Nitro, yes you are right about scale-free templates... I believe if you are going to go the wavelet route you need to formulate them in continuous-time rather than discrete. The problem is how to build the template library, and at what scale? The trick is to design some algorithm that can automatically detect 'new' situations at any, possible nested timescales and know when to add them to its library, update existing templates, etc. Maybe there is no 'when to update' but everything is always adapted continuosly as data flows thru the system. There are models based on differential equations, where if you tune the parameters correctly the continuous-time system will automatically relax to a fixed-point attractor when a certain input pattern is present and it doesn't need to wait until the signal "arrives", it starts responding as soon as the pattern begins to emerge. The issue is, there are patterns within patterns, and you are never completely within a single pattern, but any number of them simultaenously and at any number of timescales. Btw, if you solve this problem generally, you will be a very very rich man because it can be applied to nearly any complex system and not just the markets.
Finding M tops and W bottoms functions and indicators used for charting and Radarscreen by ekam https://www.tradestation.com/discussions/Topic.aspx?Topic_ID=16941
Ivo, Bulkowski has a free pattern recognition program on his website. Might give you some ideas. http://mysite.verizon.net/resppzq7/ Smitty
Hi Ivo, I have been playing with with creating neural networks (NN) and was successful at creating some simple ones. I grabbed most of my stuff from Generation 5 http://www.generation5.org/content/2005/NeuralArchitecture1.asp although I have gotten several textbooks on the subject as well. If you dig around and look at the numerous examples posted there, you should be able to come up with a general idea of how they work. I re-created them in C# from the C++ examples. The examples are readily expanded to do what you need I believe. A NN can be trained for pattern recognition. However, the ability of the NN to recognize a pattern is dependant on how good you describe it, the robustness of the rules you use, and the training. Provided you can come up with these rules for the patterns and defined them, and trained the network, I believe they could be made to work very well. Then set the developed NN loose on some historical data, and see how well it performs. If it performs poorly, tweak the model and re-train. Then evaluate the historical data again, etc, etc. As far as speed of platform, the speed aspect comes into play only when training the NN, not necessarily the evaluation of the historical data or the live operation of the NN. This is because the trained NN has constants associated with each node that were developed in the training phase (training can take between 10^3 and 10^6 cycles). I did not develop mine any further regarding pattern recognition as I am using to evaluate the output of different trade logic, something that is, at least in my mind, a better fit. Presently I have set NN aside as I have been working on the IB TWS interface using C#, something that I continue to chew on, w/o success I might add. I hope this is of some help.
If you want to experiment with neural nets, http://www.jooneworld.com is interesting, with a GUI for constructing nets, with input layers for Excel, SQL, Yahoo finance and others. Free with Java source code including distributed training if you ever get to the point of needing serious computational power.
Detecting pattern is one thing. Finding profitable next movement is another. Regardless of language, it comes to whether the pattern can predict direction. Even constant up do not necessarily lead to the next bar being up. Pattern directional probabilities are often weak.