A python package to get historical market data of cryptocurrencies, and calculate & plot different price technical indicators.
Readme
curl -sSL https://install.python-poetry.org | python3 -
poetry install
poetry add
poetry add dependency_name
Poetry documentation for more.pip install PriceIndics
poetry add PriceIndices
git clone https://github.com/dc-aichara/Price-Indices.git
cd Price-Indices
python3 setup.py install
from PriceIndices import MarketHistory, Indices
>>> 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
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 will be saved in plots directory as vola_index.png.
>>> 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 will be saved in plots directory as rsi.png.
>>> 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.
"""
>>> 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
>>> 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
>>> 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
>>>
All content provided here, is for educational purpose and your general information only, procured from third party sources.
I make no warranties of any kind in relation to this content, including but not limited to accuracy
and updatedness. No part of the content that I provide constitutes financial advice, legal advice
or any other form of advice meant for your specific reliance for any purpose. Any use or reliance on
my content is solely at your own risk and discretion. You should conduct your own research, review,
analyse and verify my content before relying on them. Trading is a highly risky activity that can
lead to major losses, please therefore consult your financial advisor before making any decision.
No content on this Site is meant to be a solicitation or offer.
FAQs
A python package to get historical market data of cryptocurrencies, and calculate & plot different price technical indicators.
We found that priceindices demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
The Python Software Foundation has secured a 5-year sponsorship from Fastly that supports PSF's activities and events, most notably the security and reliability of the Python Package Index (PyPI).
Security News
LDAPjs, an LDAP Client and Server API for Node.js, was decommissioned after its maintainer received an abusive email from a user, raising concerns about this form of abuse as a potential attack vector.
Security News
CISA launched a new project called Vulnrichment to enrich CVEs with details that help prioritize patching and mitigation efforts, as the NVD backlog of unenriched CVEs awaiting analysis surpasses 10,000.