Merge pull request #1258 from femtoghoti/add_aroon_indicator

ENH: Add Aroon indicator.

tests/pipeline/test_technical.py

@@ -1,3 +1,6 @@
+from __future__ import division
+
+from nose_parameterized import parameterized
 import numpy as np
 import pandas as pd
 import talib
@@ -7,7 +10,7 @@ from zipline.pipeline import TermGraph
 from zipline.pipeline.data import USEquityPricing
 from zipline.pipeline.engine import SimplePipelineEngine
 from zipline.pipeline.term import AssetExists
-from zipline.pipeline.factors import BollingerBands
+from zipline.pipeline.factors import BollingerBands, Aroon
 from zipline.testing import ExplodingObject, parameter_space
 from zipline.testing.fixtures import WithAssetFinder, ZiplineTestCase
 from zipline.testing.predicates import assert_equal
@@ -138,3 +141,36 @@ class BollingerBandsTestCase(WithTechnicalFactor, ZiplineTestCase):
         self.assertIs(lower, bbands.lower)
         self.assertIs(middle, bbands.middle)
         self.assertIs(upper, bbands.upper)
+
+
+class AroonTestCase(ZiplineTestCase):
+    window_length = 10
+    nassets = 5
+    dtype = [('down', 'f8'), ('up', 'f8')]
+
+    @parameterized.expand([
+        (np.arange(window_length),
+         np.arange(window_length) + 1,
+         np.recarray(shape=(nassets,), dtype=dtype,
+                     buf=np.array([0, 100] * nassets, dtype='f8'))),
+        (np.arange(window_length, 0, -1),
+         np.arange(window_length, 0, -1) - 1,
+         np.recarray(shape=(nassets,), dtype=dtype,
+                     buf=np.array([100, 0] * nassets, dtype='f8'))),
+        (np.array([10, 10, 10, 1, 10, 10, 10, 10, 10, 10]),
+         np.array([1, 1, 1, 1, 1, 10, 1, 1, 1, 1]),
+         np.recarray(shape=(nassets,), dtype=dtype,
+                     buf=np.array([100 * 3 / 9, 100 * 5 / 9] * nassets,
+                                  dtype='f8'))),
+    ])
+    def test_aroon_basic(self, lows, highs, expected_out):
+        aroon = Aroon(window_length=self.window_length)
+        today = pd.Timestamp('2014', tz='utc')
+        assets = pd.Index(np.arange(self.nassets, dtype=np.int64))
+        shape = (self.nassets,)
+        out = np.recarray(shape=shape, dtype=self.dtype,
+                          buf=np.empty(shape=shape, dtype=self.dtype))
+
+        aroon.compute(today, assets, out, lows, highs)
+
+        assert_equal(out, expected_out)

zipline/pipeline/factors/__init__.py

@@ -14,6 +14,7 @@ from .events import (
     BusinessDaysUntilNextExDate,
 )
 from .technical import (
+    Aroon,
     AverageDollarVolume,
     BollingerBands,
     EWMA,
@@ -32,6 +33,7 @@ from .technical import (
 )
 
 __all__ = [
+    'Aroon',
     'AverageDollarVolume',
     'BollingerBands',
     'BusinessDaysSince13DFilingsDate',

zipline/pipeline/factors/technical.py

@@ -2,6 +2,8 @@
 Technical Analysis Factors
 --------------------------
 """
+from __future__ import division
+
 from numbers import Number
 from numpy import (
     abs,
@@ -31,6 +33,7 @@ from zipline.utils.numpy_utils import ignore_nanwarnings
 from zipline.utils.input_validation import expect_types
 from zipline.utils.math_utils import (
     nanargmax,
+    nanargmin,
     nanmax,
     nanmean,
     nanstd,
@@ -739,3 +742,43 @@ class BollingerBands(CustomFactor):
         out.middle = middle = nanmean(close, axis=0)
         out.upper = middle + difference
         out.lower = middle - difference
+
+
+class Aroon(CustomFactor):
+    """
+    Aroon technical indicator.
+    https://www.fidelity.com/learning-center/trading-investing/technical
+    -analysis/technical-indicator-guide/aroon-indicator
+
+    **Defaults Inputs:** USEquityPricing.low, USEquityPricing.high
+
+    Parameters
+    ----------
+    window_length : int > 0
+        Length of the lookback window over which to compute the Aroon
+        indicator.
+    """
+
+    inputs = (USEquityPricing.low, USEquityPricing.high)
+    outputs = ('down', 'up')
+
+    def compute(self, today, assets, out, lows, highs):
+        wl = self.window_length
+        high_date_index = nanargmax(highs, axis=0)
+        low_date_index = nanargmin(lows, axis=0)
+        evaluate(
+            '(100 * high_date_index) / (wl - 1)',
+            local_dict={
+                'high_date_index': high_date_index,
+                'wl': wl,
+            },
+            out=out.up,
+        )
+        evaluate(
+            '(100 * low_date_index) / (wl - 1)',
+            local_dict={
+                'low_date_index': low_date_index,
+                'wl': wl,
+            },
+            out=out.down,
+        )