CNN token price prediction

Our price prediction is based on hi-resolution deal analysis from cryptocurrency exchanges. We are collecting and gather statistics to obtain price support levels that show most important zones witch traders want to buy or sell stocks. These buy/sell histograms showed in report combined with current trend analysis can be used to build high probability forecasting of future price trends. It also can be useful to set a price on calculated levels to be sure maximum profit was received.

Disclaimer
Cryptocurrency trading involves substantial risk of loss and is not suitable for every investor. All trading strategies are used at your own risk. This page performs statistical analysis of past data and can be used for assumptions about the future only taking into account the statistical probability

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CNN token price levels prediction for next 24 hours

Next 24 hours brief prediction

Price of CNN has broken last trend and will RAISE from the support level $3.3840002E-5

Historic price levels for next 24 hours

These levels are based on local historic minimums and maximums. Weight shows the power of broken trends.
Price Weight(level power) Date of formation

Volume profile price channel in 24 hours range

Edge levels of the price channel based on volume profile report.
Price Description
3.3840002E-5 Minimal price support level based on 24 hours movements

CNN token price levels prediction for next 7 days

Next 7 days brief prediction

Price of CNN has broken last trend and will RAISE from the support level $3.351E-5

Historic price levels for next 7 days

These levels are based on local historic minimums and maximums. Weight shows the power of broken trends.
Price Weight(level power) Date of formation

Volume profile price channel in 7 days range

Edge levels of the price channel based on volume profile report.
Price Description
3.351E-5 Minimal price support level based on 7 days movements

CNN token price levels prediction for next 30 days

Next 30 days brief prediction

Price of CNN has broken last trend and will RAISE from the support level $3.25E-5

Historic price levels for next 30 days

These levels are based on local historic minimums and maximums. Weight shows the power of broken trends.
Price Weight(level power) Date of formation

Volume profile price channel in 30 days range

Edge levels of the price channel based on volume profile report.
Price Description
3.25E-5 Minimal price support level based on 30 days movements
Highly correlated currencies
Coin Correlation Links
First tier
0.66569513 details, prediction
0.66471636 details, prediction
0.64514476 details, prediction
0.6043176 details, prediction
0.5596997 details, prediction
Second tier
0.8278412 details, prediction
0.81069934 details, prediction
0.7586733 details, prediction
0.7576395 details, prediction
0.7419088 details, prediction
Third tier
0.890429 details, prediction
0.87686086 details, prediction
0.8701851 details, prediction
0.82096624 details, prediction
0.8171658 details, prediction


This report should be leaned to get most info about heading trends. It means that CNN token price can be leaded by other, more mighty cryptocurrency, or maybe some market trend have affect on it.

It can be confidently asserted that any crypto currency has a significant dependence on the market as a whole and, in particular, the top 3 currencies - Bitcoin, Ethereum and Ripple, so the prediction of their prices will affect the price of CNN token
Low correlated currencies
Coin Correlation Links
First tier
-0.47830832 details, prediction
-0.25818607 details, prediction
-0.20668179 details, prediction
-0.15945093 details, prediction
0.013904221 details, prediction
Second tier
-0.37083712 details, prediction
-0.31557328 details, prediction
-0.22475518 details, prediction
-0.22188951 details, prediction
-0.14203618 details, prediction
Third tier
-0.6773653 details, prediction
-0.66059756 details, prediction
-0.64668185 details, prediction
-0.63810796 details, prediction
-0.63342494 details, prediction


This report should be leaned to build highly diversified portfolio.
It makes sense to mention that a low correlation in this case includes not only coins whose movements are not statistically related but, on the contrary, move in opposite directions (in the case of a negative value of the parameter)