Add OrderHistory.avg_daily_demand()

- the method calculates the number of daily `Order`s in a `Pixel`
  withing the `train_horizon` preceding the `predict_day`

tests/forecasts/timify/test_avg_daily_demand.py

@@ -0,0 +1,37 @@
+"""Tests for the `OrderHistory.avg_daily_demand()` method."""
+
+from tests import config as test_config
+
+
+def test_avg_daily_demand_with_constant_demand(
+    order_history, good_pixel_id, predict_at,
+):
+    """The average daily demand must be the number of time steps ...
+
+    ... if the demand is `1` at each time step.
+
+    Note: The `order_history` fixture assumes `12` time steps per day as it
+    uses `LONG_TIME_STEP=60` as the length of a time step.
+    """
+    result = order_history.avg_daily_demand(
+        pixel_id=good_pixel_id,
+        predict_day=predict_at.date(),
+        train_horizon=test_config.LONG_TRAIN_HORIZON,
+    )
+
+    assert result == 12.0
+
+
+def test_avg_daily_demand_with_no_demand(
+    order_history, good_pixel_id, predict_at,
+):
+    """Without demand, the average daily demand must be `0.0`."""
+    order_history._data.loc[:, 'n_orders'] = 0
+
+    result = order_history.avg_daily_demand(
+        pixel_id=good_pixel_id,
+        predict_day=predict_at.date(),
+        train_horizon=test_config.LONG_TRAIN_HORIZON,
+    )
+
+    assert result == 0.0