()}, payload.account_equity)[0],
+                "optimized": _summarize_wf_result({"windows": pd.DataFrame()}, payload.account_equity)[0],
+                "decision": {
+                    "accepted": False,
+                    "overfit_flag": False,
+                    "reasons": ["unknown_strategy"],
+                    "return_delta_pct": 0.0,
+                    "sharpe_delta": 0.0,
+                    "max_dd_delta_pct": 0.0,
+                    "trades_delta": 0,
+                },
+            })
+            overall_status = "fail"
+            continue
 
         try:
-            wf = WalkForwardValidator(
+            baseline_wf = WalkForwardValidator(
                 strategy_class=strategy_class,
+                fixed_params=sel.baseline_parameters,
+                param_grid=None,
+                data=df,
+                train_window=train_td,
+                test_window=test_td,
+                step_size=step_td,
+                initial_capital=float(payload.account_equity),
+                commission=float(payload.commission),
+                slippage=float(payload.slippage),
+                optimizer_metric=str(payload.optimization.optimizer_metric),
+                position_sizer=capped_sizer,
+                stop_loss=stop_loss,
+                max_combinations=int(payload.optimization.max_combinations),
+                progress_callback=lambda window_id, total_windows, sid=sid: report_progress(sid, "baseline", window_id, total_windows),
+            )
+            baseline_res = baseline_wf.run()
+            progress_done += 1
+
+            param_grid = _build_strategy_param_grid(strategy_class, sel.baseline_parameters, sel.optimization_parameters)
+            optimized_wf = WalkForwardValidator(
+                strategy_class=strategy_class,
+                fixed_params=None,
                 param_grid=param_grid,
                 data=df,
                 train_window=train_td,
@@ -255,103 +370,97 @@ def inspect_strategies_config(
                 position_sizer=capped_sizer,
                 stop_loss=stop_loss,
                 max_combinations=int(payload.optimization.max_combinations),
-                progress_callback=progress_callback,
+                progress_callback=lambda window_id, total_windows, sid=sid: report_progress(sid, "optimized", window_id, total_windows),
             )
+            optimized_res = optimized_wf.run()
+            progress_done += 1
 
-            wf_res = wf.run()
+            baseline_summary, baseline_returns, baseline_equity = _summarize_wf_result(baseline_res, payload.account_equity)
-            win_df: pd.DataFrame = wf_res["windows"]
+            optimized_summary, optimized_returns, optimized_equity = _summarize_wf_result(optimized_res, payload.account_equity)
+            decision = _decide_acceptance(baseline_summary, optimized_summary)
 
-            if win_df is None or win_df.empty:
+            optimized_parameters = sel.baseline_parameters
+            raw_results = optimized_res.get("raw_results", [])
+            if raw_results:
+                optimized_parameters = raw_results[-1].get("best_params", optimized_parameters)
+
+            status = "ok" if decision["accepted"] else "warning"
+            message = "Optimization accepted" if decision["accepted"] else "Optimization rejected by anti-overfitting rules"
+            if optimized_summary["n_windows"] == 0 or baseline_summary["n_windows"] == 0:
                 status = "fail"
-                msg = "WF produced no valid windows"
+                message = "Walk-forward produced no valid windows"
                 overall_status = "fail"
-                windows_out = []
+            elif status == "warning" and overall_status == "ok":
-                oos_returns = []
-            else:
-                trades = win_df["trades"].astype(int).tolist()
-                too_few = sum(t < int(payload.optimization.min_trades_test) for t in trades)
-
-                if too_few > 0:
-                    status = "warning"
-                    msg = f"{too_few} windows below min_trades_test"
-                    if overall_status == "ok":
                 overall_status = "warning"
-                else:
-                    status = "ok"
-                    msg = "WF OK"
-
-                windows_out = []
-                for _, r in win_df.iterrows():
-                    windows_out.append({
-                        "window": int(r["window"]),
-                        "train_start": str(r["train_start"]),
-                        "train_end": str(r["train_end"]),
-                        "test_start": str(r["test_start"]),
-                        "test_end": str(r["test_end"]),
-                        "return_pct": float(r["return_pct"]),
-                        "sharpe": float(r["sharpe"]),
-                        "max_dd_pct": float(r["max_dd_pct"]),
-                        "trades": int(r["trades"]),
-                        "params": dict(r["params"]) if isinstance(r["params"], dict) else r["params"],
-                    })
-
-                oos_returns = win_df["return_pct"].astype(float).tolist()
-
-            eq_curve = _accumulate_equity(float(payload.account_equity), oos_returns)
-            oos_final = float(eq_curve[-1]) if eq_curve else float(payload.account_equity)
-            oos_total_return = (oos_final / float(payload.account_equity) - 1.0) * 100.0
-            oos_max_dd = float(np.min(win_df["max_dd_pct"])) if (win_df is not None and not win_df.empty) else 0.0
 
             results.append({
                 "strategy_id": sid,
                 "status": status,
-                "message": msg,
+                "message": message,
-                "n_windows": int(len(windows_out)),
+                "promotion_status": sel.promotion_status,
-                "oos_final_equity": oos_final,
+                "promotion_score": sel.promotion_score,
-                "oos_total_return_pct": float(oos_total_return),
+                "selection_source": sel.selection_source,
-                "oos_max_dd_worst_pct": float(oos_max_dd),
+                "diversity_blocked_by": sel.diversity_blocked_by,
-                "degradation_sharpe": None,
+                "diversity_correlation": sel.diversity_correlation,
-                "windows": windows_out,
+                "baseline_parameters": sel.baseline_parameters,
+                "optimized_parameters": optimized_parameters,
+                "optimization_parameters": sel.optimization_parameters,
+                "baseline": baseline_summary,
+                "optimized": optimized_summary,
+                "decision": decision,
             })
 
             if include_series:
                 series["strategies"][sid] = {
-                    "window_returns_pct": oos_returns,
+                    "baseline": {
-                    "window_equity": eq_curve,
+                        "window_returns_pct": baseline_returns,
+                        "window_equity": baseline_equity,
+                    },
+                    "optimized": {
+                        "window_returns_pct": optimized_returns,
+                        "window_equity": optimized_equity,
+                    },
                 }
-
+        except Exception as exc:
-        except Exception as e:
+            log.error(f"❌ Step4 optimization error | strategy={sid} | {exc}")
-            log.error(f"❌ Step3 WF error | strategy={sid} | {e}")
             results.append({
                 "strategy_id": sid,
                 "status": "fail",
-                "message": f"Exception: {e}",
+                "message": f"Exception: {exc}",
-                "n_windows": 0,
+                "promotion_status": sel.promotion_status,
-                "oos_final_equity": float(payload.account_equity),
+                "promotion_score": sel.promotion_score,
-                "oos_total_return_pct": 0.0,
+                "selection_source": sel.selection_source,
-                "oos_max_dd_worst_pct": 0.0,
+                "diversity_blocked_by": sel.diversity_blocked_by,
-                "degradation_sharpe": None,
+                "diversity_correlation": sel.diversity_correlation,
-                "windows": [],
+                "baseline_parameters": sel.baseline_parameters,
+                "optimized_parameters": sel.baseline_parameters,
+                "optimization_parameters": sel.optimization_parameters,
+                "baseline": _summarize_wf_result({"windows": pd.DataFrame()}, payload.account_equity)[0],
+                "optimized": _summarize_wf_result({"windows": pd.DataFrame()}, payload.account_equity)[0],
+                "decision": {
+                    "accepted": False,
+                    "overfit_flag": False,
+                    "reasons": ["exception"],
+                    "return_delta_pct": 0.0,
+                    "sharpe_delta": 0.0,
+                    "max_dd_delta_pct": 0.0,
+                    "trades_delta": 0,
+                },
             })
             overall_status = "fail"
 
     valid = overall_status != "fail"
-
-    human_msg = {
-        "ok": "Strategies validation OK",
-        "warning": "Strategies validation has warnings",
-        "fail": "Strategies validation FAILED",
-    }[overall_status]
-
     out = {
         "valid": valid,
         "status": overall_status,
         "checks": checks,
-        "message": human_msg,
+        "message": {
+            "ok": "Optimization completed successfully",
+            "warning": "Optimization completed with warnings",
+            "fail": "Optimization failed",
+        }[overall_status],
         "results": results,
     }
-
     if include_series:
         out["series"] = series
-
     return out

src/web/api/v2/routers/calibration_optimization.py

@@ -1,105 +1,120 @@
 # src/web/api/v2/routers/calibration_strategies.py
-
 import logging
 import re
+import threading
 import uuid
 from pathlib import Path
 from typing import Dict
 
-from fastapi import APIRouter, Depends, HTTPException, Request
+from fastapi import APIRouter, Depends, HTTPException
-from fastapi.responses import JSONResponse, HTMLResponse
+from fastapi.responses import JSONResponse
 
-from src.data.storage import StorageManager
+from src.calibration.optimization_inspector import inspect_optimization_config, list_available_strategies
-from src.calibration.optimization_inspector import (
-    inspect_strategies_config,
-    list_available_strategies,
-)
 from src.calibration.reports.optimization_report import generate_strategies_report_pdf
+from src.data.storage import StorageManager
 
 from ..schemas.calibration_optimization import (
-    CalibrationStrategiesInspectRequest,
+    CalibrationOptimizationInspectRequest,
-    CalibrationStrategiesInspectResponse,
+    CalibrationOptimizationInspectResponse,
-    CalibrationStrategiesValidateResponse,
+    CalibrationOptimizationValidateResponse,
 )
 
 logger = logging.getLogger("tradingbot.api.v2")
 
-router = APIRouter(
+router = APIRouter(prefix="/calibration/optimization", tags=["calibration"])
-    prefix="/calibration/optimization",
-    tags=["calibration"],
-)
-
 WF_JOBS: Dict[str, Dict] = {}
 
+
 def get_storage() -> StorageManager:
     return StorageManager.from_env()
 
 
 @router.get("/catalog")
 def strategy_catalog():
-    strategies = list_available_strategies()
+    return {"strategies": list_available_strategies()}
 
-    # Añadimos defaults sugeridos
-    for s in strategies:
-        s["parameters_meta"] = [
-            {
-                "name": p,
-                "type": "int",
-                "default_min": 10,
-                "default_max": 50,
-                "default_step": 10,
-            }
-            for p in s["params"]
-        ]
 
-    return {"strategies": strategies}
+@router.post("/inspect", response_model=CalibrationOptimizationInspectResponse)
-
+def inspect_optimization(payload: CalibrationOptimizationInspectRequest, storage: StorageManager = Depends(get_storage)):
-@router.post("/inspect", response_model=CalibrationStrategiesInspectResponse)
-def inspect_strategies(
-    payload: CalibrationStrategiesInspectRequest,
-    storage: StorageManager = Depends(get_storage),
-):
     df = storage.load_ohlcv(symbol=payload.symbol, timeframe=payload.timeframe)
     if df is None or df.empty:
         raise HTTPException(status_code=400, detail="No OHLCV data found. Run Step 1 first.")
 
     from .calibration_data import analyze_data_quality
     data_quality = analyze_data_quality(df, payload.timeframe)
-
+    result = inspect_optimization_config(
-    result = inspect_strategies_config(
         storage=storage,
         payload=payload,
         data_quality=data_quality,
         include_series=False,
     )
-    return CalibrationStrategiesInspectResponse(**result)
+    return CalibrationOptimizationInspectResponse(**result)
 
-@router.post("/validate", response_model=CalibrationStrategiesValidateResponse)
+
-def validate_strategies(
+@router.post("/validate", response_model=CalibrationOptimizationValidateResponse)
-    payload: CalibrationStrategiesInspectRequest,
+def validate_optimization(payload: CalibrationOptimizationInspectRequest, storage: StorageManager = Depends(get_storage)):
-    storage: StorageManager = Depends(get_storage),
-):
     df = storage.load_ohlcv(symbol=payload.symbol, timeframe=payload.timeframe)
     if df is None or df.empty:
         raise HTTPException(status_code=400, detail="No OHLCV data found. Run Step 1 first.")
 
     from .calibration_data import analyze_data_quality
     data_quality = analyze_data_quality(df, payload.timeframe)
-
+    result = inspect_optimization_config(
-    result = inspect_strategies_config(
         storage=storage,
         payload=payload,
         data_quality=data_quality,
         include_series=True,
     )
-    return CalibrationStrategiesValidateResponse(**result)
+    return CalibrationOptimizationValidateResponse(**result)
+
+
+@router.post("/run")
+def run_optimization_async(payload: CalibrationOptimizationInspectRequest, storage: StorageManager = Depends(get_storage)):
+    job_id = uuid.uuid4().hex
+    WF_JOBS[job_id] = {
+        "status": "running",
+        "progress": 0,
+        "current_strategy": None,
+        "current_phase": "queued",
+        "result": None,
+    }
+
+    def background_job():
+        def progress_cb(current_strategy, current_phase, completed_runs, total_runs):
+            WF_JOBS[job_id]["current_strategy"] = current_strategy
+            WF_JOBS[job_id]["current_phase"] = current_phase
+            WF_JOBS[job_id]["progress"] = int(max(0, min(100, round((completed_runs / max(total_runs, 1)) * 100))))
+
+        try:
+            result = inspect_optimization_config(
+                storage=storage,
+                payload=payload,
+                data_quality={"status": "ok"},
+                include_series=True,
+                progress_callback=progress_cb,
+            )
+            WF_JOBS[job_id]["status"] = "done"
+            WF_JOBS[job_id]["progress"] = 100
+            WF_JOBS[job_id]["current_phase"] = "done"
+            WF_JOBS[job_id]["result"] = result
+        except Exception as exc:
+            WF_JOBS[job_id]["status"] = "fail"
+            WF_JOBS[job_id]["current_phase"] = "error"
+            WF_JOBS[job_id]["result"] = {"detail": str(exc)}
+
+    thread = threading.Thread(target=background_job)
+    thread.start()
+    return {"job_id": job_id}
+
+
+@router.get("/status/{job_id}")
+def get_status(job_id: str):
+    return WF_JOBS.get(job_id, {"status": "unknown"})
+
 
 @router.post("/report")
-def report_strategies(
+def report_optimization(payload: CalibrationOptimizationInspectRequest, storage: StorageManager = Depends(get_storage)):
-    payload: CalibrationStrategiesInspectRequest,
+    logger.info("🧾 Generating optimization report | %s %s", payload.symbol, payload.timeframe)
-    storage: StorageManager = Depends(get_storage),
-):
-    logger.info(f"🧾 Generating strategies report | {payload.symbol} {payload.timeframe}")
 
     df = storage.load_ohlcv(symbol=payload.symbol, timeframe=payload.timeframe)
     if df is None or df.empty:
@@ -107,30 +122,21 @@ def report_strategies(
 
     from .calibration_data import analyze_data_quality
     data_quality = analyze_data_quality(df, payload.timeframe)
-
+    result = inspect_optimization_config(
-    result = inspect_strategies_config(
         storage=storage,
         payload=payload,
         data_quality=data_quality,
         include_series=True,
     )
 
-    # ---------------------------------------------
+    project_root = Path(__file__).resolve().parents[4].parent
-    # Prepare PDF output path (outside src)
+    reports_dir = project_root / "reports" / "optimization"
-    # ---------------------------------------------
-    project_root = Path(__file__).resolve().parents[4]  # .../src
-    # project_root currently points to src/web/api/v2/routers -> parents[4] == src
-    project_root = project_root.parent  # repo root
-
-    reports_dir = project_root / "reports" / "strategies"
     reports_dir.mkdir(parents=True, exist_ok=True)
 
     safe_symbol = re.sub(r"[^a-zA-Z0-9_-]", "_", payload.symbol)
-    filename = f"strategies_report_{safe_symbol}_{payload.timeframe}_{uuid.uuid4().hex}.pdf"
+    filename = f"optimization_report_{safe_symbol}_{payload.timeframe}_{uuid.uuid4().hex}.pdf"
-
     symbol_dir = reports_dir / safe_symbol
     symbol_dir.mkdir(exist_ok=True)
-
     output_path = symbol_dir / filename
 
     generate_strategies_report_pdf(
@@ -141,9 +147,6 @@ def report_strategies(
             "Account equity": payload.account_equity,
         },
         config={
-            "Stop type": payload.stop.type,
-            "Risk per trade (%)": payload.risk.risk_fraction * 100,
-            "Max position fraction (%)": payload.risk.max_position_fraction * 100,
             "WF train_days": payload.wf.train_days,
             "WF test_days": payload.wf.test_days,
             "WF step_days": payload.wf.step_days or payload.wf.test_days,
@@ -153,54 +156,5 @@ def report_strategies(
         results=result,
     )
 
-    public_url = f"/reports/strategies/{safe_symbol}/{filename}"
+    public_url = f"/reports/optimization/{safe_symbol}/{filename}"
     return JSONResponse(content={"status": result.get("status", "ok"), "url": public_url})
-
-@router.post("/run")
-def run_strategies_async(
-    payload: CalibrationStrategiesInspectRequest,
-    storage: StorageManager = Depends(get_storage),
-):
-    import threading
-    import uuid
-
-    job_id = uuid.uuid4().hex
-
-    WF_JOBS[job_id] = {
-        "status": "running",
-        "progress": 0,
-        "current_window": 0,
-        "total_windows": 0,
-        "current_strategy": None,
-        "result": None,
-    }
-
-    def background_job():
-
-        def progress_cb(window_id, total_windows):
-            WF_JOBS[job_id]["current_window"] = window_id
-            WF_JOBS[job_id]["total_windows"] = total_windows
-            WF_JOBS[job_id]["progress"] = int(
-                window_id / total_windows * 100
-            )
-
-        result = inspect_strategies_config(
-            storage=storage,
-            payload=payload,
-            data_quality={"status": "ok"},
-            include_series=True,
-            progress_callback=progress_cb,  # ← lo pasamos
-        )
-
-        WF_JOBS[job_id]["status"] = "done"
-        WF_JOBS[job_id]["progress"] = 100
-        WF_JOBS[job_id]["result"] = result
-
-    thread = threading.Thread(target=background_job)
-    thread.start()
-
-    return {"job_id": job_id}
-
-@router.get("/status/{job_id}")
-def get_status(job_id: str):
-    return WF_JOBS.get(job_id, {"status": "unknown"})

src/web/api/v2/schemas/calibration_optimization.py

@@ -1,5 +1,4 @@
 # src/web/api/v2/schemas/calibration_strategies.py
-
 from typing import Any, Dict, List, Literal, Optional
 from pydantic import BaseModel, Field
 
@@ -9,38 +8,43 @@ from .calibration_risk import StopConfigSchema, RiskConfigSchema, GlobalRiskRule
 class WalkForwardConfigSchema(BaseModel):
     train_days: int = Field(..., gt=0)
     test_days: int = Field(..., gt=0)
-    step_days: Optional[int] = Field(None, gt=0)  # if None => step = test_days
+    step_days: Optional[int] = Field(None, gt=0)
 
 
 class OptimizationConfigSchema(BaseModel):
     optimizer_metric: str = Field("sharpe_ratio")
-    max_combinations: int = Field(500, gt=0)
+    max_combinations: int = Field(300, gt=0)
     min_trades_train: int = Field(30, ge=0)
     min_trades_test: int = Field(10, ge=0)
 
 
-class ParameterRangeSchema(BaseModel):
+class NumericParameterRangeSchema(BaseModel):
     min: float
     max: float
     step: float
 
 
-class StrategySelectionSchema(BaseModel):
+class OptimizationStrategySchema(BaseModel):
     strategy_id: str
-    parameters: Dict[str, ParameterRangeSchema]
+    baseline_parameters: Dict[str, Any]
+    optimization_parameters: Dict[str, Any]
+    promotion_status: Optional[str] = None
+    promotion_score: Optional[float] = None
+    selection_source: Optional[str] = None
+    diversity_blocked_by: Optional[str] = None
+    diversity_correlation: Optional[float] = None
 
 
-class CalibrationStrategiesInspectRequest(BaseModel):
+class CalibrationOptimizationInspectRequest(BaseModel):
     symbol: str
     timeframe: str
 
-    # snapshot from Step 2 (closed)
     stop: StopConfigSchema
     risk: RiskConfigSchema
     global_rules: GlobalRiskRulesSchema
     account_equity: float = Field(..., gt=0)
 
-    strategies: List[StrategySelectionSchema]
+    strategies: List[OptimizationStrategySchema]
     wf: WalkForwardConfigSchema
     optimization: OptimizationConfigSchema
 
@@ -54,7 +58,6 @@ class WindowRowSchema(BaseModel):
     train_end: str
     test_start: str
     test_end: str
-
     return_pct: float
     sharpe: float
     max_dd_pct: float
@@ -62,28 +65,53 @@ class WindowRowSchema(BaseModel):
     params: Dict[str, Any]
 
 
-class StrategyRunResultSchema(BaseModel):
+class RunSummarySchema(BaseModel):
-    strategy_id: str
-    status: Literal["ok", "warning", "fail"]
-    message: str
-
     n_windows: int
     oos_final_equity: float
     oos_total_return_pct: float
     oos_max_dd_worst_pct: float
-    degradation_sharpe: Optional[float] = None
+    oos_avg_sharpe: Optional[float] = None
-
+    total_trades: int = 0
     windows: List[WindowRowSchema]
 
 
-class CalibrationStrategiesInspectResponse(BaseModel):
+class OptimizationDecisionSchema(BaseModel):
+    accepted: bool
+    overfit_flag: bool = False
+    reasons: List[str] = []
+    return_delta_pct: float = 0.0
+    sharpe_delta: float = 0.0
+    max_dd_delta_pct: float = 0.0
+    trades_delta: int = 0
+
+
+class StrategyOptimizationResultSchema(BaseModel):
+    strategy_id: str
+    status: Literal["ok", "warning", "fail"]
+    message: str
+
+    promotion_status: Optional[str] = None
+    promotion_score: Optional[float] = None
+    selection_source: Optional[str] = None
+    diversity_blocked_by: Optional[str] = None
+    diversity_correlation: Optional[float] = None
+
+    baseline_parameters: Dict[str, Any]
+    optimized_parameters: Dict[str, Any]
+    optimization_parameters: Dict[str, Any]
+
+    baseline: RunSummarySchema
+    optimized: RunSummarySchema
+    decision: OptimizationDecisionSchema
+
+
+class CalibrationOptimizationInspectResponse(BaseModel):
     valid: bool
     status: Literal["ok", "warning", "fail"]
     checks: Dict[str, Any]
     message: str
-
+    results: List[StrategyOptimizationResultSchema]
-    results: List[StrategyRunResultSchema]
 
 
-class CalibrationStrategiesValidateResponse(CalibrationStrategiesInspectResponse):
+class CalibrationOptimizationValidateResponse(CalibrationOptimizationInspectResponse):
     series: Dict[str, Any]

src/web/ui/v2/static/js/pages/calibration_strategies.js

@@ -22,6 +22,8 @@ function enableNextStep() {
   btn.classList.remove("btn-outline-secondary");
   btn.classList.add("btn-outline-primary");
   btn.setAttribute("aria-disabled", "false");
+  btn.setAttribute("href", "/calibration/optimization");
+  btn.setAttribute("title", "Go to Step 4 · Optimization");
 }
 
 
@@ -31,6 +33,8 @@ function disableNextStep() {
   btn.classList.remove("btn-outline-primary");
   btn.classList.add("btn-outline-secondary");
   btn.setAttribute("aria-disabled", "true");
+  btn.setAttribute("href", "#");
+  btn.setAttribute("title", "Build a Step 4 Selection Artifact first");
 }
 
 
@@ -1959,6 +1963,7 @@ async function init() {
   });
 
   wirePromotionUI();
+  restoreStep4SelectionArtifact();
 
   document.getElementById("plot_strategy_select").addEventListener("change", function() {
     if (!lastValidationResult || !selectedStrategyId) return;
@@ -2283,9 +2288,40 @@ function buildStep4SelectionArtifact() {
   previewEl.textContent = JSON.stringify(artifact, null, 2);
   previewEl.classList.remove("d-none");
 
+  localStorage.setItem("calibration.step4.selection", JSON.stringify(artifact));
+
+  if ((artifact.selected_strategies || []).length > 0) enableNextStep();
+  else disableNextStep();
+
   updateStep4SelectionSummary();
 }
 
+function restoreStep4SelectionArtifact() {
+  const raw = localStorage.getItem("calibration.step4.selection");
+  if (!raw) {
+    disableNextStep();
+    return;
+  }
+
+  try {
+    const artifact = JSON.parse(raw);
+    const previewEl = document.getElementById("step4SelectionPreview");
+    if (previewEl && artifact) {
+      previewEl.textContent = JSON.stringify(artifact, null, 2);
+      previewEl.classList.remove("d-none");
+    }
+
+    STEP4_SELECTION = Array.isArray(artifact?.selected_strategies) ? artifact.selected_strategies : [];
+    if (STEP4_SELECTION.length > 0) enableNextStep();
+    else disableNextStep();
+
+    updateStep4SelectionSummary();
+  } catch (err) {
+    console.error("restoreStep4SelectionArtifact failed", err);
+    disableNextStep();
+  }
+}
+
 
 async function runPromotion() {
 

src/web/ui/v2/templates/pages/calibration/calibration_optimization.html

@@ -2,20 +2,10 @@
 
 {% block content %}
 <div class="container-xl">
-
-  <!-- ========================= -->
-  <!-- Wizard header -->
-  <!-- ========================= -->
   <div class="d-flex align-items-center mb-4">
-
-    <!-- Back arrow -->
     <div class="me-3">
-      <a href="/calibration/risk" class="btn btn-outline-secondary btn-icon">
+      <a href="/calibration/strategies" class="btn btn-outline-secondary btn-icon">
-        <svg xmlns="http://www.w3.org/2000/svg"
+        <svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-arrow-left" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
-            class="icon icon-tabler icon-tabler-arrow-left"
-            width="24" height="24" viewBox="0 0 24 24"
-            stroke-width="2" stroke="currentColor"
-            fill="none" stroke-linecap="round" stroke-linejoin="round">
           <path stroke="none" d="M0 0h24v24H0z"/>
           <path d="M15 6l-6 6l6 6"/>
         </svg>
@@ -23,342 +13,112 @@
     </div>
 
     <div class="flex-grow-1 text-center">
-      <h2 class="mb-0">Calibración · Paso 3 · Strategies</h2>
+      <h2 class="mb-0">Calibración · Paso 4 · Optimization</h2>
-      <div class="text-secondary">Optimización + Walk Forward (OOS)</div>
+      <div class="text-secondary">Baseline vs optimized walk-forward validation</div>
     </div>
 
-    <!-- Forward arrow (disabled until OK) -->
     <div class="ms-3">
-      <a
+      <a id="next-step-btn" href="#" class="btn btn-outline-secondary btn-icon" aria-disabled="true" title="Step 5 not implemented yet">
-        id="next-step-btn"
+        <svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-arrow-right" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
-        href="#"
-        class="btn btn-outline-secondary btn-icon"
-        aria-disabled="true"
-        title="Next step not implemented yet"
-      >
-        <svg xmlns="http://www.w3.org/2000/svg"
-            class="icon icon-tabler icon-tabler-arrow-right"
-            width="24" height="24" viewBox="0 0 24 24"
-            stroke-width="2" stroke="currentColor"
-            fill="none" stroke-linecap="round" stroke-linejoin="round">
           <path stroke="none" d="M0 0h24v24H0z"/>
           <path d="M9 6l6 6l-6 6"/>
         </svg>
       </a>
     </div>
-
   </div>
 
-  <!-- ========================= -->
-  <!-- Context -->
-  <!-- ========================= -->
   <div class="card mb-4">
-    <div class="card-header">
+    <div class="card-header"><h3 class="card-title">Step 4 input artifact</h3></div>
-      <h3 class="card-title">Context</h3>
+    <div class="card-body">
+      <div id="artifactSummary" class="text-secondary">Loading artifact…</div>
+      <pre id="artifactPreview" class="mt-3 p-3 bg-light border rounded" style="max-height: 260px; overflow:auto;"></pre>
     </div>
+  </div>
+
+  <div class="card mb-4">
+    <div class="card-header"><h3 class="card-title">Inherited context</h3></div>
     <div class="card-body">
       <div class="row g-3">
-        <div class="col-md-4">
+        <div class="col-md-3"><label class="form-label">Symbol</label><input id="symbol" class="form-control" readonly></div>
-          <label class="form-label">Symbol</label>
+        <div class="col-md-2"><label class="form-label">Timeframe</label><input id="timeframe" class="form-control" readonly></div>
-          <input id="symbol" class="form-control" placeholder="BTC/USDT">
+        <div class="col-md-2"><label class="form-label">Account equity</label><input id="account_equity" type="number" step="0.01" class="form-control"></div>
+        <div class="col-md-2"><label class="form-label">Commission</label><input id="commission" type="number" step="0.0001" class="form-control"></div>
+        <div class="col-md-2"><label class="form-label">Slippage</label><input id="slippage" type="number" step="0.0001" class="form-control"></div>
       </div>
-        <div class="col-md-4">
+      <div class="row g-3 mt-1">
-          <label class="form-label">Timeframe</label>
+        <div class="col-md-2"><label class="form-label">Train days</label><input id="wf_train_days" type="number" class="form-control"></div>
-          <input id="timeframe" class="form-control" placeholder="1h">
+        <div class="col-md-2"><label class="form-label">Test days</label><input id="wf_test_days" type="number" class="form-control"></div>
+        <div class="col-md-2"><label class="form-label">Step days</label><input id="wf_step_days" type="number" class="form-control"></div>
+        <div class="col-md-3"><label class="form-label">Optimizer metric</label><select id="opt_metric" class="form-select"><option value="sharpe_ratio">sharpe_ratio</option><option value="total_return_pct">total_return_pct</option><option value="max_drawdown_pct">max_drawdown_pct</option></select></div>
+        <div class="col-md-2"><label class="form-label">Max combinations</label><input id="opt_max_combinations" type="number" class="form-control"></div>
       </div>
-        <div class="col-md-4">
+      <div class="row g-3 mt-1">
-          <label class="form-label">Account equity</label>
+        <div class="col-md-2"><label class="form-label">Min trades train</label><input id="opt_min_trades_train" type="number" class="form-control"></div>
-          <input id="account_equity" class="form-control" type="number" step="0.01" value="10000">
+        <div class="col-md-2"><label class="form-label">Min trades test</label><input id="opt_min_trades_test" type="number" class="form-control"></div>
+        <div class="col-md-2"><label class="form-label">Risk %</label><input id="risk_fraction" type="number" step="0.01" class="form-control"></div>
+        <div class="col-md-2"><label class="form-label">Max position %</label><input id="max_position_fraction" type="number" step="0.1" class="form-control"></div>
+        <div class="col-md-2"><label class="form-label">Stop type</label><select id="stop_type" class="form-select"><option value="fixed">fixed</option><option value="trailing">trailing</option><option value="atr">atr</option></select></div>
+        <div class="col-md-2"><label class="form-label">Stop fraction %</label><input id="stop_fraction" type="number" step="0.01" class="form-control"></div>
       </div>
-      </div>
+      <div class="row g-3 mt-1">
-      <div class="mt-3 text-secondary">
+        <div class="col-md-2"><label class="form-label">ATR period</label><input id="atr_period" type="number" class="form-control"></div>
-        Tip: Symbol y timeframe se cargan desde Step 1 (localStorage). Si no aparecen, rellénalos manualmente.
+        <div class="col-md-2"><label class="form-label">ATR multiplier</label><input id="atr_multiplier" type="number" step="0.1" class="form-control"></div>
+        <div class="col-md-2"><label class="form-label">Max DD %</label><input id="max_drawdown_pct" type="number" step="0.1" class="form-control"></div>
+        <div class="col-md-2"><label class="form-label">Daily loss %</label><input id="daily_loss_limit_pct" type="number" step="0.1" class="form-control"></div>
+        <div class="col-md-2"><label class="form-label">Max consec. losses</label><input id="max_consecutive_losses" type="number" class="form-control"></div>
+        <div class="col-md-2"><label class="form-label">Cooldown bars</label><input id="cooldown_bars" type="number" class="form-control"></div>
       </div>
     </div>
   </div>
 
-  <!-- ========================= -->
-  <!-- Risk & Stops -->
-  <!-- ========================= -->
   <div class="card mb-4">
     <div class="card-header d-flex justify-content-between align-items-center">
-      <h3 class="card-title mb-0">Risk & Stops(Step 2)</h3>
+      <h3 class="card-title mb-0">Optimization ranges</h3>
-
+      <div id="strategyCount" class="text-secondary small"></div>
-      <div class="form-check form-switch m-0">
-        <input class="form-check-input" type="checkbox" id="lock_inherited" checked>
-        <label class="form-check-label" for="lock_inherited">
-          Bloquear parámetros heredados
-        </label>
-      </div>
-    </div>
-
-  <div class="card-body">
-
-    <!-- ================= -->
-    <!-- Risk Configuration -->
-    <!-- ================= -->
-    <h4 class="mb-3">Risk Configuration</h4>
-
-    <div class="row g-3 mb-4">
-      <div class="col-md-4">
-        <label class="form-label">Risk per Trade (%)</label>
-        <input id="risk_fraction" class="form-control inherited-field" type="number" step="0.01">
-      </div>
-
-      <div class="col-md-4">
-        <label class="form-label">Max Position Size (%)</label>
-        <input id="max_position_fraction" class="form-control inherited-field" type="number" step="0.1">
-      </div>
-    </div>
-
-    <!-- ================= -->
-    <!-- Stop Configuration -->
-    <!-- ================= -->
-    <h4 class="mb-3">Stop Configuration</h4>
-
-    <div class="row g-3 mb-4">
-
-      <div class="col-md-4">
-        <label class="form-label">Stop Type</label>
-        <select id="stop_type" class="form-select inherited-field">
-          <option value="fixed">fixed</option>
-          <option value="trailing">trailing</option>
-          <option value="atr">atr</option>
-        </select>
-      </div>
-
-      <div id="stop_fraction_group" class="col-md-4">
-        <label class="form-label">Stop fraction (%)</label>
-        <input id="stop_fraction" class="form-control inherited-field" type="number" step="0.01">
-      </div>
-
-      <div id="atr_group" class="col-md-4 d-none">
-        <label class="form-label">ATR period</label>
-        <input id="atr_period" class="form-control inherited-field" type="number">
-      </div>
-
-      <div id="atr_multiplier_group" class="col-md-4 d-none">
-        <label class="form-label">ATR multiplier</label>
-        <input id="atr_multiplier" class="form-control inherited-field" type="number" step="0.1">
-      </div>
-
-    </div>
-
-
-    <!-- ================= -->
-    <!-- Global Rules -->
-    <!-- ================= -->
-    <h4 class="mb-3">Global Rules</h4>
-
-    <div class="row g-3 mb-4">
-      <div class="col-md-4">
-        <label class="form-label">Max Drawdown (%)</label>
-        <input id="max_drawdown_pct" class="form-control inherited-field" type="number" step="0.1">
-      </div>
-    </div>
-
-    <!-- ================= -->
-    <!-- Optional Parameters -->
-    <!-- ================= -->
-    <h4 class="mb-3">Optional Parameters</h4>
-
-    <div class="row g-3">
-      <div class="col-md-4">
-        <label class="form-label">Daily loss limit (%)</label>
-        <input id="daily_loss_limit_pct" class="form-control optional-field" type="number" step="0.1">
-      </div>
-
-      <div class="col-md-4">
-        <label class="form-label">Max consecutive losses</label>
-        <input id="max_consecutive_losses" class="form-control optional-field" type="number">
-      </div>
-
-      <div class="col-md-4">
-        <label class="form-label">Cooldown bars</label>
-        <input id="cooldown_bars" class="form-control optional-field" type="number">
-      </div>
-    </div>
-
-  </div>
-</div>
-
-  <!-- ========================= -->
-  <!-- WF + Optimizer config -->
-  <!-- ========================= -->
-  <div class="card mb-4">
-    <div class="card-header">
-      <h3 class="card-title">Walk-Forward & Optimization</h3>
     </div>
     <div class="card-body">
-      <div class="row g-3">
+      <div id="optimizationStrategies" class="d-flex flex-column gap-3"></div>
-        <div class="col-md-3">
-          <label class="form-label">Train days</label>
-          <input id="wf_train_days" class="form-control" type="number" step="1" value="120">
-        </div>
-        <div class="col-md-3">
-          <label class="form-label">Test days</label>
-          <input id="wf_test_days" class="form-control" type="number" step="1" value="30">
-        </div>
-        <div class="col-md-3">
-          <label class="form-label">Step days (optional)</label>
-          <input id="wf_step_days" class="form-control" type="number" step="1" value="">
-        </div>
-        <div class="col-md-3">
-          <label class="form-label">Metric</label>
-          <select id="opt_metric" class="form-select">
-            <option value="sharpe_ratio">sharpe_ratio</option>
-            <option value="total_return">total_return</option>
-            <option value="max_drawdown">max_drawdown</option>
-          </select>
-        </div>
-
-        <div class="col-md-3">
-          <label class="form-label">Max combinations</label>
-          <input id="opt_max_combinations" class="form-control" type="number" step="1" value="300">
-        </div>
-        <div class="col-md-3">
-          <label class="form-label">Min trades (train)</label>
-          <input id="opt_min_trades_train" class="form-control" type="number" step="1" value="30">
-        </div>
-        <div class="col-md-3">
-          <label class="form-label">Min trades (test)</label>
-          <input id="opt_min_trades_test" class="form-control" type="number" step="1" value="10">
-        </div>
-
-        <div class="col-md-3">
-          <label class="form-label">Commission</label>
-          <input id="commission" class="form-control" type="number" step="0.0001" value="0.001">
-        </div>
-        <div class="col-md-3">
-          <label class="form-label">Slippage</label>
-          <input id="slippage" class="form-control" type="number" step="0.0001" value="0.0005">
-        </div>
-      </div>
     </div>
   </div>
 
-  <!-- ========================= -->
-  <!-- Strategy selection -->
-  <!-- ========================= -->
-  <div class="card mb-4">
-    <div class="card-header">
-      <h3 class="card-title">Strategies</h3>
-      <div class="card-actions">
-        <button id="refresh_strategies_btn" class="btn btn-sm btn-outline-secondary">Refresh</button>
-      </div>
-    </div>
-    <div class="card-body">
-      <div id="strategies_container" class="d-flex flex-column gap-4"></div>
-      <div class="card p-3">
-        <div class="d-flex justify-content-between">
-          <strong>Total combinations</strong>
-          <span id="combination_counter">0</span>
-        </div>
-      </div>
-      <div class="mt-2 text-end">
-        <small class="text-muted">
-          Estimated WF time:
-          <span id="wf_time_estimate">~ 0 sec</span>
-        </small>
-      </div>
-      <div class="mt-3 text-secondary">
-        Cada estrategia incluye un <b>param_grid</b> en JSON.
-      </div>
-    </div>
-  </div>
-
-  <!-- ========================= -->
-  <!-- Actions -->
-  <!-- ========================= -->
   <div class="d-flex gap-2 mb-4">
-    <button id="validate_strategies_btn" class="btn btn-primary">
+    <button id="runOptimizationBtn" class="btn btn-primary">Run optimization</button>
-      Validate (WF)
+    <button id="generateReportBtn" class="btn btn-outline-primary">Generate PDF report</button>
-    </button>
-    <button id="report_strategies_btn" class="btn btn-outline-primary">
-      Generate PDF report
-    </button>
   </div>
 
-  <!-- ========================= -->
+  <div id="progressCard" class="card mb-4 d-none">
-  <!-- Prograss Bar -->
+    <div class="card-header"><h3 class="card-title">Optimization progress</h3></div>
-  <!-- ========================= -->
-  <div id="wf_progress_card" class="card mb-4">
-    <div class="card-header">
-      <h3 class="card-title">Walk-Forward Progress</h3>
-    </div>
     <div class="card-body">
-
+      <div class="progress mb-2"><div id="progressBar" class="progress-bar progress-bar-striped progress-bar-animated" style="width:0%">0%</div></div>
-      <div class="progress mb-2">
+      <div id="progressText" class="text-secondary small">Waiting to start…</div>
-        <div
-          id="wfProgressBar"
-          class="progress-bar progress-bar-striped progress-bar-animated"
-          role="progressbar"
-          style="width: 0%"
-        >
-          0%
     </div>
   </div>
 
-      <div id="wf_progress_text" class="text-secondary small">
-        Waiting to start...
-      </div>
-
-    </div>
-  </div>
-
-  <!-- ========================= -->
-  <!-- Results -->
-  <!-- ========================= -->
   <div class="card mb-4">
-    <div class="card-header">
+    <div class="card-header d-flex justify-content-between align-items-center">
-      <h3 class="card-title">Results</h3>
+      <h3 class="card-title mb-0">Results</h3>
-      <div class="card-actions">
+      <span id="resultsBadge" class="badge bg-secondary">—</span>
-        <span id="strategies_status_badge" class="badge bg-secondary">—</span>
-      </div>
     </div>
     <div class="card-body">
-      <div id="strategies_message" class="mb-3 text-secondary">Run validation to see results.</div>
+      <div id="resultsMessage" class="text-secondary mb-3">Run Step 4 to compare baseline vs optimized.</div>
-
+      <div class="row g-3 mb-3">
-      <div class="row g-3">
+        <div class="col-md-4"><label class="form-label">Strategy plot</label><select id="plotStrategySelect" class="form-select"></select></div>
-        <div class="col-md-4">
-          <label class="form-label">Strategy plot</label>
-          <select id="plot_strategy_select" class="form-select"></select>
       </div>
-      </div>
-
-      <div class="mt-3">
       <div id="plot_equity" style="height: 320px;"></div>
-      </div>
+      <div class="mt-3" id="plot_returns" style="height: 320px;"></div>
-      <div class="mt-3">
-        <div id="plot_returns" style="height: 320px;"></div>
-      </div>
-
       <hr class="my-4">
-
+      <div id="resultsTableWrap"></div>
-      <div id="strategies_table_wrap"></div>
+      <details class="mt-3"><summary class="text-secondary">Debug JSON</summary><pre id="debugJson" class="mt-2" style="max-height: 320px; overflow:auto;"></pre></details>
-
-      <details class="mt-3">
-        <summary class="text-secondary">Debug JSON</summary>
-        <pre id="strategies_debug" class="mt-2" style="max-height: 300px; overflow:auto;"></pre>
-      </details>
     </div>
   </div>
 
-  <!-- ========================= -->
+  <div id="pdfViewerSection" class="card mb-4 d-none">
-  <!-- PDF Viewer -->
+    <div class="card-header d-flex justify-content-between align-items-center">
-  <!-- ========================= -->
+      <h3 class="card-title mb-0">Optimization report</h3>
-  <div id="pdf_viewer_section" class="card mb-4 d-none">
+      <button id="closePdfBtn" class="btn btn-sm btn-outline-secondary">Close</button>
-    <div class="card-header">
-      <h3 class="card-title">Strategies Report (PDF)</h3>
-      <div class="card-actions">
-        <button id="close_pdf_btn" class="btn btn-sm btn-outline-secondary">Close</button>
     </div>
+    <div class="card-body"><iframe id="pdfFrame" style="width:100%; height:800px; border:none;"></iframe></div>
   </div>
-    <div class="card-body">
-      <iframe id="pdf_frame" style="width: 100%; height: 800px; border: none;"></iframe>
-    </div>
-  </div>
-
 </div>
 
 <script src="https://cdn.plot.ly/plotly-2.27.0.min.js"></script>

src/web/ui/v2/templates/pages/calibration/calibration_strategies.html

@@ -312,7 +312,7 @@
       <div id="promotionSummary" class="mt-3"></div>
 
       <div class="mt-2 d-flex gap-2 flex-wrap">
-        <button id="btnBuildStep4Selection" class="btn btn-success btn-sm">
+        <button id="btnBuildStep4Selection" class="btn btn-success btn-sm text-white fw-semibold">
           Build Step 4 Selection
         </button>
       </div>