diff --git a/release.md b/release.md
index 9453f118..31b0616a 100644
--- a/release.md
+++ b/release.md
@@ -1,5 +1,6 @@
 | Date | Version | Comment |
 | ------------- | ------------- | ------------- |
+| 2020/05/11 | 0.5.24 | Adding extra methods for IchimokuIndicator https://github.com/bukosabino/ta/pull/155 |
 | 2020/05/10 | 0.5.23 | Fixing bug when dataset with timestamp as index https://github.com/bukosabino/ta/pull/154 |
 | 2020/05/04 | 0.5.22 | 1. Keltner Channel: adding tests; adding n atr input parametr; fixing some minor bug; adding unittests for adx https://github.com/bukosabino/ta/pull/148 |
 |            |        | 2. Refactor tests code and speed up the tests https://github.com/bukosabino/ta/pull/149 |
diff --git a/setup.py b/setup.py
index f5dfb3cd..2e38e4fd 100644
--- a/setup.py
+++ b/setup.py
@@ -4,7 +4,7 @@
 setup(
     name='ta',
     packages=['ta'],
-    version='0.5.23',
+    version='0.5.24',
     description='Technical Analysis Library in Python',
     long_description='It is a Technical Analysis library to financial time series datasets. You can use to do feature engineering. It is builded on Python Pandas library.',
     author='Dario Lopez Padial (Bukosabino)',
@@ -16,7 +16,7 @@
         'numpy',
         'pandas',
     ],
-    download_url='https://github.com/bukosabino/ta/tarball/0.5.23',
+    download_url='https://github.com/bukosabino/ta/tarball/0.5.24',
     keywords=['technical analysis', 'python3', 'pandas'],
     license='The MIT License (MIT)',
     classifiers=[
diff --git a/ta/trend.py b/ta/trend.py
index b7d4f1e5..313fb1c4 100644
--- a/ta/trend.py
+++ b/ta/trend.py
@@ -285,6 +285,31 @@ def __init__(self, high: pd.Series, low: pd.Series, n1: int = 9, n2: int = 26, n
         self._n3 = n3
         self._visual = visual
         self._fillna = fillna
+        self._run()
+
+    def _run(self):
+        self._conv = 0.5 * (
+                self._high.rolling(self._n1, min_periods=0).max() + self._low.rolling(self._n1, min_periods=0).min())
+        self._base = 0.5 * (
+                self._high.rolling(self._n2, min_periods=0).max() + self._low.rolling(self._n2, min_periods=0).min())
+
+    def ichimoku_conversion_line(self) -> pd.Series:
+        """Tenkan-sen (Conversion Line)
+
+        Returns:
+            pandas.Series: New feature generated.
+        """
+        conversion = self._check_fillna(self._conv, value=-1)
+        return pd.Series(conversion, name=f'ichimoku_conv_{self._n1}_{self._n2}')
+
+    def ichimoku_base_line(self) -> pd.Series:
+        """Kijun-sen (Base Line)
+
+        Returns:
+            pandas.Series: New feature generated.
+        """
+        base = self._check_fillna(self._base, value=-1)
+        return pd.Series(base, name=f'ichimoku_base_{self._n1}_{self._n2}')
 
     def ichimoku_a(self) -> pd.Series:
         """Senkou Span A (Leading Span A)
@@ -292,11 +317,7 @@ def ichimoku_a(self) -> pd.Series:
         Returns:
             pandas.Series: New feature generated.
         """
-        conv = 0.5 * (self._high.rolling(self._n1, min_periods=0).max()
-                      + self._low.rolling(self._n1, min_periods=0).min())
-        base = 0.5 * (self._high.rolling(self._n2, min_periods=0).max()
-                      + self._low.rolling(self._n2, min_periods=0).min())
-        spana = 0.5 * (conv + base)
+        spana = 0.5 * (self._conv + self._base)
         spana = spana.shift(self._n2, fill_value=spana.mean()) if self._visual else spana
         spana = self._check_fillna(spana, value=-1)
         return pd.Series(spana, name=f'ichimoku_a_{self._n1}_{self._n2}')
diff --git a/ta/wrapper.py b/ta/wrapper.py
index 8ac5e4aa..d7fae070 100644
--- a/ta/wrapper.py
+++ b/ta/wrapper.py
@@ -207,6 +207,8 @@ def add_trend_ta(df: pd.DataFrame, high: str, low: str, close: str, fillna: bool
 
     # Ichimoku Indicator
     indicator = IchimokuIndicator(high=df[high], low=df[low], n1=9, n2=26, n3=52, visual=False, fillna=fillna)
+    df[f'{colprefix}trend_ichimoku_conv'] = indicator.ichimoku_conversion_line()
+    df[f'{colprefix}trend_ichimoku_base'] = indicator.ichimoku_base_line()
     df[f'{colprefix}trend_ichimoku_a'] = indicator.ichimoku_a()
     df[f'{colprefix}trend_ichimoku_b'] = indicator.ichimoku_b()
     indicator = IchimokuIndicator(high=df[high], low=df[low], n1=9, n2=26, n3=52, visual=True, fillna=fillna)