1543 search results for "time series"

Blackbox trading Strategy using Rapidminer and R

January 23, 2011
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Blackbox trading Strategy using Rapidminer and R

This my first post in 2011. this post has cost me a bit more than usual, but I hope it meets expectations. The aim of this tutorial is to generate an algorithm based on black box trading, with all the necessary elements for evaluation. That is a first post of several, in order to explore the problems, features of...

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Blackbox trading Strategy using Rapidminer and R

January 23, 2011
By
Blackbox trading Strategy using Rapidminer and R

This my first post in 2011. this post has cost me a bit more than usual, but I hope it meets expectations. The aim of this tutorial is to generate an algorithm based on black box trading, with all the necessary elements for evaluation. That is a first post of several, in order to explore the problems, features of...

Read more »

Merging Multiple Data Frames in R

January 23, 2011
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Merging Multiple Data Frames in R

Earlier I had a problem that required merging 3 years of trade data, with about 12 csv files per year. Merging all of these data sets with pairwise left joins using the R merge statement worked (especially after correcting some errors pointed out by Ha...

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Posted Question for R Users

January 21, 2011
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Posted Question for R Users

I recently undertook a project where a colleague had about 12 .csv files that they wanted to merge. Each file had a common (key) variable 'Partner' (which is trading partner) with differing columns (variables) except for the common key variable. Actual...

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Learning R through baseball: sab-R-metrics

January 21, 2011
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Learning R through baseball: sab-R-metrics

The words "statistics" and "baseball" are often found near each other, but there's a lot more to statistics than dividing the number of hits by the number of swings to get a batting average. And there's a lot more to sabermetrics -- the statistical analysis of baseball -- than averages, too. Many baseball fans are also stats geeks (and...

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Interesting volatility measurement, part 2

January 21, 2011
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Interesting volatility measurement, part 2

A few weeks ago I have mentioned about an interesting volatility prediction. It is based on two periods of historical volatility (standard deviation). The remaining question was – does it really works? I could not give the answer, because I didn’t have VIX futures data at that time. Later on, I was contacted by Brian

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In case you missed it: December Roundup

January 17, 2011
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In case you missed them, here are some articles from December of particular interest to R users. A Facebook employee created a beautiful visualization of social connections around the world, which made a lot of news on the Web. The creator, Paul Butler, explained how he did it using R. With sponsorship from Revolution Analytics, the R/Finance conference in...

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Statistical podcast: Random and Pseudorandom

January 14, 2011
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Statistical podcast: Random and Pseudorandom

This morning when I downloaded the latest version of In our time, I was pleased to see that this weeks topic was “Random and Peudorandom.” If you’re not familiar with “In our time”, then I can I definitely recommend the series. Each week three academics and Melvyn Bragg discuss a particular topic from history, science,

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The number 1 novice quant mistake

January 12, 2011
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The number 1 novice quant mistake

It is ever so easy to make blunders when doing quantitative finance.  Very popular with novices is to analyze prices rather than returns. Regression on the prices When you want returns, you should understand log returns versus simple returns. Here we will be randomly generating our “returns” (with R) and we will act as if … Continue reading...

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Introducing the Lowry Plot

January 11, 2011
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Introducing the Lowry Plot

Here at the Health and Safety Laboratory* we’re big fans of physiologically-based pharmacokinetic (PBPK) models (say that 10 times fast) for predicting concentrations of chemicals around your body based upon an exposure. These models take the form of a big system of ODEs. Because they contain many equations and consequently many parameters (masses of organs

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