fix: SLIGHTLY BULLISH validation + scoring — treat as directional, not NEUTRAL

- database.py: _calibrated_snap_range now maps SLIGHTLY BULLISH to BULLISH ranges
(0.02–0.45%) instead of falling through to NEUTRAL (±5.1%), so snapshots are
measured as directional predictions
- app.py: _intraday_target_hit validates SLIGHTLY BULLISH same as BULLISH (requires
window_high >= target_price_lo); also fixes NEUTRAL logic to use intraday midpoint
touch instead of close-price-within-band (aligns with backtest evaluation)
- top5_picker.py: apply 0.65× score penalty for SLIGHTLY BULLISH in both _score_5d
and _score_1w — still accepted when no pure BULLISH picks exist, but never displaces
them for the same confidence/ML setup
- static/app.js: auto-validation runs before stat cards render (stats always show
post-validation numbers); loading spinner added; top5 loads in parallel on dashboard

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

app.py

@@ -2038,7 +2038,7 @@ def _intraday_target_hit(direction: str, window_high, window_low, close_price,
 
     BULLISH: HIT if window_high reached target_price_lo (min bull target) — stock doesn't need to close there
     BEARISH: HIT if window_low reached target_price_hi (mildest bear target)
-    NEUTRAL: HIT if closing price stayed within [target_price_lo, target_price_hi]
+    NEUTRAL: HIT if intraday range touched the midpoint (≈ entry price) during window
     NO TRADE / N/A / missing range: returns None (skip)
     """
     direction = (direction or "").upper()
@@ -2052,7 +2052,7 @@ def _intraday_target_hit(direction: str, window_high, window_low, close_price,
     if target_price_lo == target_price_hi:
         return None
 
-    if direction == "BULLISH":
+    if direction in ("BULLISH", "SLIGHTLY BULLISH"):
         if window_high is None:
             return None
         return window_high >= target_price_lo
@@ -2060,11 +2060,13 @@ def _intraday_target_hit(direction: str, window_high, window_low, close_price,
         if window_low is None:
             return None
         return window_low <= target_price_hi
-    # NEUTRAL: use close price (stock should end within the neutral band)
-    price = close_price if close_price is not None else window_high
-    if price is None:
+    # NEUTRAL: HIT if stock's intraday range touched the midpoint (≈ entry price).
+    # Aligns with backtest evaluation: midpoint touched intraday. Misses only when
+    # the stock gapped away from entry and never returned during the window.
+    if window_high is None or window_low is None:
         return None
-    return target_price_lo <= price <= target_price_hi
+    mid = (target_price_lo + target_price_hi) / 2.0
+    return window_high >= mid and window_low <= mid
 
 
 @app.route("/api/validation/execute", methods=["POST"])

database.py

@@ -28,7 +28,7 @@ def _calibrated_snap_range(direction: str, timeframe: str, current_price: float)
     """Return (target_price_lo, target_price_hi) using calibrated % ranges."""
     d = (direction or "NEUTRAL").upper()
     tf = timeframe if timeframe in _SNAP_BULL else "1D"
-    if d == "BULLISH":
+    if d in ("BULLISH", "SLIGHTLY BULLISH"):
         lo_pct, hi_pct = _SNAP_BULL[tf]
     elif d == "BEARISH":
         lo_pct, hi_pct = _SNAP_BEAR[tf]

static/app.js

@@ -875,6 +875,12 @@ function hideEl(id) { const e = document.getElementById(id); if (e) { e.classLis
 
 // ── Dashboard ─────────────────────────────────────────────────────────────────
 async function loadDashboard() {
+  // Top 5 preview — kick off immediately so it runs in parallel with portfolio/trades fetches
+  const cardsEl = document.getElementById('dash-top5-cards');
+  if (!cardsEl.hasAttribute('data-loaded')) {
+    loadTop5Cards('dash-top5-cards', 'dash-top5-loading', 3);
+  }
+
   // Portfolio summary
   try {
     const res  = await fetch('/api/portfolio', { cache: 'no-store' });
@@ -903,12 +909,6 @@ async function loadDashboard() {
     }
   } catch (e) { console.warn(e); }
 
-  // Top 5 preview (load only if not already loaded)
-  const cardsEl = document.getElementById('dash-top5-cards');
-  if (!cardsEl.hasAttribute('data-loaded')) {
-    loadTop5Cards('dash-top5-cards', 'dash-top5-loading', 3);
-  }
-
 }
 
 document.getElementById('dash-refresh-top5')?.addEventListener('click', () => {
@@ -2306,17 +2306,52 @@ async function loadValidation() {
   const missBreakdown = document.getElementById('validation-miss-breakdown');
   const missList = document.getElementById('validation-miss-list');
 
+  showEl('validation-loading');
   try {
     // Load summary and history
-    const summRes = await fetch('/api/validation/summary', { cache: 'no-store' });
-    const summData = await summRes.json();
+    let summData = await (await fetch('/api/validation/summary', { cache: 'no-store' })).json();
 
     // Load pending
     const pendRes = await fetch('/api/validation/pending', { cache: 'no-store' });
     const pendData = await pendRes.json();
     const pendingList = pendData.pending || [];
     const dueCount = pendData.due_count ?? 0;
-    const upcomingCount = (pendData.total_count ?? pendingList.length) - dueCount;
+
+    // Auto-execute validation if any items are due today — do this BEFORE rendering
+    // stats so the cards always show post-validation numbers.
+    let autoValidated = 0;
+    if (dueCount > 0) {
+      try {
+        const execRes = await fetch('/api/validation/execute', { method: 'POST', cache: 'no-store' });
+        if (execRes.ok) {
+          const execData = await execRes.json();
+          autoValidated = execData.validated || 0;
+          if (autoValidated > 0) {
+            const toast = document.createElement('div');
+            toast.className = 'val-toast';
+            toast.textContent = `✓ Auto-validated ${autoValidated} prediction${autoValidated !== 1 ? 's' : ''}`;
+            document.body.appendChild(toast);
+            setTimeout(() => toast.remove(), 4000);
+            // Reload both pending and summary so stat cards reflect new validations
+            const [summRes2, pendRes2] = await Promise.all([
+              fetch('/api/validation/summary', { cache: 'no-store' }),
+              fetch('/api/validation/pending', { cache: 'no-store' }),
+            ]);
+            summData = await summRes2.json();
+            const pendData2 = await pendRes2.json();
+            pendingList.length = 0;
+            (pendData2.pending || []).forEach(p => pendingList.push(p));
+            // If pending queue is now empty, switch to History tab
+            if (pendingList.length === 0) {
+              document.querySelectorAll('.vtab').forEach(t => t.classList.remove('active'));
+              document.querySelectorAll('.vtab-content').forEach(c => c.classList.remove('active'));
+              document.querySelector('.vtab[data-vtab="history"]')?.classList.add('active');
+              document.getElementById('vtab-history')?.classList.add('active');
+            }
+          }
+        }
+      } catch (_) {}
+    }
 
     // Update summary stats — show directional hit rate (BULLISH+BEARISH) as headline,
     // all-predictions as a footnote so NEUTRAL misses don't bury the signal quality.
@@ -2341,40 +2376,13 @@ async function loadValidation() {
       }
     });
 
-    // Pending stat card — show Due Now vs Upcoming split
+    // Pending stat card — after auto-validation, reflect the refreshed count
     const pendingEl = document.getElementById('vstat-pending');
     const pendingSubEl = document.getElementById('vstat-pending-sub');
-    if (pendingEl) pendingEl.textContent = dueCount > 0 ? dueCount : pendingList.length;
+    if (pendingEl) pendingEl.textContent = pendingList.length;
     if (pendingSubEl) {
-      if (dueCount > 0) {
-        pendingSubEl.textContent = `${upcomingCount} upcoming`;
-        pendingSubEl.style.color = '#f59e0b';
-      } else {
-        pendingSubEl.textContent = `${pendingList.length} upcoming`;
-        pendingSubEl.style.color = '';
-      }
-    }
-
-    // Auto-execute validation if any items are due today
-    if (dueCount > 0) {
-      try {
-        const execRes = await fetch('/api/validation/execute', { method: 'POST', cache: 'no-store' });
-        if (execRes.ok) {
-          const execData = await execRes.json();
-          if (execData.validated > 0) {
-            const toast = document.createElement('div');
-            toast.className = 'val-toast';
-            toast.textContent = `✓ Auto-validated ${execData.validated} prediction${execData.validated !== 1 ? 's' : ''}`;
-            document.body.appendChild(toast);
-            setTimeout(() => toast.remove(), 4000);
-            // Reload data after auto-execution
-            const pendRes2 = await fetch('/api/validation/pending', { cache: 'no-store' });
-            const pendData2 = await pendRes2.json();
-            pendingList.length = 0;
-            (pendData2.pending || []).forEach(p => pendingList.push(p));
-          }
-        }
-      } catch (_) {}
+      pendingSubEl.textContent = `${pendingList.length} upcoming`;
+      pendingSubEl.style.color = '';
     }
 
     const todayStr = new Date().toISOString().slice(0, 10);
@@ -2455,6 +2463,8 @@ async function loadValidation() {
   } catch (e) {
     console.warn('Validation load failed:', e);
     if (pending) pending.innerHTML = `<div class="error-state">Error loading validation data: ${e.message}</div>`;
+  } finally {
+    hideEl('validation-loading');
   }
 }
 

top5_picker.py

@@ -154,6 +154,10 @@ def get_top5_picks(
             sector_factor = 1.0
 
         score = base_ret * conf_mult * ml_factor * rr_factor * sector_factor
+        # SLIGHTLY BULLISH = downgraded from BULLISH (bear-market Nifty gate or weak signals).
+        # Apply a 0.65× penalty so genuine BULLISH picks always rank higher for the same setup.
+        if p.get("direction") == "SLIGHTLY BULLISH":
+            score *= 0.65
         # When AI is unavailable, apply a heavy penalty — signal-strong stocks still
         # surface but rank below LLM-confirmed ones.
         if p.get("no_trade_reason") == "ai_unavailable":
@@ -394,6 +398,8 @@ def get_weekly_picks(
         sector_data = p.get("sector") or {}
         sector_factor = 1.12 if sector_data.get("leading") else (0.90 if sector_data.get("lagging") else 1.0)
         score = ret_hi_val * conf_mult * ml_factor * rr_factor * sector_factor
+        if p.get("direction") == "SLIGHTLY BULLISH":
+            score *= 0.65
         if p.get("no_trade_reason") == "ai_unavailable":
             score *= 0.40
         return score