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## Building Blocks of Skill Sets for Financial Quants.

# Welcome to Quant Lego !

## The ultimate place for building necessary skill sets !

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## A Starting Tutorial for R Beginners

## Download financial data using R's quantmod package

## R in Time Series: Trend and Seasonal Variation

## Introduction to Autoregressive models

## Special and missing values in R: NA, NaN, NULL, Inf

## R in Time Series: Holt-Winters Smoothing and Forecast

## Import data from SQL server into R using RJDBC package

## Quick Start with R Programming

## Introduction to R Language

## R in Time Series: Linear Regression With Harmonic Seasonality

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7ads6x98yMath • Coding • Market

- Introduction to R Language

Views (536) | Comments (0) | Dec 01, 2014

- Economics Basics: A Tutorial

Views (66) | Comments (0) | Nov 11, 2013

- Introduction to Autoregressive models

Views (58) | Comments (0) | Nov 24, 2014

- A Latex Template for Technical Article

Views (37) | Comments (0) | Nov 07, 2013

- Import Data from SQL Server to Matlab Using JDBC Driver

Views (35) | Comments (0) | Nov 04, 2013

- Import Data From SQL Server Into R Using RODBC Package

Views (34) | Comments (1) | Nov 01, 2013

- Eclipse as IDE for R Programming

Views (34) | Comments (0) | Oct 30, 2013

- Stochastic Calculus Key Notes - General Probability Theory

Views (33) | Comments (0) | Nov 13, 2014

- Introduction to Dates and Times in R

Views (31) | Comments (0) | Nov 08, 2014

- Import data from SQL server into R using RJDBC package

Views (26) | Comments (0) | Nov 02, 2013

As a starting tutorial for R beginners, this article lists some basic and commonly used R commands.

This tutorial gives a short intruduction about how to use R's Quantmod package to retrieve financial time series data from internet. 1 Overview of quantmod package The quantmod package for R is designed to assist the quantitative trader in the development, testing, and deployment of statistically based trading models. It…

This tutorial talks about one aspect of R in time series: trend and seasonal variation. Trend refers to a systematic change in a time series that does not appear to be periodic. Seasonal variation refers to a repeating pattern within each year in a time series.

This tutorial presents introduction of autoregressive models, and theoir implementation in R. 1 AR(1) model 1.1 Model definition [r_t=phi_0+phi_1r_{t-1}+a_t] where ({a_t},) is a white noise series of mean zero and variance (sigma_a^2). Notes: AR(1) model is widely used not only for returns, as shown with (r_t,) here, but also for volatility…

This tutorial briefly discuss handling of missing values in R, including NA, NaN, and NULL. 1 NA vs NaN vs NULL vs Inf In statistical data sets, we often encounter missing data, which are represented with NA in R. The motivation of NA, meaning 'Not Available', is to handle the case…

This tutorial tells about how to do Holt-WInters smoothing and forecast in R. 1 Basics of Holt-Winters method 1.1 Additive model where , , and are the estimated level, slope, and seasonal effect at time t, and , , and are the smoothing parameters.

This tutorial talks about how to import data from SQL server into R using RJDBC package. About the package RJDBC is a package implementing DBI in R on the basis of JDBC. This allows the use of any DBMS in R through the JDBC interface. The only requirement is working…

This tutorial helps you quickly learn how to do R programming. 1 Must-know tips for learning R 1.1 Get helps in R To get help on a function or a dataset: ?function_name <==> help(function_name) For example, ?mean is equivalent to help(mean) To find functions using a keyword ??keyword <==> help.search(keyword)…

This tutorial presents an introduction to R programming. 1 Preliminaries R language is case sensitive R names consist of alphanumeric symbols, plus '.' and '_'， with a restriction that a name must start with '.' or a letter. Two kinds of basic R commands: (1) expressions (evalue, then print, then…

This tutorial talks about linear regression with harmonic seasonality. 1 Underlying mathematics In regression modeling with seasonality, we can use one parameter for each season. For instance, 12 parameters for 12 months in one year. However, seasonal effects often vary smoothly over the seasons, so that it may be more parameter-efficient…