In [8]:
import seaborn as sns
corr = df[['high', 'low', 'open', 'close', 'volume']].diff().dropna().corr()
sns.heatmap(corr,cmap='Blues',annot=False)
Out[8]:
<AxesSubplot:>
# plotly standard imports
import plotly.graph_objs as go
import chart_studio.plotly as py
# Cufflinks wrapper on plotly
import cufflinks
# Data science imports
import pandas as pd
import numpy as np
# Options for pandas
pd.options.display.max_columns = 30
# Display all cell outputs
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = 'all'
from plotly.offline import iplot, init_notebook_mode
cufflinks.go_offline(connected=True)
init_notebook_mode(connected=True)
# Set global theme
cufflinks.set_config_file(world_readable=True, theme='pearl')
from src.load_datasets import load_input_dataset
df = load_input_dataset()
df.head()
| timestamp | open | high | low | close | volume | |
|---|---|---|---|---|---|---|
| 0 | 1470844380000 | 579.00 | 579.00 | 579.00 | 579.00 | 1.000000 |
| 1 | 1470844440000 | 592.96 | 604.75 | 592.96 | 604.75 | 131.838200 |
| 2 | 1470844500000 | 604.75 | 604.75 | 600.00 | 600.00 | 89.437926 |
| 3 | 1470844620000 | 600.00 | 604.75 | 600.00 | 604.75 | 51.328200 |
| 4 | 1470844680000 | 604.75 | 604.75 | 604.75 | 604.75 | 4.586500 |
df.index = pd.to_datetime(df.pop('timestamp'), unit='ms')
df.head()
| open | high | low | close | volume | |
|---|---|---|---|---|---|
| timestamp | |||||
| 2016-08-10 15:53:00 | 579.00 | 579.00 | 579.00 | 579.00 | 1.000000 |
| 2016-08-10 15:54:00 | 592.96 | 604.75 | 592.96 | 604.75 | 131.838200 |
| 2016-08-10 15:55:00 | 604.75 | 604.75 | 600.00 | 600.00 | 89.437926 |
| 2016-08-10 15:57:00 | 600.00 | 604.75 | 600.00 | 604.75 | 51.328200 |
| 2016-08-10 15:58:00 | 604.75 | 604.75 | 604.75 | 604.75 | 4.586500 |
df.shape
(2270370, 5)
df.info()
<class 'pandas.core.frame.DataFrame'> DatetimeIndex: 2270370 entries, 2016-08-10 15:53:00 to 2021-03-09 18:54:00 Data columns (total 5 columns): # Column Dtype --- ------ ----- 0 open float64 1 high float64 2 low float64 3 close float64 4 volume float64 dtypes: float64(5) memory usage: 103.9 MB
df.describe()
| open | high | low | close | volume | |
|---|---|---|---|---|---|
| count | 2.270370e+06 | 2.270370e+06 | 2.270370e+06 | 2.270370e+06 | 2.270370e+06 |
| mean | 8.646184e+03 | 8.651014e+03 | 8.641306e+03 | 8.646195e+03 | 1.530163e+01 |
| std | 8.289776e+03 | 8.296865e+03 | 8.282577e+03 | 8.289813e+03 | 4.842900e+01 |
| min | 5.631000e+02 | 5.632400e+02 | 5.629900e+02 | 5.630000e+02 | 1.000000e-08 |
| 25% | 3.995000e+03 | 3.996100e+03 | 3.993400e+03 | 3.995000e+03 | 4.974970e-01 |
| 50% | 7.363400e+03 | 7.365800e+03 | 7.361200e+03 | 7.363300e+03 | 2.890435e+00 |
| 75% | 9.880000e+03 | 9.885146e+03 | 9.875000e+03 | 9.880000e+03 | 1.221400e+01 |
| max | 5.831700e+04 | 5.832124e+04 | 5.830400e+04 | 5.831700e+04 | 6.717517e+03 |
import seaborn as sns
corr = df[['high', 'low', 'open', 'close', 'volume']].diff().dropna().corr()
sns.heatmap(corr,cmap='Blues',annot=False)
<AxesSubplot:>
sns.heatmap(corr, annot=True, cmap = 'viridis')
<AxesSubplot:>
import sweetviz as sv
analyse_report = sv.analyze([df, 'Bitcoin'], target_feat="close")
analyse_report.show_notebook()
# traget dataset too big for plot
hour_df = df[59:: 60]
hour_df.iplot(
subplots=True,
)
df.describe().transpose()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| open | 2270370.0 | 8646.184272 | 8289.776487 | 5.631000e+02 | 3995.000000 | 7363.400000 | 9880.000000 | 58317.000000 |
| high | 2270370.0 | 8651.014196 | 8296.864962 | 5.632400e+02 | 3996.100000 | 7365.800000 | 9885.146379 | 58321.244032 |
| low | 2270370.0 | 8641.305688 | 8282.576625 | 5.629900e+02 | 3993.400000 | 7361.200000 | 9875.000000 | 58304.000000 |
| close | 2270370.0 | 8646.195441 | 8289.812569 | 5.630000e+02 | 3995.000000 | 7363.300000 | 9880.000000 | 58317.000000 |
| volume | 2270370.0 | 15.301625 | 48.429003 | 1.000000e-08 | 0.497497 | 2.890435 | 12.214001 | 6717.516714 |