Complexity Economics

How Complexity Anticipates the Crash of Cryptocurrencies

In the past few days we have witnessed the collapse of the Luna cryptocurrency and its associated TerraUSD (UST) stablecoin, burning billions of dollars. The chart below shows the evolution of the price of Luna. Funny that a stablecoin – designed to provide stability – has almost suddenly lost 100% of its value.

Image source.

We don’t normally monitor cryptocurrencies but, given the above situation, we have performed a quick assessment of how our QCM2 technology could have delivered early-warnings as to the crash had we been monitoring and trading Luna and other cryptocurrencies. We took the last 100 days of data, looking only at the closing price. Different analysis window sizes have been considered.

Window of 16 days.

Window of 48 days.

In both cases, the sudden peak in the C3 indicator points to an imminent crisis or disruption. In the case of the 16-day window, the early warning is of approximately 36 days. In the case of the 48-day window, the pre-alarm sounds 8 days before the crash. In reality, we always use two different window size and run two or more analyses in parallel. This way we obtain confirmation of the single alarms.

In the case of Bitcoin, the situation is as follows:

Window of 32 days.

Window of 40 days.

The early warnings before the crash are of 10 days in both cases, including an even earlier warning between 5 to 10 days.

Established originally in 2005 in the USA, Ontonix is a technology company headquartered in Como, Italy. The unusual technology and solutions developed by Ontonix focus on countering what most threatens safety, advanced products, critical infrastructures, or IT network security - the rapid growth of complexity. In 2007 the company received recognition by being selected as Gartner's Cool Vendor. What makes Ontonix different from all those companies and research centers who claim to manage complexity is that we have a complexity metric. This means that we MEASURE complexity. We detect anomalies in complex defense systems without using Machine Learning for one very good reason: our clients don’t have the luxury of multiple examples of failures necessary to teach software to recognize them. We identify anomalies without having seen them before. Sometimes, you must get it right the first and only time!

1 comment on “How Complexity Anticipates the Crash of Cryptocurrencies

  1. Reblogged this on muunyayo .

    Like

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