Development Environment

Poetry

• Install Poetry

  curl -sSL https://install.python-poetry.org | python3 -
  

• Install dependencies

  poetry install

• To add new dependencies use poetry add

  poetry add dependency_name

• Read Poetry documentation for more.

Installation

pip

pip install PriceIndics

Poetry

poetry add PriceIndices

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", "2020-03-16", "2021-03-15")  
>>> df_history.head()
         open          high           low         close        volume    market_cap        date
0  59267.429049  60540.992712  55393.165363  55907.200226  6.641937e+10  1.042946e+12  2021-03-15
1  61221.134297  61597.918396  59302.316977  59302.316977  4.390123e+10  1.106226e+12  2021-03-14
2  57343.370247  61683.864014  56217.972382  61243.084766  6.066983e+10  1.142369e+12  2021-03-13
3  57821.218747  57996.619490  55376.650088  57332.088964  5.568994e+10  1.069366e+12  2021-03-12
4  55963.180089  58091.062703  54484.593089  57805.123019  5.677234e+10  1.078136e+12  2021-03-11

# Get closing price

>>> price_data  =  history.get_price("bitcoin", "2020-03-16", "2021-03-15") 

>>> price_data.head()
         date         price
0  2021-03-15  55907.200226
1  2021-03-14  59302.316977
2  2021-03-13  61243.084766
3  2021-03-12  57332.088964
4  2021-03-11  57805.123019

• Calculate Volatility Index

indices = Indices(df=price_data, plot_dir="plots")
>>> df_bvol = indices.get_vola_index(
        plot=True,
        plot_name="vola_index.png",
        show_plot=False  
)  
>>> 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

Plot will be saved in plots directory as vola_index.png.

• Calculate Relative Strength Index (RSI)

>>> df_rsi = indices.get_rsi(
        plot=True,
        plot_name="rsi.png",
        show_plot=False,
)   

>>> 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

Plot will be saved in plots directory as rsi.png.

• Get Bollinger Bands and its plot

>>> df_bb = indices.get_bollinger_bands(
        days=20, 
        plot=True,
        plot_name="bollinger_bands.png",
        show_plot=False,
        ) 
>>> 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' in plots folder.
"""

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

>>> df_macd = indices.get_moving_average_convergence_divergence(
        plot=True,
        plot_name="macd.png",
        show_plot=False,
)
"""
This will return a pandas DataFrame and save EMA plot as 'macd.png' in in plots folder. 
""""
>>> 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(
        days=20,
        plot=True,
        plot_name="sma.png",
        show_plot=False,
) 
"""This will return a pandas DataFrame and save EMA plot as 'sma.png' in plots folder.
""""
>>> 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(
        periods=(20,70),
        plot=True,
        plot_name="ema.png",
        show_plot=False,
)
"""This will return a pandas DataFrame and save EMA plot as 'ema.png' in plots folder.
""""

>>> 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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