608 search results for "Trading"

January Update R Course Finder: Shiny, Quantitative Trading, and Much More

January 12, 2017
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January Update R Course Finder: Shiny, Quantitative Trading, and Much More

A few months ago we launched R Course Finder, an online directory that helps you to find the right R course quickly. With so many R courses available online, we thought it was a good idea to offer a tool that helps people to compare these courses, before they decide where to spend their valuable

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Trading Models and Distributed Lags

January 9, 2017
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Yesterday, I received an email from Robert Hillman.Robert wrote:"I’ve thoroughly enjoyed your recent posts and associated links on distributed lags. I’d like to throw in a slightly different perspective. To give you some brief background on myself: I did a PhD in econometrics 1993-1998 at Southampton University. ............ I now manage capital and am heavily influenced by...

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RTutor: CO2 Trading and Risk of Firm Relocation

November 3, 2016
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RTutor: CO2 Trading and Risk of Firm Relocation

Many economists would agree that the most efficient way to fight global warming would be a world-wide tax or an emmission trading system for greenhouse gases. Yet, if only a part of the world implements such a scheme, a reasonable concern is that firms...

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Financial Trading in R with Ilya Kipnis

October 19, 2016
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Financial Trading in R with Ilya Kipnis

Our new Financial Trading in R course is here! Learn from Ilya Kipnis, a professional quantitative analyst and co-author of Introduction to Quantitative Trading With R.  Master the basics of financial trading, and learn how to use quantstrat to build ...

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The Importance of Out-of-Sample Tests and Lags in Forecasts and Trading Algorithms

October 14, 2016
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The Importance of Out-of-Sample Tests and Lags in Forecasts and Trading Algorithms

I recently had the opportunity to listen to some great minds in the area of high-frequency data and trading. While I won’t go into the details about what has been said, I wanted to illustrate the importance of proper out-of-sample testing and proper variable lags in potential trade algorithms or arbitrage models that has been

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Trading strategy: Making the most of the out of sample data

August 19, 2016
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Trading strategy: Making the most of the out of sample data

When testing trading strategies a common approach is to divide the initial data set into in sample data: the part of the data designed to calibrate the model and out of sample data: the part of the data used to validate the calibration and ensure that the performance created in sample will be reflected in the

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Shorting at High: Algo Trading Strategy in R

August 11, 2016
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Shorting at High: Algo Trading Strategy in R

By Milind Paradkar Milind began his career in Gridstone Research, building earnings models and writing earnings notes for NYSE listed companies, covering Technology and REITs sectors. Milind has also worked at CRISIL and Deutsche Bank, where he was involved in modeling of Structured Finance deals covering Asset Backed Securities (ABS), and Collateralized Debt Obligations (CDOs)... The post Shorting...

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Quantitative Trading Strategy Using R: A Step by Step Guide

January 20, 2016
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Quantitative Trading Strategy Using R: A Step by Step Guide

In this post we will discuss about building a trading strategy using R. Before dwelling into the trading jargons using R let us spend some time understanding what R is. R is an open source. There are more than 4000 add on packages,18000 plus members of LinkedIn’s group and close to 80 R Meetup groups... The post

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Trading Autocorrelation?

November 15, 2015
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Trading Autocorrelation?

Markets are very smart in absorbing and reflecting information. If you think otherwise, try making money by trading. If you are new to it, make sure you don’t bet the house. In other words, markets are efficient. At least most of the time. So then why people trade? The general believe is that there are The post

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An example of a trading strategy coded in R

October 6, 2015
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An example of a trading strategy coded in R

Back-testing of a trading strategy can be implemented in four stages. Getting the historical data Formulate the trading strategy and specify the rules Execute the strategy on the historical data Evaluate performance metrics In this post, we will back-test our trading strategy in R. The quantmod package has made it really easy to pull historical... The post

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