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Dayal Chand Aichara
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V-1.0.2
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‎PriceIndices.egg-info/PKG-INFO‎

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@@ -1,12 +1,12 @@
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Metadata-Version: 2.1
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Name: PriceIndices
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Version: 1.0.1
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Version: 1.0.2
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Summary: A python package to get historical market data of cryptocurrencies from CoinMarketCap, and calculate & plot different indicators.
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Home-page: https://github.com/dc-aichara/Price-Indices
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Author: Dayal Chand Aichara
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Author-email: dc.aichara@gmail.com
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License: MIT
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Download-URL: https://github.com/dc-aichara/PriceIndices/archive/v-1.0.1.tar.gz
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Download-URL: https://github.com/dc-aichara/PriceIndices/archive/v-1.0.2.tar.gz
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Description:
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‎PriceIndices/__pycache__/price_indicators.cpython-36.pyc‎

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‎PriceIndices/price_indicators.py‎

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@@ -146,10 +146,11 @@ def get_rsi_graph(self, rsi_data):
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plt.suptitle('Price and Relative Strength Index', color='red', fontsize=24)
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plt.savefig('rsi.png', bbox_inches='tight', facecolor='orange')
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return plt.show()
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149150
except Exception as e:
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return e
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152-
def get_bollinger_bands(df, days=20):
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def get_bollinger_bands(df, days=20, plot=None):
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"""
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Type:
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Trend, volatility, momentum indicator
@@ -178,25 +179,27 @@ def get_bollinger_bands(df, days=20):
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data['plus'] = data['SMA'] + data['SD']*2
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data['minus'] = data['SMA'] - data['SMA']*2
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data1 = data.sort_values(by='date', ascending=False).reset_index(drop=True)
181-
fig, ax = plt.subplots(figsize=(16, 12))
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plt.plot(data1['date'], data1['plus'], color='g')
183-
plt.plot(data1['date'], data1['minus'], color='g')
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plt.plot(data1['date'], data1['price'], color='orange')
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plt.legend()
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plt.xlabel('Time', color='b', fontsize=22)
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plt.ylabel('Price', color='b', fontsize=22)
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plt.title('Bollinger Bands', color='b', fontsize=27)
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plt.tick_params(labelsize=17)
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fig.set_facecolor('yellow')
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plt.grid()
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plt.savefig('bollinger_bands.png', bbox_inches='tight', facecolor='orange')
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plt.show()
182+
while plot:
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fig, ax = plt.subplots(figsize=(16, 12))
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plt.plot(data1['date'], data1['plus'], color='g')
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plt.plot(data1['date'], data1['minus'], color='g')
186+
plt.plot(data1['date'], data1['price'], color='orange')
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plt.legend()
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plt.xlabel('Time', color='b', fontsize=22)
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plt.ylabel('Price', color='b', fontsize=22)
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plt.title('Bollinger Bands', color='b', fontsize=27)
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plt.tick_params(labelsize=17)
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fig.set_facecolor('yellow')
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plt.grid()
194+
plt.savefig('bollinger_bands.png', bbox_inches='tight', facecolor='orange')
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plt.show()
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break
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return data1
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196199
except Exception as e:
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return e
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199-
def get_moving_average_convergence_divergence(df):
202+
def get_moving_average_convergence_divergence(df, plot=None):
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"""
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Type
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Trend and momentum indicator
@@ -220,23 +223,24 @@ def get_moving_average_convergence_divergence(df):
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data['MACD'] = data['EMA_12'] - data['EMA_26']
221224
data1 = data.dropna()
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223-
fig, ax = plt.subplots(figsize=(14, 9))
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plt.plot(data1['date'], data1['price'], color='r', label='Price')
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plt.plot(data1['date'], data1['MACD'], color='b', label='MACD')
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plt.legend()
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plt.title('Price and MACD Plot', fontsize=28, color='b')
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plt.xlabel('Time', color='b', fontsize=19)
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plt.ylabel('Price', color='b', fontsize=19)
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plt.savefig('macd.png', bbox_inches='tight', facecolor='orange')
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fig.set_facecolor('orange')
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plt.show()
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while plot:
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fig, ax = plt.subplots(figsize=(14, 9))
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plt.plot(data1['date'], data1['price'], color='r', label='Price')
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plt.plot(data1['date'], data1['MACD'], color='b', label='MACD')
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plt.legend()
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plt.title('Price and MACD Plot', fontsize=28, color='b')
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plt.xlabel('Time', color='b', fontsize=19)
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plt.ylabel('Price', color='b', fontsize=19)
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plt.savefig('macd.png', bbox_inches='tight', facecolor='orange')
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fig.set_facecolor('orange')
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plt.show()
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break
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return data1
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236240
except Exception as e:
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return print('MACD Error - {}'.format(e))
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239-
def get_simple_moving_average(df, days=15):
243+
def get_simple_moving_average(df, days=15, plot=None):
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"""
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Simple moving average of given days
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:param price_data: pandas DataFrame
@@ -249,22 +253,24 @@ def get_simple_moving_average(df, days=15):
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data['SMA'] = data['price'].rolling(days).mean()
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data1 = data.dropna()
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data1 = data1.sort_values(by='date', ascending=False).reset_index(drop=True)
252-
fig, ax = plt.subplots(figsize=(14, 9))
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plt.plot(data1['date'], data1['price'], color='r', label='Price')
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plt.plot(data1['date'], data1['SMA'], color='b', label='SMA')
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plt.legend()
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plt.title('Price and SMA Plot', fontsize=28, color='b')
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plt.xlabel('Time', color='b', fontsize=19)
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plt.ylabel('Price', color='b', fontsize=19)
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plt.savefig('sma.png', bbox_inches='tight', facecolor='orange')
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fig.set_facecolor('orange')
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plt.show()
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while plot:
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fig, ax = plt.subplots(figsize=(14, 9))
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plt.plot(data1['date'], data1['price'], color='r', label='Price')
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plt.plot(data1['date'], data1['SMA'], color='b', label='SMA')
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plt.legend()
261+
plt.title('Price and SMA Plot', fontsize=28, color='b')
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plt.xlabel('Time', color='b', fontsize=19)
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plt.ylabel('Price', color='b', fontsize=19)
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plt.savefig('sma.png', bbox_inches='tight', facecolor='orange')
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fig.set_facecolor('orange')
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plt.show()
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break
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return data1
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264270
except Exception as e:
265271
return print('SMA Error - {}'.format(e))
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267-
def get_exponential_moving_average(df, periods=[20]):
273+
def get_exponential_moving_average(df, periods=[20], plot=None):
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"""
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The EMA is a moving average that places a greater weight and significance on the most recent data points.
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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]):
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data['EMA_{}'.format(period)] = data['price'].ewm(span=period, adjust=False).mean()
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data = data.dropna()
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data1 = data
283-
fig, ax = plt.subplots(figsize=(14, 9))
284-
plt.plot(data1['date'], data1['price'], color='r', label='Price')
285-
for period in periods:
286-
plt.plot(data1['date'], data1['EMA_{}'.format(period)], label='EMA_{}'.format(period))
287-
plt.legend()
288-
plt.title('Price and EMA Plot', fontsize=28, color='b')
289-
plt.xlabel('Time', color='b', fontsize=19)
290-
plt.ylabel('Price/EMA', color='b', fontsize=19)
291-
plt.savefig('ema.png', bbox_inches='tight', facecolor='orange')
292-
fig.set_facecolor('orange')
293-
plt.show()
289+
while plot:
290+
fig, ax = plt.subplots(figsize=(14, 9))
291+
plt.plot(data1['date'], data1['price'], color='r', label='Price')
292+
for period in periods:
293+
plt.plot(data1['date'], data1['EMA_{}'.format(period)], label='EMA_{}'.format(period))
294+
plt.legend()
295+
plt.title('Price and EMA Plot', fontsize=28, color='b')
296+
plt.xlabel('Time', color='b', fontsize=19)
297+
plt.ylabel('Price/EMA', color='b', fontsize=19)
298+
plt.savefig('ema.png', bbox_inches='tight', facecolor='orange')
299+
fig.set_facecolor('orange')
300+
plt.show()
301+
break
294302
return data1
295303

296304
except Exception as e:

‎README.md‎

Lines changed: 4 additions & 4 deletions
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@@ -113,7 +113,7 @@ This will return a plot of RSI against time and also save RSI plot in your worki
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- #### Get Bollinger Bands and its plot
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```python
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>>> df_bb = Indices.get_bollinger_bands(price_data , 20)
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>>> df_bb = Indices.get_bollinger_bands(price_data , 20, plot=True)
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>>> df_bb.tail()
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date price SMA SD pluse minus
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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
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137137
```python
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139-
>>> df_macd = Indices.get_moving_average_convergence_divergence(price_data)
139+
>>> df_macd = Indices.get_moving_average_convergence_divergence(price_data, plot=True)
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"""This will return a pandas DataFrame and save EMA plot as 'macd.png' in working directory.
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""""
142142
>>> df_macd.head()
@@ -154,7 +154,7 @@ This will also save Bollingers bands plot in your working directory as 'bollinge
154154
- #### Get Simple Moving Average (SMA) and its plot
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156156
```python
157-
>>> df_sma = Indices.get_simple_moving_average(price_data, 20)
157+
>>> df_sma = Indices.get_simple_moving_average(price_data, 20, plot=True)
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"""This will return a pandas DataFrame and save EMA plot as 'sma.png' in working directory.
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""""
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>>> df_sma.head()
@@ -172,7 +172,7 @@ This will also save Bollingers bands plot in your working directory as 'bollinge
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- ### Get Exponential Moving Average (EMA) and its plot
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```python
175-
>>> df_ema = Indices.get_exponential_moving_average(price_data, [20,70])
175+
>>> df_ema = Indices.get_exponential_moving_average(price_data, [20,70], plot=True)
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"""This will return a pandas DataFrame and save EMA plot as 'ema.png' in working directory.
177177
""""
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‎dist/PriceIndices-1.0.2-py3-none-any.whl‎

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‎dist/PriceIndices-1.0.2.tar.gz‎

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‎setup.py‎

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@@ -6,13 +6,13 @@
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setuptools.setup(
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name='PriceIndices',
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packages=['PriceIndices'],
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version='1.0.1',
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version='1.0.2',
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license='MIT',
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description='A python package to get historical market data of cryptocurrencies from CoinMarketCap, and calculate & plot different indicators.',
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author='Dayal Chand Aichara',
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author_email='dc.aichara@gmail.com',
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url='https://github.com/dc-aichara/Price-Indices',
15-
download_url='https://github.com/dc-aichara/PriceIndices/archive/v-1.0.1.tar.gz',
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download_url='https://github.com/dc-aichara/PriceIndices/archive/v-1.0.2.tar.gz',
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keywords=['Volatility', 'blockchain', 'cryptocurrency', 'Price', 'trading', 'CoinMarketCap', 'Indices', 'Indicators'],
1717
install_requires=['requests', 'pandas', 'numpy', 'matplotlib'],
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classifiers=[

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