gavindiaz/Apex_AI_MT5_EA_Optimizer
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fix: 6 workflow flaws detected via full system test

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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)
This commit is contained in:
LEGSTECH Optimizer
2026-04-13 03:45:14 +00:00
parent cf8613eb49
commit b70a6760ae
4 changed files with 67 additions and 17 deletions
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ui/static/js/dashboard.js
+17 -4
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@@ -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 => {