Introducing a robust non-parametric estimate for the likelihood of a return given the previous halving cycles closes, in a suspected cyclic market.

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THIS IS NOT FINANCIAL ADVICE AND I AM NOT A LICENSED FINANCIAL ADVISOR THIS IS FOR ENTERTAINMENT PURPOSES ONLY.

WARNING: This is not financial advice. Trading can get you REKT very quickly. Only do it if you know what you are doing and have fully comprehended all of the tax and legal obligations and sought the advice of a licensed financial advisor.

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Abstract

In this article we make the case that there is a pattern to Bitcoin bull and bear markets that could be described as cycles. We employ fourier analysis to assist in the identification of and provide experimental evidence of the suspected cycles that have been hard coded into the Bitcoin code. We then utilise the prior cycles data to estimate a robust non parametric probability of the daily log returns for the current cycle. …


Does Tether Printing Cause Bitcoin Price Movement?

Introduction

It has been speculated that Bitcoin price is manipulated by the altcoin “US Tether” printing itself into existence. This analysis attempts to test that hypothesis.

H0: Bitcoin price is unaffected by tether supply

Ha: Bitcoin price is NOT unaffected by tether supply.

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Tests for reasoning

In the Appendix below, I will test H0 by performing a Granger causality test. A variable x is said to Granger-cause a variable y if, given the past values of y, past values of x are useful for predicting y. A common method for testing Granger causality is to regress y on its own lagged values and on lagged values of x and test the null hypothesis that the estimated coefficients on the lagged values of x are jointly zero. …


Is Bitcoin an “Uncorrelated Asset?”

THIS IS NOT FINANCIAL ADVICE AND I AM NOT A LICENSED FINANCIAL ADVISOR THIS IS FOR ENTERTAINMENT PURPOSES ONLY.

WARNING: This is not financial advice. Trading can get you REKT very quickly. Only do it if you know what you are doing and have fully comprehended all of the tax and legal obligations and sought the advice of a licensed financial advisor.

Please, if you enjoy my work don’t forget to smash down the clap button!

Bitcoin is a scarce digital asset. There are only so many to go around (<21million). There has never been anything like it in existence. The closest approximation is perhaps gold, however, Bitcoin has gold overpowered on a number of its most basic properties — it’s digital, weightless, cheap to transact with, has a predetermined inflation rate that cannot be interfered with by government, has known maximum supply and is simple and inexpensive for any owner to verify. …


Using Monte-Carlo simulation and Geometric Brownian Motion

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Motivation

In a discussion panel [1], economics professor and time series guru Sebastian Kripfganz showed that the there was potentially some positive drift to the Bitcoin price (and that it could explain for the most part what people attributed various other models to[4 & 5]). The method presented here utilises that drift (and the volatility noted in my earlier articles: [2 & 3])

WARNING: This is not financial advice. Trading can get you REKT very quickly. Only do it if you know what you are doing and have fully comprehended all of the tax and legal obligations and sought the advice of a licensed financial advisor.


using mean hash rate as a predictor variable

WARNING: This is not financial advice. Trading can get you REKT very quickly. Only do it if you know what you are doing and have fully comprehended all of the tax and legal obligations and sought the advice of a licensed financial advisor.

Background

NB this is super simplified — for more detailed/correct explanation, see www.bitcoin.org. At the heart of Bitcoin is a data-structure called a “block”. A block holds a list of transactions. Blocks are generated by a process called “mining”. Mining is the process of trying to guess some of the parameters that instantiated the last block (such as parts of a timestamp). The guess has to be hashed to match the block parameters. The faster a miner can hash (aka hash rate), the more guesses they can make, and thus the more likely they are to find the next block, at which point the miner is rewarded with some Bitcoin. …


A probabilistic approach to Bitcoin trading (cont.)

In Part I, I estimated the daily percent returns of Bitcoin as Cauchy with scale 0.016 and location 0.0017. This enabled us to calculate the probability of a given daily event of profit or loss.

In this part of the series, I extend this approach to calculating the likelihood of a series of profits or loss events of a given size.

WARNING: This is not financial advice. Trading can get you REKT very quickly. …


A probabilistic approach to Bitcoin trading

SUMMARY

I present a distribution that can be used to model the percent daily returns of Bitcoin. Using this model, I identify that Bitcoin has a mild net positive return and give a formula to estimate the probability of return events. This model can be used to identify and quantify the risk thereof the high risk days (i.e. high return days) and good buying opportunities (upon a lower daily return).

WARNING: This is not financial advice. Trading can get you REKT very quickly. Only do it if you know what you are doing and have fully comprehended all of the tax and legal obligations and sought the advice of a licensed financial advisor.


A brief look into the new model and the prediction intervals it may bring

WARNING: This is not financial advice. Entertainment purposes only.

In this article I will explore the regression identified by PlanB in his latest article. In particular, I will identify the prediction interval for the next halving cycle.

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In PlanB’s article, he identifies four transitionary phases that hold in congruence to the different stock to flow ratios. The linear regression through the centre of these phases produces a line that intercepts both the gold and silver market caps providing some confidence that the asset is exiting an experimental phase and making a real economic impact.

As I am quite skeptical of these models, I will first reproduce the model using my own data driven technique (k means clustering), thus not relying on any assumptions from…


Challenging the popular understanding

Abstract

This article attempts to completely invalidate the statistics behind the idea that there is any stock-to-flow relationship to Bitcoin price. The proposed Ordinary Least Squares (OLS) model is shown to be seriously deficient due to the serial correlation in the residuals. The Engle-Granger method is shown to be not usable as a test for cointegration due to requirements for the test not being met. The Johansen method is similarly invalidated. Finally, an Auto Regressive Distributed Lag model (ARDL) is built. The ARDL model does not falsify the stock to flow relationship. …


No dog, no road — just drunk

ABSTRACT

This article explores if there is a relationship of time to Bitcoin price. The proposed log-log model [1, 2 & 3] is tested for statistical validity against the least squares assumptions, for stationarity in each variable and for potential spurious relationships using the Engle-Granger approach for cointegration. All but one of these tests are able to reject the hypothesis that time is an important predictor for Bitcoin price.

A log price ~ log time (aka log growth) model has been proposed by various source [1, 2 & 3] as explaining a large proportion of bitcoin price movements and consequently has been put forward as a mechanism to estimate future bitcoin prices. …

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