Archive for June, 2010

In this post, we travel back and disassemble how the NN works. It is paramount to understand the NN concept to see its advantages and limitations. As a start, I don’t really like the name. Instead of the term Artificial Neural Network, I prefer the less hyped Computational Network (CN) expression. When the ANN was […]


In this post, I introduce a new concept. In the current implementation, instead of relying on the decision of one NN, I run many NN and base the forecast on the vote of the committee. In theory, the aggregate decision of a committee can be derived in various ways. The simplest one is the average […]


Some articles use not the original raw time series as input, but scale the input by a log function. I haven’t seen too much justification of it, but I thought I will try it myself. How can I use this idea? My input1 is ‘RUT/SMA(30) – 1’, that is the RUT %difference between its SMA(30). […]


The subject is an oxymoron. If something is totally random, there is nothing we can do to forecast it. I was always fascinated by the idea that researchers and academic paper authors usually don’t try their learning algorithm on artificially random series instead of real world financial time series. On the one hand, it is […]


Recently, I was advised that instead of using Moving Averages that are far apart (like 20/180), a combination where the moving averages are close to each other may prove more useful. For that I test the combination of SMA(30)/SMA(45) pairs. You can convince yourself, that the crossing of the SMA(30)/SMA(45) happens faster than the crossing […]


An important feature can allow the evaluation of the strength of the trading signal. This is accomplished by looking at the unthresholded output generated by the NN. For example, a very strong buy signal corresponds to an output close to 0.5% or 1%. We study the possibility of validating a trading signal only when the […]