quantitative trading strategies in r part 2 of 3

40 60 80 100 120

40

60

80

mm

Computational Finance and Risk Management

Quantitative Trading Strategies in R

Part 2 of 3

Guy YollinPrincipal Consultant, r-programming.org

Visiting Lecturer, University of Washington

Guy Yollin (Copyright© 2011) Quantitative Trading Strategies in R quantstrat-II 1 / 68

http://www.r-programming.org

40 60 80 100 120

40

60

80

mm

Outline

1 The applyStrategy and updatePortf functions

2 Position sizing

3 Passing parameters at apply-time

4 Parameter optimization



40 60 80 100 120

40

60

80

mm

Packages for trading system development in R

Data access, charting, indicators

Time series objects

Quantitative trading rules and trading accouting

Performance metrics and graphs

zoo: ordered observations

xts: extensible time series

TTR: technical trading rules

quantmod: quantitative financial modelling framework

blotter: tools for transaction-oriented trading systems development

quantstrat: quantitative strategy model framework

PerformanceAnalytics: Econometric tools for performance and risk analysis



40 60 80 100 120

40

60

80

mm

Lecture references

TradeAnalytics project page on R-forge:http://r-forge.r-project.org/projects/blotter/

documents and demos for:

blotter packagequantstrat package

R-SIG-FINANCE:https://stat.ethz.ch/mailman/listinfo/r-sig-finance

Kent Russell’s Timely Portfolio blog:http://timelyportfolio.blogspot.com/

6-part quantstrat example


http://r-forge.r-project.org/projects/blotter/

https://stat.ethz.ch/mailman/listinfo/r-sig-finance

http://timelyportfolio.blogspot.com/


40 60 80 100 120

40

60

80

mm

Outline


2 Position sizing





40 60 80 100 120

40

60

80

mm

Basic strategy backtesting workflow for quantstrat

Initialize portfolio,

account, orders,

strategy

Add indicators,

signals, and rules

Generate

performance reports

and graphs

Initialize

currency and

instruments, and

load historic data

Apply strategy to

portfolio

InitializationBar-by-bar

processingReporting

Update portfolio,

account, equity

Define strategy Update



40 60 80 100 120

40

60

80

mm

Initialize currency and trading instruments

Initialize portfolio, account, orders,

strategy

Add indicators, signals, and rules

Generate performance reports

and graphs

Initialize currency and

instruments, and load historic data

Apply strategy to portfolio

InitializationBar-by-bar processing

Reporting

Update portfolio, account, equity


R Code:

> library(quantstrat)

> # define stock list

> stock.str=c("SPY")

> # inz currency and stocks

> dummy <- currency('USD')

> for(symbol in stock.str){

stock(symbol, currency="USD",multiplier=1)

}

> # download stocks

> start.data <- as.Date("2001-01-01")

> initDate <- start.data-1

> getSymbols(stock.str,from=start.data,adjust=T)



40 60 80 100 120

40

60

80

mm

Initialize portfolio, account, and orders object


strategy



and graphs





Reporting



R Code:

> # inz portfolio, account, orders, strategy

> strat.name <- "MAX"

> initEq=1000000

> dummy <- initPortf(name=strat.name,symbols=stock.str, initDate=initDate)

> dummy <- initAcct(name=strat.name,portfolios=strat.name,

initDate=initDate, initEq=initEq)

> initOrders(portfolio=strat.name,initDate=initDate)

> strat <- strategy(strat.name)



40 60 80 100 120

40

60

80

mm

Define indicators and signals


strategy



and graphs





Reporting



R Code:

> # indicators:

> strat <- add.indicator(strategy = strat, name = "SMA",

arguments = list(x=quote(Cl(mktdata)), n=50),label= "ma50" )


arguments = list(x=quote(Cl(mktdata)), n=200),label= "ma200")

> # signals:

> strat <- add.signal(strategy = strat,name="sigCrossover",

arguments = list(columns=c("ma50","ma200"), relationship="gte"),

label="ma50.gt.ma200")


arguments = list(column=c("ma50","ma200"),relationship="lt"),

label="ma50.lt.ma200")



40 60 80 100 120

40

60

80

mm

Define entry and exit rules


strategy



and graphs





Reporting



R Code:

> # rules:

> strat <- add.rule(strategy = strat,name='ruleSignal',

arguments = list(sigcol="ma50.gt.ma200",sigval=TRUE, orderqty=100,

ordertype='market', orderside='long'),type='enter')


arguments = list(sigcol="ma50.lt.ma200",sigval=TRUE, orderqty=-100,

ordertype='market', orderside='long'),type='exit')



40 60 80 100 120

40

60

80

mm

The blotter_portfolio object

myPortfolio

--------------------

blotter_portfolio

symbols

--------------------

listsymbols

--------------------

listsymbols

--------------------

list

summary

--------------------

portfolio_summary xts

Long.Value

Short.Value

Net.Value

Gross.Value

Period.Realized.PL

Period.Unrealized.PL

Gross.Trading.PL

Txn.Fees

Net.Trading.PL

txn

--------------------

transactions xts

posPL

--------------------

posPL xts

posPL.USD

--------------------

posPL xts

Pos.Qty

Con.Mult

Ccy.Mult

Pos.Value

Pos.Avg.Cost

Txn.Value

Period.Realized.PL


Gross.Trading.PL

Txn.Fees

Net.Trading.PL

Txn.Qty

Txn.Price

Txn.Value

Txn.Avg.Cost

Pos.Qty

Pos.Avg.Cost

Gross.Txn.Realized.PL

Txn.Fees

Net.Txn.Realized.PL

Con.Mult



40 60 80 100 120

40

60

80

mm

The str function

The str function compactly displays the internal structure of an R object

R Code: The str function

> args(str)

function (object, ...)

NULL

Main arguments:

object the R object to be inspected



40 60 80 100 120

40

60

80

mm

blotter_portfolio object before applyStrategy

R Code:> str(getPortfolio(strat.name))

List of 2

$ symbols:List of 1

..$ SPY:List of 3

.. ..$ txn :An aAYxtsaAZ object from 2000-12-31 to 2000-12-31 containing:

Data: num [1, 1:10] 0 0 0 0 0 0 0 0 0 0

- attr(*, "dimnames")=List of 2

..$ : NULL

..$ : chr [1:10] "Txn.Qty" "Txn.Price" "Txn.Value" "Txn.Avg.Cost" ...

Indexed by objects of class: [POSIXct,POSIXt] TZ:

xts Attributes:

NULL

.. ..$ posPL :An aAYxtsaAZ object from 2000-12-31 to 2000-12-31 containing:

Data: num [1, 1:11] 0 1 1 0 0 0 0 0 0 0 ...


..$ : NULL

..$ : chr [1:11] "Pos.Qty" "Con.Mult" "Ccy.Mult" "Pos.Value" ...


xts Attributes:

NULL

.. ..$ posPL.USD:An aAYxtsaAZ object from 2000-12-31 to 2000-12-31 containing:

Data: num [1, 1:11] 0 1 1 0 0 0 0 0 0 0 ...


..$ : NULL



xts Attributes:

NULL

$ summary:An aAYxtsaAZ object from 2000-12-31 to 2000-12-31 containing:

Data: num [1, 1:9] 0 0 0 0 0 0 0 0 0


..$ : NULL

..$ : chr [1:9] "Long.Value" "Short.Value" "Net.Value" "Gross.Value" ...


xts Attributes:

NULL

- attr(*, "class")= chr [1:2] "blotter_portfolio" "portfolio"

- attr(*, "currency")= chr "USD"

- attr(*, "initDate")= Date[1:1], format: "2000-12-31"



40 60 80 100 120

40

60

80

mm

Applying the strategy to a portfolio


strategy



and graphs





Reporting



R Code:

> out <- try(applyStrategy(strategy=strat , portfolios=strat.name))

[1] "2002-04-24 SPY 100 @ 91.9933301492183"

[1] "2002-04-29 SPY -100 @ 89.8492574695683"

[1] "2002-04-30 SPY 100 @ 90.6900702851173"

[1] "2002-05-14 SPY -100 @ 92.674388529813"

[1] "2003-05-12 SPY 100 @ 81.1354076349475"

[1] "2004-08-25 SPY -100 @ 96.8566013672453"

[1] "2004-10-27 SPY 100 @ 98.8180303441752"

[1] "2006-07-25 SPY -100 @ 114.662711057191"

[1] "2006-08-29 SPY 100 @ 118.211406993905"

[1] "2007-12-28 SPY -100 @ 137.165700378348"

[1] "2009-06-18 SPY 100 @ 88.5716570825431"

[1] "2010-07-06 SPY -100 @ 100.84667390332"

[1] "2010-10-15 SPY 100 @ 116.002709490181"



40 60 80 100 120

40

60

80

mm

blotter_portfolio object after applyStrategy


List of 2

$ symbols:List of 1

..$ SPY:List of 3


Data: num [1:14, 1:10] 0 100 -100 100 -100 100 -100 100 -100 100 ...


..$ : NULL



xts Attributes:

NULL


Data: num [1, 1:11] 0 1 1 0 0 0 0 0 0 0 ...


..$ : NULL



xts Attributes:

NULL


Data: num [1, 1:11] 0 1 1 0 0 0 0 0 0 0 ...


..$ : NULL



xts Attributes:

NULL


Data: num [1, 1:9] 0 0 0 0 0 0 0 0 0


..$ : NULL



xts Attributes:

NULL






40 60 80 100 120

40

60

80

mm

Transactions in the blotter_portfolio object

R Code:> getPortfolio(strat.name)$symbols$SPY$txn

Txn.Qty Txn.Price Txn.Value Txn.Avg.Cost Pos.Qty Pos.Avg.Cost Gross.Txn.Realized.PL Txn.Fees

2000-12-31 0 0.00000 0.000 0.00000 0 0.00000 0.0000 0

2002-04-24 100 91.99333 9199.333 91.99333 100 91.99333 0.0000 0

2002-04-29 -100 89.84926 -8984.926 89.84926 0 0.00000 -214.4073 0

2002-04-30 100 90.69007 9069.007 90.69007 100 90.69007 0.0000 0

2002-05-14 -100 92.67439 -9267.439 92.67439 0 0.00000 198.4318 0

2003-05-12 100 81.13541 8113.541 81.13541 100 81.13541 0.0000 0

2004-08-25 -100 96.85660 -9685.660 96.85660 0 0.00000 1572.1194 0

2004-10-27 100 98.81803 9881.803 98.81803 100 98.81803 0.0000 0

2006-07-25 -100 114.66271 -11466.271 114.66271 0 0.00000 1584.4681 0

2006-08-29 100 118.21141 11821.141 118.21141 100 118.21141 0.0000 0

2007-12-28 -100 137.16570 -13716.570 137.16570 0 0.00000 1895.4293 0

2009-06-18 100 88.57166 8857.166 88.57166 100 88.57166 0.0000 0

2010-07-06 -100 100.84667 -10084.667 100.84667 0 0.00000 1227.5017 0

2010-10-15 100 116.00271 11600.271 116.00271 100 116.00271 0.0000 0

Net.Txn.Realized.PL Con.Mult

2000-12-31 0.0000 0

2002-04-24 0.0000 1

2002-04-29 -214.4073 1

2002-04-30 0.0000 1

2002-05-14 198.4318 1

2003-05-12 0.0000 1

2004-08-25 1572.1194 1

2004-10-27 0.0000 1

2006-07-25 1584.4681 1

2006-08-29 0.0000 1

2007-12-28 1895.4293 1

2009-06-18 0.0000 1

2010-07-06 1227.5017 1

2010-10-15 0.0000 1



40 60 80 100 120

40

60

80

mm

Retrieving transactions

R Code:

> getTxns(Portfolio=strat.name, Symbol=stock.str)

Txn.Qty Txn.Price Txn.Fees Txn.Value Txn.Avg.Cost Net.Txn.Realized.PL

2000-12-31 0 0.00000 0 0.000 0.00000 0.0000

2002-04-24 100 91.99333 0 9199.333 91.99333 0.0000

2002-04-29 -100 89.84926 0 -8984.926 89.84926 -214.4073

2002-04-30 100 90.69007 0 9069.007 90.69007 0.0000

2002-05-14 -100 92.67439 0 -9267.439 92.67439 198.4318

2003-05-12 100 81.13541 0 8113.541 81.13541 0.0000

2004-08-25 -100 96.85660 0 -9685.660 96.85660 1572.1194

2004-10-27 100 98.81803 0 9881.803 98.81803 0.0000

2006-07-25 -100 114.66271 0 -11466.271 114.66271 1584.4681

2006-08-29 100 118.21141 0 11821.141 118.21141 0.0000

2007-12-28 -100 137.16570 0 -13716.570 137.16570 1895.4293

2009-06-18 100 88.57166 0 8857.166 88.57166 0.0000

2010-07-06 -100 100.84667 0 -10084.667 100.84667 1227.5017

2010-10-15 100 116.00271 0 11600.271 116.00271 0.0000



40 60 80 100 120

40

60

80

mm

Calling updatePortf


strategy



and graphs





Reporting



R Code:

> getPortfolio(strat.name)$summary

Long.Value Short.Value Net.Value Gross.Value Realized.PL

2000-12-31 0 0 0 0 0

Unrealized.PL Gross.Trading.PL Txn.Fees Net.Trading.PL

2000-12-31 0 0 0 0

> dummy <- updatePortf(Portfolio=strat.name,

Dates=paste('::',as.Date(Sys.time()),sep=''))

> # updatePortf(Portfolio=strat.name,Dates=paste('::',tail(index(SPY),1),sep=''))

> # updatePortf(Portfolio=strat.name)

> library(lattice)

> plot(xyplot(getPortfolio(strat.name)$summary,type="h",col=4,xlab=""))



40 60 80 100 120

40

60

80

mm

blotter_portfolio object after updatePortf


List of 2

$ symbols:List of 1

..$ SPY:List of 3


Data: num [1:14, 1:10] 0 100 -100 100 -100 100 -100 100 -100 100 ...


..$ : NULL



xts Attributes:

NULL


Data: num [1:2666, 1:11] 0 0 0 0 0 0 0 0 0 0 ...


..$ : NULL



xts Attributes:

NULL


Data: num [1:2666, 1:11] 0 0 0 0 0 0 0 0 0 0 ...


..$ : NULL



xts Attributes:

NULL


Data: num [1:2666, 1:9] 0 0 0 0 0 0 0 0 0 0 ...


..$ : NULL



xts Attributes:

NULL






40 60 80 100 120

40

60

80

mm

Plot of blotter_portfolio$summary object0

5000

1500

0 Long.Value

2002 2004 2006 2008 2010 2012

−0.

40.

00.

4

Short.Value

050

0015

000 Net.Value

050

0015

000 Gross.Value

010

00

Period.Realized.PL

−20

00−

500


−60

00

400

Gross.Trading.PL

−0.

40.

00.

4

Txn.Fees

−60

00

400

2002 2004 2006 2008 2010 2012

Net.Trading.PL



40 60 80 100 120

40

60

80

mm

How the blotter_portfolio object gets updated

myPortfolio

--------------------

blotter_portfolio

symbols

--------------------

listsymbols

--------------------

listsymbols

--------------------

list

summary

--------------------

portfolio_summary xts

Long.Value

Short.Value

Net.Value

Gross.Value

Period.Realized.PL


Gross.Trading.PL

Txn.Fees

Net.Trading.PL

posPL

--------------------

posPL xts

posPL.USD

--------------------

posPL xts

Pos.Qty

Con.Mult

Ccy.Mult

Pos.Value

Pos.Avg.Cost

Txn.Value

Period.Realized.PL


Gross.Trading.PL

Txn.Fees

Net.Trading.PL

Txn.Qty

Txn.Price

Txn.Value

Txn.Avg.Cost

Pos.Qty

Pos.Avg.Cost

Gross.Txn.Realized.PL

Txn.Fees

Net.Txn.Realized.PL

Con.Mult

txn

--------------------

transactions xts

Updated by

applyStrategy

Updated by

updatePortf



40 60 80 100 120

40

60

80

mm

Chart of moving average crossover performance


strategy



and graphs





Reporting



R Code:

> source("chart_Posn.R")

> chart_Posn(Portfolio=strat.name,Symbol=stock.str)

> add_SMA(n=50 , on=1,col='blue',lwd=2)

> add_SMA(n=200, on=1,col='red',lwd=2)



40 60 80 100 120

40

60

80

mm

Chart of moving average crossover performance



40 60 80 100 120

40

60

80

mm

Outline


2 Position sizing





40 60 80 100 120

40

60

80

mm

The ruleSignal function

ruleSignal is the default rule to generate a trade order on a signal

R Code: The ruleSignal function

> args(ruleSignal)

function (data = mktdata, timestamp, sigcol, sigval, orderqty = 0,

ordertype, orderside = NULL, threshold = NULL, tmult = FALSE,

replace = TRUE, delay = 1e-04, osFUN = "osNoOp", pricemethod = c("market",

"opside", "maker"), portfolio, symbol, ..., ruletype,

TxnFees = 0, prefer = NULL, sethold = FALSE)

NULL

Main arguments:

data an xts object containing market data (defaults to mktdata)sigcol column name to check for signalsigval signal value to matchorderqty quantity for order or ’all’, modified by osFUNordertype ”market”,”limit”,”stoplimit”,”stoptrailing”,”iceberg”orderside ”long”, ”short”, or NULLosFUN function or name of order sizing function (default is osNoOp)



40 60 80 100 120

40

60

80

mm

The osNoOp function

The function osNoOp is the default order sizing function

R Code: The osNoOp function

> args(osNoOp)

function (timestamp, orderqty, portfolio, symbol, ruletype, ...)

NULL

Main arguments:

timestamp timestamp (coercible into a POSIXct object) that will markthe time of order insertion

orderqty the order quanty; modified by osFUN

portfolio name of the portfolio for the order

symbol symbol of instrument

ruletype one of ”risk”, ”order”, ”rebalance”, ”enter”, ”exit”



40 60 80 100 120

40

60

80

mm

Fixed-dollar order sizing function

This order sizing function adjusts the share quantity such that thetransaction value is approximately equal to a pre-defined tradesize

R Code:

> osFixedDollar <- function(timestamp,orderqty, portfolio, symbol, ruletype, ...)

{

ClosePrice <- as.numeric(Cl(mktdata[timestamp,]))

orderqty <- sign(orderqty)*round(tradeSize/ClosePrice)

return(orderqty)

}

function retrieves the current close price and sets order quantity asfollows:

orderqty =tradeSize

ClosePrice



40 60 80 100 120

40

60

80

mm

Re-initializing the .blotter and .strategy environments

It is not possible to run initPortf, initAcct, or initOrders a secondtime in the same R session using the same names

It is necessary to either remove the individual objects or remove all objectsfrom the .blotter and .strategy environments

R Code:

> try(rm(list=ls(pos=.blotter),pos=.blotter),silent=TRUE)

> try(rm(list=ls(pos=.strategy),pos=.strategy),silent=TRUE)



40 60 80 100 120

40

60

80

mm



strategy



and graphs





Reporting



R Code:


> strat.name <- "MAX"

> initEq=1000000

> trade.percent <- 0.01

> tradeSize <- initEq * trade.percent








40 60 80 100 120

40

60

80

mm



strategy



and graphs





Reporting



R Code:

> # indicators:





> # signals:









40 60 80 100 120

40

60

80

mm

Add rules with an order sizing function specified


strategy



and graphs





Reporting



R Code:

> # rules:



ordertype='market', orderside='long', osFUN='osFixedDollar'),type='enter')


arguments = list(sigcol="ma50.lt.ma200",sigval=TRUE, orderqty='all',


The ruleSignal argument osFUN is now set to the name of ourcustom order sizing function for the entry rule

The argument orderqty is now set to ’all’ for the exit rule



40 60 80 100 120

40

60

80

mm



strategy



and graphs





Reporting



R Code:


[1] "2002-04-24 SPY 109 @ 91.9933301492183"

[1] "2002-04-29 SPY -109 @ 89.8492574695683"

[1] "2002-04-30 SPY 110 @ 90.6900702851173"

[1] "2002-05-14 SPY -110 @ 92.674388529813"

[1] "2003-05-12 SPY 123 @ 81.1354076349475"

[1] "2004-08-25 SPY -123 @ 96.8566013672453"

[1] "2004-10-27 SPY 101 @ 98.8180303441752"

[1] "2006-07-25 SPY -101 @ 114.662711057191"

[1] "2006-08-29 SPY 85 @ 118.211406993905"

[1] "2007-12-28 SPY -85 @ 137.165700378348"

[1] "2009-06-18 SPY 113 @ 88.5716570825431"

[1] "2010-07-06 SPY -113 @ 100.84667390332"

[1] "2010-10-15 SPY 86 @ 116.002709490181"



40 60 80 100 120

40

60

80

mm

Transactions with varying quantities

R Code:



2000-12-31 0 0.00000 0 0.000 0.00000 0.0000

2002-04-24 109 91.99333 0 10027.273 91.99333 0.0000

2002-04-29 -109 89.84926 0 -9793.569 89.84926 -233.7039

2002-04-30 110 90.69007 0 9975.908 90.69007 0.0000

2002-05-14 -110 92.67439 0 -10194.183 92.67439 218.2750

2003-05-12 123 81.13541 0 9979.655 81.13541 0.0000

2004-08-25 -123 96.85660 0 -11913.362 96.85660 1933.7068

2004-10-27 101 98.81803 0 9980.621 98.81803 0.0000

2006-07-25 -101 114.66271 0 -11580.934 114.66271 1600.3128

2006-08-29 85 118.21141 0 10047.970 118.21141 0.0000

2007-12-28 -85 137.16570 0 -11659.085 137.16570 1611.1149

2009-06-18 113 88.57166 0 10008.597 88.57166 0.0000

2010-07-06 -113 100.84667 0 -11395.674 100.84667 1387.0769

2010-10-15 86 116.00271 0 9976.233 116.00271 0.0000

Each entry has an approximate value of $10,000



40 60 80 100 120

40

60

80

mm

Calling updatePortf


strategy



and graphs





Reporting



R Code:





40 60 80 100 120

40

60

80

mm

Chart of fixed-dollar MA crossover performance


strategy



and graphs





Reporting



R Code:






40 60 80 100 120

40

60

80

mm

Moving average crossover with fixed-dollar entries



40 60 80 100 120

40

60

80

mm

Percent-of-available-equity order sizing function

This order sizing function adjusts the share quantity such that each tradeis approximately a fixed percentage of the available account equity

R Code:

> osPercentEquity <- function (timestamp,orderqty,portfolio,symbol,ruletype,...)

{

tempPortfolio <- getPortfolio(portfolio)

dummy <- updatePortf(Portfolio=portfolio,

Dates=paste('::',as.Date(timestamp),sep=''))

trading.pl <- sum(getPortfolio(portfolio)$summary$Net.Trading.PL)

assign(paste("portfolio.",portfolio,sep=""),tempPortfolio,pos=.blotter)

total.equity <- initEq+trading.pl

tradeSize <- total.equity * trade.percent

ClosePrice <- as.numeric(Cl(mktdata[timestamp,]))

orderqty <- sign(orderqty)*round(tradeSize/ClosePrice)

return(orderqty)

}

orderqty =(initEq + trading P&L) × trade.percent

ClosePriceGuy Yollin (Copyright© 2011) Quantitative Trading Strategies in R quantstrat-II 37 / 68


40 60 80 100 120

40

60

80

mm



strategy



and graphs





Reporting



R Code:












40 60 80 100 120

40

60

80

mm



strategy



and graphs





Reporting



R Code:

> # indicators:





> # signals:









40 60 80 100 120

40

60

80

mm



strategy



and graphs





Reporting



R Code:

> # rules:



ordertype='market', orderside='long', osFUN='osPercentEquity'),type='enter')


arguments = list(sigcol="ma50.lt.ma200",sigval=TRUE, orderqty='all',


The ruleSignal argument osFUN is now set to the name of ourcustom order sizing function for the entry rule

The argument orderqty is now set to ’all’ for the exit rule



40 60 80 100 120

40

60

80

mm



strategy



and graphs





Reporting



R Code:


[1] "2002-04-24 SPY 217 @ 91.9933301492183"

[1] "2002-04-29 SPY -217 @ 89.8492574695683"

[1] "2002-04-30 SPY 220 @ 90.6900702851173"

[1] "2002-05-14 SPY -220 @ 92.674388529813"

[1] "2003-05-12 SPY 246 @ 81.1354076349475"

[1] "2004-08-25 SPY -246 @ 96.8566013672453"

[1] "2004-10-27 SPY 203 @ 98.8180303441752"

[1] "2006-07-25 SPY -203 @ 114.662711057191"

[1] "2006-08-29 SPY 170 @ 118.211406993905"

[1] "2007-12-28 SPY -170 @ 137.165700378348"

[1] "2009-06-18 SPY 228 @ 88.5716570825431"

[1] "2010-07-06 SPY -228 @ 100.84667390332"

[1] "2010-10-15 SPY 175 @ 116.002709490181"



40 60 80 100 120

40

60

80

mm

Transactions with varying quantities

R Code:



2000-12-31 0 0.00000 0 0.00 0.00000 0.0000

2002-04-24 217 91.99333 0 19962.55 91.99333 0.0000

2002-04-29 -217 89.84926 0 -19497.29 89.84926 -465.2638

2002-04-30 220 90.69007 0 19951.82 90.69007 0.0000

2002-05-14 -220 92.67439 0 -20388.37 92.67439 436.5500

2003-05-12 246 81.13541 0 19959.31 81.13541 0.0000

2004-08-25 -246 96.85660 0 -23826.72 96.85660 3867.4137

2004-10-27 203 98.81803 0 20060.06 98.81803 0.0000

2006-07-25 -203 114.66271 0 -23276.53 114.66271 3216.4702

2006-08-29 170 118.21141 0 20095.94 118.21141 0.0000

2007-12-28 -170 137.16570 0 -23318.17 137.16570 3222.2299

2009-06-18 228 88.57166 0 20194.34 88.57166 0.0000

2010-07-06 -228 100.84667 0 -22993.04 100.84667 2798.7038

2010-10-15 175 116.00271 0 20300.47 116.00271 0.0000

Each entry is approximately 2% of the account value



40 60 80 100 120

40

60

80

mm

Call updatePortf


strategy



and graphs





Reporting



R Code:





40 60 80 100 120

40

60

80

mm

Chart of percent-equity MA crossover performance


strategy



and graphs





Reporting



R Code:






40 60 80 100 120

40

60

80

mm

Moving average crossover with percent-equity entries



40 60 80 100 120

40

60

80

mm

Outline


2 Position sizing





40 60 80 100 120

40

60

80

mm

Reload price history and convert to monthly bars


strategy



and graphs





Reporting



R Code:

> # download all of SPY



> getSymbols(stock.str,from=start.data,adjust=T)

> SPY=to.monthly(SPY, indexAt='endof')



40 60 80 100 120

40

60

80

mm



strategy



and graphs





Reporting



R Code:

> # clear .blotter and .strategy environments











40 60 80 100 120

40

60

80

mm



strategy



and graphs





Reporting



R Code:

> # indicators:


arguments = list(x = quote(Cl(mktdata)), n=10), label="SMA10")

> # signals:

> strat <- add.signal(strat,name="sigCrossover",

arguments = list(columns=c("Close","SMA10"),relationship="gt"),

label="Cl.gt.SMA")


arguments = list(columns=c("Close","SMA10"),relationship="lt"),

label="Cl.lt.SMA")

MA length (n=10) passed in arguments list to add.indicator



40 60 80 100 120

40

60

80

mm



strategy



and graphs





Reporting



R Code:

> # rules:

> strat <- add.rule(strat, name='ruleSignal',

arguments = list(sigcol="Cl.gt.SMA", sigval=TRUE, ordertype='market',

orderside='long', pricemethod='market',osFUN='osPercentEquity'), type='enter')


arguments = list(sigcol="Cl.lt.SMA", sigval=TRUE, orderqty='all',

ordertype='market', orderside='long'), type='exit')



40 60 80 100 120

40

60

80

mm



strategy



and graphs





Reporting



R Code:




40 60 80 100 120

40

60

80

mm

Call updatePortf


strategy



and graphs





Reporting



R Code:





40 60 80 100 120

40

60

80

mm

Chart Faber 10-month SMA system


strategy



and graphs





Reporting



R Code:





40 60 80 100 120

40

60

80

mm

Faber 10-month SMA system



40 60 80 100 120

40

60

80

mm



strategy



and graphs





Reporting



R Code:

> # clear .blotter and .strategy environments











40 60 80 100 120

40

60

80

mm



strategy



and graphs





Reporting



R Code:

> # indicators:


arguments = list(x = quote(Cl(mktdata))), label="SMAn")

> # signals:


arguments = list(columns=c("Close","SMAn"),relationship="gt"),

label="Cl.gt.SMA")


arguments = list(columns=c("Close","SMAn"),relationship="lt"),

label="Cl.lt.SMA")

No MA length parameter specified in add.indicator



40 60 80 100 120

40

60

80

mm



strategy



and graphs





Reporting



R Code:

> # rules:









40 60 80 100 120

40

60

80

mm

Applying the strategy with apply-time parameter passing


strategy



and graphs





Reporting



R Code:

> out <- try(applyStrategy(strategy=strat , portfolios=strat.name,

parameters=list(n=5)))

The moving average length (n=5) is passed in the named-listparameters argument



40 60 80 100 120

40

60

80

mm

Call updatePortf


strategy



and graphs





Reporting



R Code:





40 60 80 100 120

40

60

80

mm

Chart Faber 5-month SMA system


strategy



and graphs





Reporting



R Code:





40 60 80 100 120

40

60

80

mm

Faber 5-month SMA system



40 60 80 100 120

40

60

80

mm

Outline


2 Position sizing





40 60 80 100 120

40

60

80

mm

Initialize currency and trading instruments


strategy



and graphs





Reporting



R Code:

> # define GSPC instrument

> dummy <- stock("GSPC", currency="USD",multiplier=1)

> # download stocks



> getSymbols("^GSPC",from=start.data,adjust=T)

> GSPC=to.monthly(GSPC, indexAt='endof')




40 60 80 100 120

40

60

80

mm



strategy



and graphs





Reporting



R Code:


> # indicators:


arguments = list(x = quote(Cl(mktdata))), label="SMAn")

> # signals:


arguments = list(columns=c("Close","SMAn"),relationship="gt"),

label="Cl.gt.SMA")


arguments = list(columns=c("Close","SMAn"),relationship="lt"),

label="Cl.lt.SMA")

No MA length parameter specified in add.indicator



40 60 80 100 120

40

60

80

mm



strategy



and graphs





Reporting



R Code:

> # rules:









40 60 80 100 120

40

60

80

mm

Applying the strategy across range of parameter values

R Code:

> parm.seq <- seq(3,20,by=1)

> res.vec <- rep(NA,length(parm.seq))

> for(i in 1:length(parm.seq)) {

# initialize portfolio and orders

try(rm(list=ls(pos=.blotter),pos=.blotter),silent=TRUE)

try(rm(list=ls(pos=.strategy),pos=.strategy),silent=TRUE)

dummy <- initPortf(name=strat.name,symbols="GSPC", initDate=initDate)

initOrders(portfolio=strat.name,initDate=initDate)

# apply strategy

N <- parm.seq[i]

applyStrategy(strategy=strat , portfolios=strat.name, parameters=list(n=N))

# calculate performance matric

dummy <- updatePortf(Portfolio=strat.name,


tstats <- tradeStats(Portfolio=strat.name, Symbol="GSPC")

if(is.numeric(tstats$maxDrawdown))

{

returnOnMDD <- tstats$Net.Trading.PL / abs(tstats$maxDrawdown)

res.vec[i] <- returnOnMDD

}

}



40 60 80 100 120

40

60

80

mm

Plot optimization results

R Code: Plot code

> names(res.vec) <- parm.seq

> sort(res.vec,decreasing=T)

17 18 10 9 8 16 20 19 12

7.182873 7.154502 7.140314 7.125639 7.113975 6.941945 5.842226 5.210899 4.945895

15 13 11 14 6 7 5 3 4

4.938753 4.934645 4.928929 4.926389 4.557785 3.924239 3.583817 2.945360 2.925350

> barplot(height=res.vec,names.arg=parm.seq,col="lightslateblue",

xlab="length of MA (months)",ylab="Return on MaxDD")

> title("Return on Maximum Drawdown versus Moving Average Length")



40 60 80 100 120

40

60

80

mm

Optimization performance metric

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

length of MA (months)

Ret

urn

on M

axD

D

01

23

45

67

Return on Maximum Drawdown versus Moving Average Length



quantitative trading strategies in r part 2 of 3

Documents