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Why naming a cryptocurrency ‘NAS’ instead of ‘XZC’ generates familiarity and boosts purchase interest in the first week after exchange listing

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Using Postman or Curl to download Bitcoin, Ethereum and all the data from CryptoDatum.io in json or csv format


In this article we will learn how to build volume and dollar bars and we will explore what advantages they offer in respect to traditional time-based candlesticks and tick-bars. Finally, we will analyze two of their statistical properties — autocorrelation and normality of returns — in a large dataset of 16 cryptocurrency trading pairs

Introduction

In a previous post we learned how to build tick bars and assessed their particular ability to self-regulate the sampling rate based on a higher or lower activity in the market. Similar to tick bars, volume and dollar bars also allow the synchronization of the sampling rate with the activity…


In this article we will learn how to build tick bars, we will thoroughly analyze their statistical properties such as normality of returns or autocorrelation and we will explore in which scenarios these bars can be a good substitute for traditional time-based candlesticks. In order to illustrate the applicability of tick bars in the forecasting of cryptocurrency markets, we will base our analysis on a whole dataset comprising 16 cryptocurrency trading pairs including the most popular cryptoassets such as Bitcoin, Ethereum or Litecoin

1. — Introduction

In a previous article we explored why traditional time-based candlesticks are not the most suitable price data format if we are planning to train a machine learning (ML) algorithm. Namely: (1) time-based candlesticks over-sample low activity periods and under-sample high activity periods, (2) markets are increasingly controlled by trading algorithms…

Gerard Martínez

Trading strategy developer at Primer Quant Firm — Founder of CryptoDatum.io

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