Stock Market Case Study
library(xts)## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric##
## ################################### WARNING ###################################
## # We noticed you have dplyr installed. The dplyr lag() function breaks how #
## # base R's lag() function is supposed to work, which breaks lag(my_xts). #
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## # #
## # All package code is unaffected because it is protected by the R namespace #
## # mechanism. #
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## # Set `options(xts.warn_dplyr_breaks_lag = FALSE)` to suppress this warning. #
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## # You can use stats::lag() to make sure you're not using dplyr::lag(), or you #
## # can add conflictRules('dplyr', exclude = 'lag') to your .Rprofile to stop #
## # dplyr from breaking base R's lag() function. #
## ################################### WARNING ###################################data(GSPC, package="DMwR2")
first(GSPC)## GSPC.Open GSPC.High GSPC.Low GSPC.Close GSPC.Volume GSPC.Adjusted
## 1970-01-02 92.06 93.54 91.79 93 8050000 93 T.ind <- function(quotes, tgt.margin = 0.025, n.days = 10) {
+ v <- apply(HLC(quotes), 1, mean)
+ v[1] <- Cl(quotes)[1]
+ r <- matrix(NA, ncol = n.days, nrow = NROW(quotes))
+ for (x in 1:n.days) r[, x] <- Next(Delt(v, k = x), x)
+ x <- apply(r, 1, function(x) sum(x[x > tgt.margin | x < -tgt.margin]))
+ if (is.xts(quotes)) xts(x, time(quotes)) else x}T.ind <- function(quotes, tgt.margin = 0.025, n.days = 10) {
v <- apply(HLC(quotes), 1, mean)
v[1] <- Cl(quotes)[1]
r <- matrix(NA, ncol = n.days, nrow = nrow(quotes))
for (x in 1:n.days) {
r[,x] <- Next(Delt(v,k=x),x)
}
x <- apply(r, 1, function(x) sum(x[x > tgt.margin | x < -tgt.margin]))
if (is.xts(quotes)) xts(x, time(quotes)) else x
}library('quantmod')## Loading required package: TTR## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoocandleChart(last(GSPC,'3 months'),theme='white', TA=NULL) 
avgPrice <- function(p) apply(HLC(p),1,mean)
addAvgPrice <- newTA(FUN=avgPrice,col=1,legend='AvgPrice')
addT.ind <- newTA(FUN=T.ind,col='red', legend='tgtRet')
addAvgPrice(on=1) 
#addT.ind()library(TTR)
myATR<- function(x) ATR(HLC(x))[,'atr']
mySMI<- function(x) SMI(HLC(x))[, "SMI"]
myADX<- function(x) ADX(HLC(x))[,'ADX']
myAroon<- function(x) aroon(cbind(Hi(x),Lo(x)))$oscillator
myBB<- function(x) BBands(HLC(x))[, "pctB"]
myChaikinVol<- function(x) Delt(chaikinVolatility(cbind(Hi(x),Lo(x))))[, 1]
myCLV<- function(x) EMA(CLV(HLC(x)))[, 1]
myEMV<- function(x) EMV(cbind(Hi(x),Lo(x)),Vo(x))[,2]
myMACD<- function(x) MACD(Cl(x))[,2]
myMFI<- function(x) MFI(HLC(x), Vo(x))
mySAR <- function(x) SAR(cbind(Hi(x),Cl(x))) [,1]
myVolat<- function(x) volatility(OHLC(x),calc="garman")[,1]library(randomForest)## randomForest 4.7-1.1## Type rfNews() to see new features/changes/bug fixes.data.model <- specifyModel(T.ind(GSPC) ~ Delt(Cl(GSPC),k=1:10) +
myATR(GSPC) + mySMI(GSPC) + myADX(GSPC) + myAroon(GSPC) +
myBB(GSPC) + myChaikinVol(GSPC) + myCLV(GSPC) +
CMO(Cl(GSPC)) + EMA(Delt(Cl(GSPC))) + myEMV(GSPC) +
myVolat(GSPC) + myMACD(GSPC) + myMFI(GSPC) + RSI(Cl(GSPC)) +
mySAR(GSPC) + runMean(Cl(GSPC)) + runSD(Cl(GSPC)))## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
## Consider formula(paste(x, collapse = " ")) instead.set.seed(1234)
rf <- buildModel(data.model,method='randomForest',
training.per=c("1995-01-01","2005-12-30"),
ntree=1000, importance=TRUE)varImpPlot(rf@fitted.model, type = 1)
imp <- importance(rf@fitted.model,type = 1)
rownames(imp)[which(imp>30)]## [1] "myATR.GSPC" "mySMI.GSPC" "myADX.GSPC" "myAroon.GSPC"
## [5] "myEMV.GSPC" "myVolat.GSPC" "myMACD.GSPC" "myMFI.GSPC"
## [9] "mySAR.GSPC" "runMean.Cl.GSPC" "runSD.Cl.GSPC"data.model <- specifyModel(T.ind(GSPC) ~ myATR(GSPC) + mySMI(GSPC) + myADX(GSPC)+myAroon(GSPC) + myEMV(GSPC) + myVolat(GSPC) + myMACD(GSPC) + myMFI(GSPC) + mySAR(GSPC) + runMean(Cl(GSPC)) + runSD(Cl(GSPC)))## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
## Consider formula(paste(x, collapse = " ")) instead.library(DMwR2)
library(quantmod)
library(TTR)
library(zoo)
## The regression task
Tdata.train <- as.data.frame(modelData(data.model, data.window=c('1970-01-02','2005-12-30')))
Tdata.eval <- na.omit(as.data.frame(modelData(data.model, data.window=c('2006-01-01','2016-01-25'))))
Tform <- as.formula('T.ind.GSPC ~ .') ## The classification task
buy.thr <- 0.1
sell.thr <- -0.1
Tdata.trainC <- cbind(Signal=trading.signals(Tdata.train[["T.ind.GSPC"]], buy.thr,sell.thr), Tdata.train[,-1])
Tdata.evalC <- cbind(Signal=trading.signals(Tdata.eval[["T.ind.GSPC"]],
buy.thr,sell.thr),
Tdata.eval[,-1])
TformC <- as.formula("Signal ~ .")set.seed(1234)
library(nnet)
## The first column is the target variable
norm.data <- data.frame(T.ind.GSPC=Tdata.train[[1]],scale(Tdata.train[,-1]))
nn <- nnet(Tform, norm.data[1:1000, ], size = 5, decay = 0.01, maxit = 1000, linout = TRUE, trace = FALSE)
preds <- predict(nn, norm.data[1001:2000, ])sigs.nn <- trading.signals(preds,0.1,-0.1)
true.sigs <- trading.signals(Tdata.train[1001:2000, "T.ind.GSPC"], 0.1, -0.1)
sigs.PR(sigs.nn,true.sigs)## precision recall
## s 0.2809917 0.1931818
## b 0.3108108 0.2857143
## s+b 0.2973978 0.2373887set.seed(1234)
library(e1071)
sv <- svm(Tform, Tdata.train[1:1000, ], gamma = 0.001, cost = 100)
s.preds <- predict(sv, Tdata.train[1001:2000, ])
sigs.svm <- trading.signals(s.preds, 0.1, -0.1)
true.sigs <- trading.signals(Tdata.train[1001:2000, "T.ind.GSPC"], 0.1, -0.1)
sigs.PR(sigs.svm, true.sigs)## precision recall
## s 0.375 0.017045455
## b NaN 0.000000000
## s+b 0.375 0.008902077set.seed(1234)
library(kernlab)
ksv <- ksvm(Signal ~ ., Tdata.trainC[1:1000, ], C = 10)
ks.preds <- predict(ksv, Tdata.trainC[1001:2000, ])
sigs.PR(ks.preds, Tdata.trainC[1001:2000, 1])## precision recall
## s 0.2365591 0.2500000
## b 0.3246753 0.1552795
## s+b 0.2623574 0.2047478#install.packages('earth')
library(earth)## Loading required package: Formula## Loading required package: plotmo## Loading required package: plotrix## Loading required package: TeachingDemose <- earth(Tform, Tdata.train[1:1000, ])
e.preds <- predict(e, Tdata.train[1001:2000, ])
sigs.e <- trading.signals(e.preds, 0.1, -0.1)
true.sigs <- trading.signals(Tdata.train[1001:2000, "T.ind.GSPC"], 0.1, -0.1)
sigs.PR(sigs.e, true.sigs)## precision recall
## s 0.2894737 0.2500000
## b 0.3504274 0.2546584
## s+b 0.3159851 0.2522255summary(e)## Call: earth(formula=Tform, data=Tdata.train[1:1000,])
##
## coefficients
## (Intercept) 0.5241811
## h(myATR.GSPC-2.56817) 1.2724353
## h(-61.825-mySMI.GSPC) 0.0594203
## h(myADX.GSPC-40.6215) -0.0104803
## h(50.657-myADX.GSPC) -0.0025279
## h(myADX.GSPC-50.657) 0.0823181
## h(0.204717-myVolat.GSPC) -0.5105416
## h(myVolat.GSPC-0.204717) -5.6100523
## h(myVolat.GSPC-0.271459) 5.6725474
## h(mySAR.GSPC-74.7031) -0.0693496
## h(87.944-mySAR.GSPC) -0.0575104
## h(mySAR.GSPC-87.944) 0.0800171
## h(runMean.Cl.GSPC-79.265) 0.2074780
## h(81.942-runMean.Cl.GSPC) 0.1058493
## h(runMean.Cl.GSPC-81.942) -0.2185753
##
## Selected 15 of 18 terms, and 6 of 11 predictors
## Termination condition: Reached nk 23
## Importance: myVolat.GSPC, runMean.Cl.GSPC, myATR.GSPC, mySMI.GSPC, ...
## Number of terms at each degree of interaction: 1 14 (additive model)
## GCV 0.01470628 RSS 13.86568 GRSq 0.3536668 RSq 0.38939evimp(e, trim=FALSE)## nsubsets gcv rss
## myVolat.GSPC 13 100.0 100.0
## runMean.Cl.GSPC 13 100.0 100.0
## myATR.GSPC 12 96.7 96.4
## mySMI.GSPC 11 81.5 82.4
## mySAR.GSPC 7 44.1 47.7
## myADX.GSPC 5 57.8> 58.5>
## myAroon.GSPC-unused 0 0.0 0.0
## myEMV.GSPC-unused 0 0.0 0.0
## myMACD.GSPC-unused 0 0.0 0.0
## myMFI.GSPC-unused 0 0.0 0.0
## runSD.Cl.GSPC-unused 0 0.0 0.0plot(e)
policy.1 <- function(signals,market,opened.pos,money,
bet=0.2,hold.time=10,
exp.prof=0.025, max.loss= 0.05
)
{
d <- NROW(market) # this is the ID of today
orders <- NULL
nOs <- NROW(opened.pos)
# nothing to do!
if (!nOs && signals[d] == 'h') return(orders)
# First lets check if we can open new positions
# i) long positions
if (signals[d] == 'b' && !nOs) {
quant <- round(bet*money/Cl(market)[d],0)
if (quant > 0)
orders <- rbind(orders,
data.frame(order=c(1,-1,-1),order.type=c(1,2,3),
val = c(quant,
Cl(market)[d]*(1+exp.prof),
Cl(market)[d]*(1-max.loss)
),
action = c('open','close','close'),
posID = c(NA,NA,NA)
)
)
# ii) short positions
} else if (signals[d] == 's' && !nOs) {
# this is the nr of stocks we already need to buy
# because of currently opened short positions
need2buy <- sum(opened.pos[opened.pos[,'pos.type']==-1,
"N.stocks"])*Cl(market)[d]
quant <- round(bet*(money-need2buy)/Cl(market)[d],0)
if (quant > 0)
orders <- rbind(orders,
data.frame(order=c(-1,1,1),order.type=c(1,2,3),
val = c(quant,
Cl(market)[d]*(1-exp.prof),
Cl(market)[d]*(1+max.loss)
),
action = c('open','close','close'),
posID = c(NA,NA,NA)
)
)
}
# Now lets check if we need to close positions
# because their holding time is over
if (nOs)
for(i in 1:nOs) {
if (d - opened.pos[i,'Odate'] >= hold.time)
orders <- rbind(orders,
data.frame(order=-opened.pos[i,'pos.type'],
order.type=1,
val = NA,
action = 'close',
posID = rownames(opened.pos)[i]
)
)
}
orders
}policy.2 <- function(signals,market,opened.pos,money,
bet=0.2,exp.prof=0.025, max.loss= 0.05
)
{
d <- NROW(market) # this is the ID of today
orders <- NULL
nOs <- NROW(opened.pos)
# nothing to do!
if (!nOs && signals[d] == 'h') return(orders)
# First lets check if we can open new positions
# i) long positions
if (signals[d] == 'b') {
quant <- round(bet*money/Cl(market)[d],0)
if (quant > 0)
orders <- rbind(orders,
data.frame(order=c(1,-1,-1),order.type=c(1,2,3),
val = c(quant,
Cl(market)[d]*(1+exp.prof),
Cl(market)[d]*(1-max.loss)
),
action = c('open','close','close'),
posID = c(NA,NA,NA)
)
)
# ii) short positions
} else if (signals[d] == 's') {
# this is the money already committed to buy stocks
# because of currently opened short positions
need2buy <- sum(opened.pos[opened.pos[,'pos.type']==-1,
"N.stocks"])*Cl(market)[d]
quant <- round(bet*(money-need2buy)/Cl(market)[d],0)
if (quant > 0)
orders <- rbind(orders,
data.frame(order=c(-1,1,1),order.type=c(1,2,3),
val = c(quant,
Cl(market)[d]*(1-exp.prof),
Cl(market)[d]*(1+max.loss)
),
action = c('open','close','close'),
posID = c(NA,NA,NA)
)
)
}
orders
}start <- 1
len.tr <- 1000
len.ts <- 500
tr <- start:(start+len.tr-1)
ts <- (start+len.tr):(start+len.tr+len.ts-1)
## getting the quotes for the testing period
data(GSPC)
date <- rownames(Tdata.train[start+len.tr,])
marketTP <- GSPC[paste(date,'/',sep='')][1:len.ts]
## learning the model and obtaining its signal predictions for the test period
library(e1071)
s <- svm(Tform, Tdata.train[tr,], cost=10,gamma=0.01)
p <- predict(s, Tdata.train[ts,])
sig <- trading.signals(p, 0.1, -0.1)
## now using the simulated trader during the testing period
t1 <- trading.simulator(marketTP, signals=sig, policy.func='policy.1',
policy.pars=list(exp.prof=0.05,bet=0.2,hold.time=30))tradingEvaluation(t1)## NTrades NProf PercProf PL Ret RetOverBH
## 8.00 5.00 62.50 19712.54 1.97 -4.88
## MaxDD SharpeRatio AvgProf AvgLoss AvgPL MaxProf
## 25630.72 0.04 5.11 -5.00 1.32 5.26
## MaxLoss
## -5.00t2 <- trading.simulator(marketTP, sig, "policy.2", list(exp.prof = 0.05, bet = 0.3))tradingEvaluation(t2)## NTrades NProf PercProf PL Ret RetOverBH
## 37.00 26.00 70.27 152332.30 15.23 8.38
## MaxDD SharpeRatio AvgProf AvgLoss AvgPL MaxProf
## 67492.23 0.06 4.99 -4.89 2.05 5.26
## MaxLoss
## -5.00plot(t1,marketTP, theme = "white", name = "SP500")
## Rentability = 1.971254 %plot(t2,marketTP, theme = "white", name = "SP500")
## Rentability = 15.23323 %start <- 2000
len.tr <- 1000
len.ts <- 500
tr <- start:(start + len.tr - 1)
ts <- (start + len.tr):(start + len.tr + len.ts - 1)
data(GSPC)
date <- rownames(Tdata.train[start+len.tr,])
marketTP <- GSPC[paste(date,'/',sep='')][1:len.ts]
s <- svm(Tform, Tdata.train[tr, ], cost = 10, gamma = 0.01)
p <- predict(s, Tdata.train[ts, ])
sig <- trading.signals(p, 0.1, -0.1)
t2 <- trading.simulator(marketTP, sig,
"policy.2", list(exp.prof = 0.05, bet = 0.3))tradingEvaluation(t2)## NTrades NProf PercProf PL Ret RetOverBH
## 231.00 29.00 12.55 -784779.95 -78.48 -111.74
## MaxDD SharpeRatio AvgProf AvgLoss AvgPL MaxProf
## 973177.31 0.02 5.19 -2.59 -1.62 5.56
## MaxLoss
## -4.89tradingWF <- function(form, train, test,
quotes, pred.target="signals",
learner, learner.pars=NULL,
predictor.pars=NULL,
learn.test.type='fixed', relearn.step=30,
b.t, s.t,
policy, policy.pars,
trans.cost=5, init.cap=1e+06)
{
## obtain the model(s) and respective predictions for the test set
if (learn.test.type == 'fixed') { # a single fixed model
m <- do.call(learner,c(list(form,train),learner.pars))
preds <- do.call("predict",c(list(m,test),predictor.pars))
} else { # either slide or growing window strategies
data <- rbind(train,test)
n <- NROW(data)
train.size <- NROW(train)
sts <- seq(train.size+1,n,by=relearn.step)
preds <- vector()
for(s in sts) { # loop over each relearn step
tr <- if (learn.test.type=='slide') data[(s-train.size):(s-1),]
else data[1:(s-1),]
ts <- data[s:min((s+relearn.step-1),n),]
m <- do.call(learner,c(list(form,tr),learner.pars))
preds <- c(preds,do.call("predict",c(list(m,ts),predictor.pars)))
}
}
## Getting the trading signals
if (pred.target != "signals") { # the model predicts the T indicator
predSigs <- trading.signals(preds,b.t,s.t)
tgtName <- all.vars(form)[1]
trueSigs <- trading.signals(test[[tgtName]],b.t,s.t)
} else { # the model predicts the signals directly
tgtName <- all.vars(form)[1]
if (is.factor(preds))
predSigs <- preds
else {
if (preds[1] %in% levels(train[[tgtName]]))
predSigs <- factor(preds,labels=levels(train[[tgtName]]),
levels=levels(train[[tgtName]]))
else
predSigs <- factor(preds,labels=levels(train[[tgtName]]),
levels=1:3)
}
trueSigs <- test[[tgtName]]
}
## obtaining the trading record from trading with the signals
date <- rownames(test)[1]
market <- get(quotes)[paste(date,"/",sep='')][1:length(preds),]
tradeRec <- trading.simulator(market,predSigs,
policy.func=policy,policy.pars=policy.pars,
trans.cost=trans.cost,init.cap=init.cap)
return(list(trueSigs=trueSigs,predSigs=predSigs,tradeRec=tradeRec))
}tradingEval <- function(trueSigs,predSigs,tradeRec,...)
{
## Signals evaluation
st <- sigs.PR(predSigs,trueSigs)
dim(st) <- NULL
names(st) <- paste(rep(c('prec','rec'),each=3),c('s','b','sb'),sep='.')
## Trading record evaluation
tradRes <- tradingEvaluation(tradeRec)
return(c(st,tradRes))
}I will load the files in manually given by the book
###library(performanceEstimation) #library(e1071) #library(earth) #library(nnet)
LEARNERS <- c(‘#svm’,‘earth’,‘nnet’) ’’’ EST.TASK <- EstimationTask(method=MonteCarlo(nReps=20, szTrain=2540,szTest=1270, seed=1234), evaluator=“tradingEval”) VARS <- list()
VARS\(svm <- list(learner.pars=list(cost=c(10,50,150), gamma=c(0.01,0.05))) VARS\)earth <- list(learner.pars=list(nk=c(10,17), degree=c(1,2), thresh=c(0.01,0.001))) VARS$nnet <- list(learner.pars=list(linout=TRUE, trace=FALSE, maxit=750, size=c(5,10), decay=c(0.001,0.01,0.1)))
VARS\(learning <- list(learn.test.type=c("fixed","slide","grow"), relearn.step=120) VARS\)trading <- list(policy=c(“policy.1”,“policy.2”), policy.pars=list(bet=c(0.2,0.5),exp.prof=0.05,max.loss=0.05), b.t=c(0.01,0.05),s.t=c(-0.01,-0.05))
Regression (forecast T indicator) Workflows
for(lrn in LEARNERS) { objName <- paste(lrn,“res”,“regr”,sep=“_“) assign(objName, performanceEstimation(PredTask(Tform,Tdata.train,”SP500”), do.call(“workflowVariants”, c(list(“tradingWF”, varsRootName=paste0(lrn,“Regr”), quotes=“GSPC”, learner=lrn, pred.target=“indicator”), VARS[[lrn]], VARS\(learning, VARS\)trading) ), EST.TASK, cluster=TRUE) # for parallel computation ) save(list=objName,file=paste(objName,’ #Rdata’,sep=‘.’)) #} #’’’ ## Specific settings to make nnet work as a classifier VARS\(nnet\)learner.pars\(linout <- FALSE VARS\)nnet$predictor.pars <- list(type=“class”)
Classification (forecast signal) workflows
for(lrn in c(“svm”,“nnet”)) { # only these because MARS is only for regression objName <- paste(lrn,“res”,“class”,sep=“_“) assign(objName, performanceEstimation(PredTask(TformC,Tdata.trainC,”SP500”), do.call(“workflowVariants”, c(list(“tradingWF”, varsRootName=paste0(lrn,“Class”), quotes=“GSPC”, learner=lrn, pred.target=“signals”), VARS[[lrn]], VARS\(learning, VARS\)trading) ), EST.TASK, cluster=TRUE) # for parallel computation ) save(list=objName,file=paste(objName,‘##Rdata’,sep=‘.’))’’’}
#getwd()library(xts)
library(DMwR2)
library(quantmod)
library(TTR)
library(zoo)
library(performanceEstimation)
setwd("~/Downloads")
load("svm_res_regr.Rdata")
load("nnet_res_regr.Rdata")
load("earth_res_regr.Rdata")
load("svm_res_class.Rdata")
load("nnet_res_class.Rdata")
allResults <- mergeEstimationRes(svm_res_regr, earth_res_regr, nnet_res_regr,
svm_res_class, nnet_res_class,
by="workflows")
rm(svm_res_regr, earth_res_regr, nnet_res_regr, svm_res_class, nnet_res_class)tgtStats <- c('NTrades','prec.sb','Ret','RetOverBH','PercProf',
'MaxDD','SharpeRatio')
toMax <- c(rep(TRUE,5),FALSE,TRUE)
rankWorkflows(subset(allResults,
metrics=tgtStats,
partial=FALSE),
top=3,
maxs=toMax)## $SP500
## $SP500$NTrades
## Workflow Estimate
## 1 svmRegr.v24 985.35
## 2 svmRegr.v168 960.15
## 3 svmRegr.v23 958.95
##
## $SP500$prec.sb
## Workflow Estimate
## 1 nnetClass.v1 0.3199433
## 2 nnetClass.v19 0.3199433
## 3 nnetClass.v37 0.3199433
##
## $SP500$Ret
## Workflow Estimate
## 1 svmRegr.v138 155.1225
## 2 svmRegr.v60 82.7015
## 3 svmRegr.v204 81.3495
##
## $SP500$RetOverBH
## Workflow Estimate
## 1 svmRegr.v138 63.1045
## 2 svmRegr.v60 -9.3155
## 3 svmRegr.v204 -10.6670
##
## $SP500$PercProf
## Workflow Estimate
## 1 nnetRegr.v169 63.876
## 2 nnetRegr.v175 62.751
## 3 nnetRegr.v176 62.640
##
## $SP500$MaxDD
## Workflow Estimate
## 1 nnetClass.v1 12594.36
## 2 nnetClass.v73 12594.36
## 3 nnetClass.v145 12594.36
##
## $SP500$SharpeRatio
## Workflow Estimate
## 1 nnetRegr.v177 0.0400
## 2 nnetRegr.v167 0.0395
## 3 nnetRegr.v171 0.0385#getWorkflow("svmRegr.v138",analysisSet)best <- rankWorkflows(subset(allResults,
metrics=tgtStats,
partial=FALSE),
top=100,
maxs=toMax)
bestWFs <- unique(as.vector(sapply(best$SP500,function(x) x$Workflow)))
analysisSet <- subset(allResults, workflows=bestWFs, partial=FALSE)
rm(allResults)(tps <- topPerformers(subset(analysisSet,metrics=tgtStats,partial=FALSE), maxs=toMax))## $SP500
## Workflow Estimate
## NTrades svmRegr.v24 985.35
## prec.sb nnetClass.v1 0.32
## Ret svmRegr.v138 155.122
## RetOverBH svmRegr.v138 63.104
## PercProf nnetRegr.v169 63.876
## MaxDD nnetClass.v1 12594.358
## SharpeRatio nnetRegr.v177 0.04summary(subset(analysisSet,
workflows=tps$SP500[c("prec.sb","Ret","PercProf","MaxDD"),
"Workflow"],
metrics=tgtStats[-c(1,4,7)],
partial=FALSE))##
## == Summary of a Monte Carlo Performance Estimation Experiment ==
##
## Task for estimating all metrics of the selected evaluation function using
## 20 repetitions Monte Carlo Simulation using:
## seed = 1234
## train size = 2540 cases
## test size = 1270 cases
##
## * Predictive Tasks :: SP500
## * Workflows :: nnetClass.v1, svmRegr.v138, nnetRegr.v169, nnetClass.v1
##
## -> Task: SP500
## *Workflow: nnetClass.v1
## prec.sb Ret PercProf MaxDD
## avg 0.3199433 0.198500 21.7040 12594.36
## std 0.2134790 2.048353 28.8249 20844.75
## med 0.2329298 0.000000 0.0000 0.00
## iqr 0.2459596 0.000000 43.5525 16435.03
## min 0.0000000 -4.180000 0.0000 0.00
## max 0.6250000 6.280000 75.0000 64942.07
## invalid 12.0000000 0.000000 0.0000 0.00
##
## *Workflow: svmRegr.v138
## prec.sb Ret PercProf MaxDD
## avg 0.22493318 155.1225 52.384500 2081116.6
## std 0.06968098 390.1813 4.616856 1352582.7
## med 0.21721373 16.3600 51.975000 1652147.9
## iqr 0.08365744 195.7275 7.060000 1439520.3
## min 0.10979548 -92.3400 45.860000 789925.2
## max 0.35475352 1519.3300 62.350000 6537727.0
## invalid 0.00000000 0.0000 0.000000 0.0
##
## *Workflow: nnetRegr.v169
## prec.sb Ret PercProf MaxDD
## avg 0.29545145 25.83700 63.87600 248605.8
## std 0.09640889 31.57113 10.47377 158502.6
## med 0.33147567 21.38500 66.22500 235386.7
## iqr 0.15400154 37.89000 12.34750 175996.6
## min 0.11247803 -45.33000 40.25000 69260.9
## max 0.41073826 77.11000 77.50000 601398.2
## invalid 0.00000000 0.00000 0.00000 0.0
##
## *Workflow: nnetClass.v1
## prec.sb Ret PercProf MaxDD
## avg 0.3199433 0.198500 21.7040 12594.36
## std 0.2134790 2.048353 28.8249 20844.75
## med 0.2329298 0.000000 0.0000 0.00
## iqr 0.2459596 0.000000 43.5525 16435.03
## min 0.0000000 -4.180000 0.0000 0.00
## max 0.6250000 6.280000 75.0000 64942.07
## invalid 12.0000000 0.000000 0.0000 0.00ms <- metricsSummary(subset(analysisSet,
metrics=c("NTrades","Ret","PercProf"),
partial=FALSE),
summary="median")[["SP500"]]
candidates <- subset(analysisSet,
workflows=colnames(ms)[which(ms["NTrades",] > 120)],
partial=FALSE)
ms <- metricsSummary(subset(candidates,
metrics=c("Ret","PercProf"),
partial=FALSE),
summary="median")[["SP500"]]
(sms <- apply(ms,1,function(x) names(x[order(x,decreasing=TRUE)][1:15])))## Ret PercProf
## [1,] "nnetRegr.v200" "nnetRegr.v169"
## [2,] "svmRegr.v168" "nnetRegr.v167"
## [3,] "svmRegr.v204" "nnetRegr.v179"
## [4,] "svmRegr.v102" "nnetRegr.v177"
## [5,] "svmRegr.v30" "svmRegr.v169"
## [6,] "svmRegr.v24" "svmRegr.v175"
## [7,] "svmRegr.v174" "nnetRegr.v203"
## [8,] "nnetRegr.v211" "nnetRegr.v175"
## [9,] "nnetRegr.v213" "nnetRegr.v176"
## [10,] "svmRegr.v60" "nnetRegr.v205"
## [11,] "nnetRegr.v202" "nnetRegr.v172"
## [12,] "svmRegr.v246" "nnetRegr.v173"
## [13,] "svmRegr.v36" "nnetRegr.v178"
## [14,] "nnetRegr.v175" "nnetRegr.v213"
## [15,] "nnetRegr.v203" "nnetRegr.v215"(winners <- unique(c(intersect(sms[,1],sms[,2]),sms[1:3,1],sms[1:3,2])))## [1] "nnetRegr.v213" "nnetRegr.v175" "nnetRegr.v203" "nnetRegr.v200"
## [5] "svmRegr.v168" "svmRegr.v204" "nnetRegr.v169" "nnetRegr.v167"
## [9] "nnetRegr.v179"winnersResults <- subset(analysisSet,
metrics=tgtStats,workflows=winners,
partial=FALSE)#p <-pairedComparisons(winnersResults,baseline="nnetRegr.v200",maxs=toMax)
#p$Ret$WilcoxonSignedRank.testnnetRegr.v200 nnetRegr.v213 nnetRegr.v175 nnetRegr.v203 svmRegr.v168 svmRegr.v204 nnetRegr.v169 nnetRegr.v167 nnetRegr.v179 MedScore 56.685 39.210 35.535 35.460 49.105 48.900 21.385 33.480 30.735 DiffMedScores NA 17.475 21.150 21.225 7.580 7.785 35.300 23.205 25.950 p.value NA 0.1893482 0.3883762 0.3883762 0.6215134 0.7011814 0.6476555 0.4090977 0.498008
#p <- pairedComparisons(winnersResults,"nnetRegr.v175",maxs=toMax)
#p$MaxDD$WilcoxonSignedRank.testnnetRegr.v175 nnetRegr.v213 nnetRegr.v203 nnetRegr.v200 svmRegr.v168 svmRegr.v204 nnetRegr.v169 nnetRegr.v167 nnetRegr.v179 MedScore 190874.9 299346.4 402566.6 550732.9 429145.4 777845.6 235386.7 289129.3 250383.0 DiffMedScores NA -108471.47 -211691.76 -359857.96 -238270.56 -586970.71 -44511.78 -98254.37 -59508.14 p.value NA 1.678467e-04 8.506775e-04 4.768372e-05 7.076263e-04 1.335144e-05 8.983173e-01 3.998947e-02 3.117943e-01
#sds <- signifDiffs(p,p.limit=0.05,metrics="MaxDD")
#sds$MaxDD$WilcoxonSignedRank.test$SP500nnetRegr.v175 nnetRegr.v213 nnetRegr.v203 nnetRegr.v200 svmRegr.v168 svmRegr.v204 nnetRegr.v167 MedScore 190874.9 299346.4 402566.6 550732.9 429145.4 777845.6 289129.3 DiffMedScores NA -108471.47 -211691.76 -359857.96 -238270.56 -586970.71 -98254.37 p.value NA 1.678467e-04 8.506775e-04 4.768372e-05 7.076263e-04 1.335144e-05 3.998947e-02
#getWorkflow("nnetRegr.v200", winnersResults)
#getWorkflow("nnetRegr.v175", winnersResults)set.seed(1234)
data <- tail(Tdata.train, 2540) # the last 10 years of the training dataset
results <- list()
wfsOut <- list()
for (name in winners) {
sys <- getWorkflow(name, analysisSet)
wfsOut[[name]] <- runWorkflow(sys, Tform, data, Tdata.eval)
results[[name]] <- do.call("tradingEval",wfsOut[[name]])
}## # weights: 66
## initial value 723.764266
## iter 10 value 73.179826
## iter 20 value 72.759087
## iter 30 value 70.092639
## iter 40 value 67.614682
## iter 50 value 65.698379
## iter 60 value 63.482332
## iter 70 value 62.753709
## iter 80 value 62.681277
## iter 90 value 62.410345
## iter 100 value 61.731266
## iter 110 value 61.091629
## iter 120 value 60.992712
## iter 130 value 60.737050
## iter 140 value 60.630585
## iter 150 value 60.492789
## iter 160 value 60.437327
## iter 170 value 60.426776
## iter 180 value 60.402193
## iter 190 value 60.399652
## final value 60.399504
## converged
## # weights: 66
## initial value 622.805289
## iter 10 value 73.595530
## iter 20 value 72.352158
## iter 30 value 69.666734
## iter 40 value 67.814443
## iter 50 value 67.025363
## iter 60 value 66.491513
## iter 70 value 65.471019
## iter 80 value 64.676243
## iter 90 value 64.184149
## iter 100 value 63.962654
## iter 110 value 63.902796
## iter 120 value 63.660059
## iter 130 value 63.532076
## iter 140 value 63.481999
## iter 150 value 63.408091
## iter 160 value 63.165357
## iter 170 value 62.870520
## iter 180 value 62.094510
## iter 190 value 61.830294
## iter 200 value 61.464983
## iter 210 value 60.880333
## iter 220 value 60.643233
## iter 230 value 60.446388
## iter 240 value 60.134531
## iter 250 value 59.897100
## iter 260 value 59.513828
## iter 270 value 58.484565
## iter 280 value 58.234765
## iter 290 value 58.168127
## iter 300 value 58.081600
## iter 310 value 57.956227
## iter 320 value 57.882173
## iter 330 value 57.631168
## iter 340 value 57.486889
## iter 350 value 57.477118
## iter 360 value 57.476770
## iter 370 value 57.476751
## final value 57.476749
## converged
## # weights: 66
## initial value 4173.512946
## iter 10 value 74.008731
## iter 20 value 73.121326
## iter 30 value 71.236849
## iter 40 value 69.586461
## iter 50 value 67.873161
## iter 60 value 67.011604
## iter 70 value 65.497158
## iter 80 value 64.484458
## iter 90 value 64.007932
## iter 100 value 63.486304
## iter 110 value 62.447800
## iter 120 value 61.571170
## iter 130 value 60.629628
## iter 140 value 60.260785
## iter 150 value 59.716086
## iter 160 value 58.657452
## iter 170 value 56.655527
## iter 180 value 55.735653
## iter 190 value 55.608624
## iter 200 value 55.531010
## iter 210 value 54.914499
## iter 220 value 53.907700
## iter 230 value 53.453181
## iter 240 value 53.340382
## iter 250 value 53.326165
## iter 260 value 53.310385
## iter 270 value 53.209653
## iter 280 value 52.883955
## iter 290 value 51.981213
## iter 300 value 51.778652
## iter 310 value 51.733108
## iter 320 value 51.715045
## iter 330 value 51.567406
## iter 340 value 51.267792
## iter 350 value 50.851350
## iter 360 value 50.753686
## iter 370 value 50.721220
## iter 380 value 50.719205
## iter 390 value 50.717608
## iter 400 value 50.713360
## iter 410 value 50.705192
## iter 420 value 50.487486
## iter 430 value 50.405945
## iter 440 value 50.390873
## iter 450 value 50.388968
## iter 460 value 50.388893
## final value 50.388889
## converged
## # weights: 66
## initial value 1660.581205
## iter 10 value 74.414775
## iter 20 value 73.894249
## iter 30 value 72.934734
## iter 40 value 71.888229
## iter 50 value 69.317229
## iter 60 value 65.237257
## iter 70 value 64.746255
## iter 80 value 64.242702
## iter 90 value 64.126249
## iter 100 value 64.082530
## iter 110 value 63.910426
## iter 120 value 63.168754
## iter 130 value 62.641999
## iter 140 value 62.516684
## iter 150 value 62.487606
## iter 160 value 62.436782
## iter 170 value 62.368511
## iter 180 value 62.289678
## iter 190 value 62.241112
## iter 200 value 61.815192
## iter 210 value 61.472792
## iter 220 value 61.254885
## iter 230 value 61.233776
## iter 240 value 61.229215
## iter 250 value 61.228634
## iter 260 value 61.228515
## final value 61.228503
## converged
## # weights: 66
## initial value 1085.291876
## iter 10 value 76.675745
## iter 20 value 76.343265
## iter 30 value 72.203558
## iter 40 value 70.906852
## iter 50 value 68.705023
## iter 60 value 66.833513
## iter 70 value 66.600781
## iter 80 value 66.246697
## iter 90 value 65.513153
## iter 100 value 65.481764
## iter 110 value 65.400363
## iter 120 value 65.216122
## iter 130 value 65.181724
## iter 140 value 65.150558
## iter 150 value 65.037477
## iter 160 value 64.840810
## iter 170 value 64.351585
## iter 180 value 64.276520
## iter 190 value 63.997107
## iter 200 value 63.628999
## iter 210 value 63.183535
## iter 220 value 62.928104
## iter 230 value 62.872859
## iter 240 value 62.844895
## iter 250 value 62.780372
## iter 260 value 62.708552
## iter 270 value 62.391221
## iter 280 value 62.233336
## iter 290 value 61.878907
## iter 300 value 61.432419
## iter 310 value 61.239667
## iter 320 value 61.214572
## iter 330 value 61.211942
## iter 340 value 61.211320
## iter 350 value 61.211091
## final value 61.211077
## converged
## # weights: 66
## initial value 2847.074107
## iter 10 value 80.049947
## iter 20 value 78.441717
## iter 30 value 76.870690
## iter 40 value 75.748968
## iter 50 value 74.847268
## iter 60 value 73.387677
## iter 70 value 73.150924
## iter 80 value 73.023120
## iter 90 value 72.958369
## iter 100 value 72.861016
## iter 110 value 72.743092
## iter 120 value 72.554616
## iter 130 value 72.435033
## iter 140 value 72.416711
## iter 150 value 72.376994
## iter 160 value 72.348279
## iter 170 value 72.195078
## iter 180 value 71.411369
## iter 190 value 69.698341
## iter 200 value 68.986130
## iter 210 value 68.819299
## iter 220 value 68.715724
## iter 230 value 68.691813
## iter 240 value 68.441545
## iter 250 value 67.892089
## iter 260 value 67.434757
## iter 270 value 67.121670
## iter 280 value 66.984886
## iter 290 value 66.935436
## iter 300 value 66.923227
## iter 310 value 66.916797
## iter 320 value 66.915705
## iter 330 value 66.915574
## iter 340 value 66.915555
## iter 340 value 66.915555
## iter 340 value 66.915555
## final value 66.915555
## converged
## # weights: 66
## initial value 103.976470
## iter 10 value 101.388796
## iter 20 value 101.302515
## iter 30 value 100.663713
## iter 40 value 97.936644
## iter 50 value 95.773340
## iter 60 value 94.059452
## iter 70 value 90.990859
## iter 80 value 89.090158
## iter 90 value 85.765277
## iter 100 value 85.413089
## iter 110 value 84.563078
## iter 120 value 83.820921
## iter 130 value 82.437191
## iter 140 value 82.322600
## iter 150 value 81.822964
## iter 160 value 81.401294
## iter 170 value 80.751643
## iter 180 value 79.659846
## iter 190 value 78.446625
## iter 200 value 76.270702
## iter 210 value 75.959531
## iter 220 value 75.643840
## iter 230 value 75.395345
## iter 240 value 74.587926
## iter 250 value 72.897149
## iter 260 value 71.880409
## iter 270 value 71.514580
## iter 280 value 71.487524
## iter 290 value 71.482438
## iter 300 value 71.475323
## iter 310 value 71.469670
## iter 320 value 71.469118
## final value 71.469111
## converged
## # weights: 66
## initial value 4323.975862
## iter 10 value 121.902385
## iter 20 value 121.723494
## iter 30 value 121.109191
## iter 40 value 119.433112
## iter 50 value 118.511948
## iter 60 value 117.575171
## iter 70 value 117.309256
## iter 80 value 116.839410
## iter 90 value 111.602439
## iter 100 value 109.016932
## iter 110 value 106.485449
## iter 120 value 103.658927
## iter 130 value 101.949734
## iter 140 value 100.083582
## iter 150 value 96.380925
## iter 160 value 95.319011
## iter 170 value 94.414916
## iter 180 value 93.401419
## iter 190 value 92.983012
## iter 200 value 92.876373
## iter 210 value 92.853400
## iter 220 value 92.434175
## iter 230 value 92.036074
## iter 240 value 91.750985
## iter 250 value 91.144160
## iter 260 value 89.647635
## iter 270 value 87.498612
## iter 280 value 85.650576
## iter 290 value 84.520580
## iter 300 value 84.131226
## iter 310 value 83.973163
## iter 320 value 83.841871
## iter 330 value 83.313808
## iter 340 value 82.910334
## iter 350 value 82.824494
## iter 360 value 82.716009
## iter 370 value 82.598046
## iter 380 value 82.429833
## iter 390 value 81.946130
## iter 400 value 81.029058
## iter 410 value 80.808432
## iter 420 value 80.793080
## iter 430 value 80.792473
## final value 80.792431
## converged
## # weights: 66
## initial value 7388.724811
## iter 10 value 126.663240
## iter 20 value 125.398720
## iter 30 value 122.359238
## iter 40 value 120.454376
## iter 50 value 118.200450
## iter 60 value 115.241017
## iter 70 value 110.170113
## iter 80 value 104.208146
## iter 90 value 99.399232
## iter 100 value 95.376790
## iter 110 value 93.454611
## iter 120 value 93.047761
## iter 130 value 92.954586
## iter 140 value 92.895098
## iter 150 value 92.854089
## iter 160 value 92.831145
## iter 170 value 92.816121
## iter 180 value 92.808275
## iter 190 value 92.797893
## iter 200 value 92.778263
## iter 210 value 92.767897
## final value 92.767651
## converged
## # weights: 66
## initial value 1051.560850
## iter 10 value 128.345730
## iter 20 value 127.399488
## iter 30 value 124.978401
## iter 40 value 118.610594
## iter 50 value 116.623095
## iter 60 value 115.996664
## iter 70 value 115.845186
## iter 80 value 115.180919
## iter 90 value 113.453199
## iter 100 value 112.204806
## iter 110 value 108.780836
## iter 120 value 107.044764
## iter 130 value 105.882516
## iter 140 value 105.256635
## iter 150 value 104.951530
## iter 160 value 104.445678
## iter 170 value 103.711037
## iter 180 value 102.681563
## iter 190 value 101.913056
## iter 200 value 101.472433
## iter 210 value 101.083042
## iter 220 value 100.838402
## iter 230 value 100.331180
## iter 240 value 99.445003
## iter 250 value 98.785301
## iter 260 value 98.411508
## iter 270 value 97.430942
## iter 280 value 96.630889
## iter 290 value 96.338049
## iter 300 value 96.269569
## iter 310 value 96.105436
## iter 320 value 95.965627
## iter 330 value 95.957126
## iter 340 value 95.953373
## iter 350 value 95.945821
## iter 360 value 95.931841
## iter 370 value 95.924136
## final value 95.923332
## converged
## # weights: 66
## initial value 4937.572947
## iter 10 value 133.755869
## iter 20 value 133.009717
## iter 30 value 131.761132
## iter 40 value 129.669594
## iter 50 value 127.126928
## iter 60 value 123.919946
## iter 70 value 122.528125
## iter 80 value 122.215977
## iter 90 value 121.470439
## iter 100 value 120.474568
## iter 110 value 119.875613
## iter 120 value 119.411893
## iter 130 value 119.338773
## iter 140 value 119.328115
## iter 150 value 119.017838
## iter 160 value 116.965629
## iter 170 value 115.225862
## iter 180 value 111.398892
## iter 190 value 107.435350
## iter 200 value 105.653137
## iter 210 value 104.043078
## iter 220 value 103.435802
## iter 230 value 103.330535
## iter 240 value 103.314366
## iter 250 value 103.309223
## iter 260 value 103.293287
## iter 270 value 103.267529
## iter 280 value 103.260977
## iter 290 value 103.259344
## iter 300 value 103.259230
## final value 103.259227
## converged
## # weights: 66
## initial value 375.456151
## iter 10 value 134.573065
## iter 20 value 134.088239
## iter 30 value 130.918460
## iter 40 value 126.874079
## iter 50 value 125.583339
## iter 60 value 124.280421
## iter 70 value 123.626448
## iter 80 value 121.854615
## iter 90 value 120.636905
## iter 100 value 118.237145
## iter 110 value 116.268383
## iter 120 value 115.342139
## iter 130 value 114.842590
## iter 140 value 114.015264
## iter 150 value 111.943953
## iter 160 value 107.225902
## iter 170 value 105.820311
## iter 180 value 104.869753
## iter 190 value 104.225089
## iter 200 value 103.695417
## iter 210 value 103.562182
## iter 220 value 103.531431
## iter 230 value 103.459485
## iter 240 value 103.385409
## iter 250 value 103.382716
## iter 260 value 103.360619
## iter 270 value 103.318506
## iter 280 value 103.022259
## iter 290 value 102.052811
## iter 300 value 100.171809
## iter 310 value 98.800265
## iter 320 value 96.047595
## iter 330 value 95.089733
## iter 340 value 94.904729
## iter 350 value 94.812682
## iter 360 value 94.786565
## iter 370 value 94.780232
## iter 380 value 94.772265
## iter 390 value 94.747511
## iter 400 value 94.676492
## iter 410 value 94.655856
## iter 420 value 94.654442
## final value 94.654419
## converged
## # weights: 66
## initial value 1054.035528
## iter 10 value 140.879253
## iter 20 value 140.490742
## iter 30 value 138.943418
## iter 40 value 137.083625
## iter 50 value 130.281960
## iter 60 value 124.707599
## iter 70 value 122.108253
## iter 80 value 119.642616
## iter 90 value 118.418549
## iter 100 value 117.501558
## iter 110 value 117.049071
## iter 120 value 116.100135
## iter 130 value 115.362536
## iter 140 value 114.889503
## iter 150 value 114.774160
## iter 160 value 114.350845
## iter 170 value 113.860447
## iter 180 value 113.121173
## iter 190 value 112.169519
## iter 200 value 110.784534
## iter 210 value 108.176161
## iter 220 value 107.492556
## iter 230 value 106.756133
## iter 240 value 106.689030
## iter 250 value 106.508390
## iter 260 value 106.245033
## iter 270 value 106.174341
## iter 280 value 106.043130
## iter 290 value 106.008438
## iter 300 value 106.007210
## final value 106.007181
## converged
## # weights: 66
## initial value 1909.059674
## iter 10 value 144.956119
## iter 20 value 142.769564
## iter 30 value 136.488129
## iter 40 value 135.134707
## iter 50 value 129.805381
## iter 60 value 125.534298
## iter 70 value 123.337041
## iter 80 value 119.697824
## iter 90 value 115.368351
## iter 100 value 112.814359
## iter 110 value 111.007453
## iter 120 value 110.134599
## iter 130 value 109.788280
## iter 140 value 109.676696
## iter 150 value 109.611546
## iter 160 value 109.552201
## iter 170 value 109.537364
## iter 180 value 109.536628
## iter 190 value 109.535453
## iter 200 value 109.530946
## iter 210 value 109.522360
## iter 220 value 109.514033
## final value 109.513638
## converged
## # weights: 66
## initial value 493.219765
## iter 10 value 146.170285
## iter 20 value 145.930775
## iter 30 value 144.207361
## iter 40 value 141.598275
## iter 50 value 134.938247
## iter 60 value 133.640992
## iter 70 value 133.352813
## iter 80 value 133.275009
## iter 90 value 133.259057
## iter 100 value 133.226522
## iter 110 value 133.170027
## iter 120 value 133.167867
## iter 130 value 133.167569
## iter 140 value 133.166604
## final value 133.166118
## converged
## # weights: 66
## initial value 449.314942
## iter 10 value 147.014069
## iter 20 value 144.989731
## iter 30 value 144.312846
## iter 40 value 140.541592
## iter 50 value 136.708949
## iter 60 value 132.164009
## iter 70 value 129.610343
## iter 80 value 128.289786
## iter 90 value 127.814150
## iter 100 value 127.644966
## iter 110 value 126.819421
## iter 120 value 126.638641
## iter 130 value 126.495606
## iter 140 value 126.423129
## iter 150 value 126.407312
## iter 160 value 126.366426
## iter 170 value 126.316298
## iter 180 value 126.223877
## iter 190 value 126.147591
## iter 200 value 126.131287
## iter 210 value 126.118335
## iter 220 value 126.088465
## iter 230 value 126.035787
## iter 240 value 125.603111
## iter 250 value 124.212388
## iter 260 value 123.436513
## iter 270 value 123.055268
## iter 280 value 120.911274
## iter 290 value 117.166631
## iter 300 value 115.370674
## iter 310 value 113.883093
## iter 320 value 113.181720
## iter 330 value 112.918522
## iter 340 value 112.838894
## iter 350 value 112.780538
## iter 360 value 112.706169
## iter 370 value 112.477514
## iter 380 value 112.161054
## iter 390 value 112.026242
## iter 400 value 112.001676
## iter 410 value 111.996848
## iter 420 value 111.989914
## iter 430 value 111.961658
## iter 440 value 111.916483
## iter 450 value 111.858784
## iter 460 value 111.855904
## final value 111.855832
## converged
## # weights: 66
## initial value 467.905159
## iter 10 value 146.661533
## iter 20 value 141.650704
## iter 30 value 138.273919
## iter 40 value 137.354611
## iter 50 value 136.599880
## iter 60 value 135.928651
## iter 70 value 134.719284
## iter 80 value 133.565591
## iter 90 value 132.575072
## iter 100 value 131.337656
## iter 110 value 126.807039
## iter 120 value 123.007732
## iter 130 value 122.139471
## iter 140 value 121.904178
## iter 150 value 121.734036
## iter 160 value 121.502344
## iter 170 value 121.045372
## iter 180 value 120.255457
## iter 190 value 118.412672
## iter 200 value 116.555082
## iter 210 value 115.567254
## iter 220 value 115.249750
## iter 230 value 115.148593
## iter 240 value 115.084052
## iter 250 value 114.603566
## iter 260 value 114.430462
## iter 270 value 114.423936
## iter 280 value 114.423655
## iter 290 value 114.423408
## iter 300 value 114.422095
## iter 310 value 114.420426
## iter 320 value 114.419328
## final value 114.419280
## converged
## # weights: 66
## initial value 5388.295308
## iter 10 value 148.779957
## iter 20 value 148.129155
## iter 30 value 144.124890
## iter 40 value 138.069245
## iter 50 value 135.865466
## iter 60 value 134.280227
## iter 70 value 133.001161
## iter 80 value 130.117204
## iter 90 value 128.904341
## iter 100 value 128.283041
## iter 110 value 128.249716
## iter 120 value 128.230241
## iter 130 value 128.218153
## iter 140 value 128.208726
## iter 150 value 128.156872
## iter 160 value 127.184587
## iter 170 value 125.167182
## iter 180 value 125.049825
## iter 190 value 124.929814
## iter 200 value 124.671981
## iter 210 value 124.254815
## iter 220 value 123.456422
## iter 230 value 122.929980
## iter 240 value 122.606935
## iter 250 value 122.309749
## iter 260 value 122.259916
## iter 270 value 122.232966
## iter 280 value 122.230617
## iter 290 value 122.230484
## iter 300 value 122.230369
## final value 122.230296
## converged
## # weights: 66
## initial value 270.288640
## iter 10 value 148.966431
## iter 20 value 148.687996
## iter 30 value 146.557893
## iter 40 value 144.239414
## iter 50 value 141.984877
## iter 60 value 136.578914
## iter 70 value 132.197896
## iter 80 value 130.052649
## iter 90 value 128.463615
## iter 100 value 127.528797
## iter 110 value 126.132495
## iter 120 value 124.110426
## iter 130 value 123.320380
## iter 140 value 123.015082
## iter 150 value 122.836783
## iter 160 value 122.604251
## iter 170 value 122.380086
## iter 180 value 122.247592
## iter 190 value 122.186142
## iter 200 value 122.182241
## iter 210 value 122.167970
## iter 220 value 122.096652
## iter 230 value 121.967025
## iter 240 value 121.875297
## iter 250 value 121.871708
## final value 121.871586
## converged
## # weights: 66
## initial value 1569.110778
## iter 10 value 150.872614
## iter 20 value 147.684034
## iter 30 value 145.419866
## iter 40 value 142.919427
## iter 50 value 141.494973
## iter 60 value 139.302756
## iter 70 value 137.503924
## iter 80 value 133.434408
## iter 90 value 131.107152
## iter 100 value 128.922156
## iter 110 value 128.370414
## iter 120 value 127.900642
## iter 130 value 127.628472
## iter 140 value 127.336404
## iter 150 value 127.043460
## iter 160 value 125.907241
## iter 170 value 124.669686
## iter 180 value 123.755462
## iter 190 value 122.456746
## iter 200 value 120.772521
## iter 210 value 118.104734
## iter 220 value 116.734187
## iter 230 value 116.220215
## iter 240 value 115.718651
## iter 250 value 115.456311
## iter 260 value 115.246254
## iter 270 value 114.674993
## iter 280 value 113.850162
## iter 290 value 113.312595
## iter 300 value 113.223412
## iter 310 value 113.204429
## iter 320 value 113.070198
## iter 330 value 112.954064
## iter 340 value 112.800730
## iter 350 value 112.624389
## iter 360 value 112.471549
## iter 370 value 112.145059
## iter 380 value 111.822846
## iter 390 value 111.150825
## iter 400 value 110.797833
## iter 410 value 110.721978
## iter 420 value 110.711745
## final value 110.711306
## converged
## # weights: 66
## initial value 4401.922586
## iter 10 value 151.086911
## iter 20 value 150.640849
## iter 30 value 147.244733
## iter 40 value 145.632413
## iter 50 value 141.115282
## iter 60 value 139.867862
## iter 70 value 137.472633
## iter 80 value 135.592241
## iter 90 value 135.294650
## iter 100 value 135.244999
## iter 110 value 135.120416
## iter 120 value 134.885439
## iter 130 value 134.518055
## iter 140 value 134.304477
## iter 150 value 134.045965
## iter 160 value 133.482117
## iter 170 value 131.326877
## iter 180 value 128.999308
## iter 190 value 128.107276
## iter 200 value 127.357904
## iter 210 value 126.436058
## iter 220 value 125.983027
## iter 230 value 125.616451
## iter 240 value 125.196110
## iter 250 value 124.815775
## iter 260 value 124.271052
## iter 270 value 124.113115
## iter 280 value 123.774572
## iter 290 value 123.168214
## iter 300 value 122.936789
## iter 310 value 122.922009
## iter 320 value 122.921047
## final value 122.921015
## converged
## # weights: 66
## initial value 204.220078
## iter 10 value 155.135446
## iter 20 value 154.382172
## iter 30 value 153.594658
## iter 40 value 150.301268
## iter 50 value 144.398676
## iter 60 value 143.722075
## iter 70 value 142.339082
## iter 80 value 140.815987
## iter 90 value 139.897340
## iter 100 value 138.424699
## iter 110 value 137.905711
## iter 120 value 137.838852
## iter 130 value 137.592093
## iter 140 value 137.234521
## iter 150 value 136.367739
## iter 160 value 134.800362
## iter 170 value 133.757490
## iter 180 value 131.434852
## iter 190 value 129.488605
## iter 200 value 129.121501
## iter 210 value 128.971822
## iter 220 value 128.808542
## iter 230 value 128.694776
## iter 240 value 128.368476
## iter 250 value 128.292973
## iter 260 value 128.247514
## iter 270 value 128.112147
## iter 280 value 128.029916
## iter 290 value 127.954793
## iter 300 value 127.876011
## iter 310 value 127.858779
## iter 320 value 127.855798
## iter 330 value 127.855344
## final value 127.855325
## converged
## Borrowing money ( 124288.5 ) for closing a short position (PosID= 25 )
## Borrowing money ( 10306.17 ) for closing a short position (PosID= 148 )
## Borrowing money ( 461933.3 ) for closing a short position (PosID= 150 )
## Borrowing money ( 334578.7 ) for closing a short position (PosID= 160 )
## Borrowing money ( 740881.7 ) for closing a short position (PosID= 163 )
## Borrowing money ( 1171232 ) for closing a short position (PosID= 167 )
## Borrowing money ( 1603782 ) for closing a short position (PosID= 170 )
## Borrowing money ( 2019227 ) for closing a short position (PosID= 172 )
## Borrowing money ( 254537.5 ) for closing a short position (PosID= 176 )
## Borrowing money ( 109197.9 ) for closing a short position (PosID= 181 )
## Borrowing money ( 455224.9 ) for closing a short position (PosID= 182 )
## Borrowing money ( 819610.3 ) for closing a short position (PosID= 183 )
## Borrowing money ( 14785.35 ) for closing a short position (PosID= 366 )
## # weights: 66
## initial value 2184.640405
## iter 10 value 73.270611
## iter 20 value 72.978586
## iter 30 value 72.631956
## iter 40 value 71.729029
## iter 50 value 70.560813
## iter 60 value 70.189213
## iter 70 value 70.100579
## iter 80 value 70.061869
## iter 90 value 70.026379
## iter 100 value 69.403146
## iter 110 value 66.958755
## iter 120 value 66.030529
## iter 130 value 65.439352
## iter 140 value 65.101401
## iter 150 value 64.353131
## iter 160 value 63.241864
## iter 170 value 62.736276
## iter 180 value 62.245188
## iter 190 value 61.990775
## iter 200 value 61.588357
## iter 210 value 61.101998
## iter 220 value 60.691046
## iter 230 value 60.323284
## iter 240 value 59.602215
## iter 250 value 59.432097
## iter 260 value 59.253008
## iter 270 value 59.233721
## iter 280 value 59.115314
## iter 290 value 58.930522
## iter 300 value 58.084012
## iter 310 value 57.638612
## iter 320 value 57.393168
## iter 330 value 57.309397
## iter 340 value 57.190330
## iter 350 value 57.153643
## iter 360 value 57.144318
## iter 370 value 57.126032
## iter 380 value 57.108911
## iter 390 value 57.043013
## iter 400 value 56.804640
## iter 410 value 56.195574
## iter 420 value 55.850583
## iter 430 value 55.781161
## iter 440 value 55.759246
## iter 450 value 55.755540
## iter 460 value 55.750498
## iter 470 value 55.730649
## iter 480 value 55.722378
## iter 490 value 55.720413
## final value 55.719795
## converged
## # weights: 66
## initial value 207.410306
## iter 10 value 73.654016
## iter 20 value 73.593355
## iter 30 value 73.414819
## iter 40 value 72.990820
## iter 50 value 70.886254
## iter 60 value 69.784152
## iter 70 value 68.367373
## iter 80 value 66.787276
## iter 90 value 65.918707
## iter 100 value 63.203816
## iter 110 value 61.942981
## iter 120 value 61.695377
## iter 130 value 61.493056
## iter 140 value 61.254377
## iter 150 value 60.801095
## iter 160 value 60.343942
## iter 170 value 59.564552
## iter 180 value 59.300455
## iter 190 value 58.878326
## iter 200 value 58.786041
## iter 210 value 58.717205
## iter 220 value 58.619746
## iter 230 value 58.064043
## iter 240 value 57.544827
## iter 250 value 57.380270
## iter 260 value 56.921508
## iter 270 value 56.803843
## iter 280 value 56.744926
## iter 290 value 56.731201
## iter 300 value 56.715974
## iter 310 value 56.670644
## iter 320 value 56.599534
## iter 330 value 56.436445
## iter 340 value 56.094506
## iter 350 value 55.471924
## iter 360 value 54.146126
## iter 370 value 53.835158
## iter 380 value 53.770194
## iter 390 value 53.704731
## iter 400 value 53.647704
## iter 410 value 53.592548
## iter 420 value 53.590080
## iter 430 value 53.541988
## iter 440 value 53.509806
## iter 450 value 53.466145
## iter 460 value 53.218386
## iter 470 value 53.138614
## iter 480 value 53.127032
## iter 490 value 53.126567
## iter 500 value 53.125986
## iter 510 value 53.124647
## iter 520 value 53.110219
## iter 530 value 53.105067
## iter 540 value 53.101192
## iter 550 value 53.100010
## iter 560 value 53.099784
## final value 53.099721
## converged
## # weights: 66
## initial value 258.246314
## iter 10 value 73.780497
## iter 20 value 73.293033
## iter 30 value 71.593654
## iter 40 value 70.200168
## iter 50 value 68.955077
## iter 60 value 66.506347
## iter 70 value 65.506163
## iter 80 value 65.025816
## iter 90 value 64.977865
## iter 100 value 64.189126
## iter 110 value 63.272091
## iter 120 value 62.747466
## iter 130 value 62.615535
## iter 140 value 62.552138
## iter 150 value 62.541190
## iter 160 value 62.539983
## iter 160 value 62.539983
## iter 160 value 62.539982
## final value 62.539982
## converged
## # weights: 66
## initial value 476.543742
## iter 10 value 74.539675
## iter 20 value 74.497558
## iter 30 value 73.929810
## iter 40 value 73.670973
## iter 50 value 72.720273
## iter 60 value 70.747856
## iter 70 value 70.002362
## iter 80 value 69.504136
## iter 90 value 68.373725
## iter 100 value 67.322750
## iter 110 value 66.448738
## iter 120 value 64.799302
## iter 130 value 64.554325
## iter 140 value 63.911274
## iter 150 value 62.231924
## iter 160 value 61.154702
## iter 170 value 60.637766
## iter 180 value 60.286047
## iter 190 value 60.103049
## iter 200 value 60.018074
## iter 210 value 59.974823
## iter 220 value 59.957752
## iter 230 value 59.947029
## iter 240 value 59.945707
## iter 250 value 59.913487
## iter 260 value 59.785834
## iter 270 value 59.454889
## iter 280 value 58.866184
## iter 290 value 58.292900
## iter 300 value 57.706954
## iter 310 value 57.418546
## iter 320 value 57.329673
## iter 330 value 57.266459
## iter 340 value 57.016032
## iter 350 value 56.790487
## iter 360 value 56.507368
## iter 370 value 56.287046
## iter 380 value 56.141646
## iter 390 value 55.969298
## iter 400 value 55.883744
## iter 410 value 55.850085
## iter 420 value 55.842142
## iter 430 value 55.835527
## iter 440 value 55.832601
## final value 55.832067
## converged
## # weights: 66
## initial value 9184.506236
## iter 10 value 76.909855
## iter 20 value 76.909280
## final value 76.909271
## converged
## # weights: 66
## initial value 244.810632
## final value 80.268293
## converged
## # weights: 66
## initial value 321.067310
## iter 10 value 101.379662
## iter 20 value 101.376814
## iter 30 value 101.339250
## iter 40 value 100.728293
## iter 50 value 100.198604
## iter 60 value 99.870680
## iter 70 value 99.819422
## iter 80 value 99.738218
## iter 90 value 99.722537
## iter 100 value 99.721482
## iter 110 value 99.279779
## iter 120 value 98.072211
## iter 130 value 95.290888
## iter 140 value 94.169646
## iter 150 value 93.496760
## iter 160 value 92.756233
## iter 170 value 92.580825
## iter 180 value 92.538332
## iter 190 value 92.529642
## iter 200 value 92.523954
## iter 210 value 92.218705
## iter 220 value 91.594031
## iter 230 value 90.804548
## iter 240 value 90.524475
## iter 250 value 90.448402
## iter 260 value 90.327775
## iter 270 value 90.259024
## iter 280 value 90.233736
## iter 290 value 90.227779
## final value 90.227552
## converged
## # weights: 66
## initial value 4114.361628
## iter 10 value 121.606385
## iter 20 value 120.101077
## iter 30 value 118.140053
## iter 40 value 116.218925
## iter 50 value 116.128989
## iter 60 value 116.040073
## iter 70 value 115.983101
## iter 80 value 115.973615
## iter 90 value 115.972246
## iter 100 value 115.969347
## iter 110 value 115.952246
## iter 120 value 114.070931
## iter 130 value 113.404251
## iter 140 value 110.744158
## iter 150 value 109.438816
## iter 160 value 103.578236
## iter 170 value 102.814748
## iter 180 value 102.461442
## iter 190 value 102.427177
## iter 200 value 102.403148
## iter 210 value 102.399779
## iter 220 value 101.916482
## iter 230 value 101.635609
## iter 240 value 100.715504
## iter 250 value 99.763355
## iter 260 value 99.438088
## iter 270 value 99.298296
## iter 280 value 99.283970
## iter 290 value 99.258495
## iter 300 value 99.203107
## iter 310 value 99.135480
## iter 320 value 98.879037
## iter 330 value 97.746758
## iter 340 value 95.803424
## iter 350 value 95.549503
## iter 360 value 95.404872
## iter 370 value 95.203257
## iter 380 value 95.163524
## iter 390 value 95.099749
## iter 400 value 94.837159
## iter 410 value 94.430007
## iter 420 value 92.748432
## iter 430 value 89.149924
## iter 440 value 85.666252
## iter 450 value 83.714139
## iter 460 value 81.260891
## iter 470 value 80.783292
## iter 480 value 80.228290
## iter 490 value 80.075544
## iter 500 value 80.012517
## iter 510 value 79.996409
## iter 520 value 79.993070
## iter 530 value 79.992080
## iter 540 value 79.990803
## iter 550 value 79.989073
## iter 560 value 79.985827
## iter 570 value 79.979119
## iter 580 value 79.978241
## iter 590 value 79.977464
## iter 600 value 79.977206
## final value 79.976919
## converged
## # weights: 66
## initial value 135.072962
## iter 10 value 126.557548
## iter 20 value 125.639262
## iter 30 value 124.250799
## iter 40 value 123.106317
## iter 50 value 116.711829
## iter 60 value 110.583309
## iter 70 value 108.238372
## iter 80 value 106.075438
## iter 90 value 105.398170
## iter 100 value 105.084009
## iter 110 value 104.948663
## iter 120 value 104.621021
## iter 130 value 104.245919
## iter 140 value 103.703532
## iter 150 value 102.893267
## iter 160 value 102.415284
## iter 170 value 102.276797
## iter 180 value 102.224204
## iter 190 value 102.217386
## iter 200 value 102.211190
## iter 210 value 102.188440
## iter 220 value 102.045085
## iter 230 value 101.902703
## iter 240 value 101.790161
## iter 250 value 101.205574
## iter 260 value 100.595431
## iter 270 value 99.894408
## iter 280 value 99.407415
## iter 290 value 99.072950
## iter 300 value 98.941804
## iter 310 value 98.909034
## iter 320 value 98.780990
## iter 330 value 98.500563
## iter 340 value 97.574018
## iter 350 value 97.174881
## iter 360 value 96.939485
## iter 370 value 96.837532
## iter 380 value 96.643911
## iter 390 value 96.320280
## iter 400 value 96.092728
## iter 410 value 95.910667
## iter 420 value 95.888072
## iter 430 value 95.861616
## iter 440 value 95.820703
## iter 450 value 95.775602
## iter 460 value 95.670664
## iter 470 value 95.603857
## iter 480 value 95.542280
## iter 490 value 95.511792
## iter 500 value 95.498289
## iter 510 value 95.493897
## iter 520 value 95.492393
## iter 530 value 95.491608
## final value 95.491412
## converged
## # weights: 66
## initial value 1022.300799
## iter 10 value 128.292386
## iter 20 value 126.695900
## iter 30 value 126.012434
## iter 40 value 124.788027
## iter 50 value 123.190540
## iter 60 value 119.716797
## iter 70 value 110.503423
## iter 80 value 106.347191
## iter 90 value 102.061640
## iter 100 value 98.594669
## iter 110 value 97.253096
## iter 120 value 96.151931
## iter 130 value 95.746893
## iter 140 value 95.316330
## iter 150 value 95.053367
## iter 160 value 94.958489
## iter 170 value 94.850642
## iter 180 value 94.716808
## iter 190 value 94.270321
## iter 200 value 91.878832
## iter 210 value 90.499214
## iter 220 value 89.866273
## iter 230 value 89.607243
## iter 240 value 89.111994
## iter 250 value 88.896980
## iter 260 value 88.762742
## iter 270 value 88.730341
## iter 280 value 88.719818
## iter 290 value 88.719695
## iter 300 value 88.719585
## iter 310 value 88.719465
## iter 320 value 88.719362
## iter 330 value 88.718258
## iter 340 value 88.715675
## iter 350 value 88.714630
## final value 88.714477
## converged
## # weights: 66
## initial value 368.441893
## iter 10 value 133.706608
## iter 20 value 132.245777
## iter 30 value 130.049620
## iter 40 value 125.842429
## iter 50 value 124.285388
## iter 60 value 123.321495
## iter 70 value 121.752317
## iter 80 value 120.529081
## iter 90 value 117.119089
## iter 100 value 114.737180
## iter 110 value 113.160136
## iter 120 value 112.593104
## iter 130 value 112.067981
## iter 140 value 111.581111
## iter 150 value 108.126850
## iter 160 value 105.863394
## iter 170 value 105.192870
## iter 180 value 104.499214
## iter 190 value 103.384678
## iter 200 value 102.691853
## iter 210 value 102.292575
## iter 220 value 101.787535
## iter 230 value 101.311317
## iter 240 value 101.212780
## iter 250 value 101.079646
## iter 260 value 100.694773
## iter 270 value 100.085447
## iter 280 value 99.177519
## iter 290 value 98.558721
## iter 300 value 98.354849
## iter 310 value 98.146315
## iter 320 value 97.596785
## iter 330 value 96.928151
## iter 340 value 96.595612
## iter 350 value 95.956672
## iter 360 value 95.353475
## iter 370 value 95.057687
## iter 380 value 94.441995
## iter 390 value 94.126841
## iter 400 value 94.028418
## iter 410 value 94.010975
## iter 420 value 93.980789
## iter 430 value 93.914312
## iter 440 value 93.861274
## iter 450 value 93.795875
## iter 460 value 93.785931
## iter 470 value 93.783906
## iter 480 value 93.783427
## iter 490 value 93.783209
## final value 93.783207
## converged
## # weights: 66
## initial value 7116.477438
## iter 10 value 134.407491
## iter 20 value 133.589974
## iter 30 value 130.141781
## iter 40 value 127.935807
## iter 50 value 122.212997
## iter 60 value 115.621757
## iter 70 value 111.998456
## iter 80 value 108.805486
## iter 90 value 106.339352
## iter 100 value 105.479058
## iter 110 value 103.449035
## iter 120 value 103.046185
## iter 130 value 102.690402
## iter 140 value 102.417769
## iter 150 value 102.283964
## iter 160 value 102.253340
## iter 170 value 102.209679
## iter 180 value 102.180562
## iter 190 value 102.155740
## iter 200 value 102.038086
## iter 210 value 101.973140
## iter 220 value 101.565714
## iter 230 value 100.613257
## iter 240 value 98.683751
## iter 250 value 98.446894
## iter 260 value 98.285332
## iter 270 value 98.188198
## iter 280 value 98.123095
## iter 290 value 98.122685
## iter 300 value 98.122271
## iter 310 value 98.122051
## iter 320 value 98.121457
## iter 330 value 98.115063
## iter 340 value 98.088801
## iter 350 value 97.974595
## iter 360 value 97.913664
## iter 370 value 97.896684
## iter 380 value 97.888674
## iter 390 value 97.884948
## iter 400 value 97.881115
## iter 410 value 97.880764
## final value 97.880723
## converged
## # weights: 66
## initial value 920.006771
## iter 10 value 140.879267
## iter 20 value 139.946009
## iter 30 value 134.260905
## iter 40 value 131.913862
## iter 50 value 131.593278
## iter 60 value 131.270012
## iter 70 value 130.747028
## iter 80 value 130.002641
## iter 90 value 129.022808
## iter 100 value 128.589108
## iter 110 value 127.830460
## iter 120 value 127.292305
## iter 130 value 127.120852
## iter 140 value 126.696899
## iter 150 value 125.237300
## iter 160 value 124.536530
## iter 170 value 123.429631
## iter 180 value 122.906593
## iter 190 value 122.425791
## iter 200 value 121.848766
## iter 210 value 121.569673
## iter 220 value 121.437363
## iter 230 value 121.409507
## iter 240 value 121.407264
## iter 250 value 121.397008
## iter 260 value 121.284003
## iter 270 value 121.152354
## iter 280 value 120.150145
## iter 290 value 118.621001
## iter 300 value 118.225038
## iter 310 value 118.160067
## iter 320 value 118.157270
## iter 330 value 118.142659
## iter 340 value 118.116102
## iter 350 value 118.035963
## iter 360 value 118.021502
## iter 370 value 118.020462
## iter 380 value 118.011232
## iter 390 value 117.984240
## iter 400 value 117.918357
## iter 410 value 117.868484
## iter 420 value 117.848121
## iter 430 value 117.840476
## iter 440 value 117.836822
## iter 450 value 117.836264
## final value 117.836150
## converged
## # weights: 66
## initial value 207.204535
## iter 10 value 144.984706
## iter 20 value 143.828447
## iter 30 value 140.391373
## iter 40 value 138.745466
## iter 50 value 132.149641
## iter 60 value 128.732127
## iter 70 value 127.890815
## iter 80 value 127.452844
## iter 90 value 126.922808
## iter 100 value 126.691078
## iter 110 value 126.321030
## iter 120 value 125.873447
## iter 130 value 125.268723
## iter 140 value 124.715366
## iter 150 value 124.135120
## iter 160 value 122.766125
## iter 170 value 117.289354
## iter 180 value 116.297107
## iter 190 value 115.769157
## iter 200 value 115.438328
## iter 210 value 115.029719
## iter 220 value 113.344635
## iter 230 value 110.888309
## iter 240 value 109.785213
## iter 250 value 108.436455
## iter 260 value 107.656469
## iter 270 value 106.932473
## iter 280 value 106.662000
## iter 290 value 106.486050
## iter 300 value 106.446632
## iter 310 value 106.415340
## iter 320 value 106.366150
## iter 330 value 106.158310
## iter 340 value 105.862268
## iter 350 value 105.763329
## iter 360 value 105.739068
## iter 370 value 105.715145
## iter 380 value 105.696974
## iter 390 value 105.665977
## iter 400 value 105.640538
## iter 410 value 105.628544
## iter 420 value 105.617424
## final value 105.617322
## converged
## # weights: 66
## initial value 286.033133
## iter 10 value 146.119169
## iter 20 value 145.374010
## iter 30 value 142.762354
## iter 40 value 137.126718
## iter 50 value 134.990317
## iter 60 value 130.506501
## iter 70 value 123.517501
## iter 80 value 117.821242
## iter 90 value 115.750763
## iter 100 value 114.910539
## iter 110 value 114.574130
## iter 120 value 114.374698
## iter 130 value 114.241622
## iter 140 value 114.103997
## iter 150 value 113.984211
## iter 160 value 113.746354
## iter 170 value 113.732924
## iter 180 value 113.681800
## iter 190 value 113.464763
## iter 200 value 113.375736
## iter 210 value 113.343874
## iter 220 value 113.342542
## iter 230 value 113.336922
## iter 240 value 113.324039
## iter 250 value 113.256063
## iter 260 value 113.227459
## final value 113.224917
## converged
## # weights: 66
## initial value 10376.069194
## iter 10 value 147.162587
## iter 20 value 145.599417
## iter 30 value 143.613439
## iter 40 value 143.298024
## iter 50 value 143.208551
## iter 60 value 143.200446
## iter 70 value 143.196799
## iter 80 value 142.849477
## iter 90 value 141.802658
## iter 100 value 140.879683
## iter 110 value 139.921717
## iter 120 value 139.704848
## iter 130 value 139.675837
## iter 140 value 139.660636
## iter 150 value 139.659220
## iter 150 value 139.659220
## iter 150 value 139.659220
## final value 139.659220
## converged
## # weights: 66
## initial value 312.200936
## final value 147.728570
## converged
## # weights: 66
## initial value 274.305971
## iter 10 value 148.990630
## iter 20 value 148.988256
## iter 30 value 148.922230
## iter 40 value 148.336679
## iter 50 value 147.447808
## iter 60 value 144.322999
## iter 70 value 142.777983
## iter 80 value 142.329327
## iter 90 value 142.263524
## iter 100 value 142.169076
## iter 110 value 142.112445
## iter 120 value 141.987760
## iter 130 value 141.879059
## iter 140 value 141.724040
## iter 150 value 141.689872
## iter 160 value 141.675511
## final value 141.674859
## converged
## # weights: 66
## initial value 2772.909781
## iter 10 value 148.999198
## iter 20 value 147.859739
## iter 30 value 142.664338
## iter 40 value 141.312995
## iter 50 value 140.474320
## iter 60 value 138.062381
## iter 70 value 136.321733
## iter 80 value 135.279932
## iter 90 value 134.309546
## iter 100 value 133.034140
## iter 110 value 132.217237
## iter 120 value 131.936370
## iter 130 value 131.698245
## iter 140 value 131.635711
## iter 150 value 131.612829
## iter 160 value 131.601006
## iter 170 value 131.599935
## iter 180 value 131.593707
## iter 190 value 131.584279
## iter 200 value 131.579772
## iter 210 value 131.576105
## iter 220 value 131.573883
## final value 131.573613
## converged
## # weights: 66
## initial value 347.748771
## iter 10 value 150.865667
## iter 20 value 147.765836
## iter 30 value 145.504605
## iter 40 value 142.410904
## iter 50 value 139.655030
## iter 60 value 136.424957
## iter 70 value 134.013428
## iter 80 value 131.842339
## iter 90 value 126.314434
## iter 100 value 123.232185
## iter 110 value 122.557967
## iter 120 value 120.057394
## iter 130 value 117.201714
## iter 140 value 115.097280
## iter 150 value 114.247319
## iter 160 value 113.925626
## iter 170 value 113.733675
## iter 180 value 113.595832
## iter 190 value 113.149545
## iter 200 value 112.607106
## iter 210 value 112.372197
## iter 220 value 112.129761
## iter 230 value 111.991260
## iter 240 value 111.926996
## iter 250 value 111.921241
## iter 260 value 111.916341
## iter 270 value 111.914143
## iter 280 value 111.912464
## final value 111.912121
## converged
## # weights: 66
## initial value 8987.133787
## iter 10 value 161.890213
## iter 20 value 149.532705
## iter 30 value 146.291971
## iter 40 value 146.173746
## iter 50 value 146.031730
## iter 60 value 146.011433
## iter 70 value 145.812158
## iter 80 value 145.227214
## iter 90 value 142.145091
## iter 100 value 138.611536
## iter 110 value 136.445684
## iter 120 value 134.689078
## iter 130 value 134.064292
## iter 140 value 131.464270
## iter 150 value 128.311750
## iter 160 value 127.928129
## iter 170 value 127.753961
## iter 180 value 127.703176
## iter 190 value 127.670927
## iter 200 value 127.657576
## iter 210 value 127.655444
## iter 220 value 127.652868
## iter 230 value 127.652661
## iter 230 value 127.652661
## iter 230 value 127.652661
## final value 127.652661
## converged
## # weights: 66
## initial value 157.052466
## iter 10 value 155.432993
## iter 20 value 154.695894
## iter 30 value 150.946530
## iter 40 value 148.153517
## iter 50 value 147.150537
## iter 60 value 143.869085
## iter 70 value 140.943108
## iter 80 value 137.753858
## iter 90 value 135.304279
## iter 100 value 132.938641
## iter 110 value 131.842688
## iter 120 value 131.408599
## iter 130 value 131.304107
## iter 140 value 131.228369
## iter 150 value 131.161334
## iter 160 value 131.134473
## iter 170 value 131.118390
## iter 180 value 131.096048
## iter 190 value 131.085998
## iter 200 value 131.075150
## iter 210 value 131.060303
## iter 220 value 131.052459
## iter 230 value 131.050759
## iter 240 value 131.049785
## iter 250 value 131.049642
## final value 131.049603
## converged
## Borrowing money ( 46854.89 ) for closing a short position (PosID= 1 )
## Borrowing money ( 26764.78 ) for closing a short position (PosID= 15 )
## Borrowing money ( 203003.9 ) for closing a short position (PosID= 2 )
## Borrowing money ( 14403.46 ) for closing a short position (PosID= 5 )
## Borrowing money ( 87582.06 ) for closing a short position (PosID= 347 )
## Borrowing money ( 178911.2 ) for closing a short position (PosID= 343 )
## Borrowing money ( 391642.2 ) for closing a short position (PosID= 344 )
## Borrowing money ( 604335 ) for closing a short position (PosID= 345 )
## Borrowing money ( 413393.7 ) for closing a short position (PosID= 352 )
## Borrowing money ( 626260.7 ) for closing a short position (PosID= 342 )
## Borrowing money ( 386978.7 ) for closing a short position (PosID= 351 )
## Borrowing money ( 377642 ) for closing a short position (PosID= 359 )
## Borrowing money ( 364211.6 ) for closing a short position (PosID= 357 )
## Borrowing money ( 353389 ) for closing a short position (PosID= 358 )
## Borrowing money ( 54835.71 ) for closing a short position (PosID= 350 )
## # weights: 66
## initial value 2382.245838
## iter 10 value 73.234197
## iter 20 value 73.043050
## iter 30 value 71.840041
## iter 40 value 70.299729
## iter 50 value 69.761647
## iter 60 value 68.824640
## iter 70 value 67.520664
## iter 80 value 66.885593
## iter 90 value 66.326936
## iter 100 value 65.630971
## iter 110 value 64.483989
## iter 120 value 63.990387
## iter 130 value 63.968483
## iter 140 value 63.962849
## iter 150 value 63.960235
## iter 160 value 63.960158
## final value 63.960152
## converged
## Borrowing money ( 36994.5 ) for closing a short position (PosID= 22 )
## Borrowing money ( 136448.2 ) for closing a short position (PosID= 23 )
## Borrowing money ( 141650.5 ) for closing a short position (PosID= 21 )
## Borrowing money ( 23161.06 ) for closing a short position (PosID= 26 )
## Borrowing money ( 265282.2 ) for closing a short position (PosID= 57 )
## Borrowing money ( 775815.9 ) for closing a short position (PosID= 58 )
## Borrowing money ( 1282862 ) for closing a short position (PosID= 59 )
## Borrowing money ( 1801226 ) for closing a short position (PosID= 55 )
## Borrowing money ( 2316300 ) for closing a short position (PosID= 56 )
## Borrowing money ( 2835448 ) for closing a short position (PosID= 61 )
## Borrowing money ( 2373546 ) for closing a short position (PosID= 68 )
## Borrowing money ( 2143219 ) for closing a short position (PosID= 69 )
## Borrowing money ( 2667945 ) for closing a short position (PosID= 53 )
## Borrowing money ( 3199713 ) for closing a short position (PosID= 60 )
## Borrowing money ( 3749280 ) for closing a short position (PosID= 62 )
## Borrowing money ( 57245.56 ) for closing a short position (PosID= 65 )
## Borrowing money ( 541945.3 ) for closing a short position (PosID= 77 )
## Borrowing money ( 1161391 ) for closing a short position (PosID= 78 )
## Borrowing money ( 1780670 ) for closing a short position (PosID= 79 )
## Borrowing money ( 1391144 ) for closing a short position (PosID= 82 )
## Borrowing money ( 1197494 ) for closing a short position (PosID= 83 )
## Borrowing money ( 1099545 ) for closing a short position (PosID= 84 )
## Borrowing money ( 320244.9 ) for closing a short position (PosID= 81 )
## Borrowing money ( 565181.6 ) for closing a short position (PosID= 88 )
## Borrowing money ( 1149291 ) for closing a short position (PosID= 89 )
## Borrowing money ( 1734220 ) for closing a short position (PosID= 97 )
## Borrowing money ( 2319509 ) for closing a short position (PosID= 86 )
## Borrowing money ( 2932846 ) for closing a short position (PosID= 94 )
## Borrowing money ( 3543806 ) for closing a short position (PosID= 95 )
## Borrowing money ( 4131158 ) for closing a short position (PosID= 85 )
## Borrowing money ( 4757643 ) for closing a short position (PosID= 96 )
## Borrowing money ( 372695.4 ) for closing a short position (PosID= 98 )
## Borrowing money ( 496008.9 ) for closing a short position (PosID= 103 )
## Borrowing money ( 610479.1 ) for closing a short position (PosID= 107 )
## Borrowing money ( 1224397 ) for closing a short position (PosID= 109 )
## Borrowing money ( 1223089 ) for closing a short position (PosID= 121 )
## Borrowing money ( 1841344 ) for closing a short position (PosID= 106 )
## Borrowing money ( 1743935 ) for closing a short position (PosID= 114 )
## Borrowing money ( 1719573 ) for closing a short position (PosID= 116 )
## Borrowing money ( 1713118 ) for closing a short position (PosID= 118 )
## Borrowing money ( 1711819 ) for closing a short position (PosID= 120 )
## Borrowing money ( 1518330 ) for closing a short position (PosID= 113 )
## Borrowing money ( 1470923 ) for closing a short position (PosID= 115 )
## Borrowing money ( 1459396 ) for closing a short position (PosID= 117 )
## Borrowing money ( 1456850 ) for closing a short position (PosID= 119 )
## Borrowing money ( 584575.9 ) for closing a short position (PosID= 122 )
## Borrowing money ( 420244.1 ) for closing a short position (PosID= 127 )
## Borrowing money ( 88941.69 ) for closing a short position (PosID= 126 )
## Borrowing money ( 5616.597 ) for closing a short position (PosID= 128 )
## Borrowing money ( 660338.8 ) for closing a short position (PosID= 123 )
## Borrowing money ( 480604.5 ) for closing a short position (PosID= 158 )
## Borrowing money ( 1244258 ) for closing a short position (PosID= 159 )
## Borrowing money ( 2002295 ) for closing a short position (PosID= 160 )
## Borrowing money ( 2792440 ) for closing a short position (PosID= 164 )
## Borrowing money ( 1701346 ) for closing a short position (PosID= 167 )
## Borrowing money ( 805221.3 ) for closing a short position (PosID= 161 )
## Borrowing money ( 1618660 ) for closing a short position (PosID= 162 )
## Borrowing money ( 1542788 ) for closing a short position (PosID= 173 )
## Borrowing money ( 2379148 ) for closing a short position (PosID= 163 )
## Borrowing money ( 652484.7 ) for closing a short position (PosID= 412 )
## Borrowing money ( 112211.2 ) for closing a short position (PosID= 466 )
## Borrowing money ( 871583.4 ) for closing a short position (PosID= 467 )
## Borrowing money ( 856938.1 ) for closing a short position (PosID= 475 )
## Borrowing money ( 849617.5 ) for closing a short position (PosID= 476 )
## Borrowing money ( 578431.3 ) for closing a short position (PosID= 481 )
## Borrowing money ( 476569.1 ) for closing a short position (PosID= 465 )
## Borrowing money ( 921867.1 ) for closing a short position (PosID= 494 )
## Borrowing money ( 1967897 ) for closing a short position (PosID= 496 )
## Borrowing money ( 2986291 ) for closing a short position (PosID= 497 )
## Borrowing money ( 286991.9 ) for closing a short position (PosID= 503 )
## Borrowing money ( 177973.9 ) for closing a short position (PosID= 507 )
## Borrowing money ( 216757.4 ) for closing a short position (PosID= 521 )
## Borrowing money ( 812280.4 ) for closing a short position (PosID= 522 )
## Borrowing money ( 1437031 ) for closing a short position (PosID= 525 )
## Borrowing money ( 2070591 ) for closing a short position (PosID= 529 )
## Borrowing money ( 2682824 ) for closing a short position (PosID= 531 )
## Borrowing money ( 3306803 ) for closing a short position (PosID= 532 )
## Borrowing money ( 3938537 ) for closing a short position (PosID= 534 )
## Borrowing money ( 3936033 ) for closing a short position (PosID= 517 )
## Borrowing money ( 572233.7 ) for closing a short position (PosID= 641 )
## Borrowing money ( 1376188 ) for closing a short position (PosID= 633 )
## Borrowing money ( 2182439 ) for closing a short position (PosID= 634 )
## Borrowing money ( 2979461 ) for closing a short position (PosID= 631 )
## Borrowing money ( 3747239 ) for closing a short position (PosID= 638 )
## Borrowing money ( 4514935 ) for closing a short position (PosID= 639 )
## Borrowing money ( 5286709 ) for closing a short position (PosID= 643 )
## Borrowing money ( 6025011 ) for closing a short position (PosID= 642 )
## Borrowing money ( 6764137 ) for closing a short position (PosID= 640 )
## Borrowing money ( 7555999 ) for closing a short position (PosID= 632 )
## Borrowing money ( 8271924 ) for closing a short position (PosID= 644 )
## Borrowing money ( 7812812 ) for closing a short position (PosID= 648 )
## Borrowing money ( 2395667 ) for closing a short position (PosID= 645 )
## Borrowing money ( 502385.7 ) for closing a short position (PosID= 676 )
## Borrowing money ( 1162433 ) for closing a short position (PosID= 677 )
## Borrowing money ( 1809384 ) for closing a short position (PosID= 679 )
## Borrowing money ( 2454640 ) for closing a short position (PosID= 682 )
## Borrowing money ( 3107657 ) for closing a short position (PosID= 683 )
## Borrowing money ( 3752830 ) for closing a short position (PosID= 684 )
## Borrowing money ( 4381730 ) for closing a short position (PosID= 680 )
## Borrowing money ( 5009244 ) for closing a short position (PosID= 681 )
## Borrowing money ( 246738.6 ) for closing a short position (PosID= 722 )
## Borrowing money ( 503707.4 ) for closing a short position (PosID= 795 )
## Borrowing money ( 493198.7 ) for closing a short position (PosID= 809 )
## Borrowing money ( 1044836 ) for closing a short position (PosID= 798 )
## Borrowing money ( 1021503 ) for closing a short position (PosID= 808 )
## Borrowing money ( 760515.7 ) for closing a short position (PosID= 813 )
## Borrowing money ( 1315784 ) for closing a short position (PosID= 793 )
## Borrowing money ( 1309408 ) for closing a short position (PosID= 810 )
## Borrowing money ( 1747777 ) for closing a short position (PosID= 801 )
## Borrowing money ( 121613.9 ) for closing a short position (PosID= 820 )
## Borrowing money ( 476831 ) for closing a short position (PosID= 821 )
## Borrowing money ( 586496.1 ) for closing a short position (PosID= 825 )
## Borrowing money ( 946226.7 ) for closing a short position (PosID= 822 )
## Borrowing money ( 698711.8 ) for closing a short position (PosID= 828 )
## Borrowing money ( 1104186 ) for closing a short position (PosID= 819 )
## Borrowing money ( 613588.1 ) for closing a short position (PosID= 827 )
## Borrowing money ( 104320.1 ) for closing a short position (PosID= 840 )
## Borrowing money ( 230505.7 ) for closing a short position (PosID= 844 )
## Borrowing money ( 346706.2 ) for closing a short position (PosID= 845 )
## Borrowing money ( 474737.7 ) for closing a short position (PosID= 846 )
## Borrowing money ( 588485.1 ) for closing a short position (PosID= 850 )
## Borrowing money ( 704008.6 ) for closing a short position (PosID= 851 )
## Borrowing money ( 823187.8 ) for closing a short position (PosID= 852 )
## Borrowing money ( 927838.2 ) for closing a short position (PosID= 853 )
## Borrowing money ( 1043495 ) for closing a short position (PosID= 854 )
## Borrowing money ( 1171655 ) for closing a short position (PosID= 855 )
## Borrowing money ( 714250.2 ) for closing a short position (PosID= 829 )
## Borrowing money ( 848714.2 ) for closing a short position (PosID= 843 )
## Borrowing money ( 985045.8 ) for closing a short position (PosID= 848 )
## Borrowing money ( 1112894 ) for closing a short position (PosID= 849 )
## # weights: 131
## initial value 6549.908247
## iter 10 value 73.303219
## iter 20 value 73.157269
## iter 30 value 71.291114
## iter 40 value 69.984708
## iter 50 value 69.292514
## iter 60 value 69.130329
## iter 70 value 68.447853
## iter 80 value 68.117307
## iter 90 value 67.736122
## iter 100 value 67.113822
## iter 110 value 65.451735
## iter 120 value 64.301640
## iter 130 value 63.171945
## iter 140 value 62.702564
## iter 150 value 62.610175
## iter 160 value 62.447062
## iter 170 value 62.431381
## iter 180 value 62.409862
## iter 190 value 62.270937
## iter 200 value 62.078849
## iter 210 value 61.966373
## iter 220 value 61.620231
## iter 230 value 61.143780
## iter 240 value 60.651176
## iter 250 value 60.260338
## iter 260 value 60.042256
## iter 270 value 59.946472
## iter 280 value 59.896859
## iter 290 value 59.888143
## iter 300 value 59.791261
## iter 310 value 59.317285
## iter 320 value 59.064501
## iter 330 value 58.931344
## iter 340 value 58.865456
## iter 350 value 58.854086
## iter 360 value 58.839806
## iter 370 value 58.741373
## iter 380 value 57.768297
## iter 390 value 56.890461
## iter 400 value 56.398752
## iter 410 value 55.767475
## iter 420 value 55.560951
## iter 430 value 55.339716
## iter 440 value 55.282291
## iter 450 value 55.276154
## iter 460 value 55.217016
## iter 470 value 54.863073
## iter 480 value 54.240016
## iter 490 value 53.649019
## iter 500 value 53.517613
## iter 510 value 53.369423
## iter 520 value 53.340530
## iter 530 value 53.335679
## iter 540 value 53.327214
## iter 550 value 53.304212
## iter 560 value 53.191200
## iter 570 value 53.053516
## iter 580 value 52.963852
## iter 590 value 52.712746
## iter 600 value 52.514887
## iter 610 value 52.450874
## iter 620 value 52.423860
## iter 630 value 52.402229
## iter 640 value 52.380923
## iter 650 value 52.336128
## iter 660 value 52.311125
## iter 670 value 52.300066
## iter 680 value 52.291536
## iter 690 value 52.286957
## iter 700 value 52.285458
## iter 710 value 52.285375
## iter 720 value 52.275998
## iter 730 value 52.262244
## iter 740 value 52.177562
## iter 750 value 51.972319
## final value 51.972319
## stopped after 750 iterations
## Borrowing money ( 1787.274 ) for closing a short position (PosID= 11 )
## Borrowing money ( 4251.824 ) for closing a short position (PosID= 12 )
## Borrowing money ( 3268.653 ) for closing a short position (PosID= 43 )
## Borrowing money ( 537549 ) for closing a short position (PosID= 44 )
## Borrowing money ( 1070890 ) for closing a short position (PosID= 45 )
## Borrowing money ( 1596643 ) for closing a short position (PosID= 46 )
## Borrowing money ( 2123367 ) for closing a short position (PosID= 47 )
## Borrowing money ( 2654463 ) for closing a short position (PosID= 48 )
## Borrowing money ( 2407219 ) for closing a short position (PosID= 52 )
## Borrowing money ( 1416299 ) for closing a short position (PosID= 50 )
## Borrowing money ( 921064 ) for closing a short position (PosID= 51 )
## Borrowing money ( 112916.5 ) for closing a short position (PosID= 77 )
## Borrowing money ( 51267.07 ) for closing a short position (PosID= 72 )
## Borrowing money ( 35836.93 ) for closing a short position (PosID= 74 )
## Borrowing money ( 43607.04 ) for closing a short position (PosID= 75 )
## Borrowing money ( 180142.1 ) for closing a short position (PosID= 76 )
## Borrowing money ( 3163.426 ) for closing a short position (PosID= 79 )
## Borrowing money ( 73941.49 ) for closing a short position (PosID= 170 )
## Borrowing money ( 163416.5 ) for closing a short position (PosID= 194 )
## Borrowing money ( 130138.9 ) for closing a short position (PosID= 199 )
## Borrowing money ( 251716.2 ) for closing a short position (PosID= 200 )
## Borrowing money ( 141240.2 ) for closing a short position (PosID= 274 )
## Borrowing money ( 279395.4 ) for closing a short position (PosID= 277 )
## Borrowing money ( 107029.5 ) for closing a short position (PosID= 276 )
## Borrowing money ( 63157.8 ) for closing a short position (PosID= 296 )
## Borrowing money ( 57928.09 ) for closing a short position (PosID= 299 )
## Borrowing money ( 55307.59 ) for closing a short position (PosID= 300 )
## Borrowing money ( 37827.65 ) for closing a short position (PosID= 304 )
## Borrowing money ( 29103.1 ) for closing a short position (PosID= 305 )
## Borrowing money ( 26476.61 ) for closing a short position (PosID= 307 )
## Borrowing money ( 65708.28 ) for closing a short position (PosID= 311 )
## Borrowing money ( 64684.56 ) for closing a short position (PosID= 320 )
## Borrowing money ( 53204.82 ) for closing a short position (PosID= 316 )
## Borrowing money ( 66011.77 ) for closing a short position (PosID= 405 )
## Borrowing money ( 116318.5 ) for closing a short position (PosID= 423 )
## Borrowing money ( 113584.5 ) for closing a short position (PosID= 421 )
## Borrowing money ( 111029.7 ) for closing a short position (PosID= 430 )
## Borrowing money ( 100870.7 ) for closing a short position (PosID= 428 )
## Borrowing money ( 95801.71 ) for closing a short position (PosID= 429 )
## Borrowing money ( 939.196 ) for closing a short position (PosID= 506 )
## Borrowing money ( 205802.4 ) for closing a short position (PosID= 507 )
## Borrowing money ( 408776.8 ) for closing a short position (PosID= 508 )
## Borrowing money ( 611123.4 ) for closing a short position (PosID= 509 )
## Borrowing money ( 220116.1 ) for closing a short position (PosID= 513 )
## Borrowing money ( 404459.1 ) for closing a short position (PosID= 511 )
## Borrowing money ( 162311.9 ) for closing a short position (PosID= 590 )
## Borrowing money ( 386028.2 ) for closing a short position (PosID= 591 )
## Borrowing money ( 604153.3 ) for closing a short position (PosID= 596 )
## Borrowing money ( 19931.77 ) for closing a short position (PosID= 603 )
## Borrowing money ( 161359.1 ) for closing a short position (PosID= 616 )
## Borrowing money ( 292132.8 ) for closing a short position (PosID= 604 )
## Borrowing money ( 423042.9 ) for closing a short position (PosID= 606 )
## Borrowing money ( 555669.2 ) for closing a short position (PosID= 609 )
## Borrowing money ( 685024 ) for closing a short position (PosID= 608 )
## Borrowing money ( 814316.5 ) for closing a short position (PosID= 610 )
## Borrowing money ( 945530.5 ) for closing a short position (PosID= 615 )
## Borrowing money ( 1075282 ) for closing a short position (PosID= 605 )
## Borrowing money ( 1203052 ) for closing a short position (PosID= 607 )
## Borrowing money ( 1330844 ) for closing a short position (PosID= 611 )
## Borrowing money ( 230335.8 ) for closing a short position (PosID= 617 )
## Borrowing money ( 64204.22 ) for closing a short position (PosID= 626 )
## Borrowing money ( 271203.6 ) for closing a short position (PosID= 627 )
## Borrowing money ( 64829.52 ) for closing a short position (PosID= 633 )
## Borrowing money ( 291535.5 ) for closing a short position (PosID= 632 )
## Borrowing money ( 516763.8 ) for closing a short position (PosID= 634 )
## Borrowing money ( 62059.11 ) for closing a short position (PosID= 635 )
## Borrowing money ( 43390.61 ) for closing a short position (PosID= 655 )
## Borrowing money ( 229795.6 ) for closing a short position (PosID= 656 )
## Borrowing money ( 419534 ) for closing a short position (PosID= 657 )
## Borrowing money ( 609057.7 ) for closing a short position (PosID= 658 )
## Borrowing money ( 800509.8 ) for closing a short position (PosID= 659 )
## Borrowing money ( 77053.81 ) for closing a short position (PosID= 653 )
## Borrowing money ( 267081.3 ) for closing a short position (PosID= 667 )
## Borrowing money ( 127988.9 ) for closing a short position (PosID= 672 )
## Borrowing money ( 301533.7 ) for closing a short position (PosID= 648 )
## Borrowing money ( 498677.9 ) for closing a short position (PosID= 649 )
## Borrowing money ( 697412.6 ) for closing a short position (PosID= 650 )
## Borrowing money ( 895639.7 ) for closing a short position (PosID= 651 )
## Borrowing money ( 1093184 ) for closing a short position (PosID= 654 )
## Borrowing money ( 1294603 ) for closing a short position (PosID= 660 )
## Borrowing money ( 1494349 ) for closing a short position (PosID= 661 )
## Borrowing money ( 1699364 ) for closing a short position (PosID= 662 )
## Borrowing money ( 1924835 ) for closing a short position (PosID= 663 )
## Borrowing money ( 2143300 ) for closing a short position (PosID= 665 )
## Borrowing money ( 2363419 ) for closing a short position (PosID= 666 )
## Borrowing money ( 138009.9 ) for closing a short position (PosID= 668 )
## Borrowing money ( 152002.6 ) for closing a short position (PosID= 43 )
## Borrowing money ( 361900.2 ) for closing a short position (PosID= 44 )
## Borrowing money ( 571687.4 ) for closing a short position (PosID= 46 )
## Borrowing money ( 780561.9 ) for closing a short position (PosID= 42 )
## Borrowing money ( 989467.3 ) for closing a short position (PosID= 45 )
## Borrowing money ( 1195532 ) for closing a short position (PosID= 47 )
## Borrowing money ( 1406321 ) for closing a short position (PosID= 48 )
## Borrowing money ( 1616867 ) for closing a short position (PosID= 50 )
## Borrowing money ( 1827683 ) for closing a short position (PosID= 51 )
## Borrowing money ( 2036127 ) for closing a short position (PosID= 49 )
## Borrowing money ( 2245687 ) for closing a short position (PosID= 52 )
## Borrowing money ( 2454062 ) for closing a short position (PosID= 53 )
## Borrowing money ( 2659380 ) for closing a short position (PosID= 54 )
## Borrowing money ( 2863306 ) for closing a short position (PosID= 55 )
## Borrowing money ( 3074458 ) for closing a short position (PosID= 58 )
## Borrowing money ( 3285699 ) for closing a short position (PosID= 59 )
## Borrowing money ( 2145523 ) for closing a short position (PosID= 60 )
## Borrowing money ( 1560841 ) for closing a short position (PosID= 63 )
## Borrowing money ( 1768386 ) for closing a short position (PosID= 56 )
## Borrowing money ( 855094.3 ) for closing a short position (PosID= 61 )
## Borrowing money ( 519904.9 ) for closing a short position (PosID= 64 )
## Borrowing money ( 33544.69 ) for closing a short position (PosID= 86 )
## Borrowing money ( 157169.7 ) for closing a short position (PosID= 135 )
## Borrowing money ( 338805.1 ) for closing a short position (PosID= 139 )
## Borrowing money ( 49647.9 ) for closing a short position (PosID= 113 )
## Borrowing money ( 175319.6 ) for closing a short position (PosID= 114 )
## Borrowing money ( 300383.5 ) for closing a short position (PosID= 115 )
## Borrowing money ( 460497.8 ) for closing a short position (PosID= 121 )
## Borrowing money ( 618803.3 ) for closing a short position (PosID= 124 )
## Borrowing money ( 817074 ) for closing a short position (PosID= 140 )
## Borrowing money ( 69388.9 ) for closing a short position (PosID= 189 )
## Borrowing money ( 233506 ) for closing a short position (PosID= 199 )
## Borrowing money ( 97032.21 ) for closing a short position (PosID= 203 )
## Borrowing money ( 7483.347 ) for closing a short position (PosID= 263 )
## Borrowing money ( 162103.4 ) for closing a short position (PosID= 264 )
## Borrowing money ( 56465.35 ) for closing a short position (PosID= 254 )
## Borrowing money ( 122797.9 ) for closing a short position (PosID= 255 )
## Borrowing money ( 188872.6 ) for closing a short position (PosID= 256 )
## Borrowing money ( 254693.1 ) for closing a short position (PosID= 257 )
## Borrowing money ( 410805.2 ) for closing a short position (PosID= 262 )
## Borrowing money ( 26586.16 ) for closing a short position (PosID= 291 )
## Borrowing money ( 202550.2 ) for closing a short position (PosID= 292 )
## Borrowing money ( 379396.2 ) for closing a short position (PosID= 296 )
## Borrowing money ( 77852.57 ) for closing a short position (PosID= 285 )
## Borrowing money ( 256344.5 ) for closing a short position (PosID= 295 )
## Borrowing money ( 113647 ) for closing a short position (PosID= 395 )
## Borrowing money ( 16832.94 ) for closing a short position (PosID= 510 )
## Borrowing money ( 213228.5 ) for closing a short position (PosID= 511 )
## Borrowing money ( 409178.4 ) for closing a short position (PosID= 512 )
## Borrowing money ( 316599 ) for closing a short position (PosID= 518 )
## Borrowing money ( 28861.46 ) for closing a short position (PosID= 749 )
## Borrowing money ( 16910.46 ) for closing a short position (PosID= 917 )
## Borrowing money ( 32853.57 ) for closing a short position (PosID= 926 )
## Borrowing money ( 51402.49 ) for closing a short position (PosID= 910 )
## Borrowing money ( 69931.47 ) for closing a short position (PosID= 916 )
## Borrowing money ( 87006.64 ) for closing a short position (PosID= 927 )
## Borrowing money ( 104148.1 ) for closing a short position (PosID= 930 )
## Borrowing money ( 98372.44 ) for closing a short position (PosID= 906 )
## Borrowing money ( 239434.3 ) for closing a short position (PosID= 907 )
## Borrowing money ( 8647.001 ) for closing a short position (PosID= 941 )
## Borrowing money ( 92578.63 ) for closing a short position (PosID= 942 )
## Borrowing money ( 106770.4 ) for closing a short position (PosID= 944 )
## Borrowing money ( 222757.5 ) for closing a short position (PosID= 945 )
## Borrowing money ( 75847.59 ) for closing a short position (PosID= 950 )
## Borrowing money ( 191383.9 ) for closing a short position (PosID= 946 )
## Borrowing money ( 187197.3 ) for closing a short position (PosID= 937 )
## Borrowing money ( 271144.7 ) for closing a short position (PosID= 940 )
## Borrowing money ( 153680.6 ) for closing a short position (PosID= 951 )
## Borrowing money ( 58792.12 ) for closing a short position (PosID= 952 )
## Borrowing money ( 114575.4 ) for closing a short position (PosID= 973 )
## Borrowing money ( 117240.2 ) for closing a short position (PosID= 972 )
## Borrowing money ( 242176.9 ) for closing a short position (PosID= 974 )
## Borrowing money ( 368340.1 ) for closing a short position (PosID= 975 )
## Borrowing money ( 493264.9 ) for closing a short position (PosID= 976 )
## Borrowing money ( 618122.1 ) for closing a short position (PosID= 977 )
## Borrowing money ( 742970 ) for closing a short position (PosID= 978 )
## Borrowing money ( 587211.2 ) for closing a short position (PosID= 984 )
## Borrowing money ( 487288.6 ) for closing a short position (PosID= 986 )
## Borrowing money ( 407074.6 ) for closing a short position (PosID= 987 )
## Borrowing money ( 386347 ) for closing a short position (PosID= 993 )
## Borrowing money ( 369697.6 ) for closing a short position (PosID= 994 )
## Borrowing money ( 355857.9 ) for closing a short position (PosID= 995 )
## Borrowing money ( 72792.12 ) for closing a short position (PosID= 1007 )
## Borrowing money ( 199867.7 ) for closing a short position (PosID= 1008 )
## Borrowing money ( 52733.81 ) for closing a short position (PosID= 965 )
## Borrowing money ( 152177.8 ) for closing a short position (PosID= 966 )
## Borrowing money ( 251455.8 ) for closing a short position (PosID= 968 )
## Borrowing money ( 350685.3 ) for closing a short position (PosID= 969 )
## Borrowing money ( 449834.5 ) for closing a short position (PosID= 970 )
## Borrowing money ( 442420.7 ) for closing a short position (PosID= 998 )
## Borrowing money ( 436509 ) for closing a short position (PosID= 999 )
## Borrowing money ( 432063.1 ) for closing a short position (PosID= 1000 )
## Borrowing money ( 427620.5 ) for closing a short position (PosID= 1001 )
## Borrowing money ( 424660 ) for closing a short position (PosID= 1002 )
## Borrowing money ( 61420.24 ) for closing a short position (PosID= 1009 )
## Borrowing money ( 43516.27 ) for closing a short position (PosID= 1047 )
## Borrowing money ( 92213.97 ) for closing a short position (PosID= 1049 )
## Borrowing money ( 139452.4 ) for closing a short position (PosID= 1040 )
## Borrowing money ( 186730.4 ) for closing a short position (PosID= 1042 )
## Borrowing money ( 232438.3 ) for closing a short position (PosID= 1043 )
## Borrowing money ( 279704.8 ) for closing a short position (PosID= 1046 )
## Borrowing money ( 326958.7 ) for closing a short position (PosID= 1051 )
## Borrowing money ( 372642.2 ) for closing a short position (PosID= 1057 )
## Borrowing money ( 418313.6 ) for closing a short position (PosID= 1059 )
## Borrowing money ( 542197 ) for closing a short position (PosID= 1064 )
## Borrowing money ( 667682 ) for closing a short position (PosID= 1066 )
## Borrowing money ( 713439.8 ) for closing a short position (PosID= 1041 )
## Borrowing money ( 759217.4 ) for closing a short position (PosID= 1050 )
## Borrowing money ( 803540 ) for closing a short position (PosID= 1044 )
## Borrowing money ( 847790.8 ) for closing a short position (PosID= 1045 )
## Borrowing money ( 891985.2 ) for closing a short position (PosID= 1048 )
## Borrowing money ( 934681.2 ) for closing a short position (PosID= 1052 )
## Borrowing money ( 978993 ) for closing a short position (PosID= 1053 )
## Borrowing money ( 1020189 ) for closing a short position (PosID= 1054 )
## Borrowing money ( 1062841 ) for closing a short position (PosID= 1055 )
## Borrowing money ( 1104065 ) for closing a short position (PosID= 1056 )
## Borrowing money ( 1148296 ) for closing a short position (PosID= 1058 )
## Borrowing money ( 1190973 ) for closing a short position (PosID= 1060 )
## Borrowing money ( 685665.2 ) for closing a short position (PosID= 1068 )
## Borrowing money ( 806361 ) for closing a short position (PosID= 1065 )
## Borrowing money ( 401882.6 ) for closing a short position (PosID= 1069 )
## Borrowing money ( 78630.32 ) for closing a short position (PosID= 1070 )
## Borrowing money ( 12533.41 ) for closing a short position (PosID= 1225 )
## Borrowing money ( 105685.3 ) for closing a short position (PosID= 1345 )
## Borrowing money ( 214855 ) for closing a short position (PosID= 1346 )
## Borrowing money ( 352424 ) for closing a short position (PosID= 1347 )
## Borrowing money ( 489802.4 ) for closing a short position (PosID= 1348 )
## Borrowing money ( 625942.3 ) for closing a short position (PosID= 1349 )
## Borrowing money ( 363971.8 ) for closing a short position (PosID= 1352 )
## Borrowing money ( 499589.8 ) for closing a short position (PosID= 1350 )
## Borrowing money ( 173314.9 ) for closing a short position (PosID= 1351 )
## Borrowing money ( 15628.89 ) for closing a short position (PosID= 1401 )
## Borrowing money ( 46520.43 ) for closing a short position (PosID= 1404 )
## Borrowing money ( 89699.99 ) for closing a short position (PosID= 1409 )
## Borrowing money ( 133076.7 ) for closing a short position (PosID= 1410 )
## Borrowing money ( 115734.7 ) for closing a short position (PosID= 3 )
## Borrowing money ( 113280.9 ) for closing a short position (PosID= 12 )
## Borrowing money ( 80144.03 ) for closing a short position (PosID= 14 )
## Borrowing money ( 77550.41 ) for closing a short position (PosID= 18 )
## Borrowing money ( 59083.68 ) for closing a short position (PosID= 15 )
## Borrowing money ( 49838.19 ) for closing a short position (PosID= 16 )
## Borrowing money ( 372424.4 ) for closing a short position (PosID= 21 )
## Borrowing money ( 930541.5 ) for closing a short position (PosID= 19 )
## Borrowing money ( 1477783 ) for closing a short position (PosID= 22 )
## Borrowing money ( 2026193 ) for closing a short position (PosID= 24 )
## Borrowing money ( 2573621 ) for closing a short position (PosID= 25 )
## Borrowing money ( 3123596 ) for closing a short position (PosID= 27 )
## Borrowing money ( 3669219 ) for closing a short position (PosID= 23 )
## Borrowing money ( 4213502 ) for closing a short position (PosID= 26 )
## Borrowing money ( 4738673 ) for closing a short position (PosID= 28 )
## Borrowing money ( 5259184 ) for closing a short position (PosID= 29 )
## Borrowing money ( 5783394 ) for closing a short position (PosID= 31 )
## Borrowing money ( 6302539 ) for closing a short position (PosID= 32 )
## Borrowing money ( 6814299 ) for closing a short position (PosID= 30 )
## Borrowing money ( 7331015 ) for closing a short position (PosID= 33 )
## Borrowing money ( 7842603 ) for closing a short position (PosID= 34 )
## Borrowing money ( 8326974 ) for closing a short position (PosID= 35 )
## Borrowing money ( 8810026 ) for closing a short position (PosID= 36 )
## Borrowing money ( 9295957 ) for closing a short position (PosID= 39 )
## Borrowing money ( 9780649 ) for closing a short position (PosID= 40 )
## Borrowing money ( 3506035 ) for closing a short position (PosID= 41 )
## Borrowing money ( 2721633 ) for closing a short position (PosID= 44 )
## Borrowing money ( 3187515 ) for closing a short position (PosID= 37 )
## Borrowing money ( 49786.9 ) for closing a short position (PosID= 42 )
## Borrowing money ( 252276 ) for closing a short position (PosID= 38 )
## Borrowing money ( 482397.7 ) for closing a short position (PosID= 51 )
## Borrowing money ( 1016949 ) for closing a short position (PosID= 49 )
## Borrowing money ( 1552956 ) for closing a short position (PosID= 52 )
## Borrowing money ( 2083629 ) for closing a short position (PosID= 53 )
## Borrowing money ( 2621007 ) for closing a short position (PosID= 54 )
## Borrowing money ( 3152884 ) for closing a short position (PosID= 48 )
## Borrowing money ( 3669618 ) for closing a short position (PosID= 55 )
## Borrowing money ( 4196887 ) for closing a short position (PosID= 50 )
## Borrowing money ( 4700598 ) for closing a short position (PosID= 56 )
## Borrowing money ( 629184.6 ) for closing a short position (PosID= 62 )
## Borrowing money ( 297007.6 ) for closing a short position (PosID= 95 )
## Borrowing money ( 693274.5 ) for closing a short position (PosID= 96 )
## Borrowing money ( 1120874 ) for closing a short position (PosID= 97 )
## Borrowing money ( 682543.2 ) for closing a short position (PosID= 104 )
## Borrowing money ( 464373.5 ) for closing a short position (PosID= 105 )
## Borrowing money ( 355102.4 ) for closing a short position (PosID= 106 )
## Borrowing money ( 812237 ) for closing a short position (PosID= 86 )
## Borrowing money ( 1264282 ) for closing a short position (PosID= 87 )
## Borrowing money ( 1718932 ) for closing a short position (PosID= 88 )
## Borrowing money ( 2177371 ) for closing a short position (PosID= 89 )
## Borrowing money ( 2652558 ) for closing a short position (PosID= 91 )
## Borrowing money ( 3121279 ) for closing a short position (PosID= 92 )
## Borrowing money ( 3550131 ) for closing a short position (PosID= 93 )
## Borrowing money ( 3977638 ) for closing a short position (PosID= 94 )
## Borrowing money ( 4459108 ) for closing a short position (PosID= 98 )
## Borrowing money ( 2708211 ) for closing a short position (PosID= 102 )
## Borrowing money ( 1832889 ) for closing a short position (PosID= 103 )
## Borrowing money ( 2232981 ) for closing a short position (PosID= 77 )
## Borrowing money ( 2689937 ) for closing a short position (PosID= 79 )
## Borrowing money ( 3135856 ) for closing a short position (PosID= 84 )
## Borrowing money ( 3614420 ) for closing a short position (PosID= 85 )
## Borrowing money ( 35631.59 ) for closing a short position (PosID= 71 )
## Borrowing money ( 348268.4 ) for closing a short position (PosID= 72 )
## Borrowing money ( 656086.2 ) for closing a short position (PosID= 73 )
## Borrowing money ( 951685 ) for closing a short position (PosID= 74 )
## Borrowing money ( 1434852 ) for closing a short position (PosID= 80 )
## Borrowing money ( 1905480 ) for closing a short position (PosID= 83 )
## Borrowing money ( 2496033 ) for closing a short position (PosID= 99 )
## Borrowing money ( 45823.11 ) for closing a short position (PosID= 148 )
## Borrowing money ( 314861.3 ) for closing a short position (PosID= 149 )
## Borrowing money ( 592482.9 ) for closing a short position (PosID= 146 )
## Borrowing money ( 877658.5 ) for closing a short position (PosID= 153 )
## Borrowing money ( 1160194 ) for closing a short position (PosID= 154 )
## Borrowing money ( 1443894 ) for closing a short position (PosID= 145 )
## Borrowing money ( 1740727 ) for closing a short position (PosID= 155 )
## Borrowing money ( 27270.81 ) for closing a short position (PosID= 156 )
## Borrowing money ( 107948.4 ) for closing a short position (PosID= 164 )
## Borrowing money ( 468338.8 ) for closing a short position (PosID= 163 )
## Borrowing money ( 192414.3 ) for closing a short position (PosID= 162 )
## Borrowing money ( 511608.5 ) for closing a short position (PosID= 165 )
## Borrowing money ( 231313.5 ) for closing a short position (PosID= 172 )
## Borrowing money ( 211984.7 ) for closing a short position (PosID= 178 )
## Borrowing money ( 173012.4 ) for closing a short position (PosID= 177 )
## Borrowing money ( 163295.5 ) for closing a short position (PosID= 179 )
## Borrowing money ( 400837.6 ) for closing a short position (PosID= 171 )
## Borrowing money ( 277281.5 ) for closing a short position (PosID= 185 )
## Borrowing money ( 570114.8 ) for closing a short position (PosID= 186 )
## Borrowing money ( 859799.4 ) for closing a short position (PosID= 187 )
## Borrowing money ( 1154486 ) for closing a short position (PosID= 189 )
## Borrowing money ( 1449059 ) for closing a short position (PosID= 190 )
## Borrowing money ( 1369033 ) for closing a short position (PosID= 195 )
## Borrowing money ( 1403565 ) for closing a short position (PosID= 180 )
## Borrowing money ( 1438060 ) for closing a short position (PosID= 181 )
## Borrowing money ( 1473743 ) for closing a short position (PosID= 182 )
## Borrowing money ( 1509288 ) for closing a short position (PosID= 183 )
## Borrowing money ( 1808278 ) for closing a short position (PosID= 188 )
## Borrowing money ( 528065.2 ) for closing a short position (PosID= 191 )
## Borrowing money ( 161709.5 ) for closing a short position (PosID= 218 )
## Borrowing money ( 223868.1 ) for closing a short position (PosID= 204 )
## Borrowing money ( 527411.1 ) for closing a short position (PosID= 205 )
## Borrowing money ( 841109.8 ) for closing a short position (PosID= 214 )
## Borrowing money ( 1153700 ) for closing a short position (PosID= 215 )
## Borrowing money ( 1467319 ) for closing a short position (PosID= 216 )
## Borrowing money ( 1781576 ) for closing a short position (PosID= 220 )
## Borrowing money ( 531817.3 ) for closing a short position (PosID= 222 )
## Borrowing money ( 528212.2 ) for closing a short position (PosID= 228 )
## Borrowing money ( 527006.4 ) for closing a short position (PosID= 229 )
## Borrowing money ( 839901.1 ) for closing a short position (PosID= 206 )
## Borrowing money ( 1153066 ) for closing a short position (PosID= 209 )
## Borrowing money ( 1475537 ) for closing a short position (PosID= 219 )
## Borrowing money ( 899686.5 ) for closing a short position (PosID= 231 )
## Borrowing money ( 747807 ) for closing a short position (PosID= 232 )
## Borrowing money ( 671708.7 ) for closing a short position (PosID= 233 )
## Borrowing money ( 190965.2 ) for closing a short position (PosID= 235 )
## Borrowing money ( 23546.79 ) for closing a short position (PosID= 237 )
## Borrowing money ( 73212.75 ) for closing a short position (PosID= 288 )
## Borrowing money ( 80566.05 ) for closing a short position (PosID= 302 )
## Borrowing money ( 164445.6 ) for closing a short position (PosID= 298 )
## Borrowing money ( 388083.9 ) for closing a short position (PosID= 299 )
## Borrowing money ( 617978.6 ) for closing a short position (PosID= 301 )
## Borrowing money ( 277039.4 ) for closing a short position (PosID= 304 )
## Borrowing money ( 106718.4 ) for closing a short position (PosID= 305 )
## Borrowing money ( 154527.6 ) for closing a short position (PosID= 319 )
## Borrowing money ( 387363.7 ) for closing a short position (PosID= 320 )
## Borrowing money ( 624984.9 ) for closing a short position (PosID= 321 )
## Borrowing money ( 322174.5 ) for closing a short position (PosID= 407 )
## Borrowing money ( 641956.8 ) for closing a short position (PosID= 408 )
## Borrowing money ( 958567.5 ) for closing a short position (PosID= 409 )
## Borrowing money ( 950342.8 ) for closing a short position (PosID= 415 )
## Borrowing money ( 70131.06 ) for closing a short position (PosID= 469 )
## Borrowing money ( 64895.29 ) for closing a short position (PosID= 476 )
## Borrowing money ( 63784.03 ) for closing a short position (PosID= 478 )
## Borrowing money ( 15961.25 ) for closing a short position (PosID= 473 )
## Borrowing money ( 158526.2 ) for closing a short position (PosID= 470 )
## Borrowing money ( 249692 ) for closing a short position (PosID= 482 )
## Borrowing money ( 584167.9 ) for closing a short position (PosID= 484 )
## Borrowing money ( 913234 ) for closing a short position (PosID= 485 )
## Borrowing money ( 1247149 ) for closing a short position (PosID= 486 )
## Borrowing money ( 189758.5 ) for closing a short position (PosID= 489 )
## Borrowing money ( 16152.02 ) for closing a short position (PosID= 524 )
## Borrowing money ( 381154.6 ) for closing a short position (PosID= 532 )
## Borrowing money ( 53546.01 ) for closing a short position (PosID= 529 )
## Borrowing money ( 245947.1 ) for closing a short position (PosID= 559 )
## Borrowing money ( 139429.3 ) for closing a short position (PosID= 574 )
## Borrowing money ( 391963.9 ) for closing a short position (PosID= 576 )
## Borrowing money ( 628585.5 ) for closing a short position (PosID= 603 )
## Borrowing money ( 861357.3 ) for closing a short position (PosID= 605 )
## Borrowing money ( 51839.96 ) for closing a short position (PosID= 578 )
## Borrowing money ( 266855.5 ) for closing a short position (PosID= 604 )
## Borrowing money ( 63528.28 ) for closing a short position (PosID= 592 )
## Borrowing money ( 248411.7 ) for closing a short position (PosID= 593 )
## Borrowing money ( 435809.8 ) for closing a short position (PosID= 596 )
## Borrowing money ( 619781.4 ) for closing a short position (PosID= 597 )
## Borrowing money ( 802746.1 ) for closing a short position (PosID= 600 )
## Borrowing money ( 336184.7 ) for closing a short position (PosID= 609 )
## Borrowing money ( 291063.1 ) for closing a short position (PosID= 623 )
## Borrowing money ( 267356.9 ) for closing a short position (PosID= 624 )
## Borrowing money ( 2762.612 ) for closing a short position (PosID= 702 )
## Borrowing money ( 65614.39 ) for closing a short position (PosID= 700 )
## Borrowing money ( 64832.82 ) for closing a short position (PosID= 721 )
## Borrowing money ( 275583.8 ) for closing a short position (PosID= 722 )
## Borrowing money ( 483955.1 ) for closing a short position (PosID= 723 )
## Borrowing money ( 691214 ) for closing a short position (PosID= 724 )
## Borrowing money ( 898445.4 ) for closing a short position (PosID= 725 )
## Borrowing money ( 1076908 ) for closing a short position (PosID= 726 )
## Borrowing money ( 1254204 ) for closing a short position (PosID= 727 )
## Borrowing money ( 64457.32 ) for closing a short position (PosID= 732 )
## Borrowing money ( 129127.1 ) for closing a short position (PosID= 733 )
## Borrowing money ( 54851.52 ) for closing a short position (PosID= 730 )
## Borrowing money ( 118377.3 ) for closing a short position (PosID= 735 )
## Borrowing money ( 181896.3 ) for closing a short position (PosID= 736 )
## Borrowing money ( 112601.8 ) for closing a short position (PosID= 741 )
## Borrowing money ( 173852.3 ) for closing a short position (PosID= 737 )
## Borrowing money ( 238213.2 ) for closing a short position (PosID= 731 )
## Borrowing money ( 203257.2 ) for closing a short position (PosID= 742 )
## Borrowing money ( 186516.3 ) for closing a short position (PosID= 743 )
## Borrowing money ( 99903.54 ) for closing a short position (PosID= 761 )
## Borrowing money ( 83248.58 ) for closing a short position (PosID= 773 )
## Borrowing money ( 185890.9 ) for closing a short position (PosID= 760 )
## Borrowing money ( 287229.4 ) for closing a short position (PosID= 762 )
## Borrowing money ( 389824.6 ) for closing a short position (PosID= 763 )
## Borrowing money ( 491153.4 ) for closing a short position (PosID= 764 )
## Borrowing money ( 593814.7 ) for closing a short position (PosID= 765 )
## Borrowing money ( 696468.2 ) for closing a short position (PosID= 766 )
## Borrowing money ( 664769.1 ) for closing a short position (PosID= 772 )
## Borrowing money ( 656446.8 ) for closing a short position (PosID= 774 )
## Borrowing money ( 652302.5 ) for closing a short position (PosID= 775 )
## Borrowing money ( 143634.3 ) for closing a short position (PosID= 768 )
## Borrowing money ( 80460.04 ) for closing a short position (PosID= 771 )
## Borrowing money ( 67257.82 ) for closing a short position (PosID= 759 )
## Borrowing money ( 33575.62 ) for closing a short position (PosID= 778 )
## Borrowing money ( 153522 ) for closing a short position (PosID= 779 )
## Borrowing money ( 264200.9 ) for closing a short position (PosID= 780 )
## Borrowing money ( 374953.1 ) for closing a short position (PosID= 781 )
## Borrowing money ( 484501.7 ) for closing a short position (PosID= 782 )
## Borrowing money ( 114347.2 ) for closing a short position (PosID= 784 )
## Borrowing money ( 166005.4 ) for closing a short position (PosID= 749 )
## Borrowing money ( 293619.7 ) for closing a short position (PosID= 753 )
## Borrowing money ( 421262.8 ) for closing a short position (PosID= 754 )
## Borrowing money ( 548692.9 ) for closing a short position (PosID= 756 )
## Borrowing money ( 676060.6 ) for closing a short position (PosID= 757 )
## Borrowing money ( 804805.2 ) for closing a short position (PosID= 758 )
## Borrowing money ( 66507.89 ) for closing a short position (PosID= 783 )
## Borrowing money ( 1230.828 ) for closing a short position (PosID= 792 )
## Borrowing money ( 109923.9 ) for closing a short position (PosID= 793 )
## Borrowing money ( 217588.6 ) for closing a short position (PosID= 794 )
## Borrowing money ( 326410.6 ) for closing a short position (PosID= 795 )
## Borrowing money ( 431278.3 ) for closing a short position (PosID= 797 )
## Borrowing money ( 536015.7 ) for closing a short position (PosID= 799 )
## Borrowing money ( 640810.6 ) for closing a short position (PosID= 800 )
## Borrowing money ( 99455.45 ) for closing a short position (PosID= 807 )
## Borrowing money ( 197259.1 ) for closing a short position (PosID= 804 )
## Borrowing money ( 295040.8 ) for closing a short position (PosID= 806 )
## Borrowing money ( 3754.567 ) for closing a short position (PosID= 805 )
## Borrowing money ( 50859.55 ) for closing a short position (PosID= 802 )
## Borrowing money ( 39122.41 ) for closing a short position (PosID= 813 )
## Borrowing money ( 35772.48 ) for closing a short position (PosID= 815 )
## Borrowing money ( 6036.304 ) for closing a short position (PosID= 869 )
## Borrowing money ( 32540.44 ) for closing a short position (PosID= 870 )
## Borrowing money ( 68452.25 ) for closing a short position (PosID= 871 )
## Borrowing money ( 102850.5 ) for closing a short position (PosID= 872 )
## Borrowing money ( 84158.23 ) for closing a short position (PosID= 916 )
## Borrowing money ( 170801.2 ) for closing a short position (PosID= 917 )
## Borrowing money ( 257314.1 ) for closing a short position (PosID= 918 )
## Borrowing money ( 363618.5 ) for closing a short position (PosID= 919 )
## Borrowing money ( 469775.7 ) for closing a short position (PosID= 920 )
## Borrowing money ( 574499.8 ) for closing a short position (PosID= 921 )
## Borrowing money ( 346332.7 ) for closing a short position (PosID= 924 )
## Borrowing money ( 450166.4 ) for closing a short position (PosID= 922 )
## Borrowing money ( 4695.789 ) for closing a short position (PosID= 964 )
## Borrowing money ( 12812.42 ) for closing a short position (PosID= 965 )
## Borrowing money ( 2635.551 ) for closing a short position (PosID= 962 )
## Borrowing money ( 4708.432 ) for closing a short position (PosID= 955 )
## Borrowing money ( 12937.16 ) for closing a short position (PosID= 960 )
## Borrowing money ( 21203.43 ) for closing a short position (PosID= 961 )
## # weights: 66
## initial value 1292.732696
## iter 10 value 73.304596
## iter 20 value 73.282704
## iter 30 value 73.270407
## iter 40 value 73.268518
## iter 50 value 73.263778
## iter 60 value 73.263234
## iter 70 value 73.262154
## final value 73.261987
## converged
## # weights: 66
## initial value 2411.534022
## iter 10 value 72.489666
## iter 20 value 72.143955
## iter 30 value 70.974580
## iter 40 value 67.104353
## iter 50 value 65.425178
## iter 60 value 64.925823
## iter 70 value 64.699131
## iter 80 value 64.502029
## iter 90 value 64.301966
## iter 100 value 63.671257
## iter 110 value 63.554367
## iter 120 value 63.548618
## iter 130 value 62.544270
## iter 140 value 62.268640
## iter 150 value 61.796744
## iter 160 value 61.089382
## iter 170 value 60.656887
## iter 180 value 60.038123
## iter 190 value 59.652562
## iter 200 value 59.538480
## iter 210 value 59.518392
## iter 220 value 59.515029
## iter 230 value 59.513406
## iter 240 value 59.512956
## iter 250 value 59.512798
## iter 260 value 59.512645
## final value 59.512555
## converged
## # weights: 66
## initial value 2553.111160
## iter 10 value 70.746444
## iter 20 value 70.480702
## iter 30 value 66.943587
## iter 40 value 61.944913
## iter 50 value 61.851561
## iter 60 value 61.223407
## iter 70 value 60.349005
## iter 80 value 59.924096
## iter 90 value 59.304336
## iter 100 value 58.992632
## iter 110 value 58.170750
## iter 120 value 57.922458
## iter 130 value 57.867793
## iter 140 value 57.840590
## iter 150 value 57.824243
## iter 160 value 57.770091
## iter 170 value 57.418179
## iter 180 value 56.998048
## iter 190 value 56.294780
## iter 200 value 55.612357
## iter 210 value 55.349133
## iter 220 value 55.256879
## iter 230 value 55.088192
## iter 240 value 54.346376
## iter 250 value 52.625761
## iter 260 value 51.208542
## iter 270 value 50.892976
## iter 280 value 50.580503
## iter 290 value 50.242598
## iter 300 value 50.043900
## iter 310 value 49.937647
## iter 320 value 49.820141
## iter 330 value 49.774625
## iter 340 value 49.665387
## iter 350 value 49.647890
## iter 360 value 49.587860
## iter 370 value 49.493340
## iter 380 value 49.427675
## iter 390 value 49.380931
## iter 400 value 49.291577
## iter 410 value 49.266933
## iter 420 value 49.248530
## iter 430 value 49.239406
## iter 440 value 49.237789
## final value 49.237546
## converged
## # weights: 66
## initial value 957.352318
## iter 10 value 68.753785
## iter 20 value 68.743721
## iter 30 value 68.540648
## iter 40 value 66.193764
## iter 50 value 65.219803
## iter 60 value 64.586161
## iter 70 value 62.948216
## iter 80 value 60.800768
## iter 90 value 60.775235
## iter 100 value 60.506707
## iter 110 value 60.053998
## iter 120 value 59.504778
## iter 130 value 58.716017
## iter 140 value 58.596979
## iter 150 value 58.590172
## iter 160 value 58.583589
## iter 170 value 58.582981
## final value 58.582974
## converged
## # weights: 66
## initial value 296.093719
## iter 10 value 69.164714
## iter 20 value 68.514327
## iter 30 value 66.392427
## iter 40 value 64.045996
## iter 50 value 61.720826
## iter 60 value 60.011821
## iter 70 value 60.004144
## iter 80 value 60.001137
## iter 90 value 59.999149
## final value 59.998886
## converged
## # weights: 66
## initial value 4603.113490
## iter 10 value 70.806996
## iter 20 value 70.034356
## iter 30 value 68.373665
## iter 40 value 65.491468
## iter 50 value 65.158200
## iter 60 value 65.078161
## iter 70 value 65.019576
## iter 80 value 64.968532
## iter 90 value 64.903989
## iter 100 value 64.879036
## iter 110 value 64.842540
## iter 120 value 64.840548
## iter 130 value 64.839921
## iter 140 value 64.707690
## iter 150 value 64.102530
## iter 160 value 63.338036
## iter 170 value 62.935269
## iter 180 value 62.433792
## iter 190 value 61.797476
## iter 200 value 60.962055
## iter 210 value 60.759650
## iter 220 value 60.702693
## iter 230 value 60.666269
## iter 240 value 60.501651
## iter 250 value 60.345978
## iter 260 value 60.023824
## iter 270 value 59.784052
## iter 280 value 59.644612
## iter 290 value 59.616670
## iter 300 value 59.603199
## final value 59.603174
## converged
## # weights: 66
## initial value 435.916786
## iter 10 value 85.428795
## iter 20 value 84.348265
## iter 30 value 82.234088
## iter 40 value 78.200167
## iter 50 value 75.291238
## iter 60 value 74.992862
## iter 70 value 74.627724
## iter 80 value 74.507294
## iter 90 value 73.875826
## iter 100 value 73.406587
## iter 110 value 72.360787
## iter 120 value 71.098259
## iter 130 value 68.093445
## iter 140 value 63.589167
## iter 150 value 61.840968
## iter 160 value 60.988151
## iter 170 value 60.674576
## iter 180 value 60.520990
## iter 190 value 60.459648
## iter 200 value 60.433890
## iter 210 value 60.418903
## iter 220 value 60.417491
## iter 220 value 60.417491
## final value 60.417491
## converged
## # weights: 66
## initial value 616.394247
## iter 10 value 99.890069
## iter 20 value 99.398798
## iter 30 value 93.885497
## iter 40 value 92.760878
## iter 50 value 91.911801
## iter 60 value 91.347988
## iter 70 value 91.178051
## iter 80 value 91.143870
## iter 90 value 91.141860
## iter 100 value 91.139238
## iter 110 value 91.135178
## iter 120 value 91.046621
## iter 130 value 90.770708
## iter 140 value 90.159149
## iter 150 value 90.004828
## iter 160 value 89.836514
## iter 170 value 89.685206
## iter 180 value 89.598715
## iter 190 value 89.582998
## iter 200 value 89.380629
## iter 210 value 88.995896
## iter 220 value 88.605282
## iter 230 value 87.940599
## iter 240 value 87.644924
## iter 250 value 87.372476
## iter 260 value 87.362850
## iter 270 value 87.361259
## iter 280 value 87.353405
## iter 290 value 87.050387
## iter 300 value 85.551772
## iter 310 value 84.269333
## iter 320 value 83.520322
## iter 330 value 83.107510
## iter 340 value 83.026031
## iter 350 value 82.990174
## iter 360 value 82.975158
## iter 370 value 82.967530
## iter 380 value 82.965511
## iter 390 value 82.964266
## iter 400 value 82.962707
## final value 82.962256
## converged
## # weights: 66
## initial value 2626.789526
## iter 10 value 102.506076
## iter 20 value 102.428685
## iter 30 value 101.993585
## iter 40 value 101.564812
## iter 50 value 98.618173
## iter 60 value 95.544846
## iter 70 value 93.602873
## iter 80 value 92.127106
## iter 90 value 91.456321
## iter 100 value 89.798681
## iter 110 value 88.109989
## iter 120 value 87.103098
## iter 130 value 86.992863
## iter 140 value 86.910985
## iter 150 value 86.903806
## iter 160 value 86.902098
## iter 170 value 86.901847
## iter 170 value 86.901847
## final value 86.901847
## converged
## # weights: 66
## initial value 7873.000750
## iter 10 value 100.100452
## iter 20 value 99.840418
## iter 30 value 99.497932
## iter 40 value 98.267025
## iter 50 value 96.753019
## iter 60 value 96.368626
## iter 70 value 96.335790
## iter 80 value 96.319367
## iter 90 value 96.310377
## iter 100 value 96.277524
## iter 110 value 96.228674
## iter 120 value 95.517369
## iter 130 value 94.812872
## iter 140 value 93.899680
## iter 150 value 93.690799
## iter 160 value 93.590222
## iter 170 value 93.512234
## iter 180 value 93.463690
## iter 190 value 93.450744
## iter 200 value 93.368147
## iter 210 value 92.569287
## iter 220 value 90.889091
## iter 230 value 85.831153
## iter 240 value 83.104236
## iter 250 value 81.809488
## iter 260 value 80.216288
## iter 270 value 80.052835
## iter 280 value 79.985455
## iter 290 value 79.952969
## iter 300 value 79.883764
## iter 310 value 79.726008
## iter 320 value 79.461998
## iter 330 value 79.347707
## iter 340 value 79.267793
## iter 350 value 79.255139
## iter 360 value 79.248281
## iter 370 value 79.240905
## iter 380 value 79.157243
## iter 390 value 78.610448
## iter 400 value 78.146200
## iter 410 value 77.888620
## iter 420 value 77.753598
## iter 430 value 77.530216
## iter 440 value 77.493590
## iter 450 value 77.464697
## iter 460 value 77.445567
## iter 470 value 77.413026
## iter 480 value 77.408231
## iter 490 value 77.406505
## iter 500 value 77.405868
## iter 510 value 77.405727
## iter 520 value 77.405542
## iter 530 value 77.404314
## iter 540 value 77.400883
## iter 550 value 77.399638
## final value 77.399560
## converged
## # weights: 66
## initial value 6800.283443
## iter 10 value 100.244301
## iter 20 value 100.127571
## iter 30 value 98.631350
## iter 40 value 98.012501
## iter 50 value 97.535405
## iter 60 value 96.265494
## iter 70 value 95.375518
## iter 80 value 94.045979
## iter 90 value 93.811165
## iter 100 value 93.778181
## iter 110 value 93.698835
## iter 120 value 88.021761
## iter 130 value 85.238726
## iter 140 value 84.010338
## iter 150 value 83.551286
## iter 160 value 82.626462
## iter 170 value 80.907877
## iter 180 value 79.249988
## iter 190 value 78.243186
## iter 200 value 77.814501
## iter 210 value 77.689548
## iter 220 value 77.593613
## iter 230 value 77.442242
## iter 240 value 77.266096
## iter 250 value 77.088333
## iter 260 value 77.061432
## iter 270 value 77.056882
## iter 280 value 77.056317
## final value 77.056313
## converged
## # weights: 66
## initial value 134.157647
## iter 10 value 97.016101
## iter 20 value 96.514567
## iter 30 value 93.400029
## iter 40 value 89.309021
## iter 50 value 88.833900
## iter 60 value 88.603412
## iter 70 value 88.468655
## iter 80 value 88.055224
## iter 90 value 87.421537
## iter 100 value 84.403177
## iter 110 value 83.860057
## iter 120 value 83.574030
## iter 130 value 83.262226
## iter 140 value 82.954417
## iter 150 value 82.496354
## iter 160 value 82.111023
## iter 170 value 81.794545
## iter 180 value 81.727547
## iter 190 value 81.503005
## iter 200 value 81.245402
## iter 210 value 80.684959
## iter 220 value 79.909428
## iter 230 value 79.234494
## iter 240 value 76.816593
## iter 250 value 75.219685
## iter 260 value 74.802091
## iter 270 value 74.747378
## iter 280 value 74.695634
## iter 290 value 74.658959
## iter 300 value 74.468625
## iter 310 value 72.327021
## iter 320 value 71.433635
## iter 330 value 70.946827
## iter 340 value 70.821378
## iter 350 value 70.702397
## iter 360 value 70.480652
## iter 370 value 70.314316
## iter 380 value 70.273432
## iter 390 value 70.251619
## iter 400 value 70.212450
## iter 410 value 70.150595
## iter 420 value 69.979065
## iter 430 value 69.915231
## iter 440 value 69.852814
## iter 450 value 69.827434
## iter 460 value 69.825354
## iter 470 value 69.824244
## iter 480 value 69.823839
## final value 69.823775
## converged
## # weights: 66
## initial value 588.314268
## iter 10 value 98.098353
## iter 20 value 98.097124
## iter 30 value 98.049543
## iter 40 value 97.309441
## iter 50 value 94.549917
## iter 60 value 93.057916
## iter 70 value 92.429167
## iter 80 value 92.222718
## iter 90 value 92.217308
## iter 100 value 92.151568
## iter 110 value 91.159844
## iter 120 value 87.089876
## iter 130 value 82.918475
## iter 140 value 81.559484
## iter 150 value 81.200094
## iter 160 value 80.967492
## iter 170 value 80.775325
## iter 180 value 80.378049
## iter 190 value 76.732935
## iter 200 value 72.325636
## iter 210 value 70.631218
## iter 220 value 70.328573
## iter 230 value 69.831868
## iter 240 value 68.460887
## iter 250 value 65.773420
## iter 260 value 64.512762
## iter 270 value 63.792987
## iter 280 value 63.069585
## iter 290 value 62.919426
## iter 300 value 62.735257
## iter 310 value 62.672480
## iter 320 value 62.600601
## iter 330 value 62.565398
## iter 340 value 62.545590
## iter 350 value 62.518671
## iter 360 value 62.505324
## iter 370 value 62.466028
## iter 380 value 62.458510
## iter 390 value 62.456305
## iter 400 value 62.455251
## iter 410 value 62.455120
## final value 62.455064
## converged
## # weights: 66
## initial value 2675.496867
## final value 95.645423
## converged
## # weights: 66
## initial value 498.151556
## iter 10 value 89.173846
## iter 20 value 89.166650
## iter 30 value 88.431096
## iter 40 value 86.586988
## iter 50 value 83.752108
## iter 60 value 78.911808
## iter 70 value 76.965466
## iter 80 value 76.729934
## iter 90 value 76.665096
## iter 100 value 76.653461
## iter 110 value 76.637413
## iter 120 value 76.492089
## iter 130 value 76.088916
## iter 140 value 75.991242
## iter 150 value 75.781611
## iter 160 value 75.663081
## iter 170 value 75.578650
## iter 180 value 75.447811
## iter 190 value 75.439256
## iter 200 value 75.438588
## iter 210 value 75.422597
## iter 220 value 75.347554
## iter 230 value 75.001626
## iter 240 value 74.820195
## iter 250 value 73.422497
## iter 260 value 69.800798
## iter 270 value 68.529520
## iter 280 value 65.730056
## iter 290 value 65.131369
## iter 300 value 64.817571
## iter 310 value 64.677888
## iter 320 value 64.581506
## iter 330 value 64.530598
## iter 340 value 64.511887
## iter 350 value 64.497007
## iter 360 value 64.489403
## iter 370 value 64.466775
## iter 380 value 64.350726
## iter 390 value 63.923123
## iter 400 value 63.723826
## iter 410 value 63.310911
## iter 420 value 63.158194
## iter 430 value 62.974541
## iter 440 value 62.696592
## iter 450 value 62.548912
## iter 460 value 62.480672
## iter 470 value 62.371047
## iter 480 value 62.320609
## iter 490 value 62.165715
## iter 500 value 62.087574
## iter 510 value 61.979903
## iter 520 value 61.912584
## iter 530 value 61.897680
## iter 540 value 61.890570
## iter 550 value 61.889285
## iter 560 value 61.888922
## iter 570 value 61.888836
## iter 570 value 61.888836
## iter 570 value 61.888836
## final value 61.888836
## converged
## # weights: 66
## initial value 3608.194080
## iter 10 value 79.462524
## iter 20 value 78.065352
## iter 30 value 75.715125
## iter 40 value 74.703919
## iter 50 value 72.203931
## iter 60 value 69.031623
## iter 70 value 65.233988
## iter 80 value 62.756462
## iter 90 value 60.804946
## iter 100 value 60.309667
## iter 110 value 60.004698
## iter 120 value 59.697304
## iter 130 value 59.605480
## iter 140 value 59.400835
## iter 150 value 59.050771
## iter 160 value 57.402861
## iter 170 value 55.837447
## iter 180 value 55.167170
## iter 190 value 54.956230
## iter 200 value 54.675625
## iter 210 value 53.882278
## iter 220 value 53.678885
## iter 230 value 53.616266
## iter 240 value 53.433635
## iter 250 value 53.293027
## iter 260 value 52.988824
## iter 270 value 52.936151
## iter 280 value 52.916996
## iter 290 value 52.844500
## iter 300 value 52.356028
## iter 310 value 51.745235
## iter 320 value 51.603872
## iter 330 value 51.434283
## iter 340 value 51.160262
## iter 350 value 50.809750
## iter 360 value 50.678261
## iter 370 value 50.580522
## iter 380 value 50.536898
## iter 390 value 50.529452
## iter 400 value 50.505456
## iter 410 value 50.463971
## iter 420 value 50.415240
## iter 430 value 50.321559
## iter 440 value 50.297952
## iter 450 value 50.295654
## iter 460 value 50.291812
## iter 470 value 50.290901
## final value 50.290820
## converged
## # weights: 66
## initial value 8431.078963
## iter 10 value 76.804232
## iter 20 value 71.744356
## iter 30 value 71.288346
## iter 40 value 71.103689
## iter 50 value 70.788327
## iter 60 value 69.940702
## iter 70 value 68.086992
## iter 80 value 67.939015
## iter 90 value 67.514740
## iter 100 value 67.376588
## iter 110 value 66.971086
## iter 120 value 66.721057
## iter 130 value 66.653627
## iter 140 value 66.609527
## iter 150 value 66.595281
## iter 160 value 66.497054
## iter 170 value 64.898895
## iter 180 value 59.737881
## iter 190 value 57.146155
## iter 200 value 54.249630
## iter 210 value 52.683752
## iter 220 value 52.180677
## iter 230 value 52.043072
## iter 240 value 51.843281
## iter 250 value 51.419895
## iter 260 value 50.643444
## iter 270 value 48.405208
## iter 280 value 47.247316
## iter 290 value 46.928686
## iter 300 value 46.295998
## iter 310 value 45.516514
## iter 320 value 45.214549
## iter 330 value 45.005234
## iter 340 value 44.778270
## iter 350 value 44.521485
## iter 360 value 44.467787
## iter 370 value 44.252315
## iter 380 value 44.247991
## iter 390 value 44.244082
## iter 400 value 44.235465
## iter 410 value 44.191714
## iter 420 value 44.106948
## iter 430 value 44.050507
## iter 440 value 44.043677
## iter 450 value 44.043079
## final value 44.043030
## converged
## # weights: 66
## initial value 884.683957
## iter 10 value 77.263060
## iter 20 value 75.931200
## iter 30 value 73.819503
## iter 40 value 69.637874
## iter 50 value 66.852656
## iter 60 value 65.027311
## iter 70 value 64.838156
## iter 80 value 64.696440
## iter 90 value 64.230636
## iter 100 value 63.890993
## iter 110 value 63.776805
## iter 120 value 63.727605
## iter 130 value 63.600980
## iter 140 value 63.583718
## iter 140 value 63.583718
## iter 140 value 63.583718
## final value 63.583718
## converged
## # weights: 66
## initial value 4351.875542
## iter 10 value 77.074409
## final value 77.074284
## converged
## # weights: 66
## initial value 2802.769655
## iter 10 value 78.264488
## iter 20 value 78.012326
## iter 30 value 75.867638
## iter 40 value 74.085554
## iter 50 value 73.999451
## iter 60 value 73.977543
## iter 70 value 73.952236
## iter 80 value 71.545473
## iter 90 value 66.549282
## iter 100 value 58.595227
## iter 110 value 55.819012
## iter 120 value 54.356884
## iter 130 value 54.197763
## iter 140 value 53.747379
## iter 150 value 53.344381
## iter 160 value 52.969479
## iter 170 value 52.938633
## iter 180 value 52.933926
## iter 190 value 52.926730
## iter 200 value 52.886068
## iter 210 value 52.256293
## iter 220 value 52.071055
## iter 230 value 52.011176
## iter 240 value 51.960729
## iter 250 value 51.920439
## iter 260 value 51.894225
## iter 270 value 51.865971
## iter 280 value 51.858971
## iter 290 value 51.847213
## iter 300 value 51.841966
## iter 310 value 51.837576
## iter 320 value 51.833597
## iter 330 value 51.829651
## iter 340 value 51.780209
## iter 350 value 51.055547
## iter 360 value 50.531016
## iter 370 value 49.299100
## iter 380 value 48.248685
## iter 390 value 47.522598
## iter 400 value 46.219857
## iter 410 value 45.674461
## iter 420 value 45.456040
## iter 430 value 45.377351
## iter 440 value 45.361423
## iter 450 value 45.351810
## iter 460 value 45.351582
## iter 470 value 45.350917
## iter 480 value 45.348240
## iter 490 value 45.344258
## iter 500 value 45.339635
## iter 510 value 45.337142
## iter 520 value 45.336905
## iter 530 value 45.336507
## iter 540 value 45.332266
## iter 550 value 45.324549
## iter 560 value 45.323170
## iter 570 value 45.322643
## final value 45.322600
## converged
## # weights: 66
## initial value 81.843900
## iter 10 value 78.159740
## iter 20 value 78.129347
## iter 30 value 74.922014
## iter 40 value 74.392872
## iter 50 value 74.313384
## iter 60 value 73.270373
## iter 70 value 72.868180
## iter 80 value 72.758081
## iter 90 value 72.252152
## iter 100 value 72.086801
## iter 110 value 72.044503
## iter 120 value 72.010932
## iter 130 value 71.508920
## iter 140 value 71.108093
## iter 150 value 70.909447
## iter 160 value 70.174824
## iter 170 value 69.374265
## iter 180 value 67.819625
## iter 190 value 67.656781
## iter 200 value 67.436519
## iter 210 value 66.333001
## iter 220 value 64.647341
## iter 230 value 63.927316
## iter 240 value 60.836404
## iter 250 value 60.183104
## iter 260 value 58.860621
## iter 270 value 58.415322
## iter 280 value 58.079099
## iter 290 value 57.506382
## iter 300 value 55.762381
## iter 310 value 53.080213
## iter 320 value 51.459581
## iter 330 value 50.745761
## iter 340 value 50.435018
## iter 350 value 50.322754
## iter 360 value 50.234461
## iter 370 value 50.214082
## iter 380 value 50.060995
## iter 390 value 49.584673
## iter 400 value 49.274197
## iter 410 value 49.061066
## iter 420 value 48.988494
## iter 430 value 48.963872
## iter 440 value 48.897019
## iter 450 value 48.826625
## iter 460 value 48.750171
## iter 470 value 48.659154
## iter 480 value 48.580383
## iter 490 value 48.549410
## iter 500 value 48.506808
## iter 510 value 48.470295
## iter 520 value 48.365610
## iter 530 value 48.178894
## iter 540 value 48.138053
## iter 550 value 48.125035
## iter 560 value 48.122657
## iter 570 value 48.115991
## iter 580 value 48.106096
## iter 590 value 48.099578
## iter 600 value 48.086570
## iter 610 value 48.084285
## iter 620 value 48.083524
## iter 630 value 48.083277
## final value 48.083227
## converged
## # weights: 66
## initial value 2571.998993
## iter 10 value 82.023842
## iter 20 value 81.584959
## iter 30 value 80.748826
## iter 40 value 79.644376
## iter 50 value 78.197921
## iter 60 value 77.812244
## iter 70 value 77.531706
## iter 80 value 77.334156
## iter 90 value 77.308711
## iter 100 value 77.299799
## iter 110 value 77.294851
## iter 120 value 77.247736
## iter 130 value 76.941197
## iter 140 value 76.701626
## iter 150 value 76.358113
## iter 160 value 75.486169
## iter 170 value 74.736483
## iter 180 value 74.697253
## iter 190 value 74.533091
## iter 200 value 74.211847
## iter 210 value 73.458925
## iter 220 value 72.840677
## iter 230 value 72.815186
## iter 240 value 72.806036
## iter 250 value 72.800175
## iter 260 value 72.797756
## iter 270 value 72.797387
## iter 280 value 72.795483
## iter 290 value 72.482475
## iter 300 value 71.302540
## iter 310 value 68.895989
## iter 320 value 68.070204
## iter 330 value 67.225994
## iter 340 value 67.021101
## iter 350 value 66.680022
## iter 360 value 66.290258
## iter 370 value 66.018402
## iter 380 value 65.970369
## iter 390 value 65.952972
## iter 400 value 65.605576
## iter 410 value 65.352128
## iter 420 value 65.158049
## iter 430 value 64.953816
## iter 440 value 64.676205
## iter 450 value 64.413263
## iter 460 value 64.352484
## iter 470 value 64.339632
## iter 480 value 64.335979
## iter 490 value 64.331234
## iter 500 value 64.328814
## iter 510 value 64.325563
## iter 520 value 64.276318
## iter 530 value 64.228514
## iter 540 value 63.988720
## iter 550 value 63.878040
## iter 560 value 63.855148
## iter 570 value 63.835264
## iter 580 value 63.829059
## iter 590 value 63.825802
## iter 600 value 63.816805
## iter 610 value 63.783052
## iter 620 value 63.780214
## iter 630 value 63.779041
## iter 640 value 63.778923
## final value 63.778889
## converged
## Borrowing money ( 15363.49 ) for closing a short position (PosID= 71 )
## # weights: 66
## initial value 947.418142
## iter 10 value 73.246081
## iter 20 value 72.320132
## iter 30 value 70.500662
## iter 40 value 68.473365
## iter 50 value 68.084405
## iter 60 value 67.871130
## iter 70 value 67.435012
## iter 80 value 66.511829
## iter 90 value 65.385685
## iter 100 value 63.885894
## iter 110 value 63.203133
## iter 120 value 62.797550
## iter 130 value 61.970989
## iter 140 value 61.647927
## iter 150 value 61.593753
## iter 160 value 61.580708
## iter 170 value 61.580628
## final value 61.580620
## converged
## # weights: 66
## initial value 2512.729722
## iter 10 value 73.064086
## iter 20 value 71.420347
## iter 30 value 69.613681
## iter 40 value 67.849252
## iter 50 value 67.191460
## iter 60 value 67.102717
## iter 70 value 66.848065
## iter 80 value 66.471427
## iter 90 value 65.451662
## iter 100 value 63.845851
## iter 110 value 63.049076
## iter 120 value 62.623555
## iter 130 value 62.122252
## iter 140 value 60.776532
## iter 150 value 60.153923
## iter 160 value 59.836994
## iter 170 value 59.195226
## iter 180 value 58.045855
## iter 190 value 57.371994
## iter 200 value 57.311262
## iter 210 value 57.292664
## iter 220 value 57.282927
## iter 230 value 57.282295
## final value 57.282293
## converged
## # weights: 66
## initial value 431.935531
## iter 10 value 73.614792
## iter 20 value 72.857094
## iter 30 value 70.339128
## iter 40 value 66.355488
## iter 50 value 65.514841
## iter 60 value 64.542044
## iter 70 value 63.217216
## iter 80 value 62.332877
## iter 90 value 62.011630
## iter 100 value 60.911958
## iter 110 value 60.161451
## iter 120 value 59.673925
## iter 130 value 59.459888
## iter 140 value 59.049592
## iter 150 value 58.094608
## iter 160 value 57.988679
## iter 170 value 57.977926
## final value 57.977851
## converged
## # weights: 66
## initial value 337.263958
## iter 10 value 72.767471
## iter 20 value 71.002933
## iter 30 value 68.516466
## iter 40 value 68.400692
## iter 50 value 68.013324
## iter 60 value 67.018488
## iter 70 value 65.897609
## iter 80 value 65.548551
## iter 90 value 65.468765
## iter 100 value 65.458212
## iter 110 value 65.457993
## iter 110 value 65.457993
## iter 110 value 65.457993
## final value 65.457993
## converged
## # weights: 66
## initial value 85.489071
## iter 10 value 74.477918
## iter 20 value 71.736115
## iter 30 value 70.074960
## iter 40 value 68.530874
## iter 50 value 68.272542
## iter 60 value 66.843040
## iter 70 value 65.197391
## iter 80 value 64.133304
## iter 90 value 63.971439
## iter 100 value 63.652358
## iter 110 value 63.378433
## iter 120 value 63.317907
## iter 130 value 63.317446
## iter 130 value 63.317445
## iter 130 value 63.317445
## final value 63.317445
## converged
## # weights: 66
## initial value 2582.432728
## iter 10 value 77.104618
## iter 20 value 77.050005
## iter 30 value 77.048967
## iter 40 value 76.839353
## iter 50 value 76.476594
## iter 60 value 75.795585
## iter 70 value 74.289790
## iter 80 value 72.688153
## iter 90 value 69.800597
## iter 100 value 68.422855
## iter 110 value 68.316642
## iter 120 value 68.277181
## iter 130 value 68.238279
## iter 140 value 67.942605
## iter 150 value 67.738207
## iter 160 value 67.679300
## iter 170 value 67.513202
## iter 180 value 67.157700
## iter 190 value 66.584743
## iter 200 value 66.039763
## iter 210 value 65.463859
## iter 220 value 64.867967
## iter 230 value 64.659525
## iter 240 value 64.594689
## iter 250 value 64.397971
## iter 260 value 64.114556
## iter 270 value 63.994994
## iter 280 value 63.930147
## iter 290 value 63.699997
## iter 300 value 63.683741
## iter 310 value 63.682431
## iter 320 value 63.679799
## iter 330 value 63.678195
## iter 340 value 63.677865
## final value 63.677862
## converged
## # weights: 66
## initial value 951.750277
## iter 10 value 80.765015
## iter 20 value 79.600549
## iter 30 value 78.000531
## iter 40 value 75.879837
## iter 50 value 73.761870
## iter 60 value 72.685158
## iter 70 value 72.410792
## iter 80 value 72.365044
## iter 90 value 72.131503
## iter 100 value 71.780373
## iter 110 value 71.645065
## iter 120 value 71.189308
## iter 130 value 70.318131
## iter 140 value 69.583789
## iter 150 value 69.175113
## iter 160 value 68.249976
## iter 170 value 67.221564
## iter 180 value 66.989988
## iter 190 value 66.668702
## iter 200 value 66.248328
## iter 210 value 66.132977
## iter 220 value 66.112051
## iter 230 value 66.100251
## final value 66.098252
## converged
## # weights: 66
## initial value 5070.337413
## iter 10 value 101.318489
## iter 20 value 101.012630
## iter 30 value 100.291292
## iter 40 value 99.110139
## iter 50 value 97.962778
## iter 60 value 97.098253
## iter 70 value 94.188136
## iter 80 value 93.061666
## iter 90 value 89.927383
## iter 100 value 86.359722
## iter 110 value 84.924760
## iter 120 value 83.074000
## iter 130 value 81.617927
## iter 140 value 79.726544
## iter 150 value 78.400768
## iter 160 value 77.816948
## iter 170 value 77.585605
## iter 180 value 77.456262
## iter 190 value 77.359187
## iter 200 value 76.932075
## iter 210 value 76.013254
## iter 220 value 75.924742
## iter 230 value 75.809760
## iter 240 value 75.774143
## iter 250 value 75.671104
## iter 260 value 75.478346
## iter 270 value 75.254825
## iter 280 value 75.176371
## final value 75.176147
## converged
## # weights: 66
## initial value 154.265995
## iter 10 value 121.637383
## iter 20 value 120.859374
## iter 30 value 118.581576
## iter 40 value 111.577550
## iter 50 value 106.735733
## iter 60 value 103.432254
## iter 70 value 100.902882
## iter 80 value 98.928596
## iter 90 value 98.866381
## iter 100 value 98.638290
## iter 110 value 97.877345
## iter 120 value 97.265852
## iter 130 value 96.413014
## iter 140 value 95.727120
## iter 150 value 94.086944
## iter 160 value 90.777463
## iter 170 value 87.888403
## iter 180 value 86.748578
## iter 190 value 86.526600
## iter 200 value 86.494881
## iter 210 value 86.483701
## iter 220 value 86.482546
## iter 230 value 86.482471
## iter 240 value 86.482437
## iter 240 value 86.482437
## iter 240 value 86.482437
## final value 86.482437
## converged
## # weights: 66
## initial value 706.353903
## iter 10 value 126.814199
## iter 20 value 125.700272
## iter 30 value 124.075104
## iter 40 value 123.657312
## iter 50 value 122.361043
## iter 60 value 120.681683
## iter 70 value 118.416814
## iter 80 value 117.835057
## iter 90 value 117.360092
## iter 100 value 117.040720
## iter 110 value 115.740071
## iter 120 value 113.815174
## iter 130 value 112.082007
## iter 140 value 111.338623
## iter 150 value 110.416474
## iter 160 value 110.129657
## iter 170 value 109.935819
## iter 180 value 109.737876
## iter 190 value 109.347132
## iter 200 value 108.647361
## iter 210 value 108.196139
## iter 220 value 108.172971
## final value 108.172257
## converged
## # weights: 66
## initial value 3335.385212
## iter 10 value 128.171832
## iter 20 value 127.544619
## iter 30 value 122.970773
## iter 40 value 120.136376
## iter 50 value 119.166233
## iter 60 value 118.364526
## iter 70 value 117.697819
## iter 80 value 116.962614
## iter 90 value 115.588555
## iter 100 value 113.037218
## iter 110 value 110.730410
## iter 120 value 109.527601
## iter 130 value 109.193299
## iter 140 value 108.783705
## iter 150 value 108.260520
## iter 160 value 107.564955
## iter 170 value 106.525740
## iter 180 value 104.537593
## iter 190 value 104.327239
## iter 200 value 103.615426
## iter 210 value 102.971375
## iter 220 value 100.380704
## iter 230 value 96.897902
## iter 240 value 94.405607
## iter 250 value 93.514234
## iter 260 value 93.042566
## iter 270 value 92.584854
## iter 280 value 92.078400
## iter 290 value 91.958059
## iter 300 value 91.848799
## iter 310 value 91.591284
## iter 320 value 91.493174
## iter 330 value 91.458654
## iter 340 value 91.446752
## iter 350 value 91.428267
## iter 360 value 91.376376
## iter 370 value 91.340967
## final value 91.340964
## converged
## # weights: 66
## initial value 4636.158155
## iter 10 value 133.667400
## iter 20 value 133.163641
## iter 30 value 128.381043
## iter 40 value 127.237243
## iter 50 value 124.343557
## iter 60 value 123.272601
## iter 70 value 122.440777
## iter 80 value 120.116428
## iter 90 value 119.139184
## iter 100 value 117.725393
## iter 110 value 116.428684
## iter 120 value 115.246504
## iter 130 value 111.881692
## iter 140 value 111.508756
## iter 150 value 111.376699
## iter 160 value 111.281870
## iter 170 value 110.799273
## iter 180 value 109.439612
## iter 190 value 108.775107
## iter 200 value 108.436662
## iter 210 value 108.192573
## iter 220 value 107.832391
## iter 230 value 107.498457
## final value 107.495807
## converged
## # weights: 66
## initial value 843.451900
## iter 10 value 134.506344
## iter 20 value 132.978941
## iter 30 value 127.393741
## iter 40 value 125.449290
## iter 50 value 123.609455
## iter 60 value 122.877052
## iter 70 value 121.230662
## iter 80 value 119.542243
## iter 90 value 118.443708
## iter 100 value 117.365310
## iter 110 value 114.305845
## iter 120 value 112.986069
## iter 130 value 112.584880
## iter 140 value 108.201805
## iter 150 value 106.723022
## iter 160 value 105.749731
## iter 170 value 105.652227
## iter 180 value 105.170432
## iter 190 value 104.778629
## iter 200 value 104.253907
## iter 210 value 103.520823
## iter 220 value 103.319160
## iter 230 value 103.255538
## iter 240 value 103.211679
## iter 250 value 103.074224
## iter 260 value 102.998698
## iter 270 value 102.860951
## iter 280 value 102.441043
## iter 290 value 102.378941
## iter 300 value 102.370138
## final value 102.370107
## converged
## # weights: 66
## initial value 1218.899065
## iter 10 value 141.314236
## iter 20 value 140.607630
## iter 30 value 139.126195
## iter 40 value 135.623065
## iter 50 value 133.547124
## iter 60 value 126.772153
## iter 70 value 120.249390
## iter 80 value 116.811906
## iter 90 value 116.237770
## iter 100 value 114.943703
## iter 110 value 114.038468
## iter 120 value 112.636377
## iter 130 value 110.055558
## iter 140 value 108.334212
## iter 150 value 107.858823
## iter 160 value 107.628423
## iter 170 value 107.587427
## iter 180 value 107.548309
## iter 190 value 107.543142
## final value 107.543047
## converged
## # weights: 66
## initial value 4847.591249
## iter 10 value 144.975600
## iter 20 value 143.103783
## iter 30 value 138.719063
## iter 40 value 136.074749
## iter 50 value 135.722075
## iter 60 value 134.094888
## iter 70 value 131.066057
## iter 80 value 127.253846
## iter 90 value 125.351137
## iter 100 value 122.598004
## iter 110 value 120.433307
## iter 120 value 119.076013
## iter 130 value 118.288075
## iter 140 value 117.667496
## iter 150 value 116.821914
## iter 160 value 116.529447
## iter 170 value 116.355544
## iter 180 value 116.256182
## iter 190 value 116.196364
## iter 200 value 116.183489
## iter 210 value 116.178086
## iter 220 value 116.169659
## iter 230 value 116.158385
## iter 240 value 116.151541
## final value 116.151025
## converged
## # weights: 66
## initial value 12632.615064
## iter 10 value 146.355288
## iter 20 value 141.740484
## iter 30 value 137.754788
## iter 40 value 136.430517
## iter 50 value 136.070214
## iter 60 value 135.752015
## iter 70 value 135.580201
## iter 80 value 135.564133
## iter 90 value 135.556953
## iter 100 value 135.552362
## iter 110 value 135.550444
## iter 120 value 135.550074
## iter 130 value 135.547326
## iter 140 value 135.547033
## iter 150 value 135.532510
## iter 160 value 135.500423
## iter 170 value 135.498746
## final value 135.498734
## converged
## # weights: 66
## initial value 400.632710
## iter 10 value 147.113724
## iter 20 value 146.766323
## iter 30 value 145.437703
## iter 40 value 142.653859
## iter 50 value 141.390753
## iter 60 value 141.192472
## iter 70 value 140.731751
## iter 80 value 138.245162
## iter 90 value 135.195540
## iter 100 value 133.311945
## iter 110 value 130.898486
## iter 120 value 130.073202
## iter 130 value 129.788622
## iter 140 value 129.672620
## iter 150 value 129.625985
## final value 129.625857
## converged
## # weights: 66
## initial value 1348.273967
## iter 10 value 147.545314
## iter 20 value 147.062228
## iter 30 value 144.998029
## iter 40 value 142.330947
## iter 50 value 138.420938
## iter 60 value 136.979691
## iter 70 value 135.461420
## iter 80 value 133.797280
## iter 90 value 133.224116
## iter 100 value 132.607619
## iter 110 value 131.708692
## iter 120 value 129.931402
## iter 130 value 127.599811
## iter 140 value 125.902975
## iter 150 value 125.144359
## iter 160 value 124.891075
## iter 170 value 124.831099
## iter 180 value 124.802292
## iter 190 value 124.771066
## iter 200 value 124.751542
## iter 210 value 124.718300
## iter 220 value 124.648553
## iter 230 value 124.406162
## iter 240 value 123.693391
## iter 250 value 118.204345
## iter 260 value 116.351700
## iter 270 value 116.001498
## iter 280 value 115.826937
## iter 290 value 115.700899
## iter 300 value 115.282989
## iter 310 value 115.138086
## iter 320 value 115.130772
## iter 330 value 115.129589
## iter 340 value 115.129032
## iter 350 value 115.127403
## iter 360 value 115.126368
## final value 115.126319
## converged
## # weights: 66
## initial value 1024.937787
## iter 10 value 149.081992
## iter 20 value 147.847792
## iter 30 value 147.236905
## iter 40 value 142.529508
## iter 50 value 136.424195
## iter 60 value 130.095476
## iter 70 value 127.482019
## iter 80 value 125.546404
## iter 90 value 125.424473
## iter 100 value 125.155362
## iter 110 value 125.073638
## iter 120 value 124.982119
## iter 130 value 124.962777
## iter 140 value 124.909214
## iter 150 value 124.905396
## iter 160 value 124.734996
## iter 170 value 124.099081
## iter 180 value 123.549550
## iter 190 value 123.221528
## iter 200 value 120.915994
## iter 210 value 120.192608
## iter 220 value 120.015056
## iter 230 value 119.837022
## iter 240 value 119.288770
## iter 250 value 118.939331
## iter 260 value 118.559136
## iter 270 value 118.529882
## final value 118.529387
## converged
## # weights: 66
## initial value 683.405427
## iter 10 value 149.002419
## iter 20 value 147.523306
## iter 30 value 145.509883
## iter 40 value 139.646853
## iter 50 value 138.870721
## iter 60 value 138.354436
## iter 70 value 137.142236
## iter 80 value 136.080988
## iter 90 value 135.087428
## iter 100 value 134.668451
## iter 110 value 134.609625
## iter 120 value 134.586462
## iter 130 value 134.585323
## iter 140 value 134.585253
## iter 150 value 134.585078
## final value 134.585042
## converged
## # weights: 66
## initial value 2337.883405
## iter 10 value 150.985824
## iter 20 value 150.198742
## iter 30 value 144.607715
## iter 40 value 143.329812
## iter 50 value 142.595181
## iter 60 value 142.494981
## iter 70 value 142.176027
## iter 80 value 141.627813
## iter 90 value 141.481998
## iter 100 value 141.171616
## iter 110 value 140.966304
## iter 120 value 140.089717
## iter 130 value 139.827292
## iter 140 value 139.666537
## iter 150 value 137.505220
## iter 160 value 136.620520
## iter 170 value 135.796596
## iter 180 value 135.023873
## iter 190 value 134.869989
## iter 200 value 134.831297
## iter 210 value 134.689232
## iter 220 value 134.657595
## iter 230 value 134.657490
## final value 134.657487
## converged
## # weights: 66
## initial value 1894.574298
## iter 10 value 151.652263
## iter 20 value 148.004689
## iter 30 value 142.978684
## iter 40 value 142.589710
## iter 50 value 141.523074
## iter 60 value 139.634495
## iter 70 value 137.888393
## iter 80 value 133.682380
## iter 90 value 133.244264
## iter 100 value 133.020141
## iter 110 value 132.256683
## iter 120 value 131.780782
## iter 130 value 130.718945
## iter 140 value 129.810254
## iter 150 value 129.714677
## iter 160 value 129.653276
## iter 170 value 129.614631
## iter 180 value 129.614054
## final value 129.614046
## converged
## # weights: 66
## initial value 9045.868436
## iter 10 value 155.375449
## iter 20 value 154.132375
## iter 30 value 151.121222
## iter 40 value 146.572837
## iter 50 value 145.052174
## iter 60 value 142.293025
## iter 70 value 140.313211
## iter 80 value 138.693404
## iter 90 value 136.768501
## iter 100 value 136.084325
## iter 110 value 135.312471
## iter 120 value 134.164202
## iter 130 value 133.874994
## iter 140 value 133.714845
## iter 150 value 133.614358
## iter 160 value 133.600532
## iter 170 value 133.561668
## iter 180 value 133.477369
## iter 190 value 133.402633
## iter 200 value 133.360580
## iter 210 value 133.357716
## iter 210 value 133.357715
## iter 210 value 133.357715
## final value 133.357715
## converged
## Borrowing money ( 141833.6 ) for closing a short position (PosID= 4 )
## Borrowing money ( 316323.5 ) for closing a short position (PosID= 5 )
## Borrowing money ( 9254.784 ) for closing a short position (PosID= 10 )
## Borrowing money ( 120450.6 ) for closing a short position (PosID= 8 )
## Borrowing money ( 294403.7 ) for closing a short position (PosID= 7 )
## Borrowing money ( 469186.2 ) for closing a short position (PosID= 3 )
## Borrowing money ( 85769.52 ) for closing a short position (PosID= 9 )
## Borrowing money ( 259329.4 ) for closing a short position (PosID= 6 )
## Borrowing money ( 178721.1 ) for closing a short position (PosID= 16 )
## Borrowing money ( 21933.13 ) for closing a short position (PosID= 13 )
## Borrowing money ( 25252.54 ) for closing a short position (PosID= 456 )
## Borrowing money ( 211600.6 ) for closing a short position (PosID= 457 )
## Borrowing money ( 397181 ) for closing a short position (PosID= 458 )
## Borrowing money ( 153453.4 ) for closing a short position (PosID= 463 )
## Borrowing money ( 284454.8 ) for closing a short position (PosID= 449 )
## Borrowing money ( 415330.9 ) for closing a short position (PosID= 450 )
## Borrowing money ( 547002.7 ) for closing a short position (PosID= 451 )
## Borrowing money ( 676938.9 ) for closing a short position (PosID= 452 )
## Borrowing money ( 839215.5 ) for closing a short position (PosID= 455 )
## Borrowing money ( 241906.8 ) for closing a short position (PosID= 459 )
## Borrowing money ( 142576.3 ) for closing a short position (PosID= 556 )results <- t(as.data.frame(results))results[, c("NTrades","Ret","RetOverBH","PercProf","MaxDD")]## NTrades Ret RetOverBH PercProf MaxDD
## nnetRegr.v213 437 -23.93 -75.41 57.67 456315.4
## nnetRegr.v175 439 -10.73 -62.21 57.86 348273.6
## nnetRegr.v203 857 -2.75 -54.23 53.91 1412253.1
## nnetRegr.v200 672 -59.86 -111.34 47.47 880798.0
## svmRegr.v168 1473 -37.68 -89.16 50.51 576525.1
## svmRegr.v204 998 -79.79 -131.27 50.00 1067874.4
## nnetRegr.v169 555 -15.24 -66.72 46.85 377444.2
## nnetRegr.v167 1052 16.79 -34.69 54.09 416527.9
## nnetRegr.v179 660 -17.34 -68.83 58.79 415319.2getWorkflow("nnetRegr.v203", analysisSet)## Workflow Object:
## Workflow ID :: nnetRegr.v203
## Workflow Function :: tradingWF
## Parameter values:
## learner.pars -> linout=TRUE maxit=750 size=5 decay=0.1
## policy.pars -> bet=0.5 exp.prof=0.05 max.loss=0.05
## quotes -> GSPC
## learner -> nnet
## pred.target -> indicator
## learn.test.type -> fixed
## relearn.step -> 120
## policy -> policy.2
## b.t -> 0.01
## s.t -> -0.05date <- rownames(Tdata.eval)[1]
market <- GSPC[paste(date, "/", sep = "")][1:nrow(Tdata.eval), ]
plot(wfsOut[["nnetRegr.v203"]]$tradeRec, market,
theme = "white", name = "SP500 - final test")
## Rentability = -2.749164 %#install.packages('PerformanceAnalytics')
library(PerformanceAnalytics)##
## Attaching package: 'PerformanceAnalytics'## The following objects are masked from 'package:e1071':
##
## kurtosis, skewness## The following object is masked from 'package:graphics':
##
## legendequityWF <- as.xts(wfsOut[["nnetRegr.v203"]]$tradeRec@trading$Equity)
rets <- Return.calculate(equityWF)chart.CumReturns(rets, main="Cumulative returns of the strategy", ylab="returns")
yearlyReturn(equityWF)## yearly.returns
## 2006-12-29 0.06591957
## 2007-12-31 0.52067106
## 2008-12-31 -0.06119586
## 2009-12-31 0.20754778
## 2010-12-31 0.04919365
## 2011-12-30 -0.23756113
## 2012-12-31 -0.29791690
## 2013-12-31 0.25324294
## 2014-12-31 -0.30555804
## 2015-12-31 0.10831161
## 2016-01-08 -0.02305075plot(100*yearlyReturn(equityWF),
main='Yearly percentage returns of the trading system')
table.DownsideRisk(rets)ex.model <- specifyModel(T.ind(IBM) ~ Delt(Cl(IBM), k = 1:3))
data <- modelData(ex.model, data.window = c("2009-01-01", "2009-08-10"))