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I mean if you do the optimisation all together (entry and exits IS) and then isolate the optimized entry and the optimized exit by testing them alternately with a dumb entry and then a dumb exit the delta, in each case, will tell you how much was coming from the smart entry and the smart exit.
OOS will tell you whether or not you curve fitted the entry and/or the stops.
Is that right, wrong or maybe ... anyone?
--- In amibroker@xxxxxxxxxxxxxxx, "brian_z111" <brian_z111@xxx> wrote:
>
> Howard's QTS book has some good seed ideas .... pity he didn't ahve another few hundred pages to expand on them.
>
> Benchmarking the exits was one of them ... I don't think his single page did the subject justice.
>
> I am privately deconstructing the idea and it appears you are going down the same path.
>
> Off the top of my head I think the best route to take, to 'benchmark' exits that are specific to the system, is to make a dumb exit from the smart entry (Yofa should try entering with his optimised entry and then exit randomly OR every x days, as suggested by Howard) ... that would give him an exit benchmark which he can then try to beat.
>
> I agree with your walk forward caveat though.
>
> It might be fun to use a dumb entry, with optimised stops, and see how the stops walk- foward.
>
>
> --- In amibroker@xxxxxxxxxxxxxxx, "Mike" <sfclimbers@> wrote:
> >
> > > So if my trade management logic is up to its job (using the best settings) it has to produce the best distribution of drawdowns and profits of backtest runs with random entries.
> >
> > I disagree with that. The result is that you are optimizing your trade management for random entries. That means that if you actually trade using a non random entry, then you will be using the "wrong" trade management parameters given your *actual* entries.
> >
> > For example; Optimization of your trade management using noise for entries might tell you to use a 5% trailing stop. But, optimization of your trade management using non random entries (i.e. the entries that you'll *actualy* trade) might instead call for a 8% trailing stop.
> >
> > The different in performance results when applied to out of sample data will likely be substantial. I highly recommend running Walk Forward Optimization to validate your theory. Do a walk forward based on random entries, and another based on your strategy entries. See which does better.
> >
> > Mike
> >
> > --- In amibroker@xxxxxxxxxxxxxxx, "Yofa" <jtoth100@> wrote:
> > >
> > > Hi All,
> > >
> > >
> > >
> > > Aron:
> > > you got better results by removing your original entries, because your original entries were not better then random and you got more time in the market by using simple random entries (my guess).
> > >
> > >
> > >
> > > To All:
> > > Thanks for all the thoughts and consideration.
> > >
> > >
> > >
> > > To give some more hints and encourage thoughts here is a bit more info.
> > > My general idea is to divide a complete trading system into smaller independently testable/optimizable pieces. I'm building a single equity, intraday, automated trading system. To make it simple let's say it consists of a filter (when not to trade) an entry & timing logic (generate buy and short signals) and a trade management logic (initial stop, trailing logic, profit taking exits, etc.)
> > > If we accept that the price movements consists of noise and real price movements than the trade management logic's only job is to keep my stops (initial and trailing) out of the noise level, while minimizing initial loss and maximizing profit. It has to accomplish this REGARDLESS OF THE QUALITY OF THE ENTRIES AND FILTERS. If all my entries are bad it has to produce the least amount of loss. If all my entries are excellent it has to collect the most profit.
> > > If I run a number of backtest runs with random entries while keeping the settings of trade management logic constant I get a "sample" of what might happen using the settings if my entries are not better then chance. This sample has a distribution of profits, CARs, system drawdowns, etc. All the attributes of a backtest runs or a series of real life trades!
> > > If I run similar test with each possible setting (optimization) and compare the samples of each settings, I'm able to select settings that produce the best performance distribution (defined by my objective).
> > > So if my trade management logic is up to its job (using the best settings) it has to produce the best distribution of drawdowns and profits of backtest runs with random entries.
> > >
> > >
> > > Similarly, the filter's job is to keep me out of market when trading is not profitable. It's not profitable because there are more noise than real price movement (so initial stop is going to be hit sooner or later) OR because of entering the market in the wrong direction. If using random entries (in random directions) and the filter is bad, the initial stop is hit because of either cause. If the filter is good, the numbers of initial losses are minimized because initial stop is hit if I try to ride the market in wrong direction but noise is appropriately addressed.
> > > So if I use random entries and use the same initial loss with no trailing and add the "perfect" filter to it,the filtered system has to provide the smallest loss and the smallest drawdown. By running a number of random backtests for each possible filter settings, I produce a sample of that filter settings. These samples can be somehow compared and the best selected.
> > >
> > >
> > >
> > > Any opinion, thoughts or experience is appreciated.
> > > I don't really know what the best way of comparing "samples" is. Any idea?
> > >
> > >
> > >
> > > Regards,
> > >
> > >
> > >
> > > Y
> > >
> >
>
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