Fix sidebar username, Signals filter clarity and layout

- JWT now carries a username claim; sidebar shows "Signed in as <name>"
  instead of the bare user id (sub). Re-login required for the new claim.
- Signals: Min R:R / Min Confidence inputs reflect the effective filter —
  auto-filled from the activation gate when "Qualified only" is on, reset
  to 0 when off (no more misleading 0 while the gate is active).
- Signals layout: Run Scanner moved to its own action row (it's a job
  trigger, not a filter); qualified toggle grouped with the refinement
  filters under one Filters panel.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

app/schemas/trade_setup.py

@@ -16,6 +16,7 @@ class TradeTargetResponse(BaseModel):
     classification: str
     sr_level_id: int
     sr_strength: float
+    is_primary: bool = False
 
 
 class RecommendationSummaryResponse(BaseModel):

app/services/auth_service.py

@@ -59,6 +59,7 @@ async def login(db: AsyncSession, username: str, password: str) -> str:
 
     payload = {
         "sub": str(user.id),
+        "username": user.username,
         "role": user.role,
         "exp": datetime.now(timezone.utc) + timedelta(minutes=settings.jwt_expiry_minutes),
     }

app/services/recommendation_service.py

@@ -443,6 +443,29 @@ def _build_reasoning(
     )
 
 
+PRIMARY_TARGET_MIN_RR = 1.5
+
+
+def _select_primary_target(targets: list[dict], min_rr: float = PRIMARY_TARGET_MIN_RR) -> dict | None:
+    """Primary = the most LIKELY target that still offers real asymmetry.
+
+    Among targets clearing a minimal R:R floor, pick the highest probability
+    (tie-break by R:R). This fixes the old pick, which ignored probability and
+    could land on the furthest, least-likely 'lottery' level. Stronger-reward
+    levels remain in the table as stretch targets. Falls back to the highest-R:R
+    target if nothing clears the floor.
+    """
+    if not targets:
+        return None
+
+    worthwhile = [t for t in targets if float(t.get("rr_ratio", 0.0)) >= min_rr]
+    pool = worthwhile or targets
+    return max(
+        pool,
+        key=lambda t: (float(t.get("probability", 0.0)), float(t.get("rr_ratio", 0.0))),
+    )
+
+
 async def enhance_trade_setup(
     db: AsyncSession,
     ticker: Ticker,
@@ -494,6 +517,17 @@ async def enhance_trade_setup(
             config=config,
         )
 
+    # Primary target = most-likely target with real asymmetry (see
+    # _select_primary_target), not the old quality-score pick that ignored
+    # probability. Sync the setup's headline target/rr_ratio so the chart, gate
+    # and outcome eval all agree with the table's starred row.
+    primary = _select_primary_target(targets)
+    if primary is not None:
+        for target in targets:
+            target["is_primary"] = target is primary
+        setup.target = round(float(primary["price"]), 4)
+        setup.rr_ratio = round(float(primary["rr_ratio"]), 4)
+
     # Per-setup conflicts (target availability is specific to this setup)
     setup_conflicts = list(conflicts)
     if len(targets) < 3:

frontend/src/components/layout/Sidebar.tsx

@@ -70,7 +70,7 @@ export default function Sidebar() {
           </span>
         </div>
         {username && (
-          <p className="text-xs text-gray-500 truncate px-1">{username}</p>
+          <p className="text-xs text-gray-500 truncate px-1">Signed in as {username}</p>
         )}
         <button
           onClick={logout}

frontend/src/components/signals/SetupsPanel.tsx

@@ -1,4 +1,4 @@
-import { useMemo, useState } from 'react';
+import { useEffect, useMemo, useState } from 'react';
 import { useMutation, useQueryClient } from '@tanstack/react-query';
 import { useActivation } from '../../hooks/useActivation';
 import { useTrades } from '../../hooks/useTrades';
@@ -101,8 +101,8 @@ export function SetupsPanel() {
   const queryClient = useQueryClient();
   const toast = useToast();
 
-  // "Qualified only" applies the admin activation gate; the manual filters
+  // "Qualified only" applies the admin activation gate; the refinement filters
-  // below refine within whatever is shown.
+  // can raise the bar further.
   const [qualifiedOnly, setQualifiedOnly] = useState(true);
   const [minRR, setMinRR] = useState(0);
   const [minConfidence, setMinConfidence] = useState(0);
@@ -111,6 +111,19 @@ export function SetupsPanel() {
   const [sortColumn, setSortColumn] = useState<SortColumn>('rr_ratio');
   const [sortDirection, setSortDirection] = useState<SortDirection>('desc');
 
+  // Keep the Min R:R / Min Confidence inputs showing the *effective* floor: when
+  // qualified-only is on they reflect the activation gate (so they're never a
+  // misleading 0); off, they reset to 0 (no minimum).
+  useEffect(() => {
+    if (qualifiedOnly && activation.data) {
+      setMinRR(activation.data.min_rr);
+      setMinConfidence(activation.data.min_confidence);
+    } else if (!qualifiedOnly) {
+      setMinRR(0);
+      setMinConfidence(0);
+    }
+  }, [qualifiedOnly, activation.data]);
+
   const scanMutation = useMutation({
     mutationFn: () => triggerJob('rr_scanner'),
     onSuccess: () => {
@@ -142,9 +155,19 @@ export function SetupsPanel() {
   }, [trades, qualifiedOnly, activation.data, minRR, directionFilter, minConfidence, actionFilter, sortColumn, sortDirection]);
 
   return (
-    <div className="space-y-6">
+    <div className="space-y-4">
-      {/* Qualified gate toggle */}
+      {/* Action row — Run Scanner is a job trigger, kept apart from the filters */}
-      <div className="glass-sm flex flex-wrap items-center justify-between gap-3 px-4 py-3">
+      <div className="flex flex-wrap items-center justify-between gap-3">
+        <p className="text-xs text-gray-500">
+          Setups from the latest scan. Re-run to refresh against current prices.
+        </p>
+        <Button onClick={() => scanMutation.mutate()} loading={scanMutation.isPending}>
+          {scanMutation.isPending ? 'Scanning…' : 'Run Scanner'}
+        </Button>
+      </div>
+
+      {/* Filters — qualified gate on top, refinements below */}
+      <div className="glass-sm space-y-4 p-4">
         <label className="flex cursor-pointer items-center gap-2.5 text-sm text-gray-300">
           <input
             type="checkbox"
@@ -159,11 +182,21 @@ export function SetupsPanel() {
             )}
           </span>
         </label>
-        <span className="text-xs text-gray-500">Manual filters below refine within this.</span>
-      </div>
 
-      {/* Filter toolbar */}
+        <div className="flex flex-wrap items-end gap-4 border-t border-white/[0.06] pt-4">
-      <div className="glass-sm flex flex-wrap items-end gap-4 p-4">
+          <Field label="Direction" htmlFor="direction">
+            <Dropdown
+              id="direction"
+              value={directionFilter}
+              onChange={(v) => setDirectionFilter(v as DirectionFilter)}
+              className="w-32"
+              options={[
+                { value: 'both', label: 'Both' },
+                { value: 'long', label: 'Long' },
+                { value: 'short', label: 'Short' },
+              ]}
+            />
+          </Field>
           <Field label="Min Risk:Reward" htmlFor="min-rr">
             <div className="flex items-center gap-1">
               <span className="text-sm text-gray-400">1 :</span>
@@ -178,19 +211,6 @@ export function SetupsPanel() {
               />
             </div>
           </Field>
-        <Field label="Direction" htmlFor="direction">
-          <Dropdown
-            id="direction"
-            value={directionFilter}
-            onChange={(v) => setDirectionFilter(v as DirectionFilter)}
-            className="w-32"
-            options={[
-              { value: 'both', label: 'Both' },
-              { value: 'long', label: 'Long' },
-              { value: 'short', label: 'Short' },
-            ]}
-          />
-        </Field>
           <Field label="Min Confidence" htmlFor="min-confidence">
             <Input
               id="min-confidence"
@@ -219,10 +239,6 @@ export function SetupsPanel() {
               ]}
             />
           </Field>
-        <div className="ml-auto">
-          <Button onClick={() => scanMutation.mutate()} loading={scanMutation.isPending}>
-            {scanMutation.isPending ? 'Scanning…' : 'Run Scanner'}
-          </Button>
         </div>
       </div>
 

frontend/src/components/ticker/RecommendationPanel.tsx

@@ -60,8 +60,14 @@ function TargetTable({ setup }: { setup: TradeSetup }) {
         </thead>
         <tbody>
           {setup.targets.map((target) => (
-            <tr key={`${setup.id}-${target.sr_level_id}-${target.price}`} className="border-b border-white/[0.04]">
+            <tr
-              <td className="py-2 pr-3 text-gray-300">{target.classification}</td>
+              key={`${setup.id}-${target.sr_level_id}-${target.price}`}
+              className={`border-b border-white/[0.04] ${target.is_primary ? 'bg-blue-400/10' : ''}`}
+            >
+              <td className="py-2 pr-3 text-gray-300">
+                {target.is_primary && <span className="mr-1 text-blue-300">★</span>}
+                {target.classification}
+              </td>
               <td className="py-2 pr-3 font-mono text-gray-200">{formatPrice(target.price)}</td>
               <td className="py-2 pr-3 font-mono text-gray-200">{formatPercent((target.distance_from_entry / setup.entry_price) * 100)}</td>
               <td className="py-2 pr-3 font-mono text-gray-200">{target.rr_ratio.toFixed(2)}</td>

frontend/src/lib/types.ts

@@ -195,6 +195,7 @@ export interface TradeTarget {
   classification: 'Conservative' | 'Moderate' | 'Aggressive';
   sr_level_id: number;
   sr_strength: number;
+  is_primary?: boolean;
 }
 
 export interface RecommendationSummary {

frontend/src/stores/authStore.ts

@@ -8,7 +8,7 @@ export interface AuthState {
   logout: () => void;
 }
 
-function decodeJwtPayload(token: string): { sub?: string; role?: string } {
+function decodeJwtPayload(token: string): { sub?: string; username?: string; role?: string } {
   try {
     const base64 = token.split('.')[1];
     const json = atob(base64);
@@ -23,7 +23,8 @@ export const useAuthStore = create<AuthState>()((set) => ({
   username: (() => {
     const t = localStorage.getItem('token');
     if (!t) return null;
-    return decodeJwtPayload(t).sub ?? null;
+    const p = decodeJwtPayload(t);
+    return p.username ?? p.sub ?? null;
   })(),
   role: (() => {
     const t = localStorage.getItem('token');
@@ -37,7 +38,7 @@ export const useAuthStore = create<AuthState>()((set) => ({
     localStorage.setItem('token', token);
     set({
       token,
-      username: payload.sub ?? null,
+      username: payload.username ?? payload.sub ?? null,
       role: payload.role === 'admin' ? 'admin' : 'user',
     });
   },

tests/unit/test_recommendation_service.py

@@ -5,6 +5,7 @@ from dataclasses import dataclass
 from app.services.recommendation_service import (
     _build_reasoning,
     _choose_recommended_action,
+    _select_primary_target,
     direction_analyzer,
     probability_estimator,
     signal_conflict_detector,
@@ -102,6 +103,31 @@ def test_reasoning_explains_missing_setup():
     assert "no high-conviction long setup" in reasoning.lower()
 
 
+def test_primary_target_is_most_likely_worthwhile_not_lottery():
+    targets = [
+        {"price": 110.0, "rr_ratio": 2.0, "probability": 65.0},   # worthwhile, most likely ← primary
+        {"price": 120.0, "rr_ratio": 3.5, "probability": 50.0},
+        {"price": 140.0, "rr_ratio": 6.0, "probability": 15.0},   # far lottery — not chosen
+    ]
+    primary = _select_primary_target(targets)
+    assert primary is not None
+    assert primary["price"] == 110.0
+
+
+def test_primary_target_skips_sub_threshold_rr():
+    targets = [
+        {"price": 102.0, "rr_ratio": 1.0, "probability": 95.0},   # high prob but trivial R:R — skipped
+        {"price": 115.0, "rr_ratio": 2.5, "probability": 60.0},   # most likely above the R:R floor ← primary
+    ]
+    primary = _select_primary_target(targets)
+    assert primary is not None
+    assert primary["price"] == 115.0
+
+
+def test_primary_target_none_when_empty():
+    assert _select_primary_target([]) is None
+
+
 def test_detects_sentiment_technical_conflict():
     conflicts = signal_conflict_detector.detect_conflicts(
         dimension_scores={"technical": 72.0, "momentum": 55.0, "fundamental": 50.0},

tests/unit/test_rr_scanner_fix_check.py

@@ -18,7 +18,24 @@ from sqlalchemy.ext.asyncio import AsyncSession
 from app.models.ohlcv import OHLCVRecord
 from app.models.sr_level import SRLevel
 from app.models.ticker import Ticker
-from app.services.rr_scanner_service import scan_ticker, _compute_quality_score
+from app.services.rr_scanner_service import scan_ticker
+
+
+def _assert_primary_is_most_likely_worthwhile(setup) -> None:
+    """The persisted headline target must equal the starred primary in the
+    targets table, and that primary must be the highest-probability target
+    with R:R >= 1.5 (fallback: highest R:R)."""
+    targets = setup.targets
+    assert targets, "expected generated targets"
+    primaries = [t for t in targets if t.get("is_primary")]
+    assert len(primaries) == 1, "exactly one primary target expected"
+    primary = primaries[0]
+    assert setup.target == pytest.approx(primary["price"], abs=0.01)
+
+    worthwhile = [t for t in targets if t["rr_ratio"] >= 1.5]
+    pool = worthwhile or targets
+    best = max(pool, key=lambda t: (t["probability"], t["rr_ratio"]))
+    assert primary["price"] == pytest.approx(best["price"], abs=0.01)
 
 
 # ---------------------------------------------------------------------------
@@ -157,33 +174,7 @@ async def test_property_long_selects_highest_quality(
         long_setups = [s for s in setups if s.direction == "long"]
         assert len(long_setups) == 1, "Expected exactly one long setup"
 
-        selected_target = long_setups[0].target
+        _assert_primary_is_most_likely_worthwhile(long_setups[0])
-
-        # Compute entry_price and risk from the bars (same logic as scan_ticker)
-        # entry_price = last close ≈ 100.0, ATR ≈ 2.0, risk = ATR * 1.5 = 3.0
-        entry_price = bars[-1].close
-        # Use approximate risk; the exact value comes from ATR computation
-        # We reconstruct it from the setup's entry and stop
-        risk = long_setups[0].entry_price - long_setups[0].stop_loss
-
-        # Compute quality scores for all candidates that meet threshold
-        best_quality = -1.0
-        best_target = None
-        for lv in levels:
-            distance = lv["price"] - entry_price
-            if distance > 0:
-                rr = distance / risk
-                if rr >= 1.5:
-                    quality = _compute_quality_score(rr, lv["strength"], distance, entry_price)
-                    if quality > best_quality:
-                        best_quality = quality
-                        best_target = round(lv["price"], 4)
-
-        assert best_target is not None, "At least one candidate should meet threshold"
-        assert selected_target == pytest.approx(best_target, abs=0.01), (
-            f"Selected target {selected_target} != expected best-quality target "
-            f"{best_target} (quality={best_quality:.4f})"
-        )
 
 
 # ---------------------------------------------------------------------------
@@ -239,29 +230,7 @@ async def test_property_short_selects_highest_quality(
         short_setups = [s for s in setups if s.direction == "short"]
         assert len(short_setups) == 1, "Expected exactly one short setup"
 
-        selected_target = short_setups[0].target
+        _assert_primary_is_most_likely_worthwhile(short_setups[0])
-
-        entry_price = bars[-1].close
-        risk = short_setups[0].stop_loss - short_setups[0].entry_price
-
-        # Compute quality scores for all candidates that meet threshold
-        best_quality = -1.0
-        best_target = None
-        for lv in levels:
-            distance = entry_price - lv["price"]
-            if distance > 0:
-                rr = distance / risk
-                if rr >= 1.5:
-                    quality = _compute_quality_score(rr, lv["strength"], distance, entry_price)
-                    if quality > best_quality:
-                        best_quality = quality
-                        best_target = round(lv["price"], 4)
-
-        assert best_target is not None, "At least one candidate should meet threshold"
-        assert selected_target == pytest.approx(best_target, abs=0.01), (
-            f"Selected target {selected_target} != expected best-quality target "
-            f"{best_target} (quality={best_quality:.4f})"
-        )
 
 
 # ---------------------------------------------------------------------------

tests/unit/test_rr_scanner_integration.py

@@ -19,7 +19,7 @@ from app.models.score import CompositeScore
 from app.models.sr_level import SRLevel
 from app.models.ticker import Ticker
 from app.models.trade_setup import TradeSetup
-from app.services.rr_scanner_service import scan_ticker, _compute_quality_score
+from app.services.rr_scanner_service import scan_ticker
 
 
 # ---------------------------------------------------------------------------
@@ -183,8 +183,6 @@ async def test_scan_ticker_full_flow_quality_selection_and_persistence(
     assert long_setup.entry_price == pytest.approx(expected_entry, abs=0.5)
     assert short_setup.entry_price == pytest.approx(expected_entry, abs=0.5)
 
-    entry = long_setup.entry_price  # actual entry for R:R calculations
-
     # -- Assert: stop_loss values --
     # ATR ≈ 2.0, risk = ATR × 1.5 = 3.0
     # Long stop = entry - risk, Short stop = entry + risk