Setup views: primary-target column, floor-target prune, liveness cutoff

Three follow-ups to the gate probability floor (8f41143):

- Signals table shows the starred primary target (shared primaryTarget
  helper) instead of an independently computed max-probability best,
  so Overview, Signals and ticker details agree by construction.
- Targets pinned at the 3% probability clamp floor collapse to the
  nearest one (enhance_trade_setup + backtest candidates in parity):
  floor-pinned levels are indistinguishable to the model, so farther
  ones were duplicate 3% rows inviting lottery headlines.
- get_trade_setups only returns setups re-emitted within
  LIVE_SETUP_MAX_AGE_DAYS (3): an older latest row means the daily
  scan no longer confirms the setup, and such rows otherwise surface
  forever on Overview/Signals/ticker/alerts. History endpoints keep
  full history.

Backtest on the Jul-3 snapshot is metric-identical to the gate-floor
run on all qualified stats (1089 qualified, Sharpe 2.02, CAGR +49.6%,
DD -15.8%): the prune only removes noise the gate already rejected.

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

app/services/backtest_service.py

@@ -65,6 +65,7 @@ from app.services.qualification import (
 from app.services.recommendation_service import (
     _choose_recommended_action,
     _classify_by_probability,
+    _prune_floor_pinned_targets,
     _risk_level_from_conflicts,
     _select_primary_target,
     _zone_representative_levels,
@@ -179,6 +180,9 @@ def _window_setups(
                 t, dim_scores, None, direction, config
             )
             t["classification"] = _classify_by_probability(t["probability"])
+        # Collapse duplicate floor-pinned lottery targets (parity with
+        # enhance_trade_setup).
+        targets = _prune_floor_pinned_targets(targets)
         primary = _select_primary_target(targets)
         if primary is None:
             continue

app/services/recommendation_service.py

@@ -45,6 +45,12 @@ _MODERATE_MAX_ATR = 4.6
 # the same tolerance the chart and alerts use, so S/R is one model app-wide.
 _SR_ZONE_TOLERANCE = 0.02
 
+# Reach-probability estimates are clamped to this band; a target at the floor
+# means "the model considers it essentially unreachable" and floor-pinned
+# targets are mutually indistinguishable.
+_PROBABILITY_CLAMP_LOW = 3.0
+_PROBABILITY_CLAMP_HIGH = 95.0
+
 
 def _clamp(value: float, low: float, high: float) -> float:
     return max(low, min(high, value))
@@ -408,7 +414,7 @@ class ProbabilityEstimator:
         elif opposed:
             probability -= signal_weight * 100.0
 
-        return round(_clamp(probability, 3.0, 95.0), 2)
+        return round(_clamp(probability, _PROBABILITY_CLAMP_LOW, _PROBABILITY_CLAMP_HIGH), 2)
 
 
 signal_conflict_detector = SignalConflictDetector()
@@ -582,6 +588,26 @@ PRIMARY_TARGET_MIN_RR = 1.5
 PRIMARY_TARGET_MIN_PROBABILITY = MIN_TARGET_PROBABILITY
 
 
+def _prune_floor_pinned_targets(targets: list[dict]) -> list[dict]:
+    """Keep only the nearest target pinned at the probability clamp floor.
+
+    Floor-pinned targets are indistinguishable to the model (true probability
+    at/below the clamp), so farther ones add no information — they just fill
+    the table with duplicate "3%" rows whose inflated R:R invites lottery
+    picks. ``targets`` is distance-sorted by the generator, so the first
+    floor-pinned entry is the nearest (most reachable) representative.
+    """
+    pruned: list[dict] = []
+    seen_floor = False
+    for target in targets:
+        if float(target.get("probability", 0.0)) <= _PROBABILITY_CLAMP_LOW:
+            if seen_floor:
+                continue
+            seen_floor = True
+        pruned.append(target)
+    return pruned
+
+
 def _select_primary_target(
     targets: list[dict],
     min_rr: float = PRIMARY_TARGET_MIN_RR,
@@ -665,6 +691,9 @@ async def enhance_trade_setup(
         # Label follows from the reach-probability: high prob = Conservative.
         target["classification"] = _classify_by_probability(target["probability"])
 
+    # Collapse duplicate floor-pinned lottery targets to the nearest one.
+    targets = _prune_floor_pinned_targets(targets)
+
     # 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

app/services/rr_scanner_service.py

@@ -11,7 +11,7 @@ from __future__ import annotations
 import json
 import logging
 from collections.abc import Callable
-from datetime import date, datetime, timezone
+from datetime import date, datetime, timedelta, timezone
 
 from sqlalchemy import and_, func, select
 from sqlalchemy.ext.asyncio import AsyncSession
@@ -39,6 +39,15 @@ logger = logging.getLogger(__name__)
 
 STRATEGY_VERSION = "residual_highvol_80_20_atr_trail3_v1"
 
+# A setup counts as live only while the daily scan keeps re-emitting it. The
+# scan runs every day (07:00 UTC cron), so anything older than this was NOT
+# re-confirmed — typically because no level clears the R:R threshold from the
+# current price anymore. Without this cutoff such rows stay "latest" forever
+# (the scanner never writes a replacement) and keep surfacing on the live
+# views. 3 days buffers a missed pipeline run or two; history endpoints are
+# unaffected.
+LIVE_SETUP_MAX_AGE_DAYS = 3
+
 
 async def _get_ticker(db: AsyncSession, symbol: str) -> Ticker:
     normalised = symbol.strip().upper()
@@ -602,10 +611,17 @@ async def get_trade_setups(
     live_recommendation: bool = False,
     exclude_open_trade_tickers: bool = False,
 ) -> list[dict]:
-    """Get latest stored trade setups, optionally filtered."""
+    """Get latest stored trade setups, optionally filtered.
+
+    Only setups the daily scan re-emitted within ``LIVE_SETUP_MAX_AGE_DAYS``
+    are returned — an older "latest" row means the scanner no longer finds a
+    valid setup for that ticker, so it must not surface as current.
+    """
+    cutoff = datetime.now(timezone.utc) - timedelta(days=LIVE_SETUP_MAX_AGE_DAYS)
     stmt = (
         select(TradeSetup, Ticker.symbol)
         .join(Ticker, TradeSetup.ticker_id == Ticker.id)
+        .where(TradeSetup.detected_at >= cutoff)
     )
     if direction is not None:
         stmt = stmt.where(TradeSetup.direction == direction.lower())

frontend/src/components/scanner/TradeTable.tsx

@@ -1,9 +1,10 @@
 import { Link } from 'react-router-dom';
 import type { TradeSetup } from '../../lib/types';
 import { formatPrice, formatPercent, formatDateTime } from '../../lib/format';
+import { primaryTarget } from '../../lib/qualification';
 import { recommendationActionDirection, recommendationActionLabel } from '../../lib/recommendation';
 
-export type SortColumn = 'symbol' | 'direction' | 'recommended_action' | 'confidence_score' | 'entry_price' | 'stop_loss' | 'target' | 'best_target_probability' | 'risk_amount' | 'reward_amount' | 'rr_ratio' | 'stop_pct' | 'target_pct' | 'risk_level' | 'composite_score' | 'detected_at';
+export type SortColumn = 'symbol' | 'direction' | 'recommended_action' | 'confidence_score' | 'entry_price' | 'stop_loss' | 'target' | 'primary_target_probability' | 'risk_amount' | 'reward_amount' | 'rr_ratio' | 'stop_pct' | 'target_pct' | 'risk_level' | 'composite_score' | 'detected_at';
 export type SortDirection = 'asc' | 'desc';
 
 interface TradeTableProps {
@@ -21,7 +22,7 @@ const columns: { key: SortColumn; label: string }[] = [
   { key: 'entry_price', label: 'Entry' },
   { key: 'stop_loss', label: 'Stop Loss' },
   { key: 'target', label: 'Target' },
-  { key: 'best_target_probability', label: 'Best Target' },
+  { key: 'primary_target_probability', label: 'Primary Target' },
   { key: 'risk_amount', label: 'Risk $' },
   { key: 'reward_amount', label: 'Reward $' },
   { key: 'rr_ratio', label: 'R:R' },
@@ -65,10 +66,12 @@ function riskLevelClass(riskLevel: TradeSetup['risk_level']) {
   return 'text-gray-400';
 }
 
-function bestTargetText(trade: TradeSetup) {
-  if (!trade.targets || trade.targets.length === 0) return '—';
-  const best = [...trade.targets].sort((a, b) => b.probability - a.probability)[0];
-  return `${formatPrice(best.price)} (${best.probability.toFixed(0)}%)`;
+// The starred primary — the same target the Overview and ticker details
+// headline, so every view agrees on which target a setup is "about".
+function primaryTargetText(trade: TradeSetup) {
+  const primary = primaryTarget(trade);
+  if (!primary) return '—';
+  return `${formatPrice(primary.price)} (${primary.probability.toFixed(0)}%)`;
 }
 
 export function TradeTable({ trades, sortColumn, sortDirection, onSort }: TradeTableProps) {
@@ -121,7 +124,7 @@ export function TradeTable({ trades, sortColumn, sortDirection, onSort }: TradeT
                 <td className="px-4 py-3.5 font-mono text-gray-200">{formatPrice(trade.entry_price)}</td>
                 <td className="px-4 py-3.5 font-mono text-gray-200">{formatPrice(trade.stop_loss)}</td>
                 <td className="px-4 py-3.5 font-mono text-gray-200">{formatPrice(trade.target)}</td>
-                <td className="px-4 py-3.5 font-mono text-gray-200">{bestTargetText(trade)}</td>
+                <td className="px-4 py-3.5 font-mono text-gray-200">{primaryTargetText(trade)}</td>
                 <td className="px-4 py-3.5 font-mono text-gray-200">{formatPrice(analysis.risk_amount)}</td>
                 <td className="px-4 py-3.5 font-mono text-gray-200">{formatPrice(analysis.reward_amount)}</td>
                 <td className={`px-4 py-3.5 font-mono font-semibold ${rrColorClass(trade.rr_ratio)}`}>{trade.rr_ratio.toFixed(2)}</td>

frontend/src/components/signals/SetupsPanel.tsx

@@ -2,7 +2,7 @@ import { useEffect, useMemo, useState } from 'react';
 import { useMutation, useQueryClient } from '@tanstack/react-query';
 import { useActivation } from '../../hooks/useActivation';
 import { useTrades } from '../../hooks/useTrades';
-import { qualifiesSetup, activationSummary } from '../../lib/qualification';
+import { qualifiesSetup, activationSummary, primaryTargetProbability } from '../../lib/qualification';
 import { TradeTable, type SortColumn, type SortDirection, computeTradeAnalysis } from '../scanner/TradeTable';
 import { SkeletonTable } from '../ui/Skeleton';
 import { useToast } from '../ui/Toast';
@@ -42,8 +42,8 @@ function getComputedValue(trade: TradeSetup, column: SortColumn): number {
     case 'stop_pct': return analysis.stop_pct;
     case 'target_pct': return analysis.target_pct;
     case 'confidence_score': return trade.confidence_score ?? -1;
-    case 'best_target_probability':
-      return trade.targets?.length ? Math.max(...trade.targets.map((t) => t.probability)) : -1;
+    case 'primary_target_probability':
+      return primaryTargetProbability(trade) ?? -1;
     case 'risk_level':
       if (trade.risk_level === 'Low') return 1;
       if (trade.risk_level === 'Medium') return 2;
@@ -78,7 +78,7 @@ function sortTrades(
       case 'stop_pct':
       case 'target_pct':
       case 'confidence_score':
-      case 'best_target_probability':
+      case 'primary_target_probability':
       case 'risk_level':
         cmp = getComputedValue(a, column) - getComputedValue(b, column);
         break;

frontend/src/lib/qualification.ts

@@ -1,4 +1,4 @@
-import type { ActivationConfig, TradeSetup } from './types';
+import type { ActivationConfig, TradeSetup, TradeTarget } from './types';
 
 const HIGH_CONVICTION_ACTIONS = new Set(['LONG_HIGH', 'SHORT_HIGH']);
 
@@ -16,15 +16,18 @@ function actionDirection(action: TradeSetup['recommended_action']): 'long' | 'sh
   return 'neutral';
 }
 
-export function bestTargetProbability(setup: TradeSetup): number {
-  return setup.targets?.length ? Math.max(...setup.targets.map((t) => t.probability)) : 0;
+/** The starred primary target (the one the headline R:R refers to), falling
+ * back to the most likely target when no star is stored. */
+export function primaryTarget(setup: TradeSetup): TradeTarget | null {
+  const starred = setup.targets?.find((t) => t.is_primary);
+  if (starred) return starred;
+  if (!setup.targets?.length) return null;
+  return [...setup.targets].sort((a, b) => b.probability - a.probability)[0];
 }
 
 /** Probability of the starred primary target (the one the headline R:R refers to). */
 export function primaryTargetProbability(setup: TradeSetup): number | null {
-  const primary = setup.targets?.find((t) => t.is_primary);
-  if (primary) return primary.probability;
-  return setup.targets?.length ? bestTargetProbability(setup) : null;
+  return primaryTarget(setup)?.probability ?? null;
 }
 
 /** R:R recomputed from the current price (0 if no reward/risk left). */

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,
+    _prune_floor_pinned_targets,
     _select_primary_target,
     direction_analyzer,
     probability_estimator,
@@ -154,6 +155,35 @@ def test_primary_target_requires_probability_floor():
     assert primary["price"] == 112.0
 
 
+def test_prune_keeps_only_nearest_floor_pinned_target():
+    # Two targets pinned at the 3% clamp floor are indistinguishable to the
+    # model — only the nearest survives; farther ones are duplicate noise.
+    targets = [
+        {"price": 204.0, "rr_ratio": 0.7, "probability": 25.6},
+        {"price": 241.0, "rr_ratio": 2.0, "probability": 3.0},
+        {"price": 272.0, "rr_ratio": 3.1, "probability": 3.0},
+    ]
+    pruned = _prune_floor_pinned_targets(targets)
+    assert [t["price"] for t in pruned] == [204.0, 241.0]
+
+
+def test_prune_leaves_targets_above_floor_untouched():
+    targets = [
+        {"price": 110.0, "rr_ratio": 2.0, "probability": 65.0},
+        {"price": 120.0, "rr_ratio": 3.5, "probability": 20.0},
+    ]
+    assert _prune_floor_pinned_targets(targets) == targets
+
+
+def test_prune_all_floor_pinned_keeps_nearest_only():
+    targets = [
+        {"price": 241.0, "rr_ratio": 2.0, "probability": 3.0},
+        {"price": 272.0, "rr_ratio": 3.1, "probability": 3.0},
+    ]
+    pruned = _prune_floor_pinned_targets(targets)
+    assert [t["price"] for t in pruned] == [241.0]
+
+
 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_preservation.py

@@ -29,7 +29,11 @@ from app.models.trade_setup import TradeSetup
 from app.models.score import CompositeScore, DimensionScore
 from app.models.sentiment import SentimentScore
 from app.models.user import User
-from app.services.rr_scanner_service import scan_ticker, get_trade_setups
+from app.services.rr_scanner_service import (
+    LIVE_SETUP_MAX_AGE_DAYS,
+    get_trade_setups,
+    scan_ticker,
+)
 
 
 def _as_utc(value: datetime) -> datetime:
@@ -69,11 +73,11 @@ def _make_ohlcv_bars(
     num_bars: int = 20,
     base_close: float = 100.0,
 ) -> list[OHLCVRecord]:
-    """Generate OHLCV bars closing around base_close with ATR ≈ 2.0."""
+    """Generate OHLCV bars closing around base_close with ATR ≈ 2.0."""
     bars: list[OHLCVRecord] = []
     start = date(2024, 1, 1)
     for i in range(num_bars):
-        close = base_close + (i % 3 - 1) * 0.5  # oscillate ±0.5
+        close = base_close + (i % 3 - 1) * 0.5  # oscillate ±0.5
         bars.append(OHLCVRecord(
             ticker_id=ticker_id,
             date=start + timedelta(days=i),
@@ -101,7 +105,7 @@ def zero_candidate_scenario(draw: st.DrawFn) -> dict:
       but all below the R:R threshold for their respective directions
     - Levels in the right direction but below R:R threshold
 
-    Note: scan_ticker does NOT filter by SR level type — it only checks whether
+    Note: scan_ticker does NOT filter by SR level type — it only checks whether
     the price_level is above or below entry. So "wrong side" means all levels
     are clustered near entry and below threshold in both directions.
     """
@@ -111,10 +115,10 @@ def zero_candidate_scenario(draw: st.DrawFn) -> dict:
         return {"variant": variant, "levels": []}
 
     else:  # below_threshold
-        # All levels close to entry so R:R < 1.5 with risk ≈ 3
-        # For longs: reward < 4.5 → price < 104.5
-        # For shorts: reward < 4.5 → price > 95.5
-        # Place all levels in the 96–104 band (below threshold both ways)
+        # All levels close to entry so R:R < 1.5 with risk ≈ 3
+        # For longs: reward < 4.5 → price < 104.5
+        # For shorts: reward < 4.5 → price > 95.5
+        # Place all levels in the 96–104 band (below threshold both ways)
         num = draw(st.integers(min_value=1, max_value=3))
         levels = []
         for _ in range(num):
@@ -139,7 +143,7 @@ def zero_candidate_scenario(draw: st.DrawFn) -> dict:
 def single_candidate_scenario(draw: st.DrawFn) -> dict:
     """Generate a scenario with exactly one S/R level that meets the R:R threshold.
 
-    For longs: one resistance above entry with R:R >= 1.5 (price >= 104.5 with risk ≈ 3).
+    For longs: one resistance above entry with R:R >= 1.5 (price >= 104.5 with risk ≈ 3).
     """
     direction = draw(st.sampled_from(["long", "short"]))
 
@@ -175,7 +179,7 @@ async def test_property_zero_candidates_produce_no_setup(
     """**Validates: Requirements 3.1, 3.2**
 
     Property: when zero candidate S/R levels exist (no levels, wrong side,
-    or below threshold), scan_ticker produces no setup — unchanged from
+    or below threshold), scan_ticker produces no setup — unchanged from
     original behavior.
     """
     from tests.conftest import _test_engine, _test_session_factory
@@ -225,7 +229,7 @@ async def test_property_single_candidate_selected_unchanged(
     """**Validates: Requirements 3.3**
 
     Property: when exactly one candidate S/R level meets the R:R threshold,
-    scan_ticker selects it — same as the original code would.
+    scan_ticker selects it — same as the original code would.
     """
     from tests.conftest import _test_engine, _test_session_factory
     from app.database import Base
@@ -270,7 +274,7 @@ async def test_property_single_candidate_selected_unchanged(
 
 
 # ===========================================================================
-# 7.2 Unit test: no S/R levels → no setup produced
+# 7.2 Unit test: no S/R levels → no setup produced
 # ===========================================================================
 
 @pytest.mark.asyncio
@@ -296,7 +300,7 @@ async def test_no_sr_levels_produces_no_setup(scan_session: AsyncSession):
 
 
 # ===========================================================================
-# 7.3 Unit test: single candidate meets threshold → selected
+# 7.3 Unit test: single candidate meets threshold → selected
 # ===========================================================================
 
 @pytest.mark.asyncio
@@ -306,7 +310,7 @@ async def test_single_resistance_above_threshold_selected(scan_session: AsyncSes
     When exactly one resistance level above entry meets the R:R threshold,
     it should be selected as the long setup target.
 
-    Entry ≈ 100, ATR ≈ 2, risk ≈ 3. Resistance at 110 → R:R ≈ 3.33 (>= 1.5).
+    Entry ≈ 100, ATR ≈ 2, risk ≈ 3. Resistance at 110 → R:R ≈ 3.33 (>= 1.5).
     """
     ticker = Ticker(symbol="SINGL")
     scan_session.add(ticker)
@@ -343,7 +347,7 @@ async def test_single_support_below_threshold_selected(scan_session: AsyncSessio
     When exactly one support level below entry meets the R:R threshold,
     it should be selected as the short setup target.
 
-    Entry ≈ 100, ATR ≈ 2, risk ≈ 3. Support at 90 → R:R ≈ 3.33 (>= 1.5).
+    Entry ≈ 100, ATR ≈ 2, risk ≈ 3. Support at 90 → R:R ≈ 3.33 (>= 1.5).
     """
     ticker = Ticker(symbol="SINGS")
     scan_session.add(ticker)
@@ -444,6 +448,42 @@ async def test_get_trade_setups_sorting_rr_desc_composite_desc(db_session: Async
     )
 
 
+@pytest.mark.asyncio
+async def test_get_trade_setups_excludes_stale_rows(db_session: AsyncSession):
+    """A "latest" row older than LIVE_SETUP_MAX_AGE_DAYS means the daily scan
+    stopped re-emitting the setup (nothing clears the R:R threshold from the
+    current price) — it must not surface on the live views."""
+    now = datetime.now(timezone.utc)
+    ticker_fresh = Ticker(symbol="FRESH")
+    ticker_stale = Ticker(symbol="STALE")
+    db_session.add_all([ticker_fresh, ticker_stale])
+    await db_session.flush()
+
+    db_session.add_all([
+        TradeSetup(
+            ticker_id=ticker_fresh.id, direction="long",
+            entry_price=100.0, stop_loss=97.0, target=109.0,
+            rr_ratio=3.0, composite_score=50.0,
+            detected_at=now - timedelta(days=1),
+        ),
+        TradeSetup(
+            ticker_id=ticker_stale.id, direction="long",
+            entry_price=100.0, stop_loss=97.0, target=109.0,
+            rr_ratio=3.0, composite_score=50.0,
+            detected_at=now - timedelta(days=LIVE_SETUP_MAX_AGE_DAYS, hours=1),
+        ),
+    ])
+    await db_session.flush()
+
+    results = await get_trade_setups(db_session)
+    symbols = [r["symbol"] for r in results]
+    assert symbols == ["FRESH"], f"Stale setup must be excluded, got {symbols}"
+
+    # The per-symbol view applies the same liveness rule.
+    stale_rows = await get_trade_setups(db_session, symbol="STALE")
+    assert stale_rows == []
+
+
 @pytest.mark.asyncio
 async def test_get_trade_setups_can_exclude_tickers_with_open_paper_trades(
     db_session: AsyncSession,
@@ -540,9 +580,12 @@ async def test_get_trade_setups_can_exclude_tickers_with_open_paper_trades(
 
 async def _seed_stale_setup_with_current_scores(db_session: AsyncSession) -> TradeSetup:
     """Stored setup frozen at scan time (conf 82, neutral) vs. current context
-    (bullish sentiment, composite 96) that yields live confidence 97."""
-    old_scan = datetime(2026, 7, 1, tzinfo=timezone.utc)
-    current = datetime(2026, 7, 3, tzinfo=timezone.utc)
+    (bullish sentiment, composite 96) that yields live confidence 97.
+
+    The scan date stays inside the LIVE_SETUP_MAX_AGE_DAYS liveness window —
+    these tests exercise the live overlay on a still-live row, not staleness."""
+    current = datetime.now(timezone.utc)
+    old_scan = current - timedelta(days=2)
     old_reasoning = (
         "LONG (high confidence): 82% with aligned signals "
         "(technical=88, momentum=60, sentiment=neutral)."
@@ -653,7 +696,7 @@ async def test_live_recommendation_filters_apply_to_live_values(
     """min_confidence must judge the overlaid live confidence, not the stored one."""
     await _seed_stale_setup_with_current_scores(db_session)
 
-    # Stored confidence is 82 — a stored-column filter would drop this row.
+    # Stored confidence is 82 — a stored-column filter would drop this row.
     # Live confidence is 97, so it must pass.
     rows = await get_trade_setups(
         db_session,
@@ -675,7 +718,7 @@ async def test_live_recommendation_filters_apply_to_live_values(
 
 
 async def _seed_two_direction_setup(db_session: AsyncSession) -> None:
-    current = datetime(2026, 7, 3, tzinfo=timezone.utc)
+    current = datetime.now(timezone.utc)
     ticker = Ticker(symbol="BOTH")
     db_session.add(ticker)
     await db_session.flush()
@@ -776,7 +819,7 @@ async def test_live_recommendation_action_independent_of_direction_filter(
 async def test_live_overlay_preserves_setup_specific_risk_and_context(
     db_session: AsyncSession,
 ):
-    current = datetime(2026, 7, 3, tzinfo=timezone.utc)
+    current = datetime.now(timezone.utc)
     ticker = Ticker(symbol="RISK")
     db_session.add(ticker)
     await db_session.flush()
@@ -883,7 +926,7 @@ async def test_live_trade_setup_read_does_not_recompute_scores(db_session: Async
 async def test_intraday_price_update_changes_live_price_without_new_signal_rows(
     db_session: AsyncSession,
 ):
-    current = datetime(2026, 7, 3, tzinfo=timezone.utc)
+    current = datetime.now(timezone.utc)
     ticker = Ticker(symbol="LIVEP")
     db_session.add(ticker)
     await db_session.flush()