refactor: extract 6 prediction signals into Signal classes

The 803-line NationalFuelPredictionService had six private compute*Signal
methods, a private linearRegression helper, and a private disabledSignal
shape factory all crammed together. Each signal is now an independently
testable class.

- App\Services\Prediction\Signals\Signal — interface
- App\Services\Prediction\Signals\SignalContext — input value object
  (FuelType + optional lat/lng + hasCoordinates() helper)
- App\Services\Prediction\Signals\AbstractSignal — shared
  disabledSignal() and linearRegression() helpers
- TrendSignal, DayOfWeekSignal, BrandBehaviourSignal, StickinessSignal,
  RegionalMomentumSignal, OilSignal — one class each, extending
  AbstractSignal

NationalFuelPredictionService receives the 6 signal classes via constructor
injection and orchestrates them. The lat/lng null-guard for regional
momentum now lives inside RegionalMomentumSignal::compute() so the
coordinator no longer branches on coordinate presence.

Aggregation, weekly summary, and reasoning helpers stay in the service
for now — they are coupled to the public predict() output shape and are
candidates for a follow-up extraction once a stable API is locked in.

Service: 803 → 414 lines.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Ovidiu U
2026-04-29 19:43:28 +01:00
parent e39618f5df
commit 27c82ef103
10 changed files with 530 additions and 420 deletions

View File

@@ -0,0 +1,61 @@
<?php
namespace App\Services\Prediction\Signals;
use Illuminate\Support\Facades\DB;
final class BrandBehaviourSignal extends AbstractSignal
{
public function compute(SignalContext $context): array
{
$rows = DB::table('station_prices')
->join('stations', 'station_prices.station_id', '=', 'stations.node_id')
->where('station_prices.fuel_type', $context->fuelType->value)
->where('station_prices.price_effective_at', '>=', now()->subDays(7))
->selectRaw('stations.is_supermarket, DATE(station_prices.price_effective_at) as day, AVG(station_prices.price_pence) as avg_price')
->groupBy('stations.is_supermarket', 'day')
->orderBy('day')
->get();
$supermarket = $rows->where('is_supermarket', 1)->values();
$major = $rows->where('is_supermarket', 0)->values();
if ($supermarket->count() < 2 || $major->count() < 2) {
return $this->disabledSignal('Insufficient brand data for comparison');
}
$supermarketSlope = $this->linearRegression($supermarket->pluck('avg_price')->map(fn ($v) => (float) $v / 100)->values()->all())['slope'];
$majorSlope = $this->linearRegression($major->pluck('avg_price')->map(fn ($v) => (float) $v / 100)->values()->all())['slope'];
$divergence = round(abs($supermarketSlope - $majorSlope) * 7, 1);
$supermarketChange = round($supermarketSlope * 7, 1);
$majorChange = round($majorSlope * 7, 1);
if ($divergence < 1.0) {
return [
'score' => 0.0,
'confidence' => 0.5,
'direction' => 'stable',
'detail' => 'Supermarkets and majors moving in sync.',
'data_points' => $rows->count(),
'enabled' => true,
];
}
$leaderChange = abs($supermarketChange) > abs($majorChange) ? $supermarketChange : $majorChange;
$direction = $leaderChange > 0 ? 'up' : 'down';
$leader = abs($supermarketChange) > abs($majorChange) ? 'Supermarkets' : 'Majors';
$follower = $leader === 'Supermarkets' ? 'majors' : 'supermarkets';
$leaderAbs = abs($leaderChange);
$followerChange = $leader === 'Supermarkets' ? abs($majorChange) : abs($supermarketChange);
return [
'score' => $direction === 'up' ? 1.0 : -1.0,
'confidence' => min(1.0, $divergence / 5.0),
'direction' => $direction,
'detail' => "{$leader} ".($leaderChange > 0 ? 'rose' : 'fell')." {$leaderAbs}p vs {$follower} {$followerChange}p (divergence: {$divergence}p). Expect {$follower} to follow.",
'data_points' => $rows->count(),
'enabled' => true,
];
}
}