Do not believe people who claim Bitcoin is worse then the tulip bubble. Why? Simply, because no one alive today experienced the tulip bubble, and the records from that time period are scarce. Plus, Bitcoin has more utility then tulips. Now, I am not claiming whether or not Bitcoin is a bubble: I am just pointing out the obvious that it makes zero sense to make precise comparisons of Bitcoin to a phenomena that nobody alive today has experienced. The next time someone states that Bitcoin is worse then the tulip bubble: ask them what they know about the tulip bubble. Where did they get their data? What sort of charts or data are they referring too? If they have did even 5 minutes of research then that would be surprising.
The reality is people state such exaggerations to emphasize how they feel. But, do not believe for one moment they did any real research.
Speaking of feelings, I do not know whether or not Bitcoin will survive. However, I strongly suspect it will until such time as it is not able too due to technological innovation or change. I suspect that gives it a lifespan of around 20 to 30 years (but that doesn’t mean it is destined to die at the end of such period because it can be upgraded or improved) What is less certain is what price it will be. Right now, my feeling is that it is likely to remain between $8,000 and $35,000 over the next ~6 months. Something of note is that Bitcoin only has to avoid cratering to win. It doesn’t have to really do anything except avoid a massive confidence killing crash, and it has even rebounded from several of those. The longer it survives then the more data that is built that it provides evidence it can be used as a value store. Ideally, at least for long term holders, the volatility should decrease as well.
Personally, like most others, I am not convinced it should have obtained value: but it did. And, now that it has value, it appears unlikely to lose that value.
Curtis is passionate about markets. He has developed top ranked futures strategies. His core focus is (1) applying machine learning and developing systematic strategies, and (2) solving the toughest problems of discretionary trading by applying quantitative tools, machine learning, and performance discipline.
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