docs(sensors): document price rank sensors and IQR volatility band attributes

Update sensors-volatility.md to cover the three new price rank sensors and the
IQR-based volatility attributes (typical price band / price spike count).
Section headers include technical terms in parentheses for experts:
"Typical Price Band Statistics (IQR)" and "Price Rank Sensors (Percentile Rank)".
Attribute tables list Tukey fence formulas and plain-language explanations
side-by-side.

Regenerate sensor-reference.md to include price_rank_today,
price_rank_tomorrow, and price_rank_today_tomorrow with translations for all
five supported languages.

Impact: Users have full documentation for the new sensors including examples,
formulas, and a multi-language lookup table.

docs/user/docs/sensor-reference.md

@@ -217,6 +217,9 @@ explanations of each sensor's purpose, attributes, and automation examples.
 | <span id="ref-day_pattern_today" class="entity-anchor"></span>`day_pattern_today` | Today's Price Pattern | Preismuster Heute | Prismønster i dag | Prijspatroon Vandaag | Prismönster Idag | ✅ |
 | <span id="ref-day_pattern_tomorrow" class="entity-anchor"></span>`day_pattern_tomorrow` | Tomorrow's Price Pattern | Preismuster Morgen | Prismønster i morgen | Prijspatroon Morgen | Prismönster Imorgon | ❌ |
 | <span id="ref-day_pattern_yesterday" class="entity-anchor"></span>`day_pattern_yesterday` | Yesterday's Price Pattern | Preismuster Gestern | Prismønster i går | Prijspatroon Gisteren | Prismönster Igår | ❌ |
+| <span id="ref-price_rank_today" class="entity-anchor" data-refs="sensors-volatility#available-sensors"></span>`price_rank_today` | Today's Price Rank | Preisrang heute | Prisrang i dag | Prijsrang vandaag | Prisrang idag | ✅ |
+| <span id="ref-price_rank_today_tomorrow" class="entity-anchor" data-refs="sensors-volatility#available-sensors"></span>`price_rank_today_tomorrow` | Today+Tomorrow Price Rank | Preisrang heute+morgen | Prisrang i dag+i morgen | Prijsrang vandaag+morgen | Prisrang idag+imorgon | ❌ |
+| <span id="ref-price_rank_tomorrow" class="entity-anchor" data-refs="sensors-volatility#available-sensors"></span>`price_rank_tomorrow` | Tomorrow's Price Rank | Preisrang morgen | Prisrang i morgen | Prijsrang morgen | Prisrang imorgon | ❌ |
 ## Binary Sensors
 
 ### Binary Sensors

docs/user/docs/sensors-volatility.md

@@ -17,7 +17,7 @@ The sensor's state can be `low`, `moderate`, `high`, or `very_high`, based on co
 ## Available Volatility Sensors
 
 | Sensor                                                                                  | Description                               | Time Window            |
-|---|---|---|
+| --------------------------------------------------------------------------------------- | ----------------------------------------- | ---------------------- |
 | <EntityRef id="today_volatility">Today's Price Volatility</EntityRef>                   | Volatility for the current calendar day   | 00:00 - 23:59 today    |
 | <EntityRef id="tomorrow_volatility">Tomorrow's Price Volatility</EntityRef>             | Volatility for the next calendar day      | 00:00 - 23:59 tomorrow |
 | **Next 24h Price Volatility** (`next_24h_volatility`)                                   | Volatility for the next 24 hours from now | Rolling 24h forward    |
@@ -26,11 +26,13 @@ The sensor's state can be `low`, `moderate`, `high`, or `very_high`, based on co
 ## Configuration
 
 You can adjust the CV thresholds that determine the volatility level:
+
 1. Go to **Settings → Devices & Services → Tibber Prices**.
 2. Click **Configure**.
 3. Go to the **Price Volatility Thresholds** step.
 
 Default thresholds are:
+
 - **Moderate:** 15%
 - **High:** 30%
 - **Very High:** 50%
@@ -40,13 +42,19 @@ Default thresholds are:
 All volatility sensors provide these attributes:
 
 | Attribute                       | Description                                                      | Example      |
-|---|---|---|
+| ------------------------------- | ---------------------------------------------------------------- | ------------ |
 | `price_volatility`              | Volatility level (language-independent, always English)          | `"moderate"` |
 | `price_coefficient_variation_%` | The calculated Coefficient of Variation                          | `23.5`       |
 | `price_spread`                  | The difference between the highest and lowest price              | `12.3`       |
 | `price_min`                     | The lowest price in the period                                   | `10.2`       |
 | `price_max`                     | The highest price in the period                                  | `22.5`       |
 | `price_mean`                    | The arithmetic mean of all prices in the period                  | `15.1`       |
+| `price_median`                  | Median price (50th percentile, robust to outliers)               | `14.8`       |
+| `price_q25`                     | 25th percentile — lower quartile price                           | `11.0`       |
+| `price_q75`                     | 75th percentile — upper quartile price                           | `19.5`       |
+| `price_typical_spread`          | Typical price band width — IQR (Q75 − Q25, the middle 50% of prices)         | `8.5`        |
+| `price_typical_spread_%`        | Typical price band as a percentage of the median (IQR%)                      | `57.4`       |
+| `price_spike_count`             | Intervals outside the Tukey fence (Q25−1.5×IQR … Q75+1.5×IQR) — spikes/dips | `3`          |
 | `interval_count`                | Number of price intervals included in the calculation            | `96`         |
 
 ## Usage in Automations & Best Practices
@@ -61,6 +69,7 @@ For automations, it is strongly recommended to use the `price_volatility` attrib
 
 **Good Example (Robust Automation):**
 This automation triggers only if the volatility is classified as `high` or `very_high`, respecting your central settings and working independently of the system language.
+
 <details>
 <summary>Show YAML: Good Example (Robust Automation)</summary>
 
@@ -88,6 +97,7 @@ You might be tempted to use the numeric `price_coefficient_variation_%` attribut
 
 **Bad Example (Brittle Automation):**
 This automation uses a hard-coded value. If you later change the "High" threshold in the integration's options to 35%, this automation will not respect that change and might trigger at the wrong time.
+
 <details>
 <summary>Show YAML: Bad Example (Brittle Automation)</summary>
 
@@ -110,3 +120,104 @@ automation:
 </details>
 
 By following the "Good Example", your automations become simpler, more readable, and much easier to maintain.
+
+## Typical Price Band Statistics (IQR)
+
+In addition to the CV-based volatility level, every volatility sensor provides **typical price band statistics** as attributes. These are derived from the **IQR (Interquartile Range)** — the spread of the middle 50% of prices — making them more **robust to isolated price spikes** than the CV.
+
+| Metric                | CV (state)                                | IQR attributes                     |
+| --------------------- | ----------------------------------------- | ---------------------------------- |
+| Sensitive to spikes?  | ✅ Yes — spikes inflate CV                | ❌ No — IQR ignores the outer 25%  |
+| Use for optimization? | "Is today worth optimizing?"              | "How wide is the core price band?" |
+| Best for              | Triggering battery/EV charging strategies | Understanding price structure      |
+
+The `price_typical_spread_%` attribute (IQR as a percentage of the median) tells you how wide the **core** price band is relative to the median. Even on a high-CV day with isolated spikes, a low `price_typical_spread_%` means most of the day has stable prices — only a few intervals are outliers.
+
+The `price_spike_count` attribute (Tukey fence method: Q25 − 1.5×IQR to Q75 + 1.5×IQR) tells you how many intervals fall outside the normal range. A high `price_spike_count` day with a high CV is a classic "spiky" day: mostly stable prices with a few expensive or cheap peaks.
+
+---
+
+## Price Rank Sensors (Percentile Rank)
+
+The price rank sensors answer the simple question: **"Is the current price cheap or expensive compared to the rest of the day?"**
+
+Unlike the volatility sensors (which measure the _shape_ of the entire price distribution), price rank sensors place the _current price_ within that distribution — technically its **percentile rank**. A value of **0% means cheapest interval of the day**, while a value near **99% means most expensive**.
+
+### How It Works (Percentile Rank Formula)
+
+```
+Price rank (percentile rank) = (number of intervals strictly cheaper than now) ÷ total intervals × 100
+```
+
+The cheapest interval always returns 0% — you can use `state == 0` to detect the absolute cheapest moment.
+
+### Available Sensors
+
+| Sensor                                                                          | Reference Set                                   | Enabled by Default |
+| ------------------------------------------------------------------------------- | ----------------------------------------------- | ------------------ |
+| <EntityRef id="price_rank_today">Today's Price Rank</EntityRef>                 | All of today's 96 quarter-hour intervals        | ✅ Yes             |
+| <EntityRef id="price_rank_tomorrow">Tomorrow's Price Rank</EntityRef>           | All of tomorrow's 96 intervals (once available) | ❌ No              |
+| <EntityRef id="price_rank_today_tomorrow">Today+Tomorrow Price Rank</EntityRef> | Combined pool (up to 192 intervals)             | ❌ No              |
+
+### Key Attributes
+
+All price rank sensors share these attributes:
+
+| Attribute            | Description                                   | Example |
+| -------------------- | --------------------------------------------- | ------- |
+| `current_price`      | The price being ranked                        | `14.2`  |
+| `prices_below_count` | How many intervals are strictly cheaper       | `23`    |
+| `interval_count`     | Total intervals in the reference set          | `96`    |
+| `reference_min`      | The cheapest price in the reference set       | `8.1`   |
+| `reference_max`      | The most expensive price in the reference set | `27.3`  |
+| `reference_mean`     | Average price of the reference set            | `15.8`  |
+
+### When to Use Which Sensor
+
+- **`price_rank_today`** — For same-day scheduling. "Is now within the cheapest quarter of today? (< 25%)"
+- **`price_rank_tomorrow`** — To compare today's price against what tomorrow offers. "Is it worth waiting until tomorrow?"
+- **`price_rank_today_tomorrow`** — Broadest view for flexible tasks. "Is this among the cheapest moments of a 48-hour window?"
+
+### Usage in Automations
+
+<details>
+<summary>Show YAML: Start dishwasher in bottom quartile</summary>
+
+```yaml
+automation:
+    - alias: "Start dishwasher at cheapest time of day"
+      trigger:
+          - platform: numeric_state
+            entity_id: sensor.<home_name>_today_s_price_rank
+            below: 25
+      condition:
+          - condition: state
+            entity_id: binary_sensor.<home_name>_best_price_period
+            state: "on"
+      action:
+          - service: switch.turn_on
+            entity_id: switch.dishwasher
+```
+
+</details>
+
+<details>
+<summary>Show YAML: Postpone task if tomorrow is cheaper</summary>
+
+```yaml
+automation:
+    - alias: "Skip charging tonight if tomorrow is cheaper"
+      trigger:
+          - platform: time
+            at: "21:00:00"
+      condition:
+          # Only postpone if tomorrow's cheapest quartile is better than the current price
+          - condition: template
+            value_template: >
+                {{ states('sensor.<home_name>_tomorrow_s_price_rank') | float(100) < 25 }}
+      action:
+          - service: input_boolean.turn_off
+            entity_id: input_boolean.ev_charge_tonight
+```
+
+</details>