Brilliant strategy for bitcoin trading. As I tried to apply various strategies common to commodities and equity trading, I never found the secret sauce that described what’s happening in bitcoin movement.
I played with the well-known technical trading concepts, stochastics, relative strength index, various momentum algorithms, the normal moving average concepts, Bolinger bands, Gann and Fibonacci, candlesticks, FOBO analysis, trends, support and resistance, and lots of stuff I made up but never named. We call that “proprietary” when we don’t know what else to call it, because it sounds important.
The obvious explanation to me for the untradable nature of bitcoin was that “demand” of an unneeded commodity is sketchy, celebrity endorsements matter, and laws related to cryptocurrency use due to crime and money laundering evolve and differ from country to country. Not less important, by Satoshi’s very definition of crypto, Proof of Work and the like, it blows up the KYC (Know Your Customer) laws required for high value purchases and trades.
Silver, corn, yen, pork bellies, Brent sweet crude, S&P, coffee – these make sense! 70% of the time, anyway. As my mentor Joe Duffy said, “sometimes elephants walk on walls,” though.
And NOW I find out that Jurrien figured it out!
Oh boy. I still got it done, but it was more a “timing the market“ strategy — which is an absolute no-no!
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Jurrien Timmer
About four years ago when I first went down the Bitcoin rabbit hole, I developed several demand models based on historical S-curves, as well as a supply model based on a modified version of Plan B’s S2F model. They are shown below.
I developed these models as a guide to help me understand this unique asset, and my conclusion was that while the supply is known, the demand is not. The slope of the adoption curve determines the size of the network, which per Metcalfe’s Law determines the value. Even the slightest change in slope can have a profound impact on valuation, given that we are dealing with exponential math.
Of all the S-curves that are historically available, I settled on mobile phones and internet adoption as the most relevant. The latter is shown in the adoption curve chart below. As you can see, we are at the point where that curve’s exponential slope is becoming less exponential (more asymptotic). By this measure, Bitcoin is maturing.
Based on that S-curve, I built a regression model that added a layer of monetary policy outcomes (from -2% real rates to +2.5% real rates). The valuation model chart below shows this hypothetical valuation framework. Again, this is not meant as a price prediction, but as a guide to understanding Bitcoin’s use case as a network asset that also has monetary features.
