Another challenging subject (3 weekends in a row). Initially I thought you were asking a question, or questions, about benchmarking, in its various forms, but I see now that you also seem to be asking about which evaluation metric is the most suitable to measure the 'goodness' of your stops?
I think it will help if the discussion is not so hypothetical, so I will make up an example to save you revealing your trading secrets to the world ;-) Will this do for an example filter and signal? Signal = Ref(ROC(MA(C,5),1),-1) < 0 AND ROC(MA(C,5),1) > 0;//the fast MA turns up Filter = ROC(MA(C,20),1) > 0;//the slow MA is up (an uptrend in this model) disclaimer = string(I hope I got that right); I think I backtested this once but I can't remember what the result was. IMO you should think again about optimising your filter because the signal and the filter are both part of the system entry: entry = signal == 1 AND filter == 1; So 'entry' has to be true to action the stops, which are based on the price at entry) .... so you have two entry variables to optimise. Noise is a concept that came from physics and possibly maths ... I know that optimalF was an extension of the maths component of a solution to interference in telephone lines (developed by Bell?). Howard uses it with understanding and confidence (it means something to him) but I seldom use it because I don't understand its meaning, when using in a trading context etc .... all price action is real to me ... my broker seems to agree (Hey its Bob the Broker here with a margin call .... Don't worry, that was only noise!) --- In [email protected], "Yofa" <jtoth...@...> 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 >
