/R-for-Quants

Primary LanguageR

Introduction to R for Quants

This repository serves as a basic introduction to R for quantitative analysts. From basic operations to advanced statistical techniques, this guide aims to provide a foundational understanding of R's capabilities in the quantitative field.

Table of Contents:

  1. Basics
  2. Statistics
  3. Time Series Analysis
  4. Regression
  5. Optimization
  6. Machine Learning

Basics:

  • Introduction to R: Understand the basic operations in R.
  • Data Structures: Dive deep into vectors, matrices, data frames, and lists.

Statistics:

  • Descriptive Statistics: Understand the basics of statistical measures.
  • Probability Distributions: Work with common probability distributions.
  • Hypothesis Testing: Learn the foundational techniques in hypothesis testing.

Time Series Analysis:

  • Time Series Basics: Introduction to time series data in R.
  • Time Series Models: Explore AR, MA, and ARIMA models.

Regression:

  • Linear Regression: Basics of implementing and interpreting simple linear regression.
  • Multiple Regression: Dive into the world of multiple regression techniques.

Optimization:

  • Portfolio Optimization: Basics of portfolio optimization techniques in R.

Machine Learning:

  • Machine Learning Basics: Introduction to basic ML techniques using R.

Enjoy your journey into the world of R for quants!