remove min_target_probability gate + add chart time-range presets
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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>
This commit is contained in:
2026-06-24 09:24:35 +02:00
parent 605f95098c
commit f48d8705de
9 changed files with 68 additions and 63 deletions
-4
View File
@@ -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: