Louis, > Hi Howard, > > Thanks for the code and the information. What exactly is doing the > CMO Oscillator? I tried to look for information on the web but did > not find anything convincing. It sure looks interesting and I will > try to find more about it, but right now this look like mystery to > me.
The CMO is the Chande Momentum Oscillator which is actually a variation of the RSI - see, e.g., http://www.paritech.com/education/technical/indicators/momentum/chande.asp . Greetings, Thomas > > Something I wasn't sure in your book, is what you mean by objective > function. Do you mean choosing between RAR, or CAR, or K-Ratio, > etc.? I know this sound like a stupid question after I've read 75% > of the book, but... well... I wasn't sure. > > I will try to follow the steps as you write them below. However, I > am still worried about MCS; I mean, in the steps you don't use any > random optimization and you said not to worry about them. I say that > because it seems to me that in the walk-forward it can be easy to get > lucky with some very good curve-fitting results. And the more > complex the rules, the more chances there is to get a very lucky > result! Well, this was my whole point: in the walk-forward I only > get to see the absolute best return, and if there is no random > optimization I can't rule out the luck factor! > > Thanks! > > Louis > > p.s. Mike, thanks for the suggestion. Is Pardo's book really good > and is using afl code or codes that can be implemented easily to > Amibroker? > > 2008/4/22, Howard B <[EMAIL PROTECTED]>: > > Hi Louis -- > > > > If the system you are working with is actually the crossover of two > > simple moving averages, the results you get will probably not be > > very good. I often suggest a simple system when I am trying to > > make a point that requires a system and I do not want the > > definition of the system to confuse the other point. You will need > > a system that is more sophisticated to show good results. Try the > > CMO Oscillator in the code posted below. > > > > // CCT CMO Oscillator.afl > > // > > // A CMO Oscillator > > // > > // > > > > // Two variables are set up for optimizing > > CMOPeriods=Optimize("pds",61,1,101,5); > > AMAAvg=Optimize("AMAAvg",36,1,101,5); > > > > // The change in the closing price is summed > > // into two variables -- up days and down days > > SumUp = Sum(IIf(C>Ref(C,-1),(C-Ref(C,-1)),0),CMOPeriods); > > SumDown = Sum(IIf(C<Ref(C,-1),(Ref(C,-1)-C),0),CMOPeriods); > > > > // The CMO Oscillator calculation > > CMO = 100 * (SumUp - SumDown) / (SumUp + SumDown); > > > > //Plot(CMO,"CMO",colorGreen,styleLine); > > > > // Smooth the CMO Oscillator > > CMOAvg = DEMA(CMO,AMAAvg); > > // And smooth it again to form a trigger line > > Trigger = DEMA(CMOAvg,3); > > // Buy when the smoothed oscillator crosses > > // up through the trigger line > > Buy = Cross(CMOAvg,Trigger); > > // Sell on a downward cross, or 6 days, > > // whichever comes first > > Sell = Cross(Trigger,CMOAvg) OR BarsSince(Buy)>=6; > > > > Buy = ExRem(Buy,Sell); > > Sell = ExRem(Sell,Buy); > > > > Plot(C,"C",colorBlack,styleCandle); > > > > PlotShapes(Buy*shapeUpArrow+Sell*shapeDownArrow, > > IIf(Buy,colorGreen,colorRed)); > > Plot (CMOAvg,"CMOAvg",colorGreen, > > style=styleLine|styleOwnScale|styleThick,-100,100); > > //Figure 20.2 CMO Oscillator > > > > Now -- back to the issue of validating a trading system -- > > > > Tomorrow is out-of-sample. You want to increase your confidence > > that your trading system will be profitable when you trade it > > tomorrow. In order to do this, observe what happens after you have > > optimized a system over an in-sample period, then tested it on the > > immediately following out-of-sample data. The automated walk > > forward process helps you do this. Every step gives one more > > observation of the in-sample to out-of-sample transition. If the > > cumulative out-of-sample results are satisfactory to you, then you > > have increased confidence that your real trades are likely to be > > profitable. No guarantees. The best we can hope for is a high > > level of confidence. > > > > At this point, do not worry about Monte Carlo. > > > > Just concentrate on: > > > > 1. Select the objective function that You feel most comfortable > > with. 2. Design and test the systems of interest to You. > > 3. Experiment to find the length of the in-sample period. > > 4. Perform the automated walk forward analysis. > > 5. Examine the out-of-sample results. > > 6. Decide whether or not to trade your system. > > > > Thanks for listening, > > Howard > > www.quantitativetradingsystems.com > > > > > > > > On Tue, Apr 15, 2008 at 7:03 PM, Louis Préfontaine > > <[EMAIL PROTECTED]> > > > > wrote: > > > Hi, > > > > > > I've been experimenting with walking-forward, and I have some > > > questions regarding how it works. > > > > > > I ran a complete random optimization or buying/selling using the > > > variables I set (a MCS in fact), and systematically OOS results > > > were worst than IS. I don't understand how it works, because > > > whatever if the sampling is IS or OOS it is always the same > > > variables that are in place. > > > > > > Anyone could explain how this work? > > > > > > Thanks, > > > > > > Louis ------------------------------------ Please note that this group is for discussion between users only. 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