V-1.0.2

dc-aichara · Oct 30, 2019 · 5b1ac7b · 5b1ac7b
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diff --git a/PriceIndices.egg-info/PKG-INFO b/PriceIndices.egg-info/PKG-INFO
@@ -1,12 +1,12 @@
 Metadata-Version: 2.1
 Name: PriceIndices
-Version: 1.0.1
+Version: 1.0.2
 Summary: A python package to get historical market data of cryptocurrencies from CoinMarketCap, and calculate & plot different indicators.
 Home-page: https://github.com/dc-aichara/Price-Indices
 Author: Dayal Chand Aichara
 Author-email: [email protected]
 License: MIT
-Download-URL: https://github.com/dc-aichara/PriceIndices/archive/v-1.0.1.tar.gz
+Download-URL: https://github.com/dc-aichara/PriceIndices/archive/v-1.0.2.tar.gz
 Description: 
 
 

diff --git a/PriceIndices/__pycache__/price_indicators.cpython-36.pyc b/PriceIndices/__pycache__/price_indicators.cpython-36.pyc
diff --git a/PriceIndices/price_indicators.py b/PriceIndices/price_indicators.py
@@ -146,10 +146,11 @@ def get_rsi_graph(self, rsi_data):
             plt.suptitle('Price  and  Relative  Strength Index', color='red', fontsize=24)
             plt.savefig('rsi.png', bbox_inches='tight', facecolor='orange')
             return plt.show()
+
         except Exception as e:
             return e
 
-    def get_bollinger_bands(df, days=20):
+    def get_bollinger_bands(df, days=20, plot=None):
         """
         Type:
             Trend, volatility, momentum indicator
@@ -178,25 +179,27 @@ def get_bollinger_bands(df, days=20):
             data['plus'] = data['SMA'] + data['SD']*2
             data['minus'] = data['SMA'] - data['SMA']*2
             data1 = data.sort_values(by='date', ascending=False).reset_index(drop=True)
-            fig, ax = plt.subplots(figsize=(16, 12))
-            plt.plot(data1['date'], data1['plus'], color='g')
-            plt.plot(data1['date'], data1['minus'], color='g')
-            plt.plot(data1['date'], data1['price'], color='orange')
-            plt.legend()
-            plt.xlabel('Time', color='b', fontsize=22)
-            plt.ylabel('Price', color='b', fontsize=22)
-            plt.title('Bollinger Bands', color='b', fontsize=27)
-            plt.tick_params(labelsize=17)
-            fig.set_facecolor('yellow')
-            plt.grid()
-            plt.savefig('bollinger_bands.png', bbox_inches='tight', facecolor='orange')
-            plt.show()
+            while plot:
+                fig, ax = plt.subplots(figsize=(16, 12))
+                plt.plot(data1['date'], data1['plus'], color='g')
+                plt.plot(data1['date'], data1['minus'], color='g')
+                plt.plot(data1['date'], data1['price'], color='orange')
+                plt.legend()
+                plt.xlabel('Time', color='b', fontsize=22)
+                plt.ylabel('Price', color='b', fontsize=22)
+                plt.title('Bollinger Bands', color='b', fontsize=27)
+                plt.tick_params(labelsize=17)
+                fig.set_facecolor('yellow')
+                plt.grid()
+                plt.savefig('bollinger_bands.png', bbox_inches='tight', facecolor='orange')
+                plt.show()
+                break
             return data1
 
         except Exception as e:
             return e
 
-    def get_moving_average_convergence_divergence(df):
+    def get_moving_average_convergence_divergence(df, plot=None):
         """
         Type
             Trend and momentum indicator
@@ -220,23 +223,24 @@ def get_moving_average_convergence_divergence(df):
             data['MACD'] = data['EMA_12'] - data['EMA_26']
             data1 = data.dropna()
 
-            fig, ax = plt.subplots(figsize=(14, 9))
-            plt.plot(data1['date'], data1['price'], color='r', label='Price')
-            plt.plot(data1['date'], data1['MACD'], color='b', label='MACD')
-            plt.legend()
-            plt.title('Price and MACD Plot', fontsize=28, color='b')
-            plt.xlabel('Time', color='b', fontsize=19)
-            plt.ylabel('Price', color='b', fontsize=19)
-            plt.savefig('macd.png', bbox_inches='tight', facecolor='orange')
-            fig.set_facecolor('orange')
-            plt.show()
-
+            while plot:
+                fig, ax = plt.subplots(figsize=(14, 9))
+                plt.plot(data1['date'], data1['price'], color='r', label='Price')
+                plt.plot(data1['date'], data1['MACD'], color='b', label='MACD')
+                plt.legend()
+                plt.title('Price and MACD Plot', fontsize=28, color='b')
+                plt.xlabel('Time', color='b', fontsize=19)
+                plt.ylabel('Price', color='b', fontsize=19)
+                plt.savefig('macd.png', bbox_inches='tight', facecolor='orange')
+                fig.set_facecolor('orange')
+                plt.show()
+                break
             return data1
 
         except Exception as e:
             return print('MACD Error - {}'.format(e))
 
-    def get_simple_moving_average(df, days=15):
+    def get_simple_moving_average(df, days=15, plot=None):
         """
         Simple moving average of given days
         :param price_data: pandas DataFrame
@@ -249,22 +253,24 @@ def get_simple_moving_average(df, days=15):
             data['SMA'] = data['price'].rolling(days).mean()
             data1 = data.dropna()
             data1 = data1.sort_values(by='date', ascending=False).reset_index(drop=True)
-            fig, ax = plt.subplots(figsize=(14, 9))
-            plt.plot(data1['date'], data1['price'], color='r', label='Price')
-            plt.plot(data1['date'], data1['SMA'], color='b', label='SMA')
-            plt.legend()
-            plt.title('Price and SMA Plot', fontsize=28, color='b')
-            plt.xlabel('Time', color='b', fontsize=19)
-            plt.ylabel('Price', color='b', fontsize=19)
-            plt.savefig('sma.png', bbox_inches='tight', facecolor='orange')
-            fig.set_facecolor('orange')
-            plt.show()
+            while plot:
+                fig, ax = plt.subplots(figsize=(14, 9))
+                plt.plot(data1['date'], data1['price'], color='r', label='Price')
+                plt.plot(data1['date'], data1['SMA'], color='b', label='SMA')
+                plt.legend()
+                plt.title('Price and SMA Plot', fontsize=28, color='b')
+                plt.xlabel('Time', color='b', fontsize=19)
+                plt.ylabel('Price', color='b', fontsize=19)
+                plt.savefig('sma.png', bbox_inches='tight', facecolor='orange')
+                fig.set_facecolor('orange')
+                plt.show()
+                break
             return data1
 
         except Exception as e:
             return print('SMA Error - {}'.format(e))
 
-    def get_exponential_moving_average(df, periods=[20]):
+    def get_exponential_moving_average(df, periods=[20], plot=None):
         """
         The EMA is a moving average that places a greater weight and significance on the most recent data points.
         Like all moving averages, this technical indicator is used to produce buy and sell signals based on crossovers and divergences from the historical average.
@@ -280,17 +286,19 @@ def get_exponential_moving_average(df, periods=[20]):
                 data['EMA_{}'.format(period)] = data['price'].ewm(span=period, adjust=False).mean()
                 data = data.dropna()
             data1 = data
-            fig, ax = plt.subplots(figsize=(14, 9))
-            plt.plot(data1['date'], data1['price'], color='r', label='Price')
-            for period in periods:
-                plt.plot(data1['date'], data1['EMA_{}'.format(period)], label='EMA_{}'.format(period))
-            plt.legend()
-            plt.title('Price and EMA Plot', fontsize=28, color='b')
-            plt.xlabel('Time', color='b', fontsize=19)
-            plt.ylabel('Price/EMA', color='b', fontsize=19)
-            plt.savefig('ema.png', bbox_inches='tight', facecolor='orange')
-            fig.set_facecolor('orange')
-            plt.show()
+            while plot:
+                fig, ax = plt.subplots(figsize=(14, 9))
+                plt.plot(data1['date'], data1['price'], color='r', label='Price')
+                for period in periods:
+                    plt.plot(data1['date'], data1['EMA_{}'.format(period)], label='EMA_{}'.format(period))
+                plt.legend()
+                plt.title('Price and EMA Plot', fontsize=28, color='b')
+                plt.xlabel('Time', color='b', fontsize=19)
+                plt.ylabel('Price/EMA', color='b', fontsize=19)
+                plt.savefig('ema.png', bbox_inches='tight', facecolor='orange')
+                fig.set_facecolor('orange')
+                plt.show()
+                break
             return data1
 
         except Exception as e:

diff --git a/README.md b/README.md
@@ -113,7 +113,7 @@ This will return a plot of RSI against time and also save RSI plot in your worki
 - #### Get Bollinger Bands and its plot
 
 ```python
->>> df_bb = Indices.get_bollinger_bands(price_data , 20) 
+>>> df_bb = Indices.get_bollinger_bands(price_data , 20, plot=True) 
 >>> df_bb.tail()
            date   price       SMA         SD       pluse     minus
 2243 2013-05-02  105.21  115.2345   6.339257  127.913013 -115.2345
@@ -136,7 +136,7 @@ This will also save Bollingers bands plot in your working directory as 'bollinge
 
 ```python
 
->>> df_macd = Indices.get_moving_average_convergence_divergence(price_data)
+>>> df_macd = Indices.get_moving_average_convergence_divergence(price_data, plot=True)
 """This will return a pandas DataFrame and save EMA plot as 'macd.png' in working directory. 
 """"
 >>> df_macd.head()
@@ -154,7 +154,7 @@ This will also save Bollingers bands plot in your working directory as 'bollinge
 - #### Get Simple Moving Average (SMA) and its plot
 
 ```python
->>> df_sma = Indices.get_simple_moving_average(price_data, 20) 
+>>> df_sma = Indices.get_simple_moving_average(price_data, 20, plot=True) 
 """This will return a pandas DataFrame and save EMA plot as 'sma.png' in working directory. 
 """"
 >>> df_sma.head()
@@ -172,7 +172,7 @@ This will also save Bollingers bands plot in your working directory as 'bollinge
 - ### Get Exponential Moving Average (EMA) and its plot
 
 ```python
->>> df_ema = Indices.get_exponential_moving_average(price_data, [20,70])
+>>> df_ema = Indices.get_exponential_moving_average(price_data, [20,70], plot=True)
 """This will return a pandas DataFrame and save EMA plot as 'ema.png' in working directory. 
 """"
 

diff --git a/dist/PriceIndices-1.0.2-py3-none-any.whl b/dist/PriceIndices-1.0.2-py3-none-any.whl
diff --git a/dist/PriceIndices-1.0.2.tar.gz b/dist/PriceIndices-1.0.2.tar.gz
diff --git a/setup.py b/setup.py
@@ -6,13 +6,13 @@
 setuptools.setup(
   name='PriceIndices',
   packages=['PriceIndices'],
-  version='1.0.1',
+  version='1.0.2',
   license='MIT',
   description='A python package to get historical market data of cryptocurrencies from CoinMarketCap, and calculate & plot different indicators.',
   author='Dayal Chand Aichara',
   author_email='[email protected]',
   url='https://github.com/dc-aichara/Price-Indices',
-  download_url='https://github.com/dc-aichara/PriceIndices/archive/v-1.0.1.tar.gz',
+  download_url='https://github.com/dc-aichara/PriceIndices/archive/v-1.0.2.tar.gz',
   keywords=['Volatility', 'blockchain', 'cryptocurrency', 'Price', 'trading', 'CoinMarketCap', 'Indices', 'Indicators'],
   install_requires=['requests', 'pandas', 'numpy', 'matplotlib'],
   classifiers=[