Yes, although I don't use neural networks but a similar, trainable binary logic network. If you're worried about lag don't use moving averages as they will always lag -- use a band pass filter of some sort. I previously used linear regression and am now changing to inverse fourier transforms as a band pass filter. I can select the wavelengths that I want to pass through and leave any noise behind.
If you use a basic MA cross with average-to-poor rules, you deserve to lose your trading capital in the short time it will take. There are robust high frequency forex/FUT index systems, however, that utilize a weighted EMA cross for either entry/exit or both. The key is to properly incorporate market tempo filters, what snapshot to use (x time, x vol, x tick, etc), and constantly be adjusting for tempo. The naked cross itself pales in importance compared to the filters and execution rules that encapsulate it. I would strongly wager that most, including experienced traders, never dig deep enough and end up pre-maturely signing off CO development as a "newb" route. Some of the perceptions in this thread are indicative of that. Whoever stated fading a 25% win rate cross equates to 75% win rate is an absolute idiot. Perhaps that was sarcasm I didn't pick up on?
Quality post. The filter set and risk management rules are what makes the MA cross work. Most of the MA cross system hate is from people that tried it for all of an hour.
Try and test different modifiers to the strategy and see what it does to performance. For example, and entry modifier would be a certain minimum volume threshold. An exit modifier would be price not confirming a profitable crossover.
Here's an Excel spreadsheet that can perform band pass filtering on any kind of data, and inverse fourier transforms to reproduce a close approximation to the original series. Change some of the 1's to zeros to see the filtered output: http://www.ecr.unimelb.edu.au/~xlr8tr/techs/Fourier.xls
Nice to see someone share something aside from cryptic circumlocution for once. Did you write it? Although, there are a couple of problems. For one, the dc (mean) is missing from the filtered reconstructed data set. You can just add a constant of the average of the input time series to the Reconstructed data row to take care of this. 2) If you put in a real time series, it doesn't work too well. I don't have time to troubleshoot at the moment (it's likely that the freq sample resolution is too limited) but just try any stock or real series as the input column and look at the filtered output. Lastly, using fourier decomposition and inverse reconstruction typically has wraparound problems at the ends as well as limited bin resolution on financial time series data. It's not a good way to filter financial time series for a lot of other reasons. Better to use a real bandpass filter for what you are attempting.
Thanks bigdavediode for the reply and the exmples. dtrader98 seems to have analyzed your example given, please may we have your opinions regarding this? Thanks!