feat(calibration): Step 3 - Stategies preparado conceptualmente

src/calibration/reports/strategies_report.py

@@ -0,0 +1,94 @@
+# src/calibration/reports/strategies_report.py
+
+from pathlib import Path
+from typing import Any, Dict
+
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, PageBreak
+from reportlab.lib.styles import getSampleStyleSheet
+from reportlab.lib import colors
+
+
+def _simple_kv_table(title: str, dct: Dict[str, Any]):
+    rows = [["Key", "Value"]] + [[str(k), str(v)] for k, v in dct.items()]
+    t = Table(rows, hAlign="LEFT")
+    t.setStyle(TableStyle([
+        ("BACKGROUND", (0, 0), (-1, 0), colors.lightgrey),
+        ("GRID", (0, 0), (-1, -1), 0.5, colors.grey),
+        ("FONTNAME", (0, 0), (-1, 0), "Helvetica-Bold"),
+        ("VALIGN", (0, 0), (-1, -1), "TOP"),
+    ]))
+    return [Paragraph(title, getSampleStyleSheet()["Heading2"]), t, Spacer(1, 12)]
+
+
+def generate_strategies_report_pdf(
+    *,
+    output_path: Path,
+    context: Dict[str, Any],
+    config: Dict[str, Any],
+    results: Dict[str, Any],
+):
+    """Minimal v1 report. We'll enrich with charts/tables next iterations."""
+
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+
+    styles = getSampleStyleSheet()
+    doc = SimpleDocTemplate(str(output_path))
+
+    story = []
+    story.append(Paragraph("Calibration · Step 3 · Strategies", styles["Title"]))
+    story.append(Spacer(1, 12))
+
+    story.append(Paragraph(f"Status: {results.get('status')}", styles["Normal"]))
+    story.append(Spacer(1, 12))
+
+    for block in _simple_kv_table("Context", context):
+        story.append(block)
+    for block in _simple_kv_table("Configuration", config):
+        story.append(block)
+
+    story.append(Paragraph("Results (per strategy)", styles["Heading2"]))
+    story.append(Spacer(1, 6))
+
+    for r in results.get("results", []):
+        story.append(Paragraph(
+            f"<b>{r.get('strategy_id')}</b> — {r.get('status').upper()} — {r.get('message','')}",
+            styles["Normal"],
+        ))
+        story.append(Spacer(1, 4))
+
+        summary = {
+            "n_windows": r.get("n_windows"),
+            "oos_final_equity": r.get("oos_final_equity"),
+            "oos_total_return_pct": r.get("oos_total_return_pct"),
+            "oos_max_dd_worst_pct": r.get("oos_max_dd_worst_pct"),
+        }
+        for block in _simple_kv_table("Summary", summary):
+            story.append(block)
+
+        # Window table (first 20 to keep PDF light)
+        windows = r.get("windows", [])
+        if windows:
+            rows = [["Window", "Test return %", "Sharpe", "Max DD %", "Trades", "Params"]]
+            for w in windows[:20]:
+                rows.append([
+                    str(w.get("window")),
+                    f"{float(w.get('return_pct',0.0)):.2f}",
+                    f"{float(w.get('sharpe',0.0)):.2f}",
+                    f"{float(w.get('max_dd_pct',0.0)):.2f}",
+                    str(w.get("trades")),
+                    str(w.get("params")),
+                ])
+            t = Table(rows, hAlign="LEFT", colWidths=[45, 75, 55, 65, 55, 240])
+            t.setStyle(TableStyle([
+                ("BACKGROUND", (0, 0), (-1, 0), colors.lightgrey),
+                ("GRID", (0, 0), (-1, -1), 0.25, colors.grey),
+                ("FONTNAME", (0, 0), (-1, 0), "Helvetica-Bold"),
+                ("VALIGN", (0, 0), (-1, -1), "TOP"),
+            ]))
+            story.append(Paragraph("Walk-Forward windows (first 20)", styles["Heading3"]))
+            story.append(t)
+            story.append(Spacer(1, 12))
+
+        story.append(PageBreak())
+
+    doc.build(story)

src/calibration/strategies_inspector.py

@@ -0,0 +1,326 @@
+# src/calibration/strategies_inspector.py
+from __future__ import annotations
+
+from typing import Any, Dict, List
+
+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.risk.stops.fixed_stop import FixedStop
+from src.risk.stops.trailing_stop import TrailingStop
+from src.risk.stops.atr_stop import ATRStop
+
+from src.risk.sizing.percent_risk import PercentRiskSizer
+
+
+# --------------------------------------------------
+# Strategy registry (con metadata de parámetros)
+# --------------------------------------------------
+from src.strategies.moving_average import MovingAverageCrossover
+from src.strategies.rsi_strategy import RSIStrategy
+from src.strategies.buy_and_hold import BuyAndHold
+
+
+STRATEGY_REGISTRY = {
+    "moving_average": {
+        "class": MovingAverageCrossover,
+        "params": ["fast_period", "slow_period"],
+    },
+    "rsi": {
+        "class": RSIStrategy,
+        "params": ["rsi_period", "overbought", "oversold"],
+    },
+    "buy_and_hold": {
+        "class": BuyAndHold,
+        "params": [],
+    },
+}
+
+
+# --------------------------------------------------
+# Helpers
+# --------------------------------------------------
+
+def list_available_strategies() -> List[Dict[str, Any]]:
+    """
+    Devuelve metadata completa para UI.
+    """
+    out = []
+
+    for sid, entry in STRATEGY_REGISTRY.items():
+        out.append({
+            "strategy_id": sid,
+            "name": entry["class"].__name__,
+            "params": entry["params"],
+            "tags": [],  # puedes rellenar más adelante
+        })
+
+    return out
+
+
+def _build_stop_loss(stop_schema) -> object | None:
+    if stop_schema.type == "fixed":
+        return FixedStop(stop_fraction=float(stop_schema.stop_fraction))
+    if stop_schema.type == "trailing":
+        return TrailingStop(stop_fraction=float(stop_schema.stop_fraction))
+    if stop_schema.type == "atr":
+        return ATRStop(
+            atr_period=int(stop_schema.atr_period),
+            multiplier=float(stop_schema.atr_multiplier),
+        )
+    raise ValueError(f"Unknown stop type: {stop_schema.type}")
+
+
+def _build_position_sizer(risk_schema) -> PercentRiskSizer:
+    return PercentRiskSizer(risk_fraction=float(risk_schema.risk_fraction))
+
+
+def _cap_units_by_max_position_fraction(units: float, capital: float, entry_price: float, max_position_fraction: float) -> float:
+    max_units = (capital * max_position_fraction) / entry_price
+    return float(min(units, max_units))
+
+
+def _accumulate_equity(initial: float, returns_pct: List[float]) -> List[float]:
+    eq = [float(initial)]
+    cur = float(initial)
+    for r in returns_pct:
+        cur *= (1.0 + float(r) / 100.0)
+        eq.append(float(cur))
+    return eq
+
+
+# --------------------------------------------------
+# Main
+# --------------------------------------------------
+
+def inspect_strategies_config(
+    *,
+    storage: StorageManager,
+    payload,
+    data_quality: Dict[str, Any],
+    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 {
+            "valid": False,
+            "status": "fail",
+            "checks": {},
+            "message": "No OHLCV data",
+            "results": [],
+        }
+
+    checks: Dict[str, Any] = {}
+    checks["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.",
+            "results": [],
+            "series": {} 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"])
+        grid_params = set(sel.param_grid.keys())
+
+        # 🔒 Validación estricta de parámetros
+        if grid_params != valid_params:
+            msg = f"Param grid keys {grid_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
+
+        # Wrapper sizer
+        class _CappedSizer(type(base_sizer)):
+            def __init__(self, inner):
+                self.inner = inner
+
+            def calculate_size(self, *, capital, entry_price, stop_price=None, max_capital=None, volatility=None):
+                u = self.inner.calculate_size(
+                    capital=capital,
+                    entry_price=entry_price,
+                    stop_price=stop_price,
+                    max_capital=max_capital,
+                    volatility=volatility,
+                )
+                return _cap_units_by_max_position_fraction(
+                    units=float(u),
+                    capital=float(capital),
+                    entry_price=float(entry_price),
+                    max_position_fraction=float(payload.risk.max_position_fraction),
+                )
+
+        capped_sizer = _CappedSizer(base_sizer)
+
+        log.info(f"🧠 Step3 | WF run | strategy={sid}")
+
+        try:
+            wf = WalkForwardValidator(
+                strategy_class=strategy_class,
+                param_grid=sel.param_grid,
+                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=progress_callback,
+            )
+
+            wf_res = wf.run()
+            win_df: pd.DataFrame = wf_res["windows"]
+
+            if win_df is None or win_df.empty:
+                status = "fail"
+                msg = "WF produced no valid windows"
+                overall_status = "fail"
+                windows_out = []
+                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,
+                "n_windows": int(len(windows_out)),
+                "oos_final_equity": oos_final,
+                "oos_total_return_pct": float(oos_total_return),
+                "oos_max_dd_worst_pct": float(oos_max_dd),
+                "degradation_sharpe": None,
+                "windows": windows_out,
+            })
+
+            if include_series:
+                series["strategies"][sid] = {
+                    "window_returns_pct": oos_returns,
+                    "window_equity": eq_curve,
+                }
+
+        except Exception as e:
+            log.error(f"❌ Step3 WF error | strategy={sid} | {e}")
+            results.append({
+                "strategy_id": sid,
+                "status": "fail",
+                "message": f"Exception: {e}",
+                "n_windows": 0,
+                "oos_final_equity": float(payload.account_equity),
+                "oos_total_return_pct": 0.0,
+                "oos_max_dd_worst_pct": 0.0,
+                "degradation_sharpe": None,
+                "windows": [],
+            })
+            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,
+        "results": results,
+    }
+
+    if include_series:
+        out["series"] = series
+
+    return out

src/core/optimizer.py

@@ -4,11 +4,13 @@ Optimizador de parámetros para estrategias
 """
 import os
 import pandas as pd
-from typing import Dict, List, Any, Type
+from typing import Dict, List, Any, Type, Optional
 from itertools import product
 from ..utils.logger import log
 from .engine import Engine
 from .strategy import Strategy
+from src.risk.sizing.base import PositionSizer
+from src.risk.stops.base import StopLoss
 
 class ParameterOptimizer:
     """
@@ -21,7 +23,10 @@ class ParameterOptimizer:
                  initial_capital: float = 10000,
                  commission: float = 0.001,
                  slippage: float = 0.0005,
-                 position_size: float = 0.95):
+                 position_size: float = 0.95,
+                 position_sizer: Optional[PositionSizer] = None,
+                 stop_loss: Optional[StopLoss] = None,
+                 max_combinations: Optional[int] = None):
         """
         Args:
             strategy_class: Clase de estrategia (no instancia)
@@ -37,6 +42,9 @@ class ParameterOptimizer:
         self.commission = commission
         self.slippage = slippage
         self.position_size = position_size
+        self.position_sizer = position_sizer
+        self.stop_loss = stop_loss
+        self.max_combinations = max_combinations
         
         self.results: List[Dict] = []
     
@@ -63,6 +71,9 @@ class ParameterOptimizer:
         param_values = list(param_grid.values())
         combinations = list(product(*param_values))
 
+        if self.max_combinations is not None:
+            combinations = combinations[: int(self.max_combinations)]
+        
         total_tests = len(combinations)
         log.info(f"🔧 Iniciando optimización: {total_tests} combinaciones")
         
@@ -87,7 +98,9 @@ class ParameterOptimizer:
                     initial_capital=self.initial_capital,
                     commission=self.commission,
                     slippage=self.slippage,
-                    position_size=self.position_size
+                    position_size=self.position_size,
+                    position_sizer=self.position_sizer,
+                    stop_loss=self.stop_loss,
                 )
                 
                 results = engine.run(self.data)

src/core/walk_forward.py

@@ -3,6 +3,8 @@ import pandas as pd
 from typing import List, Dict, Optional
 from src.core.optimizer import ParameterOptimizer
 from src.core.engine import Engine
+from src.risk.sizing.base import PositionSizer
+from src.risk.stops.base import StopLoss
 from ..utils.logger import log
 
 class WalkForwardValidator:
@@ -28,6 +30,10 @@ class WalkForwardValidator:
         position_size: float = 0.95,
         optimizer_metric: str = "sharpe_ratio",
         verbose: bool = True,
+        position_sizer: Optional[PositionSizer] = None,
+        stop_loss: Optional[StopLoss] = None,
+        max_combinations: Optional[int] = None,
+        progress_callback: Optional[callable] = None,
     ):
         self.strategy_class = strategy_class
         self.param_grid = param_grid
@@ -43,6 +49,11 @@ class WalkForwardValidator:
         self.position_size = position_size
         self.optimizer_metric = optimizer_metric
         self.verbose = verbose
+        self.position_sizer = position_sizer
+        self.stop_loss = stop_loss
+        self.max_combinations = max_combinations
+
+        self.progress_callback = progress_callback
 
         # Validaciones básicas
         if not isinstance(self.data.index, pd.DatetimeIndex):
@@ -157,6 +168,14 @@ class WalkForwardValidator:
 
         for w in self.windows:
             wid = w["window_id"]
+            
+            if self.progress_callback:
+                self.progress_callback(
+                    window_id=wid,
+                    total_windows=len(self.windows)
+                )
+
+            wid = w["window_id"]
             log.info(f"▶️ WF #{wid} en ejecución")
 
             train_data = self.data.loc[w["train_start"]:w["train_end"]]
@@ -174,6 +193,9 @@ class WalkForwardValidator:
                 commission=self.commission,
                 slippage=self.slippage,
                 position_size=self.position_size,
+                position_sizer=self.position_sizer,
+                stop_loss=self.stop_loss,
+                max_combinations=self.max_combinations,
             )
 
             opt_df = optimizer.optimize(self.param_grid)
@@ -194,6 +216,8 @@ class WalkForwardValidator:
                 commission=self.commission,
                 slippage=self.slippage,
                 position_size=self.position_size,
+                position_sizer=self.position_sizer,
+                stop_loss=self.stop_loss,
             )
 
             test_results = engine.run(test_data)

src/web/api/v2/main.py

@@ -11,6 +11,7 @@ import time
 from .settings import settings
 from src.web.api.v2.routers.calibration_data import router as calibration_data_router
 from src.web.api.v2.routers.calibration_risk import router as calibration_risk_router
+from src.web.api.v2.routers.calibration_strategies import router as calibration_strategies_router
 
 # --------------------------------------------------
 # Logging
@@ -116,6 +117,17 @@ def create_app() -> FastAPI:
             },
         )
     
+    @app.get("/calibration/strategies", response_class=HTMLResponse)
+    def calibration_risk_page(request: Request):
+        return templates.TemplateResponse(
+            "pages/calibration/calibration_strategies.html",
+            {
+                "request": request,
+                "page": "calibration",
+                "step": 3,
+            },
+        )
+
 
     # --------------------------------------------------
     # API routers (versionados)
@@ -123,6 +135,7 @@ def create_app() -> FastAPI:
     api_prefix = settings.api_prefix
     app.include_router(calibration_data_router, prefix=api_prefix)
     app.include_router(calibration_risk_router, prefix=api_prefix)
+    app.include_router(calibration_strategies_router, prefix=api_prefix)
 
     return app
 

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

@@ -0,0 +1,191 @@
+# src/web/api/v2/routers/calibration_strategies.py
+
+import logging
+import re
+import uuid
+from pathlib import Path
+from typing import Dict
+
+from fastapi import APIRouter, Depends, HTTPException, Request
+from fastapi.responses import JSONResponse, HTMLResponse
+
+from src.data.storage import StorageManager
+from src.calibration.strategies_inspector import (
+    inspect_strategies_config,
+    list_available_strategies,
+)
+from src.calibration.reports.strategies_report import generate_strategies_report_pdf
+
+from ..schemas.calibration_strategies import (
+    CalibrationStrategiesInspectRequest,
+    CalibrationStrategiesInspectResponse,
+    CalibrationStrategiesValidateResponse,
+)
+
+logger = logging.getLogger("tradingbot.api.v2")
+
+router = APIRouter(
+    prefix="/calibration/strategies",
+    tags=["calibration"],
+)
+
+WF_JOBS: Dict[str, Dict] = {}
+
+def get_storage() -> StorageManager:
+    return StorageManager.from_env()
+
+
+@router.get("/available")
+def available_strategies():
+    return {"strategies": list_available_strategies()}
+
+@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_strategies_config(
+        storage=storage,
+        payload=payload,
+        data_quality=data_quality,
+        include_series=False,
+    )
+    return CalibrationStrategiesInspectResponse(**result)
+
+@router.post("/validate", response_model=CalibrationStrategiesValidateResponse)
+def validate_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_strategies_config(
+        storage=storage,
+        payload=payload,
+        data_quality=data_quality,
+        include_series=True,
+    )
+    return CalibrationStrategiesValidateResponse(**result)
+
+@router.post("/report")
+def report_strategies(
+    payload: CalibrationStrategiesInspectRequest,
+    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:
+        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_strategies_config(
+        storage=storage,
+        payload=payload,
+        data_quality=data_quality,
+        include_series=True,
+    )
+
+    # ---------------------------------------------
+    # Prepare PDF output path (outside src)
+    # ---------------------------------------------
+    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"
+
+    symbol_dir = reports_dir / safe_symbol
+    symbol_dir.mkdir(exist_ok=True)
+
+    output_path = symbol_dir / filename
+
+    generate_strategies_report_pdf(
+        output_path=output_path,
+        context={
+            "Symbol": payload.symbol,
+            "Timeframe": payload.timeframe,
+            "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,
+            "Optimizer metric": payload.optimization.optimizer_metric,
+            "Max combinations": payload.optimization.max_combinations,
+        },
+        results=result,
+    )
+
+    public_url = f"/reports/strategies/{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_strategies.py

@@ -0,0 +1,83 @@
+# src/web/api/v2/schemas/calibration_strategies.py
+
+from typing import Any, Dict, List, Literal, Optional
+from pydantic import BaseModel, Field
+
+from .calibration_risk import StopConfigSchema, RiskConfigSchema, GlobalRiskRulesSchema
+
+
+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
+
+
+class OptimizationConfigSchema(BaseModel):
+    optimizer_metric: str = Field("sharpe_ratio")
+    max_combinations: int = Field(500, gt=0)
+    min_trades_train: int = Field(30, ge=0)
+    min_trades_test: int = Field(10, ge=0)
+
+
+class StrategySelectionSchema(BaseModel):
+    strategy_id: str
+    param_grid: Dict[str, List[Any]]
+
+
+class CalibrationStrategiesInspectRequest(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]
+    wf: WalkForwardConfigSchema
+    optimization: OptimizationConfigSchema
+
+    commission: float = Field(0.001, ge=0)
+    slippage: float = Field(0.0005, ge=0)
+
+
+class WindowRowSchema(BaseModel):
+    window: int
+    train_start: str
+    train_end: str
+    test_start: str
+    test_end: str
+
+    return_pct: float
+    sharpe: float
+    max_dd_pct: float
+    trades: int
+    params: Dict[str, Any]
+
+
+class StrategyRunResultSchema(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
+
+    windows: List[WindowRowSchema]
+
+
+class CalibrationStrategiesInspectResponse(BaseModel):
+    valid: bool
+    status: Literal["ok", "warning", "fail"]
+    checks: Dict[str, Any]
+    message: str
+
+    results: List[StrategyRunResultSchema]
+
+
+class CalibrationStrategiesValidateResponse(CalibrationStrategiesInspectResponse):
+    series: Dict[str, Any]

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

@@ -0,0 +1,595 @@
+// src/web/ui/v2/static/js/pages/calibration_strategies.js
+
+console.log("[calibration_strategies] script loaded ✅", new Date().toISOString());
+
+// =================================================
+// WIZARD NAVIGATION
+// =================================================
+
+function enableNextStep() {
+  const btn = document.getElementById("next-step-btn");
+  if (!btn) return;
+  btn.classList.remove("btn-outline-secondary");
+  btn.classList.add("btn-outline-primary");
+  btn.setAttribute("aria-disabled", "false");
+}
+
+function disableNextStep() {
+  const btn = document.getElementById("next-step-btn");
+  if (!btn) return;
+  btn.classList.remove("btn-outline-primary");
+  btn.classList.add("btn-outline-secondary");
+  btn.setAttribute("aria-disabled", "true");
+}
+
+// =================================================
+// UTILS
+// =================================================
+
+function loadContextFromLocalStorage() {
+  const symbol = localStorage.getItem("calibration.symbol");
+  const timeframe = localStorage.getItem("calibration.timeframe");
+  if (symbol) setVal("symbol", symbol);
+  if (timeframe) setVal("timeframe", timeframe);
+
+  // Step 2 snapshot (if stored)
+  const stop_type = localStorage.getItem("calibration.stop.type");
+  const stop_fraction = localStorage.getItem("calibration.stop.stop_fraction");
+  const atr_period = localStorage.getItem("calibration.stop.atr_period");
+  const atr_multiplier = localStorage.getItem("calibration.stop.atr_multiplier");
+  const risk_fraction = localStorage.getItem("calibration.risk.risk_fraction");
+  const max_position_fraction = localStorage.getItem("calibration.risk.max_position_fraction");
+  const max_drawdown_pct = localStorage.getItem("calibration.rules.max_drawdown_pct");
+  const daily_loss_limit_pct = localStorage.getItem("calibration.rules.daily_loss_limit_pct");
+  const max_consecutive_losses = localStorage.getItem("calibration.rules.max_consecutive_losses");
+  const cooldown_bars = localStorage.getItem("calibration.rules.cooldown_bars");
+  const account_equity = localStorage.getItem("calibration.account_equity");
+
+  if (account_equity) setVal("account_equity", account_equity);
+
+  if (stop_type) setVal("stop_type", stop_type);
+  if (stop_fraction) setVal("stop_fraction", stop_fraction);
+  if (atr_period) setVal("atr_period", atr_period);
+  if (atr_multiplier) setVal("atr_multiplier", atr_multiplier);
+  if (risk_fraction) setVal("risk_fraction", risk_fraction);
+  if (max_position_fraction) setVal("max_position_fraction", max_position_fraction);
+  if (max_drawdown_pct) setVal("max_drawdown_pct", max_drawdown_pct);
+  if (daily_loss_limit_pct) setVal("daily_loss_limit_pct", daily_loss_limit_pct);
+  if (max_consecutive_losses) setVal("max_consecutive_losses", max_consecutive_losses);
+  if (cooldown_bars) setVal("cooldown_bars", cooldown_bars);
+}
+
+function buildPayload() {
+  const symbol = str("symbol");
+  const timeframe = str("timeframe");
+  const stopType = str("stop_type");
+
+  if (!symbol || !timeframe) {
+    throw new Error("symbol/timeframe missing");
+  }
+
+  const stop = { type: stopType };
+
+  if (stopType === "fixed" || stopType === "trailing") {
+    stop.stop_fraction = (num("stop_fraction") ?? 1.0) / 100;
+  }
+  if (stopType === "atr") {
+    stop.atr_period = num("atr_period") ?? 14;
+    stop.atr_multiplier = num("atr_multiplier") ?? 3.0;
+  }
+
+  const risk_fraction = (num("risk_fraction") ?? 1.0) / 100;
+  const max_position_fraction = (num("max_position_fraction") ?? 95) / 100;
+
+  const global_rules = {
+    max_drawdown_pct: (num("max_drawdown_pct") ?? 20) / 100,
+    daily_loss_limit_pct: num("daily_loss_limit_pct") ? num("daily_loss_limit_pct") / 100 : null,
+    max_consecutive_losses: num("max_consecutive_losses"),
+    cooldown_bars: num("cooldown_bars"),
+  };
+
+  const wf_train_days = num("wf_train_days") ?? 120;
+  const wf_test_days = num("wf_test_days") ?? 30;
+  const wf_step_days = num("wf_step_days");
+
+  const strategies = collectSelectedStrategies();
+
+  return {
+    symbol,
+    timeframe,
+    account_equity: num("account_equity") ?? 10000,
+
+    stop,
+    risk: {
+      risk_fraction,
+      max_position_fraction,
+    },
+    global_rules,
+
+    strategies,
+    wf: {
+      train_days: wf_train_days,
+      test_days: wf_test_days,
+      step_days: wf_step_days,
+    },
+    optimization: {
+      optimizer_metric: str("opt_metric") ?? "sharpe_ratio",
+      max_combinations: num("opt_max_combinations") ?? 300,
+      min_trades_train: num("opt_min_trades_train") ?? 30,
+      min_trades_test: num("opt_min_trades_test") ?? 10,
+    },
+
+    commission: num("commission") ?? 0.001,
+    slippage: num("slippage") ?? 0.0005,
+  };
+}
+
+function collectSelectedStrategies() {
+  const items = document.querySelectorAll("[data-strategy-item]");
+  const out = [];
+
+  items.forEach((node) => {
+    const checkbox = node.querySelector("input[type=checkbox]");
+    if (!checkbox || !checkbox.checked) return;
+
+    const sid = checkbox.getAttribute("data-strategy-id");
+    const textarea = node.querySelector("textarea");
+    let grid = {};
+    if (textarea && textarea.value.trim()) {
+      try {
+        grid = JSON.parse(textarea.value);
+      } catch (e) {
+        throw new Error(`Invalid JSON param_grid for ${sid}: ${e.message}`);
+      }
+    }
+
+    out.push({ strategy_id: sid, param_grid: grid });
+  });
+
+  if (out.length === 0) {
+    throw new Error("Select at least 1 strategy");
+  }
+  return out;
+}
+
+async function fetchAvailableStrategies() {
+  const res = await fetch("/api/v2/calibration/strategies/available");
+  const data = await res.json();
+  return data.strategies || [];
+}
+
+function num(id) {
+  const el = document.getElementById(id);
+  if (!el) return null;
+  const val = el.value;
+  if (val === "" || val === null || val === undefined) return null;
+  const n = Number(val);
+  return Number.isFinite(n) ? n : null;
+}
+
+function str(id) {
+  const el = document.getElementById(id);
+  if (!el) return null;
+  const v = el.value;
+  return v === null || v === undefined ? null : String(v);
+}
+
+function setVal(id, value) {
+  const el = document.getElementById(id);
+  if (!el) return;
+  el.value = value ?? "";
+}
+
+// =================================================
+// PROGRESS BAR
+// =================================================
+
+function startWF() {
+  document
+    .getElementById("wf_progress_card")
+    .classList.remove("d-none");
+
+  document.getElementById("wfProgressBar").style.width = "0%";
+  document.getElementById("wfProgressBar").innerText = "0%";
+}
+
+async function pollStatus(jobId) {
+  const interval = setInterval(async () => {
+    const res = await fetch(`/api/v2/calibration/strategies/status/${jobId}`);
+    const data = await res.json();
+
+    const bar = document.getElementById("wfProgressBar");
+    bar.style.width = data.progress + "%";
+    bar.innerText = data.progress + "%";
+
+    if (data.status === "done") {
+      clearInterval(interval);
+      bar.classList.remove("progress-bar-animated");
+      console.log("WF finished");
+    }
+  }, 1000);
+}
+
+// =================================================
+// RENDER RESULTS
+// =================================================
+
+function renderStrategiesList(strategies) {
+  const list = document.getElementById("strategies_list");
+  if (!list) return;
+
+  list.innerHTML = "";
+
+  strategies.forEach((s) => {
+    const col = document.createElement("div");
+    col.className = "col-12 col-lg-6";
+    col.setAttribute("data-strategy-item", "1");
+
+    const defaultGrid = s.default_grid || {};
+    const defaultGridText = JSON.stringify(defaultGrid, null, 2);
+
+    col.innerHTML = `
+      <div class="card">
+        <div class="card-body">
+          <label class="form-check form-switch">
+            <input class="form-check-input" type="checkbox" data-strategy-id="${s.strategy_id}">
+            <span class="form-check-label">
+              <b>${s.name}</b>
+              <span class="text-secondary ms-2">${(s.tags || []).join(" · ")}</span>
+            </span>
+          </label>
+
+          <div class="mt-3">
+            <label class="form-label">param_grid (JSON)</label>
+            <textarea class="form-control" rows="7" spellcheck="false">${defaultGridText}</textarea>
+            <div class="form-hint">Tip: usa listas. Ej: {"fast":[10,20],"slow":[50,100]}</div>
+          </div>
+        </div>
+      </div>
+    `;
+
+    list.appendChild(col);
+  });
+}
+
+function setBadge(status) {
+  const badge = document.getElementById("strategies_status_badge");
+  if (!badge) return;
+
+  badge.classList.remove("bg-secondary", "bg-success", "bg-warning", "bg-danger");
+  badge.classList.add(
+    status === "ok" ? "bg-success" : status === "warning" ? "bg-warning" : status === "fail" ? "bg-danger" : "bg-secondary"
+  );
+  badge.textContent = status ? status.toUpperCase() : "—";
+}
+
+function renderResultsTable(data) {
+  const wrap = document.getElementById("strategies_table_wrap");
+  if (!wrap) return;
+
+  const rows = [];
+  (data.results || []).forEach((r) => {
+    rows.push(`
+      <tr>
+        <td><b>${r.strategy_id}</b></td>
+        <td>${r.status}</td>
+        <td>${r.n_windows}</td>
+        <td>${Number(r.oos_total_return_pct).toFixed(2)}%</td>
+        <td>${Number(r.oos_max_dd_worst_pct).toFixed(2)}%</td>
+        <td>${Number(r.oos_final_equity).toFixed(2)}</td>
+        <td class="text-secondary">${r.message || ""}</td>
+      </tr>
+    `);
+  });
+
+  wrap.innerHTML = `
+    <div class="table-responsive">
+      <table class="table table-vcenter">
+        <thead>
+          <tr>
+            <th>Strategy</th>
+            <th>Status</th>
+            <th>Windows</th>
+            <th>OOS return</th>
+            <th>Worst DD</th>
+            <th>Final equity</th>
+            <th>Message</th>
+          </tr>
+        </thead>
+        <tbody>
+          ${rows.join("")}
+        </tbody>
+      </table>
+    </div>
+  `;
+}
+
+function populatePlotSelector(data) {
+  const sel = document.getElementById("plot_strategy_select");
+  if (!sel) return;
+
+  sel.innerHTML = "";
+  const ids = Object.keys((data.series && data.series.strategies) ? data.series.strategies : {});
+  ids.forEach((sid) => {
+    const opt = document.createElement("option");
+    opt.value = sid;
+    opt.textContent = sid;
+    sel.appendChild(opt);
+  });
+
+  sel.onchange = () => renderPlotsForSelected(data);
+
+  if (ids.length > 0) {
+    sel.value = ids[0];
+  }
+}
+
+function renderPlotsForSelected(data) {
+  const sel = document.getElementById("plot_strategy_select");
+  const sid = sel ? sel.value : null;
+  if (!sid) return;
+
+  const s = data.series?.strategies?.[sid];
+  if (!s) return;
+
+  const equity = s.window_equity || [];
+  const returns = s.window_returns_pct || [];
+  const xEq = [...Array(equity.length).keys()];
+  const xRet = [...Array(returns.length).keys()].map((i) => i + 1);
+
+  Plotly.newPlot("plot_equity", [
+    { x: xEq, y: equity, type: "scatter", mode: "lines", name: "Equity (OOS)" },
+  ], {
+    title: `WF OOS equity · ${sid}`,
+    margin: { t: 40, l: 50, r: 20, b: 40 },
+    xaxis: { title: "Window index" },
+    yaxis: { title: "Equity" },
+  }, { displayModeBar: false });
+
+  Plotly.newPlot("plot_returns", [
+    { x: xRet, y: returns, type: "bar", name: "Return % (per window)" },
+  ], {
+    title: `WF returns per window · ${sid}`,
+    margin: { t: 40, l: 50, r: 20, b: 40 },
+    xaxis: { title: "Window" },
+    yaxis: { title: "Return (%)" },
+  }, { displayModeBar: false });
+}
+
+function renderValidateResponse(data) {
+
+  // -------------------------------
+  // 1️⃣ Badge + message
+  // -------------------------------
+  const badge = document.getElementById("strategies_status_badge");
+  const msg = document.getElementById("strategies_message");
+
+  badge.textContent = data.status ?? "—";
+
+  badge.className = "badge";
+  if (data.status === "ok") badge.classList.add("bg-success");
+  else if (data.status === "warning") badge.classList.add("bg-warning");
+  else badge.classList.add("bg-danger");
+
+  msg.textContent = data.message ?? "";
+
+  // -------------------------------
+  // 2️⃣ Debug JSON
+  // -------------------------------
+  document.getElementById("strategies_debug").textContent =
+    JSON.stringify(data, null, 2);
+
+  // -------------------------------
+  // 3️⃣ Plots (primera estrategia por ahora)
+  // -------------------------------
+  if (data.series && data.series.strategies) {
+    const keys = Object.keys(data.series.strategies);
+    if (keys.length > 0) {
+      const s = data.series.strategies[keys[0]];
+
+      Plotly.newPlot("plot_equity", [{
+        y: s.window_equity,
+        type: "scatter",
+        mode: "lines",
+        name: "Equity"
+      }], { margin: { t: 20 } });
+
+      Plotly.newPlot("plot_returns", [{
+        y: s.window_returns_pct,
+        type: "bar",
+        name: "Window returns %"
+      }], { margin: { t: 20 } });
+    }
+  }
+
+  // -------------------------------
+  // 4️⃣ Table
+  // -------------------------------
+  const wrap = document.getElementById("strategies_table_wrap");
+  wrap.innerHTML = "";
+
+  if (data.results) {
+    let html = `<table class="table table-sm table-bordered">
+      <thead>
+        <tr>
+          <th>Strategy</th>
+          <th>Status</th>
+          <th>OOS Return %</th>
+          <th>OOS Max DD %</th>
+          <th>Windows</th>
+        </tr>
+      </thead>
+      <tbody>`;
+
+    for (const r of data.results) {
+      html += `
+        <tr>
+          <td>${r.strategy_id}</td>
+          <td>${r.status}</td>
+          <td>${r.oos_total_return_pct?.toFixed(2)}</td>
+          <td>${r.oos_max_dd_worst_pct?.toFixed(2)}</td>
+          <td>${r.n_windows}</td>
+        </tr>
+      `;
+    }
+
+    html += "</tbody></table>";
+    wrap.innerHTML = html;
+  }
+}
+
+async function validateStrategies() {
+  console.log("[calibration_strategies] validateStrategies() START");
+
+  const bar = document.getElementById("wfProgressBar");
+  const txt = document.getElementById("wf_progress_text");
+
+  const setProgress = (pct, text) => {
+    const p = Math.max(0, Math.min(100, Number(pct || 0)));
+    bar.style.width = `${p}%`;
+    bar.textContent = `${p}%`;
+    if (text) txt.textContent = text;
+  };
+
+  try {
+    // 0) Reset UI
+    setProgress(0, "Starting...");
+
+    // 1) Construye payload igual que antes (usa tu función existente)
+    const payload = buildPayload(); // <-- NO CAMBIES tu builder, reutilízalo
+
+    // 2) Arranca job async
+    const runResp = await fetch("/api/v2/calibration/strategies/run", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(payload),
+    });
+
+    if (!runResp.ok) {
+      const errText = await runResp.text();
+      throw new Error(`Run failed: ${runResp.status} ${errText}`);
+    }
+
+    const { job_id } = await runResp.json();
+    if (!job_id) throw new Error("No job_id returned from /run");
+
+    // 3) Poll status
+    const pollEveryMs = 500;
+    const maxMinutes = 30;
+    const maxPolls = Math.ceil((maxMinutes * 60 * 1000) / pollEveryMs);
+
+    for (let i = 0; i < maxPolls; i++) {
+      await new Promise((r) => setTimeout(r, pollEveryMs));
+
+      const stResp = await fetch(`/api/v2/calibration/strategies/status/${job_id}`);
+      if (!stResp.ok) continue;
+
+      const st = await stResp.json();
+
+      const pct = st.progress ?? 0;
+      const cw = st.current_window ?? 0;
+      const tw = st.total_windows ?? 0;
+
+      const label =
+        tw > 0
+          ? `WF running... window ${cw}/${tw}`
+          : "WF running...";
+
+      setProgress(pct, label);
+
+      if (st.status === "done") {
+        setProgress(100, "WF completed ✅");
+
+        // 4) Renderiza resultados usando el MISMO renderer que usabas con /validate
+        //    (ojo: el resultado viene dentro de st.result)
+        if (!st.result) throw new Error("Job done but no result in status payload");
+
+        renderValidateResponse(st.result); // <-- usa tu función existente de render (plots, tablas, etc.)
+
+        console.log("[calibration_strategies] validateStrategies() DONE ok");
+        return;
+      }
+
+      if (st.status === "unknown") {
+        setProgress(0, "Unknown job (server lost state?)");
+        break;
+      }
+    }
+
+    throw new Error("Timeout waiting for WF job to finish");
+
+  } catch (err) {
+    console.error(err);
+    // deja un estado visible
+    const txt = document.getElementById("wf_progress_text");
+    if (txt) txt.textContent = `Error: ${err.message}`;
+    console.log("[calibration_strategies] validateStrategies() DONE fail");
+  }
+}
+
+async function generateReport() {
+  console.log("[calibration_strategies] generateReport() START");
+
+  let payload;
+  try {
+    payload = buildPayload();
+  } catch (e) {
+    alert(e.message);
+    return;
+  }
+
+  const res = await fetch("/api/v2/calibration/strategies/report", {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify(payload),
+  });
+
+  const data = await res.json();
+
+  if (data.url) {
+    const viewer = document.getElementById("pdf_viewer_section");
+    const frame = document.getElementById("pdf_frame");
+    frame.src = data.url;
+    viewer.classList.remove("d-none");
+    viewer.scrollIntoView({ behavior: "smooth" });
+  } else {
+    alert("Failed to generate report");
+  }
+}
+
+function wireButtons() {
+  document.getElementById("validate_strategies_btn")?.addEventListener("click", validateStrategies);
+  document.getElementById("report_strategies_btn")?.addEventListener("click", generateReport);
+
+  document.getElementById("refresh_strategies_btn")?.addEventListener("click", async () => {
+    const strategies = await fetchAvailableStrategies();
+    renderStrategiesList(strategies);
+  });
+
+  document.getElementById("load_step2_btn")?.addEventListener("click", () => {
+    loadContextFromLocalStorage();
+  });
+
+  document.getElementById("close_pdf_btn")?.addEventListener("click", () => {
+    const viewer = document.getElementById("pdf_viewer_section");
+    const frame = document.getElementById("pdf_frame");
+    frame.src = "";
+    viewer.classList.add("d-none");
+  });
+}
+
+async function init() {
+  loadContextFromLocalStorage();
+  wireButtons();
+
+  const strategies = await fetchAvailableStrategies();
+  renderStrategiesList(strategies);
+
+  // Pre-select 1 strategy by default (moving_average) if exists
+  setTimeout(() => {
+    const first = document.querySelector('input[type=checkbox][data-strategy-id="moving_average"]');
+    if (first) first.checked = true;
+  }, 0);
+}
+
+init();

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

@@ -0,0 +1,322 @@
+{% extends "layout.html" %}
+
+{% 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">
+        <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">
+          <path stroke="none" d="M0 0h24v24H0z"/>
+          <path d="M15 6l-6 6l6 6"/>
+        </svg>
+      </a>
+    </div>
+
+    <div class="flex-grow-1 text-center">
+      <h2 class="mb-0">Calibración · Paso 3 · Strategies</h2>
+      <div class="text-secondary">Optimización + Walk Forward (OOS)</div>
+    </div>
+
+    <!-- Forward arrow (disabled until OK) -->
+    <div class="ms-3">
+      <a
+        id="next-step-btn"
+        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">
+      <h3 class="card-title">Context</h3>
+    </div>
+    <div class="card-body">
+      <div class="row g-3">
+        <div class="col-md-4">
+          <label class="form-label">Symbol</label>
+          <input id="symbol" class="form-control" placeholder="BTC/USDT">
+        </div>
+        <div class="col-md-4">
+          <label class="form-label">Timeframe</label>
+          <input id="timeframe" class="form-control" placeholder="1h">
+        </div>
+        <div class="col-md-4">
+          <label class="form-label">Account equity</label>
+          <input id="account_equity" class="form-control" type="number" step="0.01" value="10000">
+        </div>
+      </div>
+      <div class="mt-3 text-secondary">
+        Tip: Symbol y timeframe se cargan desde Step 1 (localStorage). Si no aparecen, rellénalos manualmente.
+      </div>
+    </div>
+  </div>
+
+  <!-- ========================= -->
+  <!-- Risk & Stops snapshot -->
+  <!-- ========================= -->
+  <div class="card mb-4">
+    <div class="card-header">
+      <h3 class="card-title">Risk & Stops snapshot (Step 2)</h3>
+      <div class="card-actions">
+        <button id="load_step2_btn" class="btn btn-sm btn-outline-primary">Load from Step 2</button>
+      </div>
+    </div>
+
+    <div class="card-body">
+      <div class="row g-3">
+        <div class="col-md-3">
+          <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-3">
+          <label class="form-label">Stop fraction (%)</label>
+          <input id="stop_fraction" class="form-control" type="number" step="0.01" value="1.0">
+        </div>
+
+        <div class="col-md-3">
+          <label class="form-label">ATR period</label>
+          <input id="atr_period" class="form-control" type="number" step="1" value="14">
+        </div>
+
+        <div class="col-md-3">
+          <label class="form-label">ATR multiplier</label>
+          <input id="atr_multiplier" class="form-control" type="number" step="0.1" value="3.0">
+        </div>
+
+        <div class="col-md-3">
+          <label class="form-label">Risk per trade (%)</label>
+          <input id="risk_fraction" class="form-control" type="number" step="0.01" value="1.0">
+        </div>
+
+        <div class="col-md-3">
+          <label class="form-label">Max position fraction (%)</label>
+          <input id="max_position_fraction" class="form-control" type="number" step="0.1" value="95">
+        </div>
+
+        <div class="col-md-3">
+          <label class="form-label">Max DD (%)</label>
+          <input id="max_drawdown_pct" class="form-control" type="number" step="0.1" value="20">
+        </div>
+
+        <div class="col-md-3">
+          <label class="form-label">Daily loss limit (%) (optional)</label>
+          <input id="daily_loss_limit_pct" class="form-control" type="number" step="0.1" value="">
+        </div>
+
+        <div class="col-md-3">
+          <label class="form-label">Max consecutive losses (optional)</label>
+          <input id="max_consecutive_losses" class="form-control" type="number" step="1" value="">
+        </div>
+
+        <div class="col-md-3">
+          <label class="form-label">Cooldown bars (optional)</label>
+          <input id="cooldown_bars" class="form-control" type="number" step="1" value="">
+        </div>
+      </div>
+      <div class="mt-3 text-secondary">
+        Este snapshot se envía al backend para reproducibilidad y para que WF/optimizer use el mismo sizing/stop.
+      </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 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_list" class="row g-3"></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">
+      Validate (WF)
+    </button>
+    <button id="report_strategies_btn" class="btn btn-outline-primary">
+      Generate PDF report
+    </button>
+  </div>
+
+  <!-- ========================= -->
+  <!-- Prograss Bar -->
+  <!-- ========================= -->
+  <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="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">
+      <h3 class="card-title">Results</h3>
+      <div class="card-actions">
+        <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 class="row g-3">
+        <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">
+        <div id="plot_returns" style="height: 320px;"></div>
+      </div>
+
+      <hr class="my-4">
+
+      <div id="strategies_table_wrap"></div>
+
+      <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>
+
+  <!-- ========================= -->
+  <!-- PDF Viewer -->
+  <!-- ========================= -->
+  <div id="pdf_viewer_section" class="card mb-4 d-none">
+    <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>
+    <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>
+<script src="/static/js/pages/calibration_strategies.js"></script>
+{% endblock %}