algorithmic trading in different landscapes

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© Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited Algorithmic Trading in different geographies - technologies - regulations - competition - markets Rajib Ranjan Borah Market Microstructure, Liquidity and Automated Trading Workshop. Fitch Learning, London. 16-17 June 2014. 4 th Annual Conference on ‘Behavioral Models and Sentiment Analysis Applied to Finance’, London, 16-20 June 2014.

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Presentation on "Algorithmic Trading in different geographies" This presentation highlights the trading landscape in different geographies and compares them on four parameters: i) Technological protocols in various geographies ii) Regulatory environments iii) Competitive landscape iv) Market Volumes This presentation was presented by senior QI faculty and co-founder Rajib Ranjan Borah at the pre-conference workshop of the "4th Annual Conference: Behavioral Models and Sentiment Analysis Applied to Finance", in London in June 2014. The two day pre-conference workshop on "Market Microstructure, Liquidity and Automated Trading Workshop" was conducted at Fitch Learnings, London. To view a video recording of the presentation, please contact [email protected]

TRANSCRIPT

Page 1: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Algorithmic Trading in different geographies

- technologies- regulations-  competition

- markets

Rajib Ranjan Borah

Market Microstructure, Liquidity and Automated Trading Workshop.Fitch Learning, London. 16-17 June 2014.

4th Annual Conference on ‘Behavioral Models and Sentiment Analysis Applied to Finance’, London, 16-20 June 2014.

Page 2: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Agenda

Most relevant factors in analyzing different geographies

Current AlgoTrading landscape in various geographies

The road ahead ?

Q&A

Page 3: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Agenda

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Most relevant factors in analyzing different geographies

Current AlgoTrading landscape in various geographies

The road ahead ?

Q&A

Page 4: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

How do you decide: where to trade?

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

As a trading manager, how do you decide which geographies to trade ?

Page 5: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

How do you decide: where to trade?

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

As a trading manager, how do you decide which geographies to trade ?– Am I allowed to trade in that country (i.e. how conducive are regulations) ?

• Are foreigners allowed to trade ?• Are there restrictions on position-sizing / short-selling ?

– Are these markets interesting to trade ?• What are the listed instruments ?• What is the volume traded ?

– Will I be able to make profits ?• What is the level of sophistication of competition ?• Who are the leading players ?

– What are the complexities to connecting to these markets ?• Technological protocols used by these exchanges – common standard protocols OR unique 

custom protocols• Brokers to connect to these markets

Page 6: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

How do you decide: where to trade?

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

As a trading manager, how do you decide which geographies to trade ?– Am I allowed to trade in that country (i.e. how conducive are regulations) ?

• Are foreigners allowed to trade ?• Are there restrictions on position-sizing / short-selling ?

– Are these markets interesting to trade ?• What are the listed instruments ?• What is the volume traded ?

– Will I be able to make profits ?• What is the level of sophistication of competition ?• Who are the leading players ?

– What are the complexities to connecting to these markets ?• Technological protocols used by these exchanges – common standard protocols OR unique 

custom protocols• Brokers to connect to these markets

Regulations

Market  Size

Competition

Technology

Page 7: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

How do you decide: where to trade?

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

As a trading manager, how do you decide which geographies to trade ?– Am I allowed to trade in that country (i.e. how conducive are regulations) ?

• Are foreigners allowed to trade ?• Are there restrictions on position-sizing / short-selling ?

– Are these markets interesting to trade ?• What are the listed instruments ?• What is the volume traded ?

– Will I be able to make profits ?• What is the level of sophistication of competition ?• Who are the leading players ?

– What are the complexities to connecting to these markets ?• Technological protocols used by these exchanges – common standard protocols OR unique 

custom protocols• Brokers to connect to these markets

Regulations

Market  Size

Competition

Technology

Page 8: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Agenda

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Most relevant factors in analyzing different geographies

Current AlgoTrading landscape in various geographies

The road ahead ?

Q&A

Page 9: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Algo trading in various landscapes

Page 10: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Americas

Bermuda SE                 4 

BM&FBOVESPA        67,550 

Buenos Aires SE             279 

Colombia SE          2,162 

Lima SE             338 

Mexican Exchange        14,780 

NASDAQ OMX      798,729 

NYSE Euronext (US)   1,141,704 

Santiago SE          3,640 

TMX Group      114,290 

Total region 2,143,474

APAC

Australian SE        73,463 

BSE India          7,046 

Bursa Malaysia        12,323 

Colombo SE             130 GreTai Securities Market        11,241 

HoChiMinh SE             869 Hong Kong Exchanges      110,281 

Indonesia SE          9,664 Japan Exchange Group - Osaka        17,594 Japan Exchange Group - Tokyo      525,411 

Korea Exchange      107,050 National Stock Exchange India        39,913 New Zealand Exchange             752 

Philippine SE          3,889 

Shanghai SE      310,927 

Shenzhen SE      321,542 

Singapore Exchange        23,410 

Taiwan SE Corp.        51,996 The Stock Exchange of Thailand         31,335 

Total region 1,658,838

Europe

Athens Exchange          1,981 BME Spanish Exchanges        74,464 

Budapest SE             869 

Casablanca SE             263 

Cyprus SE                 3 

Deutsche Börse      111,212 

Irish SE          1,206 

Ljubljana SE               33 

Luxembourg SE               12 

Malta SE                 6 

Moscow Exchange        20,167 NASDAQ OMX Nordic Exchange         52,153 NYSE Euronext (Europe)      138,490 

Oslo Børs        10,200 

SIX Swiss Exchange        56,413 

Wiener Börse          2,154 

Total region     469,626

MEA: Middle East + Africa

Abu Dhabi SE          1,816 

Amman SE              285 

Borsa Istanbul        34,947 

Cyprus SE                 3 

Egyptian Exchange          1,077 

Irish SE          1,206 

Johannesburg SE        28,608 

Kazakhstan SE               58 

Mauritius SE               26 Muscat Securities Market             478 

Qatar Exchange          1,714 Saudi Stock Exchange - Tadawul        30,202 

Tel Aviv SE          4,475 

Total region 104,895

Equity volumes – avg monthly volumes (USD million)

Page 11: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Americas Options Future

BM&FBOVESPA 238 119.0 504 342.0Bourse de Montreal 3 550.8 555 764.0CBOE Future Exchange x NA

Chicago Board Options Exchange NA x

CME Group 9 987 400.0 46 628 400.0

Colombia SE x 19.2

ICE Futures US 6 516.0 2 962 600.0

International Securities Exchange NA NA

MexDer 1 761.5 30 896.1NASDAQ OMX (US) NA NANYSE Euronext (US) NA NA

APAC Options FutureASX Derivatives Trading 441 915.0 2 561.6

ASX SFE Derivatives Trading 46 330.5 1 204 770.0Bombay SE 1 168 300.0 10 210.9Bursa Malaysia Derivatives NA 71 423.6

China Financial Futures Exchange NA 22 909 400.0Hong Kong Exchanges 1 838 560.0 4 539 370.0Korea Exchange 67 864 200.0 5 855 830.0National Stock Exchange India 4 668 890.0 498 918.0

Osaka SE NA 7 468 830.0

Shanghai Futures Exchange 0.0 0.0Singapore Exchange NA NA

TAIFEX 1 485 740.0 1 480 410.0

Thailand Futures Exchange NA NATokyo SE Group NA 2 630 990.0

EMEA Options Future

Athens Derivatives Exchange 432.3 28 113.5BME Spanish Exchanges 60 489.2 672 335.0

Borsa Istanbul 35.1 63 631.4

Budapest SE 0.0 341.8

EUREX 13 764 800.0 18 691 000.0ICE Futures Europe 0.0 0.0Johannesburg SE 2 946.5 487 400.0Moscow Exchange 118 079.0 752 618.0NYSE.Liffe Europe 3 297 390.0 6 282 720.0OMX Nordic Exchange 151 241.0 573 599.0

Oslo Børs 439.1 1 752.9

Tel Aviv SE 641 250.0 976.1

Wiener Börse 56.2 22 030.8

Index FO – annual volumes (USD million)

Page 12: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Americas Options Future

BM&FBOVESPA 994 498.0 NABourse de Montreal 73 960.1 0.0

Buenos Aires SE NA NA

Chicago Board Options Exchange NA NA

Colombia SE NA 480.6

International Securities Exchange NA NA

MexDer 90.0 13.4NASDAQ OMX (US) NA NANYSE Euronext (US) 104 464.0 NA

BM&FBOVESPA 994 498.0 NABourse de Montreal 73 960.1 0.0

APAC Options Future

ASX Derivatives Trading 284 474.0 11 867.0

Bombay SE 3 076.7 7 442.2Hong Kong Exchanges 167 335.0 1 485.5Korea Exchange 0.0 56 761.0

National Stock Exchange India 416 644.0 811 791.0

New Zealand 0.0 NA

Osaka SE NA NA

Shanghai Futures Exchange 0.0 0.0

TAIFEX 177.6 22 743.6

Thailand Futures Exchange NA NATokyo SE Group NA NA

EMEA Options Future

Athens Derivatives Exchange 17.0 2 450.0BME Spanish Exchanges 31 668.0 15 271.2

Borsa Istanbul 14.4 11.9

Budapest SE 0.0 2 684.7

EUREX 784 435.0 752 475.0ICE Futures Europe 0.0 0.0Johannesburg SE 311.3 16 625.5Moscow Exchange 2 829.0 106 339.0NYSE.Liffe Europe 343 406.0 455 497.0OMX Nordic Exchange 52 967.4 4 658.3

Oslo Børs 1 874.3 1 836.7

Tel Aviv SE 4 335.2 NA

Wiener Börse 307.0 0.0

Equity FO – annual volumes (USD million)

Page 13: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Americas Options Future

BM&FBOVESPA 435 060.6 4 196 493.1Bourse de Montreal 68.2 0.0

CME Group 2 076 378.0 25 281 026.0

Colombia SE NA 10 674.9

ICE Futures US 3 410.0 658 019.0

MexDer 181.9 135 749.3

APAC Options FutureHong Kong Exchanges NA 13 948.8Korea Exchange NA 532 393.2

National Stock Exchange India 252 897.5 614 399.2

Osaka SE NA 61 932.8

Thailand Futures Exchange NA NA

EMEA Options Future

Borsa Istanbul NA 4 658.1Johannesburg SE 8 934.8 25 155.6Moscow Exchange 4 040.0 483 914.0NYSE.Liffe Europe 2 324.6 37.3

Tel Aviv SE 108 322.8 0.0

Currency – annual volumes (USD million)

Page 14: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Americas Options Future

BM&FBOVESPA 3 600 770.0 18 193 900.0Bourse de Montreal 569 518.0 23 217 400.0

Buenos Aires SE NA NA

CME Group163 885 

000.0614 271 

000.0

Colombia SE NA 32 850.9

MexDer NA 93 456.0NYSE Euronext (US) NA NA

APAC Options Future

ASX SFE Derivatives Trading 393 778.0 47 596 300.0

Bombay SE NA NA

China Financial Futures Exchange NA 50 232.7Hong Kong Exchanges NA 7.2Korea Exchange NA 4 084 080.0

National Stock Exchange India NA NA

Shanghai Futures Exchange 0.0 0.0

TAIFEX NA 2.2Tokyo SE Group NA NA

EMEA Options FutureBME Spanish Exchanges 0.0 1 413.4

EUREX 11 544 400.0 76 220 100.0Johannesburg SE 47.7 54 109.3London Metal Exchange NA NAMoscow Exchange 0.0 5 323.7NYSE.Liffe Europe 175 658 000.0 443 350 000.0OMX Nordic Exchange 915 175.0 3 560 020.0

Tel Aviv SE NA NA

Interest Rate – annual volumes (USD million)

Page 15: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Americas ETF ETF Opt

BM&FBOVESPA 11 366.8 138.7

Colombia SE 1 070.1

Lima SE 9.8Mexican Exchange 104 903.9 1.8

NASDAQ OMX 6 695 703.4 NANYSE Euronext (US) 3 589 241.4 48 077.3

Santiago SE 117.1

TMX Group 73 824.8Bourse de Montreal NA 5 123.0

CME NA NA

ISE NA NA

APAC ETF ETF Opt

Australian SE 7 487.3

BSE India 1 118.3Bursa Malaysia 43.6Hong Kong Exchanges 116 431.5 16 776.7

Indonesia SE 2.1

Japan Exchange Group - Osaka 73 386.2 NA

Japan Exchange Group - Tokyo 163 170.1 NAKorea Exchange 178 724.6

National Stock Exchange India 2 132.8New Zealand Exchange 59.6

Shanghai SE 109 240.5

Shenzhen SE 36 707.2

Singapore Exchange 2 591.1

Taiwan SE Corp. 9 496.8

The Stock Exchange of Thailand  250.3

EMEA ETF ETF OptAthens Exchange 14.7BME Spanish Exchanges 5 732.2

Borsa Istanbul 4 350.9

Budapest SE 1.9Deutsche Börse 162 958.7

Irish SE 19.3Johannesburg SE 5 624.3 ~ 0

Ljubljana SE 0.0Luxembourg SE 0.0

NASDAQ OMX Nordic Exchange  15 593.7NYSE Euronext (Europe) 100 747.8

Oslo Børs 4 736.7

Saudi Stock Exchange - Tadawul 18.6SIX Swiss Exchange 98 074.8

Wiener Börse 5.3

ETF – annual volumes (USD million)

Page 16: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Americas Options Future

BM&FBOVESPA 602.9 21 666.2

CME Group10 561 200.0 47 823 300.0

Colombia SE NA 6.6ICE Futures Canada 47.4 57 599.8

ICE Futures US 305 023.0 1 347 450.0NYSE Euronext (US) NA NA

APAC Options Future

ASX SFE Derivatives Trading 5 284.0 17 687.1

Bursa Malaysia Derivatives NA 151 944.0

Dalian Commodity Exchange NA 7 676 520.0Korea Exchange NA 161.1

New Zealand 0.0 132.1

Shanghai Futures Exchange NA 9 852 290.0

TAIFEX 717.9 1 146.7

Zhengzhou Commodity Exchange NA 3 074 910.0

EMEA Options Future

Borsa Istanbul NA 807.7

Budapest SE 0.0 138.5ICE Futures Europe 31 495.0 29 469 200.0Johannesburg SE 349.9 50 124.3London Metal Exchange 663 968.0 13 965 900.0Moscow Exchange 309.6 46 291.3NYSE.Liffe Europe 1 918.0 403 586.0

Commodity– annual volumes (USD million)

Page 17: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

 

Automated Trading System components

 

Application

Order Manager

Market Data

Complex Event Processing engine

Exchange 1

Storage

Application Server Exchange

Strategy Settings UI

State Mgmt (PnL + Position)

Order / Execution Monitor

Within application RMS

Maths Calc

RMS

Admin Monitor

Exchange 2

FIX

FIX

Data Normalizer

Order Router

Back office record

MktData Store

Event History

Adaptor for third party apps – R, Matlab, etc

Data Retrieval

Data Vendor

Replay of stored data

Simulator exchange

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

To connect to any exchange, you need to build(i) market data adaptor for that exchange’s protocol

Page 18: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

 

Automated Trading System components

 

Application

Order Manager

Market Data

Complex Event Processing engine

Exchange 1

Storage

Application Server Exchange

Strategy Settings UI

State Mgmt (PnL + Position)

Order / Execution Monitor

Within application RMS

Maths Calc

RMS

Admin Monitor

Exchange 2

FIX

FIX

Data Normalizer

Order Router

Back office record

MktData Store

Event History

Adaptor for third party apps – R, Matlab, etc

Data Retrieval

Data Vendor

Replay of stored data

Simulator exchange

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

To connect to any exchange, you need to build(ii) OMS adaptor to convey transaction information as per exchange protocol

Page 19: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Technology Protocols globally

One of the most popular protocol is FIXFinancial Information eXchange protocol         Data transferred as sequence of tag=value pairs

e.g.: 8=FIX.4.2 | 9=77 | 35=0 | 49=MCXSXTRADE | 56=xxxxx | 34=24 | 43=N | 52=20140607-05:14:05 | 112=DNLDCOMPLETE | 10=127

1 Account2 AdvId3 AdvRefID4 AdvSide5 AdvTransType6 AvgPx7 BeginSeqNo8 BeginString9 BodyLength10 CheckSum11 ClOrdID12 Commission13 CommType14 CumQty15 Currency16 EndSeqNo17 ExecID18 ExecInst19 ExecRefID

20 ExecTransType21 HandlInst22 IDSource23 IOIid24 IOIOthSvc (no longer used)25 IOIQltyInd26 IOIRefID27 IOIShares28 IOITransType29 LastCapacity30 LastMkt31 LastPx32 LastShares33 LinesOfText34 MsgSeqNum35 MsgType36 NewSeqNo37 OrderID38 OrderQty

39 OrdStatus40 OrdType41 OrigClOrdID42 OrigTime43 PossDupFlag44 Price45 RefSeqNum46 RelatdSym47 Rule80A(aka OrderCapacity)48 SecurityID49 SenderCompID50 SenderSubID51 SendingDate (no longer used)52 SendingTime53 Shares54 Side55 Symbol56 TargetCompID57 TargetSubID

Page 20: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

ExchangeTransaction / Market Data Protocol Family Protocol

ASX - Australian Stock Exchange  Transaction  FIX  ASX-Trade24-FIX

ASX - Australian Stock Exchange  Transaction  OUCH  ASX-OUCH

BATS  Transaction  FIX  BATS-FIX

BATS  Transaction BATS Europe Binary Order Entry (BOE)   BATS-Europe-BinaryOrderEntry 

BATS  Transaction  BATS Binary Order Entry (BOE)  BATS-BinaryOrderEntry

BME Spanish Exchanges  Transaction  FIX  BME-SpanishExchanges-FIX

Bombay Stock Exchange Transaction IML IML/ETI

Bombay Stock Exchange Market Data EMDI NFCAST, ACAST, EMDI

Borsa Italia  Transaction  TradElect  LSE-TradElect

Borsa Italia  Transaction  FIX  Borsaltaliana-FIX

Borsa Italia  Transaction  Millennium Native Trading  Borsaltaliana-Native Trading

Boston Exchange (BOX)  Transaction SOLA Access Information Language (SAIL)  BOX-SAIL

Bursa Malaysia  Transaction/MarketData  AMP  BursaMalaysia-AMP

CBOE  Transaction CMi Protocol (trading spec only)  CBOE-CMi

CBOE  Transaction  CMi version 2  CBOE-CMi-v2

CHI-X  Transaction  CHI-X Australia FIX  CHIX-Australia-FIX

CHI-X  Transaction  CHI-X Australia OUCH  CHI-X-Australia-OUCH

CHI-X  Transaction  CHI-X Canada FIX  CHIX-Canada-FIX

CHI-X  Transaction  CHI-X Japan OUCH  CHI-X-Japan-OUCH

Technology Protocols globally

Page 21: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

ExchangeTransaction / Market Data Protocol Family Protocol

Chicago Mercantile Exchange (CME)  Transaction  FIX  CME-iLink-FIX

Deutsche Boerse  Transaction Eurex v12 Enhanced Transaction Solution  DBS-Eurex-ETS

Deutsche Boerse  Transaction Eurex v13 Enhanced Transaction Solution  DBS-Eurex-ETS-13

Deutsche Boerse  Transaction Eurex v14 Enhanced Transaction Solution  DBS-Eurex-ETS-14

Deutsche Boerse  Transaction Eurex Enhanced Trading Interface  DBS-Eurex-ETI

Deutsche Boerse  Transaction  Eurex FIX  DBS-Eurex-FIX

Deutsche Boerse  Transaction Xetra Enhanced Transaction Solution (v14 and v13)  DBS-Xetra-ETS

Direct Edge  Transaction  FIX  DirectEdge-FIX

Direct Edge  Transaction MEP / XPRS (binary order entry)  DirectEdge-Edge-XPRS

Fidessa   Transaction/MarketData Fidessa API (European MultiMarket Access)  Fidessa-EMMA

Hong Kong Exchanges (HKEx)  Transaction  AMS3 OpenGateway  HKEx-AMS3-OpenGateway

Hong Kong Exchanges (HKEx)  MarketData FIX FAST

Hong Kong Mercantile Exchange (HKMEx)  Transactions EMAPI  HKMEx-EMAPIICAP  Transaction  ICAP BrokerTec OUCH  ICAP-BrokerTec-OUCHICE  Transaction  FIX  ICE-FIXInternational Securities Exchange (ISE)  Transaction  FIX  FIX

International Securities Exchange (ISE)  Transaction Optimise Direct Trading Interface (DTI)  ISE-Optimise-DTI

Johannesburg Stock Exchange  Transaction  Millennium Native Trading  JSE-Millennium-Native-Trading

Technology Protocols globally

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Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

ExchangeTransaction / Market Data Protocol Family Protocol

Johannesburg Stock Exchange  Transaction  TradElect  LSE-TradElect

Korea (KRX)  Transaction KRX/Koscom PowerBASE Order-Entry API  KRX-Korea-PowerBASE-NFS

Korea (KRX)  Transaction  KRX KMAP order-entry API  KRX-KMAP

London Metals Exchange  Transaction  LME FIX  LME-FIX

London Stock Exchange  Transaction  LSE-FIX  LSE-FIX

London Stock Exchange  Transaction  Millennium Native Trading  LSE-Millennium-NativeTrading

London Stock Exchange  Transaction  Turquoise FIX  Turquoise-FIX

London Stock Exchange  Transaction  Turquoise Native Trading  Turquoise-NativeTrading

London Stock Exchange  Transaction  TradElect  LSE-TradElect

London Stock Exchange  Transaction  Interactive  LSE-TradElect

Miami Exchange (MIAX)  Transaction  MIAX Express Interface (MEI)  MIAX-MEI

NASDAQ OMX  Transaction  FIX  NASDAQ-FIX

NASDAQ OMX  Transaction  Nasdaq FIX Lite  NASDAQ-Flite

NASDAQ OMX  Transaction  Nasdaq OUCH v3.3  NASDAQ-OUCH-3

NASDAQ OMX  Transaction  Nasdaq OUCH v4.2  NASDAQ-OUCH-4

NASDAQ OMX  Transaction  NASDAQ OMX Nordic OUCH  NASDAQ-OMX-OUCH-NORDIC

NASDAQ OMX  Transaction NOM2 Ouch to Trade Options (OTTO)  NASDAQ-NOM2-OTTO

NASDAQ OMX  Transaction NOM2 Specialized Quote Interface (SQF)  NASDAQ-NOM2-SQF

NASDAQ OMX  Transactions Nasdaq RASHport  NASDAQ-RASHport

Technology Protocols globally

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© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

ExchangeTransaction / Market Data Protocol Family Protocol

National Stock Exchange of India (NSE)  Transactions NEAT Futures and Options NSE-India-NEAT-FuturesAndOptions

National Stock Exchange of India (NSE)  TransactionsNon-NEAT Futures and Options 

NSE-India-NonNEAT-FuturesAndOptions

NYSE  Transaction Designated MarketMaker protocol  NYSE-DMM

NYSE Euronext  Transaction  NYSE Euronext UTP-Direct  NYSE-UTPDirect

NYSE Euronext  Transaction NYSE US CCG Binary (UTP-Direct)  NYSE-UTPDirect-US

NYSE Euronext  Transaction  NYSE CCG FIX  NYSE-UTP-CCG-FIX

NYSE Euronext  Transaction  NYSE Liffe CCG Binary  NYSE-LIFFE-CCG-Binary

NYSE Euronext  Transaction  ArcaDirect 4.x  NYSE-ArcaDirect-4

NYSE Euronext  Transaction  Arca MarketMaker Direct  NYSE-ArcaMarketMakerDirect

Osaka Stock Exchange  Transactions Trading Interface  OsakaSE-TradingInterface

Oslo Bors  Transaction  Native Trading  OsloBors-NativeTrading

Pure Trading (CNSX market)  Transaction/MarketData  STAMP  TorontoSX-STAMP

Singapore Stock Exchange (SGX)  Transaction  SGX FIX Trading Interface  SGX-QuestST-FIX

SIX Swiss Exchange  Transaction  Quote Trading Interface  SWX-QTI

SIX Swiss Exchange  Transaction SWX Capacity Trading Interface  SWX-CTI

SIX Swiss Exchange  Transaction SWX Standard Trading Interface  SWX-STI

SIX Swiss Exchange  Transaction Swiss Exchange OUCH market data interface (OTI)  SIX-OUCH

Technology Protocols globally

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© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

ExchangeTransaction / Market Data Protocol Family Protocol

Taifex  Transaction/MarketData  FIX Taifex-FIX

Thailand Stock Exchange (SET)  Transaction/MarketData  EMAPI  SET-EMAPI

TMX group  Transaction/MarketData  STAMP  TorontoSX-STAMP

TMX group  Transaction/MarketData  MX SAIL  MontrealExchange-SAIL

Tokyo Stock Exchange (TSE)  Transaction  Arrowhead ESP  TokyoSE-Arrowhead-ESP

Tokyo Stock Exchange (TSE)  Transaction  Tdex+order-entry  TokyoSE-Tdex+

Tokyo Stock Exchange (TSE)  Transaction  FIX J-FIX

Warsaw Stock Exchange  Transaction  Order Entry Protocol  WSE-CCG

SHFE Transaction FIX FAST

CZCE Transaction FIX FAST

Montreal Stock Exchange Transaction FIX FIX

Montreal Stock Exchange TransactionSOLA Access Information Language (SAIL)  STAMP/ SOLA

Technology Protocols globally

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© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Country Regulation

European Union (HFT = 39%)

 MiFID IIFTT proposed (11 countries agreed to participate – Austria, Belgium, Estonia, France, Germany, Greece, Italy, Portugal, Slovakia, Slovenia, Spain)

Germany (40%)

HFT firms to register with BaFin and obtain HFT licensesSupervisors at BaFin and the Exchange Supervisory Authority are allowed to requestdocument all algorithms used - descriptions of trading strategies or parameters and are permitted to deny the use of anyalgorithmic trading strategy they believe is undesirableappropriate order-to-trade ratio, todevelop a minimum tick size, fee for excessive system usage, includingamendments and cancelations of orders, and it requires traders to flag each order generated by analgorithm

France

Non-Transaction tax of 0.01% (replace / cancel)exceeding 80% of all orders transmitted in a monthTax if orders modified or cancelled in less than 0.5 second0.20% trasaction tax on purchased shares of companies with market cap > €1 billion (France’s share of EU equity turnover dropped from 23% in 2011 to <13% in 2013)

Regulations

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© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Country Regulation

USA (48.5% in 2013. 61% in 2009)

Pre-  trade  controls; post-  trade controls; system safeguards; mandatory registration and ‐ ‐reporting;Standardized order types; testing of algorithms; documented evidence of capacity, integrity, resiliency, availability, and security adequate to maintain operational capability; scheduled testing; continuity and disaster recovery plans; system redundancy; financial transaction TaxesProposed: Inclusive Property Act 0.5% sales tax on most financial instruments,. Wall Street Trading and Speculators Tax Act: 0.03% tax on trading by financial institutions

Singapore 0.20% stamp duty on equity trading

Hong Kong (20%)0.1 % stamp taxHFT – detailed records and audit logs for at least 2 years. System testing once a year

ChinaStamp duty between 0.1% to 0.3%Complex rules wrt FII in China

Taiwan All orders to go through FMC

Regulations

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© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Algo trading in various landscapesChina Singapore Hong 

Kong Japan Korea Taiwan

Regulations Market –Equity Market - Currency x x xMarket - Cmdty x Market– Interest Rate x x x

Market - ETF Market - Options x TechnologyConstraints

Competition

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© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Algo trading in various landscapesIndia Thailand Malaysia Israel Turkey Indonesia

Regulations Market –Equity Market - Currency x xMarket - Cmdty xMarket– Interest Rate x

Market - ETF x x xMarket - Options ~ x ~ x x xTechnologyConstraints Competition

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© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Algo trading in various landscapesPhilippines Dubai Abu Dhabi Saudi 

Arabia Vietnam Bahrain

Regulations Market –Equity x Market - Currency x x x x Market - Cmdty x x x x xMarket– Interest Rate x x x x x x

Market - ETF x x x x x xMarket - Options x x x x x xTechnologyConstraints

Competition

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© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Algo trading in various landscapesEuronext OMX Eurex

Regulations Market –Equity Market - Currency xMarket - Cmdty xMarket– Interest Rate

Market - ETF Market - Options TechnologyConstraints Competition

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© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Algo trading in various landscapesSpain SIX Swiss Moscow Oslo

Regulations Market –Equity Market - Currency x x xMarket - Cmdty x x xMarket– Interest Rate x x

Market - ETF x Market - Options TechnologyConstraints

Competition

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© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Algo trading in various landscapesBM&F  Bovespa

NASDAQ NYSE TMX

/ Mn CME MexDER ICE

Regulations

Market –Equity x xMarket - Currency x x Market - Cmdty x x x x Market– Interest Rate x x Market - ETF x xMarket - Options TechnologyConstraints Competition

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© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Algo trading in various landscapesJohannesburg Nigeria

Regulations Market –Equity Market - Currency xMarket - Cmdty xMarket– Interest Rate x

Market - ETF Market - Options xTechnologyConstraints Competition

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© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Agenda

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Most relevant factors in analyzing different geographies

Current AlgoTrading landscape in various geographies

The road ahead ?

Q&A

Page 35: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Shift in market volumes

Likely threat of introduction of a HFT tax (financial transaction tax), 

+Over crowding of US and European automated trading markets 

 

Likely shift of volumes towards Asian, LatAm(increasing number of global firms setting up shop in India, Singapore, Hong Kong, 

Brazil, etc)

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

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© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Growth of market segments

Regulations in certain markets are going to introduce new destinations for algorithmic traders:

• Swaps traded electronically in SEFs instead of OTC– Post Dodd-Frank Wall Street Reform and Consumer Protection Act, four 

categories of interest rate swaps and two categories of credit default swaps are going to be listed on SEFs (Swap Execution Facilities)

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

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© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Technology Simplification/Consolidation

Different exchanges shifting to standardized technology protocols instead of maintaining proprietary protocols

(Bombay Stock Exchange in India adopting Eurex Platform.Budapest, Bursa Malaysia adopting CME GlobexMultiple developing economy exchanges like Bursa Istanbul adopting FIX protocol)

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

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© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Technology Arms race no more ?

In a lot of aspects, we are reaching the limits of technology

+The cost of technology is also coming down

↓Therefore technology might no longer be the sole asset of privileged 

market participants

↓Trading Alpha will again shift towards statistical excellence rather than 

technological upper-hand

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

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© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Agenda

Relevant factors for analyzing different markets → Current landscape in different geographies → The future →    QA

Most relevant factors in analyzing different geographies

Current AlgoTrading landscape in various geographies

The road ahead ?

Q&A

Page 40: Algorithmic Trading in Different Landscapes

© Copyright 2010-2014 QuantInsti Quantitative Learning Private  Limited

Copyright © 2014 by QuantInsti Quantitative Learning Private Limited.

Although great care has been taken to ensure accuracy of the information in this presentation – however the author (and QuantInsti) accepts no liability or warranty for the precision, correctness or completeness of any statement, estimate or opinion. QuantInsti also accepts no liability for the consequences of any actions taken on the basis of the information provided.

The slides of this presentation cannot be taken separately from the whole set of slides.

Prior approval from QuantInsti is necessary before usage of this presentation for educational and (or) commercial purposes.

This document provides an outline of a presentation and is incomplete without the accompanying oral commentary and discussion.

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