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/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
-tighteners (off by default). The momentum percentile is computed across the
-universe and attached to each setup upstream; when it's absent the gate falls
-back to the floors.
+remain as floors, and conviction/conflict survive as optional tighteners (off by
+default). The momentum percentile is computed across the universe and attached to
+each setup upstream; when it's absent the gate falls 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 /
-    target-probability tighteners. ``min_momentum_percentile`` defaults to 0 (off)
-    for callers that pass a legacy config without the key.
+    percentile (the core selection) → optional conviction / conflict tighteners.
+    ``min_momentum_percentile`` defaults to 0 (off) for callers that pass a legacy
+    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