Rose premium signals 🌹

We just witnessed one of largest liquidation cascades 2 days ago. That's called  a big move. Normally after big moves market might continue moving in chop zone before continuing next big moves. Data says 30 days in chopzone ahead

BTC is no longer young and the movement is no longer wild. In my view, SOL in this cycle is moving like BTC, ETH when they were young.

40% from last ATH 70k is the result of Nonlinear regression model. It's also the realistic target. At this rate, retail miners could not join the game of mining in this cycle.

40% from last ATH 70k is the result of Nonlinear regression model. It's also the realistic target. At this rate, retail miners could not join the game of mining in this cycle.

To identify a function that describes the pattern of percentage increases from the last all-time high (ATH) across cycles, we can analyze the given data points: 1. First cycle: up 3500% from last ATH 2. Second cycle: up 1700% from last ATH 3. Third cycle: up 240% from last ATH Let's denote these percentages as \( P_1, P_2, P_3 \) respectively: - \( P_1 = 3500 \) - \( P_2 = 1700 \) - \( P_3 = 240 \) If we aim to find a function \( f(n) \) where \( n \) is the cycle number, we might consider an exponential decay function of the form: \[ P_n = a \cdot b^n \] where \( a \) is the initial value, and \( b \) is the decay factor. We need to find \( a \) and \( b \) using the given data points. Let's set up the equations using the given points: 1. \( P_1 = a \cdot b^1 = 3500 \) 2. \( P_2 = a \cdot b^2 = 1700 \) 3. \( P_3 = a \cdot b^3 = 240 \) Using these equations, we can solve for \( a \) and \( b \). First, divide the second equation by the first: \[ \frac{P_2}{P_1} = \frac{a \cdot b^2}{a \cdot b^1} = b \] \[ \frac{1700}{3500} = b \] \[ b \approx 0.4857 \] Next, use \( b \approx 0.4857 \) in the first equation to find \( a \): \[ 3500 = a \cdot 0.4857^1 \] \[ a \approx 3500 / 0.4857 \] \[ a \approx 7203.61 \] Now, let's verify this with the third equation: \[ P_3 = a \cdot b^3 \] \[ 240 \approx 7203.61 \cdot 0.4857^3 \] \[ 240 \approx 7203.61 \cdot 0.1143 \] \[ 240 \approx 822.99 \] The approximation is not exact, so the exponential model might not perfectly fit. Another potential model could be a polynomial regression. However, for simplicity, let's use the exponential decay model to predict the percentage for the fourth cycle: \[ P_4 = 7203.61 \cdot 0.4857^4 \] \[ P_4 \approx 7203.61 \cdot 0.0555 \] \[ P_4 \approx 400 \] Therefore, the percentage increase for the fourth cycle, based on the exponential decay model, would be approximately 40%.

up around 40-50% from last ATH. are you disappointed ?

around 100k-110k Then It will be over

First cycle: up 3500% from last ATH Second cycle: up 1700% from last ATH Third cycle: Up 240% from last ATH Anyone are good at Math ? Can you use these numbers to point out an function then we can point out the percentage of this cycle ?

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