pip install PriceIndics
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', '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
>>> df_bvol = Indices.get_bvol_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
>>> 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'
"""
>>> 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
>>> 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'
"""
>>> 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'
"""
>>> 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
>>> 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
>>> 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
>>>
Check out webpage of PriceIndices package.
I have created a cryptocurrency technical indicators dashboard which uses this library.
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