My Hidden Markov Model labels the regime of the Solana market in 15 minute intervals.
8 regime labels:
• Bull / Super Low Vol
• Bull / Low Vol
• Bull / High Vol
• Bull / Super High Vol
• Bear / Super Low Vol
• Bear / Low Vol
• Bear / High Vol
• Bear / Super High Vol…
Solana’s daily moves look like a Weibull distribution. If that holds, we can put numbers on risk—pick any threshold (e.g., 10%) and get daily odds, hits/year, and average wait; estimate the “1% worst day” size; and the likely biggest day over 30/90/365 days.
Zoom out to 1 Yr and Active Addresses to Sol Price has a Pearson's Correlation of .608. The last 30 days it has been -.05. Price has disconnected from chain activity.
The Pearson correlation between Solana’s price and active addresses (excluding voting transactions) is -0.05. Something other than network use is driving the price.
The lifetime data for Bitcoin is clear. Bitcoin does not have a power law distribution. It has a lognormal distribution. This basic statistical insight will change your price outlook.
Bitcoin Top Model (Power-Law Decay)
Predicts ~1.26× above the 111d/250×2 MA crossover.
Current 350 x 2 crossover is $186K → top ≈ $235K.
and trending upward.
Rₙ = 1 + 7.38 · n⁻²·⁴³ , n = 4
R₄ ≈ 1.26
Ptop ≈ 1.26 × Crossover (>186K) ⇒ >$235K
3,865 10 to 1 long shots at GulfStream Park in the last 8 years. Bet them all and get a negative expected value of -.55 per $2 wager. Is there a subset of these horses that will deliver value?
Built a Python ticket selector for Pick and Pray horse competitions: Calculates number of tickets needed per contest based on number of participants and median payouts of similar competitions. (log(0.2)/log(1-p), p=0.15–0.4) Inspired by Hellmers & Sumja’s NHC 4th/12th in 540…
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