The annoucement below went out to R-SIG-Finance earlier today. For information is as usual the the R / Finance 2010 page: Now open for registrations: R / Finance 2010: Applied Finance with R April 16 and 17, 2010 Chicago, IL, USA The second annu...
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[Event] R / Finance 2010: Applied Finance with R
![[Event] R / Finance 2010: Applied Finance with R](http://feeds.wordpress.com/1.0/comments/statsravingmad.wordpress.com/819/)
One of the greatest event on R is under way…
R / Finance 2010: Applied Finance with R
April 16 & 17, Chicago, IL, US
The second annual R / Finance conference for applied finance using R,
the premier free software system for statistical computation and graphics,
will be held this spring in Chicago, IL, USA on Friday April 16 
R/Finance 2010: Registration Open
As posted by Dirk Eddelbuettel on R-SIG-Finance: R / Finance 2010: Applied Finance with R April 16 & 17, Chicago, IL, US The second annual R / Finance conference for applied finance using R, the premier free software system for statistical comput...
Practical Implementation of Neural Network based time series (stock) prediction -PART 4
Consider this an introduction to how we need to pre-process the data.I mentioned earlier that a financial time series is typically a unit root or non-stationary signal, what this means is that if you sample statistical properties over time, they will o...
Practical Implementation of Neural Network based Time Series (Stock) Prediction – PART 3
Ok, now that we have seen how well the perfect sine wave signal was learned, let's turn it up a notch and see how well the complex sine wave was learned.Fig 1. Summary of Actual Vs. Predicted out of sample complex sine waveformUh Oh. What happened, the...
Practical Implementation of Neural Network based time series (stock) prediction – PART 2
As a brief follow up to the series, I want to take a moment to describe a bit about Weka, which is the machine learning tool that we will be using to implement the neural network. It is a fantastic open source JAVA based tool that was developed at the...
Practical Implementation of Neural Network based time series (stock) prediction – PART 1
The following introduction is to allow viewers to understand the basic concepts and practical implementation of neural nets towards a financial time series. I will not go too deep into detail about the mathematics behind the neural net at the moment. ...
LSPM with snow
My last post provided examples of how to use the LSPM package. Those who experimented with the code have probably found that constrained optimizations with horizons > 6 have long run-times (when calc.max >= horizon).This post will illustrate how the s...
Programming a custom Backtest Profile in R
One of the many issues with systems trading is trying to make sense of the vast amounts of data you accumulate with the backtest of a system. Historical backtesting is the first step in testing your trading idea. If it is a trading idea that ought to w...
LSPM Examples
I have received several requests for additional LSPM documentation over the past couple days and a couple months ago I promised an introduction to LSPM. In this long-overdue post, I will show how to optimize a Leverage Space Portfolio with the LSPM pa...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
