By now you should be familiar with most of the core functionality of the R programming language. We can perform simulations, graph results, create summary statistics, export our results to files and accomplish almost any programming feat by simply using the base installation of R. In this lecture I want to introduce a few tools […]

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## Lecture 6 – Stochastic Processes and Monte Carlo

The use of probability and statistics is ubiquitous in quantitative finance. All of the observable prices, volumes, order arrival rates, etc, are due to supply and demand imbalances. However, keeping track of all the supply and demand imbalances becomes cumbersome as the number of variables increases. Statistical tools are vital in explaining and modeling these […]

## Lecture 5 – Applied Statistical Concepts

In this lecture we will discuss statistical estimators, investigate the law of large numbers, the central limit theorem and look at implementing all these concepts within R. Population vs. Sample Statistics Consider the set of numbers: 102, 103.2, 102, 101.2, 499, 103.2 101.23, 99.2. Here are some questions we might want to ask about these […]

## Lecture 4 – Regression and Pairs Trading

Regression Analysis Regression is a very important topic. It is a widely used statistical tool in economics, ﬁnance and trading. R provides pre-written functions that perform linear regressions in a very straightforward manner. There exist multiple add-on packages that allow for more advanced functionality. In this class, we will only utilize the lm() function which […]

## Lecture 3 – Matrices and Libraries

Matrices in R A matrix is a very useful mathematical construct. Matrices provide a mechanism for easily manipulating large collections of data. Matrix Mathematics is a vast topic and there exist numerous papers and publications that talk about all the possible uses of matrices. Suffice it to say that this class is only going to […]

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- Great online courses for learning R
- Lecture 7 – Visualization and Reporting with R
- Lecture 6 – Stochastic Processes and Monte Carlo
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- Lecture 5 – Applied Statistical Concepts
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- A tutorial on Rcpp
- Lecture 3 – Matrices and Libraries
- Lecture 2 – Functions in R
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