Wanchain 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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Wanchain price levels prediction for next 7 days

Next 7 days brief prediction

Price of WAN is now on EDGE at $0.062459998. So the next scenario is able. If price will go up it will spend next in CHANNEL near to $0.0 with $0.062459998 as upper border and $0.062459998 as bottom. If price will go down, it will FALL with support level $0.062459998

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
0.062459998 Edge price dividing trends based on 7 days movements
0.062459998 Channel maximum price resist level based in case of RAISE trend on 7 days movements
0.062459998 Channel minimum price resist level based in case of RAISE trend on 7 days movements
0.062459998 Minimum price support level in case of FALL trend based on 7 days movements

Wanchain price levels prediction for next 30 days

Next 30 days brief prediction

Price of WAN has broken last trend and will FALL from the support level $0.06692

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
0.06692 Maximum price support level based on 30 days movements
Highly correlated currencies
Coin Correlation Links
First tier
0.7741506 details, prediction
0.763341 details, prediction
0.7505852 details, prediction
0.7465219 details, prediction
0.74105257 details, prediction
Second tier
0.9761371 details, prediction
0.9411534 details, prediction
0.9398446 details, prediction
0.9395165 details, prediction
0.9381695 details, prediction
Third tier
0.971667 details, prediction
0.9675527 details, prediction
0.9652391 details, prediction
0.96152174 details, prediction
0.9482445 details, prediction


This report should be leaned to get most info about heading trends. It means that Wanchain 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 Wanchain
Low correlated currencies
Coin Correlation Links
First tier
-0.17488332 details, prediction
0.14975141 details, prediction
0.39105758 details, prediction
0.47677353 details, prediction
0.58208716 details, prediction
Second tier
-0.20163609 details, prediction
-0.0035053687 details, prediction
0.21779798 details, prediction
0.5482449 details, prediction
0.58373797 details, prediction
Third tier
-0.47104257 details, prediction
-0.31104758 details, prediction
0.27558953 details, prediction
0.2801627 details, prediction
0.62051684 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)