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Nick, Howard, Tech et al;

Apologies if any of this has been covered earlier in the thread,

I think the distinction between curve fitting and optimization is worth noting,
though accurately distinguishing one from the other is certainly beyond me.

Finding the best parameters over an arbitrary length of time and then expecting them to hold true for a multitude of conditions that may be encountered in the future is IMO a fools game, but optimizing chosen variables over a specified time period with the expectation that the performance of these variables should decay into the future is perhaps a more feasible approach.

I am aware there has been a fair amount research into this area by various academics in this field, but it would be fair to say the people who are profitably using this method prefer not to disclose. Bastards

From what I have read, machine learning applications (genetic algorithms, etc) are used to dynamicaly evaluate and update variables (or perhaps even overhaul the whole model) to optimize the next periods performance, based on a previous number of periods. Considering the calibre of individuals who subscribe to 'market cycles' and similar, I don't find it that hard to swallow.

Interested to hear others thoughts on this area.

I completely agree, though could this apply more to swing trading systems than trend following? What effect would the frequency of trades have on the effective life of a system?

Also, to Nick & Howard, what has your experience been with short term trading systems? Most of the discussion in this area seems to be on medium to longer time frame systems, have you seen short term systems employed profitably?

<blockquote data-quote="julius" data-source="post: 196377" data-attributes="member: 9072">Nick, Howard, Tech et al;Apologies if any of this has been covered earlier in the thread,I think the distinction between curve fitting and optimization is worth noting,though accurately distinguishing one from the other is certainly beyond me.Finding the best parameters over an arbitrary length of time and then expecting them to hold true for a multitude of conditions that may be encountered in the future is IMO a fools game, but optimizing chosen variables over a specified time period with the expectation that the performance of these variables should decay into the future is perhaps a more feasible approach.I am aware there has been a fair amount research into this area by various academics in this field, but it would be fair to say the people who are profitably using this method prefer not to disclose. Bastards <img src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" class="smilie smilie--sprite smilie--sprite11" alt=":rolleyes:" title="Roll Eyes&nbsp; &nbsp; :rolleyes:" loading="lazy" data-shortname=":rolleyes:" />From what I have read, machine learning applications (genetic algorithms, etc) are used to dynamicaly evaluate and update variables (or perhaps even overhaul&nbsp; the whole model) to optimize the next periods performance, based on a previous number of periods. Considering the calibre of individuals who subscribe to &#039;market cycles&#039; and similar, I don&#039;t find it that hard to swallow.Interested to hear others thoughts on this area.I completely agree, though could this apply more to swing trading systems than trend following? What effect would the frequency of trades have on the effective life of a system?Also, to Nick &amp; Howard, what has your experience been with short term trading systems? Most of the discussion in this area seems to be on medium to longer time frame systems, have you seen short term systems employed profitably?</blockquote>

[QUOTE="julius, post: 196377, member: 9072"] Nick, Howard, Tech et al; Apologies if any of this has been covered earlier in the thread, I think the distinction between curve fitting and optimization is worth noting, though accurately distinguishing one from the other is certainly beyond me. Finding the best parameters over an arbitrary length of time and then expecting them to hold true for a multitude of conditions that may be encountered in the future is IMO a fools game, but optimizing chosen variables over a specified time period with the expectation that the performance of these variables [I]should[/I] decay into the future is perhaps a more feasible approach. I am aware there has been a fair amount research into this area by various academics in this field, but it would be fair to say the people who are profitably using this method prefer not to disclose. Bastards :rolleyes: From what I have read, machine learning applications (genetic algorithms, etc) are used to dynamicaly evaluate and update variables (or perhaps even overhaul the whole model) to optimize the next periods performance, based on a previous number of periods. Considering the calibre of individuals who subscribe to 'market cycles' and similar, I don't find it that hard to swallow. Interested to hear others thoughts on this area. I completely agree, though could this apply more to swing trading systems than trend following? What effect would the frequency of trades have on the effective life of a system? Also, to Nick & Howard, what has your experience been with short term trading systems? Most of the discussion in this area seems to be on medium to longer time frame systems, have you seen short term systems employed profitably? [/QUOTE]

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