Bitcoin price analysis

In this topic, we will try to analyze Bitcoin data. There are many ways to view Bitcoin data; for example, it can be viewed on the Yahoo Finance website at https://finance.yahoo.com.

In this example, we use the Python programming language and Jupyter Notebook.

Here I will try to be brief and focus more on the sequence of the code without much explanation. The goal is to focus on the main idea.

Import libraries

from datetime import datetime
import os
import numpy as np
import pandas as pd
import seaborn as sns
import plotly.graph_objects as pob
import plotly.express as pex
import matplotlib.pyplot as plt #to deal with graphic charts

Data upload

df = pd.read_csv('BTC-USD.csv')
df.head(5)

	Date	Open	High	Low	Close	Adj Close	Volume
0	2014-09-17	465.864014	468.174011	452.421997	457.334015	457.334015	21056800.0
1	2014-09-18	456.859985	456.859985	413.104004	424.440002	424.440002	34483200.0
2	2014-09-19	424.102997	427.834991	384.532013	394.795990	394.795990	37919700.0
3	2014-09-20	394.673004	423.295990	389.882996	408.903992	408.903992	36863600.0
4	2014-09-21	408.084991	412.425995	393.181000	398.821014	398.821014	26580100.0

df.tail(5)

	Date	Open	High	Low	Close	Adj Close	Volume
3175	2023-05-28	26871.158203	28193.449219	26802.751953	28085.646484	28085.646484	1.454523e+10
3176	2023-05-29	28075.591797	28432.039063	27563.876953	27745.884766	27745.884766	1.518131e+10
3177	2023-05-30	27745.123047	28044.759766	27588.501953	27702.349609	27702.349609	1.325108e+10
3178	2023-05-31	NaN	NaN	NaN	NaN	NaN	NaN
3179	2023-06-01	27236.576172	27326.533203	27235.039063	27301.021484	27301.021484	1.596423e+10

#Description of the data
df.describe()

	Open	High	Low	Close	Adj Close	Volume
count	3179.000000	3179.000000	3179.000000	3179.000000	3179.000000	3.179000e+03
mean	13432.217640	13763.078443	13068.545810	13439.410714	13439.410714	1.659098e+10
std	16028.349128	16432.296940	15565.365494	16025.439960	16025.439960	1.962819e+10
min	176.897003	211.731003	171.509995	178.102997	178.102997	5.914570e+06
25%	741.108002	750.089508	733.108490	741.312989	741.312989	1.219535e+08
50%	7500.700195	7680.430176	7349.120117	7514.470215	7514.470215	9.744636e+09
75%	19510.696289	20034.084961	19129.965821	19545.489258	19545.489258	2.776349e+10
max	67549.734375	68789.625000	66382.062500	67566.828125	67566.828125	3.509679e+11

# to know more more information about the data
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3180 entries, 0 to 3179
Data columns (total 7 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Date       3180 non-null   object 
 1   Open       3179 non-null   float64
 2   High       3179 non-null   float64
 3   Low        3179 non-null   float64
 4   Close      3179 non-null   float64
 5   Adj Close  3179 non-null   float64
 6   Volume     3179 non-null   float64
dtypes: float64(6), object(1)
memory usage: 174.0+ KB

# find null values
df.isnull().sum()

Date         0
Open         1
High         1
Low          1
Close        1
Adj Close    1
Volume       1
dtype: int64

#find duplicate values
df.duplicated().sum()

df = df.dropna()

Bitcoin Candle

fig = pob.Figure(data=[pob.Candlestick(x=df['Date'],open=df['Open'],
                high=df['High'],low=df['Low'],
                close=df['Close'],increasing_line_color= '#FF0000',
                decreasing_line_color= '#00FF00')])

Correlation

#We will display heatmap
corr = df.corr()
sns.set(rc = {'figure.figsize':(15,10)})
sns.heatmap(df.corr(), annot=True)
plt.show()

Open and Close Prices

fig = pex.line(df, x="Date", y=["Open","Close"],template = 'plotly',title='Open and Close Prices')
fig.data[0].line.color = "#00ff00"
fig.data[1].line.color = "#FF0000"
fig.show()

High and Low Prices

fig = pex.line(df, x="Date", y=["High","Low"],template = 'plotly',title='High and Low Prices')
fig.data[0].line.color = "#00FF00"
fig.data[1].line.color = "#FF0000"
fig.show()

Close and Adj close

fig = pex.line(df, x="Date", y=["Close","Adj Close"],template = 'plotly',title='High and Low Prices')
fig.data[0].line.color = "#00FF00"
fig.data[1].line.color = "#FF0000"
fig.show()

Volume traded by day

fig = pex.line(df, x="Date", y='Volume',template = 'plotly',title='Volume in by day')
fig.data[0].line.color = "#0000FF"
fig.show()

The full example is on my Kaggle account. This is the link.

https://www.kaggle.com/code/mixmore/bitcoin-price-analysis

Here is just an explanation of the example on Kaggle.

Deep Side

01/03/2023

Bitcoin price analysis by Python

Import libraries

from datetime import datetime
import os
import numpy as np
import pandas as pd
import seaborn as sns
import plotly.graph_objects as pob
import plotly.express as pex
import matplotlib.pyplot as plt #to deal with graphic charts

df = pd.read_csv('BTC-USD.csv')
df.head(5)

df.tail(5)

#Description of the data
df.describe()

# to know more more information about the data
df.info()

# find null values
df.isnull().sum()

#find duplicate values
df.duplicated().sum()

df = df.dropna()

fig = pob.Figure(data=[pob.Candlestick(x=df['Date'],open=df['Open'],
                high=df['High'],low=df['Low'],
                close=df['Close'],increasing_line_color= '#FF0000',
                decreasing_line_color= '#00FF00')])

#We will display heatmap
corr = df.corr()
sns.set(rc = {'figure.figsize':(15,10)})
sns.heatmap(df.corr(), annot=True)
plt.show()

fig = pex.line(df, x="Date", y=["Open","Close"],template = 'plotly',title='Open and Close Prices')
fig.data[0].line.color = "#00ff00"
fig.data[1].line.color = "#FF0000"
fig.show()

fig = pex.line(df, x="Date", y=["High","Low"],template = 'plotly',title='High and Low Prices')
fig.data[0].line.color = "#00FF00"
fig.data[1].line.color = "#FF0000"
fig.show()

fig = pex.line(df, x="Date", y=["Close","Adj Close"],template = 'plotly',title='High and Low Prices')
fig.data[0].line.color = "#00FF00"
fig.data[1].line.color = "#FF0000"
fig.show()

fig = pex.line(df, x="Date", y='Volume',template = 'plotly',title='Volume in by day')
fig.data[0].line.color = "#0000FF"
fig.show()

01/03/2023

Bitcoin price analysis by Python

Import libraries

from datetime import datetimeimport osimport numpy as npimport pandas as pdimport seaborn as snsimport plotly.graph_objects as pobimport plotly.express as peximport matplotlib.pyplot as plt #to deal with graphic charts

df = pd.read_csv('BTC-USD.csv') df.head(5)

df.tail(5)

#Description of the datadf.describe()

# to know more more information about the datadf.info()

# find null valuesdf.isnull().sum()

#find duplicate valuesdf.duplicated().sum()

df = df.dropna()

fig = pob.Figure(data=[pob.Candlestick(x=df['Date'],open=df['Open'], high=df['High'],low=df['Low'], close=df['Close'],increasing_line_color= '#FF0000', decreasing_line_color= '#00FF00')])

#We will display heatmapcorr = df.corr()sns.set(rc = {'figure.figsize':(15,10)})sns.heatmap(df.corr(), annot=True)plt.show()

fig = pex.line(df, x="Date", y=["Open","Close"],template = 'plotly',title='Open and Close Prices')fig.data[0].line.color = "#00ff00"fig.data[1].line.color = "#FF0000"fig.show()

fig = pex.line(df, x="Date", y=["High","Low"],template = 'plotly',title='High and Low Prices')fig.data[0].line.color = "#00FF00"fig.data[1].line.color = "#FF0000"fig.show()

fig = pex.line(df, x="Date", y=["Close","Adj Close"],template = 'plotly',title='High and Low Prices')fig.data[0].line.color = "#00FF00"fig.data[1].line.color = "#FF0000"fig.show()

fig = pex.line(df, x="Date", y='Volume',template = 'plotly',title='Volume in by day')fig.data[0].line.color = "#0000FF"fig.show()

from datetime import datetime
import os
import numpy as np
import pandas as pd
import seaborn as sns
import plotly.graph_objects as pob
import plotly.express as pex
import matplotlib.pyplot as plt #to deal with graphic charts

df = pd.read_csv('BTC-USD.csv')
df.head(5)

#Description of the data
df.describe()

# to know more more information about the data
df.info()

# find null values
df.isnull().sum()

#find duplicate values
df.duplicated().sum()

fig = pob.Figure(data=[pob.Candlestick(x=df['Date'],open=df['Open'],
high=df['High'],low=df['Low'],
close=df['Close'],increasing_line_color= '#FF0000',
decreasing_line_color= '#00FF00')])

#We will display heatmap
corr = df.corr()
sns.set(rc = {'figure.figsize':(15,10)})
sns.heatmap(df.corr(), annot=True)
plt.show()

fig = pex.line(df, x="Date", y=["Open","Close"],template = 'plotly',title='Open and Close Prices')
fig.data[0].line.color = "#00ff00"
fig.data[1].line.color = "#FF0000"
fig.show()

fig = pex.line(df, x="Date", y=["High","Low"],template = 'plotly',title='High and Low Prices')
fig.data[0].line.color = "#00FF00"
fig.data[1].line.color = "#FF0000"
fig.show()

fig = pex.line(df, x="Date", y=["Close","Adj Close"],template = 'plotly',title='High and Low Prices')
fig.data[0].line.color = "#00FF00"
fig.data[1].line.color = "#FF0000"
fig.show()

fig = pex.line(df, x="Date", y='Volume',template = 'plotly',title='Volume in by day')
fig.data[0].line.color = "#0000FF"
fig.show()