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Introduction

This post explores a concept at the heart of quantitative financial research. Most qfin researchers utilize statistical techniques that require varying degrees of stationarity. As many of you are aware financial time series violate pretty much all the rules of stationarity and yet many researchers, including me, have applied or will apply techniques when not appropriate thereby calling into question many of the resulting conclusions.

In the new book Advances in Financial Machine Learning by Marcos Lopez De Prado he proposes that qfin researchers utilize a different type of price bar. His research has shown that by using alternatives to fixed time interval bars (minute, hour, day, week, etc.), the return series will exhibit better statistical properties. In other words using alternative bar types, the return series will better approximate normality/stationarity which will make our research and conclusions more robust.

In this post we will experiment with the following bar types: Tick, Volume, Dollar Volume, and Dollar Volume Imbalance.

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