Analyse of TradePerformance
Andrei Pazniak
February 3, 2016
## Loading required package: data.table## Loading required package: ggplot2Number of executed orders by account and ReqPerSec
table(d$Account, d$OPS_Req, d$TestNumber)## , , = 1
##
##
## 1
## 100011 300 print("Top longest execution") ## [1] "Top longest execution" print(d[order(-Total)][1:10,])## TestNumber Account OPS_Req OPS_Mean OPS_Sd Order New Calculated1
## 1: 1 100011 1 1 0 5166002 603 391
## 2: 1 100011 1 1 0 5166003 114 5
## 3: 1 100011 1 1 0 5166220 30 20
## 4: 1 100011 1 1 0 5166198 21 9
## 5: 1 100011 1 1 0 5166081 8 31
## 6: 1 100011 1 1 0 5166025 17 6
## 7: 1 100011 1 1 0 5166163 12 19
## 8: 1 100011 1 1 0 5166004 25 12
## 9: 1 100011 1 1 0 5166010 19 10
## 10: 1 100011 1 1 0 5166101 19 12
## Filled Calculated2 Total
## 1: 23 11 1028
## 2: 0 1 120
## 3: 0 0 50
## 4: 0 11 41
## 5: 1 0 40
## 6: 0 15 38
## 7: 7 0 38
## 8: 0 0 37
## 9: 3 4 36
## 10: 0 5 36 print("Worst New execution") ## [1] "Worst New execution" print(d[order(-New)][1:10,])## TestNumber Account OPS_Req OPS_Mean OPS_Sd Order New Calculated1
## 1: 1 100011 1 1 0 5166002 603 391
## 2: 1 100011 1 1 0 5166003 114 5
## 3: 1 100011 1 1 0 5166220 30 20
## 4: 1 100011 1 1 0 5166004 25 12
## 5: 1 100011 1 1 0 5166154 21 6
## 6: 1 100011 1 1 0 5166198 21 9
## 7: 1 100011 1 1 0 5166191 20 7
## 8: 1 100011 1 1 0 5166010 19 10
## 9: 1 100011 1 1 0 5166101 19 12
## 10: 1 100011 1 1 0 5166025 17 6
## Filled Calculated2 Total
## 1: 23 11 1028
## 2: 0 1 120
## 3: 0 0 50
## 4: 0 0 37
## 5: 0 0 27
## 6: 0 11 41
## 7: 0 1 28
## 8: 3 4 36
## 9: 0 5 36
## 10: 0 15 38 print("Worst Calculated1 execution") ## [1] "Worst Calculated1 execution" print(d[order(-Calculated1)][1:10,])## TestNumber Account OPS_Req OPS_Mean OPS_Sd Order New Calculated1
## 1: 1 100011 1 1 0 5166002 603 391
## 2: 1 100011 1 1 0 5166081 8 31
## 3: 1 100011 1 1 0 5166269 5 28
## 4: 1 100011 1 1 0 5166170 9 27
## 5: 1 100011 1 1 0 5166211 6 22
## 6: 1 100011 1 1 0 5166059 3 21
## 7: 1 100011 1 1 0 5166220 30 20
## 8: 1 100011 1 1 0 5166257 10 20
## 9: 1 100011 1 1 0 5166163 12 19
## 10: 1 100011 1 1 0 5166016 7 18
## Filled Calculated2 Total
## 1: 23 11 1028
## 2: 1 0 40
## 3: 0 0 33
## 4: 0 0 36
## 5: 0 0 28
## 6: 1 1 26
## 7: 0 0 50
## 8: 0 0 30
## 9: 7 0 38
## 10: 0 0 25 print("Worst Filled execution") ## [1] "Worst Filled execution" print(d[order(-Filled)][1:10,])## TestNumber Account OPS_Req OPS_Mean OPS_Sd Order New Calculated1
## 1: 1 100011 1 1 0 5166002 603 391
## 2: 1 100011 1 1 0 5166151 14 5
## 3: 1 100011 1 1 0 5166163 12 19
## 4: 1 100011 1 1 0 5166049 8 7
## 5: 1 100011 1 1 0 5166116 9 10
## 6: 1 100011 1 1 0 5166113 9 18
## 7: 1 100011 1 1 0 5166047 5 9
## 8: 1 100011 1 1 0 5166219 7 10
## 9: 1 100011 1 1 0 5166010 19 10
## 10: 1 100011 1 1 0 5166182 5 8
## Filled Calculated2 Total
## 1: 23 11 1028
## 2: 10 0 29
## 3: 7 0 38
## 4: 6 4 25
## 5: 6 0 25
## 6: 5 0 32
## 7: 4 0 18
## 8: 4 0 21
## 9: 3 4 36
## 10: 3 2 18 print("Proprtion delay for each execution staqge")## [1] "Proprtion delay for each execution staqge" print ( apply( apply(d[,7:10, with=F], 1, prop.table), 1, mean) )## New Calculated1 Filled Calculated2
## 0.37987094 0.48214865 0.01396809 0.12401232Summary execution time ~ requested orders per second
d[,OPS_Req:=OPS_Req * TestNumber]
d[,OPS_Mean:=OPS_Mean * TestNumber]
si<-rbind( tapply(d$Total, d$OPS_Req, length),
tapply(d$Total, d$OPS_Req, min),
tapply(d$Total, d$OPS_Req, mean),
tapply(d$Total, d$OPS_Req, median),
tapply(d$Total, d$OPS_Req, max),
tapply(d$Total, d$OPS_Req, sd))
rownames(si)<-c("length", "min", "mean", "median", "max", "sd")
si<-round(t(si))
ggplot(melt(si), aes(Var1, value)) + geom_point() + facet_grid(Var2 ~ ., scales = "free") + xlab("Requested Orders Per Second")
Scaling
ggplot( d, aes(x=OPS_Req, y=OPS_Mean)) + geom_line() + geom_abline(intercept = 0, slope = 1, size=2, color='red')