remove min_target_probability gate + add chart time-range presets

min_target_probability is gone: it filtered on the probability model the
calibration has repeatedly shown to be weak and overconfident, it was redundant
with the momentum gate, and as an off-by-default knob it just invited bad tuning.
Removed from the backend gate, activation config/schema, the frontend mirror
(qualifiesSetup / activationSummary), and ActivationSettings. The probability
model stays where it does real work (primary-target selection + display).

Charts: with multi-year history the all-bars default was unreadable. Added
time-range presets (1M / 3M / 6M / YTD / 1Y / 3Y / 5Y / All), defaulting to 1Y;
clicking a preset always re-applies (snaps back after a manual zoom). Y-axis
autoscale and wheel-zoom / drag-pan were already there.

339 backend tests pass; frontend build clean.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

app/schemas/admin.py

@@ -62,7 +62,6 @@ class ActivationConfigUpdate(BaseModel):
     min_momentum_percentile: float | None = Field(default=None, ge=0, le=100)
     min_rr: float | None = Field(default=None, ge=0)
     min_confidence: float | None = Field(default=None, ge=0, le=100)
-    min_target_probability: float | None = Field(default=None, ge=0, le=100)
     require_high_conviction: bool | None = None
     exclude_conflicts: bool | None = None
 

app/services/admin_service.py

@@ -46,7 +46,6 @@ _ACTIVATION_FLOAT_KEYS: dict[str, str] = {
     "min_momentum_percentile": "activation_min_momentum_percentile",
     "min_rr": "activation_min_rr",
     "min_confidence": "activation_min_confidence",
-    "min_target_probability": "activation_min_target_probability",
 }
 _ACTIVATION_BOOL_KEYS: dict[str, str] = {
     "require_high_conviction": "activation_require_high_conviction",
@@ -56,7 +55,6 @@ ACTIVATION_DEFAULTS: dict[str, float | bool] = {
     "min_momentum_percentile": 80.0,
     "min_rr": 1.2,
     "min_confidence": 55.0,
-    "min_target_probability": 0.0,
     "require_high_conviction": False,
     "exclude_conflicts": False,
 }
@@ -207,8 +205,6 @@ async def update_activation_config(
         raise ValidationError("min_rr must be >= 0")
     if "min_confidence" in updates and not 0 <= updates["min_confidence"] <= 100:
         raise ValidationError("min_confidence must be between 0 and 100")
-    if "min_target_probability" in updates and not 0 <= updates["min_target_probability"] <= 100:
-        raise ValidationError("min_target_probability must be between 0 and 100")
 
     for public_key, storage_key in _ACTIVATION_FLOAT_KEYS.items():
         if public_key in updates and updates[public_key] is not None:

app/services/qualification.py

@@ -5,10 +5,9 @@ performance stats (server) and mirrored on the frontend. The core selection is
 cross-sectional momentum: a setup's ticker must rank in the top
 ``min_momentum_percentile`` of the universe by 12-1 month momentum — the one
 signal the backtest showed actually sorts forward returns. R:R and confidence
-remain as floors, and conviction/conflict/target-probability survive as optional
+remain as floors, and conviction/conflict survive as optional tighteners (off by
-tighteners (off by default). The momentum percentile is computed across the
+default). The momentum percentile is computed across the universe and attached to
-universe and attached to each setup upstream; when it's absent the gate falls
+each setup upstream; when it's absent the gate falls back to the floors.
-back to the floors.
 """
 
 from __future__ import annotations
@@ -50,9 +49,9 @@ def setup_qualifies(setup: Any, config: dict) -> bool:
     recommended_action, risk_level and a ``targets`` list of dicts.
 
     Gate order: R:R floor → freshness (live R:R) → confidence floor → momentum
-    percentile (the core selection) → optional conviction / conflict /
+    percentile (the core selection) → optional conviction / conflict tighteners.
-    target-probability tighteners. ``min_momentum_percentile`` defaults to 0 (off)
+    ``min_momentum_percentile`` defaults to 0 (off) for callers that pass a legacy
-    for callers that pass a legacy config without the key.
+    config without the key.
     """
     if setup.rr_ratio < config["min_rr"]:
         return False
@@ -85,7 +84,4 @@ def setup_qualifies(setup: Any, config: dict) -> bool:
     if config.get("exclude_conflicts"):
         if (setup.risk_level or "") != "Low":
             return False
-    min_tp = float(config.get("min_target_probability", 0.0))
-    if min_tp > 0 and best_target_probability(setup) < min_tp:
-        return False
     return True

frontend/src/components/admin/ActivationSettings.tsx

@@ -7,7 +7,6 @@ const DEFAULTS: ActivationConfig = {
   min_momentum_percentile: 80,
   min_rr: 1.2,
   min_confidence: 55,
-  min_target_probability: 0,
   require_high_conviction: false,
   exclude_conflicts: false,
 };
@@ -91,20 +90,7 @@ export function ActivationSettings() {
       <div className="border-t border-white/[0.06] pt-4">
         <p className="text-xs font-medium uppercase tracking-widest text-gray-500">Optional tighteners</p>
         <p className="mt-1 text-[11px] text-gray-600">Off by default — turn on to be more selective on top of the momentum gate.</p>
-        <div className="mt-3 grid gap-3 md:grid-cols-3">
+        <div className="mt-3 grid gap-3 md:grid-cols-2">
-          <label className="block space-y-1">
-            <span className="text-xs text-gray-400">Min Target Probability (%)</span>
-            <input
-              type="number"
-              min={0}
-              max={100}
-              step={1}
-              value={form.min_target_probability}
-              onChange={(e) => setForm((prev) => ({ ...prev, min_target_probability: Number(e.target.value) }))}
-              className="w-full input-glass px-3 py-2 text-sm"
-            />
-            <span className="text-[11px] text-gray-600">Best target's probability must clear this. 0 disables.</span>
-          </label>
           <label className="flex cursor-pointer items-start gap-2.5 text-sm text-gray-300">
             <input
               type="checkbox"

frontend/src/components/charts/CandlestickChart.tsx

@@ -51,6 +51,26 @@ interface TooltipState {
 
 const MIN_VISIBLE_BARS = 10;
 
+type RangePreset = '1M' | '3M' | '6M' | 'YTD' | '1Y' | '3Y' | '5Y' | 'All';
+const RANGE_PRESETS: RangePreset[] = ['1M', '3M', '6M', 'YTD', '1Y', '3Y', '5Y', 'All'];
+const PRESET_MONTHS: Record<string, number> = { '1M': 1, '3M': 3, '6M': 6, '1Y': 12, '3Y': 36, '5Y': 60 };
+const DEFAULT_PRESET: RangePreset = '1Y';
+
+/** First bar index to show for a time-range preset (data is ascending by date). */
+function startIndexForPreset(data: OHLCVBar[], preset: RangePreset): number {
+  if (preset === 'All' || data.length === 0) return 0;
+  const last = new Date(data[data.length - 1].date);
+  let cutoff: Date;
+  if (preset === 'YTD') {
+    cutoff = new Date(last.getFullYear(), 0, 1);
+  } else {
+    cutoff = new Date(last);
+    cutoff.setMonth(cutoff.getMonth() - PRESET_MONTHS[preset]);
+  }
+  const idx = data.findIndex((b) => new Date(b.date) >= cutoff);
+  return idx < 0 ? 0 : idx;
+}
+
 export function CandlestickChart({ data, srLevels = [], zones = [], tradeSetup, currentPrice }: CandlestickChartProps) {
   const canvasRef = useRef<HTMLCanvasElement>(null);
   const overlayCanvasRef = useRef<HTMLCanvasElement>(null);
@@ -67,11 +87,13 @@ export function CandlestickChart({ data, srLevels = [], zones = [], tradeSetup,
     start: 0,
     end: data.length,
   });
+  const [preset, setPreset] = useState<RangePreset>(DEFAULT_PRESET);
 
-  // Reset visible range when data changes
+  // Apply the active time-range preset when the data or preset changes (so the
+  // default view is a readable window, not the whole multi-year history).
   useEffect(() => {
-    setVisibleRange({ start: 0, end: data.length });
+    setVisibleRange({ start: startIndexForPreset(data, preset), end: data.length });
-  }, [data]);
+  }, [data, preset]);
 
   const draw = useCallback(() => {
     const canvas = canvasRef.current;
@@ -627,12 +649,33 @@ export function CandlestickChart({ data, srLevels = [], zones = [], tradeSetup,
   }
 
   return (
-    <div ref={containerRef} className="relative w-full" style={{ height: 400 }}>
+    <div className="w-full">
-      <canvas
+      <div className="mb-2 flex flex-wrap items-center gap-1">
-        ref={canvasRef}
+        {RANGE_PRESETS.map((p) => (
-        className="w-full"
+          <button
-        style={{ height: 400 }}
+            key={p}
-      />
+            type="button"
+            onClick={() => {
+              // Re-apply the range directly so clicking the active preset still
+              // snaps back after a manual wheel-zoom / pan.
+              setPreset(p);
+              setVisibleRange({ start: startIndexForPreset(data, p), end: data.length });
+            }}
+            className={`rounded px-2 py-1 text-[11px] font-medium tabular-nums transition-colors ${
+              preset === p ? 'bg-white/10 text-blue-300' : 'text-gray-500 hover:text-gray-300'
+            }`}
+          >
+            {p}
+          </button>
+        ))}
+        <span className="ml-1 text-[10px] text-gray-600">scroll to zoom · drag to pan</span>
+      </div>
+      <div ref={containerRef} className="relative w-full" style={{ height: 400 }}>
+        <canvas
+          ref={canvasRef}
+          className="w-full"
+          style={{ height: 400 }}
+        />
       <canvas
         ref={overlayCanvasRef}
         className="absolute top-0 left-0 w-full cursor-crosshair"
@@ -643,11 +686,12 @@ export function CandlestickChart({ data, srLevels = [], zones = [], tradeSetup,
         onMouseLeave={handleMouseLeave}
         onWheel={handleWheel}
       />
-      <div
+        <div
-        ref={tooltipRef}
+          ref={tooltipRef}
-        className="glass absolute pointer-events-none px-3 py-2 text-xs shadow-2xl z-50"
+          className="glass absolute pointer-events-none px-3 py-2 text-xs shadow-2xl z-50"
-        style={{ display: 'none' }}
+          style={{ display: 'none' }}
-      />
+        />
+      </div>
     </div>
   );
 }

frontend/src/lib/qualification.ts

@@ -46,9 +46,6 @@ export function qualifiesSetup(setup: TradeSetup, config: ActivationConfig): boo
     return false;
   }
   if (config.exclude_conflicts && (setup.risk_level ?? '') !== 'Low') return false;
-  if (config.min_target_probability > 0 && bestTargetProbability(setup) < config.min_target_probability) {
-    return false;
-  }
   return true;
 }
 
@@ -59,6 +56,5 @@ export function activationSummary(config: ActivationConfig): string {
   parts.push(`R:R ≥ ${config.min_rr.toFixed(1)}`, `conf ≥ ${config.min_confidence.toFixed(0)}%`);
   if (config.require_high_conviction) parts.push('high-conviction');
   if (config.exclude_conflicts) parts.push('clean');
-  if (config.min_target_probability > 0) parts.push(`target ≥ ${config.min_target_probability.toFixed(0)}%`);
   return parts.join(' · ');
 }

frontend/src/lib/types.ts

@@ -162,7 +162,6 @@ export interface ActivationConfig {
   min_momentum_percentile: number;
   min_rr: number;
   min_confidence: number;
-  min_target_probability: number;
   require_high_conviction: boolean;
   exclude_conflicts: boolean;
 }

tests/unit/test_activation_settings.py

@@ -28,7 +28,6 @@ class TestActivationConfig:
             "min_momentum_percentile": 80.0,
             "min_rr": 1.2,
             "min_confidence": 55.0,
-            "min_target_probability": 0.0,
             "require_high_conviction": False,
             "exclude_conflicts": False,
         }
@@ -57,12 +56,11 @@ class TestActivationConfig:
     async def test_conviction_flags_round_trip(self, session: AsyncSession):
         await update_activation_config(
             session,
-            {"require_high_conviction": False, "exclude_conflicts": False, "min_target_probability": 45.0},
+            {"require_high_conviction": True, "exclude_conflicts": True},
         )
         config = await get_activation_config(session)
-        assert config["require_high_conviction"] is False
+        assert config["require_high_conviction"] is True
-        assert config["exclude_conflicts"] is False
+        assert config["exclude_conflicts"] is True
-        assert config["min_target_probability"] == 45.0
 
     async def test_rejects_negative_rr(self, session: AsyncSession):
         with pytest.raises(ValidationError):
@@ -71,7 +69,3 @@ class TestActivationConfig:
     async def test_rejects_out_of_range_confidence(self, session: AsyncSession):
         with pytest.raises(ValidationError):
             await update_activation_config(session, {"min_confidence": 120.0})
-
-    async def test_rejects_out_of_range_target_probability(self, session: AsyncSession):
-        with pytest.raises(ValidationError):
-            await update_activation_config(session, {"min_target_probability": 150.0})

tests/unit/test_qualification.py

@@ -12,7 +12,6 @@ DEFAULT_GATE = {
     "min_momentum_percentile": 0.0,
     "min_rr": 1.2,
     "min_confidence": 55.0,
-    "min_target_probability": 0.0,
     "require_high_conviction": False,
     "exclude_conflicts": False,
 }
@@ -25,7 +24,6 @@ STRICT_GATE = {
     "min_momentum_percentile": 0.0,
     "min_rr": 2.0,
     "min_confidence": 70.0,
-    "min_target_probability": 60.0,
     "require_high_conviction": True,
     "exclude_conflicts": True,
 }
@@ -112,9 +110,6 @@ class TestStrictTighteners:
         s = _setup(risk_level="Medium", targets=[{"probability": 65.0, "is_primary": True}])
         assert setup_qualifies(s, STRICT_GATE) is False
 
-    def test_low_target_probability_fails(self):
-        assert setup_qualifies(_setup(targets=[{"probability": 40.0, "is_primary": True}]), STRICT_GATE) is False
-
 
 class TestBestTargetProbability:
     def test_returns_max(self):