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The crypto market has been bearish this weekend, and it looks like the week might still be bearish. Most of the coins have declined over the last few days, but today the market has been quite stable. Though most coins have not made a significant recovery, the decline has also been minimal. In the past 24 hours, the entire global marketcap had declined by 0.74%, which is quite small. th of this month? Well, here are price predictions for the
Bitgert (BRISE) Price PredictionThe st announcement are key factors that will grow th and 29th August.
Safemoon (SFM) Price PredictionThe Safemoon has not been doing over the 7 days. The Safemoon coin might be experiencing some rebound today, but the market is still bearish. Therefore, might see the Safemoon coin drop if the bears will remain in control. The Safemoon coin was trading at $0.0003919 and might push to $0.0004501 if the buying pressure grows on 28th and 29th August.
Baby Doge Price PredictionThe Baby Doge coin is still in the red for the past 24 hours. The Baby Doge selling pressure is easing as the crypto market grows stable. So Baby Doge might make some recovery, but the market is still bearish.
The crypto market has been bearish this weekend, and it looks like the week might still be bearish. Most of the coins have declined over the last few days, but today the market has been quite stable. Though most coins have not made a significant recovery, the decline has also been minimal. In the past 24 hours, the entire global marketcap had declined by 0.74%, which is quite small. Bitgert (BRISE) , Baby Doge, and Safemoon have been affected differently by the current gear market. The Bitgert coin, has been experiencing a surge in buying pressure, while Baby Doge and Safemoon coins have been stable. How will Bitgert (BRISE) , Baby Doge, and Safemoon prices perform on the 28th and 29of this month? Well, here are price predictions for the Bitgert , Safemoon, and Baby Doge chúng tôi Bitgert coin is the only among the three that has been surging this month. Even today, Bitgert is bullish, growing 5% in 24 hours, while Safemoon and Baby Doge stayed stable. With a bullish Bitgert coin, the price prediction is a 50% increase between today and tomorrow. The Bitgert price as of writing was $0.000000801742, and with the growing Bitgert buying pressure, Bitgert price might end the day at 0.000001203. However, crypto experts believe that Bitgert has the potential to break last week’s ATH of $0.00000146437. Bitgert has got so many factors driving its bullish price, but the Bitgert roadmap V2 is one of them. Bitgert also partnered with the Centcex team to build products and projects for the Bitgert ecosystem. The Centcex partnership, roadmap V2 products, and the widely anticipated September 1announcement are key factors that will grow Bitgert price passed 0.000001203 between 28and chúng tôi Safemoon has not been doing over the 7 days. The Safemoon coin might be experiencing some rebound today, but the market is still bearish. Therefore, might see the Safemoon coin drop if the bears will remain in control. The Safemoon coin was trading at $0.0003919 and might push to $0.0004501 if the buying pressure grows on 28and chúng tôi Baby Doge coin is still in the red for the past 24 hours. The Baby Doge selling pressure is easing as the crypto market grows stable. So Baby Doge might make some recovery, but the market is still bearish. If the Baby Doge buying pressure rises, the price might grow from the current $0.000000001353 to $0.000000002602 in the next 24 hours. So Baby Doge might be the coin to watch on 28and 29August.
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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
Doge And Shib Price Prediction – Collateral Network Set To Surpass Both
Collateral Network (COLT) has swiftly captured all the market attention due to its unique business plan, and mammoth presale growth. According to experts, early Collateral Network investors may get 100x profit by the end of this year. Therefore, bulls have rallied behind Collateral Network, while Dogecoin (DOGE) and Shiba Inu (SHIB) are still in troubled waters.
Musk’s Twitter Resignation Hurts DogecoinAs Elon Musk has stepped out from being the Twitter CEO, experts have been pondering upon its impact on Dogecoin. A few weeks back, Twitter revealed that it would soon allow crypto trading on its platform, and Dogecoin was expected to be its biggest beneficiary, given the billionaire’s support for the meme coin.
However, a change in the Twitter CEO may hurt Dogecoin, which has heavily relied on Musk’s support for growth. The bearish sentiments can also be corroborated by Dogecoin’s price trajectory. The price of Dogecoin (DOGE) has suffered a drop of 9% in the past month. Thus, Dogecoin is available at $0.0717. The immense rise of other meme coins, like Pepe Coin, has also contributed to the market struggle of Dogecoin.
Shiba Inu Sees Improvement In Key CategoriesIn positive news for the Shiba Inu community, the AAA rating of SHIB has been restored by CertiK. The development took place after Shiba Inu fixed several security issues. However, the news has not been able to support the price of Shiba Inu (SHIB). Shiba Inu’s price has plunged by 15% in the past month.
Hence, Shiba Inu is now changing hands at $0.00000856. Meanwhile, Shiba Inu has confirmed preorders for 5000 SHIB-themed cold wallets, whose delivery will start in July 2023. On the sidelines, BitFlyer, a Japanese crypto exchange, recently stated that a few cryptocurrencies, including Shiba Inu, will have to comply with the travel rule to keep operating on its platform.
Collateral Network Surges In PopularityA first-of-a-kind DeFi platform, Collateral Network, has disrupted the lending industry. It is a blockchain crowdlending platform that allows people to take loans against their physical assets at a competitive interest rate. Collateral Network accepts a range of physical assets as collateral to give loans, such as vintage cars, watches, fine wine, art, and many more.
Collateral Network is a borderless and permissionless platform, and people can take a loan by sending their physical assets directly to the company. Collateral Network accepts borrowers’ assets as collateral, and mints non-fungible tokens against them. Prior to minting NFTs, Collateral Network evaluates the assets using artificial intelligence.
Borrowers can get their collateralized assets returned to their addresses after settling the loan. But if borrowers default on their loans, their assets are auctioned to recover the loan amount. Lenders can buy these 100% asset-backed Collateral Network NFTs to lend funds to borrowers and in return, receive a fixed income every week.
Collateral Network (COLT) tokens have been developed on the Ethereum blockchain, with their smart contracts fully audited. The company has a KYC-audited team comprising experienced members. A Collateral Network token can be bought at $0.014, which is predicted to soar by 3500% during the presale round.
Find out more about the Collateral Network presale here:Binance Coin, Monero, Dogecoin Price Analysis: 29 December
Christmas seems to have come bearing gifts for Bitcoin. Adding cheer to the festival celebrations, the world’s largest cryptocurrency scaled beyond the $27,000 price level over the past few days. As a result, the broader altcoin market has also been surging on the charts. The question now is – Will altcoins be able to sustain their momentum after an inevitable correction hits Bitcoin?
Binance Coin seemed to have answered this question by posting gains at a time when Bitcoin moved slightly lower on the charts. The same could not be said for Monero and Dogecoin, as both altcoins registered negative gains in the past 24 hours.
Binance Coin [BNB]After a sluggish start to the month, Binance Coin found its feet after bouncing back from the $25.25 support level. Since then, BNB has moved upwards on the chart and traded between the channel $31.05 and $35.93. The last few trading sessions even saw BNB spike above this channel as prices hit the $40 mark, a level not seen since June 2023. The indicators on BNB suggested that the bullish momentum was nowhere close to losing steam and that prices could surge even higher in the short term.
The price surges were also backed by a healthy number of buyers in the market, as the On Balance Volume was rising constantly since mid-December.
Monero [XMR]December has been a favorable month for Monero. A look at its 4-hr chart showed that the cryptocurrency has been on an uptrend since November, with prices hitting higher highs and higher lows. However, in the past week, prices have looked to consolidate between $168.51 and $160.17. The consolidation could be an indication that either the price is looking to settle or is slowly losing its bullish momentum. Either way, certain indicators suggested that Monero could move lower in the near future.
The Awesome Oscillator showed that the bearish momentum was increasing, as the red bars moved lower towards the Zero line.
The Parabolic SAR’s dotted lines moved above the candlesticks, also indicating the said bearishness. Support at $152.54 could provide some relief to XMR if prices moved below their present support.
Dogecoin [DOGE]Dogecoin’s activity on the charts suddenly picked up in the latter half of the month after a subdued start. Since then, the cryptocurrency has managed to post gains of over 30% in the past 30 days. However, the price seems to have lost their momentum since picking up from support at $0.0036. After the bounce back from this level, the coin’s price traded between a thin channel of $0.0046 and $0.0042 and could continue to trade between this channel in the short term. The indicators on DOGE indicated that the bulls and bears had near equal control of the market and failed to push prices in either direction.
The Relative Strength Index was floating near the neutral zone, reflecting the fact that buying pressure was being matched by selling pressure.
The Bollinger Bands showed little volatility in DOGE’s prices moving forward. Since the price was trading on the lower band, a reversal could also be expected, which would see it test the next resistance at $0.0051.
Stock Market Price Trend Prediction Using Time Series Forecasting
This article was published as a part of the Data Science Blogathon.
Introductionis used to predict future values based on previously observed values and one of the best tools for trend analysis and future prediction.
What is time-series data?It is recorded at regular time intervals, and the order of these data points is important. Therefore, any predictive model based on time series data will have time as an independent variable. The output of a model would be the predicted value or classification at a specific time.
Time series analysis vs time series forecastingLet’s talk about some possible confusion about the Time Series Analysis and Forecasting. Time series forecasting is an example of predictive modeling whereas time series analysis is a form of descriptive modeling.
For a new investor general research which is associated with the stock or share market is not enough to make the decision. The common trend towards the stock market among the society is highly risky for investment so most of the people are not able to make decisions based on common trends. The seasonal variance and steady flow of any index will help both existing and new investors to understand and make a decision to invest in the share market.
To solve this kind of problem time series forecasting is the best technique.
Stock marketStock markets are where individual and institutional investors come together to buy and sell shares in a public venue. Nowadays these exchanges exist as electronic marketplaces.
That supply and demand help determine the price for each security or the levels at which stock market participants — investors and traders — are willing to buy or sell.
The concept behind how the stock market works is pretty simple. Operating much like an auction house, the stock market enables buyers and sellers to negotiate prices and make trades.
Definition of ‘Stock’A Stock or share (also known as a company’s “equity”) is a financial instrument that represents ownership in a company
Machine learning in the stock marketThe stock market is very unpredictable, any geopolitical change can impact the share trend of stocks in the share market, recently we have seen how covid-19 has impacted the stock prices, which is why on financial data doing a reliable trend analysis is very difficult. The most efficient way to solve this kind of issue is with the help of Machine learning and Deep learning.
In this tutorial, we will be solving this problem with ARIMA Model.
To know about seasonality please refer to my previous blog, And to get a basic understanding of ARIMA I would recommend you to go through this blog, this will help you to get a better understanding of how Time Series analysis works.
Implementing stock price forecastingI will be using nsepy library to extract the historical data for SBIN.
Imports and Reading DataPython Code:
The data shows the stock price of SBIN from 2023-1-1 to 2023-11-1. The goal is to create a model that will forecast the closing price of the stock.Let us create a visualization which will show per day closing price of the stock-
plt.figure(figsize=(10,6)) plt.grid(True) plt.xlabel('Dates') plt.ylabel('Close Prices') plt.plot(sbin['Close']) plt.title('SBIN closing price') plt.show() plt.figure(figsize=(10,6)) df_close = sbin['Close'] df_close.plot(style='k.') plt.title('Scatter plot of closing price') plt.show() plt.figure(figsize=(10,6)) df_close = sbin['Close'] df_close.plot(style='k.',kind='hist') plt.title('Hisogram of closing price') plt.show()First, we need to check if a series is stationary or not because time series analysis only works with stationary data.
Testing For Stationarity:
To identify the nature of the data, we will be using the null hypothesis.
H0: The null hypothesis: It is a statement about the population that either is believed to be true or is used to put forth an argument unless it can be shown to be incorrect beyond a reasonable doubt.
H1: The alternative hypothesis: It is a claim about the population that is contradictory to H0 and what we conclude when we reject H0.
#Ho: It is non-stationary
#H1: It is stationary
If we fail to reject the null hypothesis, we can say that the series is non-stationary. This means that the series can be linear.
If both mean and standard deviation are flat lines(constant mean and constant variance), the series becomes stationary.
from statsmodels.tsa.stattools import adfuller
def test_stationarity(timeseries): #Determing rolling statistics rolmean = timeseries.rolling(12).mean() rolstd = timeseries.rolling(12).std() #Plot rolling statistics: plt.plot(timeseries, color='yellow',label='Original') plt.plot(rolmean, color='red', label='Rolling Mean') plt.plot(rolstd, color='black', label = 'Rolling Std') plt.legend(loc='best') plt.title('Rolling Mean and Standard Deviation') plt.show(block=False) print("Results of dickey fuller test") adft = adfuller(timeseries,autolag='AIC') # output for dft will give us without defining what the values are. #hence we manually write what values does it explains using a for loop output = pd.Series(adft[0:4],index=['Test Statistics','p-value','No. of lags used','Number of observations used']) for key,values in adft[4].items(): output['critical value (%s)'%key] = values print(output) test_stationarity(sbin['Close']) After analysing the above graph, we can see the increasing mean and standard deviation and hence our series is not stationary. Results of dickey fuller test Test Statistics -1.914523 p-value 0.325260 No. of lags used 3.000000 Number of observations used 5183.000000 critical value (1%) -3.431612 critical value (5%) -2.862098 critical value (10%) -2.567067 dtype: float64We see that the p-value is greater than 0.05 so we cannot reject the Null hypothesis. Also, the test statistics is greater than the critical values. so the data is non-stationary.
For time series analysis we separate Trend and Seasonality from the time series.
result = seasonal_decompose(df_close, model='multiplicative', freq = 30) fig = plt.figure() fig = result.plot() fig.set_size_inches(16, 9) from pylab import rcParams rcParams['figure.figsize'] = 10, 6 df_log = np.log(sbin['Close']) moving_avg = df_log.rolling(12).mean() std_dev = df_log.rolling(12).std() plt.legend(loc='best') plt.title('Moving Average') plt.plot(std_dev, color ="black", label = "Standard Deviation") plt.plot(moving_avg, color="red", label = "Mean") plt.legend() plt.show()Now we are going to create an ARIMA model and will train it with the closing price of the stock on the train data. So let us split the data into training and test set and visualize it.
train_data, test_data = df_log[3:int(len(df_log)*0.9)], df_log[int(len(df_log)*0.9):] plt.figure(figsize=(10,6)) plt.grid(True) plt.xlabel('Dates') plt.ylabel('Closing Prices') plt.plot(df_log, 'green', label='Train data') plt.plot(test_data, 'blue', label='Test data') plt.legend() model_autoARIMA = auto_arima(train_data, start_p=0, start_q=0, test='adf', # use adftest to find optimal 'd' max_p=3, max_q=3, # maximum p and q m=1, # frequency of series d=None, # let model determine 'd' seasonal=False, # No Seasonality start_P=0, D=0, trace=True, error_action='ignore', suppress_warnings=True, stepwise=True) print(model_autoARIMA.summary()) Performing stepwise search to minimize aic ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=-16607.561, Time=2.19 sec ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=-16607.961, Time=0.95 sec ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=-16608.035, Time=2.27 sec ARIMA(0,1,0)(0,0,0)[0] : AIC=-16609.560, Time=0.39 sec ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=-16606.477, Time=2.77 sec Best model: ARIMA(0,1,0)(0,0,0)[0] Total fit time: 9.079 seconds SARIMAX Results ============================================================================== Dep. Variable: y No. Observations: 4665 Model: SARIMAX(0, 1, 0) Log Likelihood 8305.780 Date: Tue, 24 Nov 2023 AIC -16609.560 Time: 20:08:50 BIC -16603.113 Sample: 0 HQIC -16607.293 - 4665 Covariance Type: opg ============================================================================== ------------------------------------------------------------------------------ sigma2 0.0017 1.06e-06 1566.660 0.000 0.002 0.002 =================================================================================== Ljung-Box (Q): 24.41 Jarque-Bera (JB): 859838819.58 Prob(Q): 0.98 Prob(JB): 0.00 Heteroskedasticity (H): 7.16 Skew: -37.54 Prob(H) (two-sided): 0.00 Kurtosis: 2105.12 =================================================================================== model_autoARIMA.plot_diagnostics(figsize=(15,8)) plt.show() model = ARIMA(train_data, order=(3, 1, 2)) fitted = model.fit(disp=-1) print(fitted.summary()) ARIMA Model Results ============================================================================== Dep. Variable: D.Close No. Observations: 4664 Model: ARIMA(3, 1, 2) Log Likelihood 8309.178 Method: css-mle S.D. of innovations 0.041 Date: Tue, 24 Nov 2023 AIC -16604.355 Time: 20:09:37 BIC -16559.222 Sample: 1 HQIC -16588.481 ================================================================================= --------------------------------------------------------------------------------- const 8.761e-06 0.001 0.015 0.988 -0.001 0.001 ar.L1.D.Close 1.3689 0.251 5.460 0.000 0.877 1.860 ar.L2.D.Close -0.7118 0.277 -2.567 0.010 -1.255 -0.168 ar.L3.D.Close 0.0094 0.021 0.445 0.657 -0.032 0.051 ma.L1.D.Close -1.3468 0.250 -5.382 0.000 -1.837 -0.856 ma.L2.D.Close 0.6738 0.282 2.391 0.017 0.122 1.226 Roots ============================================================================= Real Imaginary Modulus Frequency ----------------------------------------------------------------------------- AR.1 0.9772 -0.6979j 1.2008 -0.0987 AR.2 0.9772 +0.6979j 1.2008 0.0987 AR.3 74.0622 -0.0000j 74.0622 -0.0000 MA.1 0.9994 -0.6966j 1.2183 -0.0969 MA.2 0.9994 +0.6966j 1.2183 0.0969 ----------------------------------------------------------------------------- # Forecast fc, se, conf = fitted.forecast(519, alpha=0.05) # 95% confidence fc_series = pd.Series(fc, index=test_data.index) lower_series = pd.Series(conf[:, 0], index=test_data.index) upper_series = pd.Series(conf[:, 1], index=test_data.index) plt.figure(figsize=(12,5), dpi=100) plt.plot(train_data, label='training') plt.plot(test_data, color = 'blue', label='Actual Stock Price') plt.plot(fc_series, color = 'orange',label='Predicted Stock Price') plt.fill_between(lower_series.index, lower_series, upper_series, color='k', alpha=.10) plt.title('SBIN Stock Price Prediction') plt.xlabel('Time') plt.ylabel('Actual Stock Price') plt.legend(loc='upper left', fontsize=8) plt.show()
Time Series forecasting is really useful when we have to take future decisions or we have to do analysis, we can quickly do that using ARIMA, there are lots of other Models from we can do the time series forecasting but ARIMA is really easy to understand.
I hope this article will help you and save a good amount of time. Let me know if you have any suggestions.
HAPPY CODING.
Prabhat Pathak (Linkedin profile) is a Senior Analyst and innovation Enthusiast.
Related
Can Cardano (Ada), Dogecoin (Doge), And Flasko (Flsk) Attract New Money In 2023?
Many investors fled the crypto market at the onset of the crypto winter, which has seen the market lose more than $2 trillion after reaching a peak of almost $3 trillion in November 2023.
The attention is on how the market will attract new money. Will it be a new frenzy, such as the 2023 NFT mania, or will it be established cryptocurrencies such as Cardano (ADA) and Dogecoin (DOGE)? There is also a good chance that Flasko, a solid cryptocurrency project developed during the bear market, will be a major force to reckon with soon.
Some investors may argue that the industry will only attract new money in the next bull run and not under the current conditions.
Will the Next Bull Run Attract New Money to CryptoMany investors are wondering what will catalyze the next bull run. The previous bull run was largely driven by the explosion of NFTs, stimulus packages, and the growing interest in the metaverse.
The 2023 – 2023 bull run is done and dusted. The current crypto bear market has been driven by macroeconomic factors outside the control of the young market. Inflation and geopolitical tensions have played a huge role in this crypto winter.
However, the bear market has seen important developments. For example, Ethereum managed to transition from a proof-of-work blockchain to a proof-of-stake. This has made Ethereum greener, and its ecosystem will likely attract institutional capital.
Bitcoin halving – an event where Bitcoin rewards for miners are slashed in half – will occur in 2024. The halving happens every four years, and the event usually precedes a bull run.
For now, it may not be clear when or how, but investors are as sure as the sun that the bull market will eventually come. And when it does, there is a strong possibility that assets such as ADA, DOGE, and FLSK will attract new money to the industry.
Dogecoin (DOGE) and New MoneyDogecoin (DOGE) has the potential to attract new money to the cryptocurrency industry. It is the first meme coin that the industry has ever seen.
Dogecoin (DOGE) has survived several bear and bull market cycles. Dogecoin’s market cap has been growing over the years, indicating that Dogecoin (DOGE) can attract new money.
Dogecoin (DOGE) has become the market leader for meme coins. Its historical success and resilience have inspired the creation of new meme coins that also aid in bringing new capital to the market.
While Dogecoin (DOGE) is a well-known popular cryptocurrency, we believe that new projects like Flasko will attract a sizable amount of investor capital to the young industry.
Cardano (ADA) in 2023Cardano (ADA) is a smart contract platform that allows developers to create and deploy decentralized apps. Cardano’s native coin is known as ADA.
ADA’s price was expected to rise significantly after the successful implementation of the Vasil hard fork on 22 September 2023. This didn’t happen due to several factors, and Cardano (ADA) nosedived. Many expect the market to gain some bullish momentum in 2023, and this should be the moment when Cardano (ADA) shines.
As more developers build on the Cardano (ADA) blockchain, there is no doubt that Cardano (ADA) will be the biggest beneficiary as new capital flows to the Cardano (ADA) ecosystem.
Why Flasko (FLSK) Will Bring New Capital to the Crypto IndustryThe is a new kid on the crypto block, and it has the potential to shine a light on the crypto industry in 2023 and beyond.
Flasko is introducing cryptocurrencies to the alternative investment world by allowing users to invest in rare whiskeys, wines, and champagnes. The investments are minted as NFTs. NFTs have become popular over the last couple of years.
Flasko’s approach will attract new money to its ecosystem, and by extension, to the entire industry. 2023 will likely be a good year as inflation might come down and spark a wave of new investors seeking large gains from new crypto projects.
Flasko is a unique project offering its FLSK token to the public via a presale. Each Flasko token is only $0.065.
We recommend investing in the FLSK token if you want to reap big gains in the future.
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