fixing ease of movement implementation and adding test: bukosabino#58

ta/tests/__init__.py

@@ -2,4 +2,5 @@
 from ta.tests.trend import (TestADXIndicator, TestCCIIndicator,
                             TestVortexIndicator)
 from ta.tests.utils import TestGeneral
-from ta.tests.volume import TestOnBalanceVolumeIndicator
+from ta.tests.volume import (TestEaseOfMovementIndicator,
+                             TestOnBalanceVolumeIndicator)

ta/tests/data/cs-easeofmovement.csv

@@ -0,0 +1,31 @@
+Date,High,Low,Volume,EMV,SMA_EMV
+18-Jun-12,63.740000000,62.630000000,32178836.000000000,,
+19-Jun-12,64.510000000,63.850000000,36461672.000000000,1.801069353,
+20-Jun-12,64.570000000,63.810000000,51372680.000000000,0.014793855,
+21-Jun-12,64.310000000,62.620000000,42476356.000000000,-2.884545934,
+22-Jun-12,63.430000000,62.730000000,29504176.000000000,-0.913430017,
+25-Jun-12,62.850000000,61.950000000,33098600.000000000,-1.849020805,
+26-Jun-12,62.700000000,62.060000000,30577960.000000000,-0.041860216,
+27-Jun-12,63.180000000,62.690000000,35693928.000000000,0.761894292,
+28-Jun-12,62.470000000,61.540000000,49768136.000000000,-1.737858938,
+29-Jun-12,64.160000000,63.210000000,44759968.000000000,3.565686195,
+2-Jul-12,64.380000000,63.870000000,33425504.000000000,0.671343654,
+3-Jul-12,64.890000000,64.290000000,15895085.000000000,1.755259566,
+5-Jul-12,65.250000000,64.480000000,37015388.000000000,0.572059382,
+6-Jul-12,64.690000000,63.650000000,40672116.000000000,-1.777138913,
+9-Jul-12,64.260000000,63.680000000,35627200.000000000,-0.325593928,-0.027667318
+10-Jul-12,64.510000000,63.120000000,47337336.000000000,-0.455137568,-0.188824955
+11-Jul-12,63.460000000,62.500000000,43373576.000000000,-1.848129838,-0.321890933
+12-Jul-12,62.690000000,61.860000000,57651752.000000000,-1.014973491,-0.188350045
+13-Jul-12,63.520000000,62.560000000,32357184.000000000,2.269665988,0.039013956
+16-Jul-12,63.520000000,62.950000000,27620876.000000000,0.402413015,0.199830657
+17-Jul-12,63.740000000,62.630000000,42467704.000000000,-0.130687546,0.193485848
+18-Jul-12,64.580000000,63.390000000,44460240.000000000,2.141239004,0.292010470
+19-Jul-12,65.310000000,64.720000000,52992592.000000000,1.146764061,0.498054970
+20-Jul-12,65.100000000,64.210000000,40561552.000000000,-0.789910603,0.186940913
+23-Jul-12,63.680000000,62.510000000,48636228.000000000,-3.752758129,-0.129066357
+24-Jul-12,63.660000000,62.550000000,57230032.000000000,0.019395411,-0.253056654
+25-Jul-12,62.950000000,62.150000000,46260856.000000000,-0.959774718,-0.362473375
+26-Jul-12,63.730000000,62.970000000,41926492.000000000,1.450157099,-0.131952232
+27-Jul-12,64.990000000,63.640000000,42620976.000000000,3.056593542,0.109632588
+30-Jul-12,65.310000000,64.600000000,37809176.000000000,1.201824658,0.227987032

ta/tests/volume.py

@@ -1,7 +1,9 @@
 import pandas as pd
 
 from ta.tests.utils import TestIndicator
-from ta.volume import OnBalanceVolumeIndicator, on_balance_volume
+from ta.volume import (EaseOfMovementIndicator, OnBalanceVolumeIndicator,
+                       ease_of_movement, on_balance_volume,
+                       sma_ease_of_movement)
 
 
 class TestOnBalanceVolumeIndicator(TestIndicator):
@@ -21,3 +23,37 @@ def test_obv2(self):
         result = OnBalanceVolumeIndicator(
             close=self._df['Close'], volume=self._df['Volume'], fillna=False).on_balance_volume()
         pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
+
+
+class TestEaseOfMovementIndicator(TestIndicator):
+    """
+    https://school.stockcharts.com/doku.php?id=technical_indicators:ease_of_movement_emv
+    """
+
+    _filename = 'ta/tests/data/cs-easeofmovement.csv'
+
+    def test_ease_of_movement(self):
+        target = 'EMV'
+        result = ease_of_movement(
+            high=self._df['High'], low=self._df['Low'], volume=self._df['Volume'], n=14, fillna=False)
+        pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
+
+    def test_ease_of_movement2(self):
+        target = 'EMV'
+        result = EaseOfMovementIndicator(
+            high=self._df['High'], low=self._df['Low'], volume=self._df['Volume'], n=14, fillna=False
+        ).ease_of_movement()
+        pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
+
+    def test_sma_ease_of_movement(self):
+        target = 'SMA_EMV'
+        result = sma_ease_of_movement(
+            high=self._df['High'], low=self._df['Low'], volume=self._df['Volume'], n=14, fillna=False)
+        pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
+
+    def test_sma_ease_of_movement2(self):
+        target = 'SMA_EMV'
+        result = EaseOfMovementIndicator(
+            high=self._df['High'], low=self._df['Low'], volume=self._df['Volume'], n=14, fillna=False
+        ).sma_ease_of_movement()
+        pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)

ta/volume.py

@@ -159,33 +159,35 @@ class EaseOfMovementIndicator(IndicatorMixin):
     https://en.wikipedia.org/wiki/Ease_of_movement
     """
 
-    def __init__(self, high: pd.Series, low: pd.Series, close: pd.Series,
-                 volume: pd.Series, n: int = 20, fillna: bool = False):
+    def __init__(self, high: pd.Series, low: pd.Series, volume: pd.Series, n: int = 14, fillna: bool = False):
         """
         Args:
             high(pandas.Series): dataset 'High' column.
             low(pandas.Series): dataset 'Low' column.
-            close(pandas.Series): dataset 'Close' column.
             volume(pandas.Series): dataset 'Volume' column.
             n(int): n period.
             fillna(bool): if True, fill nan values.
         """
         self._high = high
         self._low = low
-        self._close = close
         self._volume = volume
         self._n = n
         self._fillna = fillna
         self._run()
 
     def _run(self):
-        emv = (self._high.diff(1) + self._low.diff(1)) * (self._high - self._low) / (2 * self._volume)
-        self._emv = emv.rolling(self._n, min_periods=0).mean()
+        self._emv = (self._high.diff(1) + self._low.diff(1)) * (self._high - self._low) / (2 * self._volume)
+        self._emv *= 100000000
 
     def ease_of_movement(self) -> pd.Series:
         emv = self.check_fillna(self._emv, value=0)
         return pd.Series(emv, name=f'eom_{self._n}')
 
+    def sma_ease_of_movement(self) -> pd.Series:
+        emv = self._emv.rolling(self._n, min_periods=0).mean()
+        emv = self.check_fillna(emv, value=0)
+        return pd.Series(emv, name=f'sma_eom_{self._n}')
+
 
 class VolumePriceTrendIndicator(IndicatorMixin):
     """Volume-price trend (VPT)
@@ -336,7 +338,29 @@ def force_index(close, volume, n=2, fillna=False):
     return ForceIndexIndicator(close=close, volume=volume, n=n, fillna=fillna).force_index()
 
 
-def ease_of_movement(high, low, close, volume, n=20, fillna=False):
+def ease_of_movement(high, low, volume, n=14, fillna=False):
+    """Ease of movement (EoM, EMV)
+
+    It relate an asset's price change to its volume and is particularly useful
+    for assessing the strength of a trend.
+
+    https://en.wikipedia.org/wiki/Ease_of_movement
+
+    Args:
+        high(pandas.Series): dataset 'High' column.
+        low(pandas.Series): dataset 'Low' column.
+        volume(pandas.Series): dataset 'Volume' column.
+        n(int): n period.
+        fillna(bool): if True, fill nan values.
+
+    Returns:
+        pandas.Series: New feature generated.
+    """
+    return EaseOfMovementIndicator(
+        high=high, low=low, volume=volume, n=n, fillna=fillna).ease_of_movement()
+
+
+def sma_ease_of_movement(high, low, volume, n=14, fillna=False):
     """Ease of movement (EoM, EMV)
 
     It relate an asset's price change to its volume and is particularly useful
@@ -347,7 +371,6 @@ def ease_of_movement(high, low, close, volume, n=20, fillna=False):
     Args:
         high(pandas.Series): dataset 'High' column.
         low(pandas.Series): dataset 'Low' column.
-        close(pandas.Series): dataset 'Close' column.
         volume(pandas.Series): dataset 'Volume' column.
         n(int): n period.
         fillna(bool): if True, fill nan values.
@@ -356,7 +379,7 @@ def ease_of_movement(high, low, close, volume, n=20, fillna=False):
         pandas.Series: New feature generated.
     """
     return EaseOfMovementIndicator(
-        high=high, low=low, close=close, volume=volume, n=n, fillna=fillna).ease_of_movement()
+        high=high, low=low, volume=volume, n=n, fillna=fillna).sma_ease_of_movement()
 
 
 def volume_price_trend(close, volume, fillna=False):

ta/wrapper.py

@@ -33,20 +33,36 @@ def add_volume_ta(df: pd.DataFrame, high: str, low: str, close: str, volume: str
     Returns:
         pandas.core.frame.DataFrame: Dataframe with new features.
     """
+
+    # Accumulation Distribution Index
     df[f'{colprefix}volume_adi'] = AccDistIndexIndicator(
         high=df[high], low=df[low], close=df[close], volume=df[volume], fillna=fillna).acc_dist_index()
+
+    # On Balance Volume
     df[f'{colprefix}volume_obv'] = OnBalanceVolumeIndicator(
         close=df[close], volume=df[volume], fillna=fillna).on_balance_volume()
+
+    # Chaikin Money Flow
     df[f'{colprefix}volume_cmf'] = ChaikinMoneyFlowIndicator(
         high=df[high], low=df[low], close=df[close], volume=df[volume], fillna=fillna).chaikin_money_flow()
+
+    # Force Index
     df[f'{colprefix}volume_fi'] = ForceIndexIndicator(
         close=df[close], volume=df[volume], fillna=fillna).force_index()
-    df[f'{colprefix}volume_em'] = EaseOfMovementIndicator(
-        high=df[high], low=df[low], close=df[close], volume=df[volume], n=14, fillna=fillna).ease_of_movement()
+
+    # Ease of Movement
+    indicator = EaseOfMovementIndicator(high=df[high], low=df[low], volume=df[volume], n=14, fillna=fillna)
+    df[f'{colprefix}volume_em'] = indicator.ease_of_movement()
+    df[f'{colprefix}volume_sma_em'] = indicator.sma_ease_of_movement()
+
+    # Volume Price Trend
     df[f'{colprefix}volume_vpt'] = VolumePriceTrendIndicator(
         close=df[close], volume=df[volume], fillna=fillna).volume_price_trend()
+
+    # Negative Volume Index
     df[f'{colprefix}volume_nvi'] = NegativeVolumeIndexIndicator(
         close=df[close], volume=df[volume], fillna=fillna).negative_volume_index()
+
     return df