diff --git a/app/services/recommendation_service.py b/app/services/recommendation_service.py
index 4b9b95b..b171e48 100644
--- a/app/services/recommendation_service.py
+++ b/app/services/recommendation_service.py
@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import json
 import logging
+import math
 from typing import Any
 
 from sqlalchemy import select
@@ -27,10 +28,30 @@ DEFAULT_RECOMMENDATION_CONFIG: dict[str, float] = {
 }
 
 
+# Horizon (trading days) over which a target's reach-probability is estimated.
+# Kept in step with the outcome evaluator's window so probability predicts the
+# metric we actually measure.
+_TARGET_HORIZON_DAYS = 30.0
+
+
 def _clamp(value: float, low: float, high: float) -> float:
     return max(low, min(high, value))
 
 
+def _norm_cdf(x: float) -> float:
+    """Standard normal CDF via erf (no SciPy dependency)."""
+    return 0.5 * (1.0 + math.erf(x / math.sqrt(2.0)))
+
+
+def _classify_by_probability(probability: float) -> str:
+    """Label a target by how likely it is to be reached (derived, not assumed)."""
+    if probability >= 60.0:
+        return "Conservative"
+    if probability >= 40.0:
+        return "Moderate"
+    return "Aggressive"
+
+
 def _sentiment_value(sentiment_classification: str | None) -> str | None:
     if sentiment_classification is None:
         return None
@@ -238,20 +259,9 @@ class TargetGenerator:
         selected = candidates[:5]
         selected.sort(key=lambda row: row["distance_from_entry"])
 
-        if not selected:
-            return []
-
-        n = len(selected)
-        for idx, target in enumerate(selected):
-            if n <= 2:
-                target["classification"] = "Conservative" if idx == 0 else "Aggressive"
-            elif idx <= 1:
-                target["classification"] = "Conservative"
-            elif idx >= n - 2:
-                target["classification"] = "Aggressive"
-            else:
-                target["classification"] = "Moderate"
-
+        # Classification is assigned later from the computed reach-probability
+        # (see _classify_by_probability), not from distance rank.
+        for target in selected:
             target.pop("quality", None)
 
         return selected
@@ -266,67 +276,47 @@ class ProbabilityEstimator:
         direction: str,
         config: dict[str, float],
     ) -> float:
-        classification = str(target.get("classification", "Moderate"))
+        """Probability the target is reached within the outcome horizon.
+
+        Base = probability of price *touching* a level at the target's distance
+        within the evaluation window, under a driftless random walk (reflection
+        principle): 2·(1 − Φ(d / (ATR·√T))). Distance is in ATR multiples and T
+        is the horizon in trading days, so a far target is inherently unlikely —
+        no more 90% on a +39% move. Strength and signal alignment (drift toward
+        the target) then modulate it modestly.
+        """
         strength = float(target.get("sr_strength", 50.0))
         atr_multiple = float(target.get("distance_atr_multiple", 1.0))
 
-        if classification == "Conservative":
-            base_prob = 70.0
-        elif classification == "Aggressive":
-            base_prob = 40.0
-        else:
-            base_prob = 55.0
-
-        if strength >= 80:
-            strength_adj = 15.0
-        elif strength >= 60:
-            strength_adj = 10.0
-        elif strength >= 40:
-            strength_adj = 5.0
-        else:
-            strength_adj = -10.0
+        expected_move_atr = math.sqrt(_TARGET_HORIZON_DAYS)  # ≈ 5.48 ATR over 30d
+        z = atr_multiple / expected_move_atr if expected_move_atr > 0 else 99.0
+        touch_prob = 2.0 * (1.0 - _norm_cdf(z))  # 0..1
+        probability = touch_prob * 100.0
 
         technical = float(dimension_scores.get("technical", 50.0))
         momentum = float(dimension_scores.get("momentum", 50.0))
         sentiment = _sentiment_value(sentiment_classification)
 
-        alignment_adj = 0.0
+        # Drift toward the target raises touch probability; against it lowers.
         if direction == "long":
-            if technical > 60 and (sentiment == "bullish" or momentum > 60):
-                alignment_adj = 15.0
-            elif technical < 40 or (sentiment == "bearish" and momentum < 40):
-                alignment_adj = -15.0
+            aligned = technical > 60 and (sentiment == "bullish" or momentum > 60)
+            opposed = technical < 40 or (sentiment == "bearish" and momentum < 40)
         else:
-            if technical < 40 and (sentiment == "bearish" or momentum < 40):
-                alignment_adj = 15.0
-            elif technical > 60 or (sentiment == "bullish" and momentum > 60):
-                alignment_adj = -15.0
+            aligned = technical < 40 and (sentiment == "bearish" or momentum < 40)
+            opposed = technical > 60 or (sentiment == "bullish" and momentum > 60)
 
-        volatility_adj = 0.0
-        if atr_multiple > 5:
-            volatility_adj = 5.0
-        elif atr_multiple < 2:
-            volatility_adj = 5.0
-
-        signal_weight = float(config.get("recommendation_signal_alignment_weight", 0.15))
         sr_weight = float(config.get("recommendation_sr_strength_weight", 0.20))
-        distance_penalty = float(config.get("recommendation_distance_penalty_factor", 0.10))
+        signal_weight = float(config.get("recommendation_signal_alignment_weight", 0.15))
 
-        scaled_alignment_adj = alignment_adj * (signal_weight / 0.15)
-        scaled_strength_adj = strength_adj * (sr_weight / 0.20)
-        distance_adj = -distance_penalty * max(atr_multiple - 1.0, 0.0) * 2.0
+        # Strength magnet: ±(sr_weight·50) at the extremes (±10 by default)
+        probability += ((strength - 50.0) / 50.0) * (sr_weight * 50.0)
+        # Alignment: ±(signal_weight·100) (±15 by default)
+        if aligned:
+            probability += signal_weight * 100.0
+        elif opposed:
+            probability -= signal_weight * 100.0
 
-        probability = base_prob + scaled_strength_adj + scaled_alignment_adj + volatility_adj + distance_adj
-        probability = _clamp(probability, 10.0, 90.0)
-
-        if classification == "Conservative":
-            probability = max(probability, 61.0)
-        elif classification == "Moderate":
-            probability = _clamp(probability, 40.0, 70.0)
-        elif classification == "Aggressive":
-            probability = min(probability, 49.0)
-
-        return round(probability, 2)
+        return round(_clamp(probability, 3.0, 95.0), 2)
 
 
 signal_conflict_detector = SignalConflictDetector()
@@ -516,6 +506,8 @@ async def enhance_trade_setup(
             direction=direction,
             config=config,
         )
+        # Label follows from the reach-probability: high prob = Conservative.
+        target["classification"] = _classify_by_probability(target["probability"])
 
     # Primary target = most-likely target with real asymmetry (see
     # _select_primary_target), not the old quality-score pick that ignored
diff --git a/tests/unit/test_recommendation_service.py b/tests/unit/test_recommendation_service.py
index 71033bb..6b5ebf9 100644
--- a/tests/unit/test_recommendation_service.py
+++ b/tests/unit/test_recommendation_service.py
@@ -159,48 +159,44 @@ def test_generate_targets_respects_direction_and_order():
     assert distances == sorted(distances)
 
 
-def test_probability_ranges_by_classification():
+def test_probability_decreases_with_distance():
+    """A far target must be far less likely than a near one — no 90% at +39%."""
     config = {
         "recommendation_signal_alignment_weight": 0.15,
         "recommendation_sr_strength_weight": 0.20,
-        "recommendation_distance_penalty_factor": 0.10,
     }
-    dimension_scores = {"technical": 70.0, "momentum": 70.0}
+    dims = {"technical": 50.0, "momentum": 50.0}  # neutral, isolate the distance term
 
-    conservative = probability_estimator.estimate_probability(
-        {
-            "classification": "Conservative",
-            "sr_strength": 80,
-            "distance_atr_multiple": 1.5,
-        },
-        dimension_scores,
-        "bullish",
-        "long",
-        config,
-    )
-    moderate = probability_estimator.estimate_probability(
-        {
-            "classification": "Moderate",
-            "sr_strength": 60,
-            "distance_atr_multiple": 3.0,
-        },
-        dimension_scores,
-        "bullish",
-        "long",
-        config,
-    )
-    aggressive = probability_estimator.estimate_probability(
-        {
-            "classification": "Aggressive",
-            "sr_strength": 40,
-            "distance_atr_multiple": 6.0,
-        },
-        dimension_scores,
-        "bullish",
-        "long",
-        config,
-    )
+    def prob(atr_multiple: float, strength: float = 50.0) -> float:
+        return probability_estimator.estimate_probability(
+            {"sr_strength": strength, "distance_atr_multiple": atr_multiple},
+            dims, None, "long", config,
+        )
 
-    assert conservative > 60
-    assert 40 <= moderate <= 70
-    assert aggressive < 50
+    near = prob(1.5)
+    mid = prob(4.0)
+    far = prob(10.0)
+
+    # Monotonic decay with distance
+    assert near > mid > far
+    # Near target is genuinely likely; a 10-ATR target is a long shot
+    assert near > 60
+    assert far < 25
+
+
+def test_far_target_not_high_probability_even_with_strong_level():
+    """The AJG case: a far target stays low-probability even at max strength."""
+    config = {"recommendation_sr_strength_weight": 0.20, "recommendation_signal_alignment_weight": 0.15}
+    # ~10 ATR away, strongest possible level, fully aligned bullish
+    p = probability_estimator.estimate_probability(
+        {"sr_strength": 100, "distance_atr_multiple": 10.0},
+        {"technical": 80.0, "momentum": 80.0}, "bullish", "long", config,
+    )
+    assert p < 40   # nowhere near 90
+
+
+def test_classify_by_probability_thresholds():
+    from app.services.recommendation_service import _classify_by_probability
+    assert _classify_by_probability(75) == "Conservative"
+    assert _classify_by_probability(50) == "Moderate"
+    assert _classify_by_probability(20) == "Aggressive"