Feature/tests

examples_to_use/all_features_example.py

@@ -5,7 +5,7 @@
 import ta
 
 # Load data
-df = pd.read_csv('../data/datas.csv', sep=',')
+df = pd.read_csv('../ta/tests/data/datas.csv', sep=',')
 
 # Clean nan values
 df = ta.utils.dropna(df)

examples_to_use/bollinger_band_features_example.py

@@ -4,7 +4,7 @@
 import ta
 
 # Load data
-df = pd.read_csv('../data/datas.csv', sep=',')
+df = pd.read_csv('../ta/tests/data/datas.csv', sep=',')
 
 # Clean nan values
 df = ta.utils.dropna(df)

examples_to_use/roc.py

@@ -0,0 +1,12 @@
+"""This is a example adding volume features.
+"""
+import pandas as pd
+import ta
+
+# Load data
+df = pd.read_csv('../ta/tests/data/datas.csv', sep=',')
+
+# Clean nan values
+df = ta.utils.dropna(df)
+
+ta.momentum.roc(close=df['Close'])

examples_to_use/visualize_features.ipynb

@@ -24,18 +24,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Load data\n",
-    "df = pd.read_csv('../data/datas.csv', sep=',')\n",
+    "df = pd.read_csv('../ta/tests/data/datas.csv', sep=',')\n",
     "df = ta.utils.dropna(df)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -44,7 +44,7 @@
        "(46306, 8)"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -165,7 +165,7 @@
        "18        5.572793  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -176,16 +176,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/vant/projects/python/ta/env-ta/lib/python3.7/site-packages/ta/trend.py:468: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "/home/vant/projects/python/ta/env-ta/lib/python3.7/site-packages/ta/trend.py:543: RuntimeWarning: invalid value encountered in double_scalars\n",
       "  dip[i] = 100 * (self._dip[i]/self._trs[i])\n",
-      "/home/vant/projects/python/ta/env-ta/lib/python3.7/site-packages/ta/trend.py:472: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "/home/vant/projects/python/ta/env-ta/lib/python3.7/site-packages/ta/trend.py:547: RuntimeWarning: invalid value encountered in double_scalars\n",
       "  din[i] = 100 * (self._din[i]/self._trs[i])\n"
      ]
     }
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -206,7 +206,7 @@
        "(46306, 69)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }

examples_to_use/volume_features_example.py

@@ -4,7 +4,7 @@
 import ta
 
 # Load data
-df = pd.read_csv('../data/datas.csv', sep=',')
+df = pd.read_csv('../ta/tests/data/datas.csv', sep=',')
 
 # Clean nan values
 df = ta.utils.dropna(df)

ta/momentum.py

@@ -34,14 +34,12 @@ def __init__(self, close: pd.Series, n: int = 14, fillna: bool = False):
 
     def _run(self):
         diff = self._close.diff(1)
-        which_dn = diff < 0
-
-        up, dn = diff, diff*0
-        up[which_dn], dn[which_dn] = 0, -up[which_dn]
-
-        emaup = up.ewm(com=self._n-1, min_periods=0).mean()
-        emadn = dn.ewm(com=self._n-1, min_periods=0).mean()
-        self._rsi = 100 * emaup / (emaup + emadn)
+        up = diff.where(diff > 0, 0.0)
+        dn = -diff.where(diff < 0, 0.0)
+        emaup = up.ewm(alpha=1/self._n, min_periods=0, adjust=False).mean()
+        emadn = dn.ewm(alpha=1/self._n, min_periods=0, adjust=False).mean()
+        rs = emaup / emadn
+        self._rsi = pd.Series(np.where(emadn == 0, 100, 100-(100/(1+rs))), index=self._close.index)
 
     def rsi(self) -> pd.Series:
         """Relative Strength Index (RSI)
@@ -121,7 +119,7 @@ class TSIIndicator(IndicatorMixin):
 
     Shows both trend direction and overbought/oversold conditions.
 
-    https://en.wikipedia.org/wiki/True_strength_index
+    https://school.stockcharts.com/doku.php?id=technical_indicators:true_strength_index
 
     Args:
         close(pandas.Series): dataset 'Close' column.
@@ -138,9 +136,11 @@ def __init__(self, close: pd.Series, r: int = 25, s: int = 13, fillna: bool = Fa
         self._run()
 
     def _run(self):
-        m = self._close - self._close.shift(1, fill_value=self._close.mean())
-        m1 = m.ewm(self._r).mean().ewm(self._s).mean()
-        m2 = abs(m).ewm(self._r).mean().ewm(self._s).mean()
+        m = self._close - self._close.shift(1)
+        m1 = m.ewm(span=self._r, min_periods=0, adjust=False).mean().ewm(
+            span=self._s, min_periods=0, adjust=False).mean()
+        m2 = abs(m).ewm(span=self._r, min_periods=0, adjust=False).mean().ewm(
+            span=self._s, min_periods=0, adjust=False).mean()
         self._tsi = m1 / m2
         self._tsi *= 100
 
@@ -154,7 +154,7 @@ def tsi(self) -> pd.Series:
         return pd.Series(tsi, name='tsi')
 
 
-class UltimateOscillatorIndicator(IndicatorMixin):
+class UltimateOscillator(IndicatorMixin):
     """Ultimate Oscillator
 
     Larry Williams' (1976) signal, a momentum oscillator designed to capture
@@ -207,13 +207,13 @@ def __init__(self,
         self._run()
 
     def _run(self):
-        min_l_or_pc = self._close.shift(1, fill_value=self._close.mean()).combine(self._low, min)
-        max_h_or_pc = self._close.shift(1, fill_value=self._close.mean()).combine(self._high, max)
+        min_l_or_pc = self._close.shift(1).combine(self._low, min)
+        max_h_or_pc = self._close.shift(1).combine(self._high, max)
         bp = self._close - min_l_or_pc
         tr = max_h_or_pc - min_l_or_pc
-        avg_s = bp.rolling(self._s, min_periods=0).sum() / tr.rolling(self._s, min_periods=0).sum()
-        avg_m = bp.rolling(self._m, min_periods=0).sum() / tr.rolling(self._m, min_periods=0).sum()
-        avg_l = bp.rolling(self._len, min_periods=0).sum() / tr.rolling(self._len, min_periods=0).sum()
+        avg_s = bp.rolling(self._s, min_periods=self._s).sum() / tr.rolling(self._s, min_periods=self._s).sum()
+        avg_m = bp.rolling(self._m, min_periods=self._m).sum() / tr.rolling(self._m, min_periods=self._m).sum()
+        avg_l = bp.rolling(self._len, min_periods=self._len).sum() / tr.rolling(self._len, min_periods=self._len).sum()
         self._uo = (100.0 * ((self._ws * avg_s) + (self._wm * avg_m) + (self._wl * avg_l))
                     / (self._ws + self._wm + self._wl))
 
@@ -227,15 +227,15 @@ def uo(self) -> pd.Series:
         return pd.Series(uo, name='uo')
 
 
-class StochIndicator(IndicatorMixin):
+class StochasticOscillator(IndicatorMixin):
     """Stochastic Oscillator
 
     Developed in the late 1950s by George Lane. The stochastic
     oscillator presents the location of the closing price of a
     stock in relation to the high and low range of the price
     of a stock over a period of time, typically a 14-day period.
 
-    https://www.investopedia.com/terms/s/stochasticoscillator.asp
+    https://school.stockcharts.com/doku.php?id=technical_indicators:stochastic_oscillator_fast_slow_and_full
 
     Args:
         close(pandas.Series): dataset 'Close' column.
@@ -472,7 +472,8 @@ class WilliamsRIndicator(IndicatorMixin):
     Highest High = highest high for the look-back period
     %R is multiplied by -100 correct the inversion and move the decimal.
 
-    From: https://www.investopedia.com/terms/w/williamsr.asp
+    https://school.stockcharts.com/doku.php?id=technical_indicators:williams_r
+
     The Williams %R oscillates from 0 to -100. When the indicator produces
     readings from 0 to -20, this indicates overbought market conditions. When
     readings are -80 to -100, it indicates oversold market conditions.
@@ -607,7 +608,7 @@ def uo(high, low, close, s=7, m=14, len=28, ws=4.0, wm=2.0, wl=1.0, fillna=False
         pandas.Series: New feature generated.
 
     """
-    return UltimateOscillatorIndicator(
+    return UltimateOscillator(
         high=high, low=low, close=close, s=7, m=14, len=28, ws=4.0, wm=2.0, wl=1.0, fillna=fillna).uo()
 
 
@@ -632,7 +633,7 @@ def stoch(high, low, close, n=14, fillna=False):
         pandas.Series: New feature generated.
     """
 
-    return StochIndicator(high=high, low=low, close=close, n=n, d_n=3, fillna=fillna).stoch()
+    return StochasticOscillator(high=high, low=low, close=close, n=n, d_n=3, fillna=fillna).stoch()
 
 
 def stoch_signal(high, low, close, n=14, d_n=3, fillna=False):
@@ -653,7 +654,7 @@ def stoch_signal(high, low, close, n=14, d_n=3, fillna=False):
     Returns:
         pandas.Series: New feature generated.
     """
-    return StochIndicator(high=high, low=low, close=close, n=n, d_n=d_n, fillna=fillna).stoch_signal()
+    return StochasticOscillator(high=high, low=low, close=close, n=n, d_n=d_n, fillna=fillna).stoch_signal()
 
 
 def wr(high, low, close, lbp=14, fillna=False):

ta/pipeline_wrapper.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 from sklearn.base import BaseEstimator, TransformerMixin
 
 import ta

ta/tests/__init__.py

@@ -1,9 +1,12 @@
-# from ta.tests.pipeline_wrapper import TestTAFeaturesTransform
+from ta.tests.momentum import (TestKAMAIndicator, TestMFIIndicator,
+                               TestRateOfChangeIndicator, TestRSIIndicator,
+                               TestStochasticOscillator, TestTSIIndicator,
+                               TestUltimateOscillator, TestWilliamsRIndicator)
 from ta.tests.trend import (TestADXIndicator, TestCCIIndicator,
                             TestMACDIndicator, TestPSARIndicator,
                             TestVortexIndicator)
 from ta.tests.utils import TestGeneral
-from ta.tests.volatility import TestAverageTrueRange
+from ta.tests.volatility import TestAverageTrueRange, TestBollingerBands
 from ta.tests.volume import (TestAccDistIndexIndicator,
                              TestEaseOfMovementIndicator,
                              TestForceIndexIndicator,

ta/tests/data/cs-bbands.csv

@@ -0,0 +1,43 @@
+Date,Close,MiddleBand,20-day Standard Deviation,HighBand,LowBand,WidthBand,CrossUp,CrossDown
+1-May-09,86.155700,,,,,,,
+4-May-09,89.086700,,,,,,,
+5-May-09,88.782900,,,,,,,
+6-May-09,90.322800,,,,,,,
+7-May-09,89.067100,,,,,,,
+8-May-09,91.145300,,,,,,,
+11-May-09,89.439700,,,,,,,
+12-May-09,89.175000,,,,,,,
+13-May-09,86.930200,,,,,,,
+14-May-09,87.675200,,,,,,,
+15-May-09,86.959600,,,,,,,
+18-May-09,89.429900,,,,,,,
+19-May-09,89.322100,,,,,,,
+20-May-09,88.724100,,,,,,,
+21-May-09,87.449700,,,,,,,
+22-May-09,87.263400,,,,,,,
+26-May-09,89.498500,,,,,,,
+27-May-09,87.900600,,,,,,,
+28-May-09,89.126000,,,,,,,
+29-May-09,90.704300,88.707940,1.291961,91.291862,86.124018,5.167845,0,0
+1-Jun-09,92.900100,89.045160,1.452054,91.949268,86.141052,5.808215,1,0
+2-Jun-09,92.978400,89.239745,1.686425,92.612595,85.866895,6.745700,1,0
+3-Jun-09,91.802100,89.390705,1.771747,92.934200,85.847210,7.086989,0,0
+4-Jun-09,92.664700,89.507800,1.902075,93.311950,85.703650,7.608300,0,0
+5-Jun-09,92.684300,89.688660,2.019895,93.728450,85.648870,8.079580,0,0
+8-Jun-09,92.302100,89.746500,2.076546,93.899592,85.593408,8.306184,0,0
+9-Jun-09,92.772500,89.913140,2.176557,94.266255,85.560025,8.706230,0,0
+10-Jun-09,92.537300,90.081255,2.241921,94.565096,85.597414,8.967682,0,0
+11-Jun-09,92.949000,90.382195,2.202359,94.786912,85.977478,8.809435,0,0
+12-Jun-09,93.203900,90.658630,2.192185,95.043001,86.274259,8.768742,0,0
+15-Jun-09,91.066900,90.863995,2.021805,94.907605,86.820385,8.087220,0,0
+16-Jun-09,89.831800,90.884090,2.009411,94.902912,86.865268,8.037643,0,0
+17-Jun-09,89.743500,90.905160,1.995080,94.895320,86.915000,7.980320,0,0
+18-Jun-09,90.399400,90.988925,1.936044,94.861013,87.116837,7.744176,0,0
+19-Jun-09,90.738700,91.153375,1.760127,94.673629,87.633121,7.040508,0,0
+22-Jun-09,88.017700,91.191090,1.682751,94.556593,87.825587,6.731006,0,0
+23-Jun-09,88.086700,91.120500,1.779126,94.678752,87.562248,7.116503,0,0
+24-Jun-09,86.843900,91.067665,1.886418,94.840502,87.294828,7.545674,0,1
+25-Jun-09,90.778100,91.150270,1.835059,94.820387,87.480153,7.340234,0,0
+26-Jun-09,90.541600,91.142135,1.837377,94.816889,87.467381,7.349507,0,0
+29-Jun-09,91.389400,91.066600,1.794097,94.654793,87.478407,7.176387,0,0
+30-Jun-09,90.650000,90.950180,1.741022,94.432224,87.468136,6.964089,0,0

ta/tests/data/cs-kama.csv

@@ -0,0 +1,50 @@
+Date,Close,ABS (Close - Close 10 periods ago),ABS (Close - Prior Close),ER,2/(fastest + 1),2/(slowest + 1),SC,KAMA
+28/05/2015,110.460000,,,,,,,
+29/05/2015,109.800000,,0.660000,,,,,
+01/06/2015,110.170000,,0.370000,,,,,
+02/06/2015,109.820000,,0.350000,,,,,
+03/06/2015,110.150000,,0.330000,,,,,
+04/06/2015,109.310000,,0.840000,,,,,
+05/06/2015,109.050000,,0.260000,,,,,
+08/06/2015,107.940000,,1.110000,,,,,
+09/06/2015,107.760000,,0.180000,,,,,
+10/06/2015,109.240000,,1.480000,,,,,109.240000
+11/06/2015,109.400000,1.060000,0.160000,0.184669,0.064516,0.666667,0.030876,109.244940
+12/06/2015,108.500000,1.300000,0.900000,0.217391,0.064516,0.666667,0.038188,109.216492
+15/06/2015,107.960000,2.210000,0.540000,0.359350,0.064516,0.666667,0.078904,109.117350
+16/06/2015,108.550000,1.270000,0.590000,0.198748,0.064516,0.666667,0.033927,109.098101
+17/06/2015,108.850000,1.300000,0.300000,0.204403,0.064516,0.666667,0.035193,109.089370
+18/06/2015,110.440000,1.130000,1.590000,0.158931,0.064516,0.666667,0.025669,109.124040
+19/06/2015,109.890000,0.840000,0.550000,0.113514,0.064516,0.666667,0.017654,109.137562
+22/06/2015,110.700000,2.760000,0.810000,0.388732,0.064516,0.666667,0.089157,109.276864
+23/06/2015,110.790000,3.030000,0.090000,0.432240,0.064516,0.666667,0.105488,109.436482
+24/06/2015,110.220000,0.980000,0.570000,0.160656,0.064516,0.666667,0.026003,109.456856
+25/06/2015,110.000000,0.600000,0.220000,0.097403,0.064516,0.666667,0.015170,109.465096
+26/06/2015,109.270000,0.770000,0.730000,0.128548,0.064516,0.666667,0.020142,109.461166
+29/06/2015,106.690000,1.270000,2.580000,0.158157,0.064516,0.666667,0.025520,109.390445
+30/06/2015,107.070000,1.480000,0.380000,0.189258,0.064516,0.666667,0.031854,109.316529
+01/07/2015,107.920000,0.930000,0.850000,0.111111,0.064516,0.666667,0.017272,109.292409
+02/07/2015,107.950000,2.490000,0.030000,0.365639,0.064516,0.666667,0.081046,109.183612
+06/07/2015,107.700000,2.190000,0.250000,0.336406,0.064516,0.666667,0.071333,109.077781
+07/07/2015,107.970000,2.730000,0.270000,0.457286,0.064516,0.666667,0.115513,108.949818
+08/07/2015,106.090000,4.700000,1.880000,0.605670,0.064516,0.666667,0.184230,108.422953
+09/07/2015,106.030000,4.190000,0.060000,0.577931,0.064516,0.666667,0.170171,108.015742
+10/07/2015,107.650000,2.350000,1.620000,0.271676,0.064516,0.666667,0.052032,107.996712
+13/07/2015,109.540000,0.270000,1.890000,0.027523,0.064516,0.666667,0.006575,108.006859
+14/07/2015,110.260000,3.570000,0.720000,0.449057,0.064516,0.666667,0.112169,108.259591
+15/07/2015,110.380000,3.310000,0.120000,0.430429,0.064516,0.666667,0.104781,108.481770
+16/07/2015,111.940000,4.020000,1.560000,0.478571,0.064516,0.666667,0.124389,108.911936
+17/07/2015,113.590000,5.640000,1.650000,0.562874,0.064516,0.666667,0.162773,109.673398
+20/07/2015,113.980000,6.280000,0.390000,0.618110,0.064516,0.666667,0.190717,110.494740
+21/07/2015,113.910000,5.940000,0.070000,0.596386,0.064516,0.666667,0.179462,111.107650
+22/07/2015,112.620000,6.530000,1.290000,0.696905,0.064516,0.666667,0.234409,111.462159
+23/07/2015,112.200000,6.170000,0.420000,0.634121,0.064516,0.666667,0.199231,111.609159
+24/07/2015,111.100000,3.450000,1.100000,0.374593,0.064516,0.666667,0.084145,111.566316
+27/07/2015,110.180000,0.640000,0.920000,0.077670,0.064516,0.666667,0.012384,111.549147
+28/07/2015,111.130000,0.870000,0.950000,0.102715,0.064516,0.666667,0.015968,111.542454
+29/07/2015,111.550000,1.170000,0.420000,0.133409,0.064516,0.666667,0.020981,111.542613
+30/07/2015,112.080000,0.140000,0.530000,0.018088,0.064516,0.666667,0.005686,111.545668
+31/07/2015,111.950000,1.640000,0.130000,0.263666,0.064516,0.666667,0.049855,111.565826
+03/08/2015,111.600000,2.380000,0.350000,0.385113,0.064516,0.666667,0.087860,111.568829
+04/08/2015,111.390000,2.520000,0.210000,0.398734,0.064516,0.666667,0.092790,111.552235
+05/08/2015,112.250000,0.370000,0.860000,0.062818,0.064516,0.666667,0.010474,111.559544