Upload
prasadpatankar9
View
215
Download
0
Embed Size (px)
Citation preview
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
1/55
Chapter1
AlgorithmTrading
DefinitionandtypeofalgorithmtradingAlgorithmtradingreferstoautomatedtradingbasedonalgorithm.Thepurposeof
usingautomatic
trading
is
to
analyze
price
and
market
conditions
in
order
to
trade
at
the
minimumcostbasedontherelevantalgorithmmethod.
Algorithmic Trading can be defined as the use of computer programs for entering
trading orders where the computer algorithm decides on aspects of the trade execution
such as the timing, price, or quantity of the order. Algorithm Trading is also known as
automatedtrading,algotrading,blackboxtradingorrobotrading.Algorithmsdynamically
monitormarketconditionsacrossdifferentsecuritiesandtradingvenuestomakethetrade
executiondecision.Algorithmictrading(AT) ismorecomplexthanelectronictradingand it
encompassesmanyformofcomputeraidedtradingincludingHighFrequencyTrading.
Algorithmic
Trading
is
widely
used
by
pension
funds,
mutual
funds,
and
other
buy
side(investordriven)institutionaltraders,todividelargetradesintoseveralsmallertrades
inordertomanagemarketimpact,andrisk.Infact,algorithmswereoriginallydevelopedfor
use by the buyside to manage orders and to reduce market impact by optimising trade
executiononcethebuyandselldecisionshadbeenmadeelsewhere.Sellsidetraders,such
asmarketmakersandsomehedgefunds,provideliquiditytothemarketbygeneratingand
executingordersautomaticallywiththehelpofalgorithms.
AlgorithmTradingisbroadlycategorisedintoHighfrequencytrading,BasketTrading,
MultiExchangetradingandothercategory.BasketTrading(Programtrading)isgenerally
usedforstockbaskettrading.Multiexchangetradingistradingaproductacrossdifferent
exchangesusingcomputeralgorithm.MultiexchangetradingisfacilitatedbySmartOrder
Routingalgorithms.
Smart
order
routing
is
atechnique
of
using
the
close
correlation
of
majorexchangesandcrosslistingofacompanyi.e.,placingordersonanexchangewhere
themostfavourableconditionsareseen.InIndia,SEBIalloweduseofSmartOrderrouting
inAugust2010andcurrentlyitisoperationalinbothCapitalMarketaswellasCurrency
DerivativeSegmentOthercategoryheretoreferredasAlgorithmTrading/Algorithmic
Trading/ATinterchangeably,referstoanyandallkindofcomputeraidedtradingwhichdoes
notfallunderfirstthreecategories.Algorithmtradingfocusesonminimizingthemarket
impactbysplittingtrades.Algorithmtradingwasoriginallydevelopedasanorderplacement
systemforthepurposeofminimizingtradingcost,butitisnowbeingusedasanoverall
termtodescribeitsstrategyandprocess.
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
2/55
Strategiesoralgorithmsusedbythecomputertoautomate(enter,modifyorcancel)trade
ordersarecalledalgorithmictradingstrategiesandtheyarebroadlyclassifiedasexecution
strategies,profitseekingstrategies(Alphastrategies)andarbitragestrategies.
Arbitragestrategies
AlphaSeekingstrategies
Executionstrategies
Algorithmtrading
is
widely
used
among
institutional
investors
like
pension
funds
and
investmentcompaniesbecausecostcanbesavedthroughbulkorders,reducingtheimpact
onthemarketandpreventingtheexposureoftradinginformation.Algorithmtradingalso
helpsreducetradingcost.
ThemostcommontypesofexecutionalgorithmareVWAPandTWAP.VWAPsplitsorder
volumebasedonhistoricaldata.TWAPsplitsorderovertime.Themarketparticipation
strategyismanipulatingtradingvolumesothatspecificordersdonotaccountfora
significantpartoftotaltradingvolumeonthemarket.Ontheotherhand,theinlinestrategy
ismanipulatingordersorpricessothattheydonotsurpassalimitprice.
AlgorithmTrading
HighFrequency
Trading
BasketTrading
(ProgrammTrading)
MultiExchangeTrading(Trading
usingSOR)
OtherAlgorithmic
Trading
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
3/55
Whatar
Buyside
securitie
propriet
Buyside
firmspa
moveme
Sellside
theorde
mostco
Sellside
required
Sellside
andthe
Arbit
Delt
Nuet
EventBArbitr
Buysidefi
firmsrefer
s.Privateeq
rytrading
firmsusuall
ticipateina
nts.
firmsrefers
rsbyslicing
monexam
brokerages
tobemark
firmsprofit
idoffersp
agestrat
alStaAr
sedge
rmsandSe
toinstitutio
uityfunds,
esksareth
ytakespec
smallernu
toinstituti
thenintos
plesofsell
areregister
tmakersin
fromCom
ead.
egies
tisticalitrage
llsidefirms
nsconcerne
mutualfun
mostcom
lativeposit
berofove
nsthattak
allorders.
idefirms.
dmember
agivensec
ission(brok
Alpha
Stra
DirectionalTrading
Strategies
ScalpingStrategies
?
dwithbuyi
s,unittrust
ontypeso
ionsormak
ralltransact
ordersfro
Brokeragefi
ofastock
urity.
erage),advi
Seeking
tegies
MeanRevesio
Strategi
DarkLiquiditSeekin
Strategi
ng,rathert
s,hedgefu
fbuysidee
relativeva
ions,andai
buysidef
irmsandM
xchange,a
soryfeecha
ns
y
s
Ex
Ben
alg
(VImpleonSParti(%VTarg
TWA
anselling,
ds,pension
ntities.
luetrades.
mtoprofitf
irmsandth
rketmakin
ndsometim
rgedtobuy
ost
RedcutionS
hmarkrithms
WAP,mentati
hortfall,ipation
olume),tClose,
,InLine,
tc)
ssetsor
funds,and
Buyside
rommarket
n"work"
firmsare
estheyare
sidefirm
ction
/
trategies
Liquidityseeking
algorithm
(CrossFireHunt,Icebe
etc)
,rg,
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
4/55
Algorithmtradingsystem
Source:Exchangehandbook
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
5/55
Chapter2
AT WherewestandgloballyandinAsia?
OnMarketEnvironmentFront
Singapore&HongKongScoreTopinchart
India
Japan
Australia
Taiwan
China
OnRegulatoryForcesFront
HongKong
Australia
Singapore
Japan
Taiwan
India
China(MostRestrictedmarketinworldfortrading)
OnTechnologyEnablersFront
Singapore(HighestinvestmentontechnologyfrontinAsia)
Australia
Japan
HongKong
India
Taiwan
China
OnAlgorithmicTradingFront
HongKong(48%ofvolumetradedalgorithmically)
Singapore
Australia
India&Japan
China
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
6/55
AlgorithmicTradinginAsiaPacific
Amongasiapacificcountries,algorithmicactivityishighestinHongKong(39%)followedby
Singapore(31%),Japan(26%),Australia(20%) andIndia(15%).Algorithmictradingin
HongKongisslatedtoreach61%in2012fromcurrent39%.Indiawascautiousinitsuseof
AlgoTrading,
but
has
lot
of
potential
to
grow.
By
the
end
of
2012,
AT
activity
in
India
is
expectedat30%fromcurrent12%.
Source:CelenetResearch
Asiaisaveryexcitingmarketforelectronictradingofcashequityandderivatives.Whilethelevelofdevelopmentofeachmarketdiffers,theleadingexchangesinJapan,Australia,Singapore,HongKong,andIndiashouldsetthetone,withfastermatchingengines,enhancedmarketdata,andcolocationservices.Withahighproportionofitsdomesticbuysidebeginningtousealgorithmictrading,HongKongisbestpoisedtogrowintheregion.ItisfollowedbySingapore,wheretheregulatoryenvironmentisveryconducivetoalgotrading.Similarly,Japan,andAustraliaareexpectedtohavehigheralgotradingincomingyears.Finally,Indiahasbeenaslowstarterbutisrapidlycatchingupwithsomeofitscounterpartsandisexpectedtohavealgotradinglevelsofaround30%by2012 AnshumanJaswal,CelentSeniorAnalyst
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
7/55
EvolutionofElectronicTradinginIndia
HFTisnotyetintroducedinIndia.
Thefollowing
is
the
solution
presented
by
the
Singapore
Stock
Exchange
to
reduce
order
fulfillmenttime:1)improvingdataqualitybyusingColocationandexpandingnetwork
bandwidth;and2)reducingorderfulfillmenttimebycombiningdifferenttechnologiesvia
efficientapplicationsolutionsandlayerednetworks.Inaddition,theexchangeisusing
advancedsoftware,whiledevelopingappliancebasedchiptechnology.
1994' 200001 200304 2008' 2010' 201011 2011'
MilestonesinIndianEquityMarkets
DomesticBuySide startedAlgoTradingoperations
SmartOrderRoutingIntroduced
(DomesticSellSide startedAlgoTradingoperations)
DMAIntroduced
AlgorithmicTradingBegins
IntroductionofDerivativesinIndia
InceptionofNSEandStartofelectronicTrading
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
8/55
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
9/55
DirectMarketAccessFacility
DirectMarketAccess(DMA)isafacilitywhichallowsbrokerstoofferclientsdirectaccessto
theexchangetradingsystemthroughthebrokersinfrastructurewithoutmanual
interventionbythebroker.
Someof
the
advantages
offered
by
DMA
are
direct
control
of
clients
over
orders,
faster
executionofclientorders,reducedriskoferrorsassociatedwithmanualorderentry,
greatertransparency,increasedliquidity,lowerimpactcostsforlargeorders,betteraudit
trailsandbetteruseofhedgingandarbitrageopportunitiesthroughtheuseofdecision
supporttools/algorithmsfortrading.
DMAtrafficaccountsfor15 18percentoftotaltradingvolumesintheUSand8percentin
Europe.DirectMarketAccesstradersinhighfrequencyprogramshavethegreatest
demand.
SEBIallowedTradingMemberstoprovideDMAfacilityinApril2008fortradinginequities,
futuresand
options
segment.
Connectivity
IndianbrokersneedFIXconnectivitytoattractinternationalorderflowandmanyhave
madesignificantinvestmentsinFIXnetworksandordermanagementsystems(OMSes).The
widespreadadoptionoftheFIXprotocoloverthelasttwoyears,allowedmanycountriesto
allowDMAfacility.
InIndiabothNSEandBSEhaveFIXconnectivity.
Colocation
IndianExchangeshavestartedprovidingcolocationfacilitytotheirmemberbrokers,
wherebytheycanplacetheirtradingserversclosetotheexchangesengineonafirstcome
firstservedbasis.Colocationsavescrucialmillisecondsfromthetimeittakestoplacean
orderanditsreceiptattheotherend.Thebrokerwithhisservernexttotheexchange
enginegetsapricefeedthatisupdatedeverythreefourmilliseconds,whileabrokerata
remoteplacewillgetthisfeedupdatedevery3040milliseconds.
NSEstartedcolocationfacilitiesforitsmembersinJanuary2010andthepaceofadoption
isfascinating.
With
co
location
facilities,
members
can
set
up
automated
trading
systems
in
thesamebuildingastheexchange.Withthis,thetimetakenformarketdatagoingoutfrom
theexchangeandforordermessagestocomeinfrommembers(alsoknownaslatency)
reducesconsiderably
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
10/55
K
80
Inarece
National
colocat
Latency
Latency
relyonv
exchang
NYSETe
TimeLa
TimeLa
Acknowl
TimeLa
at(time
executio
R
microse
Ordersp
Capacity
Capacity
Withint
pacewit
increase
Inrespo
to20,00
RX
,000S
H
10,
ntFuturesI
StockExcha
dservers.
refersto
am
erylowlate
sareinves
hnologies
fordatafe
fororder&
edgment
forRound
fromorder
nto
trade
c
ecently,NY
ondslatenc
eed(Round
refersto
ab
oductionof
heverincre
capacityin
setochan
ordersper
KE
000S
SG
10,0
dustryAss
nge(NSE)s
ountof
tim
ncy(i.every
inginnew
niversalTr
d
cancel
ripExecuti
confirmatio
nfirmation
Elaunched
yatrateso
TripExecu
ilityof
an
e
Algorithm
asingcapaci
rdertofaci
ingenviron
second.NS
X
00BS
10,0
S
ciation(FIA
aid60%oft
taken
to
e
quickdata
echnologyt
adingPlatf
n
ntotrade
Ethernetb
1million2
ionTime)a
changeto
radingnow
tydemands
ilitateMultil
ment,BSEi
Ealsoiswo
0LSE
6,00
S
)conferenc
heordersc
lectronicallytransmissio
oreducela
rm
Nasdaq
NYSElis
BATS:4
DirectEd
sedmarke
0bytemes
tmajorexc
andleorde
manystoc
.Theyarei
lateralTradi
creasedca
rkingtowar
TSE
5,000S
inMumba
mingintot
send
or
rec
)fortradin
ency.
3mi
2mi
listedissue
edissue
0microsec
ge:30050
datadeliv
sagesperse
anges
flowat
an
exchanges
vestinginn
ngFacility.
acityfrom
sincreasin
NSE
5,000S
i,anofficial
heexchang
eiveinform
gstrategies.
lliseconds
lliseconds
650micro
50microse
nds
microseco
rysolution
condperc
given
poin
arestruggli
ewtechnol
1000orders
capacity.
ASX
250
S
fromthe
werefrom
ation.HFTs
.Worldwide
econds
onds
ds
thathas25
re.
intime.
ngtokeep
giesto
persecond
NDAQ
143S
NYSE
25
S
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
11/55
AverageTradesizeandNumberoftrades
Averagesharespertrade(AverageTradesize)decreasedby83%from1,477sharesin1997
to244sharesin2009,whileAveragetradesperDay(NumberofTrades)increasedby26
timesfrom743tradesin1997to19,943tradesin2009.AveragesharesPerDayincreased
from1.09
mn
(1477*743)
in
1997
to
4.86
mn(19943*244)
in
2009.
The
figures
show
that
thereisbeenincreaseinnumberoftradesexecutedanddecreaseinordersize.Thiscanbe
attributedtointroductionofalgotradingwhichslicelargeorderintomanysmallorders.The
negativeimpactofthisisthatitmayfloodthesystemwithlargenumberoforders
increasingtheloadonexchangestradingsystem.Alsoitincreasesclearingandsettlement
costsfortraders.
AverageOrderCancellationRate
SinceintroductionofAT,OrdertoTraderatiohassignificantlygoneupinmanycountriesincluding
India. AtNYSEordertotraderatiois30:1,whichmeansonlyoneorder,outof30ordersentered,
getsexecutedand29othersareeithermodifiedorcancelled.ATBSEordertotraderatiois19:1.
Theincreaseintheaverageordertotraderatioandaverageordercancellationratemaynobethe
concern
as
long
as
exchanges
have
built
sufficiently
large
capacity
and
have
limited
order
entry
rate
throughalgorithms.
0
200
400
600
800
1,000
1,200
1,400
1,600
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
Avg.
Shares/Trade
US Equity Share volume and trades
Avg Shares Per Day (mm) Avg Trades Per Day Shares / Trade
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
12/55
Chapter2
RegulatoryActionsWorldwide
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
13/55
Chapter3
Reasonsforusingalgorithmsintrading
TypesofAlgorithmsUsedbythebuysidefirms
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
14/55
Source:Algorithm
Trading
Survey
2010
conducted
by
Trading
screen
Overtheyears,algousagehasevolvedfromlesssophisticatedparticipation
strategiessuchasVWAPandTWAPtomorecomplexpriceimprovementapproachesthat
seektominimiseslippagefromatargetprice.Asaresultin2010,Implementationshortfall
(IS)forsinglestockshasriseninpopularity,upfrom39%in2008to68%.VWAPsawthefirst
notabledeclinesince2008initsuseamongbuysiderespondentsdownfrom64%to58%.
UseofTWAPalsodeclinedmarginallyfrom23%in2010to20%in2009.
Darkliquidityseekingalgorithmssearchesallthedarkpoolseliminatingtheneedto
splitanorderinsearchofliquidity.Darkliquidityseekingalgorithmsaremostwidelyused
algorithmsbybuysidefirmsin2010,constituting81%ofrespondentsuse,upfrom51%a
yearago.Similarly,albeitfromalowbase,therehasbeenafivefoldincreaseintheuseof
algorithmsto
take
advantage
of
internal
crossing
opportunities,
up
from
5%
in
2009
to
25%
in2010.Percentageofrespondentsusingvolumeparticipationstrategyandbasket
implementationshortfallstandat62%and20%respectively.
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
15/55
61%volumeinUS
39%volumeinUS
SourceTABBGroup
HFTs(MarketMakers/LiquidityProviders/SellSidefirms)
NonHFTs(Investors/BuySidefirms)
4%
28% 29%
3%
7%
13%16%
HFTHedge
Funds
HFTBroker/
MarketMakers
Independent
HFT R
etail
HedgeFunds
LongOnly
Investment
BankProp
US Equity Share Volume by Market Participant-2009
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
16/55
AlgorithmicTrading HighFrequencyTrading
AT istheuseofcomputerprogramsforentering
trading orders with the computer algorithm
deciding on aspects of the order such as the
timing,price,orquantityoftheorder
HFT is highly quantitative, employing
computerized algorithms to analyze incoming
market data and implement proprietary trading
strategies
Algorithmic Trading is computer aided but not
necessarilyHighfrequencyorlowlatencytrading
HFTispartofAlgorithmicTrading
Accounts for70%volume traded inUSequity in
2010.NonHigh frequencyAlgorithmicTrading is
only14%inUS.
Accounts for56%volume traded inUSEquity in
2010.
ATfirmsholdingperioddependsonstrategythey
employ.Theymayholdpositionsforfewminutes
or for few years or they might never sell it.
Typicallyaverageholdingperiodislong.
HFT usually implies a firm holds an investment
positiononlyforverybriefperiodsoftime even
justseconds andrapidlytrades intoandoutof
thosepositions
AT firms include both DayTraders and Position
Traders
and
may
carry
overnight
position
dependingonstrategytheyuse
HFT firms are typically DayTraders and end a
trading
day
with
no
net
investment
position
in
thesecuritiestheytrade.
AToperationsareusuallyfoundinBuySidefirms
(such as Mutual funds, Insurance companies,
Pension funds, FII, Investment Banks, some
HedgefundsandsomeProprietarytradingfirms)
HFT operations are usually found in proprietary
firms or on proprietary trading desks in larger,
diversifiedfirms
Selecting High Profit Making and properly
backtested algorithms is more important than
speed.SpeeddoesmatterinATtoo,butneedfor
speed
is
not
an
utmost
priority.
HFT strategies are usually very sensitive to the
processingspeedofmarkets(alsocalledLatency)
andoftheirownaccesstothemarket.
ApplicationofAlgoTrading
Directional Trading : Trend Following, Pair
Trading,MeanReversion,Scalping
Arbitrage : Delta Neutral Strategies, Statistical
Arbitrage
Transaction cost reduction(Trade Execution) :
VWAP,TWAP,Liquidityseeking, Implementation
shortfall
ApplicationofHFT
RebateTrading,MarketMaking,
FilterTrading,MomentumTrading,
StatisticalArbitrageandTechnicalTrading
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
17/55
4.1Dat
Datasa
areasfo
AA
AAPL
ABD
ADBE
AGN
AINV
AMAT
AMED
AMGN
AMZN
ANGO
APOG
aSample
plechosen
llows
Sa
ARCC
AXP
AYI
AZZ
BARE
BAS
BHI
BIIB
BRCM
BRE
BW
BXS
HFTand
forstudyin
ple:List
Z C
B C
BEY C
BT C
BZ C
CO C
DR C
ELG C
ETV C
HTT C
KH C
MCSA C
Ch
itsimpa
impactof
f120stock
NQR CTS
OO DC
OST DE
PSI DIS
PWR DK
R DO
RI EB
RVL EBF
SCO ERI
SE ES
SL EW
TRN FC
apter4
tonMa
FTinstock
listedone
H FFIC
M FL
L FME
FPO
FRED
FULT
Y GAS
GE
E GEN
X GILD
BC GLW
GOO
rketQua
listedone
itherNASD
GPS
HON
HPQ
IMGN
INTC
IPAR
ISIL
ISRG
JKHY
KMB
KNOL
KR
lity
itherNASD
QorNYSE
KTII
LANC
LECO
LPNT
LSTR
MAKO
MANT
MDCO
MELI
MFB
MIG
MMM
QorNYSE
MOD
MOS
MRTN
MXWL
NC
NSR
NUS
NXTM
PBH S
PFE S
PG S
PNC S
NY
PD
TP
IGL
OC
OCK
OG
VI
F
FG
JW
WN
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
18/55
Source:HighFrequencyTradingandItsImpactonMarketQualitybyJonathanBrogaard,
Sept2010
Note:Thesestatisticsareanaggregatefor26HFTfirmstradingactivityin120stocksfor
fiscalyearendingonDecember31,2009.
4.2HFTactivityinUS
HFTsareinvolvedin68.5%ofalldollarvolumetradedinthesample.Theirlevelofdaily
involvementvariesfrom60.4%to75.9%.Theydemandliquidityin42.7%ofalldollar
volumetradedandsupplyitin41.1%.
HFTsparticipatein73.8%ofalltradesandparticipationvariesatthedaylevelfrom65.1%to
81.9%.Theydemandliquidityin43.6%ofalltradesandsupplyitin48.7%.
PanelA >DollarVolumetradedbyHFTsaspercentageofTotalDollarVolumetradedinthe
sample
of
120
stocks
PanelB >QuantitytradedbyHFTsaspercentageofTotalQuantitytradedinthesampleof
120stocks
Source:HighFrequencyTradingandItsImpactonMarketQualitybyJonathanBrogaard,
Sept2010
Note:Thesestatisticsareanaggregatefor26HFTfirmstradingactivityin120stocksfor
fiscalyearendingonDecember31,2009.
4.3DoHFTsfleeinvolatilemarkets?
WhileHFTmaybeincreasinglyprovidingliquiditytothemarketintheplaceofmore
traditionalmarketmakingactivities,someinvestorssuggestedthatunlikeregisteredmarket
makersontradingvenuesthereisnoobligationorincentiveforhighfrequencytradersto
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
19/55
continuetoprovideliquiditytothemarketintheeventofadversemarketconditions(high
volatility).
ResearchshowsasindicatedinGraph1,HFTsactivityappearstobealmostflat
acrossvolatilitylevels.EvenonthemostvolatiledaysHFToverallactivitydoesnotseemto
increaseordecreasesubstantially.However,whenvolatilityislowHFTsactivityislessthan
average.
Where,
HFT%ChangePercentchangeinHFTactivity
HFT%MA(9) 9DaymovingaverageofpercentchangeinHFTactivity
95%confMA(9)UpperandLowerpricebandofHFT%MA(9)
Source:
HFTprovide
about
10%
more
liquidity
than
usual
on
very
low
volatility
days.
The
level
of
HFT
liquidityslowlydeclinesasvolatilitypicksup,atthehighestvolatilitytheHFTliquidityis
about10%lessthanonanaverageday.
HFTactivitymorethanaverage
HFTactivityLessthanaverage
HFTactivityLessthanaverage
HFTactivitymorethanaverage
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
20/55
OntheleastvolatiledaysHFTtakeabout7%lessliquiditythannormal,andonthemost
volatiledaystheytakearound5%moreliquiditythannormal.
TheseresultsshowthatHFTactivitydoeschangewithvolatility,butnotprecipitously.In
particularonthemostvolatiledays,HFTdonotpulloutofthemarket.Onthesedays,there
doesseemtobea510%transferofHFTactivityfromsupplyingliquiditytodemanding
liquidity.
4.4DoHFTscontributetoPriceDiscoveryProcess?
Ofthe118stocks,68stocksshowashavinggreatercontributiontopricediscoveryprocess
and28
of
those
stocks
HFT
Non
HFT
contribution
is
statistically
significant.
In
the
50
stocks,
wherenonHFTcontributionisgreaterthanthatofHFT,thedifferenceisstatistically
significantfor7firms.
OnaverageHFTcontribute86%,[(0.1950.105)/0.105*100],moretopricediscoverythando
nonHFT.BasedontheseresultswecansafelysaythatHFTcontributetopricediscovery
process.
Table14:HFT nonHFTVarianceDecomposition
HFTactivitymorethanaverage
HFTactivityLessthanaverage
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
21/55
WhereHFT%showsVariance(EfficientPrice,HFTtradeprice)
NonHFT%showsVariance(EfficientPrice,NonHFTtradeprice)
TStattstatisticsfordifferencebetweenHFTandNonHFTcontributiontopricediscovery
Source:
4.5WhatroleHFTplaysinprovidingmarketliquidity?
ThissectionanalyzesHFTandthesupplyofliquidity.
a) DoHFTordersprovideinsidequotesmoreoftenthanNonHFTorders?
Insidequoteiseitherbestbuyquoteorbestsellquote
Table16:TablereportsnumberofminutesduringwhichHFTsareatthebestbidoroffer.
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
22/55
(ThetimeduringwhichbothHFTsandnonHFTsarebothatthebestquotesisalsoincluded
inthevalue.)
PanelAallstocksatalltimes,
PanelBthestocksthatareofferinglowerspreadsthanaveragealltime
PanelCthestocksthatareofferinglowerspreadsthanaveragealltime
IneachPanelthedataaredividedintothreegroups,with40firmseach,basedonfirmsize
Viz.Small,MediumandLarge
Sample=780=2(Providingeitherinsidebid/offerevery
minute)*60(minutes/hour)*6.5(tradinghours)=NumberofminutesthataHFTcould
potentiallybeprovidingatleastoneinsidequote
PanelAshowsthatHFTfirmsfrequently(ataround65.3%(=509.3/780*100)ofthetimeof
theday)providethebestbidorofferquotes.AsthefirmsizeincreasesHFTsaremore
competitiveintheirquotes,matchingorbeatingnonHFTsquotesforasignificantportionof
theday.
AlsothereisnosignificantdifferencebetweenliquidityprovidedbyHFTfirmsinHighspread
&Lowspreadstocks
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
23/55
b) DoesHFTprovidedepthtotheorderbook?
Inprevioussection,theanalysisonliquidityhasbeenbylookingatthebestinsidebidand
asks.AnotherwayoflookingatHFTimpactonliquidityisbylookingatthedepthofthe
booksuppliedbyHFT.PanelAandBshowsincreaseinimpactcostduetowithdrawalof
eitherHFTordersorNonHFTorders.
Firmsare
divided
based
on
their
market
capitalizations.
Very
Small
includes
firms
under
$
400million,Smallarethosebetween$400millionand$1.5billion,Mediumarethose
between$1.5billionand$3billion,andlargeareforfirmsvaluedatmorethan$3billion.
DollarDollarincreaseinimpactcost
BasisPercentincreaseinimpactcost
PanelAshowstheresultsofremovingHFTfromthebook.Asthetradesizeincreases,the
priceimpactincreasesacrossfirmsofallsizesandforalltentradesizeincreases.TheSmall
categorytendstobemoreimpactedbythewithdrawalofHFTliquiditythanistheVery
Smallcategory.
PanelB
shows
the
results
of
removing
non
HFTs
from
the
book.
AcrossallcategoriestheremovingofnonHFTshasamuchlargerimpactthandoesthe
removalofHFTs.ThismeansthatalthoughHFTssupplyliquidityin41%ofalldollarstraded,
theyprovideonlyafractionofthedepthcomparedtononHFTs.
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
24/55
Conclusion:Beyondsupplyingliquidityin51.4%ofalltrades,HFTsfrequentlysupplythe
insidequotesthroughouttheday.Whileremovingeithertypeoftraderwouldresultina
priceimpactsoftrades,removingnonHFTshasalargerimpact
4.6DoHFTgenerateordampenvolatility?
Assumption:Prices
would
have
achieved
their
actual
levels
even
in
the
absence
of
high
frequencytradesbutwouldjumparoundmore.
Ofthe120firms,72ofthemhaveahighervolatilitywhenHFTrinitiatedtradesare
removed.Further,AsmallmajorityoffirmsexperienceslightlyhighervolatilitywithoutHFTr
initiatedtrades.Howeverofthese72stocks,onlyoneisstatisticallysignificant.This
indicatesvolatilitywouldhaveincreasedintheabsenceofHFT.
Ofthe48stockswheretheremovalofHFTrinitiatedtradesreducesvolatility,suggestiveof
HFTscausingvolatility,noneshowastatisticallysignificantdifferenceinvolatility.
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
25/55
Conclusion:TheoverallresultsshowthatwhenHFTrinitiatedtradesareremoved,volatility
increasesandthisdifferenceisstatisticallysignificant.Admittedlytheresultsarenotstrong
inonedirectionoranother;theyleaninfavourofHFTreducingvolatility.
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
26/55
Chapter5
PossibleManipulationsUsingHFT/AlgorithmicTrading1) FrontRunning
Frontrunningisanillegalactivityinwhichatradertakesapositioninanequityin
advanceofanactionwhichhe/sheknowshis/herbrokeragewilltakethatwillmove
theequity'spriceinapredictablefashion.
ApartfromFlashTrades(discussedbelow),whichsomebelieveispartofFront
Running,thereisnooccurrenceoffrontrunningusingAlgorithmicTrading.
FlashTradingServicesshowstockpricinginformationtosomeselectmember
around500millisecondsbeforeroutingittopublicmarket.Ifsuchordersareoflarge
quantity,fewmemberscantakeadvantageofitbyfrontrunning.
In2008,SEBIallowedstockexchangestoofferDMAfacility,whichmanybelieve
helpsInstitutionswithlargeorderstoexecutethetradesdirectlyonstockexchange
withoutmanualinterventionofTradingMember.
ThereisnoconclusiveevidencethatwoulddefinerelationshipbetweenFront
runninganduseofAlgotrading.Andhence,useofAlgorithmicTrading(without
usingDMA)willnothaveanysignificantimpactonIncreaseordecreaseofnumber
ofinstancesoffrontrunning.
2) Orderstuffing/Quotestuffing
Quotestuffingisessentiallyadenialofserviceattack,aimedattryingtoslowdown
amarketinanenvironmentwheremillisecondsmatter.
Withquotestuffing,highfrequencytradersenteranenormousnumberofbidsand
offers,significantlyoutsidethecurrentbidofferspread,justtointroduceavast
amountofnoiseintothequotefeed.Allthatnoisetakestime(maybejustafew
extrananoseconds)forrivalHFTshopsandExchangesTradingSystemstoprocess,
givingthequotestufferacrucialtimeadvantage.
TheSECislookingintowhetherquotestuffingexists,andwhetheritsastrategythat
anybodyhasactuallyusedtomakemoney.Theyarealsoinvestigatingwhether
Quote
stuffinghasanyinvolvementinFlashCrashthathappenedonMay6,2010.
InIndia,myguessisquotestuffingmaynotexistsatthispointintime,givenvery
lowparticipationbyHFTsbutagainwecannotruleoutchanceofhappeninginthe
futurewith
more
and
more
penetration
by
HFTs.
Quote
stuffing
is
worldwide
regardedasveryseriousactandshouldbepunishedseriouslyifattemptedbyany
marketparticipants.
3) FlashTrades
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
27/55
Flashordersshowsstockpricinginformationforabriefperiodtoalimitedgroupof
membertraderswhocanthendecidewhethertofillanorderbeforeitisroutedout
tothepublicmarket.
DirectEdge,anelectroniccommunicationsnetworkbackedbyGoldmanSachs,
CitadelandKnightTrading,wasthefirsttooffersuchanordertypetoitsmembers
in2006.
NASDAQ
and
Bats
(U.S.
exchanges)
created
their
own
flash
market
in
early
2009inresponsetotheDirectEdgemarket.Bothvoluntarilydiscontinuedthe
practiceinAugust2009.
DirectEdgebecameaU.S.exchangeinJuly2010andusedflashtradingtogreat
successinsiphoningmarketvolumeawayfromNYSE,BatsandNasdaqandnowit
claimsthirdlargestexchangeinUS.CurrentlySECisconsideringbanningFlash
TradinginUSfollowingcomplaintbyEuronextoperatorofNYSEandGletcoOneof
thetopmarketmakersinUS.
Officially,sofar,nostockexchangeinIndiaisofferingthisservicebutintroductionof
SORmay
open
the
gates
of
opportunities
for
stock
exchanges
to
attract
institutional
tradersbyprovidingflashtradingfacility.Flashtradingservicegivesunfairadvantage
toselectgroupofpeople/institutionsattheexpenseretailinvestors.Thisserviceis
detrimentaltothedevelopmentofsecuritiesmarketandSEBIshouldenact
regulationstobanitsoperationinIndia.
4) Internalisation
Internalisationreferstoabilityofbrokerstomatchcustomersawayfrompublic
markets.
InIndia,SEBIhasbannedinternalcrossingofordersandmandatedthatall
ordersenteredbyclientsmustbesubmittedtocentralordermatchingsystemofthe
exchanges.Thispreventssettingupofstealthstockexchanges,darkpoolsorprivate
tradingvenues,stealingliquidityawayfromthemarket.
Inspiteofhavingsuchstrongregulations,Icannotruleoutthepossibilityof
useofalgorithmstodevelopInternalCrossingsystems,whichIamsuremany
brokeragefirmswouldbeetchingtostartinordertoreducetransactioncostfor
theirproprietarytrades.HighcostoftransactioninIndiacouldactuallyworkasan
impetusforuseofsuchsystems.
5) Layering
LayeringisatechniqueofenteringLimitOrderswithlargequantityatdifferentprice
levelonanysideoforderbookinordertocreateafalseappearanceofbuy orsell
sidepressure.
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
28/55
Person,whowantstobuyastock,entershiddenbuyorderatbestbidandenters
numerousselllimitorderwithlargequantityatsuchapricelevelsthathavelow
probabilityofgettingexecuted.Thiscreatesanimpressionthattherearemany
sellersinthescriptandhenceitmaygodowninducingotherpeopletoselltheir
stockstobuyer(manipulator)
InIndia,Layeringmaynotbeusedthatextensivelymainlybecauseabsenceof
DeepOrderBook.Only5bestbidsand5bestoffersareprovidedinIndiafora
particularscriptasagainstallpossibleordersprovidedinUS.Thislimitstheabilityof
DayTraderstotakeacallbasedondemandandsupplyofthestock. Layering
requiresasmanylimitorderstobevisibletoeffectivelyimplementit.
Havingsaidthis,IstillsuspectmanycasesofmanipulationusinglayeringinIndiain
LowVolumeorilliquidscriptswithoutthehelpofHFT.Permittinguseofalgorithmic
tradinginLowVolumestocks,mayaggravatethisproblem.Soifpermitted,itshould
beverifiedthatLayeringalgosarenotpermittedtouse.
FINRAimposed$1millionpenaltyonTrilliumBrokerageServices,LLCforusinganillicithighfrequencytradingstrategy
Trillium,throughnineproprietarytraders,enterednumerouslayered,nonbonafide
marketmovingorderstogeneratesellingorbuyinginterestinspecificstocks.By
enteringthenonbonafideorders,ofteninsubstantialsizerelativetoastock's
overalllegitimatependingordervolume,Trilliumtraderscreatedafalseappearance
ofbuy orsellsidepressure.
Thistradingstrategyinducedothermarketparticipantstoenterorderstoexecute
againstlimitorderspreviouslyenteredbytheTrilliumtraders.Oncetheirorders
werefilled,theTrilliumtraderswouldthenimmediatelycancelordersthathadonly
beendesignedtocreatethefalseappearanceofmarketactivity.Asaresultofthis
improperhighfrequencytradingstrategy,Trillium'stradersobtainedadvantageous
pricesthatotherwisewouldnothavebeenavailabletothemon46,000occasions.
Othermarketparticipantswereunawarethattheywereactingonthelayered,
illegitimateordersenteredbyTrilliumtraders.
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
29/55
6) Darkpools
7) WashTrades
Anillegalstocktradingpracticewhereaninvestorsimultaneouslybuysandsells
sharesinacompanythroughtwodifferentbrokersinordertoincreaseturnoverand
inturncapturetheattentionofgeneralpublicenticingthemtotradethestock.
Theresearch
conducted
by
ASX
suggests
that
there
is
been
an
increase
in
the
numberoftradesbeingcancelled,majorityofwhichwereWashTrades,from0.16%
inJan2009to0.39%inAug2009.ThereasonbehindthisismanyAlgoTradingfirms
employdifferenttradingStrategiesvizarbitragestrategies,marketmaking
strategies,directionalstrategiessimultaneously.Thisincreaselikelihoodthatbids
andoffersenteredfromthosealgoswillinadvertentlyexecuteagainsteachother.
8) Liquidity Detection
Liquiditydetectionisanumbrellatermfortradingstrategiesthatinvolvesendingsmall
ordersto
look
for
where
large
undisclosed
orders
might
be
resting,
on
the
assumption
that
Whenasmallorderisfilledquicklythereislikelytobealargeorderbehindit.Someofthe
commonliquiditydetectionstrategiesare:
Pinging:sendingoutlargenumbersofsmallorderswiththeintentionofgettingafillorto
gaininformationaboutelectroniclimitorderbooks;
Sniper:anAlgorithmthattriestodetecthiddenliquiditybytradinginroundoroddlots
untilitcompletesorreachesaninvestorslimitprice;
Sniffing:Usedtosniffoutalgorithmictradingandthealgorithmsbeingusedbysendinga
smallportionofanorderwaitingtoseeifsomeonecomesandgetsit.Sniffersattemptto
outsmartmanybuysidealgorithmictechniqueslikeiceberging.
AsurveywasconductedbyTABBgrouptofindwhetherliquiditydetectionisaformof
manipulation.
74%
of
market
participants
said
that
they
did
not
regard
liquidity
detection
as
aformofmanipulation,becausethetraderdoesnothavedirectknowledgeofanorder.
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
30/55
Concerns:
a) AreNSEandBSEequippedforpotentialattackontheirservers/Ordermatching
systemsbyQuoteStuffers?Ifyes,havetheydonestresstesting?
b) Havetheybuiltsufficientlystrongordermatchingsystemwithlargecapacity,
Highthroughout&LowLatency(TurnaroundTime)tohandleLargeOrderFlow
attremendous
speed?
c) Arestockexchangescapabletodetectandstopalloftheabovemanipulations
onrealtimebasis?AndIfyes,How?
d) HowstockexchangeswillensuresApprovedLimitsarenotviolatedbytrading
membersandtheirclientsonrealtimebasis?
e) Whatpunitiveactionswillbetakenifexchangesfoundanyofthe
abovementionedmalpracticesusedbyTMortheirclients?Orwilltheyoncase
tocasebasis?
Recommendations:
Iseenorealdangerinallowinginternalisationofproprietarytradingasit
avoidsextracostssuchasSTTonintradaytransactionsforTM.However,internal
matchingofclientsorderinternallyoracrossdifferentTradingMembershouldnever
beallowedasitraisesseveralClearingandSettlementissuesandalsothereisreal
riskofnewunregulatedtradingvenuessuchasdarkpoolbeingsetup.
SEBIneedstobeproactiveindefiningalloftheaboveoperationsas
fraudulentpracticesandhencebanningitsoperationinIndia.
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
31/55
Chapter6
HighFrequencyTradingHighfrequencytrading(HFT)isasubsetofalgorithmictradingwherealargenumber
oforders(whichareusuallyfairlysmallinsize)aresentintothemarketathighspeed,with
roundtripexecutiontimesmeasuredinmicroseconds.Programsrunningonhighspeed
computersanalyse
massive
amounts
of
market
data,
using
sophisticated
algorithms
to
exploittradingopportunitiesthatmayopenupformillisecondsorseconds.Participantsare
constantlytakingadvantageofverysmallpriceimbalances;bydoingthatatahighrateof
recurrence,theyareabletogeneratesizeableprofits.
Typically,ahighfrequencytraderwouldnotholdapositionopenformorethanafew
seconds.EmpiricalevidencerevealsthattheaverageU.S.stockisheldfor22seconds.
TABBgroup,afinancialservicesindustryresearchfirm,estimatesthatHFTnow
accountsfor56%ofturnoverinUSAand38%turnoverinEuropeintheequity
segment.
AsperLondonstockExchangesresponsetoCESRquestionnaire,inthefirstquarter
of2010,numberofordersexecutedthroughHighFrequencyTradingaccountedfor
33%oftotalturnoverintheEuropeanmarket.
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
32/55
(Details
algorith
Arbit
DeltaN
EventBArbitr
boutTradi
sandLiqui
ragestra
etralStA
asedage
gstrategie
dityseeking
tegies
atisticalrbitrage
Ch
lgorithmic
areoutline
algorithms
AlphSt
DirectionaTrading
Strategies
ScalpingStrategies
apter6
radingstra
dinAnnexu
areprovide
aSeekinategies
l MeReve
Strat
DarkLiSee
Strat
egies
re3&Deta
inannexu
ansiongies
uidityinggies
V
Sh
Vo
Cl
ilsaboutBe
re2)
CostRExecutio
enchmarklgorithms
(AP,Impleentationortfall,Partipation(%lume),Targ
tose,TWAP,InLine,etc)
nchMark
ductionnStrate
i
Liqui
see
algori(CrossF
nt,Iceb)
/ies
ditying
hmsire,Hurg,etc
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
33/55
HighFrequencytradingstrategies
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
34/55
Chapter7
CurrentRiskManagementframeworkinIndia
PriceBandsinEquityandF&OSegment
InIndia,therearepricebandsof2%,5%,10%and20%onindividualsecurity.
Nopricebandisapplicableonscripsonwhichderivativeproductsareavailable.Restall
securitiesarecategorisedindifferentcategoriesbasedontheirdailyvolumeandimpact
cost.2%,5%,10%and20%bandsareapplicableonthosecategories.
Oncescriphitapricebandoneitherside,tradingishaltedforremainingday.
InF&O,therearenopricebandsbuttopreventerroneoustradeentry.Operationalprice
bandsaredefinedas10%forIndexfutures,20%forstockfuturesandDeltabasedvaluefor
Stock&indexoptions.
Indexwise
Circuit
Filter/
Circuit
Breakers
IndexwiseCircuitFilterisanautomatedtradinghaltmechanism,whichstopstradingonthe
exchangeforspecifiedperiodoftime.
Thereare10%,15%and20%circuitbreakersontwomainindicesNifty&Sensex.
9:15am 1.00pm 1.00pm 2.30pm 2.30pm3.30pm
10%movement 1HrmarketHalt 1/2HrmarketHalt NoTradingHalt
9:15am1.00pm 1.00pm2.00pm 2.00pm3.30pm
15%movement 2HrmarketHalt 1HrmarketHalt TradingHalted
20%movement
Trading
Halted
for
rest
of
the
day
MarginingSysteminEquityandF&O
IndianmarketsaremuchbetterplacedvisvistheUSmarkets,thankstothepracticeof
collectingmarginsonarealtimebasis.Ifamemberfirmexhaustsitsmarginwithan
exchange,itisbarredfromtakingfreshpositions.IfclientortradingmemberorClearing
membercrossesspecifiedlimits,tradingfacilityissuspendedforallclientstradingthrough
TM(incaseTMviolateslimit)andalltradingmemberclearingthroughCM(incaseCM
violateslimit).
StocksarecategorizedinGroupI,II&IIIonthebasisofimpactcostandstockstradedinlast
6monthsforthepurposeofassigningdifferentmarginsondifferentcategorieshaving
differentrisk.
InEquity,InitialMargin=VaRmargin+Exposuremarginiscollectedonupfrontbasis,
TypicallyVaRMarginisintherangeof7.5 15%andExposuremarginrangesbetween5
10%.Sotypically,totalinitialMarginVariesfrom12.525%.MTMmarginiscalculatedby
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
35/55
markingeachtransactioninsecuritytotheclosingpriceofthesecurityattheendoftrading.
Andcollectedbeforestartofnexttradingday
InF&O,InitialMarginandExposuremarginarecollectedupfront.InitialMargin=Total
SPANMargin(VaRmargin)+BuyPremium+AssignmentMargin. AndExposureMargin3%
of
the
notional
value
of
a
futures
contract
(for
futures)
&
The
higher
of
5%
or
1.5
standard
deviationofthenotionalvalueofgrossopenposition (foroptions).TotalInitialMarginis
typically1520%ofTransactionValue.MTMmarginsarecollectedbeforestartofnext
tradingday.
PositionLimits
AttheendofeachdaytheExchangedisseminatestheaggregateopeninterestacrossall
Exchangesinthefuturesandoptionsonindividualscripsalongwiththemarketwide
positionlimitforthatscripandtestswhethertheaggregateopeninterestforanyscrip
exceeds95%ofthemarketwidepositionlimitforthatscrip.Ifyes,theExchangetakesnote
ofopen
positions
of
all
client/
TMs
as
at
the
end
of
that
day
in
that
scrip,
and
from
next
day
onwardstheclient/TMsshouldtradeonlytodecreasetheirpositionsthroughoffsetting
positionstillthenormaltradinginthescripisresumed.
InIndia,wehaveTMwise&clientwisepositionlimits,whichpreventasingleTMora
singleInvestorfromtakinghugepositioninasinglestock.Forclientsitis1%ofTotalFree
floatcapitalisation(forcash)and5%ofMWPL(forF&O)
NSEAlsomonitorFIIandMFpositionsforsectoralandcompanyspecificlimitssetbyRBI&
SEBI.
Thereare
stiff
penalties
for
violation
of
any
of
the
above
mentioned
limits
and
margining
requirements.
PretradeControlinF&Osegment
QuantityFreeze: AnyordercomingtotheexchangefortradeinNIFTY,S&P500,
BANKNIFTY,CNXITorMINIFTYfuture/optionwithquantitymorethan15,000willbeFreezed
untilbrokerconfirmitasagenuineorder.Fortradeinfuture/optiononindividualstocks,
freezequantityisasdecidedbyexchangefromtimetotime.
PriceFreeze: PricefreezeisoperationalonlyintheF&Osegment.Operationalpricebands
aredefined
as
10%
for
Index
futures,
20%
for
stock
futures
and
Delta
based
value
for
Stock
&indexoptions.
PretradeControlinEquitysegment
Apartfrompricebandstherearenopretradecontrolsinequitysegmenttocheck
erroneoustrades.InUSerroneoustradesthatareexecutedwayoutsidelasttradedprice
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
36/55
FirstcasewasofpunchingerrorinTulipITservicesonJan5,2006.TulipITServicessawtwo
unusualtradeshappeningonitslistingday.Onedealsaw4,04,800sharessoldat25paiseeach
againsttheaveragepriceofRs185attheBombayStockExchange,whiletheothersaw5,95,575
sharessoldatRs100each.ThosedealscausedtheselleralossofRs12.69croreinjust38
seconds.ThefirsttransactionledtoanetlossofRs7.40crorewhilethesecondtransactioncost
Rs5.06crore.
ThesecondcaseisfreaktradeinRelianceonJune1,2010.Amarketordertosell1.6lakhshares
wasenteredinReliancebyadealeratabrokermemberfirmwhichgotmatchedwithlimit
purchaseordersinthetradingsystem.Withinfewsecondsofenteringorder,Reliancestock
plungedtoRs840.55frompretradepriceofRs1,010causingscripttofallby19.56%.Many
analystsbelievethistrademighthavecostbrokingfirmamorethanRs1crore.
maybecontestedandcancelledaftercomplainingtotherespectivestockexchange.Ihave
giventwocaseswheretradeswerecancelled.
PersonallyIdontthinkanytradeshouldbecancelledevenifitisthepunchingerror.
Recommenduseof5%DynamicPriceBand
Whyis
it
important
to
implement
such
stringent
operational
dynamic
price
bands?
ThereweretwocasesinthehistoryofIndianSecuritiesMarketthatcausedtremendousloss
tothebrokingfirmsandtheirclientsduetopunchingerrorsbyoperators.
Bothofthesecasescouldhavebeenavoided,hadtherebeen5%pricebandoperational.
Havingnarrowpricebandof5%ensuresthatasingletradecantmakeanystockfallbymore
than5%.
Case1:NYSEEuronextcancelledalltradesinthe$74.8billionSPDRS&P500ETFTrustthat
occurredatalmost10percentbelowthesecuritysopeningprice,accordingtoanemailsentby
theexchange.
ThetradesoccurredontheNYSEsArcaplatformat4:15p.m.NewYorktimeon18/10/2010and
pricedthe
exchange
traded
fund
that
tracks
the
Standard
&
Poors
500
Index
at
$106.46
comparedwithitsopeningpriceof$117.74.TheETFplunged9.6percentovereightsecondsas
7.2millionsharestradedonNYSEArca,accordingtodatacompiledbyBloomberg.TheS&P500
rose0.7percenttocloseat1,184.71today.Theglitchoccurredaftertheclosingauctionwas
delayedduetoasoftwareupgradeattheexchange.Officialclosingpriceineachsecurityis
decidedinanauctionprocessconductedaftercloseofthemarket.
Case2:Nasdaq&NYSEcancelledalltradesin296securitiesthatwereexecutedatgreateror
lessthan60%oftheirpricesat2:40p.m.ETonMay6,2010
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
37/55
ProcedureforgrantingpermissiontoTradingMemberforAlgorithmTrading
a) NSE,onMay17,2011,issuedacirculartosmoothentheprocessandfacilitateearly
approvalofdecisionsupporttools/algorithmsfortradingthroughnonneatfront
end. Itcategorisedapplicationforseekingapprovalinalgotradinginthreemain
categories
viz.
Approved
Algorithms
through
CTCL,
Non
Approved
Algorithms
throughCTCLandDMAAlgorithms.ThiscategorisationgivesNSEoptiontoscrutinize
newalgosmorefortheirpretradeandposttraderiskcontrolsthanalready
approvedalgos.
b) OnfulfilmentofconditionsassatisfactoryandmeetingSEBIandExchangesminimum
requirements,theexchangewillidentifythevendors/membersalgorithmas
Approvedalgorithm.
c) Totalproceduretakesaround30daystogetalgoapprovedfromdayofapplication.
(Fordetailsrefertoannexure4.
Concerns
1) NSEis
not
disclosing
risk
control
limits
to
public.
So
it
raises
concerns
that
process
maynotbetransparentandtheremayexistdifferentlimitsfordifferentclients.
Recommendations
1) NSEshoulddiscloseriskcontrollimitsontheirwebsiteasspecifiedintheformat
below.
AlgorithmWise FirmWise
MaxNumberofAlgosallowedto
operatesimultaneously
NA
MaxNumberofOrderspersecond
2) NSEtoprepareMonthlyReportonAlgoTradinginformatsspecifiedinTable1and
shouldprovidetoSEBIasandwhenneeded.Sinceturnoverbeingcompanyspecific
datawecannotpublishitinourMonthlyBulletinandannualReports.
3) ButfortheinformationofInvestors,exchangesneedtopublishtotalturnover
throughalgotradingeverydayonitswebsite.TheyshouldalsoprovidedatatoSEBI
informatspecifiedinTable2.ThishelpsSEBIensurethattherequiredinfrastructure
forAlgoTradingisinplaceandalsoitprovidesInvestorsanoptiontoexitthe
market,iftheyfeelthereishighamountofAlgoTradingActivity,whichisnot
suitabletotheirriskprofile.
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
38/55
Table1
Nameof
theFirm
Typeoffirm
(BuySide/
Sellside)
Dateof
approving
1stalgo
Numberof
algos
approved
Orders
entered
Orders
Cancelled
Orders
Traded
1 2 3 4 5 6 7
Average
Size(Qty)
ofthe
order
Average
Valueofthe
Order
TotalTurnover
(Monthly) Optedfor
DMA
facility?
OptedForCo
Location?Through
Active
Orders
Through
Passive
Orders
8
9
10 11 12 13
Table2
BSE NSE
Capacity(Orderspersecond)
Latency(RoundtripExecution
time)
Latency(Datafeeds/Info
disseminationtime)
AverageDailyOrders
OrderFlow(throughDMA
facility)
Orderflow(throughAlgo
Trading)
AlgoTradingvolumeas
PercentageofTotalVolume
OrderFlow(throughCoLocation
facility
Numberofordersdivertedto
other
Stock
Exchanges
through
SOR
NumberofAlgosregistered
NumberofBuysideFirms
registeredforAT
NumberofSellsideFirms
registeredforAT
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
39/55
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
40/55
Directcontroloffilters:DeterminedbywhethertheParticipanthasexclusiveandcontinualaccess
tosetandamendavailableprereleaseorderfilters.
Presubmissionorderlevelfilters:Filtersthatassessanorderasadiscreteunitratherthanaspart
ofanaccount.Filtersaredesignedtopreventerrorsinprice,valueanddirectcompliancesuchas
shortselling.
Presubmissionaccountlevelfilters:Filtersthatassessanorderinthecontextofaccountlevel
exposure.Such
filters
depend
on
aconsolidate
account
position
irrespective
or
execution
venue.
Exchange Filtered
1. Introduction
of
5%
dynamic
price
bands
on
all
securities
OperationalPriceBandrequirementshouldbemadestringentforalgorithmtrading
topreventsystemsfromenteringerroneoustrades.
Forstockswithpricelessthanorequalto10,operationalpricebandshouldbe5%of
thelasttradedpriceinallscriptslistedatNSE&BSE.Thispricebandwillchange
everysecondwitheverynewtradehappening.
Justforexample: IfaXYZstockiscurrentlytradingatRs100,Anyalgotrading
systemwillnotbeabletoenterbuyorderatmorethanRs105andsellorderatless
than95.
Placingabuyorderbelowcurrentmarketprice&placingasellorderaboveCMPwill
improveliquidity
as
this
would
increase
number
of
passive
orders
sitting
in
the
order
book.SoinourexamplebuyerwouldbeabletoenterbuyorderatlessthanRs95
andsellorderatmorethanRs105.
However,thisrulewillbeanexceptionforstoplossorder.InstopLossordertrader
canplacetriggerpriceandlimitpriceatanypricehedesires.
ThisruleshouldalsobeapplicableonthefirsttradingdayoftheIPO.
2. Mechanismtoflushoutallthependingorders
3. IntroductionofMakerTakerStructureAnexampleofanewtradingstrategythathasemergedoverseasandattractedthelabel
predatoryisa
Form
of
arbitrage
designed
to
ensure
that
the
HFT
receives
a
rebate
for
providing
liquidity
to
aretailorinstitutionalinvestor.Thisstrategyistheresultofmarketmicrostructurechanges
whichmakecertaintypesoftradingprofitableforhighfrequencytraders,wherepreviously
thiswasnotthecase.
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
41/55
Chapter9
Conclusion
Highfrequencytraderstendtosticktoliquidstocksandliquidderivativescontracts.Hence,
theyhavent
yet
contributed
meaningfully
in
enhancing
liquidity
of
the
other
stocks
and
illiquidderivativescontractssuchasstockoptions.
Needlesstosay,thelargerconcernabouthighfrequencytrading(HFT)isthatitcould
destabilizemarkets,especiallyaftertheflashcrashintheUSon6May2010.Itmustbe
notedthatsuchasituationcanariseeveninaworldwithoutHFT.Aflashcrashsituation
ariseswhenalargeorderisplacedintoathinorderbookinadvertently.
Therearemixedviewsontheimpactsofalgorithmtradingwillbeonthemarket.Positive
Impactsmayincludeenhancedliquidityandtransactioncostsavings,whilenegativesmay
Includeundermined
market
stability
due
to
frequent
trading.
Ibelievethatactivealgorithmictradingwillservegreatlytomanagethemarketimpact,as
wellasreducinganysideeffectscausedbyalgorithmsniffing.Howeveritistruethat
algorithmtradingitselfhascreatedsomenegativessuchaslowsettlementratesandsystem
overloadissues,whichneedtoberesolved.
SGXemphasizeshighspeedmessaging,theshorteningofmarketdatadelays,andresearch
onnew
products.
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
42/55
AnnexureIII
A) GeneralAlgorithmicStrategies
1) TrendFollowing
Trendfollowingisaninvestmentstrategythattriestotakeadvantageoflongterm
movesthatseemtoplayoutinvariousmarkets.Thesystemaimstoworkonthemarket
trendmechanismandtakebenefitfrombothsidesofthemarketenjoyingtheprofitsfrom
theupsanddownsofthestockorfuturesmarkets.Traderswhousethisapproachcanuse
currentmarketpricecalculation,movingaveragesandchannelbreakoutstodeterminethe
generaldirectionofthemarketandtogeneratetradesignals.Traderswhosubscribetoa
trendfollowingstrategydonotaimtoforecastorpredictspecificpricelevels;theysimply
jumponthetrendandrideit.
2) PairTrading
Thepairtradingisamarketneutraltradingstrategyenablingtraderstoprofitfrom
virtuallyanymarketconditions:uptrend,downtrend,orsidewisemovement.Thistrading
strategyiscategorizedasastatisticalarbitrageandconvergencetradingstrategy.
3) DeltaNeutralStrategies
DeltaNeutralstrategyisalsoknownasCash F&Oarbitragestrategy.Deltadenotes
changeinoptionpricewithonerupeechangeintheunderlyingassets.Deltaforcalloption
isintherangeof0to1,whereasforputoptionitvariesfrom 1to0.Deltaforstock/future
isalways
1.
So
buying/
selling
any
of
the
three
assets
in
specific
quantity
can
make
our
portfoliodeltaneutral.
Arbitragerbenefitsfromdecayingoptionvalue(theta)ifheisshort.Arbitragercanalso
benefitfromincreaseinvolatilityifheislongvolatilityandviceversa.
4) Arbitrage
Basically,arbitrageisexploitingbenefitofmispricedassetstoearnriskfreeprofit.
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
43/55
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
44/55
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
45/55
currentpriceinordertoprovideliquiditytoothermarketplayersand,inthisway,benefit
fromthebidaskspread.
Profitabilityisenhancedbythefactthatmanytradingcentresapplythemakertakerfee
structure.Thisstrategyisoftencalledmarketmakingbuttodosomaybeinappropriate.
SomeHFTfirmsregisterwiththetradingvenuesonwhichtheyarememberstomeetthe
ongoingobligations
associated
with
being
an
official
market
maker.
However,
this
is
often
notthecase,asHFTfirmsmayactinsteadasinformalliquidityproviders,avoiding
prescribedmarketmakingobligationsandenjoyingbenefitsofthetraditionalmarket
makers.Bylookingattradedvolumes,HFTfirmshavebecomesignificantparticipantsinthe
liquidityandpriceformationprocessinmanymarketsandinstrumentsand,evenwhen
actinginformallyinthisrole,havepartlyreplacedtraditionalmarketmakers.Lowlatencyis
oftheutmostimportanceforthisstrategysinceprovidingliquiditymightinvolveholdinga
riskyinventorypositiononaninstrumentforsometime.Marketriskisminimizedbyrapidly
adjustingpostedquotestoreflectthearrivalofnewinformationortoadjustinventory.Asa
consequence,theratiooforderstotradesandthenumberofcancelledordersareveryhigh
inthisstrategy.
2) ArbitrageStrategies
Arbitragestrategiestakeadvantageofpricingdiscrepanciesandmayinvolvepurearbitrage
betweenthesameinstrumentstradedacrossdifferenttradingvenues(e.g.thesamestock
tradedatanexchangeandanATS/MTF),betweenanindexandtheunderlyingbasketof
securities,orbetweenrelatedinstruments(e.g.asecurityandanassociatedderivative).
Otherformsofarbitragelookatstatisticaldeviationsfromlongterm,historicalstatistical
relationships(e.g.correlations)amongsecurities.Assumingreversiontothemean,
significantdeviationsfromtheserelationshipsofferprofitabletradingopportunities.
Arbitragestrategiestendtoimprovepriceefficiencybyeliminatinginconsistenciesbetween
prices.They
also
tend
to
consume
rather
than
provide
liquidity
to
the
market,
as
the
short
livednatureofarbitrageopportunitiesmakesrapidexecutionoftradescritical.
3) Directionalstrategies
Directionalstrategies, includingeventstrategies,involveunhedgedpositionsbeingcarried
for some (albeit often short) period of time, in anticipation of small but lasting intraday
pricechanges.Basedonpastpatterns,HFTfirmsestimateexpectedpricechangestriggered
bythereleaseofmacroeconomicnews,corporateannouncementsorindustryreportswith
asignificantimpactonmarketprices.Aspasteventsgeneraterecognizableandstatistically
robust patterns, HFT firms estimate expected price responses to anticipated events.
Anotherdirectional
strategy
is
aliquidity
detection
strategy
which
involves
afirm
searching
forhiddendemand for liquidity in themarket.Undiscloseddemand is liquidity that isnot
reflectedintheorderbookandinthemarketprice.Thestrategyprofitsbymovingtheprice
againstlargehiddenbuyingorsellinginterest.
(SourceIOSCOreportonregulatoryissuesraisedbytheimpactoftechnologicalchanges
onmarketintegrityandefficiency)
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
46/55
AnnexureIV
ProcedureforgrantingpermissiontoTradingMemberforAlgorithmTrading
1. Procedureformakinganapplication
Tradingmember
desirous
of
seeking
approval
for
trading
using
algorithms
should
make
anapplicationinformatasspecifiedinAnnexure1
NSEcategorizedalgorithmapplicationsforapprovalinthreecategories,whichareas
follows
d) ApprovedAlgorithmsthroughCTCL
TradingMember,desirousofusingalgorithmswhicharealreadyapprovedbyNSEto
thevendors,canmakeanapplicationinthiscategory.Memberneednotgive
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
47/55
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
48/55
The Stocks which have traded at least 80% of the days for the previous six months shall
constitutetheGroupIandGroupII.
Outofthescripsidentifiedabove,thescripshavingmeanimpactcostoflessthanorequal
to1%arecategorizedunderGroupIandthescripswherethe impactcost ismorethan1,
arecategorizedunderGroupII.
TheremainingstocksareclassifiedintoGroupIII.
The impactcost iscalculatedonthe15thofeachmonthonarollingbasisconsideringthe
order book snapshots of the previous six months. On the basis of the impact cost so
calculated, the scrips move from one group to another group from the 1st of the next
month.
B) MarginsintheEquitysegments
Dailymarginspayablebymembersconsistsofthefollowing:
1. ValueatRiskMargin
2. ExtremeLossMargin
3. MarktoMarketMargin
AllthreeMarginsarecollectedonthegrossopenpositionofthemember.Thegrossopen
positionforthispurposemeansthegrossofallnetpositionsacrossalltheclientsofa
memberincludingitsproprietaryposition.
ValueatRiskMargin:
AllsecuritiesareclassifiedintothreegroupsforthepurposeofVaRmargin
ForthesecuritieslistedinGroupI,scripwisedailyvolatilitycalculatedusingthe
exponentiallyweightedmovingaveragemethodology.ThescripwisedailyVaRis3.5
timesthevolatilitysocalculatedsubjecttoaminimumof7.5%.
ForthesecuritieslistedinGroupII,theVaRmarginishigherofscripVaR(3.5sigma)
orthreetimestheindexVaR,anditisscaledupbyroot3.
ForthesecuritieslistedinGroupIIItheVaRmarginisequaltofivetimestheindex
VaRandscaledupbyroot3.
TheindexVaR,forthepurpose,isthehigherofthedailyIndexVaRbasedonS&PCNXNIFTY
orBSESENSEX,subjecttoaminimumof5%.
TheVaRmarginiscollectedonanupfrontbasisbyadjustingagainstthetotalliquidassetsof
thememberatthetimeoftrade.
TheVaRmarginsocollectedisreleasedoncompletionofpayinofthesettlement.
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
49/55
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
50/55
NSCCLhasdevelopedacomprehensiveriskcontainmentmechanismfortheFutures&
Optionssegment.ThemostcriticalcomponentofariskcontainmentmechanismforNSCCL
istheonlinepositionmonitoringandmarginingsystem.Theactualmarginingandposition
monitoringisdoneonline,onanintradaybasis.NSCCLusestheSPAN(StandardPortfolio
AnalysisofRisk)systemforthepurposeofmargining,whichisaportfoliobasedsystem.
InitialMargin
a.SpanMargin
NSCCLcollectsinitialmarginupfrontforalltheopenpositionsofaCMbasedonthe
marginscomputedbyNSCCLSPAN.ACMisinturnrequiredtocollecttheinitialmargin
fromtheTMsandhisrespectiveclients.Similarly,aTMshouldcollectupfrontmarginsfrom
hisclients.
Initialmarginrequirementsarebasedon99%valueatriskoveraonedaytimehorizon.
However,inthecaseoffuturescontracts(onindexorindividualsecurities),whereitmay
notbe
possible
to
collect
mark
to
market
settlement
value,
before
the
commencement
of
tradingonthenextday,theinitialmarginiscomputedoveratwodaytimehorizon,
applyingtheappropriatestatisticalformula.ThemethodologyforcomputationofValueat
RiskpercentageisaspertherecommendationsofSEBIfromtimetotime.
Initialmarginrequirementforamember:
Forclientpositions isnettedatthelevelofindividualclientandgrossedacrossallclients,
attheTrading/ClearingMemberlevel,withoutanysetoffsbetweenclients.
Forproprietarypositions isnettedatTrading/ClearingMemberlevelwithoutanysetoffs
betweenclient
and
proprietary
positions.
b.PremiumMargin
InadditiontoSpanMargin,PremiumMarginischargedtomembers.Thepremiummarginis
theclientwisepremiumamountpayablebythebuyeroftheoptionandisleviedtillthe
completionofpayintowardsthepremiumsettlement.
c.AssignmentMargin
AssignmentMargin
is
levied
on
aCM
in
addition
to
SPAN
margin
and
Premium
Margin.
It
is
leviedonassignedpositionsofCMstowardsinterimandfinalexercisesettlement
obligationsforoptioncontractsonindexandindividualsecuritiestillthepayintowards
exercisesettlementiscomplete.
TheAssignmentMarginisthenetexercisesettlementvaluepayablebyaClearingMember
towardsinterimandfinalexercisesettlementandisdeductedfromtheeffectivedepositsof
theClearingMemberavailabletowardsmargins.
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
51/55
AssignmentmarginisreleasedtotheCMsforexercisesettlementpayin.
InitialMarginrequirement=TotalSPANMarginRequirement+BuyPremium+Assignment
Margin
ExposureMargin
Theexposuremarginsforoptionsandfuturescontractsonindexareasfollows:
i.ForIndexoptionsandIndexfuturescontracts:
3%ofthenotionalvalueofafuturescontract.Incaseofoptionsitischargedonlyonshort
positionsandis3%ofthenotionalvalueofopenpositions.
ii.ForoptioncontractsandFuturesContractonindividualSecurities:
Thehigherof5%or1.5standarddeviationofthenotionalvalueofgrossopenpositionin
futuresonindividualsecuritiesandgrossshortopenpositionsinoptionsonindividual
securitiesin
aparticular
underlying.
MTMMargin
MTMmarginforF&OsegmentissameasthatofEquitysegment.
D) PositionLimits
ClearingMembersaresubjecttothefollowingpositionlimitsinadditiontoinitialmargins
requirements.
MarketWide
Position
Limits
(for
Derivative
Contracts
on
Underlying
Stocks)
TradingMemberwisePositionLimit
ClientLevelPositionLimits
FII/MFpositionlimits
MarketWidePositionLimits(forDerivativeContractsonUnderlyingStocks)
AttheendofeachdaytheExchangedisseminatestheaggregateopeninterestacrossall
Exchangesinthefuturesandoptionsonindividualscripsalongwiththemarketwide
positionlimitforthatscripandtestswhethertheaggregateopeninterestforanyscrip
exceeds95%
of
the
market
wide
position
limit
for
that
scrip.
If
yes,
the
Exchange
takes
note
ofopenpositionsofallclient/TMsasattheendofthatdayinthatscrip,andfromnextday
onwardstheclient/TMsshouldtradeonlytodecreasetheirpositionsthroughoffsetting
positionstillthenormaltradinginthescripisresumed.
Thenormaltradinginthescripisresumedonlyaftertheaggregateopeninterestacross
Exchangescomesdownto80%orbelowofthemarketwidepositionlimit.
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
52/55
Afacilityisavailableonthetradingsystemtodisplayanalertoncetheopeninterestonthe
NSEinthefuturesandoptionscontractinasecurityexceeds60%ofthemarketwide
positionlimitspecifiedforsuchsecurity.Suchalertsarepresentlydisplayedattimeintervals
of10minutes.
TradingMemberwisePositionLimit
Thetradingmemberwisepositionlimitinequityindexoptionandindexfuturesisasunder:
IndexFutures
ThetradingmemberpositionlimitsinequityindexfuturescontractsishigherofRs.500
croresor15%ofthetotalopeninterestinthemarketinequityindexfuturescontracts.This
limitisapplicableonopenpositionsinallfuturescontractsonaparticularunderlyingindex.
IndexOptions
ThetradingmemberpositionlimitsinequityindexoptioncontractsishigherofRs.500
croresor15%ofthetotalopeninterestinthemarketinequityindexoptioncontracts.This
limitwould
be
applicable
on
open
positions
in
all
option
contracts
on
aparticular
underlying
index
FuturesandOptioncontractsonindividualsecurities:
Thetradingmemberwisepositionlimitinfuturesandoptionsinindividualstocksisrelated
tothemarketwidepositionlimitfortheindividualstocks.
i.Forstockshavingapplicablemarketwidepositionlimit(MWPL)ofRs.500croresormore,
thecombinedfuturesandoptionspositionlimitis20%ofapplicableMWPLorRs.300
crores,whicheverislowerandwithinwhichstockfuturespositioncannotexceed10%of
applicableMWPLorRs.150crores,whicheverislower.
ii.Forstockshavingapplicablemarketwidepositionlimit(MWPL)lessthanRs.500crores,
thecombinedfuturesandoptionspositionlimitis20%ofapplicableMWPLandfutures
positioncannotexceed20%ofapplicableMWPLorRs.50crorewhicheverislower.
ClientLevelPositionLimits
Thegrossopenpositionforeachclient,acrossallthederivativecontractsonaunderlying,
shouldnotexceed:
1%ofthefreefloatmarketcapitalization(intermsofnumberofshares)or
5%oftheopeninterestinallderivativecontractsinthesameunderlyingstock(intermsof
numberof
shares)
whicheverishigher
Clientlevelpositionlimitsunderlyingwise,areavailabletomembersonNSE'swebsite.
E) Violations
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
53/55
PRISM(ParallelRiskManagementSystem)istherealtimepositionmonitoringandrisk
managementsystemfortheFuturesandOptionsmarketsegmentatNSCCL.Theriskofeach
tradingandclearingmemberismonitoredonarealtimebasisandalerts/disablement
messagesaregeneratedifthemembercrossesthesetlimits.
InitialMargin
Violation
ExposureLimitViolation
TradingMemberwisePositionLimitViolation
ClientLevelPositionLimitViolation
MarketWidePositionLimitViolation
Violationarisingoutofmisutilisationoftradingmember/constituentcollaterals
and/ordeposits
ViolationofExercisedPositions
Clearingmembers,whohaveviolatedanyrequirementand/orlimits,mayreducethe
positionbyclosingoutitsexistingpositionor,bringinadditionalcashdepositbywayof
cashor
bank
guarantee
or
FDR
or
securities.
Similarly,
in
case
of
margin
violation
by
Trading
members,clearingmemberhastosetitslimitforenablement.
InitialMarginviolation
TheinitialmarginonpositionsofaCMiscomputedonarealtimebasisi.e.foreachtrade.
TheinitialmarginamountisreducedfromtheeffectivedepositsoftheCMwiththeClearing
Corporation.Forthispurpose,effectivedepositsarecomputedbyreducingthetotal
depositsoftheCMbyRs.50lakhs(referredtoasminimumliquidnetworth).TheCM
receiveswarningmessagesonhisterminalwhen70%,80%,and90%oftheeffective
depositsareutilised.At100%theclearingfacilityprovidedtotheCMiswithdrawn.
Withdrawalof
clearing
facility
of
aCM
in
case
of
aviolation
will
lead
to
withdrawal
of
tradingfacilityforallTMsand/orcustodialparticipantsclearingandsettlingthroughthe
CM.
Similarly,theinitialmarginsonpositionstakenbyaTMarecomputedonarealtimebasis
andcomparedwiththeTMlimitssetbyhisCM.Theinitialmarginamountisreducedfrom
theTMlimitsetbytheCM.OncetheTMlimithasbeenutilisedtotheextentof70%,80%,
and90%,awarningmessageisreceivedbytheTMonhisterminal.At100%utilization,the
tradingfacilityprovidedtotheTMiswithdrawn.
Amemberisprovidedwithwarningsat70%,80%and90%levelbeforehistrading/clearing
facilityis
withdrawn.
A
CM
may
thus
accordingly
reduce
his
exposure
to
contain
the
violationoralternatelybringinAdditionalBaseCapital.
ExposureLimitViolation
ThisviolationoccurswhentheexposuremarginofaClearingMemberexceedshisliquid
networth,atanytime,includingduringtradinghours.Theliquidnetworthmeansthe
effectivedepositsasreducedbyinitialmarginandnetbuypremium.Incaseofviolation,the
clearingfacilityoftheclearingmemberiswithdrawnleadingtowithdrawalofthetrading
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
54/55
facilitiesofalltradingmembersand/orclearingfacilityofcustodialparticipantsclearing
throughtheclearingmember.
TradingMemberwisePositionLimitViolation
Thisviolationoccurswhentheopenpositionofthetradingmember/custodialparticipant
exceedstheTradingMemberwisePositionLimitatanytime,includingduringtradinghours.
Incase
of
violation
the
trading
facility
of
the
trading
member
is
withdrawn.
Inrespectofinitialmarginviolation,exposuremarginviolationandpositionlimitviolation,
penaltyisleviedonamonthlybasisbasedonslabsasmentionedbelow.
ClientLevelPositionLimitViolation
Thisoccurswhentheopenpositionofanyclientexceedsthe clientwidepositonlimit
TheTM/CMthroughwhomtheclienttrades/clearshisdealsisliableforsuchviolation.
Inthe
event
of
such
aviolation,
TM
/CM
should
immediately
ensure,
(i)thattheclientdoesnottakefreshpositionsand
(ii)reducesthepositionsofsuchclientstobewithinpermissiblelimits.
Additionally,intheeventofsuchaviolation,penaltywouldbechargedtoClearing
Membersforeverydayofviolation.
1%ofthevalueofthequantityinviolation(i.e.,excessquantityovertheallowedquantity,
valuedattheclosingpriceoftheunderlyingstock)perclientor
Rs.1,00,000perclient,whicheverislower,subjecttoaminimumpenaltyofRs.5,000/ per
violation/perclient.whentheclientlevelviolationisonaccountofopenpositionofclient
exceeding5%ofopeninterest,apenaltyofRs.5,000/ perinstanceischargedtoclearing
member.
TheClearingMembercanrecoverthepenaltysochargedfromtherespectiveTrading
Member/Clientviolatingtherequirementofpositionlimitsandincaseswhereitislevied
andcollectedfromTradingMember,suchtradingmember,inturn,canrecoverthesame
fromtherespectiveclientswhoviolatedthepositionlimits.
DisclosureforClientPositionsinIndexbasedcontracts
Anypersonorpersonsactinginconcertwhotogetherown15%ormoreoftheopen
interestonaparticularunderlyingindexisrequiredtoreportthisfacttotheExchange/
ClearingCorporation.Failuretodosoistreatedasaviolationandattractsappropriatepenal
anddisciplinary
action
in
accordance
with
the
Rules,
Byelaws
and
Regulations
of
the
ClearingCorporation.
Forfuturescontracts,openinterestisequivalenttotheopenpositionsinthefutures
contractmultipliedbylastavailabletradedpriceorclosingprice,asthecasemaybe.For
optioncontracts,openinterestisequivalenttothenotionalvaluewhichiscomputedby
multiplyingtheopenpositioninthatoptioncontractwiththelastavailableclosingpriceof
7/31/2019 Final-Issues Concerning Algorithmic Trading in India
55/55
theunderlying.
MarketWidePositionLimitsforderivativecontractsonunderlyingstocks
Attheendofeachdayduringwhichthebanonfreshpositionsisinforceforanyscrip,when
anymemberorclienthasincreasedhisexistingpositionsorhascreatedanewpositionin
thatscrip
the
client/
TMs
are
charged
apenalty.
Thepenaltyisrecoveredfromtheclearingmemberaffiliatedwithsuchtrading
members/clientsonaT+1daybasisalongwithpayin.Theamountofpenaltyisinformedto
theclearingmemberattheendoftheday..
Violationarisingoutofmisutilisationoftradingmember/constituentcollateralsand/or
deposits
ThisviolationtakesplacewhenaclearingmemberutilisesthecollateralofoneTMand/or
constituenttowardstheexposureand/orobligationsaTM/constituent,otherthanthe
sameTM
and/
or
constituent.
ViolationofExercisedPositions
WhenoptioncontractsareexercisedbyaCM,wherenoopenlongpositionsforsuchCM/
TMand/orconstituentexistattheendoftheday,atthetimetheexerciseprocessingis
carriedout,itistermedasviolationofexercisedpositions.