Add activation thresholds: qualified-signal defaults and views
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Admin-configurable thresholds (min R:R, default 2.0; min confidence,
default 70%) defining what counts as an actionable signal:

- Admin Settings: new Activation Thresholds panel
  (GET/PUT /admin/settings/activation)
- GET /trades/activation exposes values to all users with access
- Signals/Setups: filters initialize from activation values
- Track Record: "Qualified signals only" toggle (default on) via
  min_rr/min_confidence params on /trades/performance; the
  confidence breakdown always covers the full population so the
  thresholds can be validated against outcomes
- Dashboard: "Qualified" metric and qualified-first Top Setups
- Outcome evaluator unchanged: every setup is still evaluated

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
2026-06-12 18:16:04 +02:00
parent d139dd0390
commit 6da65b8d8f
20 changed files with 440 additions and 29 deletions
+23
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@@ -9,6 +9,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
from app.dependencies import get_db, require_admin
from app.models.user import User
from app.schemas.admin import (
ActivationConfigUpdate,
CreateUserRequest,
DataCleanupRequest,
JobToggle,
@@ -148,6 +149,28 @@ async def update_recommendation_settings(
return APIEnvelope(status="success", data=updated)
@router.get("/admin/settings/activation", response_model=APIEnvelope)
async def get_activation_settings(
_admin: User = Depends(require_admin),
db: AsyncSession = Depends(get_db),
):
config = await admin_service.get_activation_config(db)
return APIEnvelope(status="success", data=config)
@router.put("/admin/settings/activation", response_model=APIEnvelope)
async def update_activation_settings(
body: ActivationConfigUpdate,
_admin: User = Depends(require_admin),
db: AsyncSession = Depends(get_db),
):
updated = await admin_service.update_activation_config(
db,
body.model_dump(exclude_unset=True, exclude_none=True),
)
return APIEnvelope(status="success", data=updated)
@router.get("/admin/settings/ticker-universe", response_model=APIEnvelope)
async def get_ticker_universe_setting(
_admin: User = Depends(require_admin),
+24 -1
View File
@@ -6,6 +6,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
from app.dependencies import get_db, require_access
from app.schemas.common import APIEnvelope
from app.schemas.trade_setup import RecommendationSummaryResponse, TradeSetupResponse
from app.services import admin_service
from app.services.outcome_service import get_performance_stats
from app.services.rr_scanner_service import get_trade_setup_history, get_trade_setups
@@ -49,8 +50,27 @@ async def list_trade_setups(
return APIEnvelope(status="success", data=data)
@router.get("/trades/activation", response_model=APIEnvelope)
async def get_activation_thresholds(
_user=Depends(require_access),
db: AsyncSession = Depends(get_db),
) -> APIEnvelope:
"""Activation thresholds (min R:R, min confidence) for actionable signals.
Readable by any user with access — drives Signals-page default filters
and the Dashboard's qualified-setup metrics. Configured by admins via
PUT /admin/settings/activation.
"""
config = await admin_service.get_activation_config(db)
return APIEnvelope(status="success", data=config)
@router.get("/trades/performance", response_model=APIEnvelope)
async def get_trade_performance(
min_rr: float | None = Query(None, ge=0, description="Only setups with R:R >= this"),
min_confidence: float | None = Query(
None, ge=0, le=100, description="Only setups with confidence >= this"
),
_user=Depends(require_access),
db: AsyncSession = Depends(get_db),
) -> APIEnvelope:
@@ -58,8 +78,11 @@ async def get_trade_performance(
Outcomes are written by the nightly outcome_evaluator job (win = target
hit first, loss = stop hit first, expired = neither within the window).
Optional min_rr / min_confidence filters apply to the overall, direction
and action breakdowns; the confidence breakdown always covers all setups
so thresholds can be validated against it.
"""
stats = await get_performance_stats(db)
stats = await get_performance_stats(db, min_rr=min_rr, min_confidence=min_confidence)
return APIEnvelope(status="success", data=stats)
+6
View File
@@ -56,3 +56,9 @@ class RecommendationConfigUpdate(BaseModel):
class TickerUniverseUpdate(BaseModel):
universe: Literal["sp500", "nasdaq100", "nasdaq_all"]
class ActivationConfigUpdate(BaseModel):
"""Activation thresholds: what counts as an actionable signal."""
min_rr: float | None = Field(default=None, ge=0)
min_confidence: float | None = Field(default=None, ge=0, le=100)
+52
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@@ -31,6 +31,16 @@ RECOMMENDATION_CONFIG_DEFAULTS: dict[str, float] = {
DEFAULT_TICKER_UNIVERSE = "sp500"
SUPPORTED_TICKER_UNIVERSES = {"sp500", "nasdaq100", "nasdaq_all"}
# Activation thresholds: what counts as a signal worth acting on.
# Used as Signals-page default filters, the Dashboard's qualified-setup
# metrics, and the Track Record's "qualified only" view. The outcome
# evaluator deliberately ignores these — every setup gets evaluated so the
# thresholds themselves can be validated against outcomes.
ACTIVATION_DEFAULTS: dict[str, float] = {
"activation_min_rr": 2.0,
"activation_min_confidence": 70.0,
}
# ---------------------------------------------------------------------------
# User management
@@ -143,6 +153,48 @@ async def update_setting(db: AsyncSession, key: str, value: str) -> SystemSettin
return setting
# ---------------------------------------------------------------------------
# Activation thresholds
# ---------------------------------------------------------------------------
async def get_activation_config(db: AsyncSession) -> dict[str, float]:
"""Return activation thresholds with public keys (min_rr, min_confidence)."""
result = await db.execute(
select(SystemSetting).where(SystemSetting.key.like("activation_%"))
)
config = dict(ACTIVATION_DEFAULTS)
for setting in result.scalars().all():
if setting.key in config:
try:
config[setting.key] = float(setting.value)
except (TypeError, ValueError):
pass
return {
"min_rr": config["activation_min_rr"],
"min_confidence": config["activation_min_confidence"],
}
async def update_activation_config(
db: AsyncSession, updates: dict[str, float]
) -> dict[str, float]:
"""Update activation thresholds. Accepts public keys min_rr / min_confidence."""
if "min_rr" in updates and updates["min_rr"] < 0:
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")
key_map = {
"min_rr": "activation_min_rr",
"min_confidence": "activation_min_confidence",
}
for public_key, storage_key in key_map.items():
if public_key in updates:
await update_setting(db, storage_key, str(float(updates[public_key])))
return await get_activation_config(db)
def _recommendation_public_to_storage_key(key: str) -> str:
return f"recommendation_{key}"
+23 -3
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@@ -178,12 +178,20 @@ def _confidence_bucket(score: float | None) -> str | None:
return None
async def get_performance_stats(db: AsyncSession) -> dict:
async def get_performance_stats(
db: AsyncSession,
min_rr: float | None = None,
min_confidence: float | None = None,
) -> dict:
"""Aggregate outcome statistics over all evaluated trade setups.
avg_r is the expectancy per trade in R-multiples (win = +rr_ratio,
loss = -1R, expired = 0R). A positive avg_r means the signals have
been profitable on a risk-adjusted basis.
min_rr / min_confidence filter the overall, direction and action
breakdowns. The confidence breakdown deliberately stays unfiltered:
it is the instrument for validating the thresholds themselves.
"""
result = await db.execute(
select(TradeSetup).where(TradeSetup.actual_outcome.is_not(None))
@@ -195,14 +203,26 @@ async def get_performance_stats(db: AsyncSession) -> dict:
)
pending_count = len(pending_result.scalars().all())
def qualifies(setup: TradeSetup) -> bool:
if min_rr is not None and setup.rr_ratio < min_rr:
return False
if min_confidence is not None and (setup.confidence_score or 0.0) < min_confidence:
return False
return True
qualified = [s for s in evaluated if qualifies(s)]
by_direction: dict[str, list[TradeSetup]] = {}
by_action: dict[str, list[TradeSetup]] = {}
by_confidence: dict[str, list[TradeSetup]] = {}
for setup in evaluated:
for setup in qualified:
by_direction.setdefault(setup.direction, []).append(setup)
action = setup.recommended_action or "NONE"
by_action.setdefault(action, []).append(setup)
# Confidence buckets always cover the full evaluated population
for setup in evaluated:
bucket = _confidence_bucket(setup.confidence_score)
if bucket is not None:
by_confidence.setdefault(bucket, []).append(setup)
@@ -210,7 +230,7 @@ async def get_performance_stats(db: AsyncSession) -> dict:
bucket_order = [label for label, _, _ in _CONFIDENCE_BUCKETS]
return {
"overall": _bucket_stats(evaluated),
"overall": _bucket_stats(qualified),
"pending": pending_count,
"by_direction": {k: _bucket_stats(v) for k, v in sorted(by_direction.items())},
"by_action": {k: _bucket_stats(v) for k, v in sorted(by_action.items())},
+6
View File
@@ -0,0 +1,6 @@
import apiClient from './client';
import type { ActivationConfig } from '../lib/types';
export function getActivation() {
return apiClient.get<ActivationConfig>('trades/activation').then((r) => r.data);
}
+13
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@@ -1,5 +1,6 @@
import apiClient from './client';
import type {
ActivationConfig,
AdminUser,
PipelineReadiness,
RecommendationConfig,
@@ -68,6 +69,18 @@ export function updateRecommendationSettings(payload: Partial<RecommendationConf
.then((r) => r.data);
}
export function getActivationSettings() {
return apiClient
.get<ActivationConfig>('admin/settings/activation')
.then((r) => r.data);
}
export function updateActivationSettings(payload: Partial<ActivationConfig>) {
return apiClient
.put<ActivationConfig>('admin/settings/activation', payload)
.then((r) => r.data);
}
export function getTickerUniverseSetting() {
return apiClient
.get<TickerUniverseSetting>('admin/settings/ticker-universe')
+9 -2
View File
@@ -1,6 +1,13 @@
import apiClient from './client';
import type { PerformanceStats } from '../lib/types';
export function getPerformance() {
return apiClient.get<PerformanceStats>('trades/performance').then((r) => r.data);
export interface PerformanceParams {
min_rr?: number;
min_confidence?: number;
}
export function getPerformance(params?: PerformanceParams) {
return apiClient
.get<PerformanceStats>('trades/performance', { params })
.then((r) => r.data);
}
@@ -0,0 +1,81 @@
import { useEffect, useState } from 'react';
import type { ActivationConfig } from '../../lib/types';
import { useActivationSettings, useUpdateActivationSettings } from '../../hooks/useAdmin';
import { SkeletonTable } from '../ui/Skeleton';
const DEFAULTS: ActivationConfig = {
min_rr: 2,
min_confidence: 70,
};
export function ActivationSettings() {
const { data, isLoading, isError, error } = useActivationSettings();
const update = useUpdateActivationSettings();
const [form, setForm] = useState<ActivationConfig>(DEFAULTS);
useEffect(() => {
if (data) setForm(data);
}, [data]);
const onSave = () => {
update.mutate(form as unknown as Record<string, number>);
};
const onReset = () => {
setForm(DEFAULTS);
update.mutate(DEFAULTS as unknown as Record<string, number>);
};
if (isLoading) return <SkeletonTable rows={2} cols={2} />;
if (isError) return <p className="text-sm text-red-400">{(error as Error)?.message || 'Failed to load activation thresholds'}</p>;
return (
<div className="glass p-5 space-y-4">
<div>
<h3 className="text-sm font-semibold text-gray-200">Activation Thresholds</h3>
<p className="mt-1 text-xs text-gray-500">
What counts as a signal worth acting on. Used as the default Signals filters, the
Dashboard's qualified-setup metrics, and the Track Record's "qualified only" view.
All setups are still evaluated regardless, so these thresholds can be validated
against the confidence breakdown.
</p>
</div>
<div className="grid gap-4 md:grid-cols-2">
<label className="block space-y-1">
<span className="text-xs text-gray-400">Min Risk:Reward (1 : x)</span>
<input
type="number"
min={0}
step={0.1}
value={form.min_rr}
onChange={(e) => setForm((prev) => ({ ...prev, min_rr: Number(e.target.value) }))}
className="w-full input-glass px-3 py-2 text-sm"
/>
</label>
<label className="block space-y-1">
<span className="text-xs text-gray-400">Min Confidence (%)</span>
<input
type="number"
min={0}
max={100}
step={1}
value={form.min_confidence}
onChange={(e) => setForm((prev) => ({ ...prev, min_confidence: Number(e.target.value) }))}
className="w-full input-glass px-3 py-2 text-sm"
/>
</label>
</div>
<div className="flex items-center gap-2">
<button className="btn-primary px-4 py-2 text-sm" onClick={onSave} disabled={update.isPending}>
{update.isPending ? 'Saving…' : 'Save Thresholds'}
</button>
<button className="px-4 py-2 text-sm rounded border border-white/[0.1] text-gray-300 hover:text-white" onClick={onReset} disabled={update.isPending}>
Reset to Defaults
</button>
</div>
</div>
);
}
@@ -1,5 +1,6 @@
import { useMemo, useState } from 'react';
import { useMutation, useQueryClient } from '@tanstack/react-query';
import { useActivation } from '../../hooks/useActivation';
import { useTrades } from '../../hooks/useTrades';
import { TradeTable, type SortColumn, type SortDirection, computeTradeAnalysis } from '../scanner/TradeTable';
import { SkeletonTable } from '../ui/Skeleton';
@@ -94,16 +95,22 @@ function sortTrades(
export function SetupsPanel() {
const { data: trades, isLoading, isError, error } = useTrades();
const activation = useActivation();
const queryClient = useQueryClient();
const toast = useToast();
const [minRR, setMinRR] = useState(0);
// null = user hasn't touched the filter; falls back to admin-configured
// activation thresholds once loaded
const [minRROverride, setMinRROverride] = useState<number | null>(null);
const [minConfidenceOverride, setMinConfidenceOverride] = useState<number | null>(null);
const [directionFilter, setDirectionFilter] = useState<DirectionFilter>('both');
const [minConfidence, setMinConfidence] = useState(0);
const [actionFilter, setActionFilter] = useState<ActionFilter>('all');
const [sortColumn, setSortColumn] = useState<SortColumn>('rr_ratio');
const [sortDirection, setSortDirection] = useState<SortDirection>('desc');
const minRR = minRROverride ?? activation.data?.min_rr ?? 0;
const minConfidence = minConfidenceOverride ?? activation.data?.min_confidence ?? 0;
const scanMutation = useMutation({
mutationFn: () => triggerJob('rr_scanner'),
onSuccess: () => {
@@ -143,7 +150,7 @@ export function SetupsPanel() {
min={0}
step={0.1}
value={minRR}
onChange={(e) => setMinRR(Number(e.target.value) || 0)}
onChange={(e) => setMinRROverride(Number(e.target.value) || 0)}
className="w-20"
/>
</div>
@@ -167,7 +174,7 @@ export function SetupsPanel() {
max={100}
step={1}
value={minConfidence}
onChange={(e) => setMinConfidence(Number(e.target.value) || 0)}
onChange={(e) => setMinConfidenceOverride(Number(e.target.value) || 0)}
className="w-24"
/>
</Field>
@@ -1,4 +1,6 @@
import { useState } from 'react';
import { useMutation, useQueryClient } from '@tanstack/react-query';
import { useActivation } from '../../hooks/useActivation';
import { usePerformance } from '../../hooks/usePerformance';
import { triggerJob } from '../../api/admin';
import { Button } from '../ui/Button';
@@ -89,7 +91,14 @@ function BreakdownTable({ rows, labelHeader, mapLabel }: {
}
export function TrackRecordPanel() {
const { data, isLoading, isError, error } = usePerformance();
const [qualifiedOnly, setQualifiedOnly] = useState(true);
const activation = useActivation();
const params = qualifiedOnly && activation.data
? { min_rr: activation.data.min_rr, min_confidence: activation.data.min_confidence }
: undefined;
const { data, isLoading, isError, error } = usePerformance(params);
const queryClient = useQueryClient();
const toast = useToast();
@@ -106,6 +115,26 @@ export function TrackRecordPanel() {
return (
<div className="space-y-6">
<div className="glass-sm flex flex-wrap items-center justify-between gap-3 px-4 py-3">
<label className="flex cursor-pointer items-center gap-2.5 text-sm text-gray-300">
<input
type="checkbox"
checked={qualifiedOnly}
onChange={(e) => setQualifiedOnly(e.target.checked)}
className="h-4 w-4 cursor-pointer accent-blue-400"
/>
<span>
Qualified signals only
{activation.data && (
<span className="num ml-2 text-xs text-gray-500">
R:R {activation.data.min_rr.toFixed(1)} · conf {activation.data.min_confidence.toFixed(0)}%
</span>
)}
</span>
</label>
<p className="text-xs text-gray-500">Confidence breakdown always covers all setups.</p>
</div>
<div className="flex items-start justify-between gap-4">
<Disclosure summary="How outcomes are measured">
<p className="text-xs text-gray-400">
@@ -138,8 +167,9 @@ export function TrackRecordPanel() {
{data && data.overall.total === 0 && (
<Callout variant="empty">
No evaluated setups yet. Outcomes appear once setups are old enough for their stop or
target to be hit the evaluator runs nightly, or click Evaluate Now.
{qualifiedOnly
? 'No evaluated setups meet the activation thresholds yet. Untick "Qualified signals only" to see all evaluated setups, or wait for more outcomes.'
: 'No evaluated setups yet. Outcomes appear once setups are old enough for their stop or target to be hit — the evaluator runs nightly, or click Evaluate Now.'}
{data.pending > 0 && ` ${data.pending} setup${data.pending === 1 ? '' : 's'} pending evaluation.`}
</Callout>
)}
+10
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@@ -0,0 +1,10 @@
import { useQuery } from '@tanstack/react-query';
import { getActivation } from '../api/activation';
export function useActivation() {
return useQuery({
queryKey: ['activation'],
queryFn: getActivation,
staleTime: 5 * 60 * 1000,
});
}
+26
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@@ -114,6 +114,32 @@ export function useUpdateRecommendationSettings() {
});
}
export function useActivationSettings() {
return useQuery({
queryKey: ['admin', 'activation-settings'],
queryFn: () => adminApi.getActivationSettings(),
});
}
export function useUpdateActivationSettings() {
const qc = useQueryClient();
const { addToast } = useToast();
return useMutation({
mutationFn: (payload: Record<string, number>) =>
adminApi.updateActivationSettings(payload),
onSuccess: () => {
qc.invalidateQueries({ queryKey: ['admin', 'activation-settings'] });
qc.invalidateQueries({ queryKey: ['activation'] });
qc.invalidateQueries({ queryKey: ['performance'] });
addToast('success', 'Activation thresholds updated');
},
onError: (error: Error) => {
addToast('error', error.message || 'Failed to update activation thresholds');
},
});
}
export function useTickerUniverseSetting() {
return useQuery({
queryKey: ['admin', 'ticker-universe'],
+4 -4
View File
@@ -1,9 +1,9 @@
import { useQuery } from '@tanstack/react-query';
import { getPerformance } from '../api/performance';
import { getPerformance, type PerformanceParams } from '../api/performance';
export function usePerformance() {
export function usePerformance(params?: PerformanceParams) {
return useQuery({
queryKey: ['performance'],
queryFn: getPerformance,
queryKey: ['performance', params ?? null],
queryFn: () => getPerformance(params),
});
}
+6
View File
@@ -152,6 +152,12 @@ export interface PerformanceStats {
by_confidence: Record<string, OutcomeBucketStats>;
}
// Activation thresholds: what counts as an actionable signal
export interface ActivationConfig {
min_rr: number;
min_confidence: number;
}
export interface TradeTarget {
price: number;
distance_from_entry: number;
+2
View File
@@ -1,4 +1,5 @@
import { useState } from 'react';
import { ActivationSettings } from '../components/admin/ActivationSettings';
import { DataCleanup } from '../components/admin/DataCleanup';
import { JobControls } from '../components/admin/JobControls';
import { PipelineReadinessPanel } from '../components/admin/PipelineReadinessPanel';
@@ -28,6 +29,7 @@ export default function AdminPage() {
{activeTab === 'Tickers' && <TickerManagement />}
{activeTab === 'Settings' && (
<div className="space-y-4">
<ActivationSettings />
<TickerUniverseBootstrap />
<RecommendationSettings />
<SettingsForm />
+21 -11
View File
@@ -1,5 +1,6 @@
import { useMemo } from 'react';
import { Link } from 'react-router-dom';
import { useActivation } from '../hooks/useActivation';
import { useTrades } from '../hooks/useTrades';
import { useWatchlist } from '../hooks/useWatchlist';
import { usePerformance } from '../hooks/usePerformance';
@@ -51,16 +52,25 @@ function DirectionTag({ direction }: { direction: string }) {
export default function DashboardPage() {
const trades = useTrades();
const watchlist = useWatchlist();
const activation = useActivation();
const performance = usePerformance();
const topSetups: TradeSetup[] = useMemo(
() => (trades.data ?? []).slice(0, 5),
[trades.data],
const minRR = activation.data?.min_rr ?? 2;
const minConfidence = activation.data?.min_confidence ?? 70;
const qualifiedSetups = useMemo(
() =>
(trades.data ?? []).filter(
(t) => t.rr_ratio >= minRR && (t.confidence_score ?? 0) >= minConfidence,
),
[trades.data, minRR, minConfidence],
);
const highConfidenceCount = useMemo(
() => (trades.data ?? []).filter((t) => (t.confidence_score ?? 0) >= 70).length,
[trades.data],
// Show qualified setups first; fall back to the full list when none qualify
const showingQualified = qualifiedSetups.length > 0;
const topSetups: TradeSetup[] = useMemo(
() => (showingQualified ? qualifiedSetups : trades.data ?? []).slice(0, 5),
[showingQualified, qualifiedSetups, trades.data],
);
const topWatchlist = useMemo(
@@ -100,10 +110,10 @@ export default function DashboardPage() {
sub="latest per ticker & direction"
/>
<Metric
label="High Confidence"
value={String(highConfidenceCount)}
sub="confidence ≥ 70%"
valueClass={highConfidenceCount > 0 ? 'text-blue-300' : 'text-gray-100'}
label="Qualified"
value={String(qualifiedSetups.length)}
sub={`R:R ≥ ${minRR.toFixed(1)} & conf ≥ ${minConfidence.toFixed(0)}%`}
valueClass={qualifiedSetups.length > 0 ? 'text-blue-300' : 'text-gray-100'}
/>
<Metric
label="Hit Rate"
@@ -122,7 +132,7 @@ export default function DashboardPage() {
<div className="grid gap-8 xl:grid-cols-5">
{/* Top setups */}
<div className="xl:col-span-3">
<Section title="Top Setups" hint="by confidence">
<Section title="Top Setups" hint={showingQualified ? 'qualified, by confidence' : 'none qualified — showing all'}>
{trades.isLoading && <SkeletonTable rows={5} cols={5} />}
{trades.isError && <Callout variant="error">Failed to load setups</Callout>}
{trades.data && topSetups.length === 0 && (
+1 -1
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@@ -1 +1 @@
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"""Unit tests for activation threshold configuration."""
from __future__ import annotations
import pytest
from sqlalchemy.ext.asyncio import AsyncSession
from app.exceptions import ValidationError
from app.services.admin_service import (
get_activation_config,
update_activation_config,
)
@pytest.fixture
async def session() -> AsyncSession:
"""DB session compatible with services that commit."""
from tests.conftest import _test_session_factory
async with _test_session_factory() as session:
yield session
class TestActivationConfig:
async def test_defaults_when_unset(self, session: AsyncSession):
config = await get_activation_config(session)
assert config == {"min_rr": 2.0, "min_confidence": 70.0}
async def test_update_and_read_back(self, session: AsyncSession):
updated = await update_activation_config(
session, {"min_rr": 1.5, "min_confidence": 60.0}
)
assert updated == {"min_rr": 1.5, "min_confidence": 60.0}
config = await get_activation_config(session)
assert config == {"min_rr": 1.5, "min_confidence": 60.0}
async def test_partial_update_keeps_other_value(self, session: AsyncSession):
await update_activation_config(session, {"min_confidence": 80.0})
config = await get_activation_config(session)
assert config["min_rr"] == 2.0 # default untouched
assert config["min_confidence"] == 80.0
async def test_rejects_negative_rr(self, session: AsyncSession):
with pytest.raises(ValidationError):
await update_activation_config(session, {"min_rr": -1.0})
async def test_rejects_out_of_range_confidence(self, session: AsyncSession):
with pytest.raises(ValidationError):
await update_activation_config(session, {"min_confidence": 120.0})
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@@ -270,3 +270,42 @@ class TestGetPerformanceStats:
assert stats["overall"]["losses"] == 1
assert stats["overall"]["hit_rate"] == 0.0
assert stats["overall"]["avg_r"] == -1.0
async def test_activation_filters_apply_to_overall_but_not_confidence(
self, db_session: AsyncSession
):
ticker = await _make_ticker(db_session)
# Qualified: high confidence, high R:R
db_session.add(_make_setup(
ticker, rr=3.0, confidence_score=80.0, actual_outcome=OUTCOME_TARGET_HIT,
))
# Unqualified: low confidence
db_session.add(_make_setup(
ticker, rr=3.0, confidence_score=40.0, actual_outcome=OUTCOME_STOP_HIT,
))
# Unqualified: low R:R
db_session.add(_make_setup(
ticker, rr=1.2, confidence_score=90.0, actual_outcome=OUTCOME_STOP_HIT,
))
await db_session.flush()
stats = await get_performance_stats(db_session, min_rr=2.0, min_confidence=70.0)
# Overall covers only the qualified setup
assert stats["overall"]["total"] == 1
assert stats["overall"]["wins"] == 1
assert stats["overall"]["hit_rate"] == 100.0
# Confidence breakdown still covers the full population
total_in_confidence = sum(
bucket["total"] for bucket in stats["by_confidence"].values()
)
assert total_in_confidence == 3
async def test_no_filters_returns_full_population(self, db_session: AsyncSession):
ticker = await _make_ticker(db_session)
db_session.add(_make_setup(ticker, rr=1.2, confidence_score=10.0, actual_outcome=OUTCOME_TARGET_HIT))
await db_session.flush()
stats = await get_performance_stats(db_session)
assert stats["overall"]["total"] == 1