1.
How to design quant strategies
using R
Saturday, May 16, 2015
Anil Yadav
(Head, Algorithm strategy advisory team at iRageCapital)

2.
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.

3.
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/)

4.
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

5.
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.

6.
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

7.
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

8.
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 CLOSE
11/18/2014 9:15 850.15 853 850.15 852
11/18/2014 9:19 853.89 853.89 851.8 851.8
11/18/2014 9:20 853.97 853.97 853.97 853.97
11/18/2014 9:21 853.97 853.98 853.97 853.98
11/18/2014 9:22 853.98 853.98 853.98 853.98
11/18/2014 9:23 853.97 853.97 853.97 853.97
11/18/2014 9:24 852.51 854.45 852.51 854
11/18/2014 9:25 854 854 854 854

9.
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)

10.
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.

11.
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

12.
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<-xa
tht<-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 + Thresh2
dn<- xb-Thresh2
res <- cbind(xb, dn,up)
colnames(res) <- c("THT", "DOWN", "UP")
reclass(res,CompTh)
}
THTFunc()

13.
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 signal
stratMR <- 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")

14.
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_profit
stratMR <- 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')

15.
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.UpperBand
and Cl.lt.LowerBand
Trading Rule
• Signal Value True
• Order Details

16.
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 strategy
out<-try(applyStrategy(strategy=stratMR , portfolios='MeanRev') )
# look at the order book
getOrderBook('MeanRev')
updatePortf('MeanRev', stock.str)
chart.Posn(Portfolio='MeanRev',Symbol=stock.str)
Strategy output uses tradeStats
tradeStats('MeanRev', stock.str)
View(t(tradeStats('MeanRev')))

17.
Output Blotter::Functions `
chart.Posn(Portfolio='MeanRev',Symbol=stock.str)

18.
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.

19.
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)

20.
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

21.
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.

22.
About QI & EPAT
Quantinsti Quantitative Pvt Ltd. -
Quantinsti developed the curriculum for the first dedicated educational program
on Algorithmic and High-Frequency Trading globally (EPAT) in 2009.
Launched with an aim to introduce its course participants to a world class
exposure in the domain of Algorithmic Trading,it provides participants with in-
house proprietary tools and other globally renowned applications to rise steeply
on the learning curve that they witness during the program.
Executive Program in Algorithmic Trading (EPAT)-
• 6-months long comprehensive course in Algorithmic and Quantitative Trading.
• Primary focus on financial technology trends and solutions.
• It is an online live interactive course aimed at working professionals from diverse
backgrounds such as trading-brokerage services, Analytics, Quantitative roles, and
Programming & IT industry.
• Get placement assistance and internship opportunities with leading global firms
after the program

23.
Program Delivery
• Next EPAT batch starting from 10th January, 2015.
• Weekends only program
– 3 hrs sessions on Saturday & Sunday both
– 4 months long program + 2 months project / internship
– Practical Oriented
– 100 contact hours including practical sessions
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• Virtual Classroom integration
• Student Portal
• Faculty supervision
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24.
Thank you!
Next steps
 Watch QI youtube videos for more learning
 Read more at
http://www.rinfinance.com/agenda/2013/workshop/Hum
me+Peterson.pdf
 Contact us if you wish to learn R for Algo trading
 Questions?
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25.
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