Knowing that the assumption of “normality” is weak, we should treat the properties of normal distribution the same way (i.e., skewed returns, fat tails). Numerous papers have been published with regards to the assumption of normality in financial assets time-series, yet, for the lack of a better choice, we use Gaussian (normal) distribution, as it allows us to analyze data pretty easily. The assumption of the normal distribution is, by far, the weakest assumption we can make when it comes to modeling the dynamics of financial assets. When we analyze data (especially time-series) we can easily fall into various traps when we don’t have a good understanding of statistics/probability (and statistical concepts). Statistical analysis is the foundation on which data science and quant trading are based. Based on my experience with quant trading there are four major traps when building a quantitative trading strategy :Īfter we acknowledge these traps let’s dive in to understand where we could fail… Understanding Statistics and Probability For the purpose of this article, we will focus on quant analysis and data science, as these are widely used by different types of traders (both on the institutional and the retail side). Quant trading covers a rather wide array of trading strategies (anything from big-data analysis to HFT market-making). The exponential growth in computation power, and the growing interest of the quantitative community (mostly Ph.D./MS from hard science departments) turned quant funds to the hottest area investors were flocking to. The rapid growth in quant/systematic funds came in the ’90s when funds like Millennium Partners, D.E Shaw, LTCM, and AQR (to name a few) raised substantial capital to trade systematic strategies. These funds utilized quantitative models to detect (and trade) opportunities in financial markets, using massive datasets and data science (or at least an early version of what we know today as Data Science). If we examine funds like Winton Capital, AHL(now Man AHL), Aspect Capital, and Renaissance Technologies, we can trace their roots back to the mid ’80s. The history of modern quantitative finance can be dated back to the early 1900s, with Bachelier’s option pricing model (which was later followed by the Black-Scholes option pricing model), but the real evolution of quantitative finance came in the mid ’80s when mathematicians and statisticians started developing quantitative models to predict (and trade) financial markets. Ever since I discovered the wonderland of the derivatives market I knew that my path in the world of trading was going to be the quantitative path (rather than the discretionary path). The ability to make sense of financial markets using data, math, and statistics is a mind-blowing idea in my opinion. The world of quantitative finance is a fascinating world.
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