diff --git a/tests/pipeline/test_technical.py b/tests/pipeline/test_technical.py
index 110d01ee..97734477 100644
--- a/tests/pipeline/test_technical.py
+++ b/tests/pipeline/test_technical.py
@@ -10,7 +10,11 @@ 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, Aroon
+from zipline.pipeline.factors import (
+    BollingerBands,
+    Aroon,
+    FastStochasticOscillator
+)
 from zipline.testing import ExplodingObject, parameter_space
 from zipline.testing.fixtures import WithAssetFinder, ZiplineTestCase
 from zipline.testing.predicates import assert_equal
@@ -174,3 +178,62 @@ class AroonTestCase(ZiplineTestCase):
         aroon.compute(today, assets, out, lows, highs)
 
         assert_equal(out, expected_out)
+
+
+class TestFastStochasticOscillator(WithTechnicalFactor, ZiplineTestCase):
+    """
+    Test the Fast Stochastic Oscillator
+    """
+
+    def test_fso_expected_basic(self):
+        """
+        Simple test of expected output from fast stochastic oscillator
+        """
+        fso = FastStochasticOscillator()
+
+        today = pd.Timestamp('2015')
+        assets = np.arange(3, dtype=np.float)
+        out = np.empty(shape=(3,), dtype=np.float)
+
+        highs = np.full((50, 3), 3)
+        lows = np.full((50, 3), 2)
+        closes = np.full((50, 3), 4)
+
+        fso.compute(today, assets, out, closes, lows, highs)
+
+        # Expected %K
+        assert_equal(out, np.full((3,), 200))
+
+    def test_fso_expected_with_talib(self):
+        """
+        Test the output that is returned from the fast stochastic oscillator
+        is the same as that from the ta-lib STOCHF function.
+        """
+        window_length = 14
+        nassets = 6
+        closes = np.random.random_integers(1, 6, size=(50, nassets))*1.0
+        highs = np.random.random_integers(4, 6, size=(50, nassets))*1.0
+        lows = np.random.random_integers(1, 3, size=(50, nassets))*1.0
+
+        expected_out_k = []
+        for i in range(nassets):
+            e = talib.STOCHF(
+                high=highs[:, i],
+                low=lows[:, i],
+                close=closes[:, i],
+                fastk_period=window_length,
+            )
+
+            expected_out_k.append(e[0][-1])
+        expected_out_k = np.array(expected_out_k)
+
+        today = pd.Timestamp('2015')
+        out = np.empty(shape=(nassets,), dtype=np.float)
+        assets = np.arange(nassets, dtype=np.float)
+
+        fso = FastStochasticOscillator()
+        fso.compute(
+            today, assets, out, closes, lows, highs
+        )
+
+        assert_equal(out, expected_out_k)
diff --git a/zipline/pipeline/factors/__init__.py b/zipline/pipeline/factors/__init__.py
index 53eecf03..7858920c 100644
--- a/zipline/pipeline/factors/__init__.py
+++ b/zipline/pipeline/factors/__init__.py
@@ -21,6 +21,7 @@ from .technical import (
     EWMSTD,
     ExponentialWeightedMovingAverage,
     ExponentialWeightedMovingStdDev,
+    FastStochasticOscillator,
     MaxDrawdown,
     Returns,
     RollingLinearRegressionOfReturns,
@@ -49,6 +50,7 @@ __all__ = [
     'ExponentialWeightedMovingAverage',
     'ExponentialWeightedMovingStdDev',
     'Factor',
+    'FastStochasticOscillator',
     'Latest',
     'MaxDrawdown',
     'RecarrayField',
diff --git a/zipline/pipeline/factors/technical.py b/zipline/pipeline/factors/technical.py
index 8eb9b85b..c54a6f1d 100644
--- a/zipline/pipeline/factors/technical.py
+++ b/zipline/pipeline/factors/technical.py
@@ -38,6 +38,7 @@ from zipline.utils.math_utils import (
     nanmean,
     nanstd,
     nansum,
+    nanmin,
 )
 from .factor import CustomFactor
 
@@ -782,3 +783,44 @@ class Aroon(CustomFactor):
             },
             out=out.down,
         )
+
+
+class FastStochasticOscillator(CustomFactor):
+    """
+    Fast Stochastic Oscillator Indicator [%K, Momentum Indicator]
+    https://wiki.timetotrade.eu/Stochastic
+
+    This stochastic is considered volatile, and varies a lot when used in
+    market analysis. It is recommended to use the slow stochastic oscillator
+    or a moving average of the %K [%D].
+
+    **Default Inputs:** :data: `zipline.pipeline.data.USEquityPricing.close`
+                        :data: `zipline.pipeline.data.USEquityPricing.low`
+                        :data: `zipline.pipeline.data.USEquityPricing.high`
+
+    **Default Window Length:** 14
+
+    Returns
+    -------
+    out: %K oscillator
+    """
+    inputs = (USEquityPricing.close, USEquityPricing.low, USEquityPricing.high)
+    window_safe = True
+    window_length = 14
+
+    def compute(self, today, assets, out, closes, lows, highs):
+
+        highest_highs = nanmax(highs, axis=0)
+        lowest_lows = nanmin(lows, axis=0)
+        today_closes = closes[-1]
+
+        evaluate(
+            '((tc - ll) / (hh - ll)) * 100',
+            local_dict={
+                'tc': today_closes,
+                'll': lowest_lows,
+                'hh': highest_highs,
+            },
+            global_dict={},
+            out=out,
+        )