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Np. There is also a very old book called "trading as a business." by Charlie wright. that is filled with these types of basic systems. You can get it used on amazon for about 9bucks.

Didn't you say you bought "trend following," by michael covel? It has EXACTLY what you are describing in the back of the book. appendix A, p 266. "concept, moving averages." look that up, read the whole section, then tell me how it deviates from what you are seeking.

Glad I'm not the only one who was completely stumped by the logic. So many people associated you (surf) with niedderfhoffer, and judging from some of the debates I've read, I completely assumed you wanted a rigorous approach ala. VN (i.e. no subjective TA), however, your reply stated the...

congrats on the article. Although, for some reason, after following your posts, I was under the impression that you were anti-TA (you seemed to be in the rigorous proof camp, from my vantage pt). After reading the article, I get a different take (i.e. some 200dma comments, channel lines)...

I have repaired several blue death screens. One was a virus, other times it is hardware. Aside from the good suggestion to google error code, also try to see if you can get into bios menu to look at a few things as well.

Think of a neural net as a black box, that when trained (most are supervised) with given patterns, returns an approximate function for the black box. It tries to minimize the error by adjusting the internal coefficients of your approximate function each time you train it Once the error has...

ok. Not knocking acrary's work, as I already mentioned I'd like to see more. Just wanted to try to put the rr argument to rest (and from my perspective there was plenty of contention over the basic concept maestro was arguing, which appeared crystal clear to me). Granted it's slightly off...

I think I am going to have to side with maestro on this one (hopefully I am interpreting him correctly). If you take 2 structurally independent systems (defined here as different mean/variance characteristics in w/l profiles) and run a monte carlo over a large run of trials of the two systems...

Unless I'm missing something, all you need is a spreadsheet program to add each data point of the two non-correlated graphs. There's a free one called open office on the web. ---------------------------------------------- P.S. I was looking forward to hearing more interaction from maestro...

I just started reading "the only three questions that count," by Ken Fisher. Didn't expect much, as it looked to be another qualitative type book rehashing common knowledge. However, a few chapters in and I am pretty impressed. The gist of it is to run your own experiments/studies to...

Interesting, since according to celent, in 2005 about 100,000 traders traded between 500-1,000 trades/yr (the 30,000 figure was for traders trading greater than 1,000 trades per/yr). To make your inference, we have to know something about how they calculate their average at IB. Assuming it's...

while not entirely dedicated to trading systems or TA (it has systems examples in the back). Michael Covel's trend following book has a moving average crossover example system in the back you can try to replicate and compare results to.

There are around 30,000 active daytraders (1,000 or more trades/yr). Tradestation annual report has some stats. As other posters mentioned, although small accounts are greater than the number of institutions, their collective account balances are minuscule in comparison. Here are some...

good news. I manually rescaled the inputs (which means I guess RM isn't rescaling automatically, if so, then why does it offer choice for input layers?). Also added a manual bias to the input, and we have rudimentary tracking for a start. Also, changed to hyperbolic tangents for comparators for...

Sounds good. One other point I'll throw out, since eventually someone will get to it is that there is no way to add offset bias for the hidden layer comparators (according to the developers of RM). However, I think if the input data is scaled properly and proper thresholding functions are...

That is certainly another way to do it, and an often used method. However, my goal here was to try to get an RM project that everyone could actually work "hands on," rather than observe. So the priority should be to have everyone who wants to learn it, try to emulate and add their wisdom to...

yes. well, actually out is today's adjusted close. However, it is also the training and prediction variable, so once applied to an out of sample/validation set, it becomes tomorrow's predictive close (with today's close being the n-1 variable in the new dataset).

Did you review the input data? I attached all of the files. It is not open high low close data. Each vector contains delayed versions of the adjusted close. And I have had this input data converge before on a different platform. My problem is seeing if RM is worthwhile to prototype these...

last post unless something major is discovered. summary: reran with about 4,000 data pts. Maximum training validation. Figured out how to specify input layers and neurons/layer. Playing with it. (Have to manually add it to add list). Trying to incorporate output log to monitor...

Just caught this, and agree with everything here (GI = GO). I used a very small sample set for the simple reason that I don't want to sit around and debug through each iteration over 10,000 epochs, as it would slow down my (and other nophyte's) initial progress on coming up to speed on RM...