Inicializar backend FastAPI con Dockerfile

app.py

@@ -167,3 +167,114 @@ def predict(banco: str, req: PredictRequest):
     # === Otro banco no válido ===
     else:
         return {"error": "Banco no reconocido. Usa 'bbva' o 'santander'."}
+
+# ============================================================
+# 8️⃣ Nuevo endpoint de simulación — Variar "Open"
+# ============================================================
+
+class SimulateRequest(BaseModel):
+    factor_open: float = 1.0  # por defecto, sin cambio
+    n_future: int = 3         # número de días de predicción
+
+
+@app.post("/simulate/{banco}")
+def simulate(banco: str, req: SimulateRequest):
+    banco = banco.lower()
+    n_future = req.n_future
+    factor_open = req.factor_open
+
+    # === BBVA ===
+    if banco == "bbva":
+        path = "data/bbva_pred.csv"
+        df = pd.read_csv(path)
+        df["Date"] = pd.to_datetime(df["Date"])
+        df = df.set_index("Date")
+        df = df.loc["2021-01-01":"2025-10-31"]
+
+        cols_to_scale = [
+            'Open','High','Low','Close','Adj Close',
+            'ma_5','close_lag1','close_lag2','close_lag3',
+            'Volume','ibex_momentum_5d'
+        ]
+        features = ['Open','High','Low','return_1d','return_3d']
+
+        # Escalar los datos
+        df_scaled = df.copy()
+        df_scaled[cols_to_scale] = bbva_scaler_features.transform(df[cols_to_scale])
+
+        # Tomar la última ventana y modificar solo "Open"
+        last_window = df_scaled[features].iloc[-3:].values
+        modified_window = last_window.copy()
+        modified_window[:, 0] *= factor_open  # multiplicar solo el "Open"
+
+        preds_scaled = []
+        window = modified_window.copy()
+        for _ in range(n_future):
+            X_input = torch.tensor(window, dtype=torch.float32).unsqueeze(0)
+            with torch.no_grad():
+                pred_scaled = bbva_model(X_input).cpu().numpy().flatten()[0]
+            preds_scaled.append(pred_scaled)
+            next_day = window[-1].copy()
+            window = np.vstack([window[1:], next_day])
+
+        preds_real = bbva_scaler_target.inverse_transform(np.array(preds_scaled).reshape(-1, 1)).flatten().tolist()
+
+        last_date = df.index[-1]
+        future_dates = pd.date_range(last_date + pd.Timedelta(days=1), periods=n_future, freq="B")
+        fechas = [str(d.date()) for d in future_dates]
+
+        return {
+            "banco": "BBVA",
+            "factor_open": factor_open,
+            "fechas": fechas,
+            "predicciones": preds_real
+        }
+
+    # === SANTANDER ===
+    elif banco == "santander":
+        path = "data/santander_pred.csv"
+        df = pd.read_csv(path)
+        df["Date"] = pd.to_datetime(df["Date"])
+        df = df.set_index("Date")
+        df = df.loc["2021-01-01":"2025-10-31"]
+
+        cols_to_scale = [
+            'Open','High','Low','Close','Adj Close',
+            'ma_5','close_lag1','close_lag2','close_lag3',
+            'Volume','ibex_momentum_5d'
+        ]
+        features = ['Open','High','Low','close_lag1','close_lag2']
+
+        df_scaled = df.copy()
+        df_scaled[cols_to_scale] = santander_scaler_features.transform(df[cols_to_scale])
+
+        last_window = df_scaled[features].iloc[-3:].values
+        modified_window = last_window.copy()
+        modified_window[:, 0] *= factor_open
+
+        preds_scaled = []
+        window = modified_window.copy()
+        for _ in range(n_future):
+            X_input = torch.tensor(window, dtype=torch.float32).unsqueeze(0)
+            with torch.no_grad():
+                pred_scaled = santander_model(X_input).cpu().numpy().flatten()[0]
+            preds_scaled.append(pred_scaled)
+            next_day = window[-1].copy()
+            window = np.vstack([window[1:], next_day])
+
+        preds_real = santander_scaler_target.inverse_transform(np.array(preds_scaled).reshape(-1, 1)).flatten().tolist()
+
+        last_date = df.index[-1]
+        future_dates = pd.date_range(last_date + pd.Timedelta(days=1), periods=n_future, freq="B")
+        fechas = [str(d.date()) for d in future_dates]
+
+        return {
+            "banco": "Santander",
+            "factor_open": factor_open,
+            "fechas": fechas,
+            "predicciones": preds_real
+        }
+
+    # === Error ===
+    else:
+        return {"error": "Banco no reconocido. Usa 'bbva' o 'santander'."}