pequeños cambios para que el step 1 sea mas robusto

src/calibration/strategies_inspector.py

@@ -98,8 +98,10 @@ def inspect_strategies_config(
     data_quality: Dict[str, Any],
     include_series: bool,
     progress_callback=None,
+    df: pd.DataFrame | None = None,
 ) -> Dict[str, Any]:
 
+    if df is None:
         df = storage.load_ohlcv(symbol=payload.symbol, timeframe=payload.timeframe)
     if df is None or df.empty:
         return {
@@ -117,14 +119,24 @@ def inspect_strategies_config(
     }
 
     if data_quality.get("status") == "fail":
-        return {
+        out = {
             "valid": False,
             "status": "fail",
             "checks": checks,
             "message": "Step 1 data quality is FAIL. Strategies cannot be validated.",
             "results": [],
-            "series": {} if include_series else None,
+            "config": {
+                "wf": payload.wf.model_dump(),
+                "risk": payload.risk.model_dump(),
+                "stop": payload.stop.model_dump(),
+                "global_rules": payload.global_rules.model_dump(),
+                "commission": payload.commission,
+                "slippage": payload.slippage,
+            },
         }
+        if include_series:
+            out["series"] = {"strategies": {}}
+        return out
 
     stop_loss = _build_stop_loss(payload.stop)
     base_sizer = _build_position_sizer(payload.risk)
@@ -183,7 +195,7 @@ def inspect_strategies_config(
                 "message": msg,
                 "warnings": [],
                 "series_available": False,
-                "series_error": "Unknown strategy_id (not in registry)",
+                "series_error": msg,
                 "n_windows": 0,
                 "oos_final_equity": payload.account_equity,
                 "oos_total_return_pct": 0.0,
@@ -204,9 +216,10 @@ def inspect_strategies_config(
 
 
         # Wrapper sizer
-        class _CappedSizer(type(base_sizer)):
+        class _CappedSizer:
-            def __init__(self, inner):
+            def __init__(self, inner, max_position_fraction: float):
                 self.inner = inner
+                self.max_position_fraction = max_position_fraction
 
             def calculate_size(self, *, capital, entry_price, stop_price=None, max_capital=None, volatility=None):
                 u = self.inner.calculate_size(
@@ -220,10 +233,10 @@ def inspect_strategies_config(
                     units=float(u),
                     capital=float(capital),
                     entry_price=float(entry_price),
-                    max_position_fraction=float(payload.risk.max_position_fraction),
+                    max_position_fraction=float(self.max_position_fraction),
                 )
         
-        capped_sizer = _CappedSizer(base_sizer)
+        capped_sizer = _CappedSizer(base_sizer, payload.risk.max_position_fraction)
 
         log.info(f"🧠 Step3 | WF run | strategy={sid}")
 
@@ -302,11 +315,6 @@ def inspect_strategies_config(
                 oos_dd = win_df["max_dd_pct"].tolist()
                 n_windows = len(win_df)
 
-            required_cols = {"return_pct", "max_dd_pct", "trades", "window", "train_start", "train_end", "test_start", "test_end", "sharpe", "params"}
-            missing = required_cols - set(win_df.columns)
-            if missing:
-                raise ValueError(f"WF windows missing required columns: {sorted(missing)}")
-
             trades = win_df["trades"].astype(int).tolist()
             too_few = sum(t < int(payload.wf.min_trades_test) for t in trades)
 
@@ -401,6 +409,14 @@ def inspect_strategies_config(
         "checks": checks,
         "message": human_msg,
         "results": results,
+        "config": {
+            "wf": payload.wf.model_dump(),
+            "risk": payload.risk.model_dump(),
+            "stop": payload.stop.model_dump(),
+            "global_rules": payload.global_rules.model_dump(),
+            "commission": payload.commission,
+            "slippage": payload.slippage,
+        },
     }
 
     if include_series:

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

@@ -3,7 +3,7 @@
 import logging
 import threading
 from datetime import datetime, timedelta
-from typing import Dict, Optional
+from typing import Dict, Optional, Literal
 
 import pandas as pd
 
@@ -93,7 +93,7 @@ def _db_summary(
         "candles": int(row["candles"] or 0),
     }
 
-def analyze_data_quality(df: pd.DataFrame, timeframe: str):
+def analyze_data_quality(df: pd.DataFrame, timeframe: str, mode: Literal["crypto", "session"] = "crypto"):
     # --- timeframe a timedelta ---
     tf_map = {
         "1m": timedelta(minutes=1),
@@ -105,28 +105,24 @@ def analyze_data_quality(df: pd.DataFrame, timeframe: str):
         "1d": timedelta(days=1),
     }
     tf_delta = tf_map.get(timeframe)
-    if not tf_delta or df.empty:
+    if not tf_delta or df is None or df.empty:
         return None
 
-    # --- continuidad / gaps ---
+    # sanity: DatetimeIndex ordenado
-    diffs = df.index.to_series().diff().dropna()
+    if not isinstance(df.index, pd.DatetimeIndex):
-    gap_threshold_warn = tf_delta * 1.5
+        return {
-    gap_threshold_fail = tf_delta * 5
+            "status": "fail",
-
+            "checks": {"index": "fail"},
-    big_gaps = diffs[diffs > gap_threshold_warn]
+            "message": "El dataframe no tiene DatetimeIndex.",
-    max_gap = diffs.max()
+        }
-
+    if not df.index.is_monotonic_increasing:
-    continuity = "ok"
+        df = df.sort_index()
-    if any(diffs > gap_threshold_fail):
-        continuity = "fail"
-    elif len(big_gaps) > 0:
-        continuity = "warning"
 
     # --- cobertura ---
     start, end = df.index.min(), df.index.max()
     expected = int((end - start) / tf_delta) + 1
     actual = len(df)
-    ratio = actual / expected if expected > 0 else 0
+    ratio = actual / expected if expected > 0 else 0.0
 
     coverage_status = "ok"
     if ratio < 0.98:
@@ -134,12 +130,52 @@ def analyze_data_quality(df: pd.DataFrame, timeframe: str):
     elif ratio < 0.995:
         coverage_status = "warning"
 
+    # --- continuidad / gaps ---
+    diffs = df.index.to_series().diff().dropna()
+
+    # Umbrales por timeframe (más realista para 1d)
+    if timeframe == "1d":
+        warn_gap = tf_delta * 2   # >= 2 días => warning
+        fail_gap = tf_delta * 10  # >= 10 días => fail
+    else:
+        warn_gap = max(tf_delta * 6, timedelta(hours=6))     # intradía
+        fail_gap = max(tf_delta * 48, timedelta(days=2))     # intradía
+
+    # Si es mercado por sesiones (acciones), un gap de fin de semana no es "malo".
+    # Aproximación simple: para 1d, no consideramos gaps de 2-3 días como warning/fail.
+    # (Sin calendario real, esto es la mejor opción práctica).
+    diffs_for_eval = diffs
+    if mode == "session" and timeframe == "1d":
+        diffs_for_eval = diffs[diffs > timedelta(days=3)]  # ignorar 2-3 días típicos de finde
+
+    big_gaps = diffs[diffs > tf_delta * 1.5]
+    max_gap = diffs.max() if len(diffs) else pd.NaT
+
+    gaps_warn = diffs_for_eval[diffs_for_eval >= warn_gap]
+    gaps_fail = diffs_for_eval[diffs_for_eval >= fail_gap]
+
+    gaps_warn_ratio = (len(gaps_warn) / expected) if expected > 0 else 0.0
+
+    continuity = "ok"
+    if pd.notna(max_gap):
+        if len(gaps_fail) > 0 or gaps_warn_ratio > 0.02:
+            continuity = "fail"
+        elif len(gaps_warn) > 0:
+            continuity = "warning"
+
     # --- volumen ---
-    zero_vol_ratio = (df["volume"] == 0).mean()
+    zero_vol_ratio = None
+    nan_vol_ratio = None
+    volume_status = "warning"
+
+    if "volume" in df.columns:
+        nan_vol_ratio = df["volume"].isna().mean()
+        zero_vol_ratio = (df["volume"].fillna(0) == 0).mean()
+
         volume_status = "ok"
-    if zero_vol_ratio > 0.01:
+        if nan_vol_ratio > 0.005 or zero_vol_ratio > 0.01:
             volume_status = "fail"
-    elif zero_vol_ratio > 0.001:
+        elif nan_vol_ratio > 0.0005 or zero_vol_ratio > 0.001:
             volume_status = "warning"
 
     # --- estado global ---
@@ -154,6 +190,7 @@ def analyze_data_quality(df: pd.DataFrame, timeframe: str):
         status = "ok"
         msg = "Datos continuos y completos. Aptos para calibración."
 
+    # ⬇️ Mantener shape compatible con tu schema actual
     return {
         "status": status,
         "checks": {
@@ -197,7 +234,7 @@ def inspect_calibration_data(
             start_date=payload.start_date,
             end_date=payload.end_date,
         )
-        data_quality = analyze_data_quality(df, payload.timeframe)
+        data_quality = analyze_data_quality(df, payload.timeframe, mode=payload.mode)
 
     return CalibrationDataResponse(
         symbol=payload.symbol,

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

@@ -90,6 +90,7 @@ def inspect_strategies(
         payload=payload,
         data_quality=data_quality,
         include_series=False,
+        df=df,
     )
     return CalibrationStrategiesInspectResponse(**result)
 
@@ -110,6 +111,7 @@ def validate_strategies(
         payload=payload,
         data_quality=data_quality,
         include_series=True,
+        df=df,
     )
     return CalibrationStrategiesValidateResponse(**result)
 
@@ -132,6 +134,7 @@ def report_strategies(
         payload=payload,
         data_quality=data_quality,
         include_series=True,
+        df=df,
     )
 
     # ---------------------------------------------
@@ -187,6 +190,7 @@ def run_strategies_async(
 
     WF_JOBS[job_id] = {
         "status": "running",
+        "error": None,
         "progress": 0,
         "current_window": 0,
         "total_windows": 0,
@@ -199,22 +203,45 @@ def run_strategies_async(
         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
-            )
 
+            if total_windows and total_windows > 0:
+                WF_JOBS[job_id]["progress"] = int(window_id / total_windows * 100)
+            else:
+                WF_JOBS[job_id]["progress"] = 0
+
+        try:
+            # 1) Cargar OHLCV (igual que /inspect y /validate)
+            df = storage.load_ohlcv(symbol=payload.symbol, timeframe=payload.timeframe)
+            if df is None or df.empty:
+                WF_JOBS[job_id]["status"] = "error"
+                WF_JOBS[job_id]["progress"] = 0
+                WF_JOBS[job_id]["error"] = "No OHLCV data found. Run Step 1 first."
+                return
+
+            # 2) Calcular data_quality real
+            from .calibration_data import analyze_data_quality
+            data_quality = analyze_data_quality(df, payload.timeframe)
+
+            # 3) Ejecutar con data_quality real
             result = inspect_strategies_config(
                 storage=storage,
                 payload=payload,
-            data_quality={"status": "ok"},
+                data_quality=data_quality,
                 include_series=True,
-            progress_callback=progress_cb,  # ← lo pasamos
+                progress_callback=progress_cb,
+                df=df,
             )
 
             WF_JOBS[job_id]["status"] = "done"
             WF_JOBS[job_id]["progress"] = 100
             WF_JOBS[job_id]["result"] = result
 
+        except Exception as e:
+            logger.exception("❌ WF async job failed")
+            WF_JOBS[job_id]["status"] = "error"
+            WF_JOBS[job_id]["error"] = str(e)
+            WF_JOBS[job_id]["progress"] = 0
+
     thread = threading.Thread(target=background_job)
     thread.start()
 

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

@@ -7,26 +7,6 @@ from typing import Optional, Dict, Literal
 
 from pydantic import BaseModel, Field
 
-# ==================================================
-# Inspect (DB)
-# ==================================================
-
-class CalibrationDataRequest(BaseModel):
-    symbol: str = Field(..., examples=["BTC/USDT"])
-    timeframe: str = Field(..., examples=["1h"])
-    start_date: Optional[datetime] = None
-    end_date: Optional[datetime] = None
-
-
-class CalibrationDataResponse(BaseModel):
-    symbol: str
-    timeframe: str
-    first_available: Optional[datetime]
-    last_available: Optional[datetime]
-    candles_count: int
-    valid: bool
-
-
 # ==================================================
 # Download (Exchange -> DB)  (modo "sync" / síncrono)
 # ==================================================
@@ -147,7 +127,7 @@ class CalibrationDataRequest(BaseModel):
     timeframe: str
     start_date: Optional[datetime] = None
     end_date: Optional[datetime] = None
-
+    mode: Literal["crypto", "session"] = "crypto"
 
 QualityStatus = Literal["ok", "warning", "fail"]
 

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

@@ -76,9 +76,10 @@ class CalibrationStrategiesInspectResponse(BaseModel):
     status: Literal["ok", "warning", "fail"]
     checks: Dict[str, Any]
     message: str
-
     results: List[StrategyRunResultSchema]
 
+    config: Optional[Dict[str, Any]] = None
+
 
 class CalibrationStrategiesValidateResponse(CalibrationStrategiesInspectResponse):
     series: Dict[str, Any]

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

@@ -15,6 +15,7 @@ let downloadPollTimer = null;
 async function inspectCalibrationData() {
   console.log("[calibration_data] inspectCalibrationData() START ✅");
 
+  const mode = document.getElementById("market_mode")?.value || "crypto";
   const symbol = document.getElementById("symbol")?.value;
   const timeframe = document.getElementById("timeframe")?.value;
   const start_date = document.getElementById("start_date")?.value || null;
@@ -22,7 +23,7 @@ async function inspectCalibrationData() {
 
   const resultEl = document.getElementById("inspect-output");
 
-  const payload = { symbol, timeframe };
+  const payload = { symbol, timeframe, mode };
   if (start_date) payload.start_date = start_date;
   if (end_date) payload.end_date = end_date;
 
@@ -57,8 +58,11 @@ async function inspectCalibrationData() {
     const dqStatus = data.data_quality?.status ?? "ok";
     if (data.valid && dqStatus !== "fail") {
       enableNextStep();
+      localStorage.setItem("calibration.mode", mode);
       localStorage.setItem("calibration.symbol", data.symbol);
       localStorage.setItem("calibration.timeframe", data.timeframe);
+      localStorage.setItem("calibration.start_date", start_date || "");
+      localStorage.setItem("calibration.end_date", end_date || "");
     }
 
   } catch (err) {
@@ -77,17 +81,25 @@ async function inspectCalibrationData() {
 async function startDownloadJob() {
   console.log("[calibration_data] startDownloadJob()");
 
+  const mode = document.getElementById("market_mode")?.value || "crypto";
   const symbol = document.getElementById("symbol")?.value;
   const timeframe = document.getElementById("timeframe")?.value;
   const start_date = document.getElementById("start_date")?.value || null;
   const end_date = document.getElementById("end_date")?.value || null;
   
-  const payload = { symbol, timeframe };
+  const payload = { symbol, timeframe, mode };
   if (start_date) payload.start_date = start_date;
   if (end_date) payload.end_date = end_date;
 
   console.log("[calibration_data] download payload:", payload);
 
+  // ✅ Persist UI state (para restore tras recargar)
+  if (mode) localStorage.setItem("calibration.mode", mode);
+  if (symbol) localStorage.setItem("calibration.symbol", symbol);
+  if (timeframe) localStorage.setItem("calibration.timeframe", timeframe);
+  localStorage.setItem("calibration.start_date", start_date || "");
+  localStorage.setItem("calibration.end_date", end_date || "");
+
   try {
     const res = await fetch("/api/v2/calibration/data/download/job", {
       method: "POST",
@@ -290,6 +302,29 @@ function renderDataSummary(data) {
 document.addEventListener("DOMContentLoaded", () => {
   console.log("[calibration_data] DOMContentLoaded ✅");
 
+  // --- Restore persisted calibration settings ---
+  const savedMode = localStorage.getItem("calibration.mode");
+  const savedSymbol = localStorage.getItem("calibration.symbol");
+  const savedTimeframe = localStorage.getItem("calibration.timeframe");
+  const savedStartDate = localStorage.getItem("calibration.start_date");
+  const savedEndDate = localStorage.getItem("calibration.end_date");
+
+  const modeEl = document.getElementById("market_mode");
+  if (modeEl && savedMode) modeEl.value = savedMode;
+
+  const symbolEl = document.getElementById("symbol");
+  if (symbolEl && savedSymbol) symbolEl.value = savedSymbol;
+
+  const tfEl = document.getElementById("timeframe");
+  if (tfEl && savedTimeframe) tfEl.value = savedTimeframe;
+
+  const startEl = document.getElementById("start_date");
+  if (startEl && savedStartDate) startEl.value = savedStartDate;
+
+  const endEl = document.getElementById("end_date");
+  if (endEl && savedEndDate) endEl.value = savedEndDate;
+
+
   document
     .getElementById("inspect-data-btn")
     ?.addEventListener("click", inspectCalibrationData);

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

@@ -58,12 +58,21 @@
     <div class="card-body">
 
       <div class="row g-3">
-        <div class="col-md-3">
+
+        <div class="col-md-2">
+          <label class="form-label">Modo de mercado</label>
+          <select id="market_mode" class="form-select">
+            <option value="crypto" selected>Crypto (24/7)</option>
+            <option value="session">Acciones / ETFs (sesiones)</option>
+          </select>
+        </div>
+
+        <div class="col-md-2">
           <label class="form-label">Símbolo</label>
           <input id="symbol" class="form-control" value="BTC/USDT">
         </div>
 
-        <div class="col-md-3">
+        <div class="col-md-2">
           <label class="form-label">Timeframe</label>
           <select id="timeframe" class="form-select">
             <option value="1m">1m</option>
@@ -76,12 +85,12 @@
           </select>
         </div>
 
-        <div class="col-md-3">
+        <div class="col-md-2">
           <label class="form-label">Fecha inicio (opcional)</label>
           <input id="start_date" type="date" class="form-control">
         </div>
 
-        <div class="col-md-3">
+        <div class="col-md-2">
           <label class="form-label">Fecha fin (opcional)</label>
           <input id="end_date" type="date" class="form-control">
         </div>