Trending February 2024 # Ripple Xrp Price Prediction; 560% Rally For(Crno) Token Holders # Suggested March 2024 # Top 5 Popular

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The current crypto market crash has indeed made investors refrain from risky investments. Investors are extremely wary about speculative bets, and there are valid reasons behind this behavioral change. Several cryptocurrencies, which did not have real-world use cases or lacked backing from tangible assets, have fallen to the ground during this bearish market storm.

On the contrary, real-world utility tokens like chúng tôi have performed extremely well. Chronoly’s CRNO has registered a 560% return over the last two months during its pre-sale. Investors are now putting their money into projects where they feel assured of stability and positive returns.

The Double Trouble of Ripple (XRP)

Ripple XRP has been in the headlines for the past few weeks. But the recent outings of the crypto project have not been for the reasons the company may have desired. Amid the current tumultuous crypto market conditions when the prices of Ripple’s XRP are tumbling, the token is also dragged in a legal battle against the SEC.

Ripple’s XRP is a cryptocurrency token designed to facilitate the cross-border and cross-platform transfer of funds. The primary objective of Ripple XRP is to provide a faster, cost-effective, and scalable digital asset that will simplify cross-border payments. It is a payment settlement system and currency exchange network that can process transactions globally. XRP is the native token of Ripple. Besides, Ripple can facilitate exchanges for various fiat currencies and cryptocurrencies, such as Bitcoin.

Although Ripple managed to avoid the fatal crash as its case with the SEC raged on, the battle is still far from over. The crypto project also needs to maintain its market momentum to stay afloat.

In the absence of real-world asset backing, the trade volume of Ripple has fallen in proportion to the recent crypto market crash. This price fall caused by market speculation has pushed investors farther from Ripple’s XRP. Notably, Ripple’s native token, XRP, has plunged 50% from last year’s mark.

Moreover, the digital asset is still hovering way below its all-time high, sparking speculations about whether it would ever return. At the time of writing the price of Ripple XRP is $0.37 with some crypto analysts placing a price target of $1.35 over the next 9 months.

Investors Feel Confident In chúng tôi Golden Run

Chronoly.io is the world’s first blockchain marketplace for the luxury watch industry. The platform lets people buy,sell and trade fractional pieces of NFTs that are backed by luxury watches, from brands like Rolex, Patek Philippe, Richard Mille, and others. However, what makes chúng tôi distinct from other crypto projects is its unique use case and real-world utility.

According to the chúng tôi whitepaper, users can purchase small fragments of a luxury watch for as little as $10 and users that own 100% of an NFT watch can redeem it for the physical version at any time. Users can also stake their CRNO tokens for a passive income and get exclusive discounts on trading fees. The platform also provides its users with the option of borrowing against the watch-backed NFTs.

These noteworthy traits of chúng tôi have attracted crypto users from far and wide. Interestingly, many whale investors are jumping ship from Ripple to chúng tôi for its stable and robust roadmap. Chronoly.io CRNO token has grown 560% in the last few weeks. Its price, which was $0.01 in May 2023, has now touched $0.066. Moreover, experts believe that CRNO can grow by 3,000% within the next two months.

For more information about chúng tôi presale

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Ripple Vs Sec: Time For Xrp Traders To Play It Safe?

The imminent conclusion of the Ripple vs. SEC lawsuit has the entire crypto-industry eagerly anticipating the final ruling. This legal battle carries immense importance as it is poised to bring much-needed regulatory clarity to the USA, which presently lacks a comprehensive framework for governing the sector.

Is your portfolio green? Check out the Ripple Profit Calculator

The Hinman Documents

On 16 May, Judge Torres delivered a significant ruling by rejecting the Securities and Exchange Commission’s (SEC) request to keep the Hinman documents confidential. This implies that the court intends to make these documents accessible to the general public. Subsequently, the countdown began with 6 June anticipated as the date for the public disclosure of these extensively discussed documents.

XRP enthusiasts get optimistic

According to Sam Lyman, a former policy director, Ripple may have a chance to emerge victorious after the documents become available to the public.

One of the reasons why Ripple may come out on top has been the legal team that the firm has hired.  According to John E Deaton, Ripple’s legal team consists of individuals with notable backgrounds, including a former Director of Enforcement, a former Chief of Litigation, and a former Chief of the Criminal Division.

Comments from former SEC commissioner

However, in the interview, Joseph stated he had questions around the reasons why a lawsuit was filed against Ripple Labs. He also mentioned that he had questions regarding the timing and implications of this event. He further went on to claim that he does not believe this litigation will help provide a significant solution towards compliant crypto in the future.

Traders play it safe

Many in the crypto-space believe that the disclosure of the Hinman documents will provide Ripple enough ammunition to win this case, impacting XRP positively. However, traders don’t quite share the same belief.

According to Coinglass’ data, the sentiment of traders has been rather negative as short positions taken against XRP have risen over the last few days.

Over this period, XRP’s price also fell. At press time, it was trading at $0.453. The network growth of XRP had decreased as well, indicating that new users aren’t as interested in buying XRP.

Its velocity, however, remained consistent throughout the last few weeks, showcasing that the token was being traded actively despite high fluctuations in its price.

The release of the Hinman documents and the outcome of the lawsuit could provide some positive momentum to XRP’s price. Additionally, Ripple’s other developments may also help in improving the state of XRP.

Ripple enters new markets

Ripple recently revealed its selection to demonstrate a solution for tokenizing real estate assets in Hong Kong’s inaugural e-HKD Pilot Programme.

The projected growth of the real-world asset tokenization industry to a multi-trillion-dollar one by 2030 has resulted in increasing interest in utilizing this approach to tokenize commodities, particularly real estate. Ripple has developed a compelling use case that integrates the e-HKD, tokenized real estate, and lending protocols. This solution will operate on a private and secure ledger, leveraging the same technology employed on the XRP Ledger (XRPL).

Realistic or not, here’s XRP market cap in BTC’s terms

Additionally, Ripple has also been observed to be working on its CBDC platform. Ripple is actively collaborating with over 20 countries to develop plans for Central Bank Digital Currencies (CBDCs). Through this collaboration, Ripple has made significant improvements to its CBDC platform, ensuring it fulfils the specific requirements of many. This enhanced platform enables these entities to tailor their currency plans and create prototypes utilizing the new CBDC platform.

Safemoon Price Prediction, Will Sfm’s Price Hit $0.00031?

SFM should be around $0.00024.

SafeMoon price prediction 22 Jul 2023: SafeMoon’s price for 22 Jul 2023 according to our analysis should range between $0.00022 to $0.00026 and the average price of SFM should be around $0.00024.

SafeMoon price prediction 23 Jul 2023: SafeMoon’s price for 23 Jul 2023 according to our analysis should range between $0.00022 to $0.00026 and the average price of SFM should be around $0.00024.

SafeMoon price prediction 24 Jul 2023: SafeMoon’s price for 24 Jul 2023 according to our analysis should range between $0.00022 to $0.00025 and the average price of SFM should be around $0.00023.

SafeMoon price prediction 29 Jul 2023: SafeMoon’s price for 29 Jul 2023 according to our analysis should range between $0.00021 to $0.00025 and the average price of SFM should be around $0.00023.

SafeMoon price prediction 3 Aug 2023: SafeMoon’s price for 3 Aug 2023 according to our analysis should range between $0.00021 to $0.00024 and the average price of SFM should be around $0.00023.

SafeMoon price prediction 13 Aug 2023: SafeMoon’s price for 13 Aug 2023 according to our analysis should range between $0.00021 to $0.00024 and the average price of SFM should be around $0.00023.

SafeMoon price prediction September 2023: SafeMoon’s price for September 2023 according to our analysis should range between $0.00022 to $0.00025 and the average price of SFM should be around $0.00024.

SafeMoon price prediction October 2023: SafeMoon’s price for October 2023 according to our analysis should range between $0.00023 to $0.00026 and the average price of SFM should be around $0.00024.

SafeMoon price prediction November 2023: SafeMoon’s price for November 2023 according to our analysis should range between $0.00023 to $0.00027 and the average price of SFM should be around $0.00025.

SafeMoon price prediction December 2023: SafeMoon’s price for December 2023 according to our analysis should range between $0.00024 to $0.00028 and the average price of SFM should be around $0.00026.

SafeMoon price prediction 2024: SafeMoon’s price for 2024 according to our analysis should range between $0.00032 to $0.00048 and the average price of SFM should be around $0.0004.

SafeMoon price prediction 2025: SafeMoon’s price for 2025 according to our analysis should range between $0.00042 to $0.00063 and the average price of SFM should be around $0.00053.

SafeMoon price prediction 2026: SafeMoon’s price for 2026 according to our analysis should range between $0.00056 to $0.00084 and the average price of SFM should be around $0.0007.

SafeMoon price prediction 2027: SafeMoon’s price for 2027 according to our analysis should range between $0.00074 to $0.0011 and the average price of SFM should be around $0.00092.

SafeMoon price prediction 2028: SafeMoon’s price for 2028 according to our analysis should range between $0.00097 to $0.0014 and the average price of SFM should be around $0.0012.

SafeMoon price prediction 2029: SafeMoon’s price for 2029 according to our analysis should range between $0.0012 to $0.0019 and the average price of SFM should be around $0.0016.

SafeMoon price prediction 2030: SafeMoon’s price for 2030 according to our analysis should range between $0.0017 to $0.0025 and the average price of SFM should be around $0.0021.

SafeMoon price prediction 2031: SafeMoon’s price for 2031 according to our analysis should range between $0.0022 to $0.0033 and the average price of SFM should be around $0.0028.

SafeMoon price prediction 2032: SafeMoon’s price for 2032 according to our analysis should range between $0.0029 to $0.0044 and the average price of SFM should be around $0.0037.

SafeMoon price prediction 2033: SafeMoon’s price for 2033 according to our analysis should range between $0.0039 to $0.0058 and the average price of SFM should be around $0.0049.

SafeMoon price prediction 2034: SafeMoon’s price for 2034 according to our analysis should range between $0.0051 to $0.0077 and the average price of

Cryptocurrency Price Prediction Using Arima Model

This article was published as a part of the Data Science Blogathon.

Cryptocurrencies are digital tokens that can easily replace traditional currency in the future. Easy access is the reason they are becoming so popular so fast. Almost anyon

The blockchain technology on which most of these tokens are based, and their decentralized systems can have many more implementations in creating more safe and secure organizational environments in the future. In theory, it can change how economies and industries work and can almost effectively eliminate inefficiency and human error.

Like I said we are on the verge of something new and exciting, and getting on the ground floor of this change can benefit us and our successors in ways unimaginable. But how about we start this exciting crypto stuff with some good old data science analysis?

Dataset

You can find the dataset we will use here: Kaggle: Cryptocurrency

Let’s start with some EDA.

Exploratory Data Analysis

Python Code:



Open The price of the coin at the beginning of the trading day.

High: The highest price of the coin on a trading day.

Low: The lowest price of the coin on a trading day.

Close: The last price of the coin before the trading day ends.

# Transforming date to date object df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d') # Getting a dataframe containing only the latest date's data for each currency print("Latest crypto data") latest_df = df[df['date'] == max(df['date'])] latest_df.head() print("Number of cryptocurrencies listed: ") latest_df['symbol'].nunique() > Number of cryptocurrencies listed: 1461

Old and new tokens

# starting dates for all currencies start_df = pd.DataFrame({'start_date' : df.groupby( [ "name", "ranknow"] )['date'].min()}).reset_index() # List the oldest ones print("Oldest Cryptocurrencies") start_df.sort_values(['start_date']).head(x)

Oldest Cryptocurrencies

# List of the new ones print("New Cryptocurrencies") start_df.sort_values(['start_date']).tail(x)

New Cryptocurrencies

Rank

latest_df[latest_df['ranknow'] <= x].groupby('ranknow').name.unique()

Market capitalization

We can calculate this value by multiplying the current price of the token with the total amount circulating in the market.

# Plotting the top X currencies according to market valuation name = latest_df['name'].unique() currency = [] marketval = [] x_currencies = name[:x] for i, cn in enumerate(x_currencies): filtered = latest_df[(latest_df['name']==str(cn))] currency.append(str(cn)) marketval.append(filtered['market'].values[0]) f, ax = plt.subplots(figsize=(20, 8)) g = sns.barplot( y = currency, x = marketval, palette=sns.cubehelix_palette(x, reverse=True)) plt.title("Top X Cryptocurrencies Marketval") ax.set_xticklabels(ax.get_xticks()) fig=plt.gcf() plt.show()

Volume

In simple words, Volume is the amount of a token traded in a specific time interval.

# Plotting the top X currencies by volume latest_df currency = [] volume = [] x_currencies = name[:x] for i, cn in enumerate(x_currencies): filtered = latest_df[(latest_df['name']==str(cn))] currency.append(str(cn)) volume.append(filtered['volume'].values[0]) f, ax = plt.subplots(figsize=(20, 8)) g = sns.barplot( y = currency, x = volume, palette=sns.cubehelix_palette(x, reverse=True)) plt.title("Top X Cryptocurrencies Volume") ax.set_xticklabels(ax.get_xticks()) fig=plt.gcf() plt.show()

The crypto market much like the stock market depends a lot on the trader’s sentiments. The buyers are the ones who increase the price and the sellers are the ones who drive the price low. An increase in price but a decrease in volume traded shows a lack of interest, and thus can end up in a potential reversal. I know it’s counterintuitive, but that’s how financial markets all over the world works.

In this next section, we will use candlestick charts, which is the most popular chart-type used by traders, along with indicators like moving average to see if we can track some changes in volumes and uptrends and downtrends in the crypto market.

Japanese Candlestick

Image Source: Japanese Candlesticks: Find reliable signals 

There are 2 types of candles in a Japanese candlestick pattern, a green and a red. The green one signifies the price increased in the given time interval and the red one vice versa. The rectangular part of a candlestick is its body. In a green candle, the bottom end is the opening price and the upper one is the closing price. The 2 wicks out of the rectangle from both sides are called shadows, which signify the high and low price for that timeframe.

# Candlestick chart for Bitcoin rank = 1 months = 6 name = df[df.ranknow == rank].iloc[-1]['name'] OHLCfiltered_df = filtered_df[['date','open','high','low','close']] OHLCfiltered_df['date'] = mdates.date2num(OHLCfiltered_df['date'].dt.date) f,ax=plt.subplots(figsize=(15,11)) axxaxis_date() candlestick_ohlc(ax, OHLCfiltered_df.values, width=0.5, colorup='g', colordown='r',alpha=0.75) plt.xlabel("Date") axxaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d')) plt.gcf().autofmt_xdate() plt.title(name + " price") plt.ylabel("Price") plt.show() # Candlestick chart for Etherium rank = 2 months = 6 name = df[df.ranknow == rank].iloc[-1]['name'] OHLCfiltered_df = filtered_df[['date','open','high','low','close']] OHLCfiltered_df['date'] = mdates.date2num(OHLCfiltered_df['date'].dt.date) f,ax=plt.subplots(figsize=(15,11)) axxaxis_date() candlestick_ohlc(ax, OHLCfiltered_df.values, width=0.5, colorup='g', colordown='r',alpha=0.75) plt.xlabel("Date") axxaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d')) plt.gcf().autofmt_xdate() plt.title(name + " price") plt.ylabel("Price") plt.show()

Moving average

We can use SMA(simple moving average) which is a very popular indicator in stock trading, to analyze the trends in the market.

# Moving average chart for Bitcoin rank = 1 months = 6 name = df[df.ranknow == rank].iloc[-1]['name'] filtered_df.set_index('date', inplace=True) f, ax = plt.subplots(figsize=(15,11)) filtered_df.close.plot(label='Raw', ax=ax) filtered_df.close.rolling(20).mean().plot(label='20D MA', ax=ax) filtered_df.close.ewm(alpha=0.03).mean().plot(label='EWMA($alpha=.03$)', ax=ax) plt.title(name + " price with Moving Averages") plt.legend() plt.xlabel("Date") plt.gcf().autofmt_xdate() plt.ylabel("Close ($)") plt.show() # Moving average chart for Etherium rank = 2 months = 6 name = df[df.ranknow == rank].iloc[-1]['name'] filtered_df.set_index('date', inplace=True) f, ax = plt.subplots(figsize=(15,11)) filtered_df.close.plot(label='Raw', ax=ax) filtered_df.close.rolling(20).mean().plot(label='20D MA', ax=ax) filtered_df.close.ewm(alpha=0.03).mean().plot(label='EWMA($alpha=.03$)', ax=ax) plt.title(name + " price with Moving Averages") plt.legend() plt.xlabel("Date") plt.gcf().autofmt_xdate() plt.ylabel("Close ($)") plt.show()

Here are a few well-known facts about using the SMA in the stock market:

The 20 moving average (20MA) is used for the short-term analysis.

The 50 moving average (50MA) is used for medium-term analysis.

The 200 moving average (200MA) is used to determine the trend.

In a bullish run (uptrend) the price of the stock should be above 20 MA, and the 50 MA in between 20 MA and 200 MA. And in a bear run (downtrend), the vice versa.

Let’s see if that is the case with our top 2 cryptocurrencies: Bitcoin and Etherium.

# Moving average chart for BTC rank = 1 months = 10 name = df[df.ranknow == rank].iloc[-1]['name'] filtered_df.set_index('date', inplace=True) sma20 = filtered_df.close.rolling(20).mean() sma50 = filtered_df.close.rolling(50).mean() sma200 = filtered_df.close.rolling(200).mean() smaplot =pd.DataFrame({'Raw': filtered_df.close, 'SMA 20': sma20, 'SMA 50': sma50, 'SMA 200': sma200}) smaplot.plot(figsize=(9,5), legend=True, title="Bitcoin price with Moving Averages") plt.gcf().autofmt_xdate() plt.show()

As you can see a classic uptrend in mid-November 2023. After a few crosses between the coin price and the 20 SMA, we see a clear uptrend in 2023 end. But before not so long, we see a clear trend reversal in early January, indicating the beginning of a bear run.

# Moving average chart for ETH rank = 2 months = 10 name = df[df.ranknow == rank].iloc[-1]['name'] filtered_df.set_index('date', inplace=True) # simple moving averages sma20 = filtered_df.close.rolling(20).mean() sma50 = filtered_df.close.rolling(50).mean() sma200 = filtered_df.close.rolling(200).mean() smaplot = pd.DataFrame({'Raw': filtered_df.close, 'SMA 20': sma20, 'SMA 50': sma50, 'SMA 200': sma200}) smaplot.plot(figsize=(9,5), legend=True, title="Etherium price with Moving Averages") plt.gcf().autofmt_xdate() plt.show()

Etherium had a similar uptrend in late 2023 but mostly had a sideways market in most of 2023. In early 2108 we can see the 20 MA starting to change directions, which can possibly result in a bear run, but as of now, there’s no clear indication.

Price Prediction using ARIMA

In this section, we will use ARIMA(AutoRegressive Integrated Moving Average) to predict the price of Bitcoin using past data and the above analysis.

Importing libraries

import pandas as pd from pandas import DataFrame import numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = (15,7) import seaborn as sns from datetime import datetime, timedelta from statsmodels.tsa.arima_model import ARIMA from statsmodels.tsa.statespace.sarimax import SARIMAX from statsmodels.graphics.tsaplots import plot_acf, plot_pacf from statsmodels.tsa.stattools import adfuller from statsmodels.tsa.seasonal import seasonal_decompose from scipy import stats import chúng tôi as sm from itertools import product import warnings warnings.filterwarnings('ignore')

Importing data

dateparse = lambda dates: pd.datetime.strptime(dates, '%Y-%m-%d') df = pd.read_csv('../input/crypto-markets.csv', parse_dates=['date'], index_col='date', date_parser=dateparse) df.head() df.tail() # Extracting bitcoin data btc=df[df['symbol']=='BTC'] btc.drop(['slug', 'volume','symbol','name','ranknow','market', 'close_ratio', 'spread'],axis=1,inplace=True) btc.head() ARIMA Model

AutoRegressive Integrated Moving Average

The model has 3 parameters p, d, and q accounting for seasonality, trend, and noise in the dataset. We will fit the ARIMA model using a stats model which will return something called an AIC value (Akaike Information Criterion). The AIC scales how compatible the model fits the data and the complexity of the model. A model with a lot of features that fit the data will be given a larger AIC score, than a model with the same accuracy but a lesser number of features. Thus we are looking for a model which yields a low AIC score. Let’s get started :

# Initial approximation of parameters qs = range(0, 3) ps = range(0, 3) d=1 parameters = product(ps, qs) parameters_list = list(parameters) len(parameters_list) # Model Selection results = [] best_aic = float("inf") warnings.filterwarnings('ignore') for param in parameters_list: try: model = SARIMAX(btc_month.close_box, order=(param[0], d, param[1])).fit(disp=-1) except ValueError: print('bad parameter combination:', param) continue aic = model.aic if aic < best_aic: best_model = model best_aic = aic best_param = param results.append([param, model.aic]) # Best Models result_table = pd.DataFrame(results) result_table.columns = ['parameters', 'aic'] print(result_table.sort_values(by = 'aic', ascending=True).head()) print(best_model.summary())

Results

print("Dickey–Fuller test:: p=%f" % adfuller(best_model.resid[13:])[1]) best_model.plot_diagnostics(figsize=(15, 12)) plt.show()

Now it’s time for some prediction!

Prediction btc_pred = btc_month[['close']] date_list = [datetime(2024,6,31), datetime(2024,5,30), datetime(2024,3,31), datetime(2024,4,30)] future = pd.DataFrame(index=date_list, columns= btc_month.columns) btc_pred = pd.concat([btc_month_pred, future]) btc_pred['forecast'] = invboxcox(best_model.predict(start=datetime(2014,1,31),end=datetime(2024,6,30)),lmbda) plt.figure(figsize=(15,7)) btc_month_pred.close.plot() btc_month_pred.forecast.plot(color='r', ls='--', label='Predicted Close') plt.legend() plt.title('Bitcoin monthly forecast') plt.ylabel('USD') plt.show() SARIMAX Model

It stands for Seasonal ARIMA with eXogenous regressors model.

The bitcoin data above showed some seasonality which was unexpected. Therefore we can improve our model using SARIMA.

# Initial approximation of parameters Qs = range(0, 2) qs = range(0, 3) Ps = range(0, 3) ps = range(0, 3) D=1 d=1 parameters = product(ps, qs, Ps, Qs) parameters_list = list(parameters) len(parameters_list) # Model Selection results = [] best_aic = float("inf") warnings.filterwarnings('ignore') for param in parameters_list: try: model = SARIMAX(btc_month.close_box, order=(param[0], d, param[1]), seasonal_order=(param[2], D, param[3], 4)).fit(disp=-1) except ValueError: print('bad parameter combination:', param) continue aic = model.aic if aic < best_aic: best_model = model best_aic = aic best_param = param results.append([param, model.aic]) # Best Models result_table = pd.DataFrame(results) result_table.columns = ['parameters', 'aic'] print(result_table.sort_values(by = 'aic', ascending=True).head()) print(best_model.summary())

Results

print("Dickey–Fuller test:: p=%f" % adfuller(best_model.resid[13:])[1]) best_model.plot_diagnostics(figsize=(15, 12)) plt.show() Prediction of the ARIMA Model btc_month2 = btc_month[['close']] date_list = [datetime(2024,6,31),datetime(2024,5,30),datetime(2024,3,31),datetime(2024,4,30)] future = pd.DataFrame(index=date_list, columns= btc_month.columns) btc_month2 = pd.concat([btc_month2, future]) btc_month2['forecast'] = invboxcox(best_model.predict(start=0, end=75), lmbda) plt.figure(figsize=(15,7)) btc_month2.close.plot() btc_month2.forecast.plot(color='r', ls='--', label='forecast') plt.legend() plt.title('Bitcoin Monthly Close Forecast') plt.ylabel('USD') plt.savefig('bitcoin_monthly_forecast.png') plt.show() Validation

Now we will calculate how accurate our prediction is using RMSE (root mean square error). Let’s calculate the RMSE for 2024 through 2023:

y_forecasted = btc_month2.forecast y_truth = btc_month2['2024-01-01':'2024-01-01'].close # Compute the root mean square error rmse = np.sqrt(((y_forecasted - y_truth) ** 2).mean()) print('Mean Squared Error: {}'.format(round(rmse, 2))) End Notes

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Related

Filecoin, Cronos, And Budblockz Price Prediction

The cryptocurrency sector faces an uncertain future. The implosion experienced in the past few months, characterized by double-digit percentage losses, raises serious doubts. Nevertheless, it’s becoming increasingly clear that only projects with proven utility and use cases have what it takes to bounce back once the current dust settles. Filecoin, Cronos, and BudBlockz are some of the tokens worth paying attention to as we head into 2023.

BudBlockz Poised for Growth in 2023

BudBlockz might be a new kid on the block, but it is showing tremendous potential as it moves to revolutionize the cannabis sector while leveraging blockchain technology and innovation. The crypto cannabis project has set out to offer a unique transparent e-commerce project that will make it easy for people to trade real and virtual cannabis-related products.

Powered by blockchain technology, the e-commerce platform sets to revolutionize the multi-billion cannabis sector in the early stages of growth. In addition to enhancing the buying and selling of cannabis-related products, the project also plans to offer a unique NFT marketplace that will allow people to trade an array of non-fungible tokens, including the ecosystem’s native Ganja Guruz NFTs.

The proposed BudBlockz NFT marketplace will also enable partial ownership of businesses and tangible real-life assets. Therefore, marijuana enthusiasts should be able to do more than collect and trade unique NFTs.

The future can only be bright for the network’s native token, $BLUNT, poised to power the expansive network. The token will act as a medium of exchange, enabling all the transactions. Given its limited supply, its value is expected to increase significantly as demand in the ecosystem increases.

Filecoin 2023 Prediction

Filecoin is another cryptocurrency whose prospects and growth metrics look increasingly bright. The governance coin powers a blockchain-powered platform for storing files and data on users’ free spaces.

While the current data solutions revolve around Google, Amazon, and other tech giants, Filecoin seeks to offer a decentralized way of storing files away from a centralized system. Consequently, Filecoin provides a way for customers with available space to become storage providers and make some money in the process. A client will thus transfer a file or data to a storage miner for a fee.

FIL is the native token that powers the Filecoin ecosystem as a medium of exchange for all transactions. While the coin hit an all-time high in 2023, it has come under pressure amid the broader market correction.

However, the future is looking increasingly bright as people and companies look for alternatives to centralized systems for storing data prone to attacks. By offering a decentralized system that is not in the control of any authority, Filecoin stands to enjoy tremendous growth that should also see the value of the native token increase significantly.

Cronos Crypto Adoption Bet

Cronos is another project well-positioned for growth amid the growing crypto adoption. Theproject was launched in 2024, and it seeks to enhance the global adoption of cryptocurrencies by offering a variety of crypto via cards, wallets, and brokerage services.

CRO is the native token that powers the network acting as a medium of exchange. As Cronos moves to serve over a billion Web3 users, CRO demand is expected to increase significantly. Consequently, the value of the underlying token is expected to increase significantly.

Bottom Line

Cryptocurrencies short-term outlook is not good as investors flee riskier assets amid the recession. However, BudBlockz, Cronos, and Filecoin’s long-term outlook remain solid amid the growing demand for NFTs, Cannabis products storage services, and the metaverse.

Learn more about BudBlockz (BLUNT) at the links below:

Litecoin (Ltc) And Ripple (Xrp) Dip Investors Look Towards Flasko (Flsk)

Based on market capitalization, Litecoin (LTC) and Ripple (XRP) rank among the top 20 cryptocurrencies in the world. Since their inception, Litecoin (LTC) and Ripple (XRP) have hit a fantastic all-time high, but with the market crash of 2023, these coins were affected and have dipped from their initial value.

With this price downtrend, cryptocurrency enthusiasts might be keen on buying the dip, but top crypto analysts and investors are looking at other alternatives. Analysts predict that new and alternative investments like Flasko, with a unique utility and use case, might be better investments as Litecoin (LTC) and Ripple (XRP) dip.

Litecoin (LTC) Price On A Gradual Declining Rate

Based on blockchain technology, Litecoin (LTC) is a digital native cryptocurrency that the populace can use for international and domestic payments. However, as seen in data from Binance, Litecoin (LTC) has been underperforming since the coin was hit by the general bearish market trend, with the coin being down by 11.90% in the past 90 days, 19.47% in the last 60 days, 9.18% in the previous 30 days.

Litecoin (LTC) also fell by 1.29% in the past 24 hours to trade at $50.68 at press time. There is also the possibility of Litecoin (LTC) going further down in the next few hours. The poor performance of Litecoin (LTC) comes after an official tweet from Litecoin’s official Twitter page that they will add an MWEB feature to mobile phones.

This continuous price downtrend has left investors in fear of buying the dip, even with the possibility of a later uptrend. Instead, they are looking for alternative investments like Flakso that can provide enormous returns.

Ripple (XRP) Investors Not Keen On Buying Amidst Sec Lawsuits

Ripple (XRP) uses blockchain technology to simplify international transactions and allows the Ripple (XRP) platform users to receive or send money at a bit of cost and reduced time. Its traditions are transparent and safe, which makes Ripple (XRP) a competition with centralized, traditional system solutions.

However, Ripple (XRP) was still affected by the crypto winter and the ongoing Securities and Exchange Commission (SEC) lawsuits. In the last seven days, Ripple (XRP) fell by 4.90% after an initial uptrend of 2.87% to currently trade at $ 0.4857. Though some crypto enthusiasts might be enthusiastic about a positive outcome to the Ripple (XRP) and SEC case, analysts and crypto investors are more in favor of looking towards alternative investments with unique utilities guaranteed to provide huge profits in the short term.

Flasko (FLSK) Is Set To Provide Huge Returns In The Next Few Months

Buying the dip in Litecoin (LTC) and Ripple (XRP) might seem reasonable. Still, it can’t compare to investing in new coins, which offer more potential before they become of high value by market capitalization. This is why investors are keen on new and alternative investments like Flasko.

Flasko will be a revolutionary and alternative-investment platform that allows users to invest in fractionalized NFTs backed by rare, high-end, and luxury wines, champagnes, and whiskeys. Developers of Flasko put much thought into building Flasko’s platform by ensuring the liquidity will be locked for 33 years, getting a passed audit from Solid Proof, and real asset backing with premium and high-end wines, whiskeys, and champagnes that regularly increase in value.

With such details and data, Flasko is all investors need in a cryptocurrency, and with its current presale going for a price of $0.065, early investors are guaranteed huge returns. Analysts even predict that Flasko might perform over 4,000% by mid-2023. A complete sell out of stage one means stage two is now live!

If you missed getting in on Litecoin (LTC) and Ripple (XRP) before they hit their all-time highs, then checking out and investing in Flakso with the links below might be your best investment bet.

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