How to design quant strategies using R

Saturday, May 16, 2015

Anil Yadav(Head, Algorithm strategy advisory team at iRageCapital)

Content

What is R?

How can we use R packages in writing quantitative trading strategies?

Steps in development of a quantitative trading strategy

Optimizing the quantitative trading strategy

Disclaimer: The information in this presentation is intended to be general in nature and is not financial product advice.

Introduction to R

R is an open source software. It is free!

Popular because it has packages with readymade functions

Easy to find help for queries or code on internet

Installation: Download and install R-studio from (http://cran.r-project.org)

Help guide: (http://www.rseek.org/)

Packages in R

• We will use the package ‘quantstrat’ for writing our strategy today

– Install the package install.packages("quantstrat", repos=http://R-Forge.R-project.org)

– Install the dependencies (FinancialInstrument, blotter, foreach, doParallel)

install.packages("FinancialInstrument", repos=http://R-Forge.R-project.org)

• Other useful CRAN packages: TTR, quantmod, etc

Writing a quant strategy

The steps are:

1. Hypothesis Formation – what is the idea for trade

2. Testing - statistically testing the hypothesis with data, how much

confidence do you have on your strategy

3. Refining – Optimizing the strategy parameters and paper trading

4. Production - Implementing the strategy in a live trading

environment. This would involve writing the strategy on a trading platform.

Step 1: Hypothesis

What is a hypothesis? This is your trading idea. It could be any

combination of technical trading rules/it could be your “feel” for the market regime. It is the first thing to be derived out of the trading data.

Hypothesis for our strategy:

Market is mean reverting

Step 2: Testing

To test the hypothesis, we will have to write it as a strategy which has

statistical methods to compute the indicators, signals and calculate the profits for the given data.

The steps for the testing part are:

1. Get the data

2. Write the strategy (indicators, signals, trades, PnL)

3. Analyze the output

Data

• Nifty-Bees (ETF) Data from from NSE

(It is a Goldman Sachs managed ETF which trades on the Indian Stock

exchanges. National Stock Exchange has higher volumes for the instrument and therefore the data)

• OHLC data Snapshot below:

Date OPEN HIGH LOW CLOSE11/18/2014 9:15 850.15 853 850.15 852

11/18/2014 9:19 853.89 853.89 851.8 851.811/18/2014 9:20 853.97 853.97 853.97 853.97

11/18/2014 9:21 853.97 853.98 853.97 853.9811/18/2014 9:22 853.98 853.98 853.98 853.9811/18/2014 9:23 853.97 853.97 853.97 853.9711/18/2014 9:24 852.51 854.45 852.51 85411/18/2014 9:25 854 854 854 854

Plot the data

We take a look at the data and plot Bollinger bands to get the first verification on our hypothesis.

chart_Series(NSEI)zoom_Chart("2014-11-19")addBBands(n=20, sd =2)

Writing the strategy

These are the steps in writing the strategy.

Install the packages

Read the data file

Initialize of variables,

parameters

Create Indicators

Generate Signal

Trading rule for execution

Output

Optimize

For our discussion today, we will focus on the parts which are highlighted.

Indicator

•For each row, we check & compare the closing price with threshold value (Thresh)

• If price increases or decreases, threshold is updated accordingly in column THT

•The indicator prices for comparison are updated using Thresh2, saved in UP and DOWN to be used for selling and buying respectively

Signal

•For each row, the closing price is compared with UP (upper band price) and with DOWN (lower band price).

•As per the logic of in-built in ‘sigCrossover’ function, the output is ‘TRUE’ or ‘FALSE’

• If TRUE, trading rule is applied

Trading Rule

•When upper band is crossed, it generates a market order for ‘sell’ position. Orderqty = -1

•When lower band is crossed, it generates a market order for ‘buy’ position. Orderqty = 1

Writing the strategy

Indicator

Indicator

•For each row, we check & compare the closing price with threshold value (Thresh)

• If price increases or decreases, threshold(Thresh) is updated accordingly in column THT

•The indicator prices for comparison are updated using band limit (Thresh2), saved in UP and DOWN to be used for selling and buying respectively

THTFunc<-function(CompTh=NSEI,Thresh=6, Thresh2=3){ numRow<- nrow(CompTh)xa<-coredata(CompTh)[,4]xb<-xatht<-xa[1]for(i in 2:numRow){if(xa[i]>(tht+Thresh)){ tht<-xa[i]}if(xa[i]<(tht-Thresh)){ tht<-xa[i]}xb[i]<-tht}

up <- xb + Thresh2dn<- xb-Thresh2 res <- cbind(xb, dn,up)colnames(res) <- c("THT", "DOWN", "UP")reclass(res,CompTh)}

THTFunc()

Signal

Signal

•For each row, the closing price is compared with UP (upper band price) and with DOWN (lower band price).

•As per the logic of in-built in ‘sigCrossover’ function, the output is ‘TRUE’ or ‘FALSE’

• If TRUE, trading rule is applied

#add your signalstratMR <- add.signal(stratMR,name="sigCrossover",arguments = list(columns=c("Close","UP"),relationship="gt"),label="Cl.gt.UpperBand")stratMR <- add.signal(stratMR,name="sigCrossover",arguments = list(columns=c("Close","DOWN"),relationship="lt"),label="Cl.lt.LowerBand")

Trading Rule

Trading Rule

• When upper band is crossed, it generates a market order for ‘sell’ position. Orderqty = -1

• When lower band is crossed, it generates a market order for ‘buy’ position. Orderqty = 1

#add trading rule long short stop_loss, take_profitstratMR <- add.rule(stratMR,name='ruleSignal', arguments = list(sigcol="Cl.gt.UpperBand",sigval=TRUE, prefer = 'close', orderqty=-1, ordertype='market', orderside=NULL, threshold=NULL,osFUN=osMaxPos),type='enter')stratMR <- add.rule(stratMR,name='ruleSignal', arguments = list(sigcol="Cl.lt.LowerBand",sigval=TRUE, prefer = 'close', orderqty= 1, ordertype='market', orderside=NULL, threshold=NULL,osFUN=osMaxPos),type='enter')

Summarizing the code

Implementation Steps

• Function Block

• Adding Indicator

• Adding Signal

• Adding Rules

Run Strategy

Indicator

• Calls THTFunc

• Updates Up/Down/Thresh

Signal

• Crossover

• Updates Cl.gt.UpperBandand Cl.lt.LowerBand

Trading Rule

• Signal Value True

• Order Details

Analyze output

row.names NSEI

Portfolio MeanRev

Symbol NSEI

Num.Txns 102

Num.Trades 51

Net.Trading.PL 5.02

Avg.Trade.PL 0.098431

Med.Trade.PL 0.1

Largest.Winner 3.8

Largest.Loser -3

Gross.Profits 26.81

Gross.Losses -21.79

Std.Dev.Trade.PL 1.252465

Percent.Positive 54.90196

Percent.Negative 45.09804

#run the strategyout<-try(applyStrategy(strategy=stratMR , portfolios='MeanRev') )# look at the order bookgetOrderBook('MeanRev')updatePortf('MeanRev', stock.str)chart.Posn(Portfolio='MeanRev',Symbol=stock.str)

Strategy output uses tradeStats

tradeStats('MeanRev', stock.str)View(t(tradeStats('MeanRev')))

Output Blotter::Functions `

chart.Posn(Portfolio='MeanRev',Symbol=stock.str)

Writing a strategy

The steps are:

Hypothesis Formation – what is the idea for trade

Testing - statistically testing the hypothesis with data,

how much confidence do you have on your strategy

Refining – Optimizing the strategy parameters and paper

trading

Production - Implementing the strategy in a live trading

environment. This would involve writing the strategy on a trading platform.

Step 3: Optimization

.Th2 = c(.3,.4)

.Th1 = c(.5,.6)

results <- apply.paramset(stratMR, paramset.label='THTFunc', portfolio.st=portfolio.st, account.st=account.st,

nsamples=4, verbose=TRUE)

Step 3: Refining

What other techniques can you use for further refining your strategy? Run the code with more data

Bayesian update for threshold

Threshold 1, 2 can take volatility into account

Writing a strategy

The steps are:

Hypothesis Formation – what is the idea for trade

Testing - statistically testing the hypothesis with data,

how much confidence do you have on your strategy

Refining – Optimizing the strategy parameters and paper

trading

Production - Implementing the strategy in a live trading

environment. This would involve writing the strategy on a trading platform.

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