Speculation is a prominent influence on cryptocurrency prices. As seen with the collapse of Bitcoin’s price in 2018, following a bull run from around $1,000 to just shy of $20,000 in 2017, experienced and inexperienced traders alike can go long for too long – expecting the price to keep rising and fearing missing out on the party.
When Bitcoin’s price exploded in late 2017, so did the interest of the general public in cryptocurrencies. Internet searches for Bitcoin, cryptocurrencies, and crypto prices skyrocketed. Many people weren’t able to resist the fear of missing out on the massive gains the early adopters were experiencing as crypto prices rose quickly. When the inevitable correction came and crypto prices fell, those who bought towards the end of the bull run immediately saw the value of their holdings halved. Despair and anguish followed, but the enthusiasm of the crypto community hasn’t faltered.

At each point information from neighbouring data is used. As the time series considered are finite, the areas at the start and end of the data (especially at higher period bands) will not have all the data required. One solution to make computation possible, chosen here, is to pad the time series with zeros where required. However, the zero padding will impact the reliability of the results. It is standard to use a cone of influence to represent this difference in reliability of results. Pale colours represent those areas outside the cone of influence with less reliable results (as seen on Fig 3). Higher period bands require more data for computation resulting in the cone shape.
The global crypto market cap fell more than 2 per cent to $2.82 trillion mark compared to the last day. However, the total crypto market volume jumped over 31 per cent to $176.91 billion.