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/optimization/opt_moving_average.py

@@ -0,0 +1,25 @@
+from itertools import product
+from src.strategies.moving_average import MovingAverageCrossover
+
+class MACrossoverOptimization:
+
+    name = "MA_Crossover"
+
+    @staticmethod
+    def parameter_grid():
+        fast = [10, 15, 20, 25, 30]
+        slow = [40, 50, 60, 80, 100]
+        min_gap = 15
+
+        for f, s in product(fast, slow):
+            if s - f >= min_gap:
+                yield {
+                    "fast_period": f,
+                    "slow_period": s,
+                    "ma_type": "ema",
+                    "use_adx": False,
+                }
+
+    @staticmethod
+    def build_strategy(params):
+        return MovingAverageCrossover(**params)

src/strategies/optimization/opt_trend_filtered.py

@@ -0,0 +1,26 @@
+from itertools import product
+from src.strategies.trend_filtered import TrendFilteredMACrossover
+
+class TrendFilteredMAOptimization:
+
+    name = "TrendFiltered_MA"
+
+    @staticmethod
+    def parameter_grid():
+        fast = [10, 15, 20, 25, 30]
+        slow = [40, 50, 60, 80, 100]
+        adx = [15, 20, 25, 30]
+        min_gap = 15
+
+        for f, s, a in product(fast, slow, adx):
+            if s - f >= min_gap:
+                yield {
+                    "fast_period": f,
+                    "slow_period": s,
+                    "ma_type": "ema",
+                    "adx_threshold": a,
+                }
+
+    @staticmethod
+    def build_strategy(params):
+        return TrendFilteredMACrossover(**params)