diff --git a/data/datas.csv b/data/datas.csv
deleted file mode 100755
index 9db78f89..00000000
--- a/data/datas.csv
+++ /dev/null
@@ -1,52058 +0,0 @@
-Timestamp,Open,High,Low,Close,Volume_BTC,Volume_Currency,Weighted_Price
-1315918800,5.8,6.0,5.8,6.0,25.0,149.19,5.9676
-1315922400,5.95,5.95,5.76,5.76,22.35398238,132.0410233944,5.906823274252
-1315926000,5.65,5.65,5.65,5.65,2.542,14.3623,5.65
-1315929600,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1315933200,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1315936800,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1315940400,5.92,5.97,5.92,5.97,8.4754,50.504066,5.958900582863
-1315944000,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1315947600,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1315951200,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1315954800,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1315958400,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1315962000,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1315965600,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1315969200,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1315972800,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1315976400,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1315980000,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1315983600,5.58,5.62,5.54,5.62,16.42569962,91.5370320548,5.572793498753
-1315987200,5.57,5.57,5.55,5.56,12.347499,68.71109407,5.564778265623
-1315990800,5.57,5.61,5.57,5.61,4.890346,27.26142166,5.574538419163
-1315994400,5.65,5.65,5.65,5.65,5.445139,30.76503535,5.65
-1315998000,5.72,5.72,5.52,5.52,20.0,112.3265096,5.61632548
-1316001600,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316005200,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316008800,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316012400,5.52,5.53,5.52,5.53,2.0373,11.2537205,5.523840622392
-1316016000,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316019600,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316023200,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316026800,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316030400,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316034000,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316037600,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316041200,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316044800,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316048400,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316052000,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316055600,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316059200,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316062800,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316066400,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316070000,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316073600,5.12,5.12,5.12,5.12,11.9627,61.249024,5.12
-1316077200,5.02,5.02,5.02,5.02,14.1254822,70.909920644,5.02
-1316080800,5.0,5.0,5.0,5.0,24.458401,122.292005,5.0
-1316084400,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316088000,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316091600,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316095200,5.21,5.24,5.21,5.24,24.155562,125.90777802,5.212372124482
-1316098800,5.13,5.13,5.13,5.13,5.43865,27.9002745,5.13
-1316102400,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316106000,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316109600,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316113200,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316116800,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316120400,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316124000,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316127600,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316131200,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316134800,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316138400,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316142000,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316145600,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316149200,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316152800,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316156400,4.82,4.82,4.82,4.82,9.885546,47.64833172,4.82
-1316160000,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316163600,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316167200,4.8,4.8,4.8,4.8,0.3,1.44,4.8
-1316170800,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316174400,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316178000,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316181600,4.87,4.87,4.87,4.87,24.589,119.74843,4.87
-1316185200,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316188800,4.85,4.85,4.85,4.85,5.1394608,24.92638488,4.85
-1316192400,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
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-1316271600,4.87,4.87,4.87,4.87,0.3,1.461,4.87
-1316275200,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
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-1316340000,4.87,4.87,4.82,4.82,19.33213362,93.1864179294,4.820286252987
-1316343600,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
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-1316350800,4.81,4.81,4.81,4.81,30.2214,145.364934,4.81
-1316354400,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
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-1316361600,4.85,4.85,4.85,4.85,25.884562,125.5401257,4.85
-1316365200,4.81,4.82,4.81,4.82,19.49470438,93.8420251116,4.813718807035
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-1316372400,4.82,4.82,4.82,4.82,5.0,24.1,4.82
-1316376000,4.92,4.92,4.92,4.92,19.88,97.8096,4.92
-1316379600,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
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-1316422800,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
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-1316433600,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
-1316437200,4.9,4.9,4.9,4.9,20.0,98.0,4.9
-1316440800,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308,1.7e+308
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-1495324800,2013.99,2024.99,2008.28,2022.0,134.34001877,270978.8975823627,2017.112250418082
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-1495332000,2021.88,2022.6,2000.0,2013.89,274.1966786,551289.2948417505,2010.561534357514
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-1495371600,2005.0,2043.11,2000.0,2023.57,498.69591218,1005495.2125123964,2016.249156960149
-1495375200,2025.16,2037.86,1961.52,1970.0,534.97719578,1067551.8348327198,1995.50904833658
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-1503288000,4031.66,4063.31,4031.66,4045.93,95.15347123,385233.7467239762,4048.551689646813
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diff --git a/examples_to_use/Pipeline.ipynb b/examples_to_use/Pipeline.ipynb
index c468531f..79765716 100644
--- a/examples_to_use/Pipeline.ipynb
+++ b/examples_to_use/Pipeline.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,12 +21,13 @@
     "from sklearn.metrics import mean_absolute_error, accuracy_score\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "\n",
-    "from ta import *"
+    "import ta\n",
+    "from ta.pipeline_wrapper import TAFeaturesTransform"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,12 +44,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 18,
    "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",
     "\n",
     "# Labeling        \n",
     "if LABELING == \"regression\":\n",
@@ -75,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,22 +89,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/trend.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
-      "  dip[i] = 100 * (dip_mio[i]/trs[i])\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/trend.py:174: RuntimeWarning: invalid value encountered in double_scalars\n",
-      "  din[i] = 100 * (din_mio[i]/trs[i])\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/trend.py:634: FutureWarning: Currently, 'apply' passes the values as ndarrays to the applied function. In the future, this will change to passing it as Series objects. You need to specify 'raw=True' to keep the current behaviour, and you can pass 'raw=False' to silence this warning\n",
-      "  aroon_up = close.rolling(n).apply(lambda x: float(np.argmax(x) + 1) / n * 100)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/trend.py:656: FutureWarning: Currently, 'apply' passes the values as ndarrays to the applied function. In the future, this will change to passing it as Series objects. You need to specify 'raw=True' to keep the current behaviour, and you can pass 'raw=False' to silence this warning\n",
-      "  aroon_down = close.rolling(n).apply(lambda x: float(np.argmin(x) + 1) / n * 100)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\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:547: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  din[i] = 100 * (self._din[i]/self._trs[i])\n",
+      "/home/vant/projects/python/ta/env-ta/lib/python3.7/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
       "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
      ]
     },
@@ -111,7 +108,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0005068263451268888\n"
+      "0.019688640382806523\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/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:547: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  din[i] = 100 * (self._din[i]/self._trs[i])\n"
      ]
     }
    ],
@@ -126,376 +133,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/sklearn/model_selection/_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
+      "/home/vant/projects/python/ta/env-ta/lib/python3.7/site-packages/sklearn/model_selection/_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
       "  warnings.warn(CV_WARNING, FutureWarning)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:26: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df[volume], fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:27: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['volume_obv'] = on_balance_volume(df[close], df[volume], fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:29: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:31: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df[volume], fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:32: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['volume_fi'] = force_index(df[close], df[volume], fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:34: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df[volume], 14, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:35: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['volume_vpt'] = volume_price_trend(df[close], df[volume], fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:36: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['volume_nvi'] = negative_volume_index(df[close], df[volume], fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:54: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  n=14, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:56: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['volatility_bbh'] = bollinger_hband(df[close], n=20, ndev=2, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:57: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['volatility_bbl'] = bollinger_lband(df[close], n=20, ndev=2, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:58: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['volatility_bbm'] = bollinger_mavg(df[close], n=20, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:60: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:62: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:65: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  n=10, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:67: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  n=10, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:69: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  n=10, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:71: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df[close], n=10, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:73: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df[close], n=10, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:75: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['volatility_dch'] = donchian_channel_hband(df[close], n=20, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:76: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['volatility_dcl'] = donchian_channel_lband(df[close], n=20, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:78: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:80: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:97: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['trend_macd'] = macd(df[close], n_fast=12, n_slow=26, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:99: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:101: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:102: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['trend_ema_fast'] = ema_indicator(df[close], n=12, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:103: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['trend_ema_slow'] = ema_indicator(df[close], n=26, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:104: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['trend_adx'] = adx(df[high], df[low], df[close], n=14, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:105: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['trend_adx_pos'] = adx_pos(df[high], df[low], df[close], n=14, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:106: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['trend_adx_neg'] = adx_neg(df[high], df[low], df[close], n=14, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:108: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:110: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:111: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['trend_vortex_diff'] = abs(df['trend_vortex_ind_pos'] - df['trend_vortex_ind_neg'])\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:112: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['trend_trix'] = trix(df[close], n=15, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:113: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['trend_mass_index'] = mass_index(df[high], df[low], n=9, n2=25, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:115: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:116: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['trend_dpo'] = dpo(df[close], n=20, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:118: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  n2=10, n3=10, n4=15, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:120: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  n2=10, n3=10, n4=15, nsig=9, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:121: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['trend_kst_diff'] = df['trend_kst'] - df['trend_kst_sig']\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:122: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['trend_ichimoku_a'] = ichimoku_a(df[high], df[low], n1=9, n2=26, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:123: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['trend_ichimoku_b'] = ichimoku_b(df[high], df[low], n2=26, n3=52, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:124: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['trend_visual_ichimoku_a'] = ichimoku_a(df[high], df[low], n1=9, n2=26, visual=True, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:125: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['trend_visual_ichimoku_b'] = ichimoku_b(df[high], df[low], n2=26, n3=52, visual=True, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:126: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['trend_aroon_up'] = aroon_up(df[close], n=25, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:127: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['trend_aroon_down'] = aroon_down(df[close], n=25, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:128: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['trend_aroon_ind'] = df['trend_aroon_up'] - df['trend_aroon_down']\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:145: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['momentum_rsi'] = rsi(df[close], n=14, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:147: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df[volume], n=14, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:148: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['momentum_tsi'] = tsi(df[close], r=25, s=13, fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:149: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['momentum_uo'] = uo(df[high], df[low], df[close], fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:150: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['momentum_stoch'] = stoch(df[high], df[low], df[close], fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:151: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['momentum_stoch_signal'] = stoch_signal(df[high], df[low], df[close], fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:152: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['momentum_wr'] = wr(df[high], df[low], df[close], fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:153: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['momentum_ao'] = ao(df[high], df[low], fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:168: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['others_dr'] = daily_return(df[close], fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:169: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['others_dlr'] = daily_log_return(df[close], fillna=fillna)\n",
-      "/Users/lecrintech/projects/python/ta/env_ta/lib/python2.7/site-packages/ta/wrapper.py:170: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  df['others_cr'] = cumulative_return(df[close], fillna=fillna)\n"
+      "/home/vant/projects/python/ta/env-ta/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:542: FutureWarning: From version 0.22, errors during fit will result in a cross validation score of NaN by default. Use error_score='raise' if you want an exception raised or error_score=np.nan to adopt the behavior from version 0.22.\n",
+      "  FutureWarning)\n"
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.10294477 -0.00205641 -0.00436494]\n"
+     "ename": "KeyError",
+     "evalue": "None",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2656\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2657\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2658\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: None",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-21-986f509b966c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m                             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m                             scoring=score)\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresultados\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mcross_val_score\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001b[0m\n\u001b[1;32m    400\u001b[0m                                 \u001b[0mfit_params\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    401\u001b[0m                                 \u001b[0mpre_dispatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 402\u001b[0;31m                                 error_score=error_score)\n\u001b[0m\u001b[1;32m    403\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'test_score'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    404\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mcross_validate\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0mreturn_times\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_estimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_estimator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m             error_score=error_score)\n\u001b[0;32m--> 240\u001b[0;31m         for train, test in cv.split(X, y, groups))\n\u001b[0m\u001b[1;32m    241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0mzipped_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    915\u001b[0m             \u001b[0;31m# remaining jobs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 917\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    757\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    758\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 759\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    760\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    761\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    714\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    715\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 716\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    717\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    180\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    547\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    548\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 549\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 225\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 225\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    526\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 528\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    529\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    263\u001b[0m             \u001b[0mThis\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m         \"\"\"\n\u001b[0;32m--> 265\u001b[0;31m         \u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    266\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    228\u001b[0m                 Xt, fitted_transformer = fit_transform_one_cached(\n\u001b[1;32m    229\u001b[0m                     \u001b[0mcloned_transformer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m                     **fit_params_steps[name])\n\u001b[0m\u001b[1;32m    231\u001b[0m                 \u001b[0;31m# Replace the transformer of the step with the fitted\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m                 \u001b[0;31m# transformer. This is necessary when loading the transformer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/sklearn/externals/joblib/memory.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 342\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcall_and_shelve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m_fit_transform_one\u001b[0;34m(transformer, X, y, weight, **fit_params)\u001b[0m\n\u001b[1;32m    612\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_fit_transform_one\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransformer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransformer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fit_transform'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 614\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    615\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    616\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    465\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    466\u001b[0m             \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 467\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    468\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    469\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/ta/pipeline_wrapper.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, X, **transform_params)\u001b[0m\n\u001b[1;32m     38\u001b[0m                                    \u001b[0mlow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_low_column\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_close_column\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m                                    \u001b[0mvolume\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_volume_column\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfillna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fillna\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m                                    colprefix=self._colprefix)\n\u001b[0m\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/ta/wrapper.py\u001b[0m in \u001b[0;36madd_all_ta_features\u001b[0;34m(df, open, high, low, close, volume, fillna, colprefix)\u001b[0m\n\u001b[1;32m    288\u001b[0m         \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDataframe\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mnew\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m     \"\"\"\n\u001b[0;32m--> 290\u001b[0;31m     \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd_volume_ta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhigh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhigh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvolume\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvolume\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfillna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolprefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolprefix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    291\u001b[0m     \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd_volatility_ta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhigh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhigh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfillna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolprefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolprefix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m     \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd_trend_ta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhigh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhigh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfillna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolprefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolprefix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/ta/wrapper.py\u001b[0m in \u001b[0;36madd_volume_ta\u001b[0;34m(df, high, low, close, volume, fillna, colprefix)\u001b[0m\n\u001b[1;32m     37\u001b[0m     \u001b[0;31m# Accumulation Distribution Index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m     df[f'{colprefix}volume_adi'] = AccDistIndexIndicator(\n\u001b[0;32m---> 39\u001b[0;31m         high=df[high], low=df[low], close=df[close], volume=df[volume], fillna=fillna).acc_dist_index()\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0;31m# On Balance Volume\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2925\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2926\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2927\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2928\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2929\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/projects/python/ta/env-ta/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2657\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2658\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2659\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2660\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2661\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: None"
      ]
     }
    ],
@@ -506,6 +197,13 @@
     "                            scoring=score)\n",
     "print(resultados)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -517,14 +215,14 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
+   "pygments_lexer": "ipython3",
+   "version": "3.7.2"
   }
  },
  "nbformat": 4,
diff --git a/examples_to_use/all_features_example.py b/examples_to_use/all_features_example.py
index 419646ec..5d1b4ccf 100644
--- a/examples_to_use/all_features_example.py
+++ b/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)
diff --git a/examples_to_use/bollinger_band_features_example.py b/examples_to_use/bollinger_band_features_example.py
index 165f0546..07f3c167 100644
--- a/examples_to_use/bollinger_band_features_example.py
+++ b/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)
diff --git a/examples_to_use/roc.py b/examples_to_use/roc.py
new file mode 100644
index 00000000..4cb9fd13
--- /dev/null
+++ b/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'])
diff --git a/examples_to_use/visualize_features.ipynb b/examples_to_use/visualize_features.ipynb
index db94e09c..1a36fc8d 100644
--- a/examples_to_use/visualize_features.ipynb
+++ b/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"
     }
diff --git a/examples_to_use/volume_features_example.py b/examples_to_use/volume_features_example.py
index c5c73f93..70353213 100644
--- a/examples_to_use/volume_features_example.py
+++ b/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)
diff --git a/ta/momentum.py b/ta/momentum.py
index 994345c5..8dadf357 100644
--- a/ta/momentum.py
+++ b/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))
 
@@ -227,7 +227,7 @@ 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
@@ -235,7 +235,7 @@ class StochIndicator(IndicatorMixin):
     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):
diff --git a/ta/pipeline_wrapper.py b/ta/pipeline_wrapper.py
index 6c0cf588..e6e19b7e 100644
--- a/ta/pipeline_wrapper.py
+++ b/ta/pipeline_wrapper.py
@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 from sklearn.base import BaseEstimator, TransformerMixin
 
 import ta
diff --git a/ta/tests/__init__.py b/ta/tests/__init__.py
index 8c2fd44a..7667d4d4 100644
--- a/ta/tests/__init__.py
+++ b/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,
diff --git a/ta/tests/data/cs-bbands.csv b/ta/tests/data/cs-bbands.csv
new file mode 100644
index 00000000..32d68be8
--- /dev/null
+++ b/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
diff --git a/ta/tests/data/cs-kama.csv b/ta/tests/data/cs-kama.csv
new file mode 100644
index 00000000..e0fb22c2
--- /dev/null
+++ b/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
diff --git a/ta/tests/data/cs-mfi.csv b/ta/tests/data/cs-mfi.csv
new file mode 100644
index 00000000..8001e0c2
--- /dev/null
+++ b/ta/tests/data/cs-mfi.csv
@@ -0,0 +1,31 @@
+Date,High,Low,Close,Typical Price,Up or Down,Volume,Raw Money Flow,1-period Positive Money Flow,1-period Negative Money Flow,14-period Positive Money Flow,14-period Negative Money Flow,14-period Money Flow Ratio,MFI,,,
+3-Dec-10,24.82830,24.32050,24.74860,24.63247,,18730.14400,,,,,,,,,,
+6-Dec-10,24.75860,24.59930,24.70880,24.68890,1.00000,12271.74000,302975.76169,302975.76169,0.00000,,,,,,,
+7-Dec-10,25.15680,24.77850,25.03730,24.99087,1.00000,24691.41400,617059.83509,617059.83509,0.00000,,,,,,,
+8-Dec-10,25.58480,24.94770,25.54500,25.35917,1.00000,18357.60600,465533.59016,465533.59016,0.00000,,,,,,,
+9-Dec-10,25.68440,24.80830,25.06720,25.18663,-1.00000,22964.08000,578387.86280,0.00000,578387.86280,,,,,,,
+10-Dec-10,25.33600,25.05720,25.10700,25.16673,-1.00000,15918.94800,400627.91926,0.00000,400627.91926,,,,,,,
+13-Dec-10,25.28620,24.84820,24.88800,25.00747,-1.00000,16067.04400,401796.06726,0.00000,401796.06726,,,,,,,
+14-Dec-10,25.12690,24.74960,24.99750,24.95800,-1.00000,16568.48700,413516.29855,0.00000,413516.29855,,,,,,,
+15-Dec-10,25.27620,24.92780,25.04730,25.08377,1.00000,16018.72900,401810.06053,401810.06053,0.00000,,,,,,,
+16-Dec-10,25.38570,25.02740,25.33600,25.24970,1.00000,9773.56900,246779.68518,246779.68518,0.00000,,,,,,,
+17-Dec-10,25.53510,25.04730,25.05720,25.21320,-1.00000,22572.71200,569130.30220,0.00000,569130.30220,,,,,,,
+20-Dec-10,25.60480,25.06220,25.44550,25.37083,1.00000,12986.66900,329482.61475,329482.61475,0.00000,,,,,,,
+21-Dec-10,25.74410,25.53510,25.56490,25.61470,1.00000,10906.65900,279370.79829,279370.79829,0.00000,,,,,,,
+22-Dec-10,25.72420,25.45540,25.55500,25.57820,-1.00000,5799.25900,148334.60655,0.00000,148334.60655,,,,,,,
+23-Dec-10,25.67440,25.28620,25.40570,25.45543,-1.00000,7395.27400,188249.90429,0.00000,188249.90429,2643012.34568,2700042.96091,0.97888,49.46631,,,
+27-Dec-10,25.44550,25.16670,25.36580,25.32600,-1.00000,5818.16200,147350.77081,0.00000,147350.77081,2340036.58399,2847393.73172,0.82182,45.10974,,,
+28-Dec-10,25.31610,24.91780,25.03730,25.09040,-1.00000,7164.72600,179765.84123,0.00000,179765.84123,1722976.74891,3027159.57295,0.56917,36.27215,,,
+29-Dec-10,25.25630,24.90790,24.91780,25.02733,-1.00000,5672.91400,141977.90965,0.00000,141977.90965,1257443.15875,3169137.48260,0.39678,28.40665,,,
+30-Dec-10,25.03730,24.82830,24.87800,24.91453,-1.00000,5624.74200,140137.82205,0.00000,140137.82205,1257443.15875,2730887.44185,0.46045,31.52806,,,
+31-Dec-10,25.00740,24.70880,24.96760,24.89460,-1.00000,5023.46900,125057.25137,0.00000,125057.25137,1257443.15875,2455316.77396,0.51213,33.86815,,,
+3-Jan-11,25.30610,25.02740,25.04730,25.12693,1.00000,7457.09100,187373.82842,187373.82842,0.00000,1444816.98717,2053520.70670,0.70358,41.30010,,,
+4-Jan-11,25.11700,24.34040,24.45000,24.63580,-1.00000,11798.00900,290653.39012,0.00000,290653.39012,1444816.98717,1930657.79827,0.74835,42.80337,,,
+5-Jan-11,24.68890,24.27080,24.56940,24.50970,-1.00000,12366.13200,303090.18548,0.00000,303090.18548,1043006.92664,2233747.98375,0.46693,31.83048,,,
+6-Jan-11,24.54950,23.89250,24.02190,24.15463,-1.00000,13294.86500,321132.58929,0.00000,321132.58929,796227.24146,2554880.57304,0.31165,23.76012,,,
+7-Jan-11,24.27080,23.77800,23.88250,23.97710,-1.00000,9256.87000,221952.89768,0.00000,221952.89768,796227.24146,2207703.16852,0.36066,26.50618,,,
+10-Jan-11,24.27080,23.72320,24.20110,24.06503,1.00000,9690.60400,233204.70828,233204.70828,0.00000,699949.33499,2207703.16852,0.31705,24.07266,,,
+11-Jan-11,24.59930,24.20110,24.28070,24.36037,1.00000,8870.31800,216084.19893,216084.19893,0.00000,636662.73563,2207703.16852,0.28838,22.38329,,,
+12-Jan-11,24.47980,24.24090,24.33050,24.35040,-1.00000,7168.96500,174567.16534,0.00000,174567.16534,636662.73563,2233935.72731,0.28500,22.17875,,,
+13-Jan-11,24.55950,23.43450,24.44000,24.14467,-1.00000,11356.18000,274191.18071,0.00000,274191.18071,636662.73563,2319877.00372,0.27444,21.53405,,,
+14-Jan-11,25.16000,24.27000,25.00000,24.81000,1.00000,13379.37400,331942.26894,331942.26894,0.00000,968605.00457,2172526.23291,0.44584,30.83618,,,
diff --git a/ta/tests/data/cs-percentr.csv b/ta/tests/data/cs-percentr.csv
new file mode 100644
index 00000000..b583ddeb
--- /dev/null
+++ b/ta/tests/data/cs-percentr.csv
@@ -0,0 +1,31 @@
+Date,High,Low,Highest High (14),Lowest Low (14),Close,Williams_%R
+23-Feb-10,127.009000,125.357400,,,,
+24-Feb-10,127.615900,126.163300,,,,
+25-Feb-10,126.591100,124.929600,,,,
+26-Feb-10,127.347200,126.093700,,,,
+1-Mar-10,128.173000,126.819900,,,,
+2-Mar-10,128.431700,126.481700,,,,
+3-Mar-10,127.367100,126.034000,,,,
+4-Mar-10,126.422000,124.830100,,,,
+5-Mar-10,126.899500,126.392100,,,,
+8-Mar-10,126.849800,125.715600,,,,
+9-Mar-10,125.646000,124.561500,,,,
+10-Mar-10,125.715600,124.571500,,,,
+11-Mar-10,127.158200,125.068900,,,,
+12-Mar-10,127.715400,126.859700,128.431700,124.561500,127.287600,-29.561780
+15-Mar-10,127.685500,126.630900,128.431700,124.561500,127.178100,-32.391091
+16-Mar-10,128.222800,126.800100,128.431700,124.561500,128.013800,-10.797892
+17-Mar-10,128.272500,126.710500,128.431700,124.561500,127.108500,-34.189448
+18-Mar-10,128.093400,126.800100,128.431700,124.561500,127.725300,-18.252287
+19-Mar-10,128.272500,126.133500,128.431700,124.561500,127.058700,-35.476203
+22-Mar-10,127.735300,125.924500,128.272500,124.561500,127.327300,-25.470224
+23-Mar-10,128.770000,126.989100,128.770000,124.561500,128.710300,-1.418558
+24-Mar-10,129.287300,127.814800,129.287300,124.561500,127.874500,-29.895467
+25-Mar-10,130.063300,128.471500,130.063300,124.561500,128.580900,-26.943909
+26-Mar-10,129.118200,128.064100,130.063300,124.561500,128.600800,-26.582209
+29-Mar-10,129.287300,127.605900,130.063300,124.571500,127.934200,-38.768710
+30-Mar-10,128.471500,127.596000,130.063300,125.068900,128.113300,-39.043729
+31-Mar-10,128.093400,126.999000,130.063300,125.924500,127.596000,-59.613898
+1-Apr-10,128.650600,126.899500,130.063300,125.924500,127.596000,-59.613898
+5-Apr-10,129.138100,127.486500,130.063300,125.924500,128.690400,-33.171451
+6-Apr-10,128.640600,127.397000,130.063300,125.924500,128.272500,-43.268580
diff --git a/ta/tests/data/cs-roc.csv b/ta/tests/data/cs-roc.csv
new file mode 100644
index 00000000..2336867e
--- /dev/null
+++ b/ta/tests/data/cs-roc.csv
@@ -0,0 +1,21 @@
+Date,Close,ROC,
+28-Apr-10,11045.27,,
+29-Apr-10,11167.32,,
+30-Apr-10,11008.61,,
+3-May-10,11151.83,,
+4-May-10,10926.77,,
+5-May-10,10868.12,,
+6-May-10,10520.32,,
+7-May-10,10380.43,,
+10-May-10,10785.14,,
+11-May-10,10748.26,,
+12-May-10,10896.91,,
+13-May-10,10782.95,,
+14-May-10,10620.16,-3.84879682,
+17-May-10,10625.83,-4.84888048,
+18-May-10,10510.95,-4.52064339,
+19-May-10,10444.37,-6.34389154,
+20-May-10,10068.01,-7.85923013,
+21-May-10,10193.39,-6.20834146,
+24-May-10,10066.57,-4.31308173,
+25-May-10,10043.75,-3.24341092,
diff --git a/ta/tests/data/cs-rsi.csv b/ta/tests/data/cs-rsi.csv
new file mode 100644
index 00000000..75f8e591
--- /dev/null
+++ b/ta/tests/data/cs-rsi.csv
@@ -0,0 +1,34 @@
+Date,Close,Change,Gain,Loss,Avg Gain,Avg Loss,RS,RSI
+14-Dec-09,44.338900,,,,0.000000,0.000000,,
+15-Dec-09,44.090200,-0.248700,0.000000,0.248700,0.000000,0.017764,,
+16-Dec-09,44.149700,0.059500,0.059500,0.000000,0.004250,0.016495,,
+17-Dec-09,43.612400,-0.537300,0.000000,0.537300,0.003946,0.053696,,
+18-Dec-09,44.327800,0.715400,0.715400,0.000000,0.054765,0.049860,,
+21-Dec-09,44.826400,0.498600,0.498600,0.000000,0.086467,0.046299,,
+22-Dec-09,45.095500,0.269100,0.269100,0.000000,0.099512,0.042992,,
+23-Dec-09,45.424500,0.329000,0.329000,0.000000,0.115904,0.039921,,
+24-Dec-09,45.843300,0.418800,0.418800,0.000000,0.137540,0.037069,,
+28-Dec-09,46.082600,0.239300,0.239300,0.000000,0.144808,0.034422,,
+29-Dec-09,45.893100,-0.189500,0.000000,0.189500,0.134465,0.045499,,
+30-Dec-09,46.032800,0.139700,0.139700,0.000000,0.134839,0.042249,,
+31-Dec-09,45.614000,-0.418800,0.000000,0.418800,0.125207,0.069145,,
+04-Jan-10,46.282000,0.668000,0.668000,0.000000,0.163978,0.064206,,
+05-Jan-10,46.282000,0.000000,0.000000,0.000000,0.152266,0.059620,2.553927,71.862114
+06-Jan-10,46.002800,-0.279200,0.000000,0.279200,0.141389,0.075304,1.877571,65.248473
+07-Jan-10,46.032800,0.030000,0.030000,0.000000,0.133433,0.069926,1.908216,65.614661
+08-Jan-10,46.411600,0.378800,0.378800,0.000000,0.150959,0.064931,2.324923,69.924117
+11-Jan-10,46.222200,-0.189400,0.000000,0.189400,0.140176,0.073822,1.898856,65.503639
+12-Jan-10,45.643900,-0.578300,0.000000,0.578300,0.130164,0.109856,1.184862,54.230524
+13-Jan-10,46.212200,0.568300,0.568300,0.000000,0.161459,0.102009,1.582797,61.282282
+14-Jan-10,46.252100,0.039900,0.039900,0.000000,0.152777,0.094723,1.612885,61.728124
+15-Jan-10,45.713700,-0.538400,0.000000,0.538400,0.141864,0.126414,1.122219,52.879504
+19-Jan-10,46.451500,0.737800,0.737800,0.000000,0.184431,0.117384,1.571172,61.107227
+20-Jan-10,45.783500,-0.668000,0.000000,0.668000,0.171257,0.156714,1.092801,52.217151
+21-Jan-10,45.354800,-0.428700,0.000000,0.428700,0.159024,0.176141,0.902822,47.446488
+22-Jan-10,44.028800,-1.326000,0.000000,1.326000,0.147666,0.258274,0.571740,36.376226
+25-Jan-10,44.178300,0.149500,0.149500,0.000000,0.147797,0.239826,0.616266,38.128990
+26-Jan-10,44.218100,0.039800,0.039800,0.000000,0.140083,0.222696,0.629032,38.613833
+27-Jan-10,44.567200,0.349100,0.349100,0.000000,0.155012,0.206789,0.749617,42.844631
+28-Jan-10,43.420500,-1.146700,0.000000,1.146700,0.143940,0.273925,0.525472,34.446517
+29-Jan-10,42.662800,-0.757700,0.000000,0.757700,0.133659,0.308481,0.433280,30.229985
+01-Feb-10,43.131400,0.468600,0.468600,0.000000,0.157583,0.286446,0.550131,35.489326
diff --git a/ta/tests/data/cs-soo.csv b/ta/tests/data/cs-soo.csv
new file mode 100644
index 00000000..f84fbb95
--- /dev/null
+++ b/ta/tests/data/cs-soo.csv
@@ -0,0 +1,31 @@
+Date,High,Low,Highest High (14),Lowest Low (14),Close,SO,SO_SIG
+23-Feb-10,127.009000,125.357400,,,,,
+24-Feb-10,127.615900,126.163300,,,,,
+25-Feb-10,126.591100,124.929600,,,,,
+26-Feb-10,127.347200,126.093700,,,,,
+1-Mar-10,128.173000,126.819900,,,,,
+2-Mar-10,128.431700,126.481700,,,,,
+3-Mar-10,127.367100,126.034000,,,,,
+4-Mar-10,126.422000,124.830100,,,,,
+5-Mar-10,126.899500,126.392100,,,,,
+8-Mar-10,126.849800,125.715600,,,,,
+9-Mar-10,125.646000,124.561500,,,,,
+10-Mar-10,125.715600,124.571500,,,,,
+11-Mar-10,127.158200,125.068900,,,,,
+12-Mar-10,127.715400,126.859700,128.431700,124.561500,127.287600,70.438220,
+15-Mar-10,127.685500,126.630900,128.431700,124.561500,127.178100,67.608909,
+16-Mar-10,128.222800,126.800100,128.431700,124.561500,128.013800,89.202108,
+17-Mar-10,128.272500,126.710500,128.431700,124.561500,127.108500,65.810552,74.20719
+18-Mar-10,128.093400,126.800100,128.431700,124.561500,127.725300,81.747713,78.920124
+19-Mar-10,128.272500,126.133500,128.431700,124.561500,127.058700,64.523797,70.694021
+22-Mar-10,127.735300,125.924500,128.272500,124.561500,127.327300,74.529776,73.600429
+23-Mar-10,128.770000,126.989100,128.770000,124.561500,128.710300,98.581442,79.211672
+24-Mar-10,129.287300,127.814800,129.287300,124.561500,127.874500,70.104533,81.071917
+25-Mar-10,130.063300,128.471500,130.063300,124.561500,128.580900,73.056091,80.580689
+26-Mar-10,129.118200,128.064100,130.063300,124.561500,128.600800,73.417791,72.192805
+29-Mar-10,129.287300,127.605900,130.063300,124.571500,127.934200,61.231290,69.235057
+30-Mar-10,128.471500,127.596000,130.063300,125.068900,128.113300,60.956271,65.201784
+31-Mar-10,128.093400,126.999000,130.063300,125.924500,127.596000,40.386102,54.191221
+1-Apr-10,128.650600,126.899500,130.063300,125.924500,127.596000,40.386102,47.242825
+5-Apr-10,129.138100,127.486500,130.063300,125.924500,128.690400,66.828549,49.200251
+6-Apr-10,128.640600,127.397000,130.063300,125.924500,128.272500,56.731420,54.64869
diff --git a/ta/tests/data/cs-tsi.csv b/ta/tests/data/cs-tsi.csv
new file mode 100644
index 00000000..c76f8f2c
--- /dev/null
+++ b/ta/tests/data/cs-tsi.csv
@@ -0,0 +1,505 @@
+Date,Close,Momentum,25-period EMA Mo,13-Period EMA of EMA,Absolute Momentum,25-period EMA AbMo,13-Period EMA of EMA,TSI
+1-Sep-10,1080.2900000000,,,,,,,
+2-Sep-10,1090.1000000000,9.8100000000,,,9.8100000000,,,
+3-Sep-10,1104.5100000000,14.4100000000,,,14.4100000000,,,
+7-Sep-10,1091.8400000000,-12.6700000000,,,12.6700000000,,,
+8-Sep-10,1098.8700000000,7.0300000000,,,7.0300000000,,,
+9-Sep-10,1104.1800000000,5.3100000000,,,5.3100000000,,,
+10-Sep-10,1109.5500000000,5.3700000000,,,5.3700000000,,,
+13-Sep-10,1121.9000000000,12.3500000000,,,12.3500000000,,,
+14-Sep-10,1121.1000000000,-0.8000000000,,,0.8000000000,,,
+15-Sep-10,1125.0700000000,3.9700000000,,,3.9700000000,,,
+16-Sep-10,1124.6600000000,-0.4100000000,,,0.4100000000,,,
+17-Sep-10,1125.5900000000,0.9300000000,,,0.9300000000,,,
+20-Sep-10,1142.7100000000,17.1200000000,,,17.1200000000,,,
+21-Sep-10,1139.7800000000,-2.9300000000,,,2.9300000000,,,
+22-Sep-10,1134.2800000000,-5.5000000000,,,5.5000000000,,,
+23-Sep-10,1124.8300000000,-9.4500000000,,,9.4500000000,,,
+24-Sep-10,1148.6700000000,23.8400000000,,,23.8400000000,,,
+27-Sep-10,1142.1600000000,-6.5100000000,,,6.5100000000,,,
+28-Sep-10,1147.7000000000,5.5400000000,,,5.5400000000,,,
+29-Sep-10,1144.7300000000,-2.9700000000,,,2.9700000000,,,
+30-Sep-10,1141.2000000000,-3.5300000000,,,3.5300000000,,,
+1-Oct-10,1146.2400000000,5.0400000000,,,5.0400000000,,,
+4-Oct-10,1137.0300000000,-9.2100000000,,,9.2100000000,,,
+5-Oct-10,1160.7500000000,23.7200000000,,,23.7200000000,,,
+6-Oct-10,1159.9700000000,-0.7800000000,,,0.7800000000,,,
+7-Oct-10,1158.0600000000,-1.9100000000,3.1108000000,,1.9100000000,7.6444000000,,
+8-Oct-10,1165.1500000000,7.0900000000,3.4168800640,,7.0900000000,7.6017555520,,
+11-Oct-10,1165.3200000000,0.1700000000,3.1671300495,,0.1700000000,7.0301049149,,
+12-Oct-10,1169.7700000000,4.4500000000,3.2658084061,,4.4500000000,6.8316432449,,
+13-Oct-10,1178.1000000000,8.3300000000,3.6553460235,,8.3300000000,6.9468968465,,
+14-Oct-10,1173.8100000000,-4.2900000000,3.0441900074,,4.2900000000,6.7425283411,,
+15-Oct-10,1176.1900000000,2.3800000000,2.9931005120,,2.3800000000,6.4069626611,,
+18-Oct-10,1184.7100000000,8.5200000000,3.4182296206,,8.5200000000,6.5694974932,,
+19-Oct-10,1165.9000000000,-18.8100000000,1.7084341982,,18.8100000000,7.5110369460,,
+20-Oct-10,1178.1700000000,12.2700000000,2.5208298397,,12.2700000000,7.8770963841,,
+21-Oct-10,1180.2600000000,2.0900000000,2.4876904084,,2.0900000000,7.4319529302,,
+22-Oct-10,1183.0800000000,2.8200000000,2.5132516622,,2.8200000000,7.0772015109,,
+25-Oct-10,1185.6200000000,2.5400000000,2.5153091443,2.9089999951,2.5400000000,6.7281999706,7.1076366766,
+26-Oct-10,1185.6400000000,0.0200000000,2.3233699649,2.8253720268,0.0200000000,6.2122052289,6.9797690658,
+27-Oct-10,1182.4500000000,-3.1900000000,1.8992815472,2.6931263063,3.1900000000,5.9797372027,6.8369645158,
+28-Oct-10,1183.7800000000,1.3300000000,1.8554924106,2.5735121860,1.3300000000,5.6220794171,6.6634789237,
+29-Oct-10,1183.2600000000,-0.5200000000,1.6727695344,2.4448861353,0.5200000000,5.2296274683,6.4587249359,
+1-Nov-10,1184.3800000000,1.1200000000,1.6302505018,2.3285561669,1.1200000000,4.9135149234,6.2380689461,
+2-Nov-10,1193.5700000000,9.1900000000,2.2117464332,2.3118757369,9.1900000000,5.2424621555,6.0958962964,
+3-Nov-10,1197.9600000000,4.3900000000,2.3792976976,2.3215035929,4.3900000000,5.1768907665,5.9646623067,
+4-Nov-10,1221.0600000000,23.1000000000,3.9731341187,2.5573564320,23.1000000000,6.5555363288,6.0490391171,
+5-Nov-10,1225.8500000000,4.7900000000,4.0359674423,2.7685020842,4.7900000000,6.4197312744,6.1019739571,
+8-Nov-10,1223.2500000000,-2.6000000000,3.5255288266,2.8766055031,2.6000000000,6.1259175447,6.1053931014,
+9-Nov-10,1213.4000000000,-9.8500000000,2.4966831493,2.8223525909,9.8500000000,6.4123739672,6.1492299691,
+10-Nov-10,1218.7100000000,5.3100000000,2.7130834814,2.8067489621,5.3100000000,6.3275793616,6.1746982623,
+11-Nov-10,1213.5400000000,-5.1700000000,2.1067167000,2.7067843551,5.1700000000,6.2385383571,6.1838146279,
+12-Nov-10,1199.2100000000,-14.3300000000,0.8424044515,2.4405509048,14.3300000000,6.8609335867,6.2805072152,
+15-Nov-10,1197.7500000000,-1.4600000000,0.6653035011,2.1870455756,1.4600000000,6.4454937752,6.3040672960,
+16-Nov-10,1178.3400000000,-19.4100000000,-0.8788888442,1.7492301404,19.4100000000,7.4427235940,6.4666674153,
+17-Nov-10,1178.5900000000,0.2500000000,-0.7920547143,1.3863346632,0.2500000000,6.8894592952,6.5270420958,
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+19-Jun-12,1357.9800000000,13.2000000000,2.4129227432,-0.1068415625,13.2000000000,10.9063554020,10.5861405198,-1.0092588732
+20-Jun-12,1355.6900000000,-2.2900000000,2.0511739258,0.2013230492,2.2900000000,10.2435853444,10.5372236407,1.9105891274
+21-Jun-12,1325.5100000000,-30.1800000000,-0.4280479726,0.1114488673,30.1800000000,11.7770943597,10.7142771794,1.0401902567
+22-Jun-12,1335.0200000000,9.5100000000,0.3363866775,0.1435699866,9.5100000000,11.6027094616,10.8411453093,1.3243064501
+25-Jun-12,1313.7200000000,-21.3000000000,-1.3278841858,-0.0665536692,21.3000000000,12.3486250498,11.0564134162,-0.6019462796
+26-Jun-12,1319.9900000000,6.2700000000,-0.7434549342,-0.1632151699,6.2700000000,11.8810572110,11.1741725501,-1.4606465859
+27-Jun-12,1331.8500000000,11.8600000000,0.2260028194,-0.1076348410,11.8600000000,11.8794374903,11.2748843836,-0.9546425252
+28-Jun-12,1329.0400000000,-2.8100000000,-0.0075265175,-0.0933393724,2.8100000000,11.1818163585,11.2615942696,-0.8288291175
+29-Jun-12,1362.1600000000,33.1200000000,2.5406428222,0.2827932850,33.1200000000,12.8693014442,11.4911748542,2.4609605944
+2-Jul-12,1365.5100000000,3.3500000000,2.6028985763,0.6141043206,3.3500000000,12.1370767772,11.5834096488,5.3015851050
+3-Jul-12,1374.0200000000,8.5100000000,3.0572728178,0.9629887820,8.5100000000,11.8580820315,11.6226328650,8.2854615919
+5-Jul-12,1367.5800000000,-6.4400000000,2.3267425927,1.1577328262,6.4400000000,11.4413231616,11.5967418394,9.9832594551
+6-Jul-12,1354.6800000000,-12.9000000000,1.1555015525,1.1574142003,12.9000000000,11.5535245840,11.5905704153,9.9858260535
+9-Jul-12,1352.4600000000,-2.2200000000,0.8958579730,1.1200639710,2.2200000000,10.8355898730,11.4827591939,9.7543103719
+10-Jul-12,1341.4700000000,-10.9900000000,-0.0184022222,0.9574909986,10.9900000000,10.8474671000,11.3920394829,8.4049129225
+11-Jul-12,1341.4500000000,-0.0200000000,-0.0185251233,0.8181158964,0.0200000000,10.0146183306,11.1953437423,7.3076442783
+12-Jul-12,1334.7600000000,-6.6900000000,-0.5316949708,0.6253629046,6.6900000000,9.7588886887,10.9902179607,5.6901774543
+13-Jul-12,1356.7800000000,22.0200000000,1.2029814063,0.7078468266,22.0200000000,10.7020133707,10.9490623452,6.4649081749
+16-Jul-12,1353.6400000000,-3.1400000000,0.8689192766,0.7308479725,3.1400000000,10.1203433022,10.8307212659,6.7479159932
+17-Jul-12,1363.6700000000,10.0300000000,1.5735896058,0.8511914777,10.0300000000,10.1133940954,10.7282869459,7.9340856747
+18-Jul-12,1372.7800000000,9.1100000000,2.1532902933,1.0371311886,9.1100000000,10.0362130216,10.6294587895,9.7571401246
+19-Jul-12,1376.5100000000,3.7300000000,2.2745708040,1.2138375657,3.7300000000,9.5511391160,10.4754747402,11.5874229643
+20-Jul-12,1362.6600000000,-13.8500000000,1.0342688177,1.1881951485,13.8500000000,9.8818074952,10.3906990576,11.4351800767
+23-Jul-12,1350.5200000000,-12.1400000000,0.0209040603,1.0215059811,12.1400000000,10.0555076627,10.3428337264,9.8764614040
+24-Jul-12,1338.3100000000,-12.2100000000,-0.9198970801,0.7442736239,12.2100000000,10.2212312132,10.3254688875,7.2081339070
+25-Jul-12,1337.8900000000,-0.4200000000,-0.8814449967,0.5121210049,0.4200000000,9.4673205083,10.2029252990,5.0193546450
+26-Jul-12,1360.0200000000,22.1300000000,0.8885953525,0.5658815417,22.1300000000,10.4413338148,10.2369700350,5.5278225863
+27-Jul-12,1385.9700000000,25.9500000000,2.8163185980,0.8872439534,25.9500000000,11.6342604178,10.4365031017,8.5013528452
+30-Jul-12,1385.3000000000,-0.6700000000,2.5481509714,1.1244214756,0.6700000000,10.7908895065,10.4871094803,10.7219389448
+31-Jul-12,1379.3200000000,-5.9800000000,1.8921655987,1.2340553363,5.9800000000,10.4208358856,10.4776456110,11.7779831668
+1-Aug-12,1375.3200000000,-4.0000000000,1.4389402208,1.2633128978,4.0000000000,9.9269451893,10.3990055908,12.1484009872
+2-Aug-12,1365.0000000000,-10.3200000000,0.5344425391,1.1592302106,10.3200000000,9.9571789653,10.3359127486,11.2155572401
+3-Aug-12,1390.9900000000,25.9900000000,2.4924840190,1.3496188544,25.9900000000,11.1904235593,10.4579368924,12.9052113082
+6-Aug-12,1394.2300000000,3.2400000000,2.5499829482,1.5210308470,3.2400000000,10.5788769791,10.4752071368,14.5202937486
+7-Aug-12,1401.3500000000,7.1200000000,2.9015086598,1.7181630787,7.1200000000,10.3128201619,10.4520182768,16.4385770595
+8-Aug-12,1402.2200000000,0.8700000000,2.7452450137,1.8648303790,0.8700000000,9.5864784350,10.3284191874,18.0553320425
+9-Aug-12,1402.8000000000,0.5800000000,2.5786943673,1.9667701565,0.5800000000,8.8937001138,10.1235413037,19.4276893584
+10-Aug-12,1405.8700000000,3.0700000000,2.6164855965,2.0595495214,3.0700000000,8.4457411011,9.8839514347,20.8373091974
+13-Aug-12,1404.1100000000,-1.7600000000,2.2798463245,2.0910079049,1.7600000000,7.9314738956,9.6051376422,21.7696818386
+14-Aug-12,1403.9300000000,-0.1800000000,2.0906349452,2.0909546462,0.1800000000,7.3352305235,9.2809949056,22.5294234883
+15-Aug-12,1405.5300000000,1.6000000000,2.0528953052,2.0855197723,1.6000000000,6.8940765917,8.9401429704,23.3275885993
+16-Aug-12,1415.5100000000,9.9800000000,2.6626481983,2.1679337115,9.9800000000,7.1314458202,8.6818610173,24.9708410123
+17-Aug-12,1418.1600000000,2.6500000000,2.6616752989,2.2384400102,2.6500000000,6.7867330077,8.4112367376,26.6124956420
+20-Aug-12,1418.1300000000,-0.0300000000,2.4546316349,2.2693121742,0.0300000000,6.2670051048,8.1050404604,27.9987766294
+21-Aug-12,1413.1700000000,-4.9600000000,1.8842981696,2.2143321744,4.9600000000,6.1664702721,7.8282126375,28.2865614016
+22-Aug-12,1413.4900000000,0.3200000000,1.7639723544,2.1500207921,0.3200000000,5.7167597788,7.5266971693,28.5652623416
+23-Aug-12,1402.0800000000,-11.4100000000,0.7506304009,1.9501878442,11.4100000000,6.1546838166,7.3307736625,26.6027561889
+24-Aug-12,1411.1300000000,9.0500000000,1.3890179104,1.8700527777,9.0500000000,6.3773915374,7.1946306951,25.9923386886
+27-Aug-12,1410.4400000000,-0.6900000000,1.2290998528,1.7785247000,0.6900000000,5.9399173804,7.0154576337,25.3515136551
+28-Aug-12,1409.3000000000,-1.1400000000,1.0468686921,1.6740442221,1.1400000000,5.5707077355,6.8091473483,24.5852253804
+29-Aug-12,1410.4900000000,1.1900000000,1.0578783523,1.5860557358,1.1900000000,5.2337436965,6.5841797068,24.0888889198
diff --git a/ta/tests/data/cs-ultosc.csv b/ta/tests/data/cs-ultosc.csv
new file mode 100644
index 00000000..0c319d7e
--- /dev/null
+++ b/ta/tests/data/cs-ultosc.csv
@@ -0,0 +1,31 @@
+Date,High,Low,Close,Buying Pressure,True Range,AVG-7,AVG-14,AVG-28,Ult_Osc
+20-Oct-10,57.934200,56.519900,57.565700,,,,,,
+21-Oct-10,58.462000,57.067700,57.665300,0.597600,1.394300,,,,
+22-Oct-10,57.764900,56.440300,56.918300,0.478000,1.324600,,,,
+25-Oct-10,59.876300,57.525800,58.472000,1.553700,2.958000,,,,
+26-Oct-10,59.019800,57.575600,58.740900,1.165300,1.444200,,,,
+27-Oct-10,60.175100,57.894300,60.005800,2.111500,2.280800,,,,
+28-Oct-10,60.294600,58.013900,58.452100,0.438200,2.280700,,,,
+29-Oct-10,59.856400,58.432200,59.179100,0.746900,1.424200,0.541032,,,
+01-Nov-10,59.776700,58.452100,58.671200,0.219100,1.324600,0.514892,,,
+02-Nov-10,59.726900,58.581600,58.870400,0.288800,1.145300,0.507357,,,
+03-Nov-10,59.599400,58.541700,59.298600,0.756900,1.057700,0.522628,,,
+04-Nov-10,62.963700,59.617300,62.565300,3.266700,3.665100,0.594010,,,
+05-Nov-10,62.266600,61.360200,62.017600,0.657400,1.205100,0.526659,,,
+08-Nov-10,63.063300,61.250700,62.047400,0.796700,1.812600,0.578662,,,
+09-Nov-10,63.740600,62.186900,62.515500,0.468100,1.693200,0.542164,0.541571,,
+10-Nov-10,62.744600,61.021600,62.366100,1.344500,1.723000,0.616087,0.564022,,
+11-Nov-10,63.481600,61.569400,63.401900,1.832500,1.912200,0.698054,0.603482,,
+12-Nov-10,63.232600,60.792600,61.898100,1.105500,2.609300,0.647816,0.594186,,
+15-Nov-10,62.137100,60.344400,60.543600,0.199200,1.792700,0.502342,0.548936,,
+16-Nov-10,60.503700,58.203100,59.089500,0.886400,2.340500,0.477754,0.500531,,
+17-Nov-10,59.886200,58.910200,59.009800,0.099600,0.976000,0.454959,0.513271,,
+18-Nov-10,60.324500,59.090500,59.388300,0.378500,1.314700,0.461479,0.500566,,
+19-Nov-10,59.707000,58.591500,59.209000,0.617500,1.115500,0.424446,0.521215,,
+22-Nov-10,62.216800,59.440100,59.657200,0.448200,3.007800,0.283882,0.490277,,
+23-Nov-10,59.736800,57.326700,59.069600,1.742900,2.410100,0.337439,0.501987,,
+24-Nov-10,59.936000,59.109400,59.896200,0.826600,0.866400,0.415568,0.460210,,
+26-Nov-10,59.647000,58.871400,59.288700,0.417300,1.024800,0.422816,0.453823,,
+29-Nov-10,59.368300,58.242900,59.119400,0.876500,1.125400,0.488509,0.470203,,
+30-Nov-10,60.214900,58.262800,59.677100,1.414300,1.952100,0.551491,0.504313,0.523260,53.397838
+01-Dec-10,61.698900,60.583400,61.479800,1.802700,2.021800,0.606726,0.516880,0.540863,57.164680
diff --git a/ta/tests/momentum.py b/ta/tests/momentum.py
new file mode 100644
index 00000000..0a3fe617
--- /dev/null
+++ b/ta/tests/momentum.py
@@ -0,0 +1,179 @@
+import unittest
+
+import pandas as pd
+
+from ta.momentum import (KAMAIndicator, MFIIndicator, ROCIndicator,
+                         RSIIndicator, StochasticOscillator, TSIIndicator,
+                         UltimateOscillator, WilliamsRIndicator, roc)
+from ta.tests.utils import TestIndicator
+
+
+class TestRateOfChangeIndicator(TestIndicator):
+    """
+    https://school.stockcharts.com/doku.php?id=technical_indicators:on_balance_volume_obv
+    """
+
+    _filename = 'ta/tests/data/cs-roc.csv'
+
+    def test_roc(self):
+        target = 'ROC'
+        result = roc(close=self._df['Close'], n=12, fillna=False)
+        pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
+
+    def test_roc2(self):
+        target = 'ROC'
+        result = ROCIndicator(close=self._df['Close'], n=12, fillna=False).roc()
+        pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
+
+
+class TestRSIIndicator(unittest.TestCase):
+    """
+    https://school.stockcharts.com/doku.php?id=technical_indicators:relative_strength_index_rsi
+    Note: Using a more simple initilization (directly `ewm`; stockcharts uses `sma` + `ewm`)
+    """
+
+    _filename = 'ta/tests/data/cs-rsi.csv'
+
+    def setUp(self):
+        self._df = pd.read_csv(self._filename, sep=',')
+        self._indicator = RSIIndicator(close=self._df['Close'], n=14, fillna=False)
+
+    def tearDown(self):
+        del(self._df)
+
+    def test_rsi(self):
+        target = 'RSI'
+        result = self._indicator.rsi()
+        pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
+
+
+class TestMFIIndicator(unittest.TestCase):
+    """
+    https://school.stockcharts.com/doku.php?id=technical_indicators:money_flow_index_mfi
+    """
+
+    _filename = 'ta/tests/data/cs-mfi.csv'
+
+    def setUp(self):
+        self._df = pd.read_csv(self._filename, sep=',')
+        self._indicator = MFIIndicator(
+            high=self._df['High'], low=self._df['Low'], close=self._df['Close'], volume=self._df['Volume'], n=14,
+            fillna=False)
+
+    def tearDown(self):
+        del(self._df)
+
+    def test_mfi(self):
+        target = 'MFI'
+        result = self._indicator.money_flow_index()
+        pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
+
+
+class TestUltimateOscillator(unittest.TestCase):
+    """
+    https://school.stockcharts.com/doku.php?id=technical_indicators:ultimate_oscillator
+    """
+
+    _filename = 'ta/tests/data/cs-ultosc.csv'
+
+    def setUp(self):
+        self._df = pd.read_csv(self._filename, sep=',')
+        self._indicator = UltimateOscillator(
+            high=self._df['High'], low=self._df['Low'], close=self._df['Close'],
+            s=7, m=14, len=28, ws=4.0, wm=2.0, wl=1.0, fillna=False)
+
+    def tearDown(self):
+        del(self._df)
+
+    def test_uo(self):
+        target = 'Ult_Osc'
+        result = self._indicator.uo()
+        pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
+
+
+class TestStochasticOscillator(unittest.TestCase):
+    """
+    https://school.stockcharts.com/doku.php?id=technical_indicators:stochastic_oscillator_fast_slow_and_full
+    """
+
+    _filename = 'ta/tests/data/cs-soo.csv'
+
+    def setUp(self):
+        self._df = pd.read_csv(self._filename, sep=',')
+        self._indicator = StochasticOscillator(
+            high=self._df['High'], low=self._df['Low'], close=self._df['Close'], n=14, d_n=3, fillna=False)
+
+    def tearDown(self):
+        del(self._df)
+
+    def test_so(self):
+        target = 'SO'
+        result = self._indicator.stoch()
+        pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
+
+    def test_so_signal(self):
+        target = 'SO_SIG'
+        result = self._indicator.stoch_signal()
+        pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
+
+
+class TestWilliamsRIndicator(unittest.TestCase):
+    """
+    https://school.stockcharts.com/doku.php?id=technical_indicators:williams_r
+    """
+
+    _filename = 'ta/tests/data/cs-percentr.csv'
+
+    def setUp(self):
+        self._df = pd.read_csv(self._filename, sep=',')
+        self._indicator = WilliamsRIndicator(
+            high=self._df['High'], low=self._df['Low'], close=self._df['Close'], lbp=14, fillna=False)
+
+    def tearDown(self):
+        del(self._df)
+
+    def test_wr(self):
+        target = 'Williams_%R'
+        result = self._indicator.wr()
+        pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
+
+
+class TestKAMAIndicator(unittest.TestCase):
+    """
+    https://school.stockcharts.com/doku.php?id=technical_indicators:kaufman_s_adaptive_moving_average
+    """
+
+    _filename = 'ta/tests/data/cs-kama.csv'
+
+    def setUp(self):
+        self._df = pd.read_csv(self._filename, sep=',')
+        self._indicator = KAMAIndicator(close=self._df['Close'], n=10, pow1=2, pow2=30, fillna=False)
+
+    def tearDown(self):
+        del(self._df)
+
+    def test_kama(self):
+        target = 'KAMA'
+        result = self._indicator.kama()
+        pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
+
+
+class TestTSIIndicator(unittest.TestCase):
+    """
+    https://school.stockcharts.com/doku.php?id=technical_indicators:true_strength_index
+    """
+
+    _filename = 'ta/tests/data/cs-tsi.csv'
+
+    def setUp(self):
+        self._df = pd.read_csv(self._filename, sep=',')
+        self._indicator = TSIIndicator(close=self._df['Close'], r=25, s=13, fillna=False)
+
+    def tearDown(self):
+        del(self._df)
+
+    def test_kama(self):
+        target = 'TSI'
+        result = self._indicator.tsi()
+        pd.testing.assert_series_equal(
+            self._df[target].tail(), result.tail(), check_names=False, check_less_precise=True)
diff --git a/ta/tests/pipeline_wrapper.py b/ta/tests/pipeline_wrapper.py
deleted file mode 100644
index 11457b5d..00000000
--- a/ta/tests/pipeline_wrapper.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import pandas as pd
-from sklearn.ensemble import ExtraTreesRegressor
-from sklearn.metrics import mean_absolute_error
-from sklearn.model_selection import cross_val_score, train_test_split
-from sklearn.pipeline import make_pipeline
-from sklearn.preprocessing import StandardScaler
-
-import ta
-from ta.pipeline_wrapper import TAFeaturesTransform
-from ta.tests.utils import TestIndicator
-
-
-class TestTAFeaturesTransform(TestIndicator):
-    """
-    https://school.stockcharts.com/doku.php?id=technical_indicators:average_directional_index_adx
-    """
-
-    _filename = 'ta/tests/data/datas.csv'
-
-    def test_pipeline(self):
-
-        # Settings
-        target = 'target'  # name target column
-        score = "neg_mean_absolute_error"
-        model = ExtraTreesRegressor(n_estimators=50)
-
-        # Nans values
-        self._df = ta.utils.dropna(self._df)
-
-        # Labeling
-        self._df[target] = (self._df['Close'] / self._df['Close'].shift(1)) - 1
-
-        # Nans values
-        self._df = self._df.dropna()
-
-        cols = [i for i in self._df.columns if i != target]
-        X = self._df[cols]
-        y = self._df[target]
-
-        N = 500
-
-        X_train = self._df[cols][:-N]
-        y_train = self._df.target[:-N]
-        X_test = self._df[cols][-N:]
-        y_test = self._df.target[-N:]
-
-        pipe = make_pipeline(
-            TAFeaturesTransform("Open", "High", "Low", "Close", "Volume_BTC", fillna=True),
-            StandardScaler(),
-            model
-        )
-
-        y_pred = pipe.fit(X_train, y_train).predict(X_test)
-
-        # print(mean_absolute_error(y_test, y_pred))
diff --git a/ta/tests/trend.py b/ta/tests/trend.py
index b6593085..c19912e1 100644
--- a/ta/tests/trend.py
+++ b/ta/tests/trend.py
@@ -1,3 +1,5 @@
+import unittest
+
 import pandas as pd
 
 from ta.tests.utils import TestIndicator
@@ -60,32 +62,32 @@ class TestMACDIndicator(TestIndicator):
 
     def test_macd(self):
         target = 'MACD_line'
-        result = macd(close=self._df['Close'], n_slow=12, n_fast=26, fillna=False)
+        result = macd(close=self._df['Close'], n_slow=26, n_fast=12, fillna=False)
         pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
 
     def test_macd2(self):
         target = 'MACD_line'
-        result = MACD(close=self._df['Close'], n_slow=12, n_fast=26, n_sign=9, fillna=False).macd()
+        result = MACD(close=self._df['Close'], n_slow=26, n_fast=12, n_sign=9, fillna=False).macd()
         pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
 
     def test_macd_signal(self):
         target = 'MACD_signal'
-        result = macd_signal(close=self._df['Close'], n_slow=12, n_fast=26, n_sign=9, fillna=False)
+        result = macd_signal(close=self._df['Close'], n_slow=26, n_fast=12, n_sign=9, fillna=False)
         pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
 
     def test_macd_signal2(self):
         target = 'MACD_signal'
-        result = MACD(close=self._df['Close'], n_slow=12, n_fast=26, n_sign=9, fillna=False).macd_signal()
+        result = MACD(close=self._df['Close'], n_slow=26, n_fast=12, n_sign=9, fillna=False).macd_signal()
         pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
 
     def test_macd_diff(self):
         target = 'MACD_diff'
-        result = macd_diff(close=self._df['Close'], n_slow=12, n_fast=26, n_sign=9, fillna=False)
+        result = macd_diff(close=self._df['Close'], n_slow=26, n_fast=12, n_sign=9, fillna=False)
         pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
 
     def test_macd_diff2(self):
         target = 'MACD_diff'
-        result = MACD(close=self._df['Close'], n_slow=12, n_fast=26, n_sign=9, fillna=False).macd_diff()
+        result = MACD(close=self._df['Close'], n_slow=26, n_fast=12, n_sign=9, fillna=False).macd_diff()
         pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
 
 
@@ -143,33 +145,37 @@ def test_vortex_indicator_neg2(self):
         pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
 
 
-class TestPSARIndicator(TestIndicator):
+class TestPSARIndicator(unittest.TestCase):
     """
     https://school.stockcharts.com/doku.php?id=technical_indicators:parabolic_sar
     """
 
     _filename = 'ta/tests/data/cs-psar.csv'
 
+    def setUp(self):
+        self._df = pd.read_csv(self._filename, sep=',')
+        self._indicator = PSARIndicator(high=self._df['High'], low=self._df['Low'], close=self._df['Close'],
+                                        fillna=False)
+
+    def tearDown(self):
+        del(self._df)
+
     def test_psar_up(self):
         target = 'psar_up'
-        result = psar_up(high=self._df['High'], low=self._df['Low'],
-                         close=self._df['Close'])
+        result = self._indicator.psar_up()
         pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
 
     def test_psar_down(self):
         target = 'psar_down'
-        result = psar_down(high=self._df['High'], low=self._df['Low'],
-                           close=self._df['Close'])
+        result = self._indicator.psar_down()
         pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
 
     def test_psar_up_indicator(self):
         target = 'psar_up_ind'
-        result = psar_up_indicator(high=self._df['High'], low=self._df['Low'],
-                                   close=self._df['Close'])
+        result = self._indicator.psar_up_indicator()
         pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
 
     def test_psar_down_indicator(self):
         target = 'psar_down_ind'
-        result = psar_down_indicator(high=self._df['High'], low=self._df['Low'],
-                                     close=self._df['Close'])
+        result = self._indicator.psar_down_indicator()
         pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
diff --git a/ta/tests/volatility.py b/ta/tests/volatility.py
index ec909b58..ead55813 100644
--- a/ta/tests/volatility.py
+++ b/ta/tests/volatility.py
@@ -1,7 +1,9 @@
+import unittest
+
 import pandas as pd
 
 from ta.tests.utils import TestIndicator
-from ta.volatility import AverageTrueRange, average_true_range
+from ta.volatility import AverageTrueRange, BollingerBands, average_true_range
 
 
 class TestAverageTrueRange(TestIndicator):
@@ -23,3 +25,48 @@ def test_atr2(self):
             high=self._df['High'], low=self._df['Low'], close=self._df['Close'], n=14,
             fillna=False).average_true_range()
         pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
+
+
+class TestBollingerBands(unittest.TestCase):
+    """
+    https://school.stockcharts.com/doku.php?id=technical_indicators:bollinger_bands
+    """
+
+    _filename = 'ta/tests/data/cs-bbands.csv'
+
+    def setUp(self):
+        self._df = pd.read_csv(self._filename, sep=',')
+        self._indicator = BollingerBands(close=self._df['Close'], n=20, ndev=2, fillna=False)
+
+    def tearDown(self):
+        del(self._df)
+
+    def test_mavg(self):
+        target = 'MiddleBand'
+        result = self._indicator.bollinger_mavg()
+        pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
+
+    def test_hband(self):
+        target = 'HighBand'
+        result = self._indicator.bollinger_hband()
+        pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
+
+    def test_lband(self):
+        target = 'LowBand'
+        result = self._indicator.bollinger_lband()
+        pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
+
+    def test_wband(self):
+        target = 'WidthBand'
+        result = self._indicator.bollinger_wband()
+        pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
+
+    def test_hband_indicator(self):
+        target = 'CrossUp'
+        result = self._indicator.bollinger_hband_indicator()
+        pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
+
+    def test_lband_indicator(self):
+        target = 'CrossDown'
+        result = self._indicator.bollinger_lband_indicator()
+        pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
diff --git a/ta/trend.py b/ta/trend.py
index c9b5f773..2a4dcfa2 100644
--- a/ta/trend.py
+++ b/ta/trend.py
@@ -87,8 +87,8 @@ class MACD(IndicatorMixin):
     """
     def __init__(self,
                  close: pd.Series,
-                 n_slow: int = 12,
-                 n_fast: int = 26,
+                 n_slow: int = 26,
+                 n_fast: int = 12,
                  n_sign: int = 9,
                  fillna: bool = False):
         self._close = close
@@ -101,7 +101,7 @@ def __init__(self,
     def _run(self):
         self._emafast = ema(self._close, self._n_fast, self._fillna)
         self._emaslow = ema(self._close, self._n_slow, self._fillna)
-        self._macd = self._emaslow - self._emafast
+        self._macd = self._emafast - self._emaslow
         self._macd_signal = ema(self._macd, self._n_sign, self._fillna)
         self._macd_diff = self._macd - self._macd_signal
 
@@ -802,7 +802,7 @@ def ema_indicator(close, n=12, fillna=False):
     return EMAIndicator(close=close, n=n, fillna=fillna).ema_indicator()
 
 
-def macd(close, n_fast=12, n_slow=26, fillna=False):
+def macd(close, n_slow=26, n_fast=12, fillna=False):
     """Moving Average Convergence Divergence (MACD)
 
     Is a trend-following momentum indicator that shows the relationship between
@@ -822,7 +822,7 @@ def macd(close, n_fast=12, n_slow=26, fillna=False):
     return MACD(close=close, n_slow=n_slow, n_fast=n_fast, n_sign=9, fillna=fillna).macd()
 
 
-def macd_signal(close, n_fast=12, n_slow=26, n_sign=9, fillna=False):
+def macd_signal(close, n_slow=26, n_fast=12, n_sign=9, fillna=False):
     """Moving Average Convergence Divergence (MACD Signal)
 
     Shows EMA of MACD.
@@ -842,7 +842,7 @@ def macd_signal(close, n_fast=12, n_slow=26, n_sign=9, fillna=False):
     return MACD(close=close, n_slow=n_slow, n_fast=n_fast, n_sign=n_sign, fillna=fillna).macd_signal()
 
 
-def macd_diff(close, n_fast=12, n_slow=26, n_sign=9, fillna=False):
+def macd_diff(close, n_slow=26, n_fast=12, n_sign=9, fillna=False):
     """Moving Average Convergence Divergence (MACD Diff)
 
     Shows the relationship between MACD and MACD Signal.
diff --git a/ta/volatility.py b/ta/volatility.py
index 12a959f9..23afa889 100644
--- a/ta/volatility.py
+++ b/ta/volatility.py
@@ -107,8 +107,18 @@ def bollinger_lband(self) -> pd.Series:
         lband = self.check_fillna(self._lband, method='backfill')
         return pd.Series(lband, name='lband')
 
+    def bollinger_wband(self) -> pd.Series:
+        """Bollinger Channel Width Band
+
+        Returns:
+            pandas.Series: New feature generated.
+        """
+        wband = self._hband - self._lband
+        wband = self.check_fillna(wband, value=0)
+        return pd.Series(wband, name='bbiwband')
+
     def bollinger_hband_indicator(self) -> pd.Series:
-        """Bollinger Channel Indicator Crossing Low Band
+        """Bollinger Channel Indicator Crossing High Band
 
         Returns:
             pandas.Series: New feature generated.
diff --git a/ta/volume.py b/ta/volume.py
index 2d399935..a3fb4a66 100644
--- a/ta/volume.py
+++ b/ta/volume.py
@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """
 .. module:: volume
    :synopsis: Volume Indicators.
diff --git a/ta/wrapper.py b/ta/wrapper.py
index 682db95c..9a226273 100644
--- a/ta/wrapper.py
+++ b/ta/wrapper.py
@@ -1,9 +1,16 @@
+"""
+.. module:: wrapper
+   :synopsis: Wrapper of Indicators.
+
+.. moduleauthor:: Dario Lopez Padial (Bukosabino)
+"""
+
 import pandas as pd
 
 from ta.momentum import (AwesomeOscillatorIndicator, KAMAIndicator,
                          MFIIndicator, ROCIndicator, RSIIndicator,
-                         StochIndicator, TSIIndicator,
-                         UltimateOscillatorIndicator, WilliamsRIndicator)
+                         StochasticOscillator, TSIIndicator,
+                         UltimateOscillator, WilliamsRIndicator)
 from ta.others import (CumulativeReturnIndicator, DailyLogReturnIndicator,
                        DailyReturnIndicator)
 from ta.trend import (MACD, ADXIndicator, AroonIndicator, CCIIndicator,
@@ -19,7 +26,7 @@
 
 
 def add_volume_ta(df: pd.DataFrame, high: str, low: str, close: str, volume: str,
-                  fillna: bool = False, colprefix: str = ""):
+                  fillna: bool = False, colprefix: str = "") -> pd.DataFrame:
     """Add volume technical analysis features to dataframe.
 
     Args:
@@ -68,7 +75,7 @@ def add_volume_ta(df: pd.DataFrame, high: str, low: str, close: str, volume: str
 
 
 def add_volatility_ta(df: pd.DataFrame, high: str, low: str, close: str,
-                      fillna: bool = False, colprefix: str = ""):
+                      fillna: bool = False, colprefix: str = "") -> pd.DataFrame:
     """Add volatility technical analysis features to dataframe.
 
     Args:
@@ -89,9 +96,10 @@ def add_volatility_ta(df: pd.DataFrame, high: str, low: str, close: str,
 
     # Bollinger Bands
     indicator_bb = BollingerBands(close=df[close], n=20, ndev=2, fillna=fillna)
+    df[f'{colprefix}volatility_bbm'] = indicator_bb.bollinger_mavg()
     df[f'{colprefix}volatility_bbh'] = indicator_bb.bollinger_hband()
     df[f'{colprefix}volatility_bbl'] = indicator_bb.bollinger_lband()
-    df[f'{colprefix}volatility_bbm'] = indicator_bb.bollinger_mavg()
+    df[f'{colprefix}volatility_bbw'] = indicator_bb.bollinger_wband()
     df[f'{colprefix}volatility_bbhi'] = indicator_bb.bollinger_hband_indicator()
     df[f'{colprefix}volatility_bbli'] = indicator_bb.bollinger_lband_indicator()
 
@@ -114,7 +122,7 @@ def add_volatility_ta(df: pd.DataFrame, high: str, low: str, close: str,
 
 
 def add_trend_ta(df: pd.DataFrame, high: str, low: str, close: str, fillna: bool = False,
-                 colprefix: str = ""):
+                 colprefix: str = "") -> pd.DataFrame:
     """Add trend technical analysis features to dataframe.
 
     Args:
@@ -130,7 +138,7 @@ def add_trend_ta(df: pd.DataFrame, high: str, low: str, close: str, fillna: bool
     """
 
     # MACD
-    indicator_macd = MACD(close=df[close], n_fast=12, n_slow=26, n_sign=9, fillna=fillna)
+    indicator_macd = MACD(close=df[close], n_slow=26, n_fast=12, n_sign=9, fillna=fillna)
     df[f'{colprefix}trend_macd'] = indicator_macd.macd()
     df[f'{colprefix}trend_macd_signal'] = indicator_macd.macd_signal()
     df[f'{colprefix}trend_macd_diff'] = indicator_macd.macd_diff()
@@ -204,7 +212,7 @@ def add_trend_ta(df: pd.DataFrame, high: str, low: str, close: str, fillna: bool
 
 
 def add_momentum_ta(df: pd.DataFrame, high: str, low: str, close: str, volume: str,
-                    fillna: bool = False, colprefix: str = ""):
+                    fillna: bool = False, colprefix: str = "") -> pd.DataFrame:
     """Add trend technical analysis features to dataframe.
 
     Args:
@@ -230,12 +238,12 @@ def add_momentum_ta(df: pd.DataFrame, high: str, low: str, close: str, volume: s
     df[f'{colprefix}momentum_tsi'] = TSIIndicator(close=df[close], r=25, s=13, fillna=fillna).tsi()
 
     # Ultimate Oscillator
-    df[f'{colprefix}momentum_uo'] = UltimateOscillatorIndicator(
+    df[f'{colprefix}momentum_uo'] = UltimateOscillator(
         high=df[high], low=df[low], close=df[close], s=7, m=14, len=28, ws=4.0, wm=2.0, wl=1.0,
         fillna=fillna).uo()
 
     # Stoch Indicator
-    indicator = StochIndicator(high=df[high], low=df[low], close=df[close], n=14, d_n=3, fillna=fillna)
+    indicator = StochasticOscillator(high=df[high], low=df[low], close=df[close], n=14, d_n=3, fillna=fillna)
     df[f'{colprefix}momentum_stoch'] = indicator.stoch()
     df[f'{colprefix}momentum_stoch_signal'] = indicator.stoch_signal()
 
@@ -255,7 +263,7 @@ def add_momentum_ta(df: pd.DataFrame, high: str, low: str, close: str, volume: s
     return df
 
 
-def add_others_ta(df: pd.DataFrame, close: str, fillna: bool = False, colprefix: str = ""):
+def add_others_ta(df: pd.DataFrame, close: str, fillna: bool = False, colprefix: str = "") -> pd.DataFrame:
     """Add others analysis features to dataframe.
 
     Args:
@@ -280,7 +288,7 @@ def add_others_ta(df: pd.DataFrame, close: str, fillna: bool = False, colprefix:
 
 
 def add_all_ta_features(df: pd.DataFrame, open: str, high: str, low: str,
-                        close: str, volume: str, fillna: bool = False, colprefix: str = ""):
+                        close: str, volume: str, fillna: bool = False, colprefix: str = "") -> pd.DataFrame:
     """Add all technical analysis features to dataframe.
 
     Args: