Chapter 12 Analyzing Results

portfolio.st <- "Port.Luxor"
account.st <- "Acct.Luxor"
strategy.st <- "Strat.Luxor"

cwd <- getwd()
setwd("./_data/")
load.strategy(strategy.st)
setwd(cwd)

rm.strat(portfolio.st)
rm.strat(account.st)

initPortf(name = portfolio.st,
          symbols = symbols,
          initDate = init_date)

## [1] "Port.Luxor"

initAcct(name = account.st,
         portfolios = portfolio.st,
         initDate = init_date, 
         initEq = init_equity)

## [1] "Acct.Luxor"

initOrders(portfolio = portfolio.st,
           initDate = init_date)

12.1 Apply Strategy

# Results hidden to save space
applyStrategy(strategy.st, portfolios = portfolio.st)

checkBlotterUpdate(portfolio.st, account.st, verbose = TRUE)

## [1] TRUE

updatePortf(portfolio.st)

## [1] "Port.Luxor"

updateAcct(account.st)

## [1] "Acct.Luxor"

updateEndEq(account.st)

## [1] "Acct.Luxor"

12.2 Chart Positions

for(symbol in symbols) {
    chart.Posn(portfolio.st, Symbol = symbol, 
               TA = "add_SMA(n = 10, col = 4); add_SMA(n = 30, col = 2)")
}

12.3 Trade Statistics

tstats <- tradeStats(portfolio.st)
kable(t(tstats))

	IWM	QQQ	SPY
Portfolio	Port.Luxor	Port.Luxor	Port.Luxor
Symbol	IWM	QQQ	SPY
Num.Txns	23	21	23
Num.Trades	11	10	11
Net.Trading.PL	2501.460	1070.748	2251.207
Avg.Trade.PL	211.1204	101.8385	200.5545
Med.Trade.PL	51.52684	-45.13684	-120.63947
Largest.Winner	2109.166	1287.017	2927.862
Largest.Loser	-428.1350	-355.5896	-891.9164
Gross.Profits	3724.335	2463.680	5705.846
Gross.Losses	-1402.011	-1445.295	-3499.746
Std.Dev.Trade.PL	732.3649	515.1777	1149.6862
Percent.Positive	63.63636	50.00000	45.45455
Percent.Negative	36.36364	50.00000	54.54545
Profit.Factor	2.656424	1.704621	1.630360
Avg.Win.Trade	532.0479	492.7360	1141.1692
Med.Win.Trade	152.6189	351.7958	909.5703
Avg.Losing.Trade	-350.5026	-289.0589	-583.2910
Med.Losing.Trade	-352.4492	-300.4438	-726.2925
Avg.Daily.PL	211.1204	101.8385	200.5545
Med.Daily.PL	51.52684	-45.13684	-120.63947
Std.Dev.Daily.PL	732.3649	515.1777	1149.6862
Ann.Sharpe	4.576179	3.138017	2.769194
Max.Drawdown	-1691.308	-1343.709	-2942.752
Profit.To.Max.Draw	1.4790094	0.7968602	0.7650008
Avg.WinLoss.Ratio	1.517957	1.704621	1.956432
Med.WinLoss.Ratio	0.4330239	1.1709206	1.2523471
Max.Equity	2961.741	1893.009	3482.502
Min.Equity	-678.91818	-52.69256	-2053.06787
End.Equity	2501.460	1070.748	2251.207

12.3.1 Trade Related

tab.trades <- tstats %>% 
    mutate(Trades = Num.Trades, 
           Win.Percent = Percent.Positive, 
           Loss.Percent = Percent.Negative, 
           WL.Ratio = Percent.Positive/Percent.Negative) %>% 
    select(Trades, Win.Percent, Loss.Percent, WL.Ratio)

kable(t(tab.trades))

Trades	11.00000	10	11.0000000
Win.Percent	63.63636	50	45.4545455
Loss.Percent	36.36364	50	54.5454545
WL.Ratio	1.75000	1	0.8333333

12.3.2 Profit Related

tab.profit <- tstats %>% 
    select(Net.Trading.PL, Gross.Profits, Gross.Losses, Profit.Factor)
kable(t(tab.profit))

	IWM	QQQ	SPY
Net.Trading.PL	2501.459861	1070.748428	2251.20741
Gross.Profits	3724.334958	2463.679871	5705.84581
Gross.Losses	-1402.010577	-1445.294647	-3499.74600
Profit.Factor	2.656424	1.704621	1.63036

12.3.3 Averages

tab.wins <- tstats %>% 
    select(Avg.Trade.PL, Avg.Win.Trade, Avg.Losing.Trade, Avg.WinLoss.Ratio)

kable(t(tab.wins))

	IWM	QQQ	SPY
Avg.Trade.PL	211.120398	101.838522	200.554528
Avg.Win.Trade	532.047851	492.735974	1141.169162
Avg.Losing.Trade	-350.502644	-289.058929	-583.291000
Avg.WinLoss.Ratio	1.517957	1.704621	1.956432

12.3.4 Performance Summary

rets <- PortfReturns(Account = account.st)
rownames(rets) <- NULL
charts.PerformanceSummary(rets, colorset = bluefocus)

12.3.5 Per Trade Statistics

for(symbol in symbols) {
    pts <- perTradeStats(portfolio.st, Symbol = symbol)
    kable(pts, booktabs = TRUE, caption = symbol)
}
kable(pts)

Start	End	Init.Pos	Max.Pos	Num.Txns	Max.Notional.Cost	Net.Trading.PL	MAE	MFE	Pct.Net.Trading.PL	Pct.MAE	Pct.MFE	tick.Net.Trading.PL	tick.MAE	tick.MFE
2008-02-27	2008-03-07	100	100	2	13261.463	-881.91636	-881.91636	0.00000	-0.0665022	-0.0665022	0.0000000	-881.91636	-881.91636	0.00000
2008-03-07	2008-04-02	-100	-100	2	-12385.273	-725.99029	-725.99029	168.92951	-0.0586172	-0.0586172	0.0136395	-725.99029	-725.99029	168.92951
2008-04-02	2008-06-03	100	100	2	13144.834	99.74852	-395.16121	575.47610	0.0075884	-0.0300621	0.0437796	99.74852	-395.16121	575.47610
2008-06-03	2008-08-06	-100	-100	2	-13235.450	807.57595	-267.13716	1572.93130	0.0610161	-0.0201835	0.1188423	807.57595	-267.13716	1572.93130
2008-08-06	2008-09-08	100	100	2	12392.260	-151.38610	-436.70758	207.19458	-0.0122162	-0.0352404	0.0167197	-151.38610	-436.70758	207.19458
2008-09-11	2008-12-17	-100	-100	2	-11759.376	2937.86161	-394.74462	4444.47045	0.2498314	-0.0335685	0.3779512	2937.86161	-394.74462	4444.47045
2008-12-17	2009-01-21	100	100	2	8921.423	-706.59467	-1046.72109	214.09136	-0.0792020	-0.1173267	0.0239974	-706.59467	-1046.72109	214.09136
2009-02-05	2009-03-24	-100	-100	2	-7823.378	-110.63947	-689.54812	1166.48465	-0.0141422	-0.0881394	0.1491024	-110.63947	-689.54812	1166.48465
2009-03-24	2009-06-26	100	100	2	8100.427	991.08945	-344.57932	1258.95869	0.1223503	-0.0425384	0.1554188	991.08945	-344.57932	1258.95869
2009-07-06	2009-07-23	-100	-100	2	-8804.437	-863.21910	-863.21910	97.01343	-0.0980436	-0.0980436	0.0110187	-863.21910	-863.21910	97.01343
2009-07-23	2009-11-04	100	100	2	9516.246	919.57028	0.00000	1403.96276	0.0966316	0.0000000	0.1475333	919.57028	0.00000	1403.96276
2009-11-16	2009-12-31	100	100	1	10978.893	165.10760	-94.49099	293.10750	0.0150386	-0.0086066	0.0266974	165.10760	-94.49099	293.10750

12.3.6 Performance Statistics

tab.perf <- table.Arbitrary(rets,
                            metrics=c(
                                "Return.cumulative",
                                "Return.annualized",
                                "SharpeRatio.annualized",
                                "CalmarRatio"),
                            metricsNames=c(
                                "Cumulative Return",
                                "Annualized Return",
                                "Annualized Sharpe Ratio",
                                "Calmar Ratio"))
kable(tab.perf)

	IWM.DailyEndEq	QQQ.DailyEndEq	SPY.DailyEndEq
Cumulative Return	0.2317202	0.1002049	0.1306882
Annualized Return	0.1095999	0.0488073	0.0632089
Annualized Sharpe Ratio	0.5363315	0.4537899	0.1979763
Calmar Ratio	0.6821284	0.3825050	0.2385090

12.3.7 Risk Statistics

tab.risk <- table.Arbitrary(rets,
                            metrics=c(
                                "StdDev.annualized",
                                "maxDrawdown",
                                "VaR",
                                "ES"),
                            metricsNames=c(
                                "Annualized StdDev",
                                "Max DrawDown",
                                "Value-at-Risk",
                                "Conditional VaR"))
kable(tab.risk)

	IWM.DailyEndEq	QQQ.DailyEndEq	SPY.DailyEndEq
Annualized StdDev	0.2043511	0.1075549	0.3192751
Max DrawDown	0.1606735	0.1275992	0.2650170
Value-at-Risk	-0.0194849	-0.0103564	-0.0310188
Conditional VaR	-0.0266399	-0.0163183	-0.0522518

12.3.8 Buy and Hold Performance

rm.strat("buyHold")

# initialize portfolio and account
initPortf("buyHold", "SPY", initDate = init_date)

## [1] "buyHold"

initAcct("buyHold", portfolios = "buyHold",
         initDate = init_date, initEq = init_equity)

## [1] "buyHold"

# place an entry order
CurrentDate <- time(getTxns(Portfolio = portfolio.st, Symbol = "SPY"))[2]
equity = getEndEq("buyHold", CurrentDate)
ClosePrice <- as.numeric(Cl(SPY[CurrentDate,]))
UnitSize = as.numeric(trunc(equity/ClosePrice))
addTxn("buyHold", Symbol = "SPY", TxnDate = CurrentDate, TxnPrice = ClosePrice,
       TxnQty = UnitSize, TxnFees = 0)

## [1] "2008-02-27 00:00:00 SPY 75 @ 131.917425267632"

# place an exit order
LastDate <- last(time(SPY))
LastPrice <- as.numeric(Cl(SPY[LastDate,]))
addTxn("buyHold", Symbol = "SPY", TxnDate = LastDate, TxnPrice = LastPrice,
       TxnQty = -UnitSize , TxnFees = 0)

## [1] "2009-12-31 00:00:00 SPY -75 @ 111.440002"

# update portfolio and account
updatePortf(Portfolio = "buyHold")

## [1] "buyHold"

updateAcct(name = "buyHold")

## [1] "buyHold"

updateEndEq(Account = "buyHold")

## [1] "buyHold"

chart.Posn("buyHold", Symbol = "SPY")

12.3.9 Strategy vs. Market

rets <- PortfReturns(Account = account.st)
rets.bh <- PortfReturns(Account = "buyHold")
returns <- cbind(rets, rets.bh)
charts.PerformanceSummary(returns, geometric = FALSE, wealth.index = TRUE, 
                          main = "Strategy vs. Market")

12.3.10 Risk/Return Scatterplot

chart.RiskReturnScatter(returns, Rf = 0, add.sharpe = c(1, 2), 
                        main = "Return vs. Risk", colorset = c("red", "blue"))

12.3.11 Relative Performance

for(n in 1:(ncol(returns) - 1)) {
    chart.RelativePerformance(returns[, n], returns[, ncol(returns)], 
                              colorset = c("red", "blue"), lwd = 2, 
                              legend.loc = "topleft")
}

12.3.12 Portfolio Summary

#' Error
pf <- getPortfolio(portfolio.st)
xyplot(pf$summary, type = "h", col = 4)

12.3.13 Order Book

ob <- getOrderBook(portfolio.st)

12.3.14 Maximum Adverse Excursion

for(symbol in symbols) {
    chart.ME(Portfolio = portfolio.st, Symbol = symbol, type = "MAE", 
            scale = "percent")
}

12.3.15 Maximum Favorable Excursion

for(symbol in symbols) {
    chart.ME(Portfolio = portfolio.st, Symbol = symbol, type = "MFE", 
            scale = "percent")
}

12.4 Account Summary

a <- getAccount(account.st)
xyplot(a$summary, type = "h", col = 4)

12.4.1 Equity Curve

equity <- a$summary$End.Eq
plot(equity, main = "Equity Curve")

12.4.2 Account Performance Summary

ret <- Return.calculate(equity, method = "log")
charts.PerformanceSummary(ret, colorset = bluefocus, 
                          main = "Strategy Performance")

12.4.3 Cumulative Returns

rets <- PortfReturns(Account = account.st)
chart.CumReturns(rets, colorset = rich10equal, legend.loc = "topleft", 
                 main="SPDR Cumulative Returns")

12.4.4 Distribution Analysis

chart.Boxplot(rets, main = "SPDR Returns", colorset= rich10equal)

12.4.5 Annualized Returns

(ar.tab <- table.AnnualizedReturns(rets))

##                           IWM.DailyEndEq QQQ.DailyEndEq SPY.DailyEndEq
## Annualized Return                 0.1096         0.0488         0.0632
## Annualized Std Dev                0.2044         0.1076         0.3193
## Annualized Sharpe (Rf=0%)         0.5363         0.4538         0.1980

12.4.6 Performance Scatter Plot

max.risk <- max(ar.tab["Annualized Std Dev",])
max.return <- max(ar.tab["Annualized Return",])
chart.RiskReturnScatter(rets,
                        main = "SPDR Performance", colorset = rich10equal,
                        xlim = c(0, max.risk * 1.1), ylim = c(0, max.return))

12.4.7 Notional Costs

#quantstratII pp. 67/69
mnc <- pts$Max.Notional.Cost
pe <- sapply(pts$Start,getEndEq, Account = account.st)/3
barplot(rbind(pe,mnc),beside=T,col=c(2,4),names.arg=format(pts$Start,"%m/%d/%y"),
        ylim=c(0,1.5e5),ylab="$",xlab="Trade Date")
legend(x="topleft",legend=c("(Portfolio Equity)/9","Order Size"),
       pch=15,col=c(2,4),bty="n")
title("Percent of Portfolio Equity versus Trade Size for XLU")