What is the VORTECS score and how does it work?

What is Cointelegraph Markets Pro: The VORTECS™ Score

The cryptocurrency market is extremely sensitive to news and sentiment — the prevailing  attitude that investors have toward any given asset, which can be positive, negative, or neutral.

In fact, research has shown that the level of social media activity, coupled with the psychology of the crowd, is powerful enough to move crypto markets dramatically.

So imagine if you had access to a consolidated dashboard that measured these various social and sentiment indicators, combined them with actual market trading conditions, and then back tested those findings against thousands of previous situations in real-time… it would be a powerful tool in your arsenal as you researched potential investments or trades!And that’s exactly what we can share with you.

The Cointelegraph Markets Pro VORTECS™ score is at the heart of our cryptocurrency market intelligence dashboard.

The score is a comparison between current market conditions, and conditions that we have seen in the past.

A higher score means that current market conditions are bullish; a lower score is bearish.

The VORTECS™ Score is derived from a number of different indicators, each of which have multiple contributing factors and it stands for:

Volume, Outlook, RealPrice, Tweet Volume, Elevation, Confidence, Sentiment

Volume:

A measure of how much of an asset is traded across supported exchanges.

Outlook:

A measure of whether the current market conditions are favorable or not, compared with historically-similar conditions.

RealPrice by The TIE:

A composite price derived from an average of prices across relevant exchanges.

Tweet Volume:

A measure of the absolute and relative number of tweets about an asset over the past 24 hours.

Elevation:

A measure of how far up or down an asset’s price moved following historically-similar market conditions to those observed currently.

Confidence:

The degree to which current conditions are similar to historic conditions, with higher confidence also including the consistency of asset price moves following those conditions.

Sentiment:

The positivity or negativity of the chatter on Twitter surrounding the crypto asset, derived from a complex proprietary algorithm developed by The TIE.

The VORTECS™ algorithm compares these factors for supported assets to other points in time, looking for familiar patterns. 

If it finds a pattern, the VORTECS™ algorithm analyzes the direction and magnitude of any price movements following that moment in time, to see if the asset consistently rises or falls after the pattern emerges.

The VORTECS™ Score is the result of this analysis. It combines all of these factors, and also represents the algorithm’s CONFIDENCE in the patterns and trends it has found, as well as the OUTLOOK and ELEVATION of price movements that are historically associated with that pattern.How does it do this?

IT’S INSPIRED BY THE WEATHER!

Imagine it is raining in Burlington, about 170 miles east of Denver in Colorado.From that information, can you say with any certainty that it’s going to rain in Denver in the next few hours?

Of course not. Okay, so let’s add some information. It’s raining in Burlington, and the wind is blowing from the east. Enough to create a model?

Well, perhaps a very rudimentary one that’s more accurate than pure guesswork.

More information: the Rocky Mountains sit just to the west of Denver, and as air moves over mountains it cools. This is one factor in creating more humidity, and the higher the barrier, the more the air cools… which can create clouds. 73% of the time, this results in increased precipitation...

You can see what’s going on here. As the weather model is fed more information, it becomes more accurate.

But it’s not a static model: At regular intervals it compares the current conditions to historic conditions that look similar. If the current wind conditions, humidity, temperature and so on usually result in the rain moving east, the model can infer that there is a strong likelihood of Denver catching a storm.

This type of analysis is called Dynamic Modeling, and it’s exactly what we use to create the VORTECS™ Score that you see on Cointelegraph Markets Pro.It’s also a LEARNING algorithm — every time it goes back to check for patterns, it has more patterns to examine. So it becomes more accurate over time.

For more information on the model you can read the whitepaper on the VORTECS™ Score here.

USING THE SCORE

Let’s get one thing straight. We don’t offer financial advice, and nor does an algorithm.

The score is not a prediction of how an asset price WILL change over time, but an analysis of how asset prices HAVE changed over time when faced with similar market conditions.

So our score, while it is weighted to take account of the size of asset price change in the past, will not tell you HOW MUCH an asset may change.

It will also not tell you WHEN it will change — in fact, the algorithm is deliberately fuzzy on time, meaning that it is normalized and smoothed to ensure that abrupt outliers (such as a sudden viral tweet) don’t abnormally affect the overall trend. While the algorithm is generally oriented to a 12-72 hour timeframe, our backtesting revealed that efforts to “time the market” precisely introduced an element that was not supported by historical data.And just like the weather person on TV, it can’t tell you exactly WHAT is going to happen even when the algorithm has seen the same thing happen hundreds, or thousands of times.It is, after all, a model. It’s not a crystal ball.

So what does the score generally indicate? 

Three things. Confidence, outlook, and elevation.

It is a measure of the algorithm’s confidence that an asset’s current market conditions are similar enough to a representative set of historical precedents that it can see a distinct pattern.

It is also a measure of the outlook for asset prices based on these conditions, whether positive or negative.

And finally, it is a measure of the amplitude or elevation of change noted in those historical patterns, so that larger movements are reflected by higher or lower scores.

Examples of scores

Let’s take a look at an example score of 85.

This is a high score which means that there is some combination of these factors:

  • The algorithm has found market conditions in the past that look similar to current market conditions
  • Those historical conditions often led to an INCREASE in asset price over the next 12-72 hours, approximately
  • The price changes in the past have been significant
  • The algorithm maintains a high level of confidence that the set of conditions it’s looking at are similar enough to suggest that the overall direction of this asset’s price is currently bullish, or positive.

Now let’s look at a score of 55.

This is a fairly neutral score, which means that there is some combination of the following factors:

  • The algorithm has not found market conditions in the past that look similar to current market conditions
  • Even if it has found similar conditions, those conditions did not lead to a consistent price move either positively or negatively over the next 12-72 hours
  • Price changes either positively or negatively have not been significant in the past
  • The algorithm is not confident that any price momentum it may have found is sufficiently consistent among backtests.

Finally, let’s examine a score of 30.

This is a low score, which means that there is some combination of the following factors:

  • The algorithm has found historical market conditions that look similar to those that exist right now
  • Those historical conditions often led to an DECREASE in asset price over the next 12-72 hours, approximately
  • Price changes in the past have been significant
  • The algorithm is confident that price momentum is somewhat bearish, or negative when compared to similar market conditions in the past.

It can be inferred that a score of 50 is therefore easily discounted — the data isn’t showing anything particularly useful, or at least not in a way that inspires confidence.

And it can also be inferred that the further away from 50 the score gets, either positively or negatively, the more confidence the algorithm has in its assessment.

In fact, as we will explain in a deeper whitepaper, there is a direct correlation between high scores and positive price changes for a given asset, and low scores and negative price changes.




The Cointelegraph Markets Pro algorithm is in a state of almost constant evolution, improving and learning both from its own experiences and from our data scientists’ ongoing refinements and additions to the model. 

It’s also a modular algorithm, meaning that we can add additional variables as we test them offline and measure their efficacy — for instance, we have already extensively tested GitHub activity as a potential addition to the score, but have yet to find positive correlations that would affect the score’s accuracy.

It should be noted that the model has more reference points for long-term assets than for recently-launched projects — it has most likely seen the current market conditions for Bitcoin more often than it has for, say, SushiSwap, in general.

We anticipate that there will be dozens, hundreds, maybe thousands of ways that Cointelegraph Markets Pro users will interpret these scores and put them to use in their own trading strategies — and we’d love to read these discussions on Discord!

For more information on the model you can read the whitepaper on the VORTECS™ Score here.


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