fuel-price/.claude/rules/scoring.md

Scoring Engine (AlertScoringService)

Purpose

Produces a "fill up now or wait?" recommendation per user based on their local station history. Output is one of: fill_up, wait, no_signal. Never guess — stay silent (no_signal) when signals conflict or data is insufficient.

The 5 signals (in priority order)

Signal 1 — Local price trend (HIGHEST WEIGHT)

• Query station_prices for user's nearest 5 stations (within 5km of user lat/lng)
• Use last 14 days of history for e10 (or user's preferred fuel type)
• Use linear regression, not rolling averages:
  • Run least-squares regression on (recorded_at, price_pence) pairs
  • Calculate slope (pence/day) and R² (goodness of fit, 0–1)
  • Only use the regression result if R² ≥ 0.5 — below that, data is too noisy
  • Use adaptive lookback: try 5 days first (best signal on sharp moves), fall back to 14 days if R² < 0.5
• Falling: slope ≤ -0.3p/day AND R² ≥ 0.5 → wait signal, points scale with slope magnitude
• Rising: slope ≥ +0.3p/day AND R² ≥ 0.5 → fill_up signal
• Flat / noisy: |slope| < 0.3 OR R² < 0.5 → no signal from this source
• Store slope, R², lookback_days, and data_points in signal output
• Weight: 40 points max

Signal 2 — Supermarket anchor effect (HIGH WEIGHT)

• Find nearest supermarket station (is_supermarket = 1) within 10km
• Check if supermarket cut price in last 48 hours (> 1p drop)
• Check if nearest non-supermarket stations have NOT yet followed
• If supermarket cut AND independents haven't moved → strong wait signal
• Also check the inverse: if supermarket RAISED and independents haven't → mild fill_up
• Weight: 35 points max

Signal 3 — Day-of-week pattern (MEDIUM WEIGHT — needs 8+ weeks data)

• Per station: average price by day-of-week over last 90 days
• Only activate if station has 56+ days of history
• If today is statistically 1.5p+ cheaper than weekly average → mild fill_up
• If today is statistically 1.5p+ more expensive → mild wait
• Weight: 15 points max

Signal 4 — Brent crude direction (LOW WEIGHT)

• Read from price_predictions table — never query brent_prices directly in scoring
• OilPriceService::generatePrediction() runs daily at 7am and writes the prediction
• LLM (source = 'llm') is preferred; EWMA (source = 'ewma') is the fallback
• Direction rising → mild fill_up pressure; falling → mild wait; flat → no signal
• Points awarded proportionally to confidence: (confidence / 100) * 10
• Weight: 10 points max

Signal 5 — Price stickiness (CONFIDENCE MODIFIER)

• Per station: calculate average hold duration (days between price changes) from history
• Requires 30+ days of history to activate
• Use as a confidence modifier, not a directional signal:
  • avg hold < 2 days → reduce overall confidence by 5 points (volatile, hard to predict)
  • avg hold 2–4 days → neutral, no adjustment
  • avg hold > 5 days → increase overall confidence by 5 points (predictable, sticky)
• Store avg_hold_days and data_points in signal output
• Applied after all other signals are summed (±5 points)

Confidence thresholds

• Score 70–100: strong signal → fire recommendation + notification
• Score 40–69: weak signal → show in dashboard only, no push/SMS/WhatsApp
• Score 0–39: no_signal → stay silent entirely

Only send notifications when confidence ≥ 70. Never spam.

Output (stored in scoring_results)

[
    'recommendation'  => 'wait',
    'confidence'      => 78,
    'signals'         => [
        'trend'       => [
            'direction'     => 'falling',
            'slope'         => -1.07,     // pence per day
            'r_squared'     => 0.96,
            'lookback_days' => 5,
            'data_points'   => 5,
            'points'        => 32,
        ],
        'supermarket' => ['triggered' => true, 'points' => 35],
        'day_pattern' => ['triggered' => false, 'points' => 0],
        'brent'       => ['direction' => 'flat', 'points' => 0],
        'stickiness'  => ['avg_hold_days' => 2.8, 'modifier' => 0],
    ],
    'local_avg_pence' => 14380,           // 143.80p
    'trend_delta'     => -2.3,            // pence change over lookback period
]

Human-readable reason strings

Always generate a plain-English reason for the recommendation:

• "Prices near you have been falling at 1.1p/day for 5 days. Tesco {station} cut 3p yesterday — independents usually follow within 48 hours."
• "Prices are rising sharply in your area (+7.5p expected this week) — filling up today avoids paying more later."
• "No clear pattern this week — fill up at the cheapest station near you now."

Reason strings are stored in scoring_results.signals JSON and shown in the UI and notifications.

Data quality — anomaly rejection

The Fuel Finder API contains dirty data (live example: 1369.0p/litre in national index). Reject a price record before storing or scoring if:

• price_pence > 25000 (over 250p/litre — physically implausible for UK pump prices)
• price_pence < 10000 (under 100p/litre — almost certainly a decimal entry error)
• Price changed by more than 20p in a single update from the same station (flag for review, do not use in scoring)

Log rejected records to an anomalous_prices table for monitoring. Never let a dirty data point skew the regression slope or collapse R².

Accuracy self-tracking

After 3 days, check if wait recommendation was correct (prices did fall further). Store outcome in scoring_results for future display: "This signal has been right X% of the time in your area."