Ethereum technical analysis

Ethereum technical analysis

 

import os
import pandas as pd

from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import plotly as py
import plotly.io as pio
import plotly.graph_objs as go
from plotly.subplots import make_subplots

 

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

	Date	Open	High	Low	Close	Adj Close	Volume
0	2017-11-09	308.644989	329.451996	307.056000	320.884003	320.884003	893249984
1	2017-11-10	320.670990	324.717987	294.541992	299.252991	299.252991	885985984
2	2017-11-11	298.585999	319.453003	298.191986	314.681000	314.681000	842300992
3	2017-11-12	314.690002	319.153015	298.513000	307.907990	307.907990	1613479936
4	2017-11-13	307.024994	328.415009	307.024994	316.716003	316.716003	1041889984

df.tail()

	Date	Open	High	Low	Close	Adj Close	Volume
2027	2023-05-29	1909.297485	1926.421753	1879.077515	1893.078125	1893.078125	5884674572
2028	2023-05-30	1893.093140	1916.574951	1883.934692	1901.026611	1901.026611	5363439784
2029	2023-05-31	1901.098267	1907.035400	1852.094727	1874.130493	1874.130493	5984512548
2030	2023-06-01	1873.914673	1887.705322	1846.227417	1862.201416	1862.201416	5640027197
2031	2023-06-02	1862.266724	1909.625732	1851.964722	1906.706543	1906.706543	6151811584

df.describe()

	Open	High	Low	Close	Adj Close	Volume
count	2032.000000	2032.000000	2032.000000	2032.000000	2032.000000	2.032000e+03
mean	1172.894714	1209.016718	1132.495742	1173.463282	1173.463282	1.253992e+10
std	1154.144130	1188.670880	1114.491666	1153.653971	1153.653971	1.047928e+10
min	84.279694	85.342743	82.829887	84.308296	84.308296	6.217330e+08
25%	220.315762	226.314495	213.840340	220.328876	220.328876	4.845131e+09
50%	623.848511	648.783997	596.979004	630.632447	630.632447	9.944210e+09
75%	1808.816773	1840.087402	1763.558655	1810.730591	1810.730591	1.749615e+10
max	4810.071289	4891.704590	4718.039063	4812.087402	4812.087402	8.448291e+10

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

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

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

0

OHLC Chart

 

fig = go.Figure([go.Ohlc(x=df.Date,
                         open=df.Open,
                         high=df.High,
                         low=df.Low,
                         close=df.Close)])
fig.update(layout_xaxis_rangeslider_visible=False)
fig.show()

 

 

 Volume

 

fig = go.Figure(go.Bar(x=df.Date, y=df.Volume, name='Volume', marker_color='red'))
fig.show()

 

 

 

Moving Averages

 

df['EMA_9'] = df['Close'].ewm(5).mean().shift()
df['SMA_50'] = df['Close'].rolling(50).mean().shift()
df['SMA_100'] = df['Close'].rolling(100).mean().shift()
df['SMA_200'] = df['Close'].rolling(200).mean().shift()

fig = go.Figure()
fig.add_trace(go.Scatter(x=df.Date, y=df.EMA_9, name='EMA 9'))
fig.add_trace(go.Scatter(x=df.Date, y=df.SMA_50, name='SMA 50'))
fig.add_trace(go.Scatter(x=df.Date, y=df.SMA_100, name='SMA 100'))
fig.add_trace(go.Scatter(x=df.Date, y=df.SMA_200, name='SMA 200'))
fig.add_trace(go.Scatter(x=df.Date, y=df.Close, name='Close', line_color='dimgray',

opacity=0.3))
fig.show()

 

 RSI

 

def RSI(df, n=14):
    close = df['Close']
    delta = close.diff()
    delta = delta[1:]
    pricesUp = delta.copy()
    pricesDown = delta.copy()
    pricesUp[pricesUp < 0] = 0
    pricesDown[pricesDown > 0] = 0
    rollUp = pricesUp.rolling(n).mean()
    rollDown = pricesDown.abs().rolling(n).mean()
    rs = rollUp / rollDown
    rsi = 100.0 - (100.0 / (1.0 + rs))
    return rsi

num_days = 365
df['RSI'] = RSI(df).fillna(0)
fig = go.Figure(go.Scatter(x=df.Date.tail(num_days), y=df.RSI.tail(num_days)))
fig.show()

 

 

 

MACD

 

EMA_12 = pd.Series(df['Close'].ewm(span=12, min_periods=12).mean())
EMA_26 = pd.Series(df['Close'].ewm(span=26, min_periods=26).mean())
MACD = pd.Series(EMA_12 - EMA_26)
MACD_signal = pd.Series(MACD.ewm(span=9, min_periods=9).mean())

fig = make_subplots(rows=2, cols=1)
fig.add_trace(go.Scatter(x=df.Date, y=df.Close, name='Close'), row=1, col=1)
fig.add_trace(go.Scatter(x=df.Date, y=EMA_12, name='EMA 12'), row=1, col=1)
fig.add_trace(go.Scatter(x=df.Date, y=EMA_26, name='EMA 26'), row=1, col=1)
fig.add_trace(go.Scatter(x=df.Date, y=MACD, name='MACD'), row=2, col=1)
fig.add_trace(go.Scatter(x=df.Date, y=MACD_signal, name='Signal line'), row=2, col=1)
fig.show()

 

 

 

 

 

 

 

 

Stochastic

 

def stochastic(df, k, d):
    df = df.copy()
    low_min  = df['Low'].rolling(window=k).min()
    high_max = df['High'].rolling( window=k).max()
    df['stoch_k'] = 100 * (df['Close'] - low_min)/(high_max - low_min)
    df['stoch_d'] = df['stoch_k'].rolling(window=d).mean()
    return df

stochs = stochastic(df, k=14, d=3)

fig = go.Figure()
fig.add_trace(go.Scatter(x=df.Date.tail(365), y=stochs.stoch_k.tail(365), name='K stochastic'))
fig.add_trace(go.Scatter(x=df.Date.tail(365), y=stochs.stoch_d.tail(365), name='D stochastic'))
fig.show()

 

 

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

https://www.kaggle.com/code/mixmore/ethereum-technical-analysis

Here is just an explanation of the example on Kaggle

 .