Export Sites Tradersmagazine 03 Data Media Pdfs Algo Report 2007
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Transcript of Export Sites Tradersmagazine 03 Data Media Pdfs Algo Report 2007
Algos 3.0
A Supplement to Traders MagazineProduced by SourceMedia’s Custom Publishing Group
DEVELOPMENTS INALGORITHMIC TRADING
INDUSTRY PROFILES
EdgeTrade Inc.
Fidelity Capital Markets Services
Goldman Sachs
Knight Capital Group, Inc.
Morgan Stanley
Neovest, Inc.
Société Générale Corporate & Investment Banking
UBS Direct Execution
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Algorithms Find TheirRhythm with Broad,Growing BaseTwo billion U.S.- equities shares trade each day on venues that were non-existent
two years ago. This tremendous shift in the market is compelling traders to
embrace algorithms as essential equities-trading tools.
“If
DEVELOPMENTS IN ALGORITHMIC TRADING
Algos 3.0
you look from the early adopters to now, there’s been a broadening of the base of thetype of institutions, the type of traders who use algorithms,” according to Aite Group, Senior Analyst,Brad Bailey. Much of the buyside — and various swaths of buyside traders, including mutual funds,institutional managers, traditional asset managers and hedge funds — is employing algorithms.
A new research report by TABB Group highlights the growth trend: More than 90 percent of thebuyside firms TABB Group interviewed are using algorithms today, up from 77 percent one year ago. Inthe study, “Institutional Equity Trading in America 2007: Divining the Path to Liquidity,” TABB interviewed65 head traders at U.S. buyside firms.
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“Clearly they have become a top-performing solution to the challenges offinding liquidity in a fragmented market,”says Laurie Berke, a TABB Group senioranalyst. “Traders are using algorithmsin the equity market to help them seekout liquidity across the more than 40potential execution venues, includingexchanges and ECNs and dark poolsand crossing networks.”
“Certainly it’s not just a U.S.phenomenon. It’s really something that’staking off all across the planet,” Bailey says.
Aite Group estimates that algorithmic tradingrepresents 38 percent of U.S. flow, 18 percent ofEuropean flow, and 4 percent of Asian flow. Baileyexpects continued growth, predicting that half ofU.S. flow, 28 percent of European flow, and 16percent of Asian flow will be algorithmic by 2010.
Bailey points to MiFID as a catalyst in Europe’santicipated algorithm surge. Similar to the UnitedStates’ Reg NMS-driven changes, the new regulatorybackdrop will inspire a new competitive landscape,resulting in European market fragmentation.
“Exchanges will be under increasing pressurefrom new venues such as Turquoise, the recentlyannounced Smart Pool, and Chi-X,” says Bailey.
“It’s highly likely that algorithms will provide anexcellent solution in Europe, just as they have herein dealing with that fragmentation of liquidity,” saysTABB Group’s Berke.
Meanwhile, traders are finding pockets of Asiaconducive to algorithmic use, says Berke. She citesHong Kong and Japan as examples.“Some of the global asset managerswho have used algorithms here in theStates and now also in Europe arelooking to the traditional broker-dealerproviders to provide them algorithms inthose markets as well,” she says.
Worldwide, algorithms are becomingmore sophisticated. The latest iterationsfocus on liquidity as opposed tobenchmarks such as VWAP or TWAP,which characterized early algorithms.Dark algorithms have been the fastestgrowing in terms of usage this year, accordingto Bailey, who says that people have been quite
happy with how these work.“To make the algorithms really work
in the market structure that existscurrently is that they understandwhere the liquidity is, where thebest execution can be and also thecheapest way to go about finding thatliquidity,” says Bailey.
Customization is another recentadvent. Berke says that traders wantparameters they can set and use tocarefully control how algorithms behave
with an order.“Overall what people really want and what we
hear from a variety of buyside firms is just moreflexibility, more ease in a customization process,”says Bailey. Algorithm providers are enablingtraders to change parameters easily, even on the
fly, where, as conditions change, they can reflectit in the way their algorithms are operating.
Performance is another importantfactor to traders, according to Berke.She says that brokers are doing agood job of providing what they canthrough transaction cost analysis. Butequally important is the trader’s ownexperience and comfort level with analgorithm.
“Traders tell us that they spend atremendous amount of time workingwith algorithms on certain types oforders to understand how they behave,”says Berke. “Many brokers offer a suite
of algorithms with customization and parameters.And the sheer magnitude of choice poses a
challenge to the buyside trader to become adeptat using all of them.”
She describes the development of an“interpretative agent” as an emerging trend toaddress the challenge. This interpretive agentcan direct an order from one algorithmic tradingstrategy to another in reaction to market changes,such as a pricing event, a liquidity event, or a shiftin liquidity from one venue to another. The agentinterprets what’s happening in the market andoptimizes the use of various algorithms based onchanging circumstances.
Bailey describes the trend as a “smart algorithmicrouter.” “You’re really routing flow that comes inbased on the nature of that flow to different typesof algorithms’” he says. “Rather than smart-orderrouting, you’re actually routing to different algorithmsthrough some type of logic.”
In addition to broadening the buyside baseand broadening globally, algorithm providersare broadening across asset classes. Algorithmproviders are extending some of their equities-trading strategies to the futures, options andforeign-exchange markets.
An opportunity Aite Group views as ripe foralgorithms, for electronic trading and for humansis small-cap and mid-cap stocks, where it hasnot been easy to run algorithms in less-liquidnames.
Says Bailey, looking to the future: “We’re goingto see more granularity in the type of algorithms.You’re going to see more algorithms. You’re goingto see more traders become savvy around thetype of algorithms and looking to really createthe ideal algorithm for their given situation.” �
A Supplement to Traders Magazine, Produced by SourceMedia’s Custom Publishing Group
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50
40
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0
25
3 1
28
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33
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Projected Global Adoption of Algorithmic Trading
2004 2005 2006 2007 2008 2009 2010
� U.S
� Europe
� Asia
IN PERCENTAGES
Source: Aite Group.
Brad BaileySenior Analyst
Aite Group
Laurie BerkeSenior AnalystTABB Group
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DEVELOPMENTS IN ALGORITHMIC TRADING
FAN & Covert
EdgeTrade developed the FAN (“Find and Nail”) algorithm in
response to these dynamics. This SOE algorithm systematically
and proactively seeks and accesses liquidity in both displayed
and non-displayed markets. FAN acts as an aggregator of
disparate liquidity pools and as the connective tissue in the
marketplace. Rather than serially passing from one venue to
the next until the order is completed, as early SORT algorithms
did, FAN instead employs quantitative techniques in its logic and
learns from each execution, by evaluating the performance of
venues against historical and real-time trade data and adapting
to present conditions.
In real time, FAN actively seeks out locations in the
marketplace where the most trading is occurring in given shares,
moves the balance of the order in that direction and repeats this
process relentlessly over a matter of milliseconds until the fi ll
is complete. Furthermore, it has the ability to act in multiple
venues simultaneously, feeding back information on fi lls so that
no double executions occur, which gives it the facility to change
course in a millisecond.
This is a quantum leap ahead of other algorithm technologies.
To achieve this capability, FAN must be able to receive as much
as 10 times the volume of message traffic as a typical SORT
algorithm. Many algorithms hit only publicly displayed quotes;
FAN investigates both dark and displayed liquidity using two
different methodologies simultaneously.
To access dark pools, where quotes are not displayed, test
trades must be made. If there is a cursory response – a “nibble,”
or small-volume trade executed in a dark pool – then a judgment
must be made as to whether more hidden liquidity exists behind
that small trade. Drawing from its database of historical data
and proprietary analytics, FAN assesses that likelihood in a split-
second and decides whether to trade further, while constantly
maintaining contact between all parent and child orders working
in other venues.
Very few algorithmic offerings possess the capability to
learn from prior experience, balance multiple strategies, and
make intuitive decisions and adjustments on the fly. FAN also
possesses the intelligence to route itself around venues that
are experiencing technical difficulties, thus reducing wasted
A Smart Light in the Dark forBuy-Side and Sell-Side Traders
Today’s equities marketplace requires algorithms to adopt as much of a live trader’s resourcefulness and intuition as possible; it’s
no longer efficient to load an order into a black box and let it “fire away.” This concept of dynamic adaptation to real-time events
infuses EdgeTrade’s concept, “Smart Order Execution.”
Whereas smart order routing (SORT) is primarily concerned with a linear approach to choosing a destination and splitting up an
order to avoid moving the market in its constituent shares, SOE algorithms carry the greater goal of best execution in their logic.
By constantly evaluating market conditions and processing historical and proprietary analytics throughout the life of a trade, SOE
algorithms change course instantaneously as conditions warrant.
Smart order execution goes hand-in-hand with the concept of “active order placement.” Active order placement means that each
market venue is treated objectively and aggressively with best execution always driving the action. By merely handing an order
over to one venue or market participant and expecting that entity to achieve best execution on behalf of the client, the trader is
only pursuing “passive order placement.”
In today’s fast-moving market, traders and their tools must actively respond to changes in liquidity quality and availability,
independent of the agendas of any one venue.
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time. The differentiating characteristic of FAN is that it mimics
the behavior and intuition of a live trader, while juggling
information at rates far beyond a human’s comprehension,
and the capabilities of its competing offerings. The results of
this aggressive investment in quantitative trading technology
have been borne out in the marketplace: compared to average
dark pool match rates that hover between 6% and 10%, FAN
has recorded match rates as high as 28.9% in the dark on an
average daily basis.
Buy-side and sell-side traders globally have aggressively
adopted FAN since its introduction in September 2006.
Launched in June 2007, Covert follows the same principles as
FAN, but is intended for market participants to whom preventing
information leakage is of paramount importance. Covert lives up
to its name by applying the logic of FAN solely in dark pools.
Empowered Buy-Side Trader
Increasingly, sophisticated tools that were only available to
the big Wall Street brokers are now in the hands of buy-side
traders. Regulators and institutional customers such as pension
funds are applying increased pressure on portfolio managers to
fulfi ll fiduciary obligations and achieve best execution, which of
course filters directly to the trader. The convergence of these
trends obligates the trader to assume greater control, and make
intelligent use of the liquidity options available. With impartial
advice and the right tools for navigation, the empowered buy-
side trader will discover advantages that come from choice of:
strategy, technology and venue. The ability to toggle seamlessly
between passive and active strategies several times a day will
become increasingly important as liquidity moves around the
expanding trading universe.
Sell-Side Sidelined: Reinvent or Relinquish?
As more tools become available to the buy-side trader directly,
the sell-side firm must stay one step ahead of the game or risk
irrelevance. The sell-side firm of the 21st century must become
an educated leader in state-of-the-art trading technology and
the new market landscape, and offer this technology to its
clients in conjunction with all the other valuable services it has
historically provided. Increasingly, brokerage firms must confront
the “build or buy” decision with regards to trading technology.
They face the choice of attempting to keep pace with changes
and improvements in the technology, as well as evolutions in
market structure and the regulatory environment.
EdgeTrade’s alternative to the deep-pockets and historically
risky (failed) proprietary technology build is, Quantitative Service
Bureau™. Leveraged by a broad range of firms, from global
banks to regional broker-dealers, EdgeTrade’s QSB™ offers sell-
side firms all the benefits of an agency-only and anonymous
algorithmic trading infrastructure including execution (through
EdgeTrade or a firm’s own market connections) and adherence
to changing regulatory and compliance requirements. �
Kyle Zasky
President
EdgeTrade Inc.
Joseph Wald
CEO
EdgeTrade Inc.
Established in 1996, EdgeTrade has a sterling reputation
for the integrity of its unconfl icted, anonymous execution
services, next-generation algorithmic strategies and liquidity
aggregation/access technology. An agency-only broker and
software developer, EdgeTrade never conducts principal
or proprietary trading. Clients of EdgeTrade, of which
there are more than 220, include hedge funds, mutual
funds, asset management firms and broker-dealers in
North America, Europe and the Far East.
EdgeTrade Inc.
5 Hanover Square, 9th Floor
New York, NY 10004
www.edgetrade.com
Timothy Lane
Senior Vice President
212/271-6470, ext. 270
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DEVELOPMENTS IN ALGORITHMIC TRADING
How has the market for algorithmic trading evolved over the
past several years?
In recent years, the industry’s relentless search for increased
efficiency and speed of execution through automation has spurred
a dramatic expansion of algorithmic trading. The buy-side and
sell-side alike have used algorithmic trading effectively to help
improve trading results and increase productivity, while reducing
costs. Over the past few years, the technology infrastructure has
grown significantly and can now support innovative algorithmic
trading strategies that provide more scalability, less redundancy,
and more reliability.
Where do you see algorithmic trading heading in the future?
The first generation of algorithms featured static strategies,
such as Volume Weighted Average Price (VWAP), which trades
along with volume throughout the day, tracking average price
over a specified time interval. In fact, VWAP is still a very
common algorithmic trading strategy. Recently, a second
generation of dynamic algorithms that employ what is known
as “complex event processing” (CEP) are becoming more
prevalent. In contrast to the first generation algorithms that
employed relatively static execution frameworks, these dynamic
algorithms can react to market events in real time. For example,
rather than a volume scheduling strategy that does not change
throughout the day, today’s event-based algorithms can react in
real time to market events, such as a dramatic drop in price or
increase in market volatility.
Has the rise of algorithmic trading created new challenges
for traders?
The most obvious consequence has been increased market
fragmentation. From the buy-side’s perspective, the ultimate goal
has been “frictionless trading” — the creation of a seamless,
automated, “straight-through processing” network that provides
cost-saving efficiencies and broad access to liquidity. Institutional
investors are looking for the ability to enter the marketplace,
execute trades, and leave no evidence that would move the
market or tip their hands on particular investment strategies.
Fragmentation typically runs counter to these goals, so buy side
traders have gradually adopted algorithms to help counteract
this trend.
Ironically, although algorithmic strategies and other electronic
trading techniques were originally created, in part, to address
fragmentation, over time they have actually had the opposite
effect by relieving the pressure to consolidate. By synthetically
aggregating market data and access, while offering advanced
trading capabilities, algorithms may simultaneously alleviate
and contribute to market fragmentation.
At Fidelity Capital Markets Services, we have developed
enhanced strategies to help meet these challenges. For example,
our DarkSweepSM strategy aggregates dark pools of liquidity
into a single entry point and simultaneously splits orders up
among the available dark venues. DarkSweep seeks and shifts
liquidity in order to help maximize execution without revealing
your trading intentions. This strategy only executes against
The Next Generation of Liquidity
In recent years, the quest for automated trading efficiencies, anonymity, and best execution have led to an explosion of
algorithmic trading strategies. Jeff Brown, Vice President of Electronic Brokerage Services for Fidelity Capital Markets Services,
provides insight into the latest trends in algorithmic trading and discusses how Fidelity is responding to these challenges.
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nondisplayed interest within the National Best Bid and Offer,
helping to minimize market impact. Currently*, DarkSweep
aggregates liquidity from 12 market centers and alternative
trading systems, including Credit Suisse CrossFinder™, Lehman
Liquidity Cross, Fidelity CrossStreamSM and others.
What can Fidelity Capital Markets Services offer clients who
are looking to develop customized algorithms?
We work closely with our clients to offer customized solutions
and support. We meet with our clients to learn about their
style of trading and their goals. We use this information to help
design and deliver customized solutions that can leverage our
existing technology framework.
What challenges do your clients face in trying to keep pace
with the evolution of algorithmic strategies and technology?
When we talk to our clients, we hear consistently about
the challenges of navigating the current marketplace and the
complexity of modern trading products. Generally, a buy side
trader may be dealing with a variety of issues in addition
to trade execution, such as communicating with portfolio
managers, managing commissions, and post-trade operations.
In this environment, hyper-sophisticated trading strategies can
represent a sort of overkill. While the efficiency and technical
sophistication of these algorithms are attractive, it may take
a significant amount of time to master their nuances and to
integrate them into existing technology infrastructure. At
Fidelity Capital Markets Services, we work collaboratively with
our clients to understand their trading environment and develop
algorithmic strategies that help meet their needs. �
*as of October 31, 2007
Jeff BrownVice President,
Electronic Brokerage Services
Fidelity Capital Markets Services
Fidelity Capital Markets Services, a division of National
Financial Services LLC, executes equity and fi xed income
trades, provides execution services in listed options and
trades foreign exchange on behalf of a wide array of clients,
including millions of individual investors, 1,000 institutional
fi rms, 340 correspondent broker/dealers, 3,800 registered
investment advisors, bank trust and TPA clients and 18
million client accounts*. The fi rm also has full-service
Syndicate and Prime Services desks.
Fidelity Capital Markets Services200 Seaport Boulevard
Boston, MA 02210
For more information about Fidelity Capital Markets Services, call 1-800-471-0382 or visit us at
www.FidelityCapitalMarkets.com.
*as of June 30, 2007
Third party service providers are independent companies and are not affiliated
with Fidelity Investments. Listing them does not suggest a recommendation or
endorsement by Fidelity.
Fidelity Capital Markets Services is a division of National Financial Services LLC,
Member NYSE, SIPC
478981
“By synthetically aggregating
market data and access,
while offering advanced
trading capabilities, algorithms
simultaneously alleviate and
contribute to market
fragmentation.”
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DEVELOPMENTS IN ALGORITHMIC TRADING
How should a trader pick an algorithmic provider?
Traders choose algorithmic providers for a variety of reasons,
many of which are somewhat removed from the trading process.
These factors include the need to pay for research, strong
prime brokerage relationships, and the quality of an execution
management system. Goldman Sachs can compete in all of
these areas, but it is the quality of our algorithms, the size of
our liquidity pool, and our execution consulting services that are
the driving factors in our clients’ decision making.
With so many trading venues, how can I find liquidity in
this fragmented marketplace?
Liquidity access is key to successful algorithmic trading.
Although we label some of our algorithms ‘liquidity seeking’,
in fact, all of our algorithms seek and identify liquidity. The
Goldman Sachs suite of algorithms has unique access to the
largest existing dark pool, SIGMA X, which crosses over 120
million shares a day. In addition to accessing SIGMA X, we
recognize the need to tap into as many liquidity sources as
possible, so we have expanded our reach to include additional
liquidity venues. Additionally, several of our algorithms leave a
portion of their orders resting in dark pools, replenishing each
portion of the algorithm dynamically as fills come back. In this
way clients maintain a particular execution schedule while
taking advantage of additional liquidity. We plan to add as
many pools of significant liquidity as we can to our algorithmic
offering, focusing not just on the number of pools but the depth
of liquidity they provide.
How does Goldman Sachs think about the execution process?
A great deal of financial engineering goes on behind the
scenes to ensure that we can deliver promised functionality in
a consistent and effective way. There are literally hundreds of
choices that an algorithm can make to achieve its objectives on
a tick by tick basis. For example, an algorithm must determine
the number of child orders, the correct size or pricing, whether
to seek or take liquidity, the desired exchange or liquidity venue
and so forth.
How does Goldman Sachs segment its algorithmic offering?
Clients’ algorithmic objectives can be thought of falling in a
few basic categories: price and liquidity seeking, benchmark
matching and reactive participation. With global markets
increasingly diverse, liquidity centers fragmented and trading
more complex than ever, an algorithm might be called on to
achieve a number of objectives simultaneously, including:
• Searching for liquidity across multiple venues, including
dark pools
• Maintaining a level of participation in the marketplace
• Increasing the participation rate if favorable prices occur
• Capturing the spread as effectively as possible and
whenever feasible
• Minimizing the execution footprint in the marketplace
by using smart order types
• Completing the order
We will highlight an example of an algorithm in each segment of
our offering to demonstrate how we meet the above objectives:
PRICE & LIQUIDITY SEEKING
Our Sonar algorithm seeks liquidity from both displayed and non-
displayed sources and uses smart order types to source liquidity
at attractive price levels. We simplify the trading process by only
requiring a limited number of inputs when entering the order:
level of aggressiveness, limit price (if desired) and ‘would if good
prices’. Traders can also choose to use Sonar Dark which routes
to dark pools and utilizes hidden order types on public markets.
The Sonar solution reflects our philosophy of streamlining the
process without losing the power of a well designed tool.
REACTIVE PARTICIPATION
Reactive participation is a class of useful and well-designed
algorithms that have the flexibility to stray a bit from their
participation target levels based on the attractiveness of available
liquidity. One of our strategies, Dynamic Scaling, lets traders take
a view on the pricing behavior of a stock and automatically adjusts
Goldman Sachs Algorithmic Trading: Insight and OutlookBy Marie Konstance, Vice President, Goldman Sachs Execution & Clearing, L.P.
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the participation rate based on the stock’s price. If the trader
selects a reversion strategy, for example, Dynamic Scaling will
use price drops as a buying opportunity. Conversely, if trader has a
growth perspective on a stock, Dynamic Scaling will increase the
participation rate as the price moves against them.
BENCHMARK MATCHING
Benchmark matching includes standard algorithms such
as VWAP and TWAP, as well as our implementation shortfall
algorithm, 4Cast. 4Cast trades off risk and market impact
costs and lets the user determine the aggressiveness based on
their view of the riskiness of the trade. The algorithm adjusts
the participation rate based on this aggressiveness level and
keeps a portion of the trade in dark pools to maximize crossing
opportunities. At low levels of risk aversion, 4Cast’s goal is to
minimize impact. At high levels of aggressiveness, 4Cast will
minimize risk by trading quickly and activating an aggressive
leg, which will take liquidity at attractive prices if signifi cant size
becomes available.
What algorithmic strategies work best when
trading baskets of stocks?
We offer an implementation shortfall strategy for baskets
of stocks through our PortX algorithm, which optimizes the
trading of one or two-sided baskets by balancing the goals
of minimizing market impact while controlling risk. PortX
estimates the transaction costs of the portfolio and will quickly
trade names that are high risk and minimal impact. Stocks
that are correlated but on opposite sides of the basket may be
traded more slowly to reduce market impact; for example a buy
order in one tech name and a sell order in another tech name
will be traded against each other as a natural hedge. PortX will
naturally work to keep cash positions balanced.
What assistance do you give traders to help them choose
the correct algorithm?
We provide analytics and consulting to help direct traders
to the best choice for their objectives. Pre-trade analytics give
comprehensive cost analysis on both a portfolio and single
stock basis. Our systems deliver transaction cost estimates and
evaluate real-time market data to suggest which algorithm is a
better choice given the order characteristics and today’s market
conditions. Execution consulting provides an in-depth view to
tailor strategy selection to the trader’s goals. But all of this is
only the first step in the process — traders need a feedback
system to monitor their performance and evaluate their results.
We provide post-trade analytics and consulting to help with
this process. Programmable alerts indicate important market
events during trading. Post-trade reports and analysis help close
the loop so a trader can fine tune their strategy.
What product enhancements will you focus on for 2008?
We have tended away from offering new products as there is so
much confusion in marketplace with the multitude of algorithms
already available. We find that we can offer new functionality
without confusion by continually enhancing our existing product
line. An on-going effort is to add new dark pools and smart
limits to maximize crossing without disclosing information to
the market. Another goal is to build real-time optimization into
many of our algorithms, so they can immediately react to market
conditions and change course if needed. This functionality keeps
traders from having to cancel an algorithm to explore a dark
pool or react to new market developments. �
Goldman Sachs Electronic Trading provides clients with
the necessary tools to manage their trades from start
to fi nish, from pre-trade analytics to post-trade analysis.
Clients access our products via REDIPlus®, our top-ranked
EMS platform, or via FIX. Customers can seek liquidity
using our suite of multi-asset algorithms, route to optimal
destinations using our SIGMA smart router, and take
advantage of the largest U.S. crossing network, SIGMA X.
Along with providing clients access to global equity markets,
we also offer FX, Futures, and Options across North
America, Europe, and Asia.
Goldman Sachs
212.357.4255
www.gs.com/electronictrading
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DEVELOPMENTS IN ALGORITHMIC TRADING
KNIGHT’S UNIQUE LIQUIDITY
Algorithmic trading models should consider the type of order fl ow
with which it interacts. On an average trading day, Knight executes
more than 400 million shares, most of which is small retail order
flow routed from broker-dealers. Additionally, as a large provider of
this type of retail order executions, Knight has signifi cant market
share in small- and mid-cap names, which are the most diffi cult
for algorithmic models to trade without market impact. Overall,
Knight’s footprint covers nearly every U.S. equity security.
Within the last year, Knight introduced Knight Link, a new
means of access to Knight’s deep liquidity. Knight is an essential
destination for smart order routers and dark liquidity-seeking
algorithms because it provides liquidity in over 6,000 securities
— often in thin and difficult-to-trade names. Today, approximately
75 million shares daily are processed via Knight Link, with a one-
day record of over 113 million shares.
In the last 12 months, several of our new “alternative liquidity
partners” have added Knight to their routing table for the wide
spectrum of covered stocks, unparalleled depth in small- and mid-
cap issues and rapid execution speed.
Differentiating AlgorithmicTrading DestinationsAs crossing networks, ATSs, dark pools and other off-exchange destinations proliferate, it has become increasingly
difficult to differentiate between them. One of Knight’s newest products, Knight Link, offers access to a fundamentally
different source of liquidity to our clients.
As one of the world’s largest providers of retail order execution services, Knight frequently interacts with the
rest of the Street in order to manage its inventory and risk. Via Knight Link, our partners can directly trade with
Knight instead of through an ECN or exchange, saving them costly exchange and ECN fees. Knight Link also
provides a unique source of liquidity to dark liquidity-seeking algorithms and smart order routers.
Most ATSs and dark pools offer very similar, seemingly undifferentiated products. However, there are fundamental
characteristics, in addition to the type of liquidity that firms should consider when determining their connectivity
to sources of off-exchange liquidity.
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NO TRANSACTION FEES
In addition to reducing market impact, firms are looking to reduce
explicit costs. Most sources of off-exchange liquidity charge
fees. Knight Link can significantly reduce your transaction costs
by saving you exchange and ECN fees, because Knight Link is
completely free. As a free access point to liquidity, Knight Link
often sits at the top of its clients order routing systems.
FLEXIBILITY AND CUSTOMIZATION
A dark pool, crossing network or other source of off-exchange
liquidity is useless if there are too many barriers to access.
Overburdened technology teams can’t devote signifi cant time
or resources to each destination, so ease of connectivity is
imperative. No two client systems are the same, so Knight has
successfully tailored Knight Link to accommodate a wide range
of trading architectures and capacity requirements.
CERTAINTY OF EXECUTION
Knight’s fast response times and high fill rates enable our partners
to often obtain additional liquidity at the inside market, while still
being able to access other execution venues in a fast moving
market. In addition, Knight Link partners continue to benefi t from
Knight’s state-of-the-art technology and customizable streaming
data connections.
Ultimately, firms are looking for a venue that provides high
fulfillment rates, rapid response times and high quality executions.
The difference between Knight Link and other algorithmic trading
tools in terms of liquidity, cost, flexibility and market impact
can mean the difference between increasing alpha or missing
opportunities for a better trade execution. �
Jamil NazaraliManaging Director
Knight Capital Group, Inc.
Knight Capital Group, Inc. (Nasdaq: NITE) is a leading
financial services firm that provides comprehensive trade
execution solutions and asset management services.
Knight provides a broad range of customized trade
execution products and services across multiple asset
classes. We make a market or trade in nearly every
U.S. equity security and provide trade execution services
in international securities, futures, options, foreign
currencies and fixed income instruments.
For more information about how Knight Link can benefit your firm, please contact Jamil Nazarali at 201.356.1511 or [email protected].
www.knight.com
Knight Link is a product offered by Knight Equity Markets, L.P. and
Knight Capital Markets LLC, both members of FINRA and SIPC.
For more information on Knight, please visit www.knight.com
“Knight Link can significantly reduce your transaction costs by saving you exchange and ECN fees, because Knight Link is completely free.”
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DEVELOPMENTS IN ALGORITHMIC TRADING
MSET Algorithms and Trading Tools
MSET is a leading provider of algorithmic trading solutions.
Morgan Stanley’s Benchmark Execution Strategies (BXS) are a
comprehensive, yet simple suite of trading algorithms that are
designed to minimize market impact and enhance traders’ skills
and productivity while improving alpha capture and performance
relative to a specified benchmark. Electronic traders can choose
from a number of algorithms with benchmarks based on Price
(Arrival Price, VWAP and Close), Time (TWAP) or Participation
Rates (Target Percentage of Volume and Volume Dispense).
They can also utilize Morgan Stanley’s Smart Order Routing
Technology (SORT), which provides access to multiple liquidity
sources simultaneously, routing orders based on several factors
including current and historical price, liquidity and speed. SORT
is fully Regulation NMS compliant, managing connections to all
Regulation NMS protected market centers. SORT also provides
advanced order types, such as LoFloor and NoFloor, allowing
traders to execute in a style that suits their trading needs and
market views. In addition to equities, Morgan Stanley also provides
algorithms for a number of global futures contracts.
Find Hidden Liquidity in Dark Pools/Crossing Networks
With the growth of dark pools, liquidity has become much
more fragmented. Because each dark pool/crossing network
has its own set of rules and restrictions, it is extremely diffi cult
for a trader to connect to each and use them effi ciently. MSET
provides customers with an aggregator of these destinations,
Night Vision, to make finding dark liquidity easier. Night Vision
simultaneously accesses major dark pools, including Morgan
Stanley’s own MS POOLSM, and intelligently manages order
allocation and pricing to help traders achieve potential price
improvement while retaining anonymity. Night Vision can also
be utilized in conjunction with BXS algorithms by using the
“I Would” feature, which incorporates a selective dark pool
sweep.
In addition to MS POOLSM, MSET has also been providing
alternative liquidity in the form of algorithm to algorithm
crossing since 1999. MSET Trajectory Crossing reduces market
impact by crossing algorithmic orders anonymously over time
intervals.
MSET’s Perspective on Choosing TheRight Algorithmic Trading Partner
In a trading environment that grows increasingly complex, traders are seeking tools that can help them navigate markets and
simplify their workflow while accommodating different trading styles. Electronic trading has become essential in this market
structure, offering a way to bridge the myriad of exchanges, electronic communication networks, and dark pools/crossing
networks. Market fragmentation and growing competition among electronic trading providers have driven the development of a
new generation of algorithms that are more sophisticated and diverse to meet this challenge. With so many algorithmic options
out there, choosing an electronic trading partner should depend on more than just the particular products and systems that they
can provide. Value-added services like stability under extreme market moves, high-level support, access to alternative liquidity,
customization, multi-asset class execution and transaction cost analysis are what help to differentiate and distinguish the leaders
in the electronic trading space. Morgan Stanley Electronic Trading (MSET) provides all of these services and more.
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Customize Algorithms to Trade the Way You Want
In the new generation of algorithms, along with smarter strategies
come more choices – sometimes too many choices. Because most
traders have their own style of trading, should an algorithm not
adapt to their trading style and be easy to use? MSET’s new “ONE”
algorithm is a customizable strategy that combines the strengths of
algorithmic trading, direct market access, dark liquidity and smart
order routing into “one” simple order to meet a trader’s specifi c goal.
Traders can take a strong price view while still using algorithms to
control market impact within the context of a disciplined approach
to execution. It is designed to replicate how an individual trader
trades a particular stock and can be tailored to each trader’s needs
depending on their execution demands. ONE can also help alleviate
pressure on a trader to constantly modify his orders as market
conditions change.
Trade Multiple Asset Classes Around the Globe
In the constant quest for alpha, the importance of alternative
asset classes is on the rise. Exposure to these different products
offers a new way of looking at investments and strategies. MSET
offers electronic access to global equities, options, futures, swaps
and foreign exchange trading. Trading various asset classes can
be done simply from one platform, whether it is via a third party
order management system, a proprietary front-end system or
through Morgan Stanley Passport.
Learn From Your Trades
Morgan Stanley continues to add value upon execution,
providing performance analysis showing daily, weekly and
monthly results, by summary statistics and by strategy. MSET
also helps traders understand their executions and assist them
in designing future execution strategies. Each trade becomes
an opportunity to adjust strategies or trading parameters to
improve future performance.
Conclusion
The current market of algorithmic providers could be considered
overwhelming. With so many similar products, value-added
services are truly the differentiating factor. MSET is proud to be a
leader in electronic execution offering superior trading tools that
can be tailored to any trading style. MSET’s best in class service,
market structure knowledge, and access to alternative liquidity
in conjunction with a global, multi-asset platform provide a
strong foundation to build an electronic trading partnership. �
Morgan Stanley is a leading provider of electronic
trading products and solutions delivering a complete
spectrum of tools, from pre-trade analytics to execution
and post-trade execution performance analysis. MSET is
part of Morgan Stanley’s Institutional Securities Group,
and clients can have access to Morgan Stanley’s full
range of services. Access to MSET products is available
through Passport, our fully integrated front-end system,
through FIX, or from a client’s proprietary trading
system. MSET clients can trade global equities, futures,
options, swaps and foreign exchange — all from one
platform, as well as utilize an MSET representative
designated to know a particular client’s account and
provide execution assistance.
US:
(877) 761-MSET
EU:
(888) 465-2554 (from US)
+44 20 7425 3222
Asia-Pacifi c:
(800) 932-8918 (from US)
+852 2848 8222 (HK)
+813 5424 5709 (Tokyo)
www.morganstanley.com/mset
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DEVELOPMENTS IN ALGORITHMIC TRADING
True Multi-Broker Algo Neutrality
AlgoGenetics enables traders to chain algos from every
major broker and technology provider to build a best-of-breed
sequence.
Adam Sussman, an analyst at research firm TABB Group,
notes that the functionality in Neovest’s AlgoGenetics represents
the “next step” for multi-broker algorithmic aggregation
platforms, as execution management becomes more complex
in a fragmented marketplace. The initial advance, spearheaded
by Bloomberg, was enabling traders to access multiple algos
from different brokers on one system. Many vendors of order
management systems (OMSs) and [execution management
systems] (EMSs), including Neovest, now aggregate algos from
dozens of brokers.*
Brad Bailey, a senior analyst at research firm Aite Group,
points out that buy-side traders increasingly want more fl exibility
around the algos they use. “[AlgoGenetics] is a tool that makes
absolute sense if it optimizes a trader’s use of algorithms, leads
to cost mitigation and has a rules-based approach to execution,”
he says.*
The first clients to use Neovest’s AlgoGenetics are three New
York-based hedge funds, including Scopus Asset Management.
Bill Skutch, the head trader at Scopus, says the platform
“allows us to maintain neutrality among brokers and take more
control over algos by customizing them for individual stocks or
sectors.”*
Sussman sees two main benefits to this type of algorithmic
management platform. First, it enables users to switch between
algos without manually overseeing that process by setting
conditions “that will automatically drive an order from one algo
to another.” In Sussman’s view, this is particularly important for
firms shuttling 20 to 25 percent of their flow through algorithms,
since those firms are likely to encounter workfl ow-management
issues. Second, Sussman adds, creating meta-algorithms
AlgoGenetics, the Industry’s First AMSNeovest is the first to establish a new class of software, a true multi-broker algorithmic management system (AMS).
AlgoGenetics provides buy-side traders with the revolutionary ability to centralize and manage more than 100 unique
algorithms from brokers and technology fi rms.
Traders don’t have to be boxed-in anymore — overseeing and manually adjusting their electronic execution strategies
as market conditions fluctuate. Neovest’s AlgoGenetics, the logical evolution toward creating, customizing, and improving
the workflow for trading algorithmically, gives traders the freedom to adapt the behavior of broker algos to suit their
individual execution needs.
As the number of executing venues and algorithms continue to rise, traders are challenged with the need to be increasingly
vigilant in watching market conditions and movement. Traders are charged with making quick decisions on which venue
best suits their trading needs to achieve optimum fills. This can quickly turn into a workflow nightmare as traders try to
adjust positions quickly in fast changing markets.
AlgoGenetics gives traders an intuitive, point-and-click system to build their own execution sequences by combining
existing algorithms from multiple brokers, dark pools, and direct-market-access (DMA) tools. By building their own
sequences in AlgoGenetics, traders have confidence that their meta-algorithm will perform based on their established
trading style, thus improving trading effi ciency.
Many products claim to improve the trading process, but they require traders to change their individual trading style.
AlgoGenetics lets traders replicate the way they trade manually through self-customized algos.
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forces a trader “to automate his thought process, enabling him
to map what he wants to see happen with an order onto an
algorithm.”*
Designed for Ease of UseTraders know which broker’s algorithms they want to use for
specific situations and when to switch algos based on market
conditions, price, time, or other criteria. Traders can now leverage
the strength of all their algo providers to trade exactly the way
they want.
Using AlgoGenetics, a trader doesn’t have to corner a
programmer or ask the sell side to customize an existing algo
to meet his specifications. Using AlgoGenetics’ drag-and-drop
interface, a trader may create algos on the fl y and initialize
them for operation immediately.
Using time, price, order status, and market data, traders can
establish conditions for sequencing orders to algos and DMA
destinations. According to the trader’s design, these customized
sequences react in an intuitive and timely manner to market
fl uctuations.
For example, a trader may create an AlgoGenetics sequence
that will participate on exchange supported opening crosses
and then, after the open, submit the balance to participate as a
percentage of volume with JP Morgan while constantly seeking
any dark liquidity from any one of Neovest’s industry leading
dark connections. The sequence may also feature a stampede
price where, as soon as it is triggered, the trader’s order would
be represented at the trader’s favorite aggressive algorithm. The
stampede price can be a price that is specific to the instrument
being traded or can reference any other instrument or index.
These new algos, generated through AlgoGenetics, may then
be named, shared on the desk, and updated at a moment’s
notice. Traders may also take advantage of AlgoGenetics’
features to adapt the behavior of existing broker algos to suit a
trader’s individual execution needs.
Other technology providers offer mechanisms for traders
to build their own algos, but these typically require some
programming or advanced computer skills and are not set
up to readily use broker algos as components within a larger
execution strategy.
Fully Integrated EMSAlgoGenetics enhances Neovest’s already potent core EMS
application for ultimate workfl ow effi ciency. The Neovest EMS gives
users a suite of DMA tools including routing to 27 dark pools, over
150 brokers, and the ability to electronically trade actionable IOIs.
Neovest set the original standard for broker neutrality and
continues to lead the industry with its low latency EMS offering. Its
new Trade Manager module combines all of Neovest’s proven single
stock EMS features with next generation list and portfolio trading.
Neovest is a powerful tool to streamline effi ciency through
managing positions, P&L, risk, reporting, allocation & bunching,
commission management, and regulatory compliance in a tightly
integrated and comprehensive product.
Combining the workflow management advances of AlgoGenetics
and Neovest’s EMS makes Neovest the strongest algo platform
available, empowering traders not just to trade better, but to build
their own high-performance execution system. �
*Excerpted from Traders Magazine, October, 2007.
BryceByersPresident and CEO
Neovest, Inc.
Neovest, Inc., a wholly-owned subsidiary of JPMorgan,
provides a comprehensive suite of broker-neutral EMS
services, low latency market data functions, technical
analysis, filtering technologies, portfolio trading, and
analytics in an easily deployed and managed system.
Neovest’s cross-asset, multi-currency platform is a
single source of liquidity to exchanges, ECNs, crossing
networks, dark pools, IOIs, broker algorithms, and
routing to 150 brokers.
Orem, Utah Corporate Offi ceNeovest, Inc.
1145 S. 800 East, Suite 310
Orem, UT 84097
Phone: +1 800.433.4276
Fax: +1 801.373.2775
New York, New York Offi ceNeovest, Inc.
277 Park Avenue, 11th Floor
New York, NY 10172
Phone: +1 212.622.6651
Fax: +1 646.349.2567
For more information, please contact Neovest’s sales team:Phone: +1 800.433.4276
E-mail: [email protected]
Visit our Web site at www.neovest.com
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DEVELOPMENTS IN ALGORITHMIC TRADING
ALPHA is developed around a set of highly sophisticated
quantitative models. At its heart is the Relative Value Engine
(RVE). RVE, using more than 30 proprietary signals, isolates
micro-arbitrage opportunities throughout the duration of
an order. Once a mis-pricing is identified, the algorithm
automatically adjusts tactics to capture any additional alpha.
The RVE’s signals have been back-tested against a database of
millions of client and proprietary trades compiled over the past
10 years.
ALPHA’s suite of algorithms are easily adapted to the nuances
of any market. All the typical benchmarks such as VWAP,
Previous Close, Arrival Price, etc. are supported. However,
ALPHA demonstrates some of its strongest capabilities where
trades involve an element of contingency such as Pairs and
Ratio trades. ALPHA is flexible enough so that Société Générale’s
Execution Strategy team can customize to a customer’s exact
algorithimic specifi cations.
ALPHA’s capabilities are underpinned by Société Générale’s
world-class, global technology platform. This allows for consistent
and reliable execution performance in every market. Data
centers in New York, London, Hong Kong, and Tokyo ensure that
the networks, application servers, and databases are backed up
in real-time on a transaction-by-transaction basis with duplicate
market access.
Full Customer Integration and Confi dentiality
ALPHA is fully-integrated with multi-broker platforms and
connectivity via FIX and major OMS/EMS vendors. Société
Générale works with the OMS/EMS vendors to closely integrate
ALPHA seamlessly into a customer’s daily workflow. Moreover, it
accesses liquidity through multiple sources: primary exchanges,
ECNs, dark pools, MTFs, and crossing networks. ALPHA
reintegrates an increasingly fragmented US marketplace.
In addition to system controls and support services, great
lengths are taken to keep all client trades confidential and to
prevent any possibility of information leakage. Société Générale
sales-traders and principal trading desks do not have access to
customer order flow coming through ALPHA.
24/6 Support and Controls
Société Générale operates a 24-hour service-desk 6 days per
week at locations around the globe. Service desks are equipped
with advanced monitoring systems to track system performance
and trades in real-time. Once a client is trading with the fi rm,
Société Générale will provide a single point of contact for
support. Société Générale will manage the connection with your
network provider, discuss your specifi c requirements, monitor
your flows and contact clients in case of issues. All service-
desk consultants have trading, technical, and quantitative
backgrounds to better provide training and support on the fi rm’s
algorithmic offering and are knowledgeable with the front-end
trading systems each client uses.
Introducing Société Générale’s AlphaCorporate and institutional clients in over 60 countries usually think of Société Générale for its class-leading reputation
in structured products and quantitative risk trading. A stream of prestigious industry awards certainly testify to these
accomplishments. The workhorse behind Société Générale’s success is ALPHA (ALgorithmic Portfolio Handling Application).
With total daily global trading principal exceeding $7 billion, ALPHA is used for approximately 80% of the firm’s internal risk
management and agency portfolio executions. The bank’s latest innovation is to make ALPHA available to all of its customers.
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The electronic execution product provides a wide range
of trading controls to protect against “fat-fi ngers” and
pricing mistakes. These safeguards are configured to avoid
compromising the speed and performance of Société Générale’s
algos. To ensure that Société Générale’s clients’ performance
expectations are achieved, the bank provides execution
guidance based on experience and quantitative analysis. The
strategies are applied optimally for the client’s trading needs
and the ALPHA design helps to provide transparency into the
algorithms.
Quantum Provides Easy Access to All Equity Trading Products
Global markets are changing faster than ever before and
technology remains a driving force. Keeping pace has become
highly complex and hard to understand. That is why Société
Générale brought together its wide range of client execution
products into a single offering.
The Quantum platform is a single integrated portal for access
to all Société Générale’s equity trading products. Direct Market
Access, Algorithmic Trading and Program Trading can all be
utilized on a no-touch basis. Often specialist intervention is
needed, especially for Capital Commitment, Sales-Trading, ETFs,
ADR trading plus Delta One products. Quantum’s dedicated
trading teams in New York, London, Paris and Hong Kong are
highly reactive and access an impressive array of execution and
risk management tools to provide Best Execution.
Quantum provides true global market coverage, spanning
60 world equity markets, including 27 direct exchange
memberships and significant market share in the key markets
of Euronext, Switzerland, Spain, Germany, Hong Kong, and Tokyo.
High-quality service with competitive pricing is provided through
major efficiencies derived from Société Générale’s signifi cant
global market share.
MiFID Advantage
The much anticipated MiFID regulations are now the law of
the land in Europe. The change in the prioritization hierarchy
of the Euronext Matching Facility from price/time to price/
membership/time will result in orders from member fi rms
now matching against those of their own firm regardless of
where those orders may stand in the order book queue. To
accommodate this change Société Générale has developed
tools to consolidate its order flows in most markets to improve
execution performances for its clients. Given the bank’s
considerable market share throughout Europe, the consolidation
of flow is a substantial benefit to clients that might otherwise
have been shut out of trades prior to MiFID. �
John Shaw, III
Managing Director
Program Trading and
Electronic Services
Société Générale Corporate & Investment Banking is
introducing new innovative products on the market
with a critical competitive advantage from the maturity
and expertise of its global derivatives and electronic
trading businesses. For more information please
contact sales at 212.278.5469.
SG Americas Securities, LLC
1221 Avenue of the Americas, 6th Floor
New York, NY 10020
www.sgcib.com
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DEVELOPMENTS IN ALGORITHMIC TRADING
What influences you to develop new algorithms at UBS?
The greatest influence is our clients. The buy side is acutely
aware of what they want — what solutions they need to
address the complexities they’re facing today. We’ve created a
continuous cycle of development, client deployment, post-trade
evaluation, client feedback, and more development. It’s the best
way to stay ahead of the curve.
We’ve found that flexibility is the key. Giving clients the ability to
modify their algos’ behavior based on order-specifi c requirements
— such as risk appetite or market impact — means that an
algorithm has the opportunity to fit optimally each time. It’s a
strategic choice. We can make a dozen algorithms that each
have highly specific behaviors, or we can make one algorithm
that has a variety of client-directed permutations — allowing
that single strategy to act like many different algorithms for
each trader.
So even within one fi rm, a trader can tailor UBS algorithms to
meet individual needs. While one trader might want a particular
order to be passive and execute gradually, another at the next
desk can use the same algorithm to be very aggressive. In our
opinion, that’s just good client responsiveness.
How do UBS algorithms perform globally?
Our algorithms are designed to be both “top-down” and “bottom-
up.” This means that while each strategy has a consistent global
mission, on the ground they are built specifically for that location
— its market structure, regulation, liquidity characteristics, etc.
That’s what we mean when we say “global in a local way.”
For example, our closest neighbor has signifi cant differences.
The Canadian market is less liquid with fewer stocks, its
exchanges have more stringent restrictions, and it differs from
the United States in terms of how orders are placed, minimum
order size, how to short sell, etc. That means when we create an
Implementation Shortfall algorithm for Canada to deliver what
IS does for the US markets — that algorithm must be developed
with extremely different logic.
That’s where UBS has a great advantage. As one of the largest
trading organizations in the world, we have expertise and access
that is both broad and deep — we know how to bring an order
to market anywhere in the world. That experience helps us to
make our algorithms as efficient and effective as possible.
What about TCA — how is the buy side using these kinds of
tools, and how are they evolving?
In years past, TCA grew primarily as a simple replication of
what program desks and single stock trading desks were sending
to their clients, as opposed to being focused on the needs of an
algorithmic trading client. This was a disconnect that resulted
in TCA not being terribly useful, and thus, not actively used.
But changes in market structure and the heightened state of
competition are changing all that — clients are demanding real
intelligence, not just fi ltered data.
UBS decided to approach our suite of analytical tools the same
way we approach designing our algorithms — working from the
client backwards. Our analytics suite had to provide traders with
analysis and data throughout the lifecycle of a trade: enabling them
to develop informed, anticipatory strategies, and effi ciently track
what is happening with their orders so they can modify them as
conditions change. It also had to assist traders to do a comprehensive
post-mortem on their orders so they can continuously refi ne their
strategies and understand what works and why.
At UBS, our pre-trade cost analysis allows traders to estimate
likely costs related to applying a variety of strategies, given the
situation. This web-based tool helps clients predict execution
costs and risks by comparing various strategies with each other.
It delves into details of individual names, UBS’s internal liquidity
in UBS Price Improvement Network (PIN) in these names, and
the expected PIN crossing rates.
While an order is live, UBS Alerts, combined with consulting
from our experienced sales trading desk, keeps clients on top
of their order’s behavior, alerting them to any signifi cant activity
and enabling them to quickly react to fl uctuating conditions.
The Finely-Tailored Algorithm,Designed By UBS
Jatin Suryawanshi, Managing Director and head of U.S. Algorithmic Trading, discusses trends in their new, smarter
generation of algorithms, why they’re “global in a local way,” and why flexibility and “made to measure” strategies are
becoming more important than ever.
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UBS post-trade reports are an offering in which I take
very particular pride — they deliver far more then the usual
benchmark report card. We build this tool to factor in the entire
story behind how the order performed: how, when and where
did it source liquidity; how did the available liquidity impact
the overall goal; what was happening in the market external
to the order and its parameters; and what happened to the
stock after the execution. That goes way beyond data — it’s
actual intelligence. This helps traders truly optimize their use of
strategies based on their trading objectives, nature of fl ow, and
appetite for risk.
What’s ahead for UBS Algorithmic Trading?
As always, our focus is on ensuring our current strategies
continue to effectively adapt to the changing microstructure,
improving performance based on quantitative research and the
insights of our clients, and building new strategies to address
evolving buy side needs. We expect that sell side priorities will
be more aligned with those of their large buy side clients. This
will result in unique, un-replicable made-to-measure strategies.
And multiple asset classes are on the horizon. UBS already
supports algorithmic trading in futures and options. Though
these strategies are in their early stages, the clients partnering
with us as we develop these new algorithms will end up having
a significant role in how they evolve.
In the United States and possibly soon in Europe, the smart
router is going to play a key role in improving execution quality. At
UBS, we are working toward combined DMA/algorithm offerings
that will merge into one smart-trading suite of products. The
distinctions between what is a DMA order and an algorithm are
fast disappearing. They will all just be really smart order types.
Because at the end of the day — it’s not important what we call
these orders — it’s only important that they have the right fi t. �
This material has no regard to the specific investment objectives, financial situation or
particular needs of any specific recipient and is published solely for information purposes.
No representation or warranty, either express or implied is provided in relation to the accuracy,
completeness or reliability of the information contained herein, nor is it intended to be a
complete statement or summary of the developments referred to in this material. This
document does not constitute an offer to sell or a solicitation to offer to buy or sell any
securities or investment instruments, to effect any transactions or to conclude any legal act of
any kind whatsoever. Nothing herein shall limit or restrict the particular terms of any specifi c
offering. No offer of any interest in any product will be made in any jurisdiction in which the
offer, solicitation or sale is not permitted, or to any person to whom it is unlawful to make such
offer, solicitation or sale. Any opinions expressed in this material are subject to change without
notice and may differ or be contrary to opinions expressed by other business areas or groups
of UBS as a result of using different assumptions and criteria. UBS is under no obligation
to update or keep current the information contained herein. Neither UBS AG nor any of its
affiliates, directors, employees or agents accepts any liability for any loss or damage arising out
of the use of all or any part of this material. © UBS 2007. All rights reserved.
Jatin Suryawanshi
UBS Managing Director
and head of U.S.
Algorithmic Trading
UBS Direct Execution is the firm’s global electronic
equities trading division. Algorithmic Trading
strategies include liquidity-seeking, price-sensitive,
and time-sensitive algorithms. UBS also offers
Direct Market Access (DMA) — providing access to
the world’s markets; UBS Pinpoint — a sophisticated
desktop trading system; and a comprehensive array
of pre-, at-, and post-trade analytical tools. All of these
capabilities are enhanced by UBS-PIN, the fi rm’s large
and diverse crossing pool of dark liquidity.
UBS Direct Execution
677 Washington Boulevard
Stamford, CT 06901
Tel: +1-203-719 1750 or +1-800-563 8018
www.ubs.com
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