feat: finalize portfolio system and quantitative validation- Finalized MA_Crossover(30,100) and TrendFiltered_MA(30,100,ADX=15)

- Implemented portfolio engine with risk-based allocation (50/50)
- Added equity-based metrics for system-level evaluation
- Validated portfolio against standalone strategies
- Reduced max drawdown and volatility at system level
- Quantitative decision closed before paper trading phase

src/strategies/mean_reversion.py

@@ -0,0 +1,95 @@
+# src/strategies/mean_reversion.py
+
+import pandas as pd
+import numpy as np
+
+from src.strategies.base import Strategy
+from src.core.strategy import Signal
+
+
+class RSIMeanReversion(Strategy):
+    """
+    Estrategia de reversión a la media basada en RSI.
+
+    Idea:
+    - Compra cuando el mercado está sobrevendido
+    - Vende cuando el precio rebota hacia la media
+
+    Señales:
+    - BUY: RSI cruza por debajo de oversold
+    - SELL: RSI cruza por encima de overbought
+    - HOLD: en cualquier otro caso
+
+    Parámetros:
+        period: periodo del RSI
+        oversold: nivel de sobreventa
+        overbought: nivel de sobrecompra
+
+    Valores típicos:
+        period = 14
+        oversold = 30
+        overbought = 70
+    """
+
+    def __init__(
+        self,
+        period: int = 14,
+        oversold: float = 30.0,
+        overbought: float = 70.0,
+    ):
+        super().__init__(
+            name="RSI_MeanReversion",
+            params={
+                "period": period,
+                "oversold": oversold,
+                "overbought": overbought,
+            },
+        )
+
+        self.period = period
+        self.oversold = oversold
+        self.overbought = overbought
+
+    # --------------------------------------------------
+    def init_indicators(self, data: pd.DataFrame) -> pd.DataFrame:
+        """
+        Calcula el RSI clásico (Wilder).
+
+        Añade:
+            - data["rsi"]
+        """
+        delta = data["close"].diff()
+
+        gain = delta.clip(lower=0)
+        loss = -delta.clip(upper=0)
+
+        avg_gain = gain.ewm(alpha=1 / self.period, adjust=False).mean()
+        avg_loss = loss.ewm(alpha=1 / self.period, adjust=False).mean()
+
+        rs = avg_gain / avg_loss
+        rsi = 100 - (100 / (1 + rs))
+
+        data["rsi"] = rsi
+
+        return data
+
+    # --------------------------------------------------
+    def generate_signal(self, idx: int) -> Signal:
+        """
+        Genera señales de trading basadas en cruces del RSI.
+        """
+        if idx == 0:
+            return Signal.HOLD
+
+        rsi_prev = self.data["rsi"].iloc[idx - 1]
+        rsi_curr = self.data["rsi"].iloc[idx]
+
+        # BUY → cruce hacia abajo de oversold
+        if rsi_prev > self.oversold and rsi_curr <= self.oversold:
+            return Signal.BUY
+
+        # SELL → cruce hacia arriba de overbought
+        if rsi_prev < self.overbought and rsi_curr >= self.overbought:
+            return Signal.SELL
+
+        return Signal.HOLD