27c82ef103
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>
87 lines
3.3 KiB
PHP
87 lines
3.3 KiB
PHP
<?php
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namespace App\Services\Prediction\Signals;
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use Illuminate\Support\Facades\DB;
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final class TrendSignal extends AbstractSignal
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{
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private const float R_SQUARED_THRESHOLD = 0.5;
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private const float SLOPE_THRESHOLD_PENCE = 0.3;
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private const float SLOPE_SATURATION_PENCE = 0.5;
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private const int PREDICTION_HORIZON_DAYS = 7;
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/** @return array{score: float, confidence: float, direction: string, detail: string, data_points: int, enabled: bool, slope: float, r_squared: float} */
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public function compute(SignalContext $context): array
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{
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foreach ([5, 14] as $lookbackDays) {
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$rows = DB::table('station_prices')
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->where('fuel_type', $context->fuelType->value)
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->where('price_effective_at', '>=', now()->subDays($lookbackDays))
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->selectRaw('DATE(price_effective_at) as day, AVG(price_pence) as avg_price')
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->groupBy('day')
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->orderBy('day')
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->get();
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if ($rows->count() < 2) {
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continue;
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}
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$regression = $this->linearRegression($rows->pluck('avg_price')->map(fn ($v) => (float) $v / 100)->values()->all());
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if ($regression['r_squared'] >= self::R_SQUARED_THRESHOLD) {
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$slope = $regression['slope'];
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$direction = match (true) {
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$slope >= self::SLOPE_THRESHOLD_PENCE => 'up',
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$slope <= -self::SLOPE_THRESHOLD_PENCE => 'down',
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default => 'stable',
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};
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$absSlope = abs($slope);
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$score = $direction === 'stable' ? 0.0 : min(1.0, $absSlope / self::SLOPE_SATURATION_PENCE) * ($slope > 0 ? 1 : -1);
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$projected = round($slope * $lookbackDays, 1);
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$detail = $direction === 'stable'
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? "Prices flat over {$lookbackDays} days (slope: {$slope}p/day, R²={$regression['r_squared']})"
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: sprintf(
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'%s at %sp/day over %d days (R²=%s, ~%s%sp in %dd)',
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$slope > 0 ? 'Rising' : 'Falling',
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abs(round($slope, 2)),
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$lookbackDays,
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round($regression['r_squared'], 2),
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$projected > 0 ? '+' : '',
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$projected,
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self::PREDICTION_HORIZON_DAYS,
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);
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if ($lookbackDays === 5) {
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$detail .= ' [Adaptive lookback active]';
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}
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return [
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'score' => $score,
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'confidence' => min(1.0, $regression['r_squared']),
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'direction' => $direction,
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'detail' => $detail,
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'data_points' => $rows->count(),
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'enabled' => true,
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'slope' => round($slope, 3),
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'r_squared' => round($regression['r_squared'], 3),
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];
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}
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}
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return [
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'score' => 0.0,
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'confidence' => 0.0,
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'direction' => 'stable',
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'detail' => 'Insufficient price history or noisy data (R² below threshold)',
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'data_points' => 0,
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'enabled' => false,
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'slope' => 0.0,
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'r_squared' => 0.0,
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];
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}
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}
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