Metadata-Version: 2.1
Name: PriceIndices
Version: 0.2
Summary: This package can be useful to get historical price 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: dc.aichara@gmail.com
License: MIT
Download-URL: https://github.com/dc-aichara/PriceIndices/archive/v-0.2.tar.gz
Description: 
        ## Installation 
        
        ### pip 
        
        ```
        pip install PriceIndics
        ```
         ### From Source (Github)
         
         git clone https://github.com/dc-aichara/Price-Indices.git
         
         cd PriceIndices 
         
         python3 setup.py install
         
         ## Usages 
         
         ```python
        from PriceIndices import MarketHistory, Indices
        
        ```
        ## Examples 
        
        ```python
        >>> history = MarketHistory()
        
        >>> df = history.get_history('bitcoin', '20130428', '20190624')  # Get Market History
        >>> df.head()
                Date     Open*      High       Low   Close**       Volume    Market Cap
        0 2019-06-23  10696.69  11246.14  10556.10  10855.37  20998326502  192970090355
        1 2019-06-22  10175.92  11157.35  10107.04  10701.69  29995204861  190214124824
        2 2019-06-21   9525.07  10144.56   9525.07  10144.56  20624008643  180293241528
        3 2019-06-20   9273.06   9594.42   9232.48   9527.16  17846823784  169304784791
        4 2019-06-19   9078.73   9299.62   9070.40   9273.52  15546809946  164780855869
        
        
        >>> df =  history.get_price('bitcoin', '20130428', '20190624')  # Get closing price
        
        >>> df.head()
                date     price
        0 2019-06-23  10855.37
        1 2019-06-22  10701.69
        2 2019-06-21  10144.56
        3 2019-06-20   9527.16
        4 2019-06-19   9273.52
        
        
        >>> df_bvol = Indices.get_bvol_index(df)  # Calculate Volatility Index
        >>> df_bvol.head()
                date     price  BVOL_Index
        0 2019-06-22  10701.69    0.636482
        1 2019-06-21  10144.56    0.636414
        2 2019-06-20   9527.16    0.619886
        3 2019-06-19   9273.52    0.608403
        4 2019-06-18   9081.76    0.604174
        
        >>> indices.get_bvol_graph(df_bvol)  # Plot Volatility Index 
        
        """
        This will return a plot of BVOL index against time also save volatility index plot in your working directory as 'bvol_index.png'
        """
        
        >>> df_rsi = indices.get_rsi(df)   # Calculate RSI
        
        >>> print(df_rsi.tail())
                   date   price  price_change   gain   loss  gain_average  loss_average        RS      RSI_1  RS_Smooth      RSI_2
        2217 2013-05-02  105.21          7.46   7.46   0.00      1.532143      2.500000  0.612857  37.998229   0.561117  35.943306
        2218 2013-05-01  116.99         11.78  11.78   0.00      2.373571      2.175714  1.090939  52.174596   0.975319  49.375257
        2219 2013-04-30  139.00         22.01  22.01   0.00      3.945714      1.981429  1.991348  66.570258   1.869110  65.145981
        2220 2013-04-29  144.54          5.54   5.54   0.00      3.878571      1.981429  1.957462  66.187226   2.206422  68.812592
        2221 2013-04-28  134.21        -10.33   0.00  10.33      3.878571      2.506429  1.547449  60.745050   1.397158  58.283931
        
        >>> indices.get_rsi_graph(df_rsi)  # Plot RSI
        
        """
        This will return a plot of RSI against time and also save RSI plot in your working directory as 'rsi.png'
        """
        >>> df_bb = Indices.get_bollinger_bands(df, 20) # Get Bollinger Bands and plot
        >>> df_bb.tail()
                   date   price       SMA         SD       pluse     minus
        2243 2013-05-02  105.21  115.2345   6.339257  127.913013 -115.2345
        2244 2013-05-01  116.99  114.9400   6.097587  127.135174 -114.9400
        2245 2013-04-30  139.00  115.7900   8.016499  131.822998 -115.7900
        2246 2013-04-29  144.54  116.9175  10.217936  137.353372 -116.9175
        2247 2013-04-28  134.21  117.4530  10.842616  139.138233 -117.4530
        
        """
        This will also save Bollingers bands plot in your working directory as 'bollinger_bands.png'
        """
        
        ```
        
        ### License 
        [MIT](https://choosealicense.com/licenses/mit/) © [Dayal Chand Aichara](https://github.com/dc-aichara)
        
        
Keywords: Volatility,blockchain,cryptocurrency,Price,trading
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Education
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
