diff --git a/app.py b/app.py
index cb2a61a..df1cf04 100644
--- a/app.py
+++ b/app.py
@@ -320,10 +320,11 @@ def best_result():
         "net_profit":     round(best_run.net_profit, 2),
         "profit_factor":  round(best_run.profit_factor, 3),
         "calmar":         round(best_run.calmar, 3),
-        "max_drawdown":   round(best_run.max_drawdown, 2),
-        "win_rate":       round(best_run.win_rate, 1),
+        # max_drawdown + win_rate are stored as fractions on RankedResult; emit as %
+        "max_drawdown":   round(best_run.max_drawdown * 100, 2),
+        "win_rate":       round(best_run.win_rate * 100, 1),
         "total_trades":   best_run.total_trades,
-        "passing":        best_run.passing,
+        "passing":        bool(best_run.passing),
         "phase":          getattr(best_run, "phase", "phase2_ai"),
         "params":         best_run.params,
         "evolution":      evolution,
@@ -357,9 +358,21 @@ def ai_insights_all():
     return jsonify([])
 
 
+def _mask_key(k: str) -> str:
+    """Mask an API key so the GET response never exposes the secret."""
+    if not k:
+        return ""
+    if k.startswith("${") or k in ("YOUR_API_KEY", "sk-ant-..."):
+        return ""           # placeholder — return empty so the field shows blank
+    if len(k) <= 12:
+        return "***"
+    return k[:8] + "…" + k[-4:]
+
+
 @app.route("/api/settings", methods=["GET"])
 def get_settings():
     import yaml
+    import os
     config_path = BASE_DIR / "config.yaml"
     try:
         with open(config_path, encoding="utf-8") as f:
@@ -368,10 +381,15 @@ def get_settings():
         mt5_cfg = cfg.get("mt5", {})
         broker_cfg = cfg.get("broker", {})
         thresh_cfg = cfg.get("thresholds", {})
+        # Resolve the active API key — placeholder in config falls back to env var.
+        raw_key = ai_cfg.get("anthropic_api_key", "")
+        if not raw_key or raw_key.startswith("${"):
+            raw_key = os.environ.get("ANTHROPIC_API_KEY", "")
         return jsonify({
             "ai": {
                 "enabled":          ai_cfg.get("enabled", True),
-                "anthropic_api_key": ai_cfg.get("anthropic_api_key", ""),
+                "anthropic_api_key": _mask_key(raw_key),
+                "anthropic_api_key_set": bool(raw_key),
                 "model":            ai_cfg.get("model", "claude-opus-4-7"),
                 "timeout_seconds":  ai_cfg.get("timeout_seconds", 30),
             },
@@ -411,10 +429,25 @@ def save_settings():
             if section in data and isinstance(data[section], dict):
                 if section not in cfg:
                     cfg[section] = {}
+                # Don't overwrite the real API key with the masked one we sent
+                # the client. We accept a key only if it's empty (clearing) or
+                # looks like a full key (sk-ant-… with no mask markers and at
+                # least 30 chars). Anything ambiguous → preserve the existing.
+                if section == "ai":
+                    incoming_key = data["ai"].get("anthropic_api_key", "")
+                    if incoming_key:
+                        looks_masked = (
+                            "…" in incoming_key
+                            or "..." in incoming_key
+                            or "***" in incoming_key
+                            or len(incoming_key) < 30
+                        )
+                        if looks_masked:
+                            data["ai"].pop("anthropic_api_key", None)
                 cfg[section].update(data[section])
         with open(config_path, "w", encoding="utf-8") as f:
             yaml.dump(cfg, f, default_flow_style=False, allow_unicode=True)
-        return jsonify({"ok": True})
+        return jsonify({"ok": True, "note": "Settings saved. Will apply on the next optimization run."})
     except Exception as e:
         return jsonify({"ok": False, "error": str(e)}), 500
 
diff --git a/optimizer/ai_guided_loop.py b/optimizer/ai_guided_loop.py
index bcffd90..74bc359 100644
--- a/optimizer/ai_guided_loop.py
+++ b/optimizer/ai_guided_loop.py
@@ -142,7 +142,7 @@ class AIGuidedLoop:
                 self._emit("ai_targets_met", {
                     "iteration": iteration - 1,
                     "profit_factor": round(self.best_result.profit_factor, 3),
-                    "max_drawdown":  round(self.best_result.max_drawdown, 2),
+                    "max_drawdown":  round(self.best_result.max_drawdown * 100, 2),
                     "calmar":        round(self.best_result.calmar, 3),
                 })
                 self.pipeline._emit_early_termination(
@@ -151,7 +151,7 @@ class AIGuidedLoop:
                     details={
                         "iteration":     iteration - 1,
                         "profit_factor": round(self.best_result.profit_factor, 3),
-                        "max_drawdown":  round(self.best_result.max_drawdown, 2),
+                        "max_drawdown":  round(self.best_result.max_drawdown * 100, 2),
                         "calmar":        round(self.best_result.calmar, 3),
                     },
                 )
@@ -298,7 +298,7 @@ class AIGuidedLoop:
                     kind="warning", iteration=iteration,
                 )
 
-            # Emit iteration complete
+            # Emit iteration complete (max_drawdown emitted as %)
             self._emit("ai_iteration_complete", {
                 "iteration":       iteration,
                 "max_iterations":  max_iterations,
@@ -306,10 +306,10 @@ class AIGuidedLoop:
                 "score":           round(result.score, 4),
                 "profit_factor":   round(result.profit_factor, 3),
                 "calmar":          round(result.calmar, 3),
-                "max_drawdown":    round(result.max_drawdown, 2),
+                "max_drawdown":    round(result.max_drawdown * 100, 2),
                 "net_profit":      round(result.net_profit, 2),
                 "total_trades":    result.total_trades,
-                "passing":         result.passing,
+                "passing":         bool(result.passing),
                 "best_score":      round(self.best_result.score, 4) if self.best_result else 0,
                 "best_pf":         round(self.best_result.profit_factor, 3) if self.best_result else 0,
                 "best_calmar":     round(self.best_result.calmar, 3) if self.best_result else 0,
@@ -327,10 +327,10 @@ class AIGuidedLoop:
                 "net_profit":    round(result.net_profit, 2),
                 "calmar":        round(result.calmar, 3),
                 "profit_factor": round(result.profit_factor, 3),
-                "win_rate":      round(result.win_rate, 1),
-                "max_drawdown":  round(result.max_drawdown, 2),
+                "win_rate":      round(result.win_rate * 100, 1),
+                "max_drawdown":  round(result.max_drawdown * 100, 2),
                 "total_trades":  result.total_trades,
-                "passing":       result.passing,
+                "passing":       bool(result.passing),
                 "score":         round(result.score, 4),
                 "progress_pct":  round(self.pipeline._run_count / max(self.pipeline._total_runs, 1) * 100),
             })
diff --git a/optimizer/pipeline.py b/optimizer/pipeline.py
index c790794..ae2a847 100644
--- a/optimizer/pipeline.py
+++ b/optimizer/pipeline.py
@@ -131,8 +131,8 @@ class OptimizationPipeline:
             "best_net_profit":    round(best.net_profit, 2) if best else None,
             "best_calmar":        round(best.calmar, 3) if best else None,
             "best_pf":            round(best.profit_factor, 3) if best else None,
-            "best_win_rate":      round(best.win_rate, 1) if best else None,
-            "best_max_drawdown":  round(best.max_drawdown, 2) if best else None,
+            "best_win_rate":      round(best.win_rate * 100, 1) if best else None,
+            "best_max_drawdown":  round(best.max_drawdown * 100, 2) if best else None,
             "best_total_trades":  best.total_trades if best else None,
         }
 
@@ -495,7 +495,7 @@ class OptimizationPipeline:
                 "net_profit":    round(oos_result.net_profit, 2),
                 "profit_factor": round(oos_result.profit_factor, 3),
                 "calmar":        round(oos_result.calmar, 3),
-                "max_drawdown":  round(oos_result.max_drawdown, 2),
+                "max_drawdown":  round(oos_result.max_drawdown * 100, 2),
                 "total_trades":  oos_result.total_trades,
                 "passing":       oos_result.passing,
             })
@@ -566,9 +566,9 @@ class OptimizationPipeline:
                         "net_profit":    round(sr.net_profit, 2),
                         "profit_factor": round(sr.profit_factor, 3),
                         "calmar":        round(sr.calmar, 3),
-                        "max_drawdown":  round(sr.max_drawdown, 2),
+                        "max_drawdown":  round(sr.max_drawdown * 100, 2),
                         "total_trades":  sr.total_trades,
-                        "passing":       sr.passing,
+                        "passing":       bool(sr.passing),
                     })
 
             # Determine verdict
@@ -603,8 +603,8 @@ class OptimizationPipeline:
             "net_profit":    round(self.final_result.net_profit, 2),
             "calmar":        round(self.final_result.calmar, 3),
             "profit_factor": round(self.final_result.profit_factor, 3),
-            "win_rate":      round(self.final_result.win_rate, 1),
-            "max_drawdown":  round(self.final_result.max_drawdown, 2),
+            "win_rate":      round(self.final_result.win_rate * 100, 1),
+            "max_drawdown":  round(self.final_result.max_drawdown * 100, 2),
             "total_trades":  self.final_result.total_trades,
             "oos_profit":    round(oos_result.net_profit, 2) if oos_result else None,
             "oos_calmar":    round(oos_result.calmar, 3) if oos_result else None,
@@ -863,7 +863,13 @@ class OptimizationPipeline:
     # ── Helpers ───────────────────────────────────────────────────────────────
 
     def _make_run_dict(self, run_id: str, result: RankedResult, phase: str) -> dict:
-        """Canonical run dict used for both _completed_runs and run_complete emits."""
+        """Canonical run dict used for both _completed_runs and run_complete emits.
+
+        IMPORTANT — unit convention: win_rate and max_drawdown are emitted as
+        PERCENTAGES (0–100), not fractions, so the dashboard can render them
+        directly with `${val}%`. RankedResult stores these as fractions so we
+        scale here at the API boundary.
+        """
         d = {
             "run_id":        run_id,
             "phase":         phase,
@@ -871,10 +877,10 @@ class OptimizationPipeline:
             "net_profit":    round(result.net_profit, 2),
             "calmar":        round(result.calmar, 3),
             "profit_factor": round(result.profit_factor, 3),
-            "win_rate":      round(result.win_rate, 1),
-            "max_drawdown":  round(result.max_drawdown, 2),
+            "win_rate":      round(result.win_rate * 100, 1),
+            "max_drawdown":  round(result.max_drawdown * 100, 2),
             "total_trades":  result.total_trades,
-            "passing":       result.passing,
+            "passing":       bool(result.passing),
             "score":         round(result.score, 4),
             "params":        result.params,
         }
@@ -891,10 +897,10 @@ class OptimizationPipeline:
             "net_profit":    round(r.net_profit, 2),
             "calmar":        round(r.calmar, 3),
             "profit_factor": round(r.profit_factor, 3),
-            "win_rate":      round(r.win_rate, 1),
-            "max_drawdown":  round(r.max_drawdown, 2),
+            "win_rate":      round(r.win_rate * 100, 1),     # fraction → %
+            "max_drawdown":  round(r.max_drawdown * 100, 2), # fraction → %
             "total_trades":  r.total_trades,
-            "passing":       r.passing,
+            "passing":       bool(r.passing),
             "params":        r.params,                     # full param dict
             "params_summary": self._params_summary(r.params),
         }
diff --git a/optimizer/result_ranker.py b/optimizer/result_ranker.py
index 4c70f0c..cd9800b 100644
--- a/optimizer/result_ranker.py
+++ b/optimizer/result_ranker.py
@@ -165,7 +165,7 @@ class ResultRanker:
                 run_id=run_id, params=params, phase=phase,
                 error=error or "run_failed",
             )
-        return RankedResult(
+        result = RankedResult(
             run_id        = run_id,
             params        = params,
             phase         = phase,
@@ -176,3 +176,8 @@ class ResultRanker:
             max_drawdown  = getattr(metrics, "max_drawdown_pct", 0.0) or 0.0,
             total_trades  = getattr(metrics, "total_trades", 0) or 0,
         )
+        # Set passing + raw_score now so single-run dispatchers (Phase 2 AI loop,
+        # validation runs) get correct values without waiting for a full rank() pass.
+        result.passing   = self._is_passing(result)
+        result.raw_score = self._raw_score(result) if result.passing else 0.0
+        return result