Cardano analysis

Import Libraries

import pandas_ta as ta
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datetime
from datetime import date, time
from math import sqrt

import sklearn
from sklearn.neural_network import MLPClassifier
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.metrics import classification_report,confusion_matrix

import warnings
warnings.filterwarnings("ignore")

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

	Date	Open	High	Low	Close	Adj Close	Volume
0	2017-11-09	0.025160	0.035060	0.025006	0.032053	0.032053	18716200.0
1	2017-11-10	0.032219	0.033348	0.026451	0.027119	0.027119	6766780.0
2	2017-11-11	0.026891	0.029659	0.025684	0.027437	0.027437	5532220.0
3	2017-11-12	0.027480	0.027952	0.022591	0.023977	0.023977	7280250.0
4	2017-11-13	0.024364	0.026300	0.023495	0.025808	0.025808	4419440.0

df.tail()

	Date	Open	High	Low	Close	Adj Close	Volume
2028	2023-05-30	0.379129	0.383068	0.375512	0.377934	0.377934	186645169.0
2029	2023-05-31	0.377937	0.380359	0.371003	0.374403	0.374403	193309518.0
2030	2023-06-01	0.374414	0.375474	0.361692	0.364724	0.364724	235187863.0
2031	2023-06-02	NaN	NaN	NaN	NaN	NaN	NaN
2032	2023-06-03	0.378149	0.378149	0.376950	0.377337	0.377337	197218784.0

   
    #Description of the data
df.describe()

	Open	High	Low	Close	Adj Close	Volume
count	2032.000000	2032.000000	2032.000000	2032.000000	2032.000000	2.032000e+03
mean	0.480802	0.501045	0.459207	0.480965	0.480965	1.082908e+09
std	0.601298	0.626942	0.573988	0.601294	0.601294	1.979570e+09
min	0.023954	0.025993	0.019130	0.023961	0.023961	2.930550e+06
25%	0.070544	0.073276	0.068434	0.070921	0.070921	8.421186e+07
50%	0.210612	0.222707	0.198475	0.211688	0.211688	2.898286e+08
75%	0.552721	0.590249	0.522270	0.552757	0.552757	1.090055e+09
max	2.966390	3.099186	2.907606	2.968239	2.968239	1.914198e+10

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

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2033 entries, 0 to 2032
Data columns (total 7 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Date       2033 non-null   object 
 1   Open       2032 non-null   float64
 2   High       2032 non-null   float64
 3   Low        2032 non-null   float64
 4   Close      2032 non-null   float64
 5   Adj Close  2032 non-null   float64
 6   Volume     2032 non-null   float64
dtypes: float64(6), object(1)
memory usage: 111.3+ KB

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

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

df = df.dropna()

   
    df.plot(y='Close', title='Cardano')
plt.show()

Cardano technical analysis

df['RSI(2)'] = ta.rsi(df['Close'],length=2)
df['RSI(7)'] = ta.rsi(df['Close'],length=7)
df['RSI(14)'] = ta.rsi(df['Close'],length=14)
df['CCI(30)'] = ta.cci(close=df['Close'],length=30,high=df['High'],low=df['Low'])
df['CCI(50)'] = ta.cci(close=df['Close'],length=50,high=df['High'],low=df['Low'])
df['CCI(100)'] = ta.cci(close=df['Close'],length=100,high=df['High'],low=df['Low'])

df.head()

	Date	Open	High	Low	Close	Adj Close	Volume	RSI(2)	RSI(7)	RSI(14)	CCI(30)	CCI(50)	CCI(100)
0	2017-11-09	0.025160	0.035060	0.025006	0.032053	0.032053	18716200.0	NaN	NaN	NaN	NaN	NaN	NaN
1	2017-11-10	0.032219	0.033348	0.026451	0.027119	0.027119	6766780.0	NaN	NaN	NaN	NaN	NaN	NaN
2	2017-11-11	0.026891	0.029659	0.025684	0.027437	0.027437	5532220.0	11.418312	NaN	NaN	NaN	NaN	NaN
3	2017-11-12	0.027480	0.027952	0.022591	0.023977	0.023977	7280250.0	3.276662	NaN	NaN	NaN	NaN	NaN
4	2017-11-13	0.024364	0.026300	0.023495	0.025808	0.025808	4419440.0	44.876387	NaN	NaN	NaN	NaN	NaN

 
    #Create a plot showing some of our indicators 

df.plot(y='RSI(2)')
df.plot(y='CCI(100)')

<AxesSubplot:>

  
    df['LABEL'] = np.where( df['Open'].shift(-2).gt(df['Open'].shift(-1)),"1","0")
df = df.dropna()

df.head()

	Date	Open	High	Low	Close	Adj Close	Volume	RSI(2)	RSI(7)	RSI(14)	CCI(30)	CCI(50)	CCI(100)	label
99	2018-02-16	0.410142	0.412404	0.392676	0.403810	0.403810	192420000.0	62.563977	48.621949	43.259957	-52.419423	-85.774875	1.793763	1
100	2018-02-17	0.402403	0.428074	0.401056	0.419883	0.419883	341415008.0	84.868071	53.778234	45.256687	-41.407653	-79.123300	4.472773	0
101	2018-02-18	0.418355	0.420010	0.380787	0.385618	0.385618	251314000.0	23.972251	43.036056	41.873633	-50.814224	-83.434954	-2.400970	1
102	2018-02-19	0.386930	0.396608	0.386224	0.391472	0.391472	169966000.0	38.942088	45.217173	42.662228	-50.390640	-81.358376	-4.642194	0
103	2018-02-20	0.390942	0.404329	0.377539	0.378779	0.378779	281249984.0	21.005952	41.224257	41.352215	-50.828816	-79.685546	-7.093281	0

Neural Network

X = df[df.columns[6:-1]].values
y = df['LABEL'].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

mlp = MLPClassifier(hidden_layer_sizes=(8,8,8), activation='relu',

solver='adam', max_iter=1000)
mlp.fit(X_train,y_train)

predict_train = mlp.predict(X_train)
predict_test = mlp.predict(X_test)

print(' Train Data Accuracy ')
print(classification_report(y_train,predict_train))

print( ' Testing Data Accuracy ' )
print( classification_report(y_test,predict_test) )

 Train Data Accuracy 
              precision    recall  f1-score   support

           0       0.00      0.00      0.00       673
           1       0.50      1.00      0.67       680

    accuracy                           0.50      1353
   macro avg       0.25      0.50      0.33      1353
weighted avg       0.25      0.50      0.34      1353

 Testing Data Accuracy 
              precision    recall  f1-score   support

           0       0.00      0.00      0.00       298
           1       0.49      1.00      0.65       282

    accuracy                           0.49       580
   macro avg       0.24      0.50      0.33       580
weighted avg       0.24      0.49      0.32       580

Backtesting Model

df['Prediction'] = np.append(predict_train,predict_test)
df['Strategy Returns'] = np.where( df['Prediction'].eq("1"),

df['Open'].shift(-2)-df['Open'].shift(-1),0)

df['Strategy Returns'] = df['Strategy Returns'].cumsum()

df.plot(y='Strategy Returns')
plt.plot()

[]

  
    prediction = df.iloc[-1]['Prediction'] 

if prediction=="1":
  print("Today's return forecast: UP")
else: 
  print("Today's return forecast: DOWN")
    

Today's return forecast: UP

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

https://www.kaggle.com/code/mixmore/cardano-analysis

This is just an explanation of the example on Kaggle.

Deep Side

12/03/2023

Cardano analysis

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

df.tail()

#Description of the data
df.describe()

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

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

df = df.dropna()

df.plot(y='Close', title='Cardano')
plt.show()

Cardano technical analysis

#Create a plot showing some of our indicators

df.plot(y='RSI(2)')
df.plot(y='CCI(100)')

df['LABEL'] = np.where( df['Open'].shift(-2).gt(df['Open'].shift(-1)),"1","0")
df = df.dropna()

df.head()

df['Prediction'] = np.append(predict_train,predict_test)
df['Strategy Returns'] = np.where( df['Prediction'].eq("1"),

df['Open'].shift(-2)-df['Open'].shift(-1),0)

df['Strategy Returns'] = df['Strategy Returns'].cumsum()

df.plot(y='Strategy Returns')
plt.plot()

prediction = df.iloc[-1]['Prediction']

if prediction=="1":
print("Today's return forecast: UP")
else:
print("Today's return forecast: DOWN")

12/03/2023

Cardano analysis

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

df.tail()

#Description of the datadf.describe()

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

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

df = df.dropna()

df.plot(y='Close', title='Cardano')plt.show()

Cardano technical analysis

#Create a plot showing some of our indicators df.plot(y='RSI(2)')df.plot(y='CCI(100)')

df['LABEL'] = np.where( df['Open'].shift(-2).gt(df['Open'].shift(-1)),"1","0")df = df.dropna()df.head()

df['Prediction'] = np.append(predict_train,predict_test)df['Strategy Returns'] = np.where( df['Prediction'].eq("1"), df['Open'].shift(-2)-df['Open'].shift(-1),0)df['Strategy Returns'] = df['Strategy Returns'].cumsum() df.plot(y='Strategy Returns')plt.plot()

prediction = df.iloc[-1]['Prediction'] if prediction=="1": print("Today's return forecast: UP")else: print("Today's return forecast: DOWN")

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

#Description of the data
df.describe()

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

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

df.plot(y='Close', title='Cardano')
plt.show()

#Create a plot showing some of our indicators

df.plot(y='RSI(2)')
df.plot(y='CCI(100)')

df['LABEL'] = np.where( df['Open'].shift(-2).gt(df['Open'].shift(-1)),"1","0")
df = df.dropna()

df.head()

df['Prediction'] = np.append(predict_train,predict_test)
df['Strategy Returns'] = np.where( df['Prediction'].eq("1"),

df['Open'].shift(-2)-df['Open'].shift(-1),0)

df['Strategy Returns'] = df['Strategy Returns'].cumsum()

df.plot(y='Strategy Returns')
plt.plot()

prediction = df.iloc[-1]['Prediction']

if prediction=="1":
print("Today's return forecast: UP")
else:
print("Today's return forecast: DOWN")