How to discover the deep side (2)

 How to discover the deep side (2)

In this part, we will talk about other factors that influence how cryptocurrencies are valued.

Decentralized autonomous organizations (DAOs)

Many cryptocurrency projects are controlled by decentralized organizations. Most of these organizations have a forum that anyone can join.

 Through this forum, members discuss all matters related to the cryptocurrency project.

 For example, any potential partnership, expenses, appointments of some employees, problems facing the organization, future plans, and solving various problems are discussed.
These discussions also continue on Discord. Anyone can join Discord easily.

By joining the forum, you can easily judge the strength and goodness of the existing administration. And knowledge about the problems they face, the proposed solutions, and their future plans.

You can also share your thoughts with the members of the organization.

But there is an important point: there are indeed decentralized organizations that are managed properly, but there are also many decentralized organizations that are not managed properly.

In some organizations you do not find any real interaction or participation, and some of them are controlled in a central way, and they delete any comment or any post that contradicts what they do.

Once you enter the decentralization organization forum, you can judge whether it is really decentralized.

Also, you can judge the quality of management and whether this project has a future or not.

By joining the organization, you can learn a lot about the cryptocurrency project and also get the latest news about it.

Because all events are mostly discussed on the forum, Always look for a decentralized cryptocurrency organization forum.

Through it, you can easily judge whether it is a good investment or not.

Will you be able to judge whether it is a failed project or a real one?

You will know if there are really serious discussions, ideas, and real management. You can also connect with the team and be part of its success.

Code and smart contracts

Many cryptocurrency projects are open source, and you can easily check out the source code on GitHub.

If you have programming experience and have experience with open source code, you can examine the project code, inspect the code, and also examine the smart contracts associated with it. To be realistic, even if you are an excellent programmer, you will not be able to fully understand the code in a short time, but at least you can judge the project. And knowing how many people are working on it.

Is the code well developed?

What is the programming language used?

Are there many volunteers?

Are smart contracts written well and without problems?

Is the code developed regularly?

The answers to all these questions give you an idea of the strength and quality of the cryptocurrency project.

You evaluate the project deeply and on strong foundations.

To be continued

How to discover the deep side (1)

How to discover the deep side (1)

The purpose of writing about these topics is not to provide investment advice.

But we try to find tools that help us develop a new and different way of thinking and be able to see the deep side of the crypto and blockchain worlds.

We are just trying to answer the question of how the cryptocurrency can be valued.

In the beginning, if you are new to crypto and the blockchain field, you need to try to learn some information about this field.

I think it is important to follow the cryptocurrency news and try to understand the basic things and terms in the world of crypto and blockchain.

There are many websites, blogs, YouTube, Telegram, Twitter, and Discord channels that explain blockchain concepts to those interested in cryptocurrency.

In many languages, you will find what you want, whether you speak English, Arabic, Spanish, French, or another language.

This is a very good start to putting your feet in this world. But the question now is, "Is that enough?"

It's not enough. But this is just the beginning. In fact, not everything you read about news, analysis, and opinion is true.

In the end, what you read is just someone's opinion. You should be able to form your own opinion.

Let's see an example of an educational website. This website,

https://academy.binance.com/en

On this website, you will find many articles and explanations about various concepts of blockchain and cryptocurrency.
There are many other websites out there, and this is just an example.

Another popular and good website that helps you learn a lot about cryptocurrencies

https://coinmarketcap.com

Now, if you are interested in a cryptocurrency, what is the suggested first step?
Search for a cryptocurrency website.

Read everything you find on the site, read the documentation, check the links, and if you can, check the app itself.

If there are links on social media, check them also.

Try to collect as much information as possible, analyze and arrange what you read, and if you find a term or something incomprehensible, search for its meaning on the internet.

Another point is the cryptocurrency project team.

It is important to know the team that is working on this project.

Sometimes team members are mentioned on a cryptocurrency website or related project.

Or if there is a LinkedIn link, you can see the LinkedIn page and know the team members.

Now you can evaluate the skills and experience of the team associated with the cryptocurrency project.

But you will find some problems in many countries cryptocurrency is illegal

So many team members hide their identities and do not give information about them or their real names.

 Therefore, you cannot collect information about them or know anything about their experiences and capabilities.

Another point Some people know that whoever wants to invest in his cryptocurrency will search for and want to know the members of the team working on the project.

So sometimes a cryptocurrency project is eager to get employees with master's degrees, Ph.D.s, great names in the crypto field, or who have previous experience in large companies.

Therefore, whoever sees the team will be convinced that this currency has a future and that this is a strong team and start investing their money in this cryptocurrency.

This may generate the wrong impression.

Don't think that when you see the bright names, it's a good project; maybe it's not. 

How do others see this cryptocurrency and its project?

Another point
By following various Discord channels, Telegram, Twitter, Facebook, forums and websites, YouTube channels, and the comments and interests of other investors.

You should keep track of the cryptocurrencies you are interested in.

This cryptocurrency may not be a good project, but because of people's interest, optimistic talk, and high expectations for it, it may eventually really rise in value.

Thus, it would be a good investment, at least in the short term

 People's opinions and feelings towards any cryptocurrency are very important and may be a factor in determining its price in the near term, even if their opinions are based only on their feelings and not on a solid foundation. You can use this to make profits.

To be continued

Cardano analysis

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.