[Returnanalytics-commits] r3334 - pkg/PerformanceAnalytics/tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sun Feb 23 17:47:33 CET 2014
Author: braverock
Date: 2014-02-23 17:47:32 +0100 (Sun, 23 Feb 2014)
New Revision: 3334
Modified:
pkg/PerformanceAnalytics/tests/Examples/PerformanceAnalytics-Ex.Rout.save
Log:
- commit updated example output
Modified: pkg/PerformanceAnalytics/tests/Examples/PerformanceAnalytics-Ex.Rout.save
===================================================================
--- pkg/PerformanceAnalytics/tests/Examples/PerformanceAnalytics-Ex.Rout.save 2014-02-23 16:21:50 UTC (rev 3333)
+++ pkg/PerformanceAnalytics/tests/Examples/PerformanceAnalytics-Ex.Rout.save 2014-02-23 16:47:32 UTC (rev 3334)
@@ -1,7 +1,6 @@
-R version 2.15.2 (2012-10-26) -- "Trick or Treat"
-Copyright (C) 2012 The R Foundation for Statistical Computing
-ISBN 3-900051-07-0
+R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
+Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
@@ -26,7 +25,7 @@
Attaching package: ‘zoo’
-The following object(s) are masked from ‘package:base’:
+The following objects are masked from ‘package:base’:
as.Date, as.Date.numeric
@@ -34,21 +33,21 @@
Attaching package: ‘PerformanceAnalytics’
-The following object(s) are masked from ‘package:graphics’:
+The following object is masked from ‘package:graphics’:
legend
>
-> assign(".oldSearch", search(), pos = 'CheckExEnv')
+> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
> cleanEx()
> nameEx("ActivePremium")
> ### * ActivePremium
>
> flush(stderr()); flush(stdout())
>
-> ### Name: ActivePremium
-> ### Title: Active Premium
-> ### Aliases: ActivePremium
+> ### Name: ActiveReturn
+> ### Title: Active Premium or Active Return
+> ### Aliases: ActivePremium ActiveReturn
> ### Keywords: distribution models multivariate ts
>
> ### ** Examples
@@ -171,7 +170,6 @@
> ### Title: Functions to calculate systematic or beta co-moments of return
> ### series
> ### Aliases: BetaCoKurtosis BetaCoMoments BetaCoSkewness BetaCoVariance
-> ### SystematicKurtosis SystematicSkewness
> ### Keywords: distribution models multivariate ts
>
> ### ** Examples
@@ -243,9 +241,11 @@
> flush(stderr()); flush(stdout())
>
> ### Name: CAPM.CML.slope
-> ### Title: utility functions for CAPM CML, SML, and RiskPremium
+> ### Title: utility functions for single factor (CAPM) CML, SML, and
+> ### RiskPremium
> ### Aliases: CAPM.CML CAPM.CML.slope CAPM.RiskPremium CAPM.SML.slope
-> ### CAPM.utils
+> ### CAPM.utils SFM.CML SFM.CML.slope SFM.RiskPremium SFM.SML.slope
+> ### SFM.utils
> ### Keywords: distribution models multivariate ts
>
> ### ** Examples
@@ -276,8 +276,8 @@
> flush(stderr()); flush(stdout())
>
> ### Name: CAPM.alpha
-> ### Title: calculate CAPM alpha
-> ### Aliases: CAPM.alpha
+> ### Title: calculate single factor model (CAPM) alpha
+> ### Aliases: CAPM.alpha SFM.alpha
> ### Keywords: distribution models multivariate ts
>
> ### ** Examples
@@ -334,8 +334,8 @@
> flush(stderr()); flush(stdout())
>
> ### Name: CAPM.beta
-> ### Title: calculate CAPM beta
-> ### Aliases: CAPM.beta CAPM.beta.bear CAPM.beta.bull TimingRatio
+> ### Title: calculate single factor model (CAPM) beta
+> ### Aliases: CAPM.beta CAPM.beta.bear CAPM.beta.bull SFM.beta TimingRatio
> ### Keywords: distribution models multivariate ts
>
> ### ** Examples
@@ -406,6 +406,94 @@
>
>
> cleanEx()
+> nameEx("CAPM.dynamic")
+> ### * CAPM.dynamic
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: CAPM.dynamic
+> ### Title: Time-varying conditional single factor model beta
+> ### Aliases: CAPM.dynamic SFM.dynamic
+>
+> ### ** Examples
+>
+> data(managers)
+> CAPM.dynamic(managers[,1,drop=FALSE], managers[,8,drop=FALSE],
++ Rf=.035/12, Z=managers[, 9:10])
+ Average alpha US 10Y TR alpha at t - 1 US 3m TR alpha at t - 1
+HAM1 to SP500 TR 0.0070965 -0.196351 0.1665381
+ Average beta US 10Y TR beta at t - 1 US 3m TR beta at t - 1
+HAM1 to SP500 TR 0.3248015 3.493336 -63.74814
+>
+> CAPM.dynamic(managers[80:120,1:6], managers[80:120,7,drop=FALSE],
++ Rf=managers[80:120,10,drop=FALSE], Z=managers[80:120, 9:10])
+ Average alpha US 10Y TR alpha at t - 1
+HAM1 to EDHEC LS EQ -0.0001741347 -0.23890464
+HAM2 to EDHEC LS EQ -0.0027673634 -0.06632217
+HAM3 to EDHEC LS EQ 0.0062624783 -0.21733015
+HAM4 to EDHEC LS EQ -0.0033262023 0.16135997
+HAM5 to EDHEC LS EQ 0.0043380559 0.26882960
+HAM6 to EDHEC LS EQ -0.0053865004 0.05000616
+ US 3m TR alpha at t - 1 Average beta
+HAM1 to EDHEC LS EQ -0.4385012 1.1793098
+HAM2 to EDHEC LS EQ -4.0176982 0.7067390
+HAM3 to EDHEC LS EQ 7.6804829 0.4260623
+HAM4 to EDHEC LS EQ -0.2091890 1.6367609
+HAM5 to EDHEC LS EQ 3.8497148 1.2224547
+HAM6 to EDHEC LS EQ -3.0664314 1.6281908
+ US 10Y TR beta at t - 1 US 3m TR beta at t - 1
+HAM1 to EDHEC LS EQ 3.861212 -51.01409
+HAM2 to EDHEC LS EQ 5.682080 171.16658
+HAM3 to EDHEC LS EQ 1.507916 -705.20354
+HAM4 to EDHEC LS EQ -7.622136 -565.85196
+HAM5 to EDHEC LS EQ 7.083956 39.70358
+HAM6 to EDHEC LS EQ -11.035136 343.52891
+>
+> CAPM.dynamic(managers[80:120,1:6], managers[80:120,8:7],
++ managers[80:120,10,drop=FALSE], Z=managers[80:120, 9:10])
+ Average alpha US 10Y TR alpha at t - 1
+HAM1 to SP500 TR 0.0036316941 -0.03538369
+HAM2 to SP500 TR 0.0016901086 -0.05484988
+HAM3 to SP500 TR 0.0072668556 -0.05978008
+HAM4 to SP500 TR -0.0015875926 0.41314240
+HAM5 to SP500 TR 0.0083363515 0.35300102
+HAM6 to SP500 TR 0.0012839717 0.03521033
+HAM1 to EDHEC LS EQ -0.0001741347 -0.23890464
+HAM2 to EDHEC LS EQ -0.0027673634 -0.06632217
+HAM3 to EDHEC LS EQ 0.0062624783 -0.21733015
+HAM4 to EDHEC LS EQ -0.0033262023 0.16135997
+HAM5 to EDHEC LS EQ 0.0043380559 0.26882960
+HAM6 to EDHEC LS EQ -0.0053865004 0.05000616
+ US 3m TR alpha at t - 1 Average beta
+HAM1 to SP500 TR 0.08506313 0.51861197
+HAM2 to SP500 TR -2.91835013 0.05157528
+HAM3 to SP500 TR 4.10231175 0.17720080
+HAM4 to SP500 TR -6.04090381 1.20562924
+HAM5 to SP500 TR 1.56695525 0.57212866
+HAM6 to SP500 TR -1.72313785 0.59611332
+HAM1 to EDHEC LS EQ -0.43850123 1.17930984
+HAM2 to EDHEC LS EQ -4.01769818 0.70673900
+HAM3 to EDHEC LS EQ 7.68048289 0.42606233
+HAM4 to EDHEC LS EQ -0.20918897 1.63676093
+HAM5 to EDHEC LS EQ 3.84971482 1.22245465
+HAM6 to EDHEC LS EQ -3.06643145 1.62819081
+ US 10Y TR beta at t - 1 US 3m TR beta at t - 1
+HAM1 to SP500 TR -1.181057 -65.73676
+HAM2 to SP500 TR 2.075534 -23.79983
+HAM3 to SP500 TR 1.063350 -256.19346
+HAM4 to SP500 TR -1.812210 162.03456
+HAM5 to SP500 TR 4.277306 183.06200
+HAM6 to SP500 TR -5.106318 189.51371
+HAM1 to EDHEC LS EQ 3.861212 -51.01409
+HAM2 to EDHEC LS EQ 5.682080 171.16658
+HAM3 to EDHEC LS EQ 1.507916 -705.20354
+HAM4 to EDHEC LS EQ -7.622136 -565.85196
+HAM5 to EDHEC LS EQ 7.083956 39.70358
+HAM6 to EDHEC LS EQ -11.035136 343.52891
+>
+>
+>
+> cleanEx()
> nameEx("CAPM.epsilon")
> ### * CAPM.epsilon
>
@@ -413,19 +501,19 @@
>
> ### Name: CAPM.epsilon
> ### Title: Regression epsilon of the return distribution
-> ### Aliases: CAPM.epsilon epsilon Regression
+> ### Aliases: CAPM.epsilon SFM.epsilon
> ### Keywords: distribution models multivariate ts
>
> ### ** Examples
>
> data(portfolio_bacon)
-> print(CAPM.epsilon(portfolio_bacon[,1], portfolio_bacon[,2])) #expected -0.013
+> print(SFM.epsilon(portfolio_bacon[,1], portfolio_bacon[,2])) #expected -0.013
[1] -0.01313932
>
> data(managers)
-> print(CAPM.epsilon(managers['1996',1], managers['1996',8]))
+> print(SFM.epsilon(managers['1996',1], managers['1996',8]))
[1] 0.07425366
-> print(CAPM.epsilon(managers['1996',1:5], managers['1996',8]))
+> print(SFM.epsilon(managers['1996',1:5], managers['1996',8]))
HAM1 HAM2 HAM3 HAM4
Regression epsilon (Risk free = 0) 0.07425366 0.5399193 0.2048063 0.05570592
HAM5
@@ -441,19 +529,19 @@
>
> ### Name: CAPM.jensenAlpha
> ### Title: Jensen's alpha of the return distribution
-> ### Aliases: CAPM.jensenAlpha Jensen'sAlpha
+> ### Aliases: CAPM.jensenAlpha SFM.jensenAlpha
> ### Keywords: distribution models multivariate ts
>
> ### ** Examples
>
> data(portfolio_bacon)
-> print(CAPM.jensenAlpha(portfolio_bacon[,1], portfolio_bacon[,2])) #expected -0.014
+> print(SFM.jensenAlpha(portfolio_bacon[,1], portfolio_bacon[,2])) #expected -0.014
[1] -0.01416944
>
> data(managers)
-> print(CAPM.jensenAlpha(managers['1996',1], managers['1996',8]))
+> print(SFM.jensenAlpha(managers['1996',1], managers['1996',8]))
[1] 0.08077871
-> print(CAPM.jensenAlpha(managers['1996',1:5], managers['1996',8]))
+> print(SFM.jensenAlpha(managers['1996',1:5], managers['1996',8]))
HAM1 HAM2 HAM3 HAM4 HAM5
Jensen's Alpha (Risk free = 0) 0.08077871 NA 0.2196026 0.06063837 NA
>
@@ -468,7 +556,7 @@
> ### Name: CDD
> ### Title: Calculate Uryasev's proposed Conditional Drawdown at Risk (CDD
> ### or CDaR) measure
-> ### Aliases: CDaR CDD
+> ### Aliases: CDD CDaR
> ### Keywords: distribution models multivariate ts
>
> ### ** Examples
@@ -590,11 +678,11 @@
> #with data used in Bacon 2008
>
> data(portfolio_bacon)
-> MAR = 0.5
-> DownsideDeviation(portfolio_bacon[,1], MAR) #expected 0.493
-[1] 0.492524
-> DownsidePotential(portfolio_bacon[,1], MAR) #expected 0.491
-[1] 0.491
+> MAR = 0.005
+> DownsideDeviation(portfolio_bacon[,1], MAR) #expected 0.0255
+[1] 0.02553674
+> DownsidePotential(portfolio_bacon[,1], MAR) #expected 0.0137
+[1] 0.01370833
>
> #with data of managers
>
@@ -938,11 +1026,11 @@
> data(managers)
> MAR = 0
> print(MSquaredExcess(managers['1996',1], managers['1996',8], MAR))
- HAM1
-HAM1 -0.127433
+ SP500 TR
+SP500 TR 0.02027322
> print(MSquaredExcess(managers['1996',1:5], managers['1996',8], MAR))
- HAM1 HAM2 HAM3 HAM4 HAM5
-MSquaredExcess (Risk free = 0) -0.127433 NA 0.1456129 -0.01310258 NA
+ HAM1 HAM2 HAM3 HAM4 HAM5
+MSquaredExcess (Risk free = 0) 0.02027322 NA 0.1409545 -0.02546609 NA
>
>
>
@@ -960,17 +1048,17 @@
> ### ** Examples
>
> data(portfolio_bacon)
-> print(MSquared(portfolio_bacon[,1], portfolio_bacon[,2])) #expected 0.1068
- portfolio.monthly.return....
-portfolio.monthly.return.... 0.1068296
+> print(MSquared(portfolio_bacon[,1], portfolio_bacon[,2])) #expected 0.10062
+ benchmark.return....
+benchmark.return.... 0.10062
>
> data(managers)
> print(MSquared(managers['1996',1], managers['1996',8]))
- HAM1
-HAM1 0.07287385
+ SP500 TR
+SP500 TR 0.2544876
> print(MSquared(managers['1996',1:5], managers['1996',8]))
- HAM1 HAM2 HAM3 HAM4 HAM5
-MSquared (Risk free = 0) 0.07287385 NA 0.4086003 0.21345 NA
+ HAM1 HAM2 HAM3 HAM4 HAM5
+MSquared (Risk free = 0) 0.2544876 NA 0.4028725 0.1982483 NA
>
>
>
@@ -988,24 +1076,66 @@
> ### ** Examples
>
> data(portfolio_bacon)
-> print(MSquaredExcess(portfolio_bacon[,1], portfolio_bacon[,2])) #expected -0.00998
- portfolio.monthly.return....
-portfolio.monthly.return.... -0.009976721
-> print(MSquaredExcess(portfolio_bacon[,1], portfolio_bacon[,2], Method="arithmetic")) #expected -0.011
- portfolio.monthly.return....
-portfolio.monthly.return.... -0.01115381
+> MSquaredExcess(portfolio_bacon[,1], portfolio_bacon[,2]) #expected -0.00998
+ benchmark.return....
+benchmark.return.... -0.01553103
>
+> MSquaredExcess(portfolio_bacon[,1], portfolio_bacon[,2], Method="arithmetic") #expected -0.011
+ benchmark.return....
+benchmark.return.... -0.01736344
+>
> data(managers)
-> print(MSquaredExcess(managers['1996',1], managers['1996',8]))
- HAM1
-HAM1 -0.127433
-> print(MSquaredExcess(managers['1996',1:5], managers['1996',8]))
- HAM1 HAM2 HAM3 HAM4 HAM5
-MSquaredExcess (Risk free = 0) -0.127433 NA 0.1456129 -0.01310258 NA
+> MSquaredExcess(managers['1996',1], managers['1996',8])
+ SP500 TR
+SP500 TR 0.02027322
+> MSquaredExcess(managers['1996',1:5], managers['1996',8])
+ HAM1 HAM2 HAM3 HAM4 HAM5
+MSquaredExcess (Risk free = 0) 0.02027322 NA 0.1409545 -0.02546609 NA
>
>
>
> cleanEx()
+> nameEx("MarketTiming")
+> ### * MarketTiming
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: MarketTiming
+> ### Title: Market timing models
+> ### Aliases: MarketTiming
+>
+> ### ** Examples
+>
+> data(managers)
+> MarketTiming(managers[,1], managers[,8], Rf=.035/12, method = "HM")
+ Alpha Beta Gamma
+HAM1 to SP500 TR 0.008275839 0.4555824 -0.1344417
+> MarketTiming(managers[80:120,1:6], managers[80:120,7], managers[80:120,10])
+ Alpha Beta Gamma
+HAM1 to EDHEC LS EQ -0.0005755802 1.3121058 -0.405150
+HAM2 to EDHEC LS EQ -0.0003616789 0.4370998 8.520620
+HAM3 to EDHEC LS EQ -0.0058148518 1.1898242 11.913786
+HAM4 to EDHEC LS EQ -0.0055113742 2.0616524 18.797340
+HAM5 to EDHEC LS EQ 0.0005125284 1.0703704 -5.077881
+HAM6 to EDHEC LS EQ 0.0003590925 1.2711094 -7.443428
+> MarketTiming(managers[80:120,1:6], managers[80:120,8:7], managers[80:120,10], method = "TM")
+ Alpha Beta Gamma
+HAM1 to SP500 TR 0.0048833318 0.5970167 -0.2801650
+HAM2 to SP500 TR 0.0050694247 0.1190405 -0.5000263
+HAM3 to SP500 TR 0.0032110848 0.5272982 -0.6645684
+HAM4 to SP500 TR 0.0094634771 0.8779523 -0.8155100
+HAM5 to SP500 TR 0.0087234498 0.2869943 -2.7728051
+HAM6 to SP500 TR 0.0048031173 0.2902262 0.6910898
+HAM1 to EDHEC LS EQ -0.0005755802 1.3121058 -0.4051500
+HAM2 to EDHEC LS EQ -0.0003616789 0.4370998 8.5206196
+HAM3 to EDHEC LS EQ -0.0058148518 1.1898242 11.9137857
+HAM4 to EDHEC LS EQ -0.0055113742 2.0616524 18.7973395
+HAM5 to EDHEC LS EQ 0.0005125284 1.0703704 -5.0778814
+HAM6 to EDHEC LS EQ 0.0003590925 1.2711094 -7.4434281
+>
+>
+>
+> cleanEx()
> nameEx("MartinRatio")
> ### * MartinRatio
>
@@ -1064,6 +1194,36 @@
>
>
> cleanEx()
+> nameEx("Modigliani")
+> ### * Modigliani
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: Modigliani
+> ### Title: Modigliani-Modigliani measure
+> ### Aliases: Modigliani
+>
+> ### ** Examples
+>
+> data(managers)
+> Modigliani(managers[,1,drop=FALSE], managers[,8,drop=FALSE], Rf=.035/12)
+[1] 0.01678381
+> Modigliani(managers[,1:6], managers[,8,drop=FALSE], managers[,8,drop=FALSE])
+ HAM1 HAM2 HAM3
+Modigliani-Modigliani measure: SP500 TR 0.01281799 0.01505458 0.0131509
+ HAM4 HAM5 HAM6
+Modigliani-Modigliani measure: SP500 TR 0.01057959 0.01053081 0.01844616
+> Modigliani(managers[,1:6], managers[,8:7], managers[,8,drop=FALSE])
+ HAM1 HAM2 HAM3
+Modigliani-Modigliani measure: SP500 TR 0.01281799 0.01505458 0.01315090
+Modigliani-Modigliani measure: EDHEC LS EQ 0.01062640 0.01168261 0.01078361
+ HAM4 HAM5 HAM6
+Modigliani-Modigliani measure: SP500 TR 0.01057959 0.010530812 0.01844616
+Modigliani-Modigliani measure: EDHEC LS EQ 0.00956933 0.009546295 0.01328426
+>
+>
+>
+> cleanEx()
> nameEx("NetSelectivity")
> ### * NetSelectivity
>
@@ -1482,6 +1642,27 @@
>
>
> cleanEx()
+> nameEx("Return.annualized.excess")
+> ### * Return.annualized.excess
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: Return.annualized.excess
+> ### Title: calculates an annualized excess return for comparing instruments
+> ### with different length history
+> ### Aliases: Return.annualized.excess
+> ### Keywords: distribution models multivariate ts
+>
+> ### ** Examples
+>
+> data(managers)
+> Return.annualized.excess(Rp = managers[,1], Rb = managers[,8])
+ HAM1
+Annualized Return 0.03718883
+>
+>
+>
+> cleanEx()
> nameEx("Return.calculate")
> ### * Return.calculate
>
@@ -1495,8 +1676,8 @@
> ### ** Examples
>
> ## Not run:
-> ##D require(tseries)
-> ##D prices = get.hist.quote("IBM", start = "1999-01-01", end = "2007-01-01", quote = "AdjClose", compression = "d")
+> ##D require(quantmod)
+> ##D prices = getSymbols("IBM", from = "1999-01-01", to = "2007-01-01")
> ##D
> ## End(Not run)
> ## Don't show:
@@ -4938,7 +5119,7 @@
>
> ### Name: SkewnessKurtosisRatio
> ### Title: Skewness-Kurtosis ratio of the return distribution
-> ### Aliases: SkewnessKurtosisRatio Skewness-KurtosisRatio
+> ### Aliases: Skewness-KurtosisRatio SkewnessKurtosisRatio
> ### Keywords: distribution models multivariate ts
>
> ### ** Examples
@@ -5101,14 +5282,28 @@
[1,] 0.01048561
$contribution
- [1] 0.0008658123 0.0006049525 0.0010509579 0.0021479823 0.0004348643
- [6] 0.0011469074 0.0006822007 0.0010512230 0.0013150652 0.0005428396
-[11] 0.0007548481 -0.0013277224 0.0012156837
+ Convertible Arbitrage CTA Global Distressed Securities
+ 0.0008658123 0.0006049525 0.0010509579
+ Emerging Markets Equity Market Neutral Event Driven
+ 0.0021479823 0.0004348643 0.0011469074
+Fixed Income Arbitrage Global Macro Long/Short Equity
+ 0.0006822007 0.0010512230 0.0013150652
+ Merger Arbitrage Relative Value Short Selling
+ 0.0005428396 0.0007548481 -0.0013277224
+ Funds of Funds
+ 0.0012156837
$pct_contrib_StdDev
- [1] 0.08257143 0.05769357 0.10022854 0.20485039 0.04147247 0.10937913
- [7] 0.06506063 0.10025383 0.12541613 0.05176994 0.07198892 -0.12662322
-[13] 0.11593824
+ Convertible Arbitrage CTA Global Distressed Securities
+ 0.08257143 0.05769357 0.10022854
+ Emerging Markets Equity Market Neutral Event Driven
+ 0.20485039 0.04147247 0.10937913
+Fixed Income Arbitrage Global Macro Long/Short Equity
+ 0.06506063 0.10025383 0.12541613
+ Merger Arbitrage Relative Value Short Selling
+ 0.05176994 0.07198892 -0.12662322
+ Funds of Funds
+ 0.11593824
>
>
@@ -5124,7 +5319,7 @@
>
> ### Name: StdDev.annualized
> ### Title: calculate a multiperiod or annualized Standard Deviation
-> ### Aliases: sd.annualized sd.multiperiod StdDev.annualized
+> ### Aliases: StdDev.annualized sd.annualized sd.multiperiod
> ### Keywords: distribution models multivariate ts
>
> ### ** Examples
@@ -5254,21 +5449,21 @@
>
> data(portfolio_bacon)
> data(managers)
-> round(TreynorRatio(managers[,1,drop=FALSE], managers[,8,drop=FALSE], Rf=.035/12),4)
+> round(TreynorRatio(managers[,1], managers[,8], Rf=.035/12),4)
[1] 0.2528
-> round(TreynorRatio(managers[,1,drop=FALSE], managers[,8,drop=FALSE], Rf = managers[,10,drop=FALSE]),4)
+> round(TreynorRatio(managers[,1], managers[,8], Rf = managers[,10]),4)
[1] 0.2428
-> round(TreynorRatio(managers[,1:6], managers[,8,drop=FALSE], Rf=.035/12),4)
+> round(TreynorRatio(managers[,1:6], managers[,8], Rf=.035/12),4)
HAM1 HAM2 HAM3 HAM4 HAM5 HAM6
Treynor Ratio: SP500 TR 0.2528 0.3925 0.201 0.1209 0.0052 0.3042
-> round(TreynorRatio(managers[,1:6], managers[,8,drop=FALSE], Rf = managers[,10,drop=FALSE]),4)
+> round(TreynorRatio(managers[,1:6], managers[,8], Rf = managers[,10]),4)
HAM1 HAM2 HAM3 HAM4 HAM5 HAM6
Treynor Ratio: SP500 TR 0.2428 0.3883 0.1956 0.1144 0.0219 0.3401
-> round(TreynorRatio(managers[,1:6], managers[,8:7,drop=FALSE], Rf=.035/12),4)
+> round(TreynorRatio(managers[,1:6], managers[,8:7], Rf=.035/12),4)
HAM1 HAM2 HAM3 HAM4 HAM5 HAM6
Treynor Ratio: SP500 TR 0.2528 0.3925 0.2010 0.1209 0.0052 0.3042
Treynor Ratio: EDHEC LS EQ 0.1297 0.1088 0.0776 0.0504 0.0014 0.0966
-> round(TreynorRatio(managers[,1:6], managers[,8:7,drop=FALSE], Rf = managers[,10,drop=FALSE]),4)
+> round(TreynorRatio(managers[,1:6], managers[,8:7], Rf = managers[,10]),4)
HAM1 HAM2 HAM3 HAM4 HAM5 HAM6
Treynor Ratio: SP500 TR 0.2428 0.3883 0.1956 0.1144 0.0219 0.3401
Treynor Ratio: EDHEC LS EQ 0.1242 0.1068 0.0753 0.0471 0.0060 0.1086
@@ -5792,7 +5987,7 @@
>
> ### Name: Return.centered
> ### Title: calculate centered Returns
-> ### Aliases: centeredcomoment centeredmoment Return.centered
+> ### Aliases: Return.centered centeredcomoment centeredmoment
> ### Keywords: distribution models multivariate ts
>
> ### ** Examples
@@ -5941,7 +6136,7 @@
>
> flush(stderr()); flush(stdout())
>
-> ### Name: chart.ACFplus
+> ### Name: chart.ACF
> ### Title: Create ACF chart or ACF with PACF two-panel chart
> ### Aliases: chart.ACF chart.ACFplus
> ### Keywords: distribution hplot models multivariate ts
@@ -6502,15 +6697,17 @@
>
> ### ** Examples
>
-> data(managers)
-> R = Drawdowns(managers[,2,drop=FALSE])
-> n = table.Drawdowns(managers[,2,drop=FALSE])
-> chart.Events(Drawdowns(managers[,2,drop=FALSE]),
-+ dates = n$Trough,
-+ prior=max(na.omit(n$"To Trough")),
-+ post=max(na.omit(n$Recovery)),
-+ lwd=2, colorset=redfocus, legend.loc=NULL,
-+ main = "Worst Drawdowns")
+> ## Not run:
+> ##D data(managers)
+> ##D R = PerformanceAnalytics:::Drawdowns(managers[,2,drop=FALSE])
+> ##D n = table.Drawdowns(managers[,2,drop=FALSE])
+> ##D chart.Events(PerformanceAnalytics:::Drawdowns(managers[,2,drop=FALSE]),
+> ##D dates = n$Trough,
+> ##D prior=max(na.omit(n$"To Trough")),
+> ##D post=max(na.omit(n$Recovery)),
+> ##D lwd=2, colorset=redfocus, legend.loc=NULL,
+> ##D main = "Worst Drawdowns")
+> ## End(Not run)
>
>
>
@@ -6672,16 +6869,6 @@
> data(edhec)
> chart.RiskReturnScatter(edhec, Rf = .04/12)
> chart.RiskReturnScatter(edhec, Rf = .04/12, add.boxplots = TRUE)
-Warning in par(original.layout) :
- graphical parameter "cin" cannot be set
-Warning in par(original.layout) :
- graphical parameter "cra" cannot be set
-Warning in par(original.layout) :
- graphical parameter "csi" cannot be set
-Warning in par(original.layout) :
- graphical parameter "cxy" cannot be set
-Warning in par(original.layout) :
- graphical parameter "din" cannot be set
>
>
>
@@ -6777,13 +6964,10 @@
> dev.new()
> chart.RollingQuantileRegression(managers[, 1, drop=FALSE],
+ managers[, 8, drop=FALSE], Rf = .04/12)
-Package SparseM (0.96) loaded.
- To cite, see citation("SparseM")
-
Attaching package: ‘SparseM’
-The following object(s) are masked from ‘package:base’:
+The following object is masked from ‘package:base’:
backsolve
@@ -6899,9 +7083,9 @@
>
> flush(stderr()); flush(stdout())
>
-> ### Name: charts.TimeSeries
+> ### Name: chart.TimeSeries
> ### Title: Creates a time series chart with some extensions.
-> ### Aliases: charts.TimeSeries chart.TimeSeries
+> ### Aliases: chart.TimeSeries charts.TimeSeries
> ### Keywords: distribution hplot models multivariate ts
>
> ### ** Examples
@@ -6964,7 +7148,13 @@
> R=edhec[,"Funds of Funds",drop=FALSE]
> Return.cumulative = cumprod(1+R) - 1
> chart.TimeSeries(Return.cumulative)
-> chart.TimeSeries(Return.cumulative, colorset = "darkblue", legend.loc = "bottomright", period.areas = cycles.dates, period.color = "lightblue", event.lines = risk.dates, event.labels = risk.labels, event.color = "red", lwd = 2)
+> chart.TimeSeries(Return.cumulative, colorset = "darkblue",
++ legend.loc = "bottomright",
++ period.areas = cycles.dates,
++ period.color = "lightblue",
++ event.lines = risk.dates,
++ event.labels = risk.labels,
++ event.color = "red", lwd = 2)
>
>
>
@@ -7022,7 +7212,11 @@
> ### ** Examples
>
> data(managers)
-> charts.RollingPerformance(managers[,1:8], Rf=managers[,10,drop=FALSE], colorset=tim8equal, main="Rolling 12-Month Performance", legend.loc="topleft")
+> charts.RollingPerformance(managers[,1:8],
++ Rf=managers[,10,drop=FALSE],
++ colorset=tim8equal,
++ main="Rolling 12-Month Performance",
++ legend.loc="topleft")
>
>
>
@@ -7502,7 +7696,7 @@
> ### Name: mean.geometric
> ### Title: calculate attributes relative to the mean of the observation
> ### series given, including geometric, stderr, LCL and UCL
-> ### Aliases: mean.geometric mean.LCL mean.stderr mean.UCL mean.utils
+> ### Aliases: mean.LCL mean.UCL mean.geometric mean.stderr mean.utils
> ### Keywords: distribution models multivariate ts
>
> ### ** Examples
@@ -7734,37 +7928,45 @@
>
> require("Hmisc")
Loading required package: Hmisc
+Loading required package: cluster
+Loading required package: grid
+Loading required package: lattice
Loading required package: survival
Loading required package: splines
+Loading required package: Formula
Hmisc library by Frank E Harrell Jr
Type library(help='Hmisc'), ?Overview, or ?Hmisc.Overview')
to see overall documentation.
-NOTE:Hmisc no longer redefines [.factor to drop unused levels when
-subsetting. To get the old behavior of Hmisc type dropUnusedLevels().
-
Attaching package: ‘Hmisc’
-The following object(s) are masked from ‘package:survival’:
+The following objects are masked from ‘package:survival’:
- untangle.specials
+ survfitKM, untangle.specials
-The following object(s) are masked from ‘package:base’:
+The following objects are masked from ‘package:base’:
format.pval, round.POSIXt, trunc.POSIXt, units
> result = t(table.AnnualizedReturns(managers[,1:8], Rf=.04/12))
>
-> textplot(format.df(result, na.blank=TRUE, numeric.dollar=FALSE, cdec=c(3,3,1)), rmar = 0.8, cmar = 2, max.cex=.9, halign = "center", valign = "top", row.valign="center", wrap.rownames=20, wrap.colnames=10, col.rownames=c("red", rep("darkgray",5), rep("orange",2)), mar = c(0,0,3,0)+0.1)
+> textplot(format.df(result, na.blank=TRUE, numeric.dollar=FALSE,
++ cdec=c(3,3,1)), rmar = 0.8, cmar = 2, max.cex=.9,
++ halign = "center", valign = "top", row.valign="center",
++ wrap.rownames=20, wrap.colnames=10, col.rownames=c("red",
++ rep("darkgray",5), rep("orange",2)), mar = c(0,0,3,0)+0.1)
+>
> title(main="Annualized Performance")
>
>
>
> cleanEx()
-detaching ‘package:Hmisc’, ‘package:survival’, ‘package:splines’
+detaching ‘package:Hmisc’, ‘package:Formula’, ‘package:survival’,
+ ‘package:splines’, ‘package:lattice’, ‘package:grid’,
+ ‘package:cluster’
> nameEx("table.Arbitrary")
> ### * table.Arbitrary
@@ -7780,7 +7982,8 @@
> ### ** Examples
>
> data(edhec)
-> table.Arbitrary(edhec,metrics=c("VaR", "ES"),metricsNames=c("Modified VaR","Modified Expected Shortfall"))
+> table.Arbitrary(edhec,metrics=c("VaR", "ES"),
++ metricsNames=c("Modified VaR","Modified Expected Shortfall"))
Convertible Arbitrage CTA Global
Modified VaR -0.03247395 -0.03380228
Modified Expected Shortfall -0.09954768 -0.04284185
@@ -7831,7 +8034,11 @@
US 3m TR 0.9224 0.9081 0.8968 0.8746 0.8363 0.8127 0.0000
>
> result = t(table.Autocorrelation(managers[,1:8]))
-> textplot(result, rmar = 0.8, cmar = 2, max.cex=.9, halign = "center", valign = "top", row.valign="center", wrap.rownames=15, wrap.colnames=10, mar = c(0,0,3,0)+0.1)
+>
+> textplot(result, rmar = 0.8, cmar = 2, max.cex=.9, halign = "center",
++ valign = "top", row.valign="center", wrap.rownames=15,
++ wrap.colnames=10, mar = c(0,0,3,0)+0.1)
+>
> title(main="Autocorrelation")
>
>
@@ -7842,15 +8049,16 @@
>
> flush(stderr()); flush(stdout())
>
-> ### Name: table.CAPM
-> ### Title: Asset-Pricing Model Summary: Statistics and Stylized Facts
-> ### Aliases: table.CAPM
+> ### Name: table.SFM
+> ### Title: Single Factor Asset-Pricing Model Summary: Statistics and
+> ### Stylized Facts
+> ### Aliases: table.CAPM table.SFM
> ### Keywords: distribution models multivariate ts
>
> ### ** Examples
>
> data(managers)
-> table.CAPM(managers[,1:3,drop=FALSE], managers[,8,drop=FALSE], Rf = managers[,10,drop=FALSE])
+> table.SFM(managers[,1:3], managers[,8], Rf = managers[,10])
HAM1 to SP500 TR HAM2 to SP500 TR HAM3 to SP500 TR
Alpha 0.0058 0.0091 0.0062
Beta 0.3901 0.3384 0.5523
@@ -7865,9 +8073,11 @@
Information Ratio 0.3604 0.5060 0.4701
Treynor Ratio 0.2428 0.3883 0.1956
>
-> result = table.CAPM(managers[,1:3,drop=FALSE], managers[,8,drop=FALSE], Rf = managers[,10,drop=FALSE])
-> textplot(result, rmar = 0.8, cmar = 1.5, max.cex=.9, halign = "center", valign = "top", row.valign="center", wrap.rownames=15, wrap.colnames=10, mar = c(0,0,3,0)+0.1)
-> title(main="CAPM-Related Statistics")
+> result = table.SFM(managers[,1:3], managers[,8], Rf = managers[,10])
+> textplot(result, rmar = 0.8, cmar = 1.5, max.cex=.9,
++ halign = "center", valign = "top", row.valign="center",
++ wrap.rownames=15, wrap.colnames=10, mar = c(0,0,3,0)+0.1)
+> title(main="Single Factor Model Related Statistics")
>
>
>
@@ -7906,36 +8116,46 @@
> # prettify with format.df in hmisc package
> require("Hmisc")
Loading required package: Hmisc
+Loading required package: cluster
+Loading required package: grid
+Loading required package: lattice
Loading required package: survival
Loading required package: splines
+Loading required package: Formula
Hmisc library by Frank E Harrell Jr
Type library(help='Hmisc'), ?Overview, or ?Hmisc.Overview')
to see overall documentation.
-NOTE:Hmisc no longer redefines [.factor to drop unused levels when
-subsetting. To get the old behavior of Hmisc type dropUnusedLevels().
-
Attaching package: ‘Hmisc’
-The following object(s) are masked from ‘package:survival’:
+The following objects are masked from ‘package:survival’:
- untangle.specials
+ survfitKM, untangle.specials
-The following object(s) are masked from ‘package:base’:
+The following objects are masked from ‘package:base’:
format.pval, round.POSIXt, trunc.POSIXt, units
> result = t(table.CalendarReturns(managers[,c(1,8)]))
-> textplot(format.df(result, na.blank=TRUE, numeric.dollar=FALSE, cdec=rep(1,dim(result)[2])), rmar = 0.8, cmar = 1, max.cex=.9, halign = "center", valign = "top", row.valign="center", wrap.rownames=20, wrap.colnames=10, col.rownames=c( rep("darkgray",12), "black", "blue"), mar = c(0,0,3,0)+0.1)
+>
+> textplot(format.df(result, na.blank=TRUE, numeric.dollar=FALSE,
++ cdec=rep(1,dim(result)[2])), rmar = 0.8, cmar = 1,
++ max.cex=.9, halign = "center", valign = "top",
++ row.valign="center", wrap.rownames=20, wrap.colnames=10,
++ col.rownames=c( rep("darkgray",12), "black", "blue"),
++ mar = c(0,0,3,0)+0.1)
+>
> title(main="Calendar Returns")
>
>
>
> cleanEx()
-detaching ‘package:Hmisc’, ‘package:survival’, ‘package:splines’
+detaching ‘package:Hmisc’, ‘package:Formula’, ‘package:survival’,
+ ‘package:splines’, ‘package:lattice’, ‘package:grid’,
+ ‘package:cluster’
> nameEx("table.CaptureRatios")
> ### * table.CaptureRatios
@@ -7977,7 +8197,10 @@
>
> result = t(table.UpDownRatios(managers[,1:6], managers[,7,drop=FALSE]))
> colnames(result)=colnames(managers[,1:6])
-> textplot(result, rmar = 0.8, cmar = 1.5, max.cex=.9, halign = "center", valign = "top", row.valign="center", wrap.rownames=15, wrap.colnames=10, mar = c(0,0,3,0)+0.1)
+> textplot(result, rmar = 0.8, cmar = 1.5, max.cex=.9,
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/returnanalytics -r 3334
More information about the Returnanalytics-commits
mailing list