priceindices 1.0.3

A python package to get historical market data of cryptocurrencies from CoinMarketCap, and calculate & plot different indicators.

Installation

pip

pip install PriceIndics

From Source (Github)

git clone https://github.com/dc-aichara/Price-Indices.git

cd Price-Indices

python3 setup.py install

Usages

from PriceIndices import MarketHistory, Indices

Examples

• Get market history and closing price

>>> history = MarketHistory()

# Get Market History 

>>> df_history = history.get_history('bitcoin', '20130428', '20190624')  
>>> df_history.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

# Get closing price

>>> price_data  =  history.get_price('bitcoin', '20130428', '20190624')  

>>> price_data .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

• Calculate Volatility Index

>>> df_bvol = Indices.get_bvol_index(price_data )  
>>> 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

• Plot Volatility Index

>>> Indices.get_bvol_graph(df_bvol)   

"""
This will return a plot of BVOL index against time also save volatility index plot in your working directory as 'bvol_index.png'
"""

• Calculate Relative Strength Index (RSI)

>>> df_rsi = Indices.get_rsi(price_data)   

>>> 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

• Plot RSI

>>> Indices.get_rsi_graph(df_rsi)  

"""
This will return a plot of RSI against time and also save RSI plot in your working directory as 'rsi.png'
"""

• Get Bollinger Bands and its plot

>>> 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
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'
"""

• Get Moving Average Convergence Divergence (MACD) and its plot

>>> 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()
         date    price      EMA_12        EMA_26        MACD
19 2019-06-18  9081.76    10415.979340  10886.327599 -470.348259
20 2019-06-17  9320.35    10247.420980  10770.329259 -522.908279
21 2019-06-16  8994.49    10054.662368  10638.785610 -584.123242
22 2019-06-15  8838.38    9867.542004   10505.422231 -637.880228
23 2019-06-14  8693.83    9686.970926   10371.230214 -684.259288

• Get Simple Moving Average (SMA) and its plot

>>> 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()
         date    price         SMA
19 2019-06-18  9081.76  10998.4180
20 2019-06-17  9320.35  10891.8930
21 2019-06-16  8994.49  10781.1900
22 2019-06-15  8838.38  10674.1860
23 2019-06-14  8693.83  10548.1055

• Get Exponential Moving Average (EMA) and its plot

>>> 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. 
""""

>>> df_ema.head()
        date     price        EMA_20        EMA_70
0 2019-07-07  11450.85  11450.850000  11450.850000
1 2019-07-06  11208.55  11427.773810  11444.024648
2 2019-07-05  10978.46  11384.982018  11430.910151
3 2019-07-04  11215.44  11368.835159  11424.840569
4 2019-07-03  11961.27  11425.257525  11439.951257

License

MIT © Dayal Chand Aichara

Check out webpage of PriceIndices package.

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