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Hi,
Sorry, here is the correct code:
PlotShapes(shapeUpTriangle*Cross(x2,Ref(C,1)),colorAqua);
//PlotShapes(shapeUpArrow*(Cross(bb,C)),colorRed);
PlotShapes(shapeDownTriangle*Cross(Ref(C,1),x1),colorPink);
//PlotShapes(shapeDownArrow*(Cross(C,bt)),colorBrightGreen);
Pal
--- In amibroker@xxxxxxxxxxxxxxx, "palsanand" <palsanand@xxxx> wrote:
> Hi,
>
> The Central Limit Theorem states:
>
> If a large number of random samples (of size 30 or more) are
> collected, the means from a sampling distribution of means where
>
> a) the mean of the sample will be equal to the mean of the
population
>
> b) The StDev of the sampling distribution is the standard error of
> the mean and
>
> c) when n is large (> 30) the sampling distribution of means is
> approximately normally distributed regardless of the shape of the
> distribution of the population as long as the sample size of each
> sample is the same.
>
> Z-Scores of the COT data can be combined with the Z-Scores of the
> close price to accurately pinpoint turning points. But, I would
> still detect, verify and interpret a Entry/Exit Trading Signal for
> precise timing.
>
> I also modified plots for the following code to indicate whether
the
> Bands have been crossed, which would warn me to look for a trading
> signal, whether a continuation signal or counter-trend
> pullback/Breakout signal:
>
> /* Anticipating the next bar BBandBot OR BBandTop Cross, by D.
> Tsokakis, Sept 2003. Both crosses come from the same 2nd degree
> equation A2*X^2+A1*X+A0=0 The solution is the X2 array. For
visual
> verification, a pink arrow is plotted when the X2 crosses the next
> bar Close AND a red arrow points the actual Cross. */
>
> n=20; f=2;
> Qn=Sum(C^2,n);Qn_1=Sum(C^2,n-1);
> Sn=Sum(C,n);Sn_1=Sum(C,n-1);
> Mn=Sn/n;Mn_1=Sn_1/(n-1);
> Kn=(1/n)*sqrt(n*Qn-Sn^2);Kn_1=(1/(n-1))*sqrt((n-1)*Qn_1-Sn_1^2);
> bb=Mn-f*Kn;bt=Mn+f*Kn;
> S=Sn_1;Q=Qn_1;
> A2=(n-1)*(f^2-n+1);
> A1=-2*(f^2+1-n)*S;
> A0=f^2*n*Q-f^2*S^2-S^2;
> x1=(-A1-sqrt(A1^2-4*A2*A0))/(2*A2);
> x2=(-A1+sqrt(A1^2-4*A2*A0))/(2*A2);
> Plot(C,"C",1,8);
> Plot(X1,"",colorBlue,1);
> Plot(X2,"",colorBlue,1);Plot(bb,"BBandBot",7,1);Plot
> (bt,"BBandTop",7,1);
> PlotShapes(shapeUpTriangle*Cross(x2,Ref(C,1)),colorPink);
> //PlotShapes(shapeUpArrow*(Cross(bb,C)),colorRed);
> PlotShapes(shapeDownTriangle*Cross(Ref(C,1),x1),colorAqua);
> //PlotShapes(shapeDownArrow*(Cross(C,bt)),colorBrightGreen);
> Title="The next "+Name()+" Close should be "+"\n *below"+WriteVal
(x2)
> +" for a BBandBot Cross"+
> "\n *above"+WriteVal(x1)+" for a BBandTop Cross"+
> "\n Actual Next Close = "+WriteIf(Cum(1)!=LastValue(Cum
(1)),WriteVal
> (Ref(C,1)),"?");
>
>
> Regards,
>
> Pal
>
>
>
>
>
> --- In amibroker@xxxxxxxxxxxxxxx, "Gary A. Serkhoshian"
> <serkhoshian777@xxxx> wrote:
> > Pal,
> >
> > That makes sense as I've visually seen what you've described. It
> seems like our primary job when interpreting the data is to
determine
> where the critical inflection points are versus noise.
> >
> > I've worked with Bollinger Bands and the net positions of the
three
> groups, but am interested in how Z-Score differs from what a
> Bollinger Band plots, and does it give a better sense of the
> inflection point we seek.
> >
> > In addition, should any changes be made to the ZScore code as
> listed below for non-normal distribution as you describe COT data
to
> be? How do you determine if the data is normally distributed?
> >
> > Sorry for all the questions, but you've piqued my interest, and
> you've been very clear in your explainations.
> >
> > I'd be happy to code any adjustments based on your suggestions,
and
> post them on the board.
> >
> > Code Below
> >
> > Kind Regards,
> > Gary
> >
> >
> > /*
> >
> > There is one interpretation of the Z-Score that takes an
> observation from a
> >
> > population and returns a Z-Score statistic, where the Z-Score is a
> >
> > measurement of the number of standard deviations that that
specific
> >
> > observation deviates from the mean. If this is the interpretation
> you
> >
> > intend, the following afl code returns the Z-Score of the Close
of
> the most recent 50 days of an end-of-day price series and plots it.
> Copy this code and paste it into Indicator Builder.
> >
> > Note that most of the Closes (95 percent, on average) will have
> ZScore
> >
> > values between -2.0 and +2.0.
> >
> > */
> >
> > // ZScore of Close
> >
> > ZLen = 50;
> >
> > ZScore = (C-MA(C,ZLen))/StDev(C,ZLen);
> >
> > Plot(C,"C",colorBlack,style=styleCandle);
> >
> > Plot(ZScore,"ZScore",colorBlue,styleOwnScale|styleNoLabel,-3,3);
> >
> > Plot(0,"",colorRed,styleOwnScale|styleNoLabel,-3,3);
> >
> > Plot(-2.0,"", colorRed,styleOwnScale|styleNoLabel,-3,3 );
> >
> > Plot(2.0,"", colorRed,styleOwnScale|styleNoLabel,-3,3);
> >
> > /*
> >
> > Version 2
> >
> > normal percentile to Z Score conversion
> >
> > Schmeiser (1979) came up with the following simple formula for p
>
> 0.5:
> >
> > z = {p ^ 0.135 - (1-p) ^ 0.135} / 0.1975
> >
> > According to a table in Shore (1982), it is accurate to two
digits
> at p = 0, 0.4, 0.8, ...,
> >
> > which may be good enough.
> >
> > */
> >
> > //p = 0.025;
> >
> > p = Param("p", 0.025, 0.0001, 0.9999, 0.0001 );
> >
> > pp = IIf(p>=0.5, p, 1.0-p);
> >
> > z = ((pp ^ 0.135) - ((1.0-pp) ^ 0.135)) / 0.1975;
> >
> > z = IIf(p>=0.5, z, -z);
> >
> > //----------------------
> >
> >
> >
> >
> >
> >
> >
> > palsanand <palsanand@xxxx> wrote:
> > Gary,
> >
> > If you plot the Net Longs of all the 3 players (Commercials,
Large
> > speculators and Small traders), you will see that the plot of the
> > Commercials and Large speculators are at opposite sides about the
> > mean (most of the time) and the small traders closer to the mean.
> >
> > You will see that the plot of the Commercials and Large
speculators
> > are either diverging from each other or going parallel (most of
the
> > time).
> >
> > You can then watch for trend-change pullbacks or breakout signals
> at
> > the specific time on the plot where the Commercials and Large
> > Speculators begin converging from their extreme positions
(visually
> > identified) on either side of the mean.
> >
> > You may use Z-Scores to identify the extreme positions. Z-Scores
> > tend to be used mainly in the context of the normal curve, and
> their
> > interpretation based on the standard normal table. It would be
> > erroneous to conclude, however, that Z-Scores are limited to
> > distributions that approximate the normal curve. Non-normal
> > distributions can also be transformed into sets of Z-Scores. In
> this
> > case the standard normal table cannot be consulted, since the
shape
> > of the distribution of Z-Scores is the same as that for the
> original
> > non-normal distribution. For instance, if the original
distribution
> > is positively skewed the distribution of Z-Scores also will be
> > positively skewed.
> >
> > Regardless of the shape of the distribution, the shift to Z-
Scores
> > always produces a distribution with a mean of 0 and a variance of
1.
> >
> > Regards,
> >
> > Pal
> >
> > --- In amibroker@xxxxxxxxxxxxxxx, "Gary A. Serkhoshian"
> > <serkhoshian777@xxxx> wrote:
> > > Pal,
> > >
> > > Thanks for the post as I've been racking my brain thinking of
> ways
> > to trade COT. Could you please elaborate on your statement
below.
> > Specifically, how are you identifying extremes (std dev?), and
when
> > you write "low points and turning up" are you referring to the
net
> > commercial position. Taking it a step further, can I assume you
> mean
> > net-short commerical?
> > >
> > > Thanks,
> > > Gary
> > >
> > > So, for those places where the
> > > Commercials are at extreme low points and turning up, and the
> Large
> > > speculators are at the opposite extreme and turning down, the
> > market
> > > will probably turn down shortly (vice-versa for an upside
move).
> > The
> > > small speculators are usually trading with the primary trend.
> > >
> > >
> > >
> > >
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