diff --git a/backtest_gpu.py b/backtest_gpu.py
index f7b0f19..cf15e0a 100644
--- a/backtest_gpu.py
+++ b/backtest_gpu.py
@@ -408,19 +408,21 @@ class VectorIndicators:
 # ══════════════════════════════════════════════════════════════════
 
 # Per-indicator base weights
-# Optimised 2026-05-07 via 36-combo sweep on H4 2022-2026 local data:
-#   ema_trend ×3, macd ×3, stochastic ×2, adx ×0.5, ichimoku ×0.5, rsi=0, bollinger=0
-#   Combined result: +21.30% / -8.52% DD / Sharpe 0.47  vs  baseline +7.12% / -9.05% DD / 0.23
+# Round 1 optimisation 2026-05-07: ema_trend=3.6, macd=3.3, stoch=1.6, adx=0.5, ichi=0.55
+#   → +21.30% / -8.52% DD / Sharpe 0.47
+# Round 2 optimisation 2026-05-08 (GPU sweep relative to round-1 baseline):
+#   ema_trend ×0.5 was best single move: +32.68% / -6.15% DD / Sharpe 0.74
+#   stochastic ×1.5 added modest improvement; macd kept unchanged
 # Original weights (engine.py defaults, pre-optimisation):
 #   ema_trend=1.2, rsi=0.3, macd=1.1, bollinger=0.9, stochastic=0.8, adx=1.0, ichimoku=1.1
 _BASE_W = {
-    "ema_trend":  3.6,
-    "macd":       3.3,
-    "stochastic": 1.6,
-    "adx":        0.5,
-    "ichimoku":   0.55,
-    "rsi":        0.0,   # ablated — net drag on H4 2022-2026
-    "bollinger":  0.0,   # ablated — counter-trend noise on trending pair
+    "ema_trend":  1.8,   # was 3.6 — over-weighted; halving lifts return +11pp, tightens DD
+    "macd":       3.3,   # unchanged — dominant signal
+    "stochastic": 2.4,   # was 1.6 — ×1.5 improved WR to 40.4%
+    "adx":        0.5,   # unchanged
+    "ichimoku":   0.55,  # unchanged
+    "rsi":        0.0,   # ablated — confirmed: all multipliers give identical result
+    "bollinger":  0.0,   # ablated — confirmed: all multipliers give identical result
 }
 
 # Regime-based weight multipliers — matches engine._compute_composite
diff --git a/engine.py b/engine.py
index f223f00..98c90e8 100644
--- a/engine.py
+++ b/engine.py
@@ -171,7 +171,7 @@ class TechnicalEngine:
         result.indicators["ema_trend"] = IndicatorSignal(
             name="EMA Trend", value=normalized,
             signal=signal, confidence=conf,
-            description=desc, weight=3.6,
+            description=desc, weight=1.8,
         )
         if t is not None:
             result.key_levels["ema_trend"] = float(t)
@@ -423,7 +423,7 @@ class TechnicalEngine:
 
         result.indicators["stochastic"] = IndicatorSignal(
             name="Stochastic", value=k_val, signal=signal,
-            confidence=conf, description=desc, weight=1.6,
+            confidence=conf, description=desc, weight=2.4,
         )
 
     def _compute_adx(self, df: pd.DataFrame, result: TechnicalAnalysis):