> Seen one fractal - seen 'em all! That should go on the back of the Tshirts.
--- In [email protected], i cs <ics4...@...> wrote: > > > Hi all, > > LeBeau and Lucas in their book "Computer Analysis of the > Futures Markets" talk about using random exits to test the efficacy > of systems which are meant to catch trends. Been a long time > since I read it, but i think it went along the lines of ," if you have > an entry/filter combination which you think should catch trends, > then any trend you are on, should by definition, trend for a > while. So you should be able to exit at any number of points > in the future at a profit" This then removes the chance that > your exit is "doing the work" > > I think they suggested that you test exits at regular periods > down the track, say 5, 10,15 days etc. Not quite random > but a similar principle. > > I think if you just test random Vs random you get into an > area of potential overoptimisation very quickly. I remember > one such study where they stuck a large bunch of data into > a data mining system, without planning their objectives > properly, and they found that the best predictor of the SP500 > index was the price change of butter in Bangladesh. So it > was either coincidence or overoptimisation. > > > BTW Brian > Seen one fractal - seen 'em all! > > Z > > > > > ________________________________ > From: brian_z111 <brian_z...@...> > To: [email protected] > Sent: Saturday, 13 June, 2009 10:43:50 AM > Subject: [amibroker] Re: Random entry & exit optimization > > > > > > Re comparing random testing to a system, to confirm the edge: > > Generally speaking I agree with Mike ... that is the obvious answer, but I > have gained a great deal by continual labttesting and whatif specualation > (actual and virtual). > > Testing the tools, the myths, the philosophy, authors ideas, strategies, > systems, data, crazy ideas, holy cows, indicators ... in short everything. > > The results from all of this weren't immediate, or direct ... over time it > revealed my trading philosophy, temperament and strategies etc which are > directly reflected in my systems. > > Re random entries: > > - the larger the sample (N) the more we can rely on the result > - so, massive random testing is more instructive than a small sample > - if we conduct a massive number of random entries (say on daily bars) we > will eventually end up entering every bar an approx equal number of times > (with a random entry every ball in the bucket has an equal chance of getting > drawn, assuming sampling with replacement) .... so why not just go straight > to buying every day (on close, on open or something else?) ... it saves the > extra work of including the random entry code and uses up less of your data > in the IS..... the only difference between your benchmark and your real > signals will be N .... the real sample set will have a smaller N and a larger > sample error. > > Re optimising random entries: > > - very interesting > - at first thoughts it looks like, at the least, you are testing the validity > of optimising > - including your filters may complicate the research ... perhaps you should > try isolating each component (filters, trade management, optimisation) . > - it would be interesting to see what type of opt results you get from a > random entry > - even more interesting to see how many 'good' optimised random entries > perform in OOS walk -forward > - if any perform above expectations, for a random system, the explanation > would be even more interesting > > Speculating on the outcomes of your research: > > - without knowing what your objectives are > - leaving out filters and trade management > - optimising random entries is likely to produce a set of parameters that > approximate the perfect buy and sell signals for the data you are analysing > (see Howard's QTS book page81 for some insight into perfect signals) ... > notice that, in Howards screenshot example, they approximate smoothed cycles. > - if you wanted to hit every perfect buy/sell then you would need to add more > lines of code and of course the number of trades in your system will increase > until the ratio of trades/number of perfect trades == 1 > > If any 'systems' produced by opting random signals survives the OOS sample > test it can only be because they are not significant, compared to random > chance OR they have indentified some persisent and recurring cycle (seasonal > patterns, moon phase, Fibonacci or a new one that you can discover .... > fascetious there ... just having some fun with it). > > An interesting variation, on your research, might be to randomly generate > data and see what optimising it can do. > Note that randomly generated data doesn't match up to real market data ... > the markets tend to have a lot more extreme results than can explained by a > normal dist (I think Mandelbrot did some work with fractal maths and > generation of more realistic distributions if you want to stress test opt in > a realistic environment) . > > (Some people claim that fractals is a pseudoscience ... according to others > it has something to say about wave patterns, or cycles, in market data ... I > don't know about that but I think the computer generated fractal art would > look great on a Tshirt). > > http://www.scientif icamerican. com/article. cfm?id=multifrac tals-explain- > wall-street > > Most of the time the text books don't answer the hard trading questions, or > answer them in enough detail, so you can only can an advantage by pushing the > envelope on optimisation theory. > > Howard's chapter on benchmarking is only scratching the surface and I haven't > found a lot of critical analysis of optimisation anywhere (only a few high > tech efforts like Whites reality check for datamining etc). > > --- In amibro...@yahoogrou ps.com, "Yofa" <jtoth100@ .> wrote: > > > > Hi All, > > > > I'm trying to improve my optimization method. So I divided my trading > > system into parts: entry logic, trade management logic (trailing, profit > > target, volatility exit, etc ), filters, etc. > > > > I created a random entry system, that uses the same trade management logic > > as my trading system. > > With random entries I optimized the parameters of the trade management > > logic. I also try to improve filters the same way. > > > > My questions: > > Is there anyone who uses similar technic for optimization? > > Is there anyone how uses similar approach to validate the trading > > system and its parameters? > > Is it reasonable optimization method? > > > > Any opinion or experiance appreciated. > > > > Regards, > > > > Y > > > > > > > > Need a Holiday? Win a $10,000 Holiday of your choice. 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