R is one of the best choices when it comes to *quantitative finance*. Here we will show you how to load financial data, plot *charts* and give you a step-by-step template to *backtest trading strategies*. So, read on…

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## The Rich didn’t earn their Wealth, they just got Lucky

Tomorrow, on the *First of May*, many countries celebrate the so called *International Workers’ Day* (or *Labour Day*): time to talk about the *unequal distribution of wealth* again, so read on!

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## Google’s Eigenvector… or: How a Random Surfer Finds the Most Relevant Webpages

Like most people, you will have used a search engine lately, like *Google*. But have you ever thought about how it manages to give you the most fitting results? How does it order the results so that the best are on top? Read on to find out!

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## Base Rate Fallacy – or why No One is justified to believe that Jesus rose

In this post we are talking about one of the most unintuitive results in statistics: the so called *false positive paradox* which is an example of the so called *base rate fallacy*. It describes a situation where a positive test result of a very sensitive medical test shows that you have the respective disease… yet you are most probably healthy!

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## Separating the Signal from the Noise: Robust Statistics for Pedestrians

One of the problems of navigating an autonomous car through a city is to extract *robust signals* in the face of all the *noise* that is present in the different sensors. Just taking something like an arithmetic mean of all the data points could possibly end in a catastrophe: if a part of a wall looks similar to the street and the algorithm calculates an average trajectory of the two this would end in leaving the road and possibly crashing into pedestrians. So we need some robust algorithm to get rid of the noise. The area of statistics that especially deals with such problems is called *robust statistics* and the methods used therein *robust estimation*.

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## Symbolic Regression, Genetic Programming… or if Kepler had R

A few weeks ago we published a post about using the power of the *evolutionary method* for *optimization* (see Evolution works!). In this post we will go a step further, so read on…

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## Inverse Statistics – and how to create Gain-Loss Asymmetry plots in R

Asset returns have certain statistical properties, also called *stylized facts*. Important ones are:

**Absence of autocorrelation**: basically the direction of the return of one day doesn’t tell you anything useful about the direction of the next day.**Fat tails**: returns are not normal, i.e. there are many more extreme events than there would be if returns were normal.**Volatility clustering**: basically financial markets exhibit high-volatility and low-volatility regimes.**Leverage effect**: high-volatility regimes tend to coincide with falling prices and vice versa.

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## Learning Data Science: Predicting Income Brackets

As promised in the post Learning Data Science: Modelling Basics we will now go a step further and try to predict income brackets with real world data and different modelling approaches. We will learn a thing or two along the way, e.g. about the so-called *Accuracy-Interpretability Trade-Off*, so read on…

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## Learning R: The Collatz Conjecture

In this post we will see that a little bit of simple R code can go a very long way! So let’s get started!

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## Evolution works!

Hamlet:

Do you see yonder cloud that’s almost in shape of a camel?

Polonius:By the mass, and ’tis like a camel, indeed.

Hamlet:Methinks it is like a weasel.

fromHamletbyWilliam Shakespeare