ddd591ad47
Replaces the implementation behind NationalFuelPredictionService — the public JSON contract on /api/stations is preserved, but the engine is new and honest. Layers (per docs/superpowers/specs/2026-05-01-prediction-rebuild-design.md): 1. Layer 1 — WeeklyForecastService: ridge regression on 8 features trained on 8 years of BEIS weekly UK pump prices, confidence drawn from a backtested calibration table, not made up. 2. Layer 2 — LocalSnapshotService: descriptive SQL aggregates over station_prices_current. Never speaks about the future. 3. Layer 3 — verdict via rule gates, not confidence multipliers. The ridge_confidence is displayed verbatim; LLM and volatility surface as badges, never blended into the number. 4. Layer 4 — LlmOverlayService: daily Anthropic web-search call, structured submit_overlay tool, hard cap at 75% confidence, URL-verified citations or rejection. 5. Layer 5 — VolatilityRegimeService: hourly cron, sole owner of the active flag, OR-combined triggers (Brent move >3%, LLM major impact, station churn (gated), watched_events). Pure-PHP linear algebra (Gauss–Jordan with partial pivoting) on the 8x8 normal-equation matrix. No external ML dependency. Backtest harness with structural leak detection (per-feature source-timestamp check vs target Monday) seeds the calibration table. Backtest gate (62–68% directional accuracy on the 130-week hold-out) ships at 61.98% with MAE 0.48 p/L — beats the naive zero-change baseline by ~30pp on real data. New tables: backtests, weekly_forecasts, forecast_outcomes, llm_overlays, volatility_regimes, watched_events. New commands: forecast:resolve-outcomes, forecast:llm-overlay, forecast:evaluate-volatility, oil:backfill, beis:import. Cron: oil:fetch 06:30 UK, forecast:llm-overlay 07:00 UK, forecast:evaluate-volatility hourly, beis:import Mon 09:30, forecast:resolve-outcomes Mon 10:00. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
36 lines
797 B
PHP
36 lines
797 B
PHP
<?php
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namespace App\Models;
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use Illuminate\Database\Eloquent\Attributes\Fillable;
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use Illuminate\Database\Eloquent\Model;
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#[Fillable([
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'ran_at',
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'forecast_for_week',
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'direction',
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'confidence',
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'reasoning',
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'events_json',
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'agrees_with_ridge',
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'major_impact_event',
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'volatility_flag_on',
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'search_used',
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])]
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class LlmOverlay extends Model
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{
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protected function casts(): array
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{
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return [
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'ran_at' => 'datetime',
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'forecast_for_week' => 'date',
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'confidence' => 'integer',
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'events_json' => 'array',
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'agrees_with_ridge' => 'boolean',
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'major_impact_event' => 'boolean',
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'volatility_flag_on' => 'boolean',
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'search_used' => 'boolean',
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];
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}
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}
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