{% load humanize %}{% if error %}
<div class="notice" role="alert">
  <p>{{ error }}</p>
  <p>{% if retry_url %}<a href="{{ retry_url }}">Run it again</a> or <a href="{% url 'home' %}">try another symbol</a>{% else %}<a href="{% url 'home' %}">Try another symbol</a>{% endif %}</p>
</div>
{% else %}{% with r=results %}
<section class="verdict" data-outcome="{% if r.verdict.significant %}significant{% elif r.verdict.beaten %}noise{% else %}unbeaten{% endif %}">
  <h2 class="headline">{{ r.headline }}</h2>
  {% if r.trend_note %}<p class="trend-note">{{ r.trend_note }}</p>{% endif %}
  <p class="verdict-meta">
    {{ r.horizon }}-trading-day forecasts made on each of {{ r.origins }} trading days,
    <time datetime="{{ r.first_origin|date:'Y-m-d' }}">{{ r.first_origin|date:'j M Y' }}</time> to
    <time datetime="{{ r.last_target|date:'Y-m-d' }}">{{ r.last_target|date:'j M Y' }}</time>.
    Prices to the close of <time datetime="{{ r.as_of|date:'Y-m-d' }}">{{ r.as_of|date:'j M Y' }}</time>.
  </p>
  <p class="verdict-meta">
    Over this period the price moved {{ r.period_change }}. It was higher {{ r.horizon }} day{{ r.horizon|pluralize }}
    later after {{ r.up_origins }} of the {{ r.origins }} forecasts.
  </p>
  <p class="verdict-meta">
    Hansen's SPA test of whether any model beats naive: p = {{ r.spa_p_value }}.
  </p>
</section>

<section class="leaderboard" aria-labelledby="leaderboard-title">
  <h2 id="leaderboard-title">Leaderboard</h2>
  <div class="table-scroll">
    <table class="data-table">
      <thead>
        <tr>
          <th scope="col">Model</th>
          <th scope="col" class="num">Mean error</th>
          <th scope="col" class="num">vs naive</th>
          <th scope="col" class="num">CRPS vs naive</th>
          <th scope="col" class="num">Interval score vs naive</th>
          <th scope="col" class="num">Days won</th>
          <th scope="col" class="num">Direction right</th>
          <th scope="col" class="num">In {{ r.level }}% interval</th>
          <th scope="col" class="num">p-value</th>
        </tr>
      </thead>
      <tbody>
        {% for row in r.rows %}
          <tr{% if row.key == 'naive' %} class="baseline-row"{% endif %}>
            <th scope="row">
              <span class="model-name">{{ row.name }}</span>
              <span class="model-family">{{ row.family }}{% if row.best and r.rivals > 1 %} · best model{% endif %}</span>
            </th>
            <td class="num">{{ row.mae }}</td>
            <td class="num {{ row.change.tone }}">{{ row.change.text }}</td>
            <td class="num {{ row.crps.tone }}">{{ row.crps.text }}</td>
            <td class="num {{ row.interval.tone }}">{{ row.interval.text }}</td>
            <td class="num">{{ row.win_rate }}{% if row.ties %}<span class="cell-note">{{ row.ties }}</span>{% endif %}</td>
            <td class="num">{{ row.direction }}</td>
            <td class="num">{{ row.coverage }}</td>
            <td class="num">{{ row.p_value }}{% if row.significant %}<span class="significant" title="p below {{ r.significance }}">*</span>{% endif %}</td>
          </tr>
        {% endfor %}
      </tbody>
    </table>
  </div>
  <dl class="notes">
    <div><dt>Mean error</dt><dd>Average gap between forecast and actual close at the horizon, measured on log prices, so it reads as a percentage at any price level.</dd></div>
    <div><dt>vs naive</dt><dd>Mean error relative to the naive forecast. Negative is better.</dd></div>
    <div><dt>CRPS vs naive</dt><dd>Scores the whole forecast distribution, not just its centre. Relative to naive; negative is better.</dd></div>
    <div><dt>Interval score vs naive</dt><dd>Rewards a narrow {{ r.level }}% interval and charges heavily for a close outside it. Relative to naive; negative is better.</dd></div>
    <div><dt>Days won</dt><dd>Share of the {{ r.origins }} forecasts where the model was closer than naive. Days it forecast exactly what naive did are ties, counted below the share.</dd></div>
    <div><dt>Direction right</dt><dd>How often the forecast moved the same way as the price did. Always predicting a rise would have scored {{ r.always_up }}.</dd></div>
    <div><dt>In {{ r.level }}% interval</dt><dd>How often the actual close landed inside the model's {{ r.level }}% interval. Close to {{ r.level }}% is well calibrated.</dd></div>
    <div><dt>p-value</dt><dd>Diebold-Mariano test against naive, allowing for overlapping forecasts and adjusted for testing {{ r.rivals|apnumber }} model{{ r.rivals|pluralize }} (Holm). An asterisk marks p below {{ r.significance }}.</dd></div>
  </dl>
</section>

<section class="trend" aria-labelledby="trend-title">
  <h2 id="trend-title">Up and down markets</h2>
  <p class="section-lede">
    Error relative to naive, split by whether the close was above or below its {{ r.trend_days }}-day average on the
    day of the forecast. An edge that only shows up on one side may just be a bet on the trend.
  </p>
  <div class="table-scroll">
    <table class="data-table">
      <thead>
        <tr>
          <th scope="col">Model</th>
          <th scope="col" class="num">Uptrend<br>{{ r.trend.days.0 }} days</th>
          <th scope="col" class="num">Downtrend<br>{{ r.trend.days.1 }} days</th>
        </tr>
      </thead>
      <tbody>
        {% for row in r.trend.rows %}
          <tr>
            <th scope="row"><span class="model-name">{{ row.name }}</span></th>
            {% for cell in row.cells %}<td class="num {{ cell.tone }}">{{ cell.text }}</td>{% endfor %}
          </tr>
        {% endfor %}
      </tbody>
    </table>
  </div>
</section>

<section class="charts" aria-labelledby="charts-title">
  <h2 id="charts-title">Forecasts</h2>
  {% if r.rivals > 1 %}
  <fieldset class="segmented model-picker">
    <legend>Compare with naive</legend>
    <div class="segmented-options">
      {% for row in r.rows %}{% if row.key != 'naive' %}
        <input type="radio" name="model" value="{{ row.key }}" id="model-{{ forloop.counter }}"{% if row.best %} checked{% endif %}>
        <label for="model-{{ forloop.counter }}">{{ row.name }}</label>
      {% endif %}{% endfor %}
    </div>
  </fieldset>
  {% endif %}

  {% if r.chart.future.dates %}
  <figure class="chart" data-chart="price">
    <figcaption>
      <h3>The next {{ r.horizon }} trading day{{ r.horizon|pluralize }}</h3>
      <p>
        Recent closes, then each model's forecast from the latest close. The band is the selected model's {{ r.level }}% interval.
        {% if not r.calendar_known %}This exchange's holiday calendar isn't known, so future dates skip weekends only.{% endif %}
      </p>
    </figcaption>
    <div class="chart-legend" aria-hidden="true"></div>
    <div class="chart-plot"></div>
    <details class="chart-data"><summary>Show the data</summary><div class="table-scroll"></div></details>
  </figure>
  {% endif %}

  <figure class="chart" data-chart="cumulative">
    <figcaption>
      <h3>Running total against naive</h3>
      <p>
        Each day adds how much closer the model's {{ r.horizon }}-day forecast was than naive's, in percentage points
        of error. The line rises while the model is winning and falls while it is losing, so a steady climb is a lasting
        edge and a jump is one lucky stretch.
      </p>
    </figcaption>
    <div class="chart-plot"></div>
    <details class="chart-data"><summary>Show the data</summary><div class="table-scroll"></div></details>
  </figure>
</section>
{{ r.chart|json_script:"chart-data" }}
{% endwith %}{% endif %}