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 [email protected], "Mike" <sfclimb...@...> 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 [email protected], "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 > > >
