The Jungle King: August 2012

Monday 27 August 2012

Stamp Duties

INSTRUMENT	RATE
1. Adoption Deed	Fifty Rupees
2. Affidavit / Declaration	Ten Rupees
3. Agreement	Fifty Rupees
4. Award	One Rupee per thousand
5. Relinquishment Deed	One Hundred Rupees
6. Lease Deed	2% five years,
	8% upto ten years,
	16% upto twenty years
	24% upto thirty years Of Annual Rent
7. Cancellation	One Hundred Rupees
8. SaleDeed, Gift Deed, Exchange Deed, Mortgage with Possession further charge	8%
9. Copy	Ten Rupees
10. Divorce	One Hundred Rupees
11. Indemnity Bond, Surety Bond Guarantee Bond	One Hundred Rupees
12. Bond, Bank Guarantee	2%
13. Mortgage Deed	2%
14. Notarial Ticket	Five Rupees
15. Partition Deed	2%
16. Partnership Deed	1%
17. Dissolution Deed	Two Hundred Rupees
18. General Power of Attorney	Fifty Rupees
19. Special Power of Attorney	Twenty Rupees
20. Re-Conveyance Deed	One Hundred Rupees

Thursday 23 August 2012

_SECTION_BEGIN("Chart Settings");
SetChartOptions(0,chartShowArrows|chartShowDates);
SetChartBkColor(ParamColor("Outer Panel",colorBlack));
SetChartBkGradientFill(ParamColor("Upper Chart",colorDarkTeal),ParamColor("Lower Chart",colorBlack));
GraphXSpace=Param("GraphXSpace",10,0,100,1);
dec = (Param("Decimals",2,0,7,1)/10)+1;
bi = BarIndex();
Lbi = LastValue(BarIndex());
sbi = SelectedValue(bi);
x1= BarCount-1;
Title = EncodeColor(55)+ Title = Name() + " " + EncodeColor(32) + Date() +
" " + EncodeColor(5) + "{{INTERVAL}} " +
EncodeColor(55)+ " Open = "+ EncodeColor(52)+ WriteVal(O,dec) +
EncodeColor(55)+ " High = "+ EncodeColor(5) + WriteVal(H,dec) +
EncodeColor(55)+ " Low = "+ EncodeColor(32)+ WriteVal(L,dec) +
EncodeColor(55)+ " Close = "+ EncodeColor(52)+ WriteVal(C,dec)+
EncodeColor(55)+ " Volume = "+ EncodeColor(52)+ WriteVal(V,1);
_SECTION_END();
_SECTION_BEGIN( "BASERECTANGEL" );
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorBlue );
GfxSelectSolidBrush( colorDarkBlue );
GfxSelectPen( colorBlue, 1 );
// broader color
GfxRectangle(0,15,203,600);
_SECTION_END();
_SECTION_BEGIN("res/sup-s1");
DayH = TimeFrameGetPrice("H", inDaily, -1);// yesterdays high
DayL = TimeFrameGetPrice("L", inDaily, -1);//low
DayC = TimeFrameGetPrice("C", inDaily, -1);//close
DayO = TimeFrameGetPrice("O", inDaily);// current day open
HiDay = TimeFrameGetPrice("H", inDaily);
LoDay = TimeFrameGetPrice("L", inDaily);
PP = (DayH + DayL + DayO + DayO) / 4 ;
R1 = (2 * PP) - DayL;
S1 = (2 * PP) - DayH;
R2 = PP + R1 - S1;
S2 = PP + S1 - R1;
R3 = R2 + (R1 - PP);
S3 = S2 - (PP - S1);
style = styleLine | styleThick + styleNoRescale;
rcolor = colorBlue;
scolor = colorRed;
pcolor = colorGreen;
GfxSelectFont("arial", 10, 700 ); GfxSetBkMode( colorWhite );
GfxSetTextColor( ParamColor("Color",colorRed) );
Hor=Param("Horizontal Position",20,10,1200,1);
Ver=Param("Vertical Position",420,300,500,500);
GfxTextOut(""+s1,Hor , Ver );
_SECTION_END();
_SECTION_BEGIN("res/sup-s2");
GfxSelectFont("arial", 10, 700 ); GfxSetBkMode( colorWhite );
GfxSetTextColor( ParamColor("Color",colorRed) );
Hor=Param("Horizontal Position",20,10,1200,1);
Ver=Param("Vertical Position",480,300,500,500);
GfxTextOut(""+s2,Hor , Ver );
_SECTION_END();
_SECTION_BEGIN("res/sup-s3");
GfxSelectFont("arial", 10, 700 ); GfxSetBkMode( colorWhite );
GfxSetTextColor( ParamColor("Color",colorRed) );
Hor=Param("Horizontal Position",20,10,1200,1);
Ver=Param("Vertical Position",540,300,600,600);
GfxTextOut(""+s3,Hor , Ver );
_SECTION_END();
_SECTION_BEGIN("res/sup-r1");
GfxSelectFont("arial", 10, 700 ); GfxSetBkMode( colorWhite );
GfxSetTextColor( ParamColor("Color",colorGreen) );
Hor=Param("Horizontal Position",150,10,1200,1);
Ver=Param("Vertical Position",420,300,500,500);
GfxTextOut(""+r1,Hor , Ver );
_SECTION_END();
_SECTION_BEGIN("res/sup-r2");
GfxSelectFont("arial", 10, 700 ); GfxSetBkMode( colorWhite );
GfxSetTextColor( ParamColor("Color",colorGreen) );
Hor=Param("Horizontal Position",150,10,1200,1);
Ver=Param("Vertical Position",480,300,500,500);
GfxTextOut(""+r2,Hor , Ver );
_SECTION_END();
_SECTION_BEGIN("res/sup-r3");
GfxSelectFont("arial", 10, 700 ); GfxSetBkMode( colorWhite );
GfxSetTextColor( ParamColor("Color",colorGreen) );
Hor=Param("Horizontal Position",150,10,1200,1);
Ver=Param("Vertical Position",540,300,500,500);
GfxTextOut(""+r3,Hor , Ver );
_SECTION_END();
_SECTION_BEGIN("MACD");
r1 = Param( "Fast avg", 12, 2, 200, 1 );
r2 = Param( "Slow avg", 26, 2, 200, 1 );
r3 = Param( "Signal avg", 9, 2, 200, 1 );
r4 = Param( "Wk slow", 17, 2, 200, 1 );
r5 = Param( "Wk fast", 8, 2, 200, 1 );
m1=MACD(r1,r2);
s1=Signal(r1,r2,r3);
_SECTION_END();
_SECTION_BEGIN("WRITE");
GfxSelectFont("arial", 8, 700 ); GfxSetBkMode( colorLightBlue );
GfxSetTextColor( ParamColor("Color",colorLightBlue) );
Hor=Param("Horizontal Position",0,10,1200,1);
Ver=Param("Vertical Position",580,100,50,50);
GfxTextOut(" R O C K E T J E T",Hor , Ver );
_SECTION_END();
_SECTION_BEGIN("Forex_Main_mrtq13");
///////////////////////////////////
Title =
EncodeColor(colorWhite)+ Title = Name () + " | "
+EncodeColor(colorYellow) + Date() + " | "
+EncodeColor(colorTurquoise)+ "O : "+ EncodeColor(colorLightGrey)+ O + " | "
+EncodeColor(colorTurquoise)+ "H : "+ EncodeColor(colorLightGrey)+ H + " | "
+EncodeColor(colorTurquoise)+ "L : "+ EncodeColor(colorLightGrey)+ L + " | "
+EncodeColor(colorTurquoise)+ "C : "+ WriteIf(C> Ref(C, -1),EncodeColor(colorBrightGreen),EncodeColor(colorRed))+ C + " | "
+EncodeColor(colorTurquoise)+ "Change : ("+ WriteIf(C> Ref(C, -1),EncodeColor(colorBrightGreen),EncodeColor(colorRed)) + WriteVal(C-Ref(C,-1))+" Rs."
+EncodeColor(colorTurquoise)+ " /"+ WriteIf(C> Ref(C, -1),EncodeColor(colorBrightGreen),EncodeColor(colorRed))+ WriteVal( ROC( C, 1 ))+""+ " % "
+EncodeColor(colorTurquoise)+ ") | Volume : " + WriteIf(V> Ref(V, -1),EncodeColor(colorBrightGreen),EncodeColor(colorRed))+ WriteVal(V,1)
;
_SECTION_END();
_SECTION_BEGIN("Flower");
Show_color = ParamToggle("Display CandleColor", "No|Yes", 1);
r1 = Param( "ColorFast avg", 5, 2, 200, 1 );
r2 = Param( "ColorSlow avg", 10, 2, 200, 1 );
r3 = Param( "ColorSignal avg", 5, 2, 200, 1 );
Prd1=Param("ATR Period",4,1,20,1);
Prd2=Param("Look Back",7,1,20,1);
green = HHV(LLV(L,Prd1)+ATR(Prd1),Prd2);
red = LLV(HHV(H,Prd1)-ATR(Prd1),Prd2);
flowerClose = EMA((Open+High+Low+Close)/4,3) ;
flowerOpen = EMA((Ref(Open,-1) + Ref(flowerClose,-1))/2,3);
Temp = Max(High, flowerOpen);
flowerHigh = EMA(Max(Temp, flowerClose),3);
Temp = Min(Low,flowerOpen);
flowerLow = EMA(Min(Temp, flowerClose),3);
m1=MACD(r1,r2);
s1=Signal(r1,r2,r3);
mycolor=IIf(m1<0 AND m1>s1, colorYellow,IIf(m1>0 AND
m1>s1,colorWhite,IIf(m1>0 AND m1<s1,colorDarkYellow,colorRed)));
if(Show_color)
{ColorHighliter = myColor;
SetBarFillColor( ColorHighliter );
}
barColor=IIf(C>Green ,colorWhite,IIf(C < RED,colorRed,colorWhite));
PlotOHLC( IIf(flowerOpen<flowerClose, flowerOpen,
flowerClose),flowerHigh,flowerLow,IIf(flowerOpen<flowerClose, flowerClose,
flowerOpen), "Close", barColor, styleNoTitle | styleCandle);
_SECTION_END();
_SECTION_BEGIN("Bands");
SupResB =Param("Sup-Res Short",6,0,100,1);
nn=SupResB;
Bandlinecol=ParamColor("SupResLineColor",ColorRGB(82,82,82));
ParmCloud = ParamToggle("Cloud", "No|Yes", 0);
BoxCloudColor=ParamColor("BoxCloudColor",ColorRGB(27,27,27));
Line2=Param("ResLineLength",100,2,500,0.1);
Daysback1 = Line2;
FirstBar1 = BarCount - DaysBack1;
Hh=HHV(flowerHigh,nn);
LL=LLV(flowerLow,nn);
Res2=Hh;
Sup2=LL;
BandRes=IIf(BarIndex() >= Firstbar1,EndValue(Res2),Null);
BandSup=IIf(BarIndex() >= Firstbar1,EndValue(Sup2),Null);
Plot(BandRes,"",Bandlinecol,ParamStyle("ShortSupBand",styleLine|styleDashed|stylehidden|styleNoTitle|styleNoLabel|styleThick,maskAll));
Plot(BandSup,"",Bandlinecol,ParamStyle("ShortResBand",styleLine|styleDashed|stylehidden|styleNoTitle|styleNoLabel|styleThick,maskAll));
CS=BandRes;
CR=BandSup;
if(parmCloud == 1)
PlotOHLC( CS, CS, CR,CR, "Band",BoxCloudColor, styleCloud | styleNoLabel|styleNoTitle);
_SECTION_END();

_SECTION_BEGIN("Breakout Setting");
Buyperiods=Param("Breakout periods best is usually 18",5,1,100,1,1);
Sellperiods=Param("Exit Breakout",5,1,100,1,1);
HaClose =EMA((O+H+L+C)/4,3); // Woodie
//HaClose =(O+H+L+C)/4;
HaOpen = AMA( Ref( HaClose, -1 ), 0.5 );
HaHigh = Max( H, Max( HaClose, HaOpen ) );
HaLow = Min( L, Min( HaClose, HaOpen ) );
Buy= C>Ref(HHV(High,Buyperiods),-1) ;
Sell= C<Ref(LLV(Low,Sellperiods),-1);
/* exrem is one method to remove surplus strade signals. It removes excessive signals of arrow */
Buy = ExRem(Buy, Sell);
Sell = ExRem(Sell, Buy);
PlotShapes( IIf( Buy, shapeSmallUpTriangle, shapeNone ), colorWhite, layer = 0,yposition = HaLow, offset = -30);
//PlotShapes( IIf( Buy, shapeSmallCircle, shapeNone ), colorWhite, layer = 0,yposition = HaLow, offset = -8);
PlotShapes( IIf( Sell, shapeSmallDownTriangle, shapeNone ), colorRed, layer = 0, yposition = HaHigh, offset = -30);
//PlotShapes( IIf( Sell, shapeSmallCircle, shapeNone ), colorRed, layer = 0, yposition = HaHigh, offset = -8);
_SECTION_END();
_SECTION_BEGIN("Graphics");
GrpPrm=Param("Graphic Space",-5,-10,10);
GraphXSpace=GrpPrm;
_SECTION_END();
//#include <T3.AFL>;
_SECTION_BEGIN("PFE");
pds=10;
x=sqrt((ROC(C,9)*ROC(C,9))+100);
y=Sum(sqrt((ROC(C,1)* ROC(C,1))+1),pds);
z=(x/y);
pfe=EMA(IIf(C>Ref(C,-9),z,-z)*100,5);
rsidn=pfe <-10 AND pfe<Ref(pfe,-1);
rsiup=pfe >10 AND pfe>Ref(pfe,-1);
rsiresult1 = WriteIf( rsiup,"cu", "");
rsiresult2 = WriteIf( rsidn,"cd", "");
RequestTimedRefresh( 0 );
GfxSelectFont( "Tahoma", 12, 120 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorWhite );
if ( rsiresult1 =="cu")
{ GfxSelectSolidBrush( ColorRGB(0,90,0) );
}
else
if ( rsiresult2 =="cd")
{ GfxSelectSolidBrush( ColorRGB(90,0,0));
}
else
if ( rsiresult2 =="")
{ GfxSelectSolidBrush( colorDarkTeal );
}
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorBlue );
GfxCircle( 100,90,50 );
_SECTION_END();
//GfxRoundRect( 15,305,135,215, 5, 5 );
GfxCircle( 100,100,70 );
_SECTION_END();
_SECTION_BEGIN("STC");
_SECTION_BEGIN("Schaff Trend Cycle");
/*
Ported directly from original STC Tradestation code
results differ from other Amibroker versions that are not based directly on original EasyLanguage code
http://mediaserver.fxstreet.com/Reports/99afdb5f-d41d-4a2c-802c-f5d787df886c/ebfbf387-4b27-4a0f-848c-039f4ab77c00.pdf
*/
MA1=23;
MA2=50;
TCLen=10;
MA1=Param("ShortMACDLen",23,5,36);
MA2=Param("LOngMACDLen",50,10,100);
TCLen=Param("TCLen(StochPeriod)",10,5,20);
Factor=.5;
//Calculate a MACD Line
XMac = MACD(MA1,MA2) ; // MACD in Amibroker always uses Close for MACD calculation
//1st Stochastic: Calculate Stochastic of a MACD
Value1 = LLV(XMac, TCLen);
Value2 = HHV(XMac, TCLen) - Value1;
//Frac1=1; // prime Frac1 to a default of 1
//Frac1 = IIf(Value2 > 0, ((XMac - Value1) / Value2) * 100, Ref(FRAC1,-1));
// have to "prime" first value so that reference to "i-1" does not result in subscript out of range
// since MACD for both periods is not defined until MA2 period, 0 seems to be mathematically correct priming value
frac1=0;
for (i = 1; i < BarCount; i++) {
if (Value2[i] > 0) {
frac1[i] = ((XMac[i] - Value1[i])/Value2[i])*100;
}
else {
frac1[i]= frac1[i-1];
}
}
//Smoothed calculation for %FastD of MACD
PF[0]=frac1[0];
PF[1]=frac1[1];
for (i = 2; i < BarCount; i++) {
PF[i]=PF[i-1]+(Factor*(frac1[i]-PF[i-1]));
}
//2nd Stochastic: Calculate Stochastic of Smoothed Percent FastD, above.
Value3 = LLV(PF, TCLen);
Value4 = HHV(PF, TCLen) - Value3;
//%FastK of PF
/*
Frac2=1;
Frac2 = IIf(Value4 > 0, ((PF - Value3) / Value4) * 100, Ref(FRAC2,-1));
*/
frac2[0]=0;
for (i = 1; i < BarCount; i++) {
if (Value4[i] > 0 ) {
frac2[i]=((PF[i] - Value3[i])/Value4[i])*100;
}
else {
frac2[i]=frac2[i-1];
}
}
//Smoothed calculation for %FastD of PF
PFF[0]=frac2[0];
PFF[1]=frac2[1];
for (i = 2; i < BarCount; i++) {
PFF[i]=PFF[i-1]+(Factor*(frac2[i]-PFF[i-1]));
}
//HT=ParamColor("HT", colorRed );
rsidn=pff <2;
rsiup=pff >98;
rsiresult1 = WriteIf( rsiup,"cu", "");
rsiresult2 = WriteIf( rsidn,"cd", "");
RequestTimedRefresh( 0 );
GfxSelectFont( "Tahoma", 12, 120 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorWhite );
if ( rsiresult1 =="cu")
{ GfxSelectSolidBrush( ColorRGB(0,120,0) );
}
else
if ( rsiresult2 =="cd")
{ GfxSelectSolidBrush( ColorRGB(120,0,0));
}
else
if ( rsiresult2 =="")
{ GfxSelectSolidBrush( colorDarkTeal );
}
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorBlue );
GfxCircle( 100,100,60 );
_SECTION_END();
_SECTION_BEGIN("rsi");
//HT=ParamColor("HT", colorRed );
rsidn=RSI(7) <30;
rsiup=RSI(7) >70;
rsiresult1 = WriteIf( rsiup,"cu", "");
rsiresult2 = WriteIf( rsidn,"cd", "");
RequestTimedRefresh( 0 );
GfxSelectFont( "Tahoma", 12, 120 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorWhite );
if ( rsiresult1 =="cu")
{ GfxSelectSolidBrush( ColorRGB(0,150,0) );
}
else
if ( rsiresult2 =="cd")
{ GfxSelectSolidBrush( ColorRGB(150,0,0));
}
else
if ( rsiresult2 =="")
{ GfxSelectSolidBrush( colorDarkTeal );
}
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorBlue );
GfxCircle( 100,100,50 );
_SECTION_END();
_SECTION_BEGIN("Rays");
//FT=ParamColor("FT", colorRed );
Pp1=3;
Pp2=2;
CS33=HHV(LLV(flowerHigh,Pp1)-ATR(Pp2),4);
CR33=HHV(LLV(flowerHigh,Pp1)-ATR(Pp2),5);
AtrupTrendCond1 = flowerClose> CS33 ;
AtrdnTrendCond1 =CS33>flowerClose ;
ATRup = WriteIf(AtrupTrendCond1,"atrup", "");
ATRdown= WriteIf( AtrdnTrendCond1,"atrdn", "");
if ( ATRup =="atrup")
{ GfxSelectSolidBrush( ColorRGB(0,180,0) );
}
else
if (ATRdown =="atrdn")
{ GfxSelectSolidBrush( ColorRGB(180,0,0));
}
else
if ( ATRdown =="")
{ GfxSelectSolidBrush( colorDarkTeal );
} GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorBlue );
GfxSelectPen( colorBlue, 1 ); // broader color
GfxCircle( 100,100,40 );
_SECTION_END();
_SECTION_BEGIN("Exit_Beast-3");
//GT=ParamColor("GT", colorRed );
EntrylookbackPeriod=10;
EntryATRperiod=1.9;
EntrySig = C > ( LLV( flowerLow, EntrylookbackPeriod ) + EntryATRperiod * ATR( 10 ) );
ExitSig = C < ( HHV( flowerHigh, EntrylookbackPeriod ) -EntryATRperiod * ATR( 10 ) );
RequestTimedRefresh( 0 );
GfxSelectFont( "Tahoma", 12, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorWhite );
EntryB = WriteIf( EntrySig,"eu", "");
ExitB = WriteIf( ExitSig,"ed", "");
if ( EntryB =="eu")
{ GfxSelectSolidBrush( ColorRGB(0,210,0) ); //
}
else
if ( ExitB =="ed")
{ GfxSelectSolidBrush( ColorRGB(210,0,0)); //
}
else
if ( ExitB =="")
{ GfxSelectSolidBrush( colorDarkTeal );
}
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorBlue );
GfxSelectPen( colorBlue, 1 ); // broader color
GfxCircle( 100,100,30 );// changing the value of x,y,rad x-70, y-90, rad-24
_SECTION_END();
_SECTION_BEGIN("CCI9-2");
//HT=ParamColor("HT", colorRed );
ccidn=CCI(8) < 0;
cciup=CCI(9) > 0;
ccresult1 = WriteIf( cciup,"cu", "");
ccresult2 = WriteIf( ccidn,"cd", "");
RequestTimedRefresh( 0 );
GfxSelectFont( "Tahoma", 12, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorWhite );
if ( ccresult1 =="cu")
{ GfxSelectSolidBrush( ColorRGB(0,240,0) );
}
else
if ( ccresult2 =="cd")
{ GfxSelectSolidBrush( ColorRGB(240,0,0));
}
else
if ( ccresult2 =="")
{ GfxSelectSolidBrush( colorDarkTeal );
}
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorBlue );
GfxSelectPen( colorBlue, 1 ); // broader color
GfxCircle( 100,100,20);
_SECTION_END();
_SECTION_BEGIN("%BB7-1");
//IT=ParamColor("IT", colorRed );
p=7;
x=((C+2*StDev(C,p)-MA(C,p))/(4*StDev(C,p)))*100;
bbdown= x < 40;
bbup= x > 40;
bbresult1 = WriteIf( bbup,"bu", "");
bbresult2 = WriteIf( bbdown,"bd", "");
bbresult3 = WriteIf( C,"bearishrevers", "");
RequestTimedRefresh( 0 );
GfxSelectFont( "Tahoma", 12, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorWhite );
if ( bbresult1 =="bu")
{ GfxSelectSolidBrush( ColorRGB(62,255,62) );
}
else
if ( bbresult2 =="bd")
{ GfxSelectSolidBrush( ColorRGB(255,62,62) );
}
else
if ( bbresult2 =="")
{ GfxSelectSolidBrush( colorDarkTeal );
}
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorBlue );
GfxSelectPen( colorBlue, 1 ); // broader color
GfxCircle( 100,100,10 );
_SECTION_END();
_SECTION_BEGIN("MACDHIGHBULLISH");
r1 = Param( "Fast avg", 12, 2, 200, 1 );
r2 = Param( "Slow avg", 26, 2, 200, 1 );
r3 = Param( "Signal avg", 9, 2, 200, 1 );
r4 = Param( "Wk slow", 17, 2, 200, 1 );
r5 = Param( "Wk fast", 8, 2, 200, 1 );
m1=MACD(r1,r2);
s1=Signal(r1,r2,r3);
rsidn=m1>0 AND m1>s1;
rsiresult2 = WriteIf( rsidn,"cd", "");
if ( rsiresult2 =="cd")
{ GfxSelectSolidBrush( ColorRGB(0,147,0));
}
else
if ( rsiresult2 =="")
{ GfxSelectSolidBrush( colorDarkTeal );
}
RequestTimedRefresh( 0 );
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorBlue );
GfxSelectPen( colorBlue, 1 );
// broader color
GfxRectangle(80,220,120,280);
_SECTION_END();

_SECTION_BEGIN("MACDLOWHBULLISH");
r1 = Param( "Fast avg", 12, 2, 200, 1 );
r2 = Param( "Slow avg", 26, 2, 200, 1 );
r3 = Param( "Signal avg", 9, 2, 200, 1 );
r4 = Param( "Wk slow", 17, 2, 200, 1 );
r5 = Param( "Wk fast", 8, 2, 200, 1 );
m1=MACD(r1,r2);
s1=Signal(r1,r2,r3);
rsidn=m1<0 AND m1>s1;
rsiresult2 = WriteIf( rsidn,"cd", "");
if ( rsiresult2 =="cd")
{ GfxSelectSolidBrush( ColorRGB(0,147,0));
}
else
if ( rsiresult2 =="")
{ GfxSelectSolidBrush( colorDarkTeal );
}
RequestTimedRefresh( 0 );
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorBlue );
GfxSelectPen( colorBlue, 1 );
// broader color
GfxRectangle(120,315,180,280);
_SECTION_END();
_SECTION_BEGIN("MACDLOWHBEARISH");
r1 = Param( "Fast avg", 12, 2, 200, 1 );
r2 = Param( "Slow avg", 26, 2, 200, 1 );
r3 = Param( "Signal avg", 9, 2, 200, 1 );
r4 = Param( "Wk slow", 17, 2, 200, 1 );
r5 = Param( "Wk fast", 8, 2, 200, 1 );
m1=MACD(r1,r2);
s1=Signal(r1,r2,r3);
rsidn=m1>0 AND m1<s1;
rsiresult2 = WriteIf( rsidn,"cd", "");
if ( rsiresult2 =="cd")
{ GfxSelectSolidBrush( ColorRGB(225,0,0));
}
else
if ( rsiresult2 =="")
{ GfxSelectSolidBrush( colorDarkTeal );
}
RequestTimedRefresh( 0 );
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorBlue );
GfxSelectPen( colorBlue, 1 );
// broader color
GfxRectangle(20,315,80,280);
_SECTION_END();
_SECTION_BEGIN("MACDHIGHHBEARISH");
r1 = Param( "Fast avg", 12, 2, 200, 1 );
r2 = Param( "Slow avg", 26, 2, 200, 1 );
r3 = Param( "Signal avg", 9, 2, 200, 1 );
r4 = Param( "Wk slow", 17, 2, 200, 1 );
r5 = Param( "Wk fast", 8, 2, 200, 1 );
m1=MACD(r1,r2);
s1=Signal(r1,r2,r3);
rsidn=m1<0 AND m1<s1;
rsiresult2 = WriteIf( rsidn,"cd", "");
if ( rsiresult2 =="cd")
{ GfxSelectSolidBrush( ColorRGB(225,0,0));
}
else
if ( rsiresult2 =="")
{ GfxSelectSolidBrush( colorDarkTeal );
}
RequestTimedRefresh( 0 );
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorBlue );
GfxSelectPen( colorBlue, 1 );
// broader color
GfxRectangle(80,375,120,315);
_SECTION_END();
_SECTION_BEGIN("bearishline");
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorRed );
GfxSelectPen( colorRed, 1 );
GfxMoveTo( 25,320 );
GfxLineTo( 75, 370 );
_SECTION_END();
_SECTION_BEGIN("bullishline");
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorGreen );
GfxSelectPen( colorGreen, 1 );
GfxMoveTo( 125,225 );
GfxLineTo( 175, 275 );
_SECTION_END();
_SECTION_BEGIN("volume1");
GfxSelectSolidBrush( colorDarkTeal );
GfxRoundRect(90,555,110,405,20,20);
_SECTION_END();
_SECTION_BEGIN("volume2");
GfxSelectSolidBrush( colorDarkTeal );
GfxCircle( 100,550,23 );
_SECTION_END();
_SECTION_BEGIN("spiker");
C1 = Ref(C, -1);
uc = C > C1; dc = C <= C1;
ud = C > O; dd = C <= O;
green = 1; blue = 2; yellow = 3; red = 4; white = 5;
VType = IIf(ud,
IIf(uc, green, yellow),
IIf(dd,
IIf(dc, red, blue), white));
/* green volume: up-day and up-close*/
gv = IIf(VType == green, V, 0);
/* yellow volume: up-day but down-close */
yv = IIf(VType == yellow, V, 0);
/* red volume: down-day and down-close */
rv = IIf(VType == red, V, 0);
/* blue volume: down-day but up-close */
bv = IIf(VType == blue, V, 0);
uv = gv + bv; uv1 = Ref(uv, -1); /* up volume */
dv = rv + yv; dv1 = Ref(dv, -1); /* down volume */
VolPer = Param("Adjust Vol. MA per.", 10, 1, 255, 1);
ConvPer = Param("Adjust Conv. MA per.", 4, 1, 255, 1);
MAuv = TEMA(uv, VolPer ); mauv1 = Ref(mauv, -1);
MAdv = TEMA(dv, VolPer ); madv1 = Ref(madv, -1);
MAtv = TEMA(V, VolPer );//total volume
Converge = (TEMA(MAuv - MAdv, ConvPer));
Converge1 = Ref(Converge, -1);
ConvergeUp = Converge > Converge1;
ConvergeOver = Converge > 0;
rising = ConvergeUp AND ConvergeOver;
falling = !ConvergeUp AND ConvergeOver;
_SECTION_END();
_SECTION_BEGIN("vol30");
rsiup=rising;
rsidn=falling;
down=Converge > 0;
rsiresult1 = WriteIf( rsiup,"ab", "");
rsiresult2 = WriteIf( rsidn,"cd", "");
rsiresult3 = WriteIf( down,"ef", "");
RequestTimedRefresh( 0 );
if ( rsiresult1 =="ab")
{
GfxSelectSolidBrush( ColorRGB(0,147,0) );
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorGreen );
GfxSelectPen( colorGreen, 1 );
}
else
if ( rsiresult2 =="cd")
{ GfxSelectSolidBrush( ColorRGB(0,85,0) );
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorDarkGreen );
GfxSelectPen( colorDarkGreen, 1 );
}
else
{ GfxSelectSolidBrush( ColorRGB(255,0,0) );
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorRed );
GfxSelectPen( colorRed, 1 );
}
GfxRectangle(91,550,109,430);
_SECTION_END();
_SECTION_BEGIN("vol31");
rsiup=rising;
rsidn=falling;
down=Converge > 0;
rsiresult1 = WriteIf( rsiup,"ab", "");
rsiresult2 = WriteIf( rsidn,"cd", "");
rsiresult3 = WriteIf( down,"ef", "");
RequestTimedRefresh( 0 );
if ( rsiresult1 =="ab")
{ GfxSelectSolidBrush( ColorRGB(0,147,0) );
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorGreen );
GfxSelectPen( colorGreen, 1 );
}
else
if ( rsiresult2 =="cd")
{ GfxSelectSolidBrush( ColorRGB(0,85,0) );
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorDarkGreen );
GfxSelectPen( colorDarkGreen, 1 );
}
else
{
GfxSelectSolidBrush( ColorRGB(255,0,0) );
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorRed );
GfxSelectPen( colorRed, 1 ); }
GfxCircle( 100,550,22 );
_SECTION_END();
_SECTION_BEGIN("Average 0");
//Average_switch = ParamToggle("Candle On/off", "Off|On");
P = HaClose;
Type = ParamList("Type", "Weighted,Lagless-21,Hull-26,Linear
Regression-45,Exponential,Double Exponential,Tripple
Exponential,Wilders,Simple");
Periods = Param("Periods", 20, 2, 200 );
Displacement = Param("Displacement", 1, -50, 50 );
m = 0;
if( Type == "Weighted" ) m= WMA( P, Periods );
if( Type == "Lagless-21" ) m= 2*EMA(P, Periods)-EMA(EMA(P, Periods), Periods);
if( Type == "Hull-26" ) m= WMA(2*(WMA(P, Periods/2))-WMA(P, Periods) ,4 );
if( Type == "Linear Regression-45" ) m= LinearReg( P, Periods );
if( Type == "Exponential" ) m = EMA( P, Periods );
if( Type == "Double Exponential" ) m = DEMA( P, Periods );
if( Type == "Tripple Exponential" ) m = TEMA( P, Periods );
if( Type == "Wilders" ) m = Wilders( P, Periods );
if( Type == "Simple" ) m = MA( P, Periods );
Plot( m, _DEFAULT_NAME(), ParamColor("Color", colorLightGrey),ParamStyle("Style",styleLine |styleThick|styleNoTitle ,maskAll),Displacement );
_SECTION_END();
_SECTION_BEGIN("Average 1");
//Average_switch = ParamToggle("Candle On/off", "Off|On");
P = HaClose;
Type = ParamList("Type", "Weighted,Lagless-21,Hull-26,Linear
Regression-45,Exponential,Double Exponential,Tripple
Exponential,Wilders,Simple");
Periods = Param("Periods", 60, 2, 200 );
Displacement = Param("Displacement", 1, -50, 50 );
m = 0;
if( Type == "Weighted" ) m= WMA( P, Periods );
if( Type == "Lagless-21" ) m= 2*EMA(P, Periods)-EMA(EMA(P, Periods), Periods);
if( Type == "Hull-26" ) m= WMA(2*(WMA(P, Periods/2))-WMA(P, Periods) ,4 );
if( Type == "Linear Regression-45" ) m= LinearReg( P, Periods );
if( Type == "Exponential" ) m = EMA( P, Periods );
if( Type == "Double Exponential" ) m = DEMA( P, Periods );
if( Type == "Tripple Exponential" ) m = TEMA( P, Periods );
if( Type == "Wilders" ) m = Wilders( P, Periods );
if( Type == "Simple" ) m = MA( P, Periods );
Plot( m, _DEFAULT_NAME(), ParamColor("Color", colorGrey50),ParamStyle("Style",styleLine |styleNoTitle ,maskAll),Displacement );
_SECTION_END();
_SECTION_BEGIN("Average 5");
P = HaClose;
Type = ParamList("Type", "Weighted,Lagless-21,Hull-26,Linear
Regression-45,Exponential,Double Exponential,Tripple
Exponential,Wilders,Simple");
Periods = Param("Periods", 120 ,2, 200 );
Displacement = Param("Displacement", 1, -50, 50 );
m = 0;
if( Type == "Weighted" ) m= WMA( P, Periods );
if( Type == "Lagless-21" ) m= 2*EMA(P, Periods)-EMA(EMA(P, Periods), Periods);
if( Type == "Hull-26" ) m= WMA(2*(WMA(P, Periods/2))-WMA(P, Periods) ,4 );
if( Type == "Linear Regression-45" ) m= LinearReg( P, Periods );
if( Type == "Exponential" ) m = EMA( P, Periods );
if( Type == "Double Exponential" ) m = DEMA( P, Periods );
if( Type == "Tripple Exponential" ) m = TEMA( P, Periods );
if( Type == "Wilders" ) m = Wilders( P, Periods );
if( Type == "Simple" ) m = MA( P, Periods );
Plot( m, _DEFAULT_NAME(), ParamColor("Color", colorGrey40),ParamStyle("Style",styleLine |styleNoTitle ,maskAll),Displacement );
_SECTION_END();
_SECTION_BEGIN("Sell Average 3");
P = HaClose;
Type = ParamList("Type", "Weighted,Lagless-21,Hull-26,Linear
Regression-45,Exponential,Double Exponential,Tripple
Exponential,Wilders,Simple");
Periods = Param("Periods", 180, 2, 800 );
Displacement = Param("Displacement", 1, -50, 50 );
m = 0;
if( Type == "Weighted" ) m= WMA( P, Periods );
if( Type == "Lagless-21" ) m= 2*EMA(P, Periods)-EMA(EMA(P, Periods), Periods);
if( Type == "Hull-26" ) m= WMA(2*(WMA(P, Periods/2))-WMA(P, Periods) ,4 );
if( Type == "Linear Regression-45" ) m= LinearReg( P, Periods );
if( Type == "Exponential" ) m = EMA( P, Periods );
if( Type == "Double Exponential" ) m = DEMA( P, Periods );
if( Type == "Tripple Exponential" ) m = TEMA( P, Periods );
if( Type == "Wilders" ) m = Wilders( P, Periods );
if( Type == "Simple" ) m = MA( P, Periods );
Plot( m, _DEFAULT_NAME(), ParamColor("Color", ColorRGB(40,40,40)),ParamStyle("Style",styleLine |styleNoTitle ,maskAll),Displacement );
_SECTION_END();

_SECTION_BEGIN("trendline");
farback=Param("How Far back to go",100,50,5000,10);
nBars = Param("Number of bars", 12, 5, 40);
aHPivs = H - H;aLPivs = L - L;
aHPivHighs = H - H;aLPivLows = L - L;aHPivIdxs = H - H;aLPivIdxs = L - L;
nHPivs = 0;nLPivs = 0;lastHPIdx = 0;lastLPIdx = 0;lastHPH = 0;lastLPL = 0;
curPivBarIdx = 0;
aHHVBars = HHVBars(H, nBars);aLLVBars = LLVBars(L, nBars);
aHHV = HHV(H, nBars);aLLV = LLV(L, nBars);
aVisBars = Status("barvisible");nLastVisBar = LastValue(Highest(IIf(aVisBars, BarIndex(), 0)));
_TRACE("Last visible bar: " + nLastVisBar);
curBar = (BarCount-1);curTrend = "";if (aLLVBars[curBar] < aHHVBars[curBar]) {
curTrend = "D";}else {curTrend = "U";}
for (i=0; i<farback; i++) {curBar = (BarCount - 1) - i;
if (aLLVBars[curBar] < aHHVBars[curBar]) {
if (curTrend == "U") {curTrend = "D";
curPivBarIdx = curBar - aLLVBars[curBar];aLPivs[curPivBarIdx] = 1;aLPivLows[nLPivs] = L[curPivBarIdx];
aLPivIdxs[nLPivs] = curPivBarIdx;nLPivs++;}
} else {if (curTrend == "D") {curTrend = "U";curPivBarIdx = curBar - aHHVBars[curBar];
aHPivs[curPivBarIdx] = 1;aHPivHighs[nHPivs] = H[curPivBarIdx];
aHPivIdxs[nHPivs] = curPivBarIdx;nHPivs++;}} }
curBar = (BarCount-1);candIdx = 0;candPrc = 0;lastLPIdx = aLPivIdxs[0];lastLPL = aLPivLows[0];
lastHPIdx = aHPivIdxs[0];lastHPH = aHPivHighs[0];if (lastLPIdx > lastHPIdx) {
candIdx = curBar - aHHVBars[curBar];candPrc = aHHV[curBar];
if (lastHPH < candPrc AND candIdx > lastLPIdx AND candIdx < curBar) {
aHPivs[candIdx] = 1;
for (j=0; j<nHPivs; j++) {aHPivHighs[nHPivs-j] = aHPivHighs[nHPivs-(j+1)];
aHPivIdxs[nHPivs-j] = aHPivIdxs[nHPivs-(j+1)];}aHPivHighs[0] = candPrc ;
aHPivIdxs[0] = candIdx;nHPivs++;}} else {
candIdx = curBar - aLLVBars[curBar];candPrc = aLLV[curBar];if (lastLPL > candPrc AND
candIdx > lastHPIdx AND candIdx < curBar) {
aLPivs[candIdx] = 1;
for (j=0; j<nLPivs; j++) {aLPivLows[nLPivs-j] = aLPivLows[nLPivs-(j+1)];
aLPivIdxs[nLPivs-j] = aLPivIdxs[nLPivs-(j+1)];}aLPivLows[0] = candPrc;
aLPivIdxs[0] = candIdx;nLPivs++;}}
for (k=0; k<nHPivs; k++) {_TRACE("High pivot no. " + k
+ " at barindex: " + aHPivIdxs[k] + ", "
+ WriteVal(ValueWhen(BarIndex()==aHPivIdxs[k],
DateTime(), 1), formatDateTime)+ ", " + aHPivHighs[k]);}
a1=ahpivs==1;a2=alpivs==1;
x = Cum(1);s1=L;s11=H;pS = a2 == 1;
endt= SelectedValue(ValueWhen( pS, x, 1 ));
startt=SelectedValue(ValueWhen( pS, x, 2 ));dtS =endt-startt;
endS = SelectedValue(ValueWhen( pS, s1, 1 ) );
startS = SelectedValue( ValueWhen( pS, s1, 2 ));aS = (endS-startS)/dtS;
bS = endS;trendlineS = aS * ( x -endt ) + bS;
g3= IIf(x>startt-10,trendlineS,-1e10);
pR = a1== 1;endt1= SelectedValue(ValueWhen( pR, x, 1 ));
startt1=SelectedValue(ValueWhen( pR, x, 2 ));
dtR =endt1-startt1;endR = SelectedValue(ValueWhen( pR, s11, 1 ) );
startR = SelectedValue( ValueWhen( pR, s11, 2 ));
aR = (endR-startR)/dtR;bR = endR;
trendlineR = aR * ( x -endt1 ) + bR;
g4= IIf(x>startT1-10,trendlineR,-1e10);
_SECTION_END();
_SECTION_BEGIN("trendlineA");
dn=g3>C;
up=g3<C;
bbresult1 = WriteIf( dn,"dn", "");
bbresult2 = WriteIf( up,"up", "");
RequestTimedRefresh( 0 );
if ( bbresult1 =="dn")
{ GfxSelectSolidBrush( ColorRGB(255,0,0) );
}
else
if ( bbresult2 =="up")
{ GfxSelectSolidBrush( ColorRGB(0,147,0) );
}
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorWhite );
GfxSelectPen( colorWhite, 1 );
GfxCircle( 30,229,8 );
_SECTION_END();
_SECTION_BEGIN("trendlineA");
dn=g3>C;
up=g3<C;
bbresult1 = WriteIf( dn,"dn", "");
bbresult2 = WriteIf( up,"up", "");
RequestTimedRefresh( 0 );
if ( bbresult1 =="dn")
{ GfxSelectSolidBrush( ColorRGB(255,0,0) );
}
else
if ( bbresult2 =="up")
{ GfxSelectSolidBrush( ColorRGB(0,147,0) );
}
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorWhite );
GfxSelectPen( colorWhite, 1 );
GfxCircle( 168,367,8 );
_SECTION_END();
_SECTION_BEGIN("TRENDLINEline");
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorWhite );
GfxSelectPen( colorWhite, 1 );
GfxMoveTo( 35,234 );
GfxLineTo( 163, 362 );
_SECTION_END();
_SECTION_BEGIN("traing sl");
function vstop_func(trBull,trBear)
{ trailArray[ 0 ] = C[ 0 ]; // initialize
for( i = 1; i < BarCount; i++ )
{ prev = trailArray[ i - 1 ];
if (C[ i ] > prev AND C[ i - 1 ] > prev)
{ trailArray[ i ] = Max(prev,C[ i ] - trBull[ i ]);
}
else if (C[ i ] < prev AND C[ i - 1 ] < prev)
{ trailArray[ i ] = Min(prev,C[ i ] + trBear[ i ]);
}
else if (C[ i ] > prev)
{ trailArray[ i ] = C[ i ] - trBull[ i ];
}
else
{ trailArray[ i ] = C[ i ] + trBear[ i ];
}
} return trailArray;
}
per = Param("per",20, 1, 150, 1);
multBull = Param("multBull",2, 1, 4, 0.05);
multBear = Param("multBear",2, 1, 4, 0.05);
trBull = multBull * ATR(per);
trBear = multBear * ATR(per);
trailArray = vstop_func(trBull,trBear);
s0=trailArray;
s1= s0 > C ;
s2= s0 <C ;
ccresult1 = WriteIf( s1,"cu", "");
ccresult2 = WriteIf( s2,"cd", "");
GfxSelectFont("arial", 13, 700 ); GfxSetBkMode( colorRed);
GfxSetTextColor( ParamColor("Color",colorRed) );
Hor=Param("Horizontal Position",30,10,1200,1);
Ver=Param("Vertical Position",185,100,50,50);
if ( ccresult1 =="cu")
{ GfxTextOut(""+s0,Hor , Ver );
}
else
GfxSelectFont("arial", 13, 700 ); GfxSetBkMode( colorGreen );
GfxSetTextColor( ParamColor("Color",colorGreen) );
Hor=Param("Horizontal Position",75,10,1200,1);
Ver=Param("Vertical Position",185,100,50,50);
if ( ccresult2 =="")
{GfxTextOut(""+s0,Hor , Ver );
}
_SECTION_END();
_SECTION_BEGIN("traing s2");
s0=trailArray;
s1= s0 > C ;
s2= s0 <C ;
ccresult1 = WriteIf( s1,"cu", "");
ccresult2 = WriteIf( s2,"cd", "");
GfxSelectFont("arial", 13, 700 ); GfxSetBkMode( colorBrightGreen );
GfxSetTextColor( ParamColor("Color",colorBrightGreen) );
Hor=Param("Horizontal Position",30,10,1200,1);
Ver=Param("Vertical Position",185,100,50,50);
if ( ccresult2 =="cd")
{GfxTextOut(""+s0,Hor , Ver );
}
else
GfxSelectFont("arial", 13, 700 ); GfxSetBkMode( colorWhite );
GfxSetTextColor( ParamColor("Color",colorWhite) );
Hor=Param("Horizontal Position",75,10,1200,1);
Ver=Param("Vertical Position",185,100,50,50);
if ( ccresult1 =="")
{GfxTextOut(""+s0,Hor , Ver );
}
_SECTION_END();
_SECTION_BEGIN("CMP");
GfxSelectFont("arial", 13, 700 ); GfxSetBkMode( colorWhite );
GfxSetTextColor( ParamColor("Color",colorWhite) );
Hor=Param("Horizontal Position",120,10,1200,1);
Ver=Param("Vertical Position",185,100,50,50);
GfxTextOut(""+C,Hor , Ver );
_SECTION_END();
_SECTION_BEGIN("buycircle");
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorBlue );
GfxSelectPen( colorBlue, 1 );
if ( ccresult2 =="cd")
{GfxSelectSolidBrush( ColorRGB(0,255,0) );
}
else
{GfxSelectSolidBrush( ColorRGB(0,0,94) );
}
GfxCircle( 20,40,7 );
_SECTION_END();
_SECTION_BEGIN("sellcircle");
GfxSelectFont( "Arial", 10, 100 );
GfxSetBkMode( 1 );
GfxSetTextColor( colorBlue );
GfxSelectPen( colorBlue, 1 );
if ( ccresult1 =="cu")
{GfxSelectSolidBrush( ColorRGB(255,0,0) );
}
else
{GfxSelectSolidBrush( ColorRGB(0,0,94) );
}
GfxCircle( 180,40,7 );
_SECTION_END();

Wednesday 22 August 2012

Diabetes

ONE GOOD AYURVEDIC MEDICINE

NAMELY

" SAPTRANGYADIVATI "

FROM

TAPOVAN PHARMA

COSTING RS. 175/- FOR 60 NO.

BEFORE START THIS MEDICINE , FIRST GOTO LAB & CHECK OR DIABITIES AND THEN READ INSTRUSCTION IN THIS MEDICINE BOTTLE AND START COURSE

ALONGWITH THIS MEDICINE , DONT STOP UR ROUTINE MEDICINE OF DIABITIES.

EVERY 15 DAYS CHECK UR DIABITIES AND SHOW TO UR ROUTINE DOCTOR

IF IT COMES IN CONTROL , SLOWLY SLWOLY REDUCE UR ROUTINE MEDICINE....

IF GOD WISH , UR DIABITIES WILL BE IN NORMAL WITHIN 6 MONTH TIME

@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@

ONE OF MY RELATIVES SUFFERING FROM DIABITIES FROM LAST 35 YEARS..

AFTER TAKING THIS MEDICINE , NOW BEFORE FOOD REPORT COMES TO 72

NOW THEY TAKE SWEET ALSO

@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@

DISCLAIMER :

FIRST OF ALL TAPOVAN PHARMA IS NOT MY COMPANY , NOT A DISTRIBUTOR , NOR A DEALER , NOR ANY KIND OF RELATION WITH HIM.. BEFORE USING THIS MEDICINE , FIRST CONSULT UR DOCTOR AND THEN TAKE UR DECISITION, I M NOT RESPONSIBLE FOR ANYKIND OF REVERSE EFFECT. THIS IS MY VIEW ONLY.

GOD IS GREAT

Monday 20 August 2012

AMAZING ANSWERS

Question and the Answer given by Candidates. Oh sorry, most of them are IAS Officers now.

Q.How can you drop a raw egg onto a concrete floor without cracking it?
A.Concrete floors are very hard to crack! (UPSC Topper)

Q.If it took eight men ten hours to build a wall, how long would it
take four men to build it?
A. No time at all it is already built. (UPSC 23 Rank Opted for IFS)

Q.If you had three apples and four oranges in one hand and four apples and three oranges in the other hand, what would you have?
A. Very large hands. (UPSC 11 Rank Opted for IPS)

Q.. How can you lift an elephant with one hand?
A. It is not a problem, since you will never find! an elephant with
one hand. (UPSC Rank 14 Opted for IES)

Q. How can a man go eight days without sleep?
A. No Probs , He sleeps at night. (UPSC IAS Rank 98)

Q. If you throw a red stone into the blue sea what it will become?
A. It will Wet or Sink as simple as that. (UPSC IAS Rank 2)

Q. What looks like half apple ?
A : The other half. (UPSC - IAS Topper )

Q. What can you never eat for breakfast ?
A : Dinner.

Q. What happened when wheel was invented ?
A : It caused a revolution.

Q.. Bay of Bengal is in which state?
A : Liquid (UPSC 33Rank)
Q. How many buckets of water does Pacific Ocean contains?
A : It depends on the size of the bucket. (CA Institute Campus
Interview Placement)
Interviewer said 'I shall either ask you ten easy questions or one really difficult question. Think well before you make up your mind!'
The boy thought for a while and said, 'my choice is one really difficult question.'

'Well, good luck to you, you have made your own choice! Now tell me this. 'What comes first, Day or Night?'

The boy was jolted into reality as his admission depends on his answer, but he thought for a while and said, 'It's the DAY sir!'

'How' the interviewer asked,

'Sorry sir, you promised me that you will not ask me a SECOND difficult question!'

He was selected for IIM!

SDA +KOLIER

// Supertrend - Translated from Kolier MQ4
// see: http://kolier.li/indicator/kolier-supertrend-indi
// translation in Amibroker AFL code by E.M.Pottasch, 2011

procedure calcTrend_proc(ATR_Period,tr,ATR_Multiplier,TrendMode,CalcPrice)
{
global buffer_line_down;
global buffer_line_up;
buffer_line_down = Null;
buffer_line_up = Null;

PHASE_NONE = 0;
PHASE_BUY = 1;
PHASE_SELL = -1;

phase=PHASE_NONE;
band_upper = 0;band_lower = 0;

for(i = ATR_Period + 1; i < BarCount; i++)
{
band_upper = CalcPrice[i] + ATR_Multiplier * tr[i];
band_lower = CalcPrice[i] - ATR_Multiplier * tr[i];

if(phase==PHASE_NONE)
{
buffer_line_up[i] = CalcPrice[i];
buffer_line_down[i] = CalcPrice[i];
}
if(phase!=PHASE_BUY && Close[i]>buffer_line_down[i-1] && !IsEmpty(buffer_line_down[i-1]))
{
phase = PHASE_BUY;
buffer_line_up[i] = band_lower;
buffer_line_up[i-1] = buffer_line_down[i-1];
}
if(phase!=PHASE_SELL && Close[i]<buffer_line_up[i-1] && !IsEmpty(buffer_line_up[i-1]))
{
phase = PHASE_SELL;
buffer_line_down[i] = band_upper;
buffer_line_down[i-1] = buffer_line_up[i-1];
}
if(phase==PHASE_BUY && ((TrendMode==0 && !IsEmpty(buffer_line_up[i-2])) || TrendMode==1) )
{
if(band_lower>buffer_line_up[i-1])
{
buffer_line_up[i] = band_lower;
}
else
{
buffer_line_up[i] = buffer_line_up[i-1];
}
}
if(phase==PHASE_SELL && ((TrendMode==0 && !IsEmpty(buffer_line_down[i-2])) || TrendMode==1) )
{
if(band_upper<buffer_line_down[i-1])
{
buffer_line_down[i] = band_upper;
}
else
{
buffer_line_down[i] = buffer_line_down[i-1];
}
}
}
}

SetBarsRequired(sbrAll,sbrAll);

TrendMode = ParamToggle("TrendMode","Off|On",1);
ATR_Multiplier = Param("ATR_Multiplier",2,0.1,10,0.1);
ATR_Period = Param( "ATR_Period",5,1,20,1);
tr = ATR(ATR_Period);

CalcPrice = (H+L)/2;
calcTrend_proc(ATR_Period,tr,ATR_Multiplier,TrendMode,CalcPrice);

SetChartOptions(0,chartShowDates);
Plot(C,"C",colorWhite,64);
Plot(buffer_line_up,"\ntu",ColorRGB(28,134,238),styleThick);
Plot(buffer_line_down,"\ntd",ColorRGB(205,51,51),styleThick);

Plot( 2,"",IIf(buffer_line_up,colorGreen,colorBlack),styleOwnScale|styleArea|styleNoLabel, -0.5, 100 );
Plot( 4,"",IIf(buffer_line_down,colorRed,colorBlack),styleOwnScale|styleArea|styleNoLabel, -0.5, 100 );

_SECTION_BEGIN("SDA2 Channel Trading System ver 2.0");

/* Coded By : Rajandran R
WebSite: www.marketcalls.in

Earlier during October 26th, 2010 SDA2 Trend Trading System is introduced in marketcalls.
After revising my old strategy( which contains too many flaws in the system) releasing the
newer Version of SDA2 Trend trading system(Version 2.0).This Version is more optimized for
Long Only Signals (Short Signals are treated as exit from the long position AND stay away
from the market). In the older Version the SDA2 trading system wont perform good if there
is any gap up OR gap down in the trading instrument. The new Version overcomes those flaw
AND it is more optimized towards those Gap Up/Gap Down. */

SetChartOptions(0,chartShowArrows|chartShowDates);
trailing=0;
_N(Title = StrFormat("{{NAME}} - {{INTERVAL}} {{DATE}} Open %g, Hi %g, Lo %g, Close %g (%.1f%%) {{VALUES}}", O, H, L, C, SelectedValue( ROC( C, 1 ) ) ));
SetTradeDelays( 1, 1, 1, 1 );
BuyPrice = Open;
SellPrice = Open;

Derived=WMA(WMA(WMA(((H+L)/2)+(O-C),2),2),2);
i=WMA(Derived,3);
Upper=(Derived+StDev(Derived,7))+ATR(2)/1.5;
Lower=(Derived-StDev(Derived,7))-ATR(2)/1;

color=IIf(BarsSince(Cross(Lower,Low))>
BarsSince(Cross(C,Upper)), colorBrightGreen,colorRed);
Plot(C,"",color,64);
Plot(Upper,"upper band",colorGreen);
Plot(Lower,"lower band",colorDarkRed);

/* Buy or Sell Condition */
Buy = Cross(Close,Upper);
Sell = Cross(Lower,Low);
Buy = ExRem(Buy,Sell);
Sell = ExRem(Sell,Buy);
Cover=Buy;
Short=Sell;

Filter = Buy OR Sell;
/* Exploration Parameters */
AddTextColumn( FullName(), "Company Name" );
AddColumn( Buy, "Buy", 1 );
AddColumn( Sell, "Sell", 1 );
AddColumn( C, "Close", 1.3 );
AddColumn( H, "High", 1.3 );
AddColumn( Lower, "Lower Band", 1.3 );
AddColumn( Upper, "Upper Band", 1.3 );

PlotShapes(IIf(Buy, shapeSquare, shapeNone),colorGreen, 0, L, Offset=-40);
PlotShapes(IIf(Buy, shapeSquare, shapeNone),colorLime, 0,L, Offset=-50);
PlotShapes(IIf(Buy, shapeUpArrow, shapeNone),colorWhite, 0,L, Offset=-45);
PlotShapes(IIf(Sell, shapeSquare, shapeNone),colorRed, 0, H, Offset=40);
PlotShapes(IIf(Sell, shapeSquare, shapeNone),colorOrange, 0,H, Offset=50);
PlotShapes(IIf(Sell, shapeDownArrow, shapeNone),colorWhite, 0,H, Offset=-45);

_SECTION_END();

/*_SECTION_BEGIN("WaterMark");
GfxSetOverlayMode(1);
GfxSelectFont("Tahoma", Status("pxheight")/12 );
GfxSetTextAlign( 6 );// center alignment
GfxSetTextColor( ColorHSB( 42, 42, 42 ) );
GfxSetBkMode(0); // transparent
GfxTextOut( "Budget 2012", Status("pxwidth")/4, Status("pxheight")/4 ); //you can change your own display name
GfxSelectFont("Tahoma", Status("pxheight")/12 );
GfxTextOut( " www.marketcalls.in", Status("pxwidth")/4, Status("pxheight")/4+90 ); //you can change your own display name
_SECTION_END(); */

_SECTION_BEGIN("AR_Prediction.afl");
//------------------------------------------------------------------------------
//
// Formula Name: AR_Prediction.afl
// Author/Uploader: Mich. - tom_borgo [at] hotmail.com
// E-mail:
// Date/Time Added: 2006-11-18 20:09:10
// Origin:
// Keywords: ar,prediction,cycle,fft,spectrum,regression,burg
// Level: basic
// Flags: indicator,function
// Formula URL: http://www.amibroker.com/library/formula.php?id=764
// Details URL: http://www.amibroker.com/library/detail.php?id=764
//
//------------------------------------------------------------------------------
//
// AR MODELING (Burg and Yule-Walker method are implemented)
//
// Main features :
//
// - Prediction : calculate future datas based on past data, past volume and
// past low/high
//
// - Spectral analysis : compute sharp and clean spectrum of the data and plot
// it in Decibels
//
// - Find up to 10 or more dominant cycles from the data
//
//------------------------------------------------------------------------------

// ******************************************************************************************
// *
// * AR MODELING (Burg and Yule-Walker method are implemented)
// *
// *
// * Main features :
// * - Prediction : calculate future datas based on past data, past volume and past low/high
// * - Spectral analysis : compute sharp and clean spectrum of the data and plot it in Decibels
// * - Find up to 10 or more dominant cycles from the data
// *
// *
// * Native AFL for maximum speed without needed external dll
// * Many criterions for auto order selection : FPE, AIC, MDL, CAT, CIC ...
// * + 2 optionnal VBS Scripts (one for experimentation about rounding, the other for PolyFit detrending)
// *
// *
// * Use and modify it freely. v0.04, tomy_frenchy, tom_borgo@hotmail.com, mich. 11/2006.
// * Thanks to Fred for PolyFit function AFL and Gaussian Elimination VBS
// *
// ******************************************************************************************

// ******************************************************************************************
// *
// * DESCRITION
// *
// * AR_Prediction.afl compute AR (Auto-Regressive) model from the data.
// * Burg method is called too Maximum Likelihood, Maximum Entropy Method (MEM),
// * Maximum Entropy Spectral Analysis (MESA). All are the same.
// * Yule-Walker method is based on the Autocorellation of the data and by minimising
// * the Least Square error between model and data.
// *
// * Purpose of AR modeling are to obtain a simple model to describe data.
// * From this model we can for two main application :
// * - Calculate future data (make prediction). It is possible because the model is
// * described by a mathematical expression.
// * - Make spectral analysis to exctract main cycle from the data. Spectral Analysis is
// * more powerfull this way than classic FFT for short horizon data and for spectrum wich
// * have sharp frequency (cyclic signal)
// *
// *
// * MAIN FUNCTION :
// * 1- AR_Burg: to compute AR Burg model
// * 2- AR_YuleWalker: to compute AR Yule Walker model
// * 3- AR_Predict: to predict future data based on the computed AR model
// * 4- AR_Freq: to compute the spectrum from the computed AR model
// * 5- AR_Sin: to find main frequency in the computed spectrum (dominant cycles from the data)
// *
// *
// * ALL THE CODE IS COMMENTED SO YOU CAN FIND PARAMETERS FOR THE FUNCTION IN THE DEMO SECTION AT THE END OF THE FILE.
// * All the source is commented, so you can learn a lot from it.
// * There are many sources paper on the net about the subject.
// *
// *
// ******************************************************************************************

// ******************************************************************************************
// *
// * PARAMETERS
// *
// *
// *
// * Main parameters for AR modeling :
// *
// *
// * Price field = Data to predict
// * AR Model = Burg model or Yule-Yalker model
// * Length to look back = Horizon used on the data to compute the model
// * Order AR = Order wanted for the AR model
// * Number of bar to predict = Length prediction
// * Auto AR order = Find automatically the best order
// * Auto AR criterion = Criterion to use for Auto AR order
// * FPE: Final Prediction Error (classic old one)
// * AIC: Akaine Information Criterion (classic less old one)
// * MDL: Minimum Description Length
// * CAT: Criterion Autoregressive Transfer
// * CIC: Combined Information Criterion (good one)
// * Mixed: An upper averaging from all those criterion (best, recommended)
// * Burg: Hamming Window = If Hamming Window is applyed during model computation (only Burg model)
// * Burg: Double Precision = If Double Precision is used to compute the model (only Burg model)
// * Y-W: Biased = If Autocorrelation estimator should be biased (Yes) or not-biased (No) (only Yule-Walker model)
// *
// *
// *
// * Pre-Processing begore modeling AR model :
// *
// *
// * Intraday EOD GAP Filter = For intraday data, first data of the day is same than last data of yesterday day
// *
// *
// * Periods T3 = Periods for Tillson's T3 filter (high order low pass denoiser)
// * Slope T3 = Slope for Tillson's T3 filter (0.7 to 0.83 for usual value) (Higher value mean more dynamic filter)
// *
// *
// * Detrending method = Method to use to detrend the data
// * None: no detrending is done
// * Log10: log10(data) detrending is done (usefull for trend wich increase with time)
// * Derivation: differentiation one by one from the data (prefered method because is sure to kill the linear trend)
// * Linear Regression: substract linear regression from the data (good method too if fitting is ok)
// * PolyFit: substract n-th order polynomial regression from the data (order 3 can give nice result)
// * PolyFit Order = Order for the PolyFit detrending method (usefull just if you choose this method of detrending)
// *
// *
// * Center data = Remove the bias in the data (substract the mean).
// * AR modeling NEED NOT BIASED data, so this sould be always left on "Yes".
// *
// *
// * Normalize = Scale data between [-1;1] (usefull for better computation because less rounding error)
// *
// *
// * Volume HL weight on data = Multiply data by a factor computed from volume and high/low information from the data
// * !!! This is experimental feature !!!
// * Strength Volume = Strength of volume weighting
// * Strength Fractal = Strength of High/Low (Fractal) weighting
// * Periods Fract (even) = Periods to compute the fractal factor (an even period is needed)
// * Scale Final Weights = Final dynamic of the weights (high value increase difference between low weight and high weight)
// * Translate Final Weights = Final strength of the weights (high value bring all the weights near their maximum)
// * Scale / Translate is based on Inverse Fisher Transform and work like a limiter/compressor of the weights
// *
// *
// * Time weight on data = Apply time windowing on data so more recent data are more weighted than old data to compute the AR model
// * Weighting Time Window = Choose the strength of the weights
// * Log: natural logarithm, small time weigthing on the data
// * Ramp: a ramp, linear time weigthing on the data
// * Exp: exponential, strong time weigthing on the data
// *
// *
// *
// * Post-Processing before plotting predicted data :
// *
// * Retrending method = Choose if trend should be added back to predicted data
// * Trend is automatically added back for "Log10" and "Derivation" methods,
// * so data stay in good scale
// *
// * Color PolyFit = Color to plot the curve from "PolyFit" detrending method
// * Color PolyFit = Color to plot the line from "Linear Regression" detrending method
// *
// *
// ******************************************************************************************

EnableScript("VBScript");
<%
' Called by ARG_Burg AFL funct. to compute Ki in Double Precision
function Compute_KI(f, b, W, i, LongBar)
n = LongBar - 1

for j = i to n
Num = Num + cDBL(W(j))*( cDBL(f(j))*cDBL(b(j-1)) )
Den = Den + cDBL(W(j))*( cDBL(f(j))^2 + cDBL(b(j-1))^2 )
next

KI = 2*Num/Den
Compute_KI = KI

end function

function Gaussian_Elimination (GE_Order, GE_N, GE_SumXn, GE_SumYXn)
Dim b(10, 10)
Dim w(10)
Dim Coeff(10)

for i = 1 To 10
Coeff(i) = 0
next

n = GE_Order + 1

for i = 1 to n
for j = 1 to n
if i = 1 AND j = 1 then
b(i, j) = cDBL(GE_N)
else
b(i, j) = cDbl(GE_SumXn(i + j - 2))
end if
next
w(i) = cDbl(GE_SumYXn(i))
next

n1 = n - 1
for i = 1 to n1
big = cDbl(abs(b(i, i)))
q = i
i1 = i + 1

for j = i1 to n
ab = cDbl(abs(b(j, i)))
if (ab >= big) then
big = ab
q = j
end if
next

if (big <> 0.0) then
if (q <> i) then
for j = 1 to n
Temp = cDbl(b(q, j))
b(q, j) = b(i, j)
b(i, j) = Temp
next
Temp = w(i)
w(i) = w(q)
w(q) = Temp
end if
end if

for j = i1 to n
t = cDbl(b(j, i) / b(i, i))
for k = i1 to n
b(j, k) = b(j, k) - t * b(i, k)
next
w(j) = w(j) - t * w(i)
next
next

if (b(n, n) <> 0.0) then

Coeff(n) = w(n) / b(n, n)
i = n - 1

while i > 0
SumY = cDbl(0)
i1 = i + 1
for j = i1 to n
SumY = SumY + b(i, j) * Coeff(j)
next
Coeff(i) = (w(i) - SumY) / b(i, i)
i = i - 1
wend

Gaussian_Elimination = Coeff

end if
end function
%>

function PolyFit(GE_Y, GE_BegBar, GE_EndBar, GE_Order, GE_ExtraB, GE_ExtraF)
{
BI = BarIndex();

GE_N = GE_EndBar - GE_BegBar + 1;
GE_XBegin = -(GE_N - 1) / 2;
GE_X = IIf(BI < GE_BegBar, 0, IIf(BI > GE_EndBar, 0, (GE_XBegin + BI - GE_BegBar)));

GE_X_Max = LastValue(Highest(GE_X));

GE_X = GE_X / GE_X_Max;

X1 = GE_X;

GE_Y = IIf(BI < GE_BegBar, 0, IIf(BI > GE_EndBar, 0, GE_Y));

GE_SumXn = Cum(0);
GE_SumXn[1] = LastValue(Cum(GE_X));
GE_X2 = GE_X * GE_X; GE_SumXn[2] = LastValue(Cum(GE_X2));
GE_X3 = GE_X * GE_X2; GE_SumXn[3] = LastValue(Cum(GE_X3));
GE_X4 = GE_X * GE_X3; GE_SumXn[4] = LastValue(Cum(GE_X4));
GE_X5 = GE_X * GE_X4; GE_SumXn[5] = LastValue(Cum(GE_X5));
GE_X6 = GE_X * GE_X5; GE_SumXn[6] = LastValue(Cum(GE_X6));
GE_X7 = GE_X * GE_X6; GE_SumXn[7] = LastValue(Cum(GE_X7));
GE_X8 = GE_X * GE_X7; GE_SumXn[8] = LastValue(Cum(GE_X8));
GE_X9 = GE_X * GE_X8; GE_SumXn[9] = LastValue(Cum(GE_X9));
GE_X10 = GE_X * GE_X9; GE_SumXn[10] = LastValue(Cum(GE_X10));
GE_X11 = GE_X * GE_X10; GE_SumXn[11] = LastValue(Cum(GE_X11));
GE_X12 = GE_X * GE_X11; GE_SumXn[12] = LastValue(Cum(GE_X12));
GE_X13 = GE_X * GE_X12; GE_SumXn[13] = LastValue(Cum(GE_X13));
GE_X14 = GE_X * GE_X13; GE_SumXn[14] = LastValue(Cum(GE_X14));
GE_X15 = GE_X * GE_X14; GE_SumXn[15] = LastValue(Cum(GE_X15));
GE_X16 = GE_X * GE_X15; GE_SumXn[16] = LastValue(Cum(GE_X16));

GE_SumYXn = Cum(0);
GE_SumYXn[1] = LastValue(Cum(GE_Y));
GE_YX = GE_Y * GE_X; GE_SumYXn[2] = LastValue(Cum(GE_YX));
GE_YX2 = GE_YX * GE_X; GE_SumYXn[3] = LastValue(Cum(GE_YX2));
GE_YX3 = GE_YX2 * GE_X; GE_SumYXn[4] = LastValue(Cum(GE_YX3));
GE_YX4 = GE_YX3 * GE_X; GE_SumYXn[5] = LastValue(Cum(GE_YX4));
GE_YX5 = GE_YX4 * GE_X; GE_SumYXn[6] = LastValue(Cum(GE_YX5));
GE_YX6 = GE_YX5 * GE_X; GE_SumYXn[7] = LastValue(Cum(GE_YX6));
GE_YX7 = GE_YX6 * GE_X; GE_SumYXn[8] = LastValue(Cum(GE_YX7));
GE_YX8 = GE_YX7 * GE_X; GE_SumYXn[9] = LastValue(Cum(GE_YX8));

GE_Coeff = Cum(0);

GE_VBS = GetScriptObject();
GE_Coeff = GE_VBS.Gaussian_Elimination(GE_Order, GE_N, GE_SumXn, GE_SumYXn);

for (i = 1; i <= GE_Order + 1; i++)
printf(NumToStr(i, 1.0) + " = " + NumToStr(GE_Coeff[i], 1.9) + "\n");

GE_X = IIf(BI < GE_BegBar - GE_ExtraB - GE_ExtraF, 0, IIf(BI > GE_EndBar, 0, (GE_XBegin + BI - GE_BegBar + GE_ExtraF) / GE_X_Max));

GE_X2 = GE_X * GE_X; GE_X3 = GE_X2 * GE_X; GE_X4 = GE_X3 * GE_X; GE_X5 = GE_X4 * GE_X; GE_X6 = GE_X5 * GE_X;
GE_X7 = GE_X6 * GE_X; GE_X8 = GE_X7 * GE_X; GE_X9 = GE_X8 * GE_X; GE_X10 = GE_X9 * GE_X; GE_X11 = GE_X10 * GE_X;
GE_X12 = GE_X11 * GE_X; GE_X13 = GE_X12 * GE_X; GE_X14 = GE_X13 * GE_X; GE_X15 = GE_X14 * GE_X; GE_X16 = GE_X15 * GE_X;

GE_Yn = IIf(BI < GE_BegBar - GE_ExtraB - GE_ExtraF, -1e10, IIf(BI > GE_EndBar, -1e10,
GE_Coeff[1] +
GE_Coeff[2] * GE_X + GE_Coeff[3] * GE_X2 + GE_Coeff[4] * GE_X3 + GE_Coeff[5] * GE_X4 + GE_Coeff[6] * GE_X5 +
GE_Coeff[7] * GE_X6 + GE_Coeff[8] * GE_X7 + GE_Coeff[9] * GE_X8));

return GE_Yn;
}

function Time_weighting(data, window, BegBar, EndBar) {
BI = BarIndex();
if (window == "Log") alphatime = log(Cum(1));
if (window == "Ramp") alphatime = Cum(1);
if (window == "Exp") alphatime = exp(Cum(0.01));
alphatime = Ref(alphatime, -BegBar);
alphatime = alphatime - alphatime[BegBar];
alphatime = alphatime / alphatime[EndBar];
alphatime = IIf(BI < BegBar, 0, IIf(BI > EndBar, 0, alphatime));
return alphatime*data;
}

function Volume_HighLow_weighting(data, coef_vol, coefdim, period, scale, translation) {
// Coefficient Volume
highvol = V; highvol = HHV(Median(V, 5), 5000);
nbr_vol = coef_vol*highvol/(1 - coef_vol) + 1;
alpha_vol = V/nbr_vol;
alpha_vol = IIf(alpha_vol > 0.99, 0.99, alpha_vol);
alpha_vol = IIf(alpha_vol < 0.01, 0.01, alpha_vol);

// Coefficient High/Low (Adpated from FRAMA from John Ehlers)
coefdim = coefdim * 2 - 0.01;
N = 2*floor(period/2);
delta1 = HHV(Ref(High, -N/2), N/2) - LLV(Ref(Low, -N/2), N/2);
delta2 = HHV(High, N/2) - LLV(Low, N/2);
delta = HHV(High, N) - LLV(Low, N);
Dimen = log((delta1 + delta2)/delta)/log(2);
alphadim = exp(-coefdim*Dimen);
alphadimnorm = Min(Max(alphadim, 0.01), 0.99);

// Mix Volume and High/Low, and scale them with Inverse Fisher Tansform
alpha = alphadimnorm + alpha_vol;
data_av = scale*(alpha - translation);
alpha = (exp(2*data_av) - 1)/(exp(2*data_av) + 1);
alpha = Min(Max(alpha,0.01),0.99);
return alpha*data;
}

function Filter_GAP_EOD(data) {
time_frame = Minute() - Ref(Minute(), -1);
time_frame = IIf(time_frame < 0, time_frame + 60, time_frame);
time_frame = time_frame[1];
day_begin = 152900 + 100*time_frame;
day_end = 215900;
delta = 0;
timequote = TimeNum();
for (i = 1; i < BarCount; i++) {
if (timequote[i] == Day_begin) delta = data[i] - data[i-1];
data[i] = data[i] - delta;
}
return data;
}

function T3(data, periods, slope) { // High Order Low-Pass filter
e1 = EMA(data, periods);
e2 = EMA(e1, periods);
e3 = EMA(e2, periods);
e4 = EMA(e3, periods);
e5 = EMA(e4, periods);
e6 = EMA(e5, periods);
c1 = -slope*slope*slope;
c2 = 3*slope*slope + 3*slope*slope*slope;
c3 = -6*slope*slope - 3*slope - 3*slope*slope*slope;
c4 = 1 + 3*slope+slope*slope*slope + 3*slope*slope;
result = c1*e6 + c2*e5 + c3*e4 + c4*e3;
return result;
}

function f_centeredT3(data, periods, slope) { // Centered T3 Moving Average
slide = floor(periods/2);
centeredT3 = data;
centeredT3 = Ref(T3(data,periods,slope),slide);
centeredT3 = IIf( IsNan(centeredT3) OR !IsFinite(centeredT3) OR IsNull(centeredT3), data, centeredT3);
return centeredT3;
}

function f_detrend(data, BegBar, EndBar, ExtraF, method_detrend, PF_order) { // Detrend data
BI = BarIndex();
LongBar = EndBar - BegBar + 1 ;
detrended[0] = 0;

if (method_detrend == "Log10") detrended = log10(data);

if (method_detrend == "Derivation") detrended = data - Ref(data, -1);

if (method_detrend == "Linear Regression") {
x = Cum(1); x = Ref(x,-1); x[0] = 0; x = Ref(x, -BegBar);
a = LinRegSlope(data, LongBar); a = a[EndBar];
b = LinRegIntercept(data, LongBar); b = b[EndBar];
y = a*x + b;

global detrending_parameters;
detrending_parameters = y;

y = IIf(BI < BegBar, -1e10, IIf(BI > EndBar + ExtraF, -1e10, y));
Plot( y, "Linear Regression", ParamColor( "Color Linear Regression", colorCycle ), ParamStyle("Style") );
detrended = data - y;
}

if (method_detrend == "PolyFit") {
Yn = PolyFit(data, BegBar, EndBar, PF_order, 0, ExtraF);
Yn = Ref(Yn, -ExtraF);

global detrending_parameters;
detrending_parameters = Yn;

Yn = IIf(BI < BegBar, -1e10, IIf(BI > EndBar + ExtraF, -1e10, Yn));
Plot( Yn, "PolyFit", ParamColor( "Color PolyFit", colorCycle ), ParamStyle("Style") );
detrended = data - Yn;
}

return detrended;
}

function f_retrend(data, Value_BegBar, BegBar, EndBar, method_detrend) {
BI = BarIndex();
retrended = -1e10;

if (method_detrend == "Log10") {
retrended = 10^(data);
}

if (method_detrend == "Derivation") {
retrended[BegBar] = Value_BegBar;
for (i = BegBar + 1; i < EndBar + 1; i++) retrended[i] = data[i] + retrended[i-1];
}

if (method_detrend == "Linear Regression") {
retrended = data + detrending_parameters;
}

if (method_detrend == "PolyFit") {
retrended = data + detrending_parameters;
}

return retrended;
}

function durbinlevison(Autocorr, OrderAR, LongBar, AutoOrder, AutoOrder_Criterion) { // for Yule Walker method only
// Initialization
AR_Coeff = 0;
alpha[1] = noise_variance[1] = Autocorr[0];
beta[1] = Autocorr[1];
k[1] = Autocorr[1] / Autocorr[0];
AR_Coeff[1] = k[1];

// Initialize recursive criterion for order AR selection
if (AutoOrder == 1) {
FPE = AIC = MDL = CAT = CIC = -1e10;
CAT_factor = 0;
CIC_product = (1 + 1/(LongBar + 1))/(1 - 1/(LongBar + 1));
CIC_add = 1 + 1/(LongBar + 1);
}

// Main iterative loop
for (n = 1; n < OrderAR; n++) {

// Compute last coefficient
for (i = 1; i < n + 1; i++) AR_Coeff_inv[n+1-i] = AR_Coeff[i];

temp = 0;
for (i = 1; i < n + 1; i++) temp = temp + Autocorr[i] * AR_Coeff_inv[i];
beta[n+1] = Autocorr[n+1] - temp;

alpha[n+1] = alpha[n] * (1 - k[n]*k[n]);
k[n+1] = beta[n+1] / alpha[n+1];
AR_Coeff[n+1] = k[n+1];

// Compute other coefficients by recursion
for (i = 1; i < n + 1; i++) New_AR_Coeff[i] = AR_Coeff[i] - k[n+1] * AR_Coeff_inv[i];
New_AR_Coeff[n+1] = AR_Coeff[n+1];

// Update
AR_Coeff = New_AR_Coeff;
noise_variance[n+1] = alpha[n+1];

if (AutoOrder == 1) {
i = n + 1;
// Compute criterions for order AR selection
FPE[i] = noise_variance[i]*(LongBar + (i + 1))/(LongBar - (i + 1)); // Final Prediction Error
AIC[i] = log(noise_variance[i])+2*i/LongBar; // Akaine Information Criterion
MDL[i] = log(noise_variance[i]) + i*log(LongBar)/LongBar; // Minimum Description Length
CAT_factor = CAT_factor + (LongBar - i)/(LongBar*noise_variance[i]);
CAT[i] = CAT_factor/LongBar - (LongBar - i)/(LongBar*noise_variance[i]); // Criterion Autoregressive Transfer
CIC_product = CIC_product * (1 + 1/(LongBar + 1 - i))/(1 - 1/(LongBar + 1 - i));
CIC_add = CIC_add + (1 + 1/(LongBar + 1 - i));
CIC[i] = log(noise_variance[i]) + Max(CIC_product - 1,3*CIC_add); // Combined Information Criterion
}

// End main iterative loop
}

if (AutoOrder == 1) {
// Clean data because of rounding number for very low or very high value, and discard small changes
FPE = IIf(abs(LinRegSlope(FPE, 2)/FPE[1]) < 0.005 OR abs(FPE) > 1e7, -1e10, FPE);
AIC = IIf(log(abs(LinRegSlope(AIC, 2)/AIC[1])) < 0.005 OR abs(AIC) > 1e7, -1e10, AIC);
MDL = IIf(log(abs(LinRegSlope(MDL, 2)/MDL[1])) < 0.005 OR abs(MDL) > 1e7, -1e10, MDL);
CAT = IIf(log(abs(LinRegSlope(CAT, 2)/CAT[1])) < 0.005 OR abs(CAT) > 1e7, -1e10, CAT);
CIC = IIf(abs(LinRegSlope(CIC, 2)/CIC[1]) < 0.005 OR abs(CIC) > 1e7, -1e10, CIC);

// Find lower value
Mixed_Order = 0;
FPE_Order = Mixed_Order[1] = LastValue(Max(ValueWhen(FPE == Lowest(FPE) AND IsFinite(FPE), Cum(1), 1) - 1, 2));
AIC_Order = Mixed_Order[2] = LastValue(Max(ValueWhen(AIC == Lowest(AIC) AND IsFinite(AIC), Cum(1), 1) - 1, 2));
MDL_Order = Mixed_Order[3] = LastValue(Max(ValueWhen(MDL == Lowest(MDL) AND IsFinite(MDL), Cum(1), 1) - 1, 2));
CAT_Order = Mixed_Order[4] = LastValue(Max(ValueWhen(CAT == Lowest(CAT) AND IsFinite(CAT), Cum(1), 1) - 1, 2));
CIC_Order = Mixed_Order[5] = LastValue(Max(ValueWhen(CIC == Lowest(CIC) AND IsFinite(CIC), Cum(1), 1) - 1, 2));

// Mixed array is an averaging from all criterions taking in consideration order choose by criterions is often too low
Mixed_Order_array = Median(Mixed_Order, 5); Mixed_Order = 2*ceil(Mixed_Order_array[5]);

// Print informations about best order for each criterion
printf("\nFPE Order : "+FPE_Order);
printf("\nAIC Order : "+AIC_Order);
printf("\nMDL Order : "+MDL_Order);
printf("\nCAT Order : "+CAT_Order);
printf("\nCIC Order : "+CIC_Order);
printf("\n*** Mixed Order *** : "+Mixed_Order);

// Mark best order in a[1] wich is returned at the end of the function
if (AutoOrder_Criterion == "FPE") AR_Coeff[1] = FPE_Order;
if (AutoOrder_Criterion == "AIC") AR_Coeff[1] = AIC_Order;
if (AutoOrder_Criterion == "MDL") AR_Coeff[1] = MDL_Order;
if (AutoOrder_Criterion == "CAT") AR_Coeff[1] = CAT_Order;
if (AutoOrder_Criterion == "CIC (good)") AR_Coeff[1] = CIC_Order;
if (AutoOrder_Criterion == "Mixed (best)") AR_Coeff[1] = Mixed_Order;
}

AR_Coeff[0] = alpha[OrderAR]; // noise variance estimator
return AR_Coeff;
}

function AR_YuleWalker(data, BegBar, EndBar, OrderAR, Biased, AutoOrder, AutoOrder_Criterion) {
BI = BarIndex();
Data_all = data;
Data = IIf(BI < BegBar, 0, IIf(BI > EndBar, 0, Data));

LongBar = EndBar - BegBar + 1;

// Window Hamming to make it more stable for non-biased estimator
if (Biased == 0) {
pi = atan(1) * 4;
x = Cum(1); x = Ref(x,-1); x[0] = 0;
W = 0.54 - 0.46*cos(2*pi*x/(LongBar-1));
Wi = Ref(W,-BegBar);
data = Wi*data;
}

// Compute Autocorrelation function
for (i = 0; i < OrderAR + 1; i++) {
temp = 0;
for (j = BegBar; j < EndBar + 1 - i; j++) {
temp = temp + data[j]*data[j+i];
}
if (Biased == 1) Autocorr[i]=(1/(LongBar))*temp; // biased estomator (best, model is always stable), small variance but less power
if (Biased == 0) Autocorr[i]=(1/(LongBar-i))*temp; // non-biased estimator (model can be unstable), large variance
}
Autocorr=Autocorr/Autocorr[0]; // normalization (don't change result, to be less exposed to bad rounding)

// Compute AR parameters with Durbin-Levison recursive algorithm for symmetric Toeplitz matrix (Hermitian matrix)
AR_Coeff = durbinlevison(Autocorr, OrderAR, LongBar, AutoOrder, AutoOrder_Criterion);

// End
AR_Coeff = IIf(BI > OrderAR, -1e10, AR_Coeff); // usefull for AR_Pred and AR_Freq
return AR_Coeff; // AR_Coeff[0] is noise variance estimator
}

function AR_Burg(data, BegBar, EndBar, OrderAR, Window, AutoOrder, AutoOrder_Criterion, DoublePrecision) {
BI = BarIndex();
LongBar = EndBar - BegBar + 1; // LongBar = the number of data

// Initialize recursive criterion for order AR selection
if (AutoOrder == 1) {
FPE = AIC = MDL = CAT = CIC = -1e10;
CAT_factor = 0;
CIC_product = (1 + 1/(LongBar + 1))/(1 - 1/(LongBar + 1));
CIC_add = 1 + 1/(LongBar + 1);
}

// Hamming window if selected
if (Window == 1) {
pi = atan(1) * 4;
x = Cum(1); x = Ref(x,-1); x[0] = 0;
W = 0.54 - 0.46*cos(2*pi*x/(LongBar-1));
}

// Create data, forward and backward coefficients arrays
y = Ref(data,BegBar); // y[0:LongBar-1] are the data
y = IIf(BI < LongBar, y, 0); // to be sure not to compute future data
f = b = y; // initialize forward and backward coefficients

// Initialize noise variance estimate with data variance (= data autocorrelation[0])
noise_variance = Cum(y^2);
noise_variance[0] = noise_variance[LongBar-1]/LongBar;

// Main loop
for (i = 1; i < OrderAR + 1; i++) {

if (Window == 1) Wi = Ref(W,-i); // center Hamming Window
else Wi = 1;

b_shifted = Ref(b,-1);
if (DoublePrecision == 0) { // single precision
k_temp = Sum(Wi*f*b_shifted, LongBar - i)/Sum(Wi*(f^2 + b_shifted^2), LongBar - i);
k[i] = 2*k_temp[LongBar - 1]; // k is reflection coefficient computed from i to LongBar - 1, like Burg tell it (!!! on Numerical Recipes it is computed from 1 to LongBar - i !!!)
}

/* -------------------------------------------------------------------------------------------------------------------------------------------- */
/* --- PART JUST BELOW ARE ONLY FOR EXPERIMENTATION ABOUT ROUNDING PROBLEM, COMPUTATION IS SLOWER AND THIS IS NOT NEEDED FOR SMALL AR ORDER --- */

// Use for FOR loop instead SUM function give different result (neither good, neither bad, but just different) because of rounding single-precision in AFL. De-comment this part to use FOR loop.
/*
Num = 0;Den = 0;
for (j = i; j < LongBar; j++) { // compute from i to LongBar - 1, like Burg tell it (!!! on Numerical Recipes it is computed from 1 to LongBar - i !!!)
Num = Num + Wi[j]*( f[j]*b[j-1] );
Den = Den + Wi[j]*( f[j]^2 + b[j-1]^2 );
}
k[i] = 2*Num/Den; // k is reflection coefficient, a is AR coefficient
*/

/* --- END OF EXPERIMENTAL PART --- */
/* -------------------------------------------------------------------------------------------------------------------------------------------- */

// If you wish Double Precision, VBS script must be used (result are more precise than SUM or FOR loop, but computation is a lot more slower)
if (DoublePrecision == 1) { // double precision
KI_VBS = GetScriptObject();
KI = KI_VBS.Compute_KI(f, b, Wi, i, LongBar);
k[i] = KI;
}

noise_variance[i] = (1 - k[i]^2)*noise_variance[i-1]; // update noise variance estimator

if (AutoOrder == 1) {
// Compute criterions for order AR selection
FPE[i] = noise_variance[i]*(LongBar + (i + 1))/(LongBar - (i + 1)); // Final Prediction Error
AIC[i] = log(noise_variance[i])+2*i/LongBar; // Akaine Information Criterion
MDL[i] = log(noise_variance[i]) + i*log(LongBar)/LongBar; // Minimum Description Length
CAT_factor = CAT_factor + (LongBar - i)/(LongBar*noise_variance[i]);
CAT[i] = CAT_factor/LongBar - (LongBar - i)/(LongBar*noise_variance[i]); // Criterion Autoregressive Transfer
CIC_product = CIC_product * (1 + 1/(LongBar + 1 - i))/(1 - 1/(LongBar + 1 - i));
CIC_add = CIC_add + (1 + 1/(LongBar + 1 - i));
CIC[i] = log(noise_variance[i]) + Max(CIC_product - 1,3*CIC_add); // Combined Information Criterion
}

// Update all AR coefficients
for (j = 1; j < i; j++) a[j] = a_old[j] - k[i]*a_old[i-j];
a[i] = k[i];

// Store them for next iteration
a_old = a;

// Update forward and backward reflection coefficients
f_temp = f - k[i]*b_shifted;
b = b_shifted - k[i]*f;
f = f_temp;

// End of the main loop
}

if (AutoOrder == 1) {
// Clean data because of rounding number for very low or very high value, and discard small changes
FPE = IIf(abs(LinRegSlope(FPE, 2)/FPE[1]) < 0.005 OR abs(FPE) > 1e7, -1e10, FPE);
AIC = IIf(log(abs(LinRegSlope(AIC, 2)/AIC[1])) < 0.005 OR abs(AIC) > 1e7, -1e10, AIC);
MDL = IIf(log(abs(LinRegSlope(MDL, 2)/MDL[1])) < 0.005 OR abs(MDL) > 1e7, -1e10, MDL);
CAT = IIf(log(abs(LinRegSlope(CAT, 2)/CAT[1])) < 0.005 OR abs(CAT) > 1e7, -1e10, CAT);
CIC = IIf(abs(LinRegSlope(CIC, 2)/CIC[1]) < 0.005 OR abs(CIC) > 1e7, -1e10, CIC);

// Find lower value
Mixed_Order = 0;
FPE_Order = Mixed_Order[1] = LastValue(Max(ValueWhen(FPE == Lowest(FPE) AND IsFinite(FPE), Cum(1), 1) - 1, 2));
AIC_Order = Mixed_Order[2] = LastValue(Max(ValueWhen(AIC == Lowest(AIC) AND IsFinite(AIC), Cum(1), 1) - 1, 2));
MDL_Order = Mixed_Order[3] = LastValue(Max(ValueWhen(MDL == Lowest(MDL) AND IsFinite(MDL), Cum(1), 1) - 1, 2));
CAT_Order = Mixed_Order[4] = LastValue(Max(ValueWhen(CAT == Lowest(CAT) AND IsFinite(CAT), Cum(1), 1) - 1, 2));
CIC_Order = Mixed_Order[5] = LastValue(Max(ValueWhen(CIC == Lowest(CIC) AND IsFinite(CIC), Cum(1), 1) - 1, 2));

// Mixed array is an averaging from all criterions taking in consideration order choose by criterions is often too low
Mixed_Order_array = Median(Mixed_Order, 5); Mixed_Order = 2*ceil(Mixed_Order_array[5]);

// Print informations about best order for each criterion
printf("\nFPE Order : "+FPE_Order);
printf("\nAIC Order : "+AIC_Order);
printf("\nMDL Order : "+MDL_Order);
printf("\nCAT Order : "+CAT_Order);
printf("\nCIC Order : "+CIC_Order);
printf("\n*** Mixed Order *** : "+Mixed_Order);

// Mark best order in a[1] wich is returned at the end of the function
if (AutoOrder_Criterion == "FPE") a[1] = FPE_Order;
if (AutoOrder_Criterion == "AIC") a[1] = AIC_Order;
if (AutoOrder_Criterion == "MDL") a[1] = MDL_Order;
if (AutoOrder_Criterion == "CAT") a[1] = CAT_Order;
if (AutoOrder_Criterion == "CIC (good)") a[1] = CIC_Order;
if (AutoOrder_Criterion == "Mixed (best)") a[1] = Mixed_Order;
}

// End of the function, return AR coefficients and noise variance estimator for the current selected order in a[0]
a = IIf(BI > OrderAR, -1e10, a); a[0] = noise_variance[i-1]; // usefull for AR_Pred and AR_Freq
return a;
}

function AR_Predict(data, AR_Coeff, EndBar, ExtraF) {
BI = BarIndex();
OrderAR = LastValue(BarCount - BarsSince(AR_Coeff) - 1);

// Print some informations about AR coefficients and noise variance
sum_coeffs = Cum(AR_Coeff) - AR_Coeff[0]; // ARCoeff[0] store noise_variance
printf("\n\nNoise Variance Estimator : " + NumToStr(abs(AR_Coeff[0]), 1.9));
printf("\n\nSum of AR coeffs : " + NumToStr(sum_coeffs[OrderAR], 1.9));
for (i = 1; i < OrderAR + 1; i++) printf("\nCoeff AR " + NumToStr(i, 1.0) + " = " + NumToStr(AR_Coeff[i], 1.9));

// Pass the data into the AR filter
data_predict = IIf(BI > EndBar, -1e10, Data); // clear data after EndBar
for (i = Endbar + 1; i < EndBar + ExtraF + 1; i++) {
data_predict[i] = 0;
for (j = 1; j < OrderAR + 1; j++) data_predict[i] = data_predict[i] + AR_Coeff[j] * data_predict[i-j];
}

/* -------------------------------------------------------------------------------------------------------------------------------------------- */
/* --- PART JUST BELOW IS ONLY IF YOU NEED RESIDUALS "u" FROM FILTERING PROCESS OR TO COMPARE IT WITH THE NOISE VARIANCE ESTIMATOR --- */
/*
global BegBar;
data_predict = IIf(BI > EndBar, -1e10, Data);
for (i = BegBar + OrderAR; i < EndBar + 1 + ExtraF; i++) {
data_predict[i] = 0;
for (j = 1; j < OrderAR + 1; j++) {
data_predict[i] = data_predict[i] + AR_Coeff[j] * data_predict[i-j];
}
if (i > EndBar) u[i] = 0; else u[i] = data[i] - data_predict[i];
data_predict[i] = data_predict[i] + u[i];
}
global noise_variance_filterburg;
noise_variance_filterburg = StDev(u, EndBar - (BegBar + OrderAR) + 1); noise_variance_filterburg = noise_variance_filterburg[EndBar]^2;
printf( "\n\nNoise variance from filtering process : " + NumToStr(noise_variance_filterburg, 1.9));
*/
/* --- END OF RESIDUAL COMPUTATION PART --- */
/* -------------------------------------------------------------------------------------------------------------------------------------------- */

// Return data and data predicted from (EndBar + 1) to (EndBar + ExtraF)
return data_predict;
}

function AR_Freq(AR_Coeff, step) {
BI = BarIndex();
pi2 = atan(1) * 8;
OrderAR = LastValue(BarCount - BarsSince(AR_Coeff) - 1);
AR_Coeff=-AR_Coeff;
noise_variance = abs(AR_Coeff[0]);
if (noise_variance == 0) noise_variance = 0.000001; //seven-th digit to one

S = -1e10; // usefull to determine the number of data for AR_Sin function

f = Cum(1); f = Ref(f,-1); f[0] = 0; f = IIf(BI > 0.5/step, -1e10, f);
f = step*f;

// Danielson-Lanczos algorithm to fast compute exponential complex multiplication, using vector formulation AFL for multiple frequency in one-time recursion
W_cos = cos(pi2*f); W_sin = sin(pi2*f); // initialize cos and sinus constant for each frequency f
W_Re = 1; W_Im = 0; // initialize for recursion real and imaginary weight for AR coeffs
Re = 1; Im = 0; // initialize real and imaginary part of the denominator from the spectrum function
for (j = 1; j < OrderAR + 1; j++) {
W_Re_temp = W_Re; // Begin Danielson-Lanczos part
W_Re = W_Re*W_cos - W_Im*W_sin;
W_Im = W_Im*W_cos + W_Re_temp*W_sin; // End Danielson-Lanczos part
Re = Re + AR_Coeff[j]*W_Re; // real part of the denominator
Im = Im + AR_Coeff[j]*W_Im; // imaginary part of the denominator
}

/* Just for experimentation about speed between classic and Danielson-Lanczos algorithm, here is basic computation */
//Re = 1; Im = 0;
//for (j = 1; j < OrderAR + 1; j++) { Re = Re + AR_Coeff[j]*cos(pi2*f*j); Im = Im + AR_Coeff[j]*sin(pi2*f*j); }

S = noise_variance/(Re^2+Im^2); // S the spectrum function
Sdb = 10*log10(abs(s/noise_variance)); // Sdb the spectrum function in dB (power) with noise_variance as reference (so if Sdb is negative, power at this frequency is less than noise power)
return Sdb;
}

function AR_Sin(S, nb_sinus) {
// Initialization
nb_data = LastValue(BarCount - BarsSince(S));
step = 0.5/nb_data;

// Process data to keep only signifiant peaks
slope = LinRegSlope(S,2); // derivation
peaks = Ref(Cross(0, slope), 1); // find maximums of the spectrum
value_peaks = IIf(peaks == 1 AND S > 0, S, 0); // erase maximum wich have less power than the noise

// Find the reduced frequency of the most powerfull cycles
sinus = -1e10; sinus[0] = 0; // to detect error or no cycle
for (i = 0; i < nb_sinus; i++) { // save nb_sinus dominant cycles
sinus[i] = BarCount - LastValue(HighestBars(value_peaks)) - 1; // save higher peak position
value_peaks[sinus[i]] = 0; // erase new detected peak for next iteration
}

sinus = 1/(sinus*step); // convert reduced frequency in periods
sinus[0] = Nz(sinus[0]); // no cycle found

return sinus;
}

/* ---------------------------------------------------------------------- */
/* -------------------------------- DEMO -------------------------------- */
/* ---------------------------------------------------------------------- */

// Choice of the parameters
empty = ParamList("Main parameters", "", 0);
data_source = ParamField("Price field",-1);
ARmodel = ParamList("AR Model", "Burg (best)|Yule-Walker", 0);
length = Param("Length to look back", 200, 1, 5000, 1);
OrderAR = Param("Order AR", 20, 0, 100, 1);
ExtraF = Param("Number of bar to predict", 50, 0, 200, 1);
AutoOrder = ParamToggle("Auto AR order", "No|Yes", 0);
AutoOrder_Criterion = ParamList("Auto AR criterion", "FPE|AIC|MDL|CAT|CIC (good)|Mixed (best)", 5);
HammingWindow = ParamToggle("Burg: Hamming Window", "No|Yes", 1);
DoublePrecision = ParamToggle("Burg: Double Precision", "No|Yes", 0);
Biased = ParamToggle("Y-W: Biased (Yes: best)", "No|Yes", 1);

empty = ParamList("*** PRE-PROCESSING : ***", "", 0);
empty = ParamList("1- Intraday EOD GAP Filter", "", 0);
FiltrageGAPEOD = ParamToggle("Filtrage GAP EOD", "No|Yes", 0);

empty = ParamList("2- Denoise", "", 0);
periodsT3 = Param("Periods T3", 5, 1, 200, 1);
slopeT3 = Param("Slope T3", 0.7, 0, 3, 0.01);

empty = ParamList("3- Detrend", "", 0);
method_detrend = ParamList("Detrending method", "None|Log10|Derivation|Linear Regression|PolyFit", 3);
PF_order = Param("PolyFit Order", 3, 1, 9, 1);

empty = ParamList("4- Center", "", 0);
PreCenter = ParamToggle("Center data", "No|Yes", 1);

empty = ParamList("5- Normalize", "", 0);
PreNormalise = ParamToggle("Normalise data", "No|Yes", 1);

empty = ParamList("6- Volume weight on data", "", 0);
PonderVolHL = ParamToggle("Weighting Volume and H/L(Fractal)", "No|Yes", 0);
coef_vol = Param( "Strength Volume (- +)", 0.5, 0.01, 0.99, 0.01);
coefdim = Param( "Strength Fractal (- +)", 0.5, 0.01, 0.99, 0.01);
period = Param( "Periods Fract (even)", 16, 2, 200, 2);
scale = Param( "Scale Final Weights", 1, 0, 10, 0.01);
translation = Param( "Translate Final Weights", 0.3, 0, 1, 0.01);

empty = ParamList("7- Time weight on data", "", 0);
PonderTime = ParamToggle("Weighting Time", "No|Yes", 0);
PonderTimeWindow = ParamList("Weighting Time Window", "Log|Ramp|Exp", 0);

empty = ParamList("*** POST-PROCESSING : ***", "", 0);
method_retrend = ParamList("Retrending method", "Add back trend|Prediction without trend", 0);
empty = ParamList("For Derivation or Log10 :", "add automatically the trend", 0);

// Initialization
SetBarsRequired(20000,20000);
Title = "";
BI = BarIndex();
current_pos = SelectedValue( BI ) - BI[ 0 ];//1500
printf( "Position: " + NumToStr(current_pos) + "\n" );

// Adjust users choice depending number of bars data available or detrending/retrending necessity for derivation method
if ( method_retrend == "Prediction without trend" AND (method_detrend == "Derivation" OR method_detrend == "Log10") ) method_retrend = "Add back trend";

BegBar = current_pos - length + 1;
slide = floor(periodsT3/2);

if (BegBar < 6*periodsT3) Title = Title + "\n\n\n\n\n\n!!! WARNING : YOU HAVE NOT ENOUGH DATA HISTORY FOR CORRECT T3 FILTERING - Reduce \"Periods T3\" parameter OR move Position Bar !!!\n\n\n\n\n\n";

if (BegBar < 1) {
BegBar = 1;
length = current_pos - slide;
Title = Title + "\n\n\n\n\n\n!!! WARNING : YOU HAVE NOT ENOUGH DATA HISTORY - Reduce \"Length to look back\" parameter OR move Position Bar !!!\n\n\n\n\n\n";
}

EndBar = current_pos - slide;
if ( EndBar + ExtraF > BarCount - 1) {
ExtraF = (BarCount - 1) - EndBar;
}

if (AutoOrder == 1) OrderAR = floor(length / 2);
if (OrderAR >= length) {
OrderAR = length - 1;
Title = Title + "\n\n\n\n\n\n!!! WARNING : ORDER AR IS MORE HIGH THAN (LENGTH TO LOOK BACK - 1) - Reduce \"Order AR\" OR increase \"Length to look back\" parameters !!!\n\n\n\n\n\n";
}
data = data_source;
data_filtred = f_centeredT3(data, periodsT3, slopeT3);

/* -------------------- BEGIN PROCESSING -------------------- */

// Filter GAP End of the days - only needed for intraday
if (FiltrageGAPEOD == 1) data = Filter_GAP_EOD(data);

// Denoise data
data = f_centeredT3(data, periodsT3, slopeT3);

// Detrend data
if (method_detrend != "None") data = f_detrend(data, BegBar, EndBar, ExtraF, method_detrend, PF_order);
data = IIf(BI < BegBar, 0, data); // fill with 0 before BegBar to be able to compute native MA Function AFL

// Center data
mean_data_before = MA(data, EndBar - BegBar + 1);
printf( "\nMean data not centered : " + WriteVal(mean_data_before[EndBar]) + "\n" );
if (PreCenter == 1) {
data = data - mean_data_before[EndBar];
mean_data_after = MA(data, EndBar - BegBar + 1);
printf( "\nMean data centered : " + WriteVal(mean_data_after[EndBar]) + "\n\n" );
}

// Normalize data
if (PreNormalise == 1) {
max_data = HHV(data, EndBar - BegBar + 1);
data = data / max_data[EndBar];
}

// Weigthing only for the data which will feed the AR_Burg function (modeling AR), not the AR_Pred function (prediction)
data_weighted = data;
// Volume and High/Low (Fractal) weighting
if (PonderVolHL == 1) data_weighted = Volume_HighLow_weighting(data_weighted, coef_vol, coefdim, period, scale, translation);
// Time weighting
if (PonderTime == 1) data_weighted = Time_weighting(data_weighted, PonderTimeWindow, BegBar, EndBar);

// Compute AR model
if (ARmodel == "Burg (best)") { // Burg model (MEM method) (better than Yule-Walker for sharp spectrum and short data history)
AR_Coeff = AR_Burg(data_weighted, BegBar, EndBar, OrderAR, HammingWindow, AutoOrder, AutoOrder_Criterion, DoublePrecision);
if (AutoOrder == 1) { // In case of AutoOrder, a new computation to find coefficients with the ideal order is needed
OrderAR = AR_Coeff[1];
AR_Coeff = AR_Burg(data_weighted, BegBar, EndBar, OrderAR, HammingWindow, 0, AutoOrder_Criterion, DoublePrecision); // Compute the AR from AutoOrder
}
}
if (ARmodel == "Yule-Walker") { // Yule-Walker model (Autocorrelation method)
AR_Coeff = AR_YuleWalker(data_weighted, BegBar, EndBar, OrderAR, Biased, AutoOrder, AutoOrder_Criterion);
if (AutoOrder == 1) { // In case of AutoOrder, a new computation to find coefficients with the ideal order is needed
OrderAR = AR_Coeff[1];
AR_Coeff = AR_YuleWalker(data_weighted, BegBar, EndBar, OrderAR, Biased, 0, AutoOrder_Criterion); // Compute the AR from AutoOrder
}
}
noise_variance = abs(AR_Coeff[0]);

// Prediction based on the computed AR model
data_predict = AR_Predict(data, AR_Coeff, EndBar, ExtraF);

// De-normalize
if (PreNormalise == 1) {
data_predict = data_predict * max_data[EndBar];
noise_variance = noise_variance * max_data[EndBar];
}

// De-center
if (PreCenter == 1) data_predict = data_predict + mean_data_before[EndBar];

// Add the trend back again
if (method_detrend != "None" AND method_retrend == "Add back trend") data_predict = f_retrend(data_predict, data_filtred[EndBar], EndBar, EndBar + ExtraF, method_detrend);

// Translate along value axis filtered EOD GAP data or not retrended data, so no gap occurs
if (FiltrageGAPEOD == 1 OR method_retrend == "Prediction without trend") {
delta = data_predict[EndBar + 1] - data_filtred[EndBar];
data_predict = data_predict - delta;
data_predict = Ref(data_predict, 1); // so there is no discontinuity (but there is one data less for forward prediction)
}

// Plot AR Prediction and Burg predicted channels based on ATR and noise variance (if prediction is bad, channel will be larger)
dataATR = ATR(periodsT3); // no need to detrend or center ATR before prediction (considerer centred without any trend)
printf("\n\nATR AR model for Channel prediction :\n");
AR_Coeff_ATR = AR_Burg(dataATR, BegBar, EndBar, OrderAR, HammingWindow, OrderAR, AutoOrder_Criterion, DoublePrecision);
data_predict_ATR = AR_Predict(dataATR, AR_Coeff_ATR, EndBar, ExtraF);
DeltaBand = 2*(data_predict_ATR + noise_variance);

Data_reconstruct = -1e10;
Data_reconstruct = IIf( BI <= EndBar AND BI >= BegBar, data_filtred, IIf( BI > EndBar, data_predict, Data_reconstruct));
Plot(Data_reconstruct, "AR("+ NumToStr(OrderAR, 1.0) +") Prediction", IIf(BI > EndBar + slide, colorRed, IIf(BI > EndBar AND BI <= EndBar + slide, colorBlue, colorBrightGreen)), styleThick, Null, Null, 0);
Plot(Data_reconstruct + DeltaBand, "AR("+ NumToStr(OrderAR, 1.0) +") Prediction Upper Channel", IIf(BI > EndBar + slide, colorRed, IIf(BI > EndBar AND BI <= EndBar + slide, colorBlue, colorBrightGreen)), styleDashed, Null, Null, 0);
Plot(Data_reconstruct - DeltaBand, "AR("+ NumToStr(OrderAR, 1.0) +") Prediction Lower Channel", IIf(BI > EndBar + slide, colorRed, IIf(BI > EndBar AND BI <= EndBar + slide, colorBlue, colorBrightGreen)), styleDashed, Null, Null, 0);

/* -------------------------------------------------------------------------------------- */
/* Demo for density spectrum power (DSP) analyse based on AR modeling (parametric method) */

// Compute spectrum based directly on AR model. Decomment following line to plot spectrum begining in first Bar (Bar[0])
Sdb = AR_Freq(AR_Coeff, 0.001); // 0.001 is the step for frequency resolution
//Plot( Sdb, "Spectrum in decibels", ParamColor( "Color Spectrum in decibels", colorCycle ), ParamStyle("Style") );

// Look for the frequency which are the more powerfull (dominant cycles)
sinus = AR_Sin(Sdb, 10); // 10 is the maximum number of frequency to look for
Title = Title + "\nDominant periods: " + WriteIf(sinus[0] >= 0 AND IsFinite(sinus[0]), NumToStr(round(sinus[0]),1.0), "");
for (i = 1; i < 10; i++) Title = Title + WriteIf(sinus[i] > 0 AND IsFinite(sinus[i]), ", "+NumToStr(round(sinus[i]),1.0), "");

/* End DSP demo - For more information look for MEM or MESA */
/* -------------------------------------------------------------------------------------- */

/* ---------------------------------------------------------------------- */
/* ------------------------------- END DEMO ----------------------------- */
/* ---------------------------------------------------------------------- */
_SECTION_END();

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