ENH: add true range technical factor

tests/pipeline/test_technical.py

@@ -18,6 +18,7 @@ from zipline.pipeline.factors import (
     IchimokuKinkoHyo,
     LinearWeightedMovingAverage,
     RateOfChangePercentage,
+    TrueRange,
 )
 from zipline.testing import ExplodingObject, parameter_space
 from zipline.testing.fixtures import WithAssetFinder, ZiplineTestCase
@@ -413,3 +414,20 @@ class TestLinearWeightedMovingAverage(ZiplineTestCase):
 
         wma2.compute(today, assets, out, data)
         assert_equal(out, np.array([30.,  31.,  32.,  33.,  34.]))
+
+
+class TestTrueRange(WithTechnicalFactor, ZiplineTestCase):
+
+    def test_tr_basic(self):
+        tr = TrueRange()
+
+        today = pd.Timestamp('2014')
+        assets = np.arange(3, dtype=np.int64)
+        out = np.empty(3, dtype=np.float64)
+
+        highs = np.full((2, 3), 3)
+        lows = np.full((2, 3), 2)
+        closes = np.full((2, 3), 1)
+
+        tr.compute(today, assets, out, highs, lows, closes)
+        assert_equal(out, np.full((3,), 2))

zipline/pipeline/factors/__init__.py

@@ -29,6 +29,7 @@ from .technical import (
     Returns,
     RSI,
     SimpleMovingAverage,
+    TrueRange,
     VWAP,
     WeightedAverageValue,
 )
@@ -58,6 +59,7 @@ __all__ = [
     'RollingSpearmanOfReturns',
     'RSI',
     'SimpleMovingAverage',
+    'TrueRange',
     'VWAP',
     'WeightedAverageValue',
 ]

zipline/pipeline/factors/technical.py

@@ -11,6 +11,7 @@ from numpy import (
     average,
     clip,
     diff,
+    dstack,
     exp,
     fmax,
     full,
@@ -642,3 +643,28 @@ class RateOfChangePercentage(CustomFactor):
                  global_dict={},
                  out=out,
                  )
+
+
+class TrueRange(CustomFactor):
+    """
+    True Range
+
+    A technical indicator originally developed by J. Welles Wilder, Jr.
+    Indicates the true degree of daily price change in an underlying.
+    """
+
+    inputs = (USEquityPricing.high, USEquityPricing.low, USEquityPricing.close, )
+    window_length = 2
+
+    def compute(self, today, assets, out, highs, lows, closes):
+        high_to_low = highs[1:] - lows[1:]
+        high_to_prev_close = abs(highs[1:] - closes[:-1])
+        low_to_prev_close = abs(lows[1:] - closes[:-1])
+        out[:] = nanmax(
+            dstack((
+                high_to_low,
+                high_to_prev_close,
+                low_to_prev_close,
+            )),
+            2
+        )