Ripple up more than 100% since a spike in social media mentions

BittsAnalytics platform offers real-time social media analytics for cryptocurrencies – determining number of social media mentions as well as sentiment on an hourly basis. This allows assessing both the current buzz about the cryptocurrency as well as its sentiment (positive or negative). Sentiment is determined by applying machine learning models on texts of social media posts.

In this post we are focusing on Ripple which had a big spike in social media mentions a few days ago, see our tweet and chart from that time:

The ripple made a rally after that initial spike from 0.31 USD to 0.64 USD for a return of more than 100%. Real-time social media analytics tools are available as part of our advanced subscription, you can find more information at:

https://www.bittsanalytics.com/subscribe.php

For other recent examples of using real-time analytics read our recent blog posts:

Nano up more than 50% since surge in social media mentions on 11th September

RChain up more than 50% since surge in social media mentions on 9th September

Fun up more than 30% since big jump in social media mentions on 12th September

Ripple fell after encountering resistance Bittsband

In this post we would like to introduce the use of our recently introduced advanced feature, Bands or BittsBands, on a concrete example. We are using deep learning on vast sets of different data to assess where could be possible price bands of resistance for cryptocurrencies, see e.g. bands for Ripple below. The more red the particular band is, the higher is the probability that the price will rebound from it when falling towards the band or that it will fall, “repelled” by the band when increasing and encountering the band from below. If the band is particularly weak, i.e. almost white, then there is a higher probability of price rapidly moving through such price band.  Of course not every price movement will follow this pattern as often there are other drivers, such as a very positive news, where the price can rise or fall through such bands. But it can often work surprisingly well.

As an example of use, yesterday we mentioned in a tweet (see below) about Ripple that it would be interesting to see what happens when Ripple reached the rather strong resistance band from 1.05 USD to 1.2 USD:

twitter.com/bittsanalytics/status/970466555074174976

And after the tweet the price first rose to the band, reached around 1.08 and then indeed fell back to 0.96 USD at the time of writing this post.  See the updated chart below:

If you want to start using our BittsBands and many other advanced analytics you can by subscribing at www.bittsanalytics.com. For other examples of using BittsBands see our post about Particl:

Return on Particl 70% – example of using social media analytics combined with advanced technical analysis

or see our recent tweet about Ethereum Classic below where we analysed that due to weak resistance bands below 32 USD it can fall rapidly below 30 USD and this is indeed what happened, it is now around 25 USD.

Ripple sentiment dipped yesterday to negative for the first time in two weeks – since then down 15%

We track millions of social media posts and use machine learning to determine sentiment for 200+ of cryptocurrencies in real-time. Below is real-time sentiment of tweets mentioning Ripple over the last month.  The hourly sentiment of Ripple dipped to negative yesterday, 4th January 8pm UTC time for the first time in several weeks. As we have already posted in other such cases in the past this means a warning signal for the price and since then the price of Ripple has since then fallen indeed by 15%. Sentiment is often ahead of the price and you can read more examples in our blog posts.

We actually went bullish on account of real-time sentiment in mid December, you can read more about it here:

www.bittsanalytics.com/blog/2017/12/14/using-real-time-sentiment-on-an-hourly-basis-gave-a-bullish-signal-for-ripple-at-13th-4am-utc-and-since-then-the-return-is-over-120/

So it was quite a ride.

You can start using sentiment and tweets mentions data like this to improve your cryptocurrency trading by subscribing to our platform at www.bittsanalytics.com.

 

Using real-time sentiment on an hourly basis gave a bullish signal for Ripple at 13th 4am UTC and since then the return is over +120%

We track millions of social media posts and use machine learning to determine sentiment for hundreds of cryptocurrencies in real-time. Below is  sentiment of tweets for Ripple over the last few days but unlike in the previous post the sentiment below are based on hourly basis not daily. We wanted to show you how much more profitable is tracking real-time sentiment than daily sentiment. If you followed the real-time sentiment and entered position on spike at 13th December 4am UTC time the return since then is even higher over +120% in around a day. [Edit on 1.1.2018: since writing this post the rally of Ripple has continued and the return achieved since our trading call is now over 340%] Real-time sentiment is turning out as a truly outstanding trading signal. And the return is twice as high as when following daily sentiment (see our previous blog post about daily sentiment charts). You can see fascinating trading signals like this by subscribing to our platform www.bittsanalytics.com

Real-time sentiment again proven as a great trading signal – Ripple sentiment surged on 13th December and since then the price is up by 60%

We track millions of social media posts and use machine learning to determine sentiment for hundreds of cryptocurrencies in real-time. Below is daily sentiment of tweets for Ripple over the last few days.  The daily sentiment of Ripple surged on 13th December. As we have already posted in other such cases in the past this means a major bullish signal for the price and indeed the price of Ripple has since then rallied by more than 60% in just one day. Our sentiment has again proven correct – read more about many past great trading signals like this in our blog posts. And you can start using it by subscribing to our platform at www.bittsanalytics.com.