technology edge in algo trading: traditional vs automated trading system architecture

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Technology Edge in Algo Trading

Sameer Kumar

14th March, 2015

FINBRIDGE 2015

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It's the Latency, Stupid

http://rescomp.stanford.edu/~cheshire/rants/Latency.html

Well known and referenced article “a network link with low bandwidth can be made better with money, but network link with bad latency cannot be helped”

This was the scene in 1996, when bandwidth was the constraint. Speeds were in Kbps.

Cheshire later become Wizard at Apple. Pioneering Zeroconf

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Misnomer – Bad TerminologyWould you say that a Boeing 747 is three times "faster" than a Boeing 737? Of course not. They both cruise at around 500 miles per hour. The difference is that the 747 carries 500 passengers where as the 737 only carries 150. The Boeing 747 is three times bigger than the Boeing 737, not faster.

Now, if you wanted to go from New York to London, the Boeing 747 is not going to get you there three times faster. It will take just as long as the 737.

In fact, if you were really in a hurry to get to London quickly, you'd take Concorde, which cruises around 1350 miles per hour. It only seats 100 passengers though, so it's actually the smallest of the three. Size and speed are not the same thing.

On the other hand, If you had to transport 1500 people and you only had one aeroplane to do it, the 747 could do it in three trips where the 737 would take ten, so you might say the Boeing 747 can transport large numbers of people three times faster than a Boeing 737, but you would never say that a Boeing 747 is three times faster than a Boeing 737.

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Traditional Trading System Architecture

• Traditionally a trading system would consist of – A system to read data from the market– A storehouse of historical data– A tool to analyse historical data– A system where the trader can input his trading decisions– A system to route orders to the exchange

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• A system to read data from the market

Workshop on Algorithmic & High Frequency Trading

Market Data Exchange

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• A storehouse of historical data– Which could also be directly purchased from third party data vendor

directly


Market Data Exchange

Data Warehouse

/

Storehouseof

historical data

Data Vendor

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• A subset of the database is queried and stored locally for operational uses


Market Data

Operational Data Store

Exchange

Data Warehouse

/

Storehouseof

historical data

Data Vendor

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• The trader’s tool would then analyze current data against patterns discovered in the operational data store


Trader’s tool

Main Centre of operations – analyzing market data wrt

to historical data in operational data store and

generating orders

Market Data


Exchange

Data Warehouse

/

Storehouseof

historical data

Data Vendor

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• The trader’s tool would then generate orders which will be forwarded to the order management tool


Order Manager

Market Data


Exchange

Data Warehouse

/

Storehouseof

historical data

Data Vendor

Trader’s tool



generating orders

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• The order manager would then route the orders to the exchange


Order Manager

Market Data


Exchange

Data Warehouse

/

Storehouseof

historical data

Data Vendor

Trader’s tool



generating orders

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• The data warehouse would also probably store records of orders sent out


Order Manager

Market Data


Exchange

Data Warehouse

/

Storehouseof

historical data

Data Vendor

Trader’s tool



generating orders

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• The whole system could be broken down to three components


Order Manager

Market Data


Exchange

Data Warehouse

/

Storehouseof

historical data

Data Vendor

Trader’s tool



generating orders

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• The exchange (and other data sources) – i.e. the external world


Order Manager

Market Data


Exchange

Data Warehouse

/

Storehouseof

historical data

Data Vendor

Trader’s tool



generating orders

Exchange

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• The server – which is mostly a data store


Order Manager

Market Data


Exchange

Data Warehouse

/

Storehouseof

historical data

Data Vendor

Trader’s tool



generating orders

Server Exchange

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• The applications in the trader’s pc which do all the processing


Order Manager

Market Data


Exchange

Data Warehouse

/

Storehouseof

historical data

Data Vendor

Trader’s tool



generating orders

Application Server Exchange

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• If data operations are simple… operational data store can be in application layer (trader’s pc)


Order Manager

Market Data

Operational Data

Store

Exchange

Data Warehouse

/

Storehouseof

historical data

Data Vendor

Trader’s tool



generating orders


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Automated Trading System Architecture

• With the advent of DMA & automated trading, the following changes in architecture took place:– Latency between Event Occurrence & Order Generation had to be

reduced to an order of milliseconds and lower.– Order Management had to be made more robust to handle

generation of thousands of orders in a second– Risk Management had to be done in real time and without human

intervention


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• To generate orders quickly, market events are now handled in the server instead of the application - in the CEP module


Application

Order Manager

Market Data

Complex Event Processing engine

Exchange 1

Storage


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• Complex mathematical operations are handled in a dedicated calculation block in the server block (e.g. Options Greeks calculations)


Application

Order Manager

Market Data


Exchange 1

Storage


Maths Calc

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• The role of the application layer has reduced drastically – (i) an input screen for strategy settings


Application

Order Manager

Market Data


Exchange 1

Storage


Strategy Settings UI

Maths Calc

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• The role of the application layer has reduced drastically – (ii) a monitor of position & orders


Application

Order Manager

Market Data


Exchange 1

Storage



State Mgmt (PnL + Position)

Order / Execution Monitor

Maths Calc

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• The role of the application layer has reduced drastically – (iii) preliminary fat finger RMS checker


Application

Order Manager

Market Data


Exchange 1

Storage





Within application RMS

Maths Calc

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• RMS is now automated and checked by the OMS before an order is generated


Application

Order Manager

Market Data


Exchange 1

Storage






Maths Calc

RMS

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• Because RMS is automated, a second level of monitoring is necessary – an overall global position monitor


Application

Order Manager

Market Data


Exchange 1

Storage






Maths Calc

RMS

Admin Monitor

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• With Multiple destinations being connected to the automated systems, standardized protocols (FIX) became the norm


Application

Order Manager

Market Data


Exchange 1

Storage






Maths Calc

RMS

Admin Monitor

Exchange 2

FIX

FIX

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• This also necessitated adding data normalizer block in the market data adaptors to convert data from multiple exchanges into a standard format


Application

Order Manager

Market Data


Exchange 1

Storage






Maths Calc

RMS

Admin Monitor

Exchange 2

FIX

FIX

Data Normalizer

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• Moreover, an Order Router had to be added to the OMS to route orders from the same OMS to multiple exchanges


Application

Order Manager

Market Data


Exchange 1

Storage






Maths Calc

RMS

Admin Monitor

Exchange 2

FIX

FIX

Data Normalizer

Order Router

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• Regulatory requirements have complicated storage requirements – requiring storage of trade information in addition to market data


Application

Order Manager

Market Data


Exchange 1

Storage






Maths Calc

RMS

Admin Monitor

Exchange 2

FIX

FIX

Data Normalizer

Order Router

Back office record

MktData Store

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• And obviously it should be able to read information from third party vendors as well


Application

Order Manager

Market Data


Exchange 1

Storage






Maths Calc

RMS

Admin Monitor

Exchange 2

FIX

FIX

Data Normalizer

Order Router

Back office record

MktData Store

Data Vendor

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• Moreover, the CEP engine has its own storage requirements of event history to identify future opportunities


Application

Order Manager

Market Data


Exchange 1

Storage






Maths Calc

RMS

Admin Monitor

Exchange 2

FIX

FIX

Data Normalizer

Order Router

Back office record

MktData Store

Event History

Data Vendor

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• Complex functionality third party applications have to be tightly integrated with all the blocks


Application

Order Manager

Market Data


Exchange 1

Storage






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 Vendor

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• Independent data management tools to verify the sanity of the information have to configured


Application

Order Manager

Market Data


Exchange 1

Storage






Maths Calc

RMS

Admin Monitor

Exchange 2

FIX

FIX

Data Normalizer

Order Router

Back office record

MktData Store

Event History


Data Retrieval Data

Vendor

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• To be able to back-test strategies, two components are required: (i) replay of stored data


Application

Order Manager

Market Data


Exchange 1

Storage






Maths Calc

RMS

Admin Monitor

Exchange 2

FIX

FIX

Data Normalizer

Order Router

Back office record

MktData Store

Event History


Data Retrieval Data

Vendor

Replay of stored data

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• To be able to back-test strategies, two components are required: (ii) a simulator destination instead of an actual exchange


Application

Order Manager

Market Data


Exchange 1

Storage






Maths Calc

RMS

Admin Monitor

Exchange 2

FIX

FIX

Data Normalizer

Order Router

Back office record

MktData Store

Event History


Data Retrieval Data

Vendor

Replay of stored data

Simulator exchange

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Introduction to low latency

• Technology – State of Art • Approach to latency improvement • Latest in Low Latency -approaches and technologies being deployed to

achieve low latency

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Why aim for Low Latency or Lowest?

• It may be necessary to lower latency just to remain competitive • The strategy demands low latency, perhaps. • It may be desirable to improve latency to stop getting picked off by

competitors • With introduction of Colocations and increasing focus in remaining fastest in

the market, significant capital is invested. However it can all go waste, if correct technology is not identified and implemented.

• The issue is that latency is difficult to quantify. As a result the value of latency improvement, though easily understood, is extremely difficult to quantify

• Lower latency systems cost a lot more to build and deploy. Hence the objective should be to find the right balance between investment and return on investment in low latency

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Latencies – Strategy wise

Citihub, 2009

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Current Scenario• Synchronized Markets – Correlated• Volumes and Trades have increased• Orders per Trade has increased drastically• Synchronized Trading Patterns• Need to increase throughput and lower latency simultaneously• CPUs not getting faster – since 2007. Only cores are being added, not the

frequency. Moore’s Law is failing

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Performance Degradation with Throughput

http://www.ibm.com/developerworks/websphere/library/techarticles/0706_lou/0706_lou.html

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Causes of Degradation • Massive headroom to allow for bursts typically 10:1 (like bridges and dams)

• Can be triggered by a shortage of any one of it’s resources: – CPU cycles – Memory – I/O channel capacity – Disk transfers – Network transfers

• Shortage of one can trigger another e.g. shortage of memory causing CPU Typical system response time against throughput and I/O thrashing

• Distributed deployments add an order of magnitude more complexity

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Parallel computing • Amdahl's law, also

known as Amdahl's argument,is used to find the maximum expected improvement to an overall system when only part of the system is improved. It is often used in parallel computing to predict the theoretical maximum speedup using multiple processors.

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Network Effects

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Latency by Distance

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Spread NetworksEstimated roundtrip time for an ordinary cable is 14.5 milliseconds, giving users of Spread Networks a slight advantage.

• In October 2012, Spread Networks announced latency improvements, bringing the estimated roundtrip time from 13.1 milliseconds to 12.98 milliseconds.

• Some companies, such as McKay Brothers and Tradeworx, are using air-based transmission to offer lower estimated roundtrip times (9 milliseconds and 8.5 milliseconds respectively) that are very close to the theoretical minimum possible (about 7.5-8 milliseconds).

http://en.wikipedia.org/w/index.php?title=McKay_Brothers&action=edit&redlink=1

http://en.wikipedia.org/w/index.php?title=Tradeworx&action=edit&redlink=1

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Microbursts

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How to ask bandwidth question

“For this feed, how much bandwidth is needed to protect 99.99% of packets from loss with no more than 100 microseconds of latency to be experienced during the busy 1 second of the trading day”

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Indian context• The scenario is generally broken down into colocation ( 1+ Gbps) and non-

colocation ( 20+ Mbps).• Exchanges provides Tick-by-tick which may require taking separate lines.

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Latency Breakdown• Latency can be broken down into the following components • L = P + N + S + I + AP

– P is Propagation time - sending the bits along the wire, speed of light constrained

– N is Network packet processing – routing, switching and protection – S is Serialization time - pulling the bits on/off the wire – I is Interrupt handling time – receiving the packet on a server – AP is Application Processing time

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Technology Mix

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Tips• Servers – Fastest Cores, Cache, • Operating Systems – RT kernels• Fastest Network Infra (Switches, Routers )• Retune the TCP stack (e.g. i/o buffer size )• Program Runtime – isolcpus, stack bypass• Solid State Drives ( SSD )• Latency Tuning – TCP_NODELAY, sendfile(2), Lock Free Codes

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Sample Solution Path

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Thank you!

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technology edge in algo trading: traditional vs automated trading system architecture

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