Timely Portfolio has been doing some interesting work with Ralph Vince’s Leverage Space Model via the LSPM R package. Here’s a short list of his most recent LSPM-related posts:
The Leverage Space Trading Model Bond Market as a Casino Game Part 1 Bond Market as a Casino Game Part 2 Slightly Different Use of Ralph Vince’s Leverage Space Trading Model Another Use of LSPM in Tactical Portfolio Allocation I encourage those of you who are interested in LSPM and/or R to check out his blog.
PRESS RELEASE
The Leverage Space Portfolio (LSP) strategy seeks to maximize the probability of equity portfolio profitability by employing a risk-control process focused on capital preservation and drawdown management. Compared to a traditional buy-and-hold portfolio, an LSP-based portfolio aims for more consistent returns with lower risk.
The indexes, scheduled to be launched in the second half of 2011, can serve as the basis of both passive and active investment funds, including exchange-traded funds, mutual funds, and institutional accounts, around the world.
This is the third post in the Backtesting in Excel and R series and it will show how to backtest a simple strategy in R. It will follow the 4 steps Damian outlined in his post on how to backtest a simple strategy in Excel.
Step 1: Get the data
The getSymbols function in quantmod makes this step easy if you can use daily data from Yahoo Finance. There are also “methods” (not in the strict sense) to pull data from other sources (FRED, Google, Oanda, R save files, databases, etc.
(This is a guest post by Damian from Skill Analytics and ETF Prophet)
Let me start by saying that I’m not an expert in backtesting in Excel – there are a load of very smart bloggers out there that have, as I would say, “mad skillz” at working with Excel including (but not limited to) Michael Stokes over at marketsci.com, Jeff Pietch over at etfprophet.com and the folks (David and Corey) over at cssanalytics.
The registration for R/Finance 2011–which will take place April 29 and 30 in Chicago–is NOW OPEN!
Building on the success of the two previous conferences in 2009 and 2010, we are expecting more than 250 attendees from around the world representing both industry and academia to join a record 30+ presentations covering all areas of finance with R.
This year we are excited to have longer tutorial sessions and an optional full-day workshop on the Thursday before the conference.
This first post of the Backtesting in Excel and R series will provide some resources to help smooth the transition from the familiarity and comfort of Excel to the potentially strange and intimidating world of R.
I made my voyage from Excel to R more than 5 years ago and learned mostly by trial and error (and reading the R manuals). Most people don’t prefer my approach of “keep at it until you figure it out”, so I don’t have a lot of personal advice to share.
This post is the introduction to a series that will illustrate how to backtest the same strategy in Excel and R. The impetus for this series started with this tweet by Jared Woodard at Condor Options. After Soren Macbeth introduced us, Jared suggested backtesting a simple DVI strategy in Excel and R.
The three-post series will show you:
Resources that make it easier to move from Excel to R How to test DVI in Excel How to test DVI in R Since I know next to nothing about testing strategies in Excel, I will be writing posts 1 and 3.
The 2011 R/Finance conference has an updated call for papers. Dirk Eddelbuettel announced it to the R-SIG-Finance mailing list. I’ve reproduced his email in its entirety below. Let me know if you plan on attending.
Subject: R/Finance 2011: Call for Papers: Now with prizes and travel money
Dear R / Finance community,
The preparations for R/Finance 2011 are progressing, and due to favourable responses from the different sponsors we contacted, we are now able to offer
I use R very frequently and take for granted much that it has to offer. I forget how R is different from similar tools, so I have trouble communicating the benefits of using R. The goal of this post is to highlight R’s main strengths, but first… my story.
How I got started with R
I was introduced to R while I was working as a Research Analyst at the Federal Reserve Bank of St.
Before you start, note that there is now a Windows binary of RQuantLib is available on CRAN.
Due to a change in how R-2.12.0 is built, CRAN maintainers could no longer provide a Windows binary of RQuantLib with the QuantLib library they had been using. I decided to try and build an updated QuantLib library from source, which would allow me (and them) to build the current RQuantLib.
Instructions for Getting Started with QuantLib and MinGW from Scratch by Terry August (found in QuantLib FAQ 3.