Getting Syntax error in this python code, how to eleminate? - amibroker
////// section begins /* use this afl when buy is different from cover and sell is different from short / RequestTimedRefresh( 1, onlyvisible = False ) ; _SECTION_BEGIN( "Algoji.com intraday.afl" ); intra = ParamToggle( "Activate Intraday Mode", "NO|YES" ); per10 = Param( "Trade Entry From(HHMM)", 920, 900, 2300, 1 ); per11 = Param( "Trade Entry Upto(HHMM)", 1445, 900, 2300, 1 ); per12 = Param( "Trade Exit(HHMM)", 1515, 900, 2300, 100 ); pop= ParamToggle( "Percentage or Points", "Points|Percentage"); slp = Param( "StopLoss", 0, 0, 1000, 0.1 ); tsl= Param("Trail Stop", 0, 0, 1000, 0.1); tgtp = Param( "Target", 0, 0, 1000, 0.1 ); Col = ParamColor( "Color of Modified Signals", colorYellow ); delay= ParamToggle("Trade Intrabar?", "YES|Candle Completion"); dlong= ParamToggle("Disable Long?", "NO|YES"); dshort= ParamToggle("Disable Short?", "NO|YES"); if(dlong){Buy=Sell=0;} if(dshort){Short=Cover=0;} dd= DaysSince1900(); d=prof= 0; if(delay) {Buy=Ref(Buy,-1); Sell=Ref(Sell,-1); Short= Ref(Short,-1); Cover= Ref(Cover,-1);} qt= Param("Trade Quantity", 0, 0, 1000000) ; exposure= Param("Exposure", 0, 0, 1000000) ; if(exposure>0) qt= round(exposure/ValueWhen(Day()!=Ref(Day(),-1), C)); maxl= Param("Qty using SL (MaxLoss)",0,0,100000 ); if(maxl>0 AND !pop) qt= round(maxl/slp); if(maxl>0 AND pop) { basicprice= LastValue(ValueWhen(Day()!=Ref(Day(),-1), C)); sl= slpbasicprice/100; qt= round(maxl/sl); } intraex = intra AND (TimeNum() > per12 * 100); intraen = !intra OR ( TimeNum() <= per11 * 100 AND TimeNum() >= per10 * 100 ); Buy1 = Buy; Sell1 = Sell; Short1 = Short; Cover1 = Cover; Buy=Sell=Short=Cover=0; bflag = sflag = sp=bp = 0; slarr = tgtarr = qtarr= Null; for ( i = 10; i < BarCount; i++ ) { if ( ( Cover1[i] OR intraex[i]OR( H[i] > slarr[i-1] AND (sl>0 OR tsl>0) ) OR ( L[i] < tgtarr[i-1] AND tgt > 0 ) ) AND sflag ) { Cover[i] = 1; CoverPrice[i]= C[i]; sflag = 0; d= dd[i]; prof= sp-C[i]; } if ( ( Sell1[i] OR intraex[i] OR( L[i] < slarr[i-1] AND (sl>0 OR tsl>0) ) OR ( H[i] > tgtarr[i-1] AND tgt > 0 ) ) AND bflag ) { Sell[i] = 1; SellPrice[i]= C[i]; bflag = 0; d= dd[i]; prof= C[i]- bp; } if ( Buy1[i] AND intraen[i] AND bflag == 0 ) { Buy[i] = 1; bflag = 1; bp= C[i]; sl=slp; tgt= tgtp; if(pop) {sl= slpbp/100; tgt= tgtpbp/100;} if ( slp ) slarr[i] = bp-sl; if ( tgtp ) tgtarr[i] = bp+tgt; } if ( bflag AND Buy[i]==0 ) { slarr[i] = slarr[i-1]; tgtarr[i] = tgtarr[i-1]; if(tsl>0 AND pop) slarr[i] = Max(slarr[i-1], H[i](1-tsl/100)); if(tsl>0 AND !pop) slarr[i] = Max(slarr[i-1], H[i]-tsl); } if ( Short1[i] AND intraen[i] AND sflag == 0 ) { Short[i] = 1; sflag = 1; Sp= C[i]; sl= slp; tgt= tgtp; if(pop) {sl= slpSp/100; tgt= tgtpSp/100;} if ( slp ) slarr[i] = sp + sl; if ( tgtp ) tgtarr[i] = sp - tgt; } if ( sflag AND Short[i] == 0 ) { slarr[i] = slarr[i-1]; tgtarr[i] = tgtarr[i-1]; if(tsl>0 AND pop) slarr[i] = Min(slarr[i-1], L[i](1+tsl/100)); if(tsl>0 AND !pop) slarr[i] = Min(slarr[i-1], L[i]+tsl); } } Plot( slarr, "SL", Col, styleThick ); Plot( tgtarr, "TGT", Col, styleThick ); PlotShapes( IIf( Buy, shapeUpArrow, shapeNone ), Col, 0, H, Offset = 15 ); PlotShapes( IIf( Short, shapeDownArrow, shapeNone ), Col, 0, L, Offset = 15 ); PlotShapes( IIf( Cover, shapeStar, shapeNone ), Col, 0, H, Offset = -25 ); PlotShapes( IIf( Sell, shapeStar, shapeNone ), Col, 0, L, Offset = -25 ); sig = IIf( BarsSince( Buy ) < BarsSince( Short ), 1, 0 ); messageboard = ParamToggle( "Message Board", "Show|Hide", 1 ); if ( messageboard == 1 ) { GfxSelectFont( "Tahoma", 13, 100 ); GfxSetBkMode( 1 ); GfxSetTextColor( colorWhite ); GfxSelectSolidBrush( colorDarkTeal ); // this is the box background color pxHeight = Status( "pxchartheight" ) ; xx = Status( "pxchartwidth" ); Left = 1100; width = 310; x = 5; x2 = 310; y = pxHeight; GfxSelectPen( colorGreen, 1 ); // broader color GfxRoundRect( x, y - 160, x2, y , 7, 7 ) ; GfxTextOut( ""+WriteIf(intra, "Intraday Mode Activated", "Intraday Mode Not Activated" ), 13, y-160 ); GfxTextOut( ( "Current Qty "+qt ), 13, y-140 ); GfxTextOut( ( "Last" + " Signal came " + ( BarsSince( Buy OR Short ) ) * Interval() / 60 + " mins ago" ), 13, y - 120 ) ; // The text format location GfxTextOut( ( "" + WriteIf( sig == 1, "BUY # " + ValueWhen(Buy,C) , "SHORT # " + ValueWhen(Short,C) ) ), 13, y - 100 ); GfxTextOut( "Stop Loss : " + WriteIf(slp==0, "Not Activated", ""+slarr), 13, y - 80 ); GfxTextOut( "Target : " + WriteIf(tgtp==0, "Not Activated", ""+tgtarr), 13, y - 60 ); GfxTextOut( ( "Current P/L : " + WriteVal( IIf( sig == 1, (C-ValueWhen(Buy,C))*qt, (ValueWhen(Short,C)-C)*qt ), 2.2 ) ), 13, y-40 ); // GfxTextOut( ( "jhjh " ), 13, y-20 ); } //section begins for auto trade instr= ParamList("Instrument Name","EQ|FUTIDX|FUTSTK|OPTIDX|OPTSTK|FUTCOM"); stag= ParamStr("Strategy Tag", "STG1"); qty= NumToStr(qt[BarCount-1], 1.0, False) ; bp= sp= NumToStr(Close[BarCount-1],1.2, False); if(dlong){Buy=Sell=0;} if(dshort){Short=Cover=0;} if(delay) {Buy=Ref(Buy,-1); Sell=Ref(Sell,-1); Short= Ref(Short,-1); Cover= Ref(Cover,-1);} global algoji; algoji = Name() + NumToStr( Interval() / 60, 1.0, False ) ; procedure aStaticVarSet( SName, Svalue ) { global algoji; StaticVarSet( Sname + algoji, Svalue ); } function aStaticVarGet( SName ) { global algoji; Var = StaticVarGet( Sname + algoji ); if ( IsNull( Var = StaticVarGet( Sname + algoji ) ) ) Var = 0; return Var; } sym = Name(); //_TRACE("t"+t); Checkdt=Nz(aStaticVarGet("lastdt")); dt = LastValue( DateTime() ); Checkdtss=Nz(aStaticVarGet("lastdtss")); dtss = LastValue( DateTime() ); Checkdtc=Nz(aStaticVarGet("lastdtc")); dtc = LastValue( DateTime() ); Checkdts=Nz(aStaticVarGet("lastdts")); dts = LastValue( DateTime() ); RTBuy = LastValue( Buy) AND Checkdt != dt; RTShort = LastValue( Short) AND Checkdtss != dtss; RTCover = LastValue( Cover) AND Checkdtc != dtc; RTSell = LastValue( Sell) AND Checkdts != dts; if ( RTCover ) { aStaticVarSet("lastdtc",dtc ); StaticVarSet("counter", Nz(StaticVarGet("counter"))+1 ); _TRACE( "#"+Nz(StaticVarGet("counter"))+",SX,"+sym+",,," +bp +","+qty+","+instr+",,"); Algoji_Signal(NumToStr(Nz(StaticVarGet("counter")),0,False), "SX",sym,"M","",bp,qty,instr,stag); } if ( RTSell ) { aStaticVarSet("lastdts",dts ); StaticVarSet("counter", Nz(StaticVarGet("counter"))+1 ); _TRACE( "#"+Nz(StaticVarGet("counter"))+",LX,"+sym+",,," +sp +","+qty+",,,"); Algoji_Signal(NumToStr(Nz(StaticVarGet("counter")),0,False), "LX",sym,"M","",sp,qty,instr,stag); } if ( RTBuy ) { aStaticVarSet("lastdt",dt ); StaticVarSet("counter", Nz(StaticVarGet("counter"))+1 ); _TRACE( "#"+Nz(StaticVarGet("counter"))+",LE,"+sym+",,," +bp +","+qty+","+instr+",,"); Algoji_Signal(NumToStr(Nz(StaticVarGet("counter")),0,False), "LE",sym,"M","",bp,qty,instr,stag); } if ( RTShort ) { aStaticVarSet("lastdtss",dtss ); StaticVarSet("counter", Nz(StaticVarGet("counter"))+1 ); sp= NumToStr(Close[BarCount-1],1.2, False); _TRACE( "#"+Nz(StaticVarGet("counter"))+",SE,"+sym+",,," +sp +","+qty+","+instr+",,"); Algoji_Signal(NumToStr(Nz(StaticVarGet("counter")),0,False), "SE",sym,"M","",bp,qty,instr,stag); } Button = ParamToggle( "Enable Button Trading", "YES|NO" ); expiry= ParamStr("Expiry",""); strike= ParamStr("Strike",""); type= ParamStr("Option Type", ""); sym = Name()+ "|"+expiry+ "|" +strike+ "|" +type; function GetSecondNum() { Time = Now( 4 ); Seconds = int( Time % 100 ); Minutes = int( Time / 100 % 100 ); Hours = int( Time / 10000 % 100 ); SecondNum = int( Hours * 60 * 60 + Minutes * 60 + Seconds ); return SecondNum; } function PopupWindowEx( popupID, bodytext, captiontext, timeout, left, top ) { displayText = bodytext + captiontext; if ( ( StaticVarGetText( "prevPopup" + popupID ) != displayText) OR ( StaticVarGet( "prevPopupTime" + popupID ) < GetSecondNum() ) ) { StaticVarSetText( "prevPopup" + popupID, displayText); StaticVarSet( "prevPopupTime" + popupID, GetSecondNum() + timeout ); PopupWindow( bodytext, Captiontext + popupID, timeout, Left, top ); } } x1= Status( "pxchartleft" )+10; y1= Status( "pxcharttop" )+20; if ( Button == 0 ) { click = GetCursorMouseButtons() == 9; Px = GetCursorXPosition( 1 ); Py = GetCursorYPosition( 1 ); x2 = x1 + 60; y2 = y1 + 60; GfxSelectSolidBrush( ColorRGB( 0, 102, 0 ) ); //buy GfxSelectFont( "Tahoma", 13, 100 ); GfxSetBkMode( 1 ); GfxSetTextColor( colorWhite ); GfxRoundRect( x1, y1, x2, y2 , 7, 7 ) ; GfxTextOut( "LE", x1 + 20, y1 + 20 ); sx1 = x2; sy1 = y1; sx2 = sx1 + 60; sy2 = sy1 + 60; GfxSelectSolidBrush( ColorRGB( 255, 204, 204 ) );//sell GfxRoundRect( sx1, sy1, sx2, sy2 , 7, 7 ) ; GfxSetTextColor( ColorRGB( 153, 0, 0 ) ); GfxTextOut( "SE", sx1 + 20, sy1 + 20 ); ex1 = x1; ey1 = y1+60; ex2 = ex1 + 60; ey2 = ey1 + 60; GfxSelectSolidBrush( ColorRGB( 255, 204, 204 ) );//sell GfxRoundRect( ex1, ey1, ex2, ey2 , 7, 7 ) ; GfxSetTextColor( ColorRGB( 153, 0, 0 ) ); GfxTextOut( "LX", ex1 + 20, ey1 + 20 ); GfxSelectSolidBrush( ColorRGB( 0, 102, 0 ) );//sell GfxRoundRect( ex2, ey1, ex2+60, ey2 , 7, 7 ) ; GfxSetTextColor( colorWhite ); GfxTextOut( "SX", ex2 + 20, ey1 + 20 ); if ( px > x1 AND pxy1 AND py < y2 AND Click ) { _TRACE( "# ," + NumToStr(Nz(StaticVarGet("counter")),0,False) + ", BUY triggered from button, " ); AlertIf( 1, "SOUND C:\Windows\Media\tada.wav", "Audio alert", 1, 2, 1 ); StaticVarSet("counter", Nz(StaticVarGet("counter"))+1 ); PopupWindowEx( "ID:1", "BUY", "Buy Triggered from Button "+Name(), 1, -1, -1 ); AlgoJi_Signal(NumToStr(Nz(StaticVarGet("counter")),0,False), "LE",sym,"M","",sp,qty,instr,stag); } //https://algoji.com/ if ( px > sx1 AND pxsy1 AND py < sy2 AND Click ) { _TRACE( "# ," + NumToStr( DateTime(), formatDateTime ) + ", SHORT triggered from button, " ); AlertIf( 2, "SOUND C:\Windows\Media\tada.wav", "Audio alert", 2, 2, 1 ); StaticVarSet("counter", Nz(StaticVarGet("counter"))+1 ); PopupWindowEx( "ID:3", "SHORT", "Short Triggered from Button "+Name(), 1, -1, -1 ); AlgoJi_Signal(NumToStr(Nz(StaticVarGet("counter")),0,False), "SE",sym,"M","",sp,qty,instr,stag); } //https://algoji.com/ if ( px > ex1 AND pxey1 AND py<ey2 AND Click ) { _TRACE( "# ," + NumToStr( DateTime(), formatDateTime ) + ", SELL triggered from button, " ); AlertIf( 3, "SOUND C:\Windows\Media\tada.wav", "Audio alert", 2, 2, 1 ); StaticVarSet("counter", Nz(StaticVarGet("counter"))+1 ); PopupWindowEx( "ID:3", "SELL", "Sell Triggered from Button "+Name(), 1, -1, -1 ); AlgoJi_Signal(NumToStr(Nz(StaticVarGet("counter")),0,False), "LX",sym,"M","",sp,qty,instr,stag); } //https://algoji.com/ if ( px > ex2 AND px<(ex2+60) AND py>ey1 AND py < ey2 AND Click ) { _TRACE( "# ," + NumToStr( DateTime(), formatDateTime ) + ", Cover triggered from button, " ); AlertIf( 4, "SOUND C:\Windows\Media\tada.wav", "Audio alert", 2, 2, 1 ); StaticVarSet("counter", Nz(StaticVarGet("counter"))+1 ); PopupWindowEx( "ID:3", "Cover", "Cover Triggered from Button "+Name(), 1, -1, -1 ); AlgoJi_Signal(NumToStr(Nz(StaticVarGet("counter")),0,False), "SX",sym,"M","",sp,qty,instr,stag); } }
It is not a Python, it is AFL. It will be much easier if you post it as a code.
If you run it in AFL Formula Editor it will help you to debug it - simply click "Apply" and you will have all errors highlined.
I've found that you missed some "*" in your formula.
Related
how to generate 2 combinations among n from an array of fixed size integers
Hi and thanks to read question,
I'm taking part in a coding challenge. I am in trouble.
I would like from a parent that has an array of int, generate all random instances if we remove two in the first array (the draw) to put in a second (the hand). It's like a card game with 10 types of cards and you draw 2 at random.
I wrote some code that shows you the answer for a solution but I'm interested in all cases.
class Stack {
Stack? p;
List<Stack> child = [];
List<int> hand = [];
List<int> draw = [];
#override
String toString() => "hand ${hand} draw ${draw}";
}
void generate( Stack _ )
{
//...
// Generate all combine 2 from card
}
void main() {
Stack start = new Stack();
start.draw = [ 0 , 3 , 2 , 0 , 0 , 1 , 2 , 0 , 1 , 2 ];
start.hand = [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ];
generate(start);
// A solution
Stack aChild = new Stack();
aChild.draw = [ 0 , 1 , 2 , 0 , 0 , 1 , 2 , 0 , 1 , 2 ];
aChild.hand = [ 0 , 2 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ];
start.child.add( aChild );
print(start);
print(aChild);
}
And the result:
hand [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] draw [0, 3, 2, 0, 0, 1, 2, 0, 1, 2]
hand [0, 2, 0, 0, 0, 0, 0, 0, 0, 0] draw [0, 1, 2, 0, 0, 1, 2, 0, 1, 2]
You can write code on Darpad. It works.
I have found(coded) an algorithm that works.
class Stack {
Stack? p;
List<Stack> child = [];
List<int> hand = [];
List<int> draw = [];
int cardToDraw = 0 , indexToDraw = -1;
#override
String toString() => "hand ${hand} draw ${draw}";
}
void generate( Stack parent , Stack _ )
{
// Generate all combine 2 from card
if( _.cardToDraw == 2 )
{
// Reset
_.cardToDraw = 0;
_.indexToDraw = -1;
parent.child.add( _ );
return;
}
else
if( _.indexToDraw == 9 )
{
return;
}
for( int i1 = 0 ; i1 <= 2 - _.cardToDraw ; i1++ )
{
Stack next = new Stack()..indexToDraw = _.indexToDraw + 1..cardToDraw = _.cardToDraw + i1;
next.hand = [..._.hand];
next.draw = [..._.draw];
if( next.draw[ next.indexToDraw ] >= i1 ) {
next.hand[ next.indexToDraw ] += i1;
next.draw[ next.indexToDraw ] -= i1;
generate( parent , next );
}
}
}
void main() {
Stack start = new Stack();
start.draw = [ 0 , 3 , 2 , 0 , 0 , 1 , 2 , 0 , 0 , 0 ];
start.hand = [ 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ];
generate(start,start);
print(start);
int i1 = 0;
for( Stack child in start.child )
{
print("[${i1}] $child");
i1++;
}
}
And output console:
Adapting Bayesian logistic regression script to my data
I'm looking to run a hierarchical logistic regression in a Bayesian framework, but am having trouble adapting codes for my data. I have the great book "Doing Bayesian Data Analysis", but I'm not sure how to modify the script the author provided (will paste below) to rerun the analysis on my thesis data. Specifically, I have the following questions:
How do I add more terms to this model? I had 5 predictors in my thesis, and this model only has 2
How do I make it hierarchical/include multiple steps or blocks?
How do I add interaction terms among my predictors?
I believe this script is set up for metric predictors (the book's example used height and weight data); If I am running a mixture of metric and nominal independent variables, how do I need to adapt it?
any help on any of these questions would be wonderful.
# Jags-Ydich-XmetMulti-Mlogistic.R
# Accompanies the book:
# Kruschke, J. K. (2015). Doing Bayesian Data Analysis, Second Edition:
# A Tutorial with R, JAGS, and Stan. Academic Press / Elsevier.
source("DBDA2E-utilities.R")
#===============================================================================
genMCMC = function( data , xName="x" , yName="y" ,
numSavedSteps=10000 , thinSteps=1 , saveName=NULL ,
runjagsMethod=runjagsMethodDefault ,
nChains=nChainsDefault ) {
require(runjags)
#-----------------------------------------------------------------------------
# THE DATA.
y = data[,yName]
x = as.matrix(data[,xName],ncol=length(xName))
# Do some checking that data make sense:
if ( any( !is.finite(y) ) ) { stop("All y values must be finite.") }
if ( any( !is.finite(x) ) ) { stop("All x values must be finite.") }
cat("\nCORRELATION MATRIX OF PREDICTORS:\n ")
show( round(cor(x),3) )
cat("\n")
flush.console()
# Specify the data in a list, for later shipment to JAGS:
dataList = list(
x = x ,
y = y ,
Nx = dim(x)[2] ,
Ntotal = dim(x)[1]
)
#-----------------------------------------------------------------------------
# THE MODEL.
modelString = "
# Standardize the data:
data {
for ( j in 1:Nx ) {
xm[j] <- mean(x[,j])
xsd[j] <- sd(x[,j])
for ( i in 1:Ntotal ) {
zx[i,j] <- ( x[i,j] - xm[j] ) / xsd[j]
}
}
}
# Specify the model for standardized data:
model {
for ( i in 1:Ntotal ) {
# In JAGS, ilogit is logistic:
y[i] ~ dbern( ilogit( zbeta0 + sum( zbeta[1:Nx] * zx[i,1:Nx] ) ) )
}
# Priors vague on standardized scale:
zbeta0 ~ dnorm( 0 , 1/2^2 )
for ( j in 1:Nx ) {
zbeta[j] ~ dnorm( 0 , 1/2^2 )
}
# Transform to original scale:
beta[1:Nx] <- zbeta[1:Nx] / xsd[1:Nx]
beta0 <- zbeta0 - sum( zbeta[1:Nx] * xm[1:Nx] / xsd[1:Nx] )
}
" # close quote for modelString
# Write out modelString to a text file
writeLines( modelString , con="TEMPmodel.txt" )
#-----------------------------------------------------------------------------
# INTIALIZE THE CHAINS.
# Let JAGS do it...
#-----------------------------------------------------------------------------
# RUN THE CHAINS
parameters = c( "beta0" , "beta" ,
"zbeta0" , "zbeta" )
adaptSteps = 500 # Number of steps to "tune" the samplers
burnInSteps = 1000
runJagsOut <- run.jags( method=runjagsMethod ,
model="TEMPmodel.txt" ,
monitor=parameters ,
data=dataList ,
#inits=initsList ,
n.chains=nChains ,
adapt=adaptSteps ,
burnin=burnInSteps ,
sample=ceiling(numSavedSteps/nChains) ,
thin=thinSteps ,
summarise=FALSE ,
plots=FALSE )
codaSamples = as.mcmc.list( runJagsOut )
# resulting codaSamples object has these indices:
# codaSamples[[ chainIdx ]][ stepIdx , paramIdx ]
if ( !is.null(saveName) ) {
save( codaSamples , file=paste(saveName,"Mcmc.Rdata",sep="") )
}
return( codaSamples )
} # end function
#===============================================================================
smryMCMC = function( codaSamples ,
saveName=NULL ) {
summaryInfo = NULL
mcmcMat = as.matrix(codaSamples)
paramName = colnames(mcmcMat)
for ( pName in paramName ) {
summaryInfo = rbind( summaryInfo , summarizePost( mcmcMat[,pName] ) )
}
rownames(summaryInfo) = paramName
if ( !is.null(saveName) ) {
write.csv( summaryInfo , file=paste(saveName,"SummaryInfo.csv",sep="") )
}
return( summaryInfo )
}
#===============================================================================
plotMCMC = function( codaSamples , data , xName="x" , yName="y" ,
showCurve=FALSE , pairsPlot=FALSE ,
saveName=NULL , saveType="jpg" ) {
# showCurve is TRUE or FALSE and indicates whether the posterior should
# be displayed as a histogram (by default) or by an approximate curve.
# pairsPlot is TRUE or FALSE and indicates whether scatterplots of pairs
# of parameters should be displayed.
#-----------------------------------------------------------------------------
y = data[,yName]
x = as.matrix(data[,xName])
mcmcMat = as.matrix(codaSamples,chains=TRUE)
chainLength = NROW( mcmcMat )
zbeta0 = mcmcMat[,"zbeta0"]
zbeta = mcmcMat[,grep("^zbeta$|^zbeta\\[",colnames(mcmcMat))]
if ( ncol(x)==1 ) { zbeta = matrix( zbeta , ncol=1 ) }
beta0 = mcmcMat[,"beta0"]
beta = mcmcMat[,grep("^beta$|^beta\\[",colnames(mcmcMat))]
if ( ncol(x)==1 ) { beta = matrix( beta , ncol=1 ) }
#-----------------------------------------------------------------------------
if ( pairsPlot ) {
# Plot the parameters pairwise, to see correlations:
openGraph()
nPtToPlot = 1000
plotIdx = floor(seq(1,chainLength,by=chainLength/nPtToPlot))
panel.cor = function(x, y, digits=2, prefix="", cex.cor, ...) {
usr = par("usr"); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r = (cor(x, y))
txt = format(c(r, 0.123456789), digits=digits)[1]
txt = paste(prefix, txt, sep="")
if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt)
text(0.5, 0.5, txt, cex=1.5 ) # was cex=cex.cor*r
}
pairs( cbind( beta0 , beta )[plotIdx,] ,
labels=c( "beta[0]" ,
paste0("beta[",1:ncol(beta),"]\n",xName) ) ,
lower.panel=panel.cor , col="skyblue" )
if ( !is.null(saveName) ) {
saveGraph( file=paste(saveName,"PostPairs",sep=""), type=saveType)
}
}
#-----------------------------------------------------------------------------
# Data with posterior predictive:
# If only 1 predictor:
if ( ncol(x)==1 ) {
openGraph(width=7,height=6)
par( mar=c(3.5,3.5,2,1) , mgp=c(2.0,0.7,0) )
plot( x[,1] , y , xlab=xName[1] , ylab=yName ,
cex=2.0 , cex.lab=1.5 , col="black" , main="Data with Post. Pred." )
abline(h=0.5,lty="dotted")
cVec = floor(seq(1,chainLength,length=30))
xWid=max(x)-min(x)
xComb = seq(min(x)-0.1*xWid,max(x)+0.1*xWid,length=201)
for ( cIdx in cVec ) {
lines( xComb , 1/(1+exp(-(beta0[cIdx]+beta[cIdx,1]*xComb ))) , lwd=1.5 ,
col="skyblue" )
xInt = -beta0[cIdx]/beta[cIdx,1]
arrows( xInt,0.5, xInt,-0.04, length=0.1 , col="skyblue" , lty="dashed" )
}
if ( !is.null(saveName) ) {
saveGraph( file=paste(saveName,"DataThresh",sep=""), type=saveType)
}
}
# If only 2 predictors:
if ( ncol(x)==2 ) {
openGraph(width=7,height=7)
par( mar=c(3.5,3.5,2,1) , mgp=c(2.0,0.7,0) )
plot( x[,1] , x[,2] , pch=as.character(y) , xlab=xName[1] , ylab=xName[2] ,
col="black" , main="Data with Post. Pred.")
cVec = floor(seq(1,chainLength,length=30))
for ( cIdx in cVec ) {
abline( -beta0[cIdx]/beta[cIdx,2] , -beta[cIdx,1]/beta[cIdx,2] , col="skyblue" )
}
if ( !is.null(saveName) ) {
saveGraph( file=paste(saveName,"DataThresh",sep=""), type=saveType)
}
}
#-----------------------------------------------------------------------------
# Marginal histograms:
decideOpenGraph = function( panelCount , saveName , finished=FALSE ,
nRow=1 , nCol=3 ) {
# If finishing a set:
if ( finished==TRUE ) {
if ( !is.null(saveName) ) {
saveGraph( file=paste0(saveName,ceiling((panelCount-1)/(nRow*nCol))),
type=saveType)
}
panelCount = 1 # re-set panelCount
return(panelCount)
} else {
# If this is first panel of a graph:
if ( ( panelCount %% (nRow*nCol) ) == 1 ) {
# If previous graph was open, save previous one:
if ( panelCount>1 & !is.null(saveName) ) {
saveGraph( file=paste0(saveName,(panelCount%/%(nRow*nCol))),
type=saveType)
}
# Open new graph
openGraph(width=nCol*7.0/3,height=nRow*2.0)
layout( matrix( 1:(nRow*nCol) , nrow=nRow, byrow=TRUE ) )
par( mar=c(4,4,2.5,0.5) , mgp=c(2.5,0.7,0) )
}
# Increment and return panel count:
panelCount = panelCount+1
return(panelCount)
}
}
# Original scale:
panelCount = 1
panelCount = decideOpenGraph( panelCount , saveName=paste0(saveName,"PostMarg") )
histInfo = plotPost( beta0 , cex.lab = 1.75 , showCurve=showCurve ,
xlab=bquote(beta[0]) , main="Intercept" )
for ( bIdx in 1:ncol(beta) ) {
panelCount = decideOpenGraph( panelCount , saveName=paste0(saveName,"PostMarg") )
histInfo = plotPost( beta[,bIdx] , cex.lab = 1.75 , showCurve=showCurve ,
xlab=bquote(beta[.(bIdx)]) , main=xName[bIdx] )
}
panelCount = decideOpenGraph( panelCount , finished=TRUE , saveName=paste0(saveName,"PostMarg") )
# Standardized scale:
panelCount = 1
panelCount = decideOpenGraph( panelCount , saveName=paste0(saveName,"PostMargZ") )
histInfo = plotPost( zbeta0 , cex.lab = 1.75 , showCurve=showCurve ,
xlab=bquote(z*beta[0]) , main="Intercept" )
for ( bIdx in 1:ncol(beta) ) {
panelCount = decideOpenGraph( panelCount , saveName=paste0(saveName,"PostMargZ") )
histInfo = plotPost( zbeta[,bIdx] , cex.lab = 1.75 , showCurve=showCurve ,
xlab=bquote(z*beta[.(bIdx)]) , main=xName[bIdx] )
}
panelCount = decideOpenGraph( panelCount , finished=TRUE , saveName=paste0(saveName,"PostMargZ") )
#-----------------------------------------------------------------------------
}
#===============================================================================
How can I relate two tables in Leaflet
I am mapping bibliographic information of Mars and would like to relate two tables(key column: feature_ID) in Leaflet. My objective is to show pop up of the csv table 2 info window when clicking a feature in the geojson table 1. However, I have no idea how to do this. Does anyone have any idea where should I refer to?
$.getJSON("mapdata.geojson",function(hoodData){
L.geoJson( hoodData , {
style: function(feature){
var fillColor,
number_of_papers = feature.properties.number_of_papers;
if ( number_of_papers > 80 ) fillColor = "#006837";
else if ( number_of_papers > 40 ) fillColor = "#31a354";
else if ( number_of_papers > 20 ) fillColor = "#78c679";
else if ( number_of_papers > 10 ) fillColor = "#c2e699";
else if ( number_of_papers > 0 ) fillColor = "#ffffcc";
else fillColor = "#f7f7f7"; // no data
return { color: "#999", weight: 1, fillColor: fillColor, fillOpacity: .6 };
},
onEachFeature: function( feature, layer ){
layer.bindPopup( "<strong>" + feature.properties.Clean_Feature_Name + "</strong><br/>" + "Number of Papers: " + feature.properties.number_of_papers + "<br/>" + "Number of Citing Papers: " + feature.properties.number_of_citing_papers + "<br/>" + "Number of self citations: " + feature.properties.number_of_self_citations + "<br/>" + "Total number of citations: " + feature.properties.total_number_of_citations + "<br/>" + "Number of referred citations: " + feature.properties.total_number_of_refereed_citations )
}
} ).addTo(map);
Map I created so far: https://shinjjo.github.io/heatmap.html
Data: https://github.com/shinjjo/shinjjo.github.io
The Table 1 has geographic information (geojson)
The Table 2 has publication information (csv)
Table 2
Fill assoc array with date
I need to fill a assoc array $pay_day with the key = date and a value of 0, from a start date $d1 till an end date $d2.
$d1 = strtotime("October 12 2014");
$d2 = strtotime("February 28 2015");
$startday = date('F j, Y', $d1 + (24 * 60 * 60));
echo "<h2>Startday is " . $startday . "</h2>";
$endday = date('F j, Y', $d2 + (24 * 60 * 60));
echo "<h2>Endday is " . $endday . "</h2>";
$n=0;
$d3=$d1;
while ($d3 <= $d2) {
$d3 = date('F j, Y', ($d1 + ++$n*(24 * 60 * 60)));
$pay_day[$d3] = 0;
}
$d1 = 'August 12 2014';
$d2 = 'February 28 2015';
$period = new \DatePeriod(
\DateTime::createFromFormat('F d Y', $d1),
new \DateInterval('P1D'),
\DateTime::createFromFormat('F d Y', $d2)->modify('+1 day'));
foreach($period as $date) {
$d3 = $date->format('F j, Y');
$pay_day[$d3] = 0;
}
print_r($pay_day);
how to program this indicator script to work with stocks and not just futures
i found this.. posted it in the Amibroker editor.. saved.. found out after looking into it further.. that it will only work on futures that are listed in the if statements within the code.. i'd like to see this for stocks.. any ideas..
// ACD Plot
// LSMA is Linreg
// ACD.afl
// v 1.2 9/13/2004
SetChartBkColor(16);
Per = Param("Periods",13);
Per2 = Param("Periods 2",34);
LSMAPer = Param("LMSA Period",25);
Offset = Param("A Level",1);
ACDFlag = 0;
IntervalFlag = IIf(Interval(format=0)==300,1,0);
strInterval = Interval(format=2);
strWeekday = StrMid("SunMonTueWedThuFriSat", SelectedValue(DayOfWeek())*3,3);
if( StrFind( Name(), "YM" ) )
{
ACDOffset = 10;
ACDFlag = 1;
ACDTime = 94500;
}
if( StrFind( Name(), "ER" ) )
{
ACDOffset = 0.5;
ACDFlag = 1;
ACDTime = 93500;
}
if( StrFind( Name(), "ES" ) )
{
ACDOffset = 2;
ACDFlag = 1;
ACDTime = 94500;
}
if( StrFind( Name(), "NQ" ) )
{
ACDOffset = 3;
ACDFlag = 1;
ACDTime = 94500;
}
if( StrFind( Name(), "ZB" ) )
{
ACDOffset = 0.15;
ACDFlag = 1;
ACDTime = 83000;
}
if( StrFind( Name(), "ZN" ) )
{
ACDOffset = 0.15;
ACDFlag = 1;
ACDTime = 83000;
}
GraphXSpace = 1;
Shift = 2;
// calculate the pivot range
PDH = TimeFrameGetPrice( "H", inDaily, -1 ); // gives previous Day High when working on intraday data
PDL = TimeFrameGetPrice( "L", inDaily, -1 );
PDC = TimeFrameGetPrice( "C", inDaily, -1 );
PP = (PDH+PDL+PDC)/3;
DIFF = abs((PDH+PDL)/2 - PP);
PRHi = PP + DIFF;
PRLo = PP - DIFF;
EMA1 = EMA(Avg,Per);
EMA2 = EMA(Avg,Per2);
LSMA = LinearReg(Avg, LSMAPer);
Plot(C, "Close",colorWhite,styleCandle);
if (ACDFlag AND IntervalFlag) {
ORHigh= ValueWhen(TimeNum()<ACDTime,HighestSince(DateNum()>Ref(DateNum(),-1),High));
ORLow = ValueWhen(TimeNum()<ACDTime,LowestSince(DateNum()>Ref(DateNum(),-1), Low));
Plot(PRHi,"PRHigh",colorWhite,styleDots+styleNoLine+styleNoLabel);
Plot(PRLo,"PRLow",colorWhite,styleDots+styleNoLine+styleNoLabel);
Plot(ORHigh,"ORHigh",colorBlue,style=styleStaircase+styleDots+styleNoLine+styleNoLabel);
Plot(ORLow,"ORLow",colorBlue,style=styleStaircase+styleDots+styleNoLine+styleNoLabel);
Plot(ORHigh+ACDOffset,"AUp",colorYellow,style=styleStaircase+styleDots+styleNoLine);
Plot(ORLow-ACDOffset,"ADn",colorYellow,style=styleStaircase+styleDots+styleNoLine);
// Plot(LSMA, "LSMA", colorYellow,style=styleThick);
}
Title=Name()+" ["+strInterval+"] "+ strWeekday + " " +Date()+ " Close: "
+WriteVal(C,format=1.2) +" "+WriteVal(per,format=1.0)+"-Per MA: "
+WriteVal(EMA1,format=1.2)+" " + WriteVal(per2,format=1.0)+"-Per MA: "
+WriteVal(EMA2,format=1.2) + " PR High: "+WriteVal(PRHi,format=1.2) + " PR Low: "
+WriteVal(PRLo,format=1.2);
this is the response i got in Amibroker forum.. thought i would share the answer.. This is both indicator and an exploration (however – this code is using very old approach, now it’s much more convenient to use PLOT or ADDCOLUMN functions instead of this obsolete coding style) There are just some errors in the formula, as it uses assignment instead of equality check, so you need to replace: HiLo=IIf(HLv=-1, with HiLo=IIf(HLv == -1, the same with - HiLoInvert=IIf(HLv=-1, This is because == (double =) is an operator used for equality check.