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 5158355 19 34
## 2: 1 100011 1 1 0 5158508 27 6
## 3: 1 100011 1 1 0 5158356 7 32
## 4: 1 100011 1 1 0 5158359 17 24
## 5: 1 100011 1 1 0 5158404 9 18
## 6: 1 100011 1 1 0 5158585 8 22
## 7: 1 100011 1 1 0 5158597 9 22
## 8: 1 100011 1 1 0 5158458 10 25
## 9: 1 100011 1 1 0 5158520 10 22
## 10: 1 100011 1 1 0 5158566 12 14
## Filled Calculated2 Total
## 1: 0 0 53
## 2: 0 13 46
## 3: 0 4 43
## 4: 0 0 41
## 5: 1 13 41
## 6: 0 11 41
## 7: 0 8 39
## 8: 0 3 38
## 9: 5 1 38
## 10: 12 0 38 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 5158508 27 6
## 2: 1 100011 1 1 0 5158355 19 34
## 3: 1 100011 1 1 0 5158302 17 0
## 4: 1 100011 1 1 0 5158330 17 11
## 5: 1 100011 1 1 0 5158359 17 24
## 6: 1 100011 1 1 0 5158399 17 10
## 7: 1 100011 1 1 0 5158317 16 0
## 8: 1 100011 1 1 0 5158599 15 8
## 9: 1 100011 1 1 0 5158328 14 0
## 10: 1 100011 1 1 0 5158354 14 6
## Filled Calculated2 Total
## 1: 0 13 46
## 2: 0 0 53
## 3: 0 1 18
## 4: 0 4 32
## 5: 0 0 41
## 6: 0 5 32
## 7: 0 0 16
## 8: 0 4 27
## 9: 1 0 15
## 10: 0 2 22 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 5158355 19 34
## 2: 1 100011 1 1 0 5158356 7 32
## 3: 1 100011 1 1 0 5158458 10 25
## 4: 1 100011 1 1 0 5158359 17 24
## 5: 1 100011 1 1 0 5158541 4 24
## 6: 1 100011 1 1 0 5158520 10 22
## 7: 1 100011 1 1 0 5158563 6 22
## 8: 1 100011 1 1 0 5158585 8 22
## 9: 1 100011 1 1 0 5158597 9 22
## 10: 1 100011 1 1 0 5158368 12 20
## Filled Calculated2 Total
## 1: 0 0 53
## 2: 0 4 43
## 3: 0 3 38
## 4: 0 0 41
## 5: 4 2 34
## 6: 5 1 38
## 7: 0 5 33
## 8: 0 11 41
## 9: 0 8 39
## 10: 1 3 36 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 5158361 8 10
## 2: 1 100011 1 1 0 5158566 12 14
## 3: 1 100011 1 1 0 5158326 6 8
## 4: 1 100011 1 1 0 5158493 7 19
## 5: 1 100011 1 1 0 5158520 10 22
## 6: 1 100011 1 1 0 5158391 5 15
## 7: 1 100011 1 1 0 5158437 6 20
## 8: 1 100011 1 1 0 5158534 4 6
## 9: 1 100011 1 1 0 5158541 4 24
## 10: 1 100011 1 1 0 5158568 5 12
## Filled Calculated2 Total
## 1: 13 0 31
## 2: 12 0 38
## 3: 6 1 21
## 4: 5 0 31
## 5: 5 1 38
## 6: 4 2 26
## 7: 4 0 30
## 8: 4 0 14
## 9: 4 2 34
## 10: 4 0 21 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
## 0.36914198 0.48108330 0.01556654 0.13420818Summary 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')