priceindices 1.4.0

Description:

priceindices 1.4.0

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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