fix: 6 workflow flaws detected via full system test

1. CRITICAL FIX - Reports 500 Internal Server Error:
   Root cause: {{}} in onclick was Jinja2 template expression
   Fix: Changed to single {} in reports_index.html onclick

2. Score history chart always empty:
   Root cause: score_update only emitted inside wfv.passed block
   Fix: Emit score_update after every hypothesis run with {promoted:false}
   Fix: Baseline run also pushes to chart via run_complete handler

3. IS gate too strict - optimizer produces 0 candidates forever:
   Root cause: min_calmar=0.35 but best EA has calmar=0.165
   Fix: Two-tier IS check - passes if composite_score improves vs baseline
   (absolute thresholds still apply as alternative pass condition)

4. Findings emitted twice (double UI display):
   Root cause: baseline analyzed once after run, then re-analyzed in iter 1
   Fix: Cache baseline findings, reuse in iteration 1 without re-emitting

5. Chart labels improved:
   'Baseline' for first point, 'It1·h1' for hypothesis runs
   Calmar normalized -0.5..2.0 -> 0..1 for chart display

6. Best score header only updates on actual promotion (not per-hypothesis)

optimizer_loop.py

@@ -146,6 +146,7 @@ class OptimizerLoop:
         self.best_score    = baseline_metrics.composite_score
         current_params     = default_params.copy()
         current_metrics    = baseline_metrics
+        current_findings   = findings   # reuse in iter 1, avoid double-emit
         no_improve_count   = 0
 
         max_iter  = cfg["optimization"]["max_iterations"]
@@ -168,11 +169,14 @@ class OptimizerLoop:
             if trades_df.empty and not baseline_trades.empty:
                 trades_df = baseline_trades
 
-            # Analysis
+            # Analysis — reuse cached findings when re-analyzing same run_id
-            self.phase = "analyze"
+            if current_findings is not None and trades_df.empty:
-            findings = self._run_analysis(
+                findings = current_findings
-                current_metrics.run_id, trades_df, current_metrics, analyzers, store
+            else:
-            )
+                findings = self._run_analysis(
+                    current_metrics.run_id, trades_df, current_metrics, analyzers, store
+                )
+            current_findings = None  # only reuse once
 
             if not findings:
                 self._emit("log", {"level": "warn", "msg": "No actionable findings. Stopping."})
@@ -251,6 +255,17 @@ class OptimizerLoop:
                 store.update_hypothesis_status(hyp.hypothesis_id, "tested", run_id)
                 self.session_tested_deltas.append(hyp.param_delta)  # track for session dedup
 
+                # Emit score update for every run so chart populates
+                self._emit("score_update", {
+                    "iteration": self.iteration,
+                    "run_id":    run_id,
+                    "score":     round(test_metrics.composite_score, 4),
+                    "calmar":    round(test_metrics.calmar_ratio, 4),
+                    "pf":        round(test_metrics.profit_factor, 4),
+                    "ts":        datetime.utcnow().isoformat(),
+                    "promoted":  False,
+                })
+
                 if iteration_best is None or test_metrics.composite_score > iteration_best.composite_score:
                     iteration_best        = test_metrics
                     iteration_best_params = test_params
@@ -262,7 +277,7 @@ class OptimizerLoop:
 
             # Validation gate
             self.phase = "validate"
-            gate_result = gate.run_is_check(iteration_best)
+            gate_result = gate.run_is_check(iteration_best, baseline_score=current_metrics.composite_score)
             if not gate_result.passed:
                 self._emit("log", {"level": "error", "msg": f"IS gate failed: {gate_result.reason}"})
                 store.update_hypothesis_status(iteration_best_hyp.hypothesis_id, "rejected")

ui/static/js/dashboard.js

@@ -258,9 +258,15 @@ function addCandidate(d) {
 
 function pushChartPoint(d) {
   const labels = scoreChart.data.labels;
-  labels.push(`Iter ${d.iteration}`);
+  // Label: 'Baseline' for first point, 'Iter N · run_id' for hypothesis runs
+  const lbl = d.run_id
+    ? (d.run_id.startsWith('baseline') ? 'Baseline' : `It${d.iteration}·${d.run_id.split('_').pop()}`)
+    : `Iter ${d.iteration}`;
+  labels.push(lbl);
   scoreChart.data.datasets[0].data.push(d.score);
-  scoreChart.data.datasets[1].data.push(d.calmar > 1 ? 1 : d.calmar / 4); // normalize calmar to 0-1
+  // Normalize calmar: clamp -0.5..2.0 → 0..1 for display
+  const calmarNorm = Math.max(0, Math.min(1, (d.calmar + 0.5) / 2.5));
+  scoreChart.data.datasets[1].data.push(calmarNorm);
   if (labels.length > 50) {
     labels.shift();
     scoreChart.data.datasets.forEach(ds => ds.data.shift());
@@ -309,10 +315,14 @@ socket.on('run_started', d => {
 
 socket.on('run_complete', d => {
   updateMetrics(d);
-  const sign = d.score > 0 ? '✓' : '•';
+  const sign = d.score > 0 ? '\u2713' : '\u2022';
   addLog(d.score > 0.3 ? 'success' : 'info',
     `${sign} ${d.run_id}: Score=${d.score} | Calmar=${d.calmar} | PF=${d.profit_factor} | DD=${d.drawdown_pct}%`
   );
+  // Push baseline run to chart immediately (hypothesis runs pushed via score_update)
+  if (d.run_id && d.run_id.startsWith('baseline')) {
+    pushChartPoint({ iteration: 0, run_id: d.run_id, score: d.score, calmar: d.calmar });
+  }
 });
 
 socket.on('run_failed', d => {
@@ -333,7 +343,10 @@ socket.on('hypothesis_testing', d => {
 
 socket.on('score_update', d => {
   pushChartPoint(d);
-  document.getElementById('hdr-score').textContent = d.score.toFixed(4);
+  // Update best score header only when a candidate is actually promoted
+  if (d.promoted) {
+    document.getElementById('hdr-score').textContent = d.score.toFixed(4);
+  }
 });
 
 socket.on('candidate_promoted', d => {

ui/templates/reports_index.html

@@ -38,7 +38,7 @@
       <h1>📁 Optimization Reports</h1>
       <div class="sub">{{ runs|length }} run(s) recorded · Click any card to open the full report</div>
     </div>
-    <a class="back-link" href="/" onclick="if(document.referrer){{history.back();return false;}}">← Back to Dashboard</a>
+    <a class="back-link" href="/" onclick="if(document.referrer){history.back();return false;}">&#8592; Back to Dashboard</a>
   </header>
 
   {% if not runs %}

validation/gate.py

@@ -38,16 +38,38 @@ class ValidationGate:
 
     # ── Phase 1: IS Check ─────────────────────────────────────────────────────
 
-    def run_is_check(self, metrics: RunMetrics) -> GateResult:
+    def run_is_check(
+        self,
+        metrics: RunMetrics,
+        baseline_score: float = 0.0,
+    ) -> GateResult:
         """
-        Hard minimum thresholds. All must pass.
+        Two-tier IS check:
+        - MUST: enough trades for statistical confidence
+        - MUST: composite_score is better than baseline (or meets abs thresholds)
+        Absolute calmar/PF thresholds are logged as warnings but are NOT blockers
+        when the hypothesis shows clear improvement over baseline.
         """
+        min_trades_ok = metrics.total_trades >= self.thresh["min_trades"]
+        abs_pf_ok     = metrics.profit_factor >= self.thresh["min_profit_factor"]
+        abs_calmar_ok = metrics.calmar_ratio  >= self.thresh["min_calmar"]
+
+        # Score-based relative pass: hypothesis is better than baseline
+        score_improve_ok = metrics.composite_score > baseline_score * 1.0 + 0.01
+
+        # Gate passes if:
+        #  a) Enough trades AND (abs thresholds met OR clearly better than baseline)
+        passed = min_trades_ok and (
+            (abs_pf_ok and abs_calmar_ok)      # standard absolute pass
+            or score_improve_ok                # OR better than baseline
+        )
+
         checks = {
-            "min_trades":   metrics.total_trades  >= self.thresh["min_trades"],
+            "min_trades":    min_trades_ok,
-            "min_pf":       metrics.profit_factor >= self.thresh["min_profit_factor"],
+            "min_pf":        abs_pf_ok,
-            "min_calmar":   metrics.calmar_ratio  >= self.thresh["min_calmar"],
+            "min_calmar":    abs_calmar_ok,
+            "score_improve": score_improve_ok,
         }
-        passed = all(checks.values())
         reason = None
         if not passed:
             failed = [k for k, v in checks.items() if not v]