feat: Feature 2.1 — Parlay Scan with correlation detection + monetization

POST /api/scan/parlay — authenticated parlay analysis:
- Supabase JWT auth middleware (auth.getUser verification)
- 5 correlation types detected between legs (same_game, same_team,
  same_player_conflicting, positive_correlation, blowout_cascade)
- Overall parlay grading (A/B/C/D) with correlation penalty adjustments
- Free tier: 5 scans/month, atomic scan count increment
- Scan 5: full analysis + personalized upgrade pitch
- Scan 6+: 403 block with upgrade pitch
- Pitch personalization from scan history (top stats, grades, tier rec)
- DB writes: picks + scan_sessions per scan

30 new tests, 158 total (131 Node.js + 27 Python), all passing

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

src/services/parlayGrader.js

@@ -0,0 +1,63 @@
+function gradeParlayFromLegs(legResults, correlationFlags) {
+  if (legResults.length === 0) {
+    return { grade: 'D', confidence: 30, composite: 0 };
+  }
+
+  // Extract leg composites and grades
+  const legComposites = legResults.map((l) => l._composite || 0);
+  const legGrades = legResults.map((l) => l.grade);
+  const legConfidences = legResults.map((l) => l.confidence);
+  const dCount = legGrades.filter((g) => g === 'D').length;
+
+  // Average of leg composites
+  const legAvg = legComposites.reduce((a, b) => a + b, 0) / legComposites.length;
+
+  // Correlation penalties
+  let correlationPenalty = 0;
+  let hasMajorNegative = false;
+  for (const flag of correlationFlags) {
+    if (flag.impact === 'minor_negative') correlationPenalty -= 0.3;
+    if (flag.impact === 'major_negative') {
+      correlationPenalty -= 1.0;
+      hasMajorNegative = true;
+    }
+  }
+
+  const parlayComposite = legAvg + correlationPenalty;
+
+  // Grade assignment
+  let grade;
+  if (parlayComposite >= 2.5 && dCount === 0 && !hasMajorNegative) {
+    grade = 'A';
+  } else if (parlayComposite >= 1.5 && dCount <= 1) {
+    grade = 'B';
+  } else if (parlayComposite >= 0.5) {
+    grade = 'C';
+  } else {
+    grade = 'D';
+  }
+
+  // 2+ D legs forces grade D
+  if (dCount >= 2) grade = 'D';
+
+  // Major negative caps at B
+  if (hasMajorNegative && (grade === 'A')) {
+    grade = 'B';
+  }
+
+  // Confidence: average of legs, adjusted for correlations
+  let confidence = legConfidences.reduce((a, b) => a + b, 0) / legConfidences.length;
+  for (const flag of correlationFlags) {
+    if (flag.impact === 'minor_negative') confidence -= 5;
+    if (flag.impact === 'major_negative') confidence -= 15;
+  }
+  confidence = Math.max(30, Math.min(95, Math.round(confidence)));
+
+  return {
+    grade,
+    confidence,
+    composite: Math.round(parlayComposite * 100) / 100,
+  };
+}
+
+module.exports = { gradeParlayFromLegs };