priceindices 1.2.0

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

indices = Indices(df=price_data)
>>> df_bvol = indices.get_vola_index(price_data )  
>>> df_bvol.head()
        date    price  BVOL_Index
0 2019-10-29  9427.69    0.711107
1 2019-10-28  9256.15    0.707269
2 2019-10-27  9551.71    0.709765
3 2019-10-26  9244.97    0.698544
4 2019-10-25  8660.70    0.692656

• Plot Volatility Index

>>> indices.get_vola_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.head())
        date    price       RSI_1  RS_Smooth      RSI_2
0 2019-10-30  9205.73      64.641855   1.624958  61.904151
1 2019-10-29  9427.69      65.707097   1.709072  63.086984
2 2019-10-28  9256.15      61.333433   1.597755  61.505224
3 2019-10-27  9551.71      66.873327   2.012345  66.803267
4 2019-10-26  9244.97      63.535368   1.791208  64.173219

• 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.head()
        date    price     BB_upper   BB_lower
0 2019-10-30  9205.73  9635.043581 -8428.5855
1 2019-10-29  9427.69  9550.707153 -8397.6225
2 2019-10-28  9256.15  9408.263164 -8356.0250
3 2019-10-27  9551.71  9268.466516 -8304.6565
4 2019-10-26  9244.97  9003.752779 -8239.3520


"""
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       MACD
0 2019-10-30  9205.73   0.000000
1 2019-10-29  9427.69  17.706211
2 2019-10-28  9256.15  17.692715
3 2019-10-27  9551.71  41.057952
4 2019-10-26  9244.97  34.426864

• 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
0 2019-10-30  9205.73  8467.488000
1 2019-10-29  9427.69  8400.797333
2 2019-10-28  9256.15  8330.597333
3 2019-10-27  9551.71  8268.254667
4 2019-10-26  9244.97  8187.244667

• 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 2019-10-30  9205.73  9205.730000  9205.730000
1 2019-10-29  9427.69  9226.869048  9211.982394
2 2019-10-28  9256.15  9229.657710  9213.226552
3 2019-10-27  9551.71  9260.329356  9222.761297
4 2019-10-26  9244.97  9258.866561  9223.386895
>>> 

License

MIT © Dayal Chand Aichara

Check out webpage of PriceIndices package.

I have created a cryptocurrency technical indicators dashboard which uses this library.

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