Automated Trading: Fueling Global FX Market Growth · PDF fileAutomated trading in FX is not...
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Automated Trading: Fueling Global FX
Market Growth
Automated FX Trading July 2011
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TABLE OF CONTENTS EXECUTIVE SUMMARY .................................................................................................................................... 3
INTRODUCTION .............................................................................................................................................. 5
MARKET TRENDS ............................................................................................................................................ 6
GLOBAL FX MARKET GROWTH .................................................................................................................. 6
DIVERSIFICATION OF MARKET PARTICIPANTS .......................................................................................... 6
MARKET FRAGMENTATION....................................................................................................................... 7
INCREASED ADOPTION IN ELECTRONIC TRADING .................................................................................... 8
INCREASED ADOPTION IN AUTOMATED TRADING ................................................................................... 9
INCREASED INTERNALIZATION BY SINGLE BANK PLATFORMS ................................................................ 10
NEW OPPORTUNITIES AND CHALLENGES ..................................................................................................... 11
OPPORTUNITIES IN PRICE AGGREGATION .............................................................................................. 11
CHALLENGES IN MANAGING MULTIPLE TRADING VENUES .................................................................... 11
CHANGING RELATIONSHIP BETWEEN BANKS AND AUTOMATED TRADING FIRMS ................................ 12
DECLINING SHELF-LIFE OF TRADING STRATEGIES ................................................................................... 12
KEY INGREDIENTS IN AUTOMATED TRADING ............................................................................................... 14
UNDERSTANDING THE STRATEGY DEVELOPMENT WORKFLOW ............................................................ 14
CHALLENGES IN FX FOR AUTOMATED TRADING .................................................................................... 15
AUTOMATED FX TRADING INFRASTRUCTURE ........................................................................................ 16
CASE STUDY .................................................................................................................................................. 19
CONCLUSION ................................................................................................................................................ 20
ABOUT QUANTHOUSE: ................................................................................................................................. 21
QUANTFACTORY – AUTOMATED TRADING PLATFORM ......................................................................... 21
DATACENTER SERVER ........................................................................................................................ 22
DEVELOPMENT PLATFORM ............................................................................................................... 22
EXECUTION PLATFORM ..................................................................................................................... 23
QUANTFEED – ULTRA LOW LATENCY MARKET DATA ............................................................................. 21
QUANTLINK – ULTRA LOW LATENCY TRADING INFRASTRUCTURE ......................................................... 24
Automated FX Trading July 2011
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EXECUTIVE SUMMARY
Automated Trading: Fueling Global FX Market Growth commissioned by QuantHouse and
produced by Aite Group, analyzes the rapidly evolving automated trading in the global FX
market. The study examines the background behind the overall growth of automated trading in
FX and highlights key technology components that are needed build an effective presence in the
marketplace. The study concludes with a case study.
Key takeaways from the study include the following:
• During the latter part of 2008 and well into 2009, customers faced a much different
market from previous years, marked by wider spreads and declining liquidity.
Consequently, average daily trade volume returned to earth in 2009, standing at
approximately US$3.7 trillion. However, the global FX market bounced back quite
nicely with a strong volume growth in 2010, reaching US$4.1 trillion.
• One of the most significant changes that the FX market is currently undergoing is the
substantial increase in trading activities from non-bank financial institutions. In
1995, banks accounted for 64% of all trading, but that figure declined to below 40%
by 2010. During the same time period, trading from non-bank financial institutions
increased to 48% from 20% in 1995, thereby becoming the largest counterparty in
the FX market.
• Electronic trading adoption continues to increase in the global FX market. Given that
markets remain fragmented, the need to source multiple liquidity pools
simultaneously has only strengthened the overall position of electronic trading. At
the end of 2010, electronic trading accounted for 68% of all FX trading.
• In recent years, a new breed of actively trading market participants has driven
significant innovation in trading technology. As traders navigate through growing
complexity within the marketplace, development and implementation of automated
trading strategies have become vital part of some of the more sophisticated FX
traders.
• Automated trading in FX is poised to grow quite rapidly over the next few years, as
the first-generation automated trading firms are joined by an influx of next-
generation equity and futures automated trading firms looking to capture
uncorrelated alpha in FX. At the end of 2010, automated trading accounted for 29%
of overall trade volume. This figure is expected to hit more than 40% by the end of
2012
• Challenges in automated FX trading include 1) latency; 2) decentralized, highly
fragmented across numerous single bank and ECN execution venues; 3) largely
unregulated market with lack of an industry benchmark and best execution
obligation; 4) venue-specific market structure and communication standards; 5) lack
of a comprehensive, consolidated view into the entire market; and 6) dispersed price
discovery process.
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• Despite existing challenges, firms have been implementing automated FX trading for
many years and developing and maintaining a robust automated FX trading
infrastructure has been vital to their continuing success. Key components of
automated FX trading infrastructure include the following:
• Access to real-time and historical data
• Robust strategy development framework
• Trading engine
• Low latency connectivity
• Trade analytics
Automated trading in FX is not driven entirely by independent low latency proprietary shops or
hedge funds. In fact, most of the global FX banks have either acquired or developed robust low
latency FX prop desks to compete in the marketplace and account for a significant percentage of
the overall market share.
In recent months, several traders from major banks have left to start their own firms, taking with
them not only their quantitative skills, but also their market structure knowledge, which could
potentially pave the way for the next phase in automated trading, in which non-bank trading
firms play a larger role in liquidity provision. The first round may be over in FX automated
trading, but many more remain before the real winners can be determined in this rapidly
evolving marketplace.
Automated FX Trading July 2011
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INTRODUCTION
After robust market growth in 2007 and 2008, the global FX market experienced lower volatility,
lower volume, and in most cases, wider spreads in 2009. The global FX market fared much better
in 2010, resuming its overall growth and seeing volume levels skyrocket, returning to close to its
record 2008 level.
In the meantime, large FX banks have pressed on, maintaining the global FX market’s overall
market dominance, increasingly relying on internalization to manage their P&L. The global FX
market remains highly fragmented with single bank, multi-bank, and interbank execution venues
vying for precious market share. On the exchange-traded market, the CME Group has developed
a formidable FX futures franchise that currently has a virtual monopoly.
With the client-to-dealer market outpacing the growth of inter-dealer market in terms of overall
trading volume, much focus has been placed on the dynamic changes within the different client
segments. In recent years, a new breed of actively trading market participants has driven
significant innovation in trading technology. As traders navigate through growing complexity
within the marketplace, development and implementation of automated trading strategies have
become vital part of some of the more sophisticated FX traders.
Another important thing to note is that automated trading in FX is not driven entirely by
independent low latency proprietary shops or hedge funds. In fact, most of the global FX banks
have either acquired or developed robust low latency FX prop desks to compete in the
marketplace and account for a significant percentage of the overall market share.
Under this competitive environment, this study analyzes the rapidly evolving automated trading
in the global FX market. The study examines the background behind the overall growth of
automated trading in FX and highlights key technology components that are needed build an
effective presence in the marketplace.
Automated FX Trading July 2011
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MARKET TRENDS
G LOBAL FX MARKET GROWTH
Thanks in large part to high volatility, 2008 yielded historical highs in terms of overall trading
volume, followed by an inevitable decline in 2009. The industry averaged approximately US$4.3
trillion in daily trading volume in 2008 compared to about US$4 trillion in 2007. During the latter
part of 2008 and well into 2009, customers faced a much different market from previous years,
marked by wider spreads and declining liquidity. Consequently, average daily trade volume
returned to earth in 2009, standing at approximately US$3.7 trillion. However, the global FX
market bounced back quite nicely with a strong volume growth in 2010, reaching US$4.1 trillion
(Figure 1).
Figure 1: Growth of Global FX Market
Source: BIS, Bank of England Foreign Exchange Joint Standing Committee (JSC),New York Foreign Exchange Committee, Singapore
Foreign Exchange Market Committee, Canadian Foreign Exchange Committee, Tokyo Foreign Exchange Joint Standing Committee,
and Aite Group
D IVE RS IF ICATION OF MARKET PARTIC IPAN TS
One of the most significant changes that the FX market is currently undergoing is the substantial
increase in trading activities from non-bank financial institutions (i.e., other financial institutions
in Figure 2). In 1995, banks accounted for 64% of all trading, but that figure declined to below
40% by 2010. During the same time period, trading from non-bank financial institutions
increased to 48% from 20% in 1995, thereby becoming the largest counterparty in the FX
market.
$-
$500
$1,000
$1,500
$2,000
$2,500
$3,000
$3,500
$4,000
$4,500
$5,000
1998 2001 2004 2007 2008 2009 2010
Average Daily Trade Volume(In US$ Billions)
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Figure 2: Diversification of Market Participants
Source: BIS
Following the theme of declining bank transactions, as banks continue to fine-tune their ability
to manage their FX risk books in real-time, the client-to-dealer market has increased its overall
market share over the last few years at the expense of the interbank market. By the end of 2010,
the client-to-dealer market accounted for 61% of overall FX trading while the interbank stood at
39%. In comparison, the interbank market represented close to 60% of the marketplace in 2001.
Figure 3: Client-to-Dealer vs. Inter-Dealer
Source: BIS
MARKET FRAGME NTATION
0%
10%
20%
30%
40%
50%
60%
70%
1995 1998 2001 2004 2007 2010
Reported FX Market Turnover by Counterparty
Dealers Other Financial Institutions Non-Financial Customers
64% 59% 53%43% 41% 40% 39%
36% 41% 47%57% 59% 60% 61%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1998 2001 2004 2007 2008 2009 2010
Client-to-Dealer vs. Interbank
Client-to-dealer
Interbank
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The global FX market remains highly fragmented, represented by the still significant voice
market, as well as numerous electronic venues, including single bank, multi-bank, and interbank
venues. In recent years, the term FX ECNs has emerged to describe mixture of multi-bank and
interbank venues that support ECN-like functionality, including streaming live quotes.
Examining the FX ECN market alone, EBS currently holds the top spot with 23% market share
followed very closely in second place by Thomson Reuters. While the CME Group is an exchange
with its FX futures product compared to the OTC products of the other FX ECNs, it is clear from a
trading value perspective that the CME Group has become a major player in the global FX market
(Figure 4).
Figure 4: Market Fragmentation in FX ECN Market
Source: ECNs, Aite Group
INCREASED ADOPTION IN ELECTRONIC TRADING
Unlike other over-the-counter (OTC) markets, driven by early acceptance in the interbank
market, electronic trading adoption continues to increase in the global FX market. Given that
markets remain fragmented, the need to source multiple liquidity pools simultaneously has only
strengthened the overall position of electronic trading. At the end of 2010, electronic trading
accounted for 68% of all FX trading. This figure is expected to reach more than 70% by end of
2012 (Figure 5).
ICAP (EBS)20%
Thomson Reuters
19%
CME Group17%
Currenex14%
FX Connect13%
Fxall10%
Hotspot FX7%
FX Venues Competitive Landscape(As of January 2011)
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Figure 5: Projected Electronic Trading in FX
Source: Aite Group
INCREASED ADOPTION IN AUTOMATED TRAD ING
Automated trading in FX is poised to grow quite rapidly over the next few years, as the first-
generation automated trading firms are joined by an influx of next-generation equity and futures
automated trading firms looking to capture uncorrelated alpha in FX. In addition, new
automated trading firms have emerged in recent months, formed by FX quants and traders who
have left large banks looking to capture new opportunities on the other side of the market. At
the end of 2010, automated trading accounted for 29% of overall trade volume. This figure is
expected to hit more than 40% by the end of 2012 (Figure 6).
0%
10%
20%
30%
40%
50%
60%
70%
80%
Projected Electronic Trading in FX
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Figure 6: Projected Adoption of Automated Trading in FX
Source: Aite Group
INCREASED INTER NALIZATION BY S INGLE BANK P LAT FORMS
Another key trend over the last few years has been the increasing effectiveness of large FX banks
in managing their risk books when trading against customers. By utilizing sophisticated pricing
engine and real-time internalization capabilities, large FX banks have become quite adept at
showing varying prices to different types of customer segments as well as efficient at deciding
when to internalize vs. utilize traditional interbank markets to lay off their risk. In a way, the
aftermath of the credit crisis of 2008 has only reinforced banks’ need to internalize, particularly
as regulators and politicians continue to emphasize banks’ need to lower their overall risk
profile. Consequently, the need to better segment customer flow has been on top of banks’
overall client-facing trading strategy so that they can optimize their balance sheets and better
manage their profit and loss (P&L).
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
2003 2004 2005 2006 2007 2008 2009 2010 e2011 e2012
Estimated Automated Trading in FX
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NEW OPPORTUNITIES AND CHALLENGES
Against these diverse market realities, plenty of opportunities currently exist for those firms
looking to capture alpha in the global FX market. At the same time, however, obvious challenges
exist, driven by the OTC nature of the FX market.
OPPORTUNIT IES IN PRICE AGGREGATION
From a technology perspective, clear opportunities currently exist in the FX market in terms of
providing effective price aggregation services to overcome the challenges that stems from
market fragmentation and lack of industry-wide consolidated tape. Price aggregation is a difficult
proposition in the FX market due to the differences that exist across multiple venues and how
they operate. While the larger FX players have developed price aggregation capabilities
internally, recent efforts made by third-party vendors hold hope for those smaller players looking
for a level playing field. From strategy development to actual execution, getting the price
aggregation piece right is the first important steps towards developing a winning automated
trading operations.
CH AL LENGES IN MANAGING MULTIPLE TRADING VE NUES
Not so long ago, the traditional FX venues, such as EBS and Reuters dominated the market,
making it less cumbersome for traders to figure out where the market is headed. However, the
recent trend of growing market presence of newer ECNs as well as single bank platforms,
coinciding with declining market share of EBS and Reuters have made it essential that firms
attempt to source liquidity from various venues. A few of the challenges arising from multiple
trading venues include the following:
• Different locations and built-in distance latency: FX execution venues are located in
many different regional and financial centers, resulting in inherent distance latency
for most firms.
• Different frequency of price distribution: Each venue has its own timeframes in
terms of price distribution. In cases of single bank platforms, they will be distributing
prices to many different FX ECNs, in addition to their own in different latency.
• Depth of book: Wide differences exist in terms how much of the order book is
actually represented in each venue.
• Tick size and order types: FX venues also support different tick sizes and order
types, adding another layer complexity in terms of firms trying to figure out the best
location for execution at any given time.
• Constant upgrades: Venues are constantly going through enhancements of new
functionality, order types, reduction in latency and more. These changes could have
an impact on effectiveness of strategies so firms need to stay on top of these
changes and tweak strategies as needed.
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CH ANGING RELATIO NSHIP BETWEE N BANKS AND AUTOMATE D TRAD ING FIRMS
While the banks have gone through a series of consolidations, leading to fewer banks making
markets, the FX market continues to evolve with new types of customer segments beyond the
traditional corporate and asset manager customer base. Since 2002, actively trading hedge funds
and proprietary trading firms have made a huge impact in the FX market, driven by a robust IT
infrastructure and development of automated trading strategies.
In fact, some would argue that the large FX banks learned a painful lesson between 2002 and
2006, driven by latency arbitrage strategies in the first wave of automated trading firms. As a
direct consequence of this experience, the banks initiated massive overhaul of their trading
infrastructure, not only focusing on drastically lowering latency levels within the single bank
platforms, but also on developing more efficient pricing engine and internalization capabilities to
better manage their risk books against different types of customer segments. It was also during
this time that the banks decided to kick out those automated trading firms whose relationships
they deemed unprofitable.
Since 2008, however, banks and certain segments of the automated trading community are
attempting to peacefully co-exist. As banks continue to increase their internalization efforts,
potential liquidity from automated trading firms has become more attractive. On the other
hand, automated trading firms have come to realize that banks have a vital position in the FX
market; in order to ensure continued success, co-opetition has become a competitive necessity.
One potential change that could change the balance in the market is successful implementations
of centralized clearing in the OTC marketplace. If non-bank automated trading firms can become
direct clearing members for OTC products, and also illustrate their commitment to taking more
risk as a legitimate liquidity provider, banks’ stranglehold in the FX market could be weakened
and hence open up a new phase of intense competition.
DECLINING SHE LF- L IFE OF TRAD ING STRATEGIES
While there are long-term quant strategies that have generated great returns, there is a growing
group of high-frequency trading shops relying on implementation of short-term alpha discovery
strategies. This requires constant monitoring of the strategies, and the ability to isolate
ineffective variables or assumptions rapidly so that the strategy can be fine-tuned and launched
live into the market again.
With a notable increase in the total number of quantitatively-driven funds, the pressure to come
up with the next big quant model has grown exponentially. This reality has also fueled the need
to streamline the overall workflow process and shorten the duration of the entire investment
selection life cycle to compete more effectively. On average, the entire process of idea
generation to implementation can take anywhere from 10 to 28 weeks. Given the fact that
certain short-term strategies only remain effective for three to four months, rapid construction
and implementation of alpha models become that much more urgent. As a result, the search for
a single unifying platform that can create a more centralized and streamlined process and
functionality appears inevitable.
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And as the shelf life of alpha strategies continues to decrease driven by competition and
changing market conditions, there is growing recognition that the links between alpha discovery
and execution cannot be ignored.
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KEY INGREDIENTS IN AUTOMATED TRADING
UNDERSTANDING THE STRATEGY DEVELOPMENT WORKFLOW
There is no industry standard when it comes to strategy analysis workflow. Depending on the
firm — and also depending very much on whether or not they have long-term or short-term
trading philosophies — specific steps within the workflow and their duration will vary.
Figure 7: Alpha Generation Workflow
Source: Aite Group
A high-level, generally accepted alpha discovery workflow goes as follows:
• Alpha thesis/philosophy: The initial starting point for most firms is the basic thesis
or philosophy of alpha discovery. During this abstract phase, analysts and managers
are contemplating basic questions like instrument alternatives, value versus growth,
domestic versus international and other items such as decisioning variables. All
funds are built around a specific alpha philosophy.
• Data acquisition and preparation: Perhaps the most time-consuming and tedious
part of the entire workflow, in order to accommodate testing and analysis of a given
model, appropriate data must be acquired, typically transformed and normalized,
and then stored and ready to be mined and manipulated. Depending on the type of
firm, types of data would include fundamental and technical data as well as
historical and real-time tick data. In addition, firms have used a wide variety of data
storage options (including Excel, proprietary programs, relational databases, and
high performance databases) depending on the type of data, size of data sets, and
acceptable level of latency in testing and analysis.
• Alpha discovery: Once the data has been loaded properly, quants will mine the data
sets to identify patterns, events, and market conditions that may lead to alpha
discovery. During this phase, the quants will also either acquire or code various
mathematical, statistical, and technical functions and routines to be used to
formulate their alpha models. Typically using C, C++, C#, Java or other languages,
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quants will create specific parameters for the models. Most quants use popular
third-party statistical packages (i.e., MATLAB, S+, and R) during this process; often
the same package they have used during their academic training.
• Back-testing: Using relevant sets of historical data, quants will back-test alpha
models through varied market and economic conditions, multiple combinations of
parameters, different portfolio attributes and more to validate or debunk the various
alpha models.
• Analysis and optimization: The results from back-testing are analyzed, and new
factors are added and/or additional coding takes place to fine-tune and optimize the
alpha model. Use of visualization and various portfolio optimization tools would be
used during this phase to ensure appropriate risk parameters and diversifications.
• Simulations: Once the alpha model has been tightened up, intensive simulation
takes place using either historical or live data under varying known and hypothetical
market conditions. These simulations determine the appropriate levels of risk and
investment parameters to ensure effectiveness and profitability of given alpha
models.
• Production and live launch: Once the alpha model has been validated, it will be
coded into production, after which integration work takes place to create an
automated investment environment. In order to ensure automated trading, the
model also needs to communicate with execution management systems (EMS)
typically via FIX.
The entire workflow in quantitative modeling is an iterative process, a system of continuous fine-
tuning. The quant manager tests the investment model for validation in light of ever-changing
market conditions.
CH ALLENGES IN FX FOR AUTOMATED TRAD ING
While there is certainly a growing market demand for low latency trading infrastructure within
the FX market, the FX market is relatively slow compared to the U.S. cash equities market (where
the accepted level of latency has fallen below hundreds of microseconds), typically operating in
the hundreds-of-milliseconds range. Still, continuing efforts to eliminate precious milliseconds
off matching engines and trading infrastructure have led to most firms being able to develop
internal trading latency levels of single-digit milliseconds.
Unfortunately, the globally distributed nature of the FX market with three major FX centers —
New York, London, and Tokyo — has made it tough to alleviate latency caused by physical
distance. As a result, depending on the location of the trader and the matching engine, latency
levels can vary widely from less than 15 milliseconds (i.e., local transaction) to close to 300
milliseconds (cross-border transactions).
In the short-term, basic latency issue is faced by all market participants. As a result, most firms
have focused on addressing the three major technology areas to address the latency issue:
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16
• Network optimization;
• Low latency connectivity; and
• Colocation/proximity solution.
In addition to the obvious latency struggles, traders in the global FX market face more pressing
issues, driven largely by the fact that the FX market is still an OTC marketplace. Consequently,
firms engaged in automated FX trading must overcome the following barriers to ensure that
their trading needs are met:
• Decentralized, highly fragmented across numerous single bank and ECN execution
venues;
• Unregulated for the most part, with lack of an industry benchmark and best
execution obligation;
• Venue-specific market structure and communication standards;
• Lack of a comprehensive, consolidated view into the entire market; and
• Dispersed price discovery process.
A trader attempting to trade even a very liquid currency pair might need to capture market data
from numerous locations scattered around the different time zones to ensure optimal trading
execution. Balancing between the inherent distance latency hurdles and decentralized nature of
the FX market is not a simple chore. For most of the large automated trading firms, solving the
market fragmentation and latency issues has meant mostly internal development with close
cooperation between FX ECNs. In recent years, however, a few technology service providers have
emerged to provide trading infrastructure to facilitate those firms looking to jumpstart in
automated FX trading.
AUTOMATE D FX TRADING INFRASTR UCTURE
Despite these obstacles, firms have been implemented automated FX trading for many years and
developing and maintaining a robust automated FX trading infrastructure has been vital to their
continuing success. Some of the key components of an automated FX trading infrastructure
include the following:
Automated FX Trading
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Figure 8: Automated FX Trading Infrastructure
Source: Aite Group
• Access to real-time and historical data
of the automated trading environment, access to accurate real
data drives the development of strategies as well as live implementation of those
strategies to capture alpha.
• Ability to capture,
• Support for handling different time slices and types
• Integration with market leading data vendors and feeds
• Robust strategy development framework
firms can start developing, back
live production. In today’s automated t
third-party alpha generation platforms that help quants move from idea generation
and development to production in a seamless fashion.
include the following:
• Support for industry standar
• Seamless workflow from development and testing to optimization and
production
• Support for multiple asset classes and currencies
. All rights reserved. Reproduction of this report by any means is strictly prohibited.
: Automated FX Trading Infrastructure
time and historical data: Considered to be the most important piece
of the automated trading environment, access to accurate real-time and historical
development of strategies as well as live implementation of those
strategies to capture alpha. Key features of this would include the following:
Ability to capture, store, replay and update live data
Support for handling different time slices and types
egration with market leading data vendors and feeds
Robust strategy development framework: Once normalized data can be acquired,
firms can start developing, back-testing, and optimizing strategies prior to going into
live production. In today’s automated trading technology market, there are viable
party alpha generation platforms that help quants move from idea generation
and development to production in a seamless fashion. Key features of this would
include the following:
Support for industry standard development languages
Seamless workflow from development and testing to optimization and
Support for multiple asset classes and currencies
July 2011
.
17
: Considered to be the most important piece
time and historical
development of strategies as well as live implementation of those
Key features of this would include the following:
Once normalized data can be acquired,
testing, and optimizing strategies prior to going into
rading technology market, there are viable
party alpha generation platforms that help quants move from idea generation
Key features of this would
Seamless workflow from development and testing to optimization and
Automated FX Trading July 2011
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18
• Full integration with external applications, including leading statistical
packages, such as MATLAB
• Robust back-testing and optimization environment
• Trading engine: Once the strategy has been thoroughly back-tested and optimized,
traders can put it into production and leverage the trading engine to send out and
manage orders to various execution venues. Key features of this would include the
following:
• Seamless transition from development to production
• Ability to start, modify, and stop strategies
• Position and order management capabilities
• Integration with third-party trading front ends
• Low latency connectivity: Firms must have access to low latency connectivity to
ensure that opportunities can be identified and acted upon in a timely manner. Key
features of this would include the following:
• Global connectivity to leading execution venues and brokers
• Access to colocation and/or proximity services
• Reliability and predictability of connectivity
• Trade analytics: As executions take place, firms can utilize various trade analytics to
measure overall performance of automated trading strategies. Intraday trade
analytics can provide real-time feedback on overall execution quality so that the
trader can make necessary adjustments to underlying strategy assumptions. Key
features of this would include the following:
• Real-time monitoring of live orders and positions
• Ability to tweak working strategies on the fly
• Feeding performance measurement data back to development environment to
improve effectiveness of strategies
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19
CASE STUDY
Founded in November 2009, Fisycs Capital is a Paris-based systematic, quantitative hedge fund
focusing exclusively on liquid markets. Fisycs Capital is currently authorized and regulated by the
AMF (Autorité des Marchés Financiers) in France. Current list of strategies implemented by
Fisycs Capital include the following:
• FX Intraday
• U.S. Equity Market Neutral
• Global Macro
• FX Fundamental
In the process of setting up its investment and trading technology infrastructure, Fisycs Capital
examined many different options, both in-house and vendor supplied platforms. At the end of its
analysis, Fisycs selected the QuantHouse’s QuantFACTORY platform for various reasons:
• Ability to connect strategy to multiple venues and brokers at the same time.
• Fisycs Capital has a reasonable number of strategies and QuantFACTORY makes
message loading quite simple and seamless
• Ability to customize and the vendor’s receptiveness to incorporating requested new
features into their development plans.
Focusing on the automated FX market, Fisycs Capital believes that the ultimate benefits of
leveraging the QuantFACTORY platform are the following:
• As a multi-strategy hedge fund, very easy to support multi-desk trading activities
and also very easy to support risk management and asset allocation off the same
platform.
• Fairly shallow learning curve in grasping the code needed to test and very easy to
use.
• QuantFACTORY is very good at handling data; can take in multiple data sources very
quickly.
• All strategies’ modules are separated but can communicate with each other.
• Openness of the platform, enabling Fisycs Capital to link their own execution algos.
• Helps Fisycs Capital to quickly improve trading ideas, thus shortening the time from
research or recalibration to live execution.
Automated FX Trading July 2011
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20
CONCLUSION
Despite the influx of automated trading flow, the banks still maintain enough market clout to
hold onto their competitive edge. In fact, the FX market remains very much a two-tiered market;
single bank platforms tend to interact against customers who can be perceived either as less
sophisticated or less sensitive to explicit transaction costs (i.e., willing to take wider spreads to
get the trades done). On the other hand, most automated trading is occurring on FX ECNs. While
the liquidity ultimately comes from banks, this two-tiered approach has enabled banks to be
more efficient in trading against different types of end customers.
In this rapidly evolving automated trading marketplace, technology has become a competitive
differentiator, enabling firms to quickly develop and launch new strategies to take advantage of
changing market conditions. While the initial stage of technology development has been driven
by large firms with significant in-house application development capabilities, the availability of
cost effective, robust third-party solutions will go a long way in leveling the playfield for the rest
of the marketplace moving forward.
In recent months, several traders from major banks have left to start their own firms, taking with
them not only their quantitative skills, but also their market structure knowledge, which could
potentially pave the way for the next phase in automated trading, in which non-bank trading
firms play a larger role in liquidity provision. The first round may be over in FX automated
trading, but many more remain before the real winners can be determined in this rapidly
evolving marketplace.
Automated FX Trading
© 2011 QuantHouse. All rights reserved. Reproduction of this report by any means is strictly prohibited
ABOUT QUANTHOUSE:
QuantHouse is an independent global provider of low
ultra-low-latency market data technologies, Automated Trading Platfo
and order routing services to help hedge funds, proprietary desks and low
side firms take the lead. With more than 14 international hosting facilities within or near more
than 45 exchanges all interconnecte
benefit from a leading global trading infrastructure for ultimate results.
QUA NTFACTORY – AUTOMATED TRADING P L
http://www.quanthouse.com/?q=node/19
QuantFACTORY, the firm’s advanced algo
Development Environment (IDE) designed to help
automated trading development cycle. The framework’s openness and industry standard
language enables quant traders, re
models, allowing them to focus on business development.
QuantFACTORY is a suite of products, designed to efficiently handle the different phases
trading alpha generation workflow
back-testing to production. Its foundation layer
Interface (API) to build computerized quantitative trading systems.
asset classes (futures, forex, equities, bonds etc.) and runs multiple models on different
timescales.
. All rights reserved. Reproduction of this report by any means is strictly prohibited.
ABOUT QUANTHOUSE:
QuantHouse is an independent global provider of low-latency trading solutions. These include
latency market data technologies, Automated Trading Platform, trading infrastructure
and order routing services to help hedge funds, proprietary desks and low-latency
side firms take the lead. With more than 14 international hosting facilities within or near more
than 45 exchanges all interconnected by our proprietary fibre optic network, QuantHouse clients
benefit from a leading global trading infrastructure for ultimate results.
AUTOMATED TRADING P LATFORM
http://www.quanthouse.com/?q=node/19
the firm’s advanced algo-trading development tool, is an Integrated
Environment (IDE) designed to help clients significantly optimize each step of
automated trading development cycle. The framework’s openness and industry standard
enables quant traders, researchers and developers to quickly build and deploy
to focus on business development.
QuantFACTORY is a suite of products, designed to efficiently handle the different phases
generation workflow process, from data acquisition to alpha discovery and from
to production. Its foundation layer provides a powerful Application Programming
Interface (API) to build computerized quantitative trading systems. QuantFACTORY
asset classes (futures, forex, equities, bonds etc.) and runs multiple models on different
July 2011
.
21
latency trading solutions. These include
rm, trading infrastructure
latency-sensitive sell
side firms take the lead. With more than 14 international hosting facilities within or near more
d by our proprietary fibre optic network, QuantHouse clients
ATFORM
is an Integrated
significantly optimize each step of the
automated trading development cycle. The framework’s openness and industry standard
and deploy alpha
QuantFACTORY is a suite of products, designed to efficiently handle the different phases of the
data acquisition to alpha discovery and from
provides a powerful Application Programming
QuantFACTORY handles all
asset classes (futures, forex, equities, bonds etc.) and runs multiple models on different
Automated FX Trading
© 2011 QuantHouse. All rights reserved. Reproduction of this report by any means is strictly prohibited
D A T A C E N T E R S E R V E R
Data acquisition and preparation
With its plug-in architecture, QuantFACTORY can communicate with any market data prov
broker, and new plug-ins can easily be written to connect to new providers.
Clients can import historical data and store real
tests. Clients can also store low latency market data,
Reuters RMTD and IDC) and be connected to liquidity platforms
Trayport). The data capture configuration
timescales). QuantFACTORY can handle diff
tick-data, best bid-offer and order
D E V E L O P M E N T P L A T F O R M
Alpha discovery
The QuantFACTORY development platform is a Visual Studio
powerful IDE. It provides several tools to ease the model development process. This combination
allows clients to manage referential and historical market data and to develop and test models.
QuantFACTORY then analyzes the performance of clients’ models and e
execution platform.
Develop, record and bask-test your alpha models
Within the same application, a list of instruments (equities, derivatives, bonds, swaps,
legged instruments, FX, commodities e
In addition, the ‘Indicator Editor’ window contains a library with more than
but clients can also customize and add indicators.
an event-based application, provid
any .Net language within the Visual Studio environment. Using the libraries
. All rights reserved. Reproduction of this report by any means is strictly prohibited.
reparation
in architecture, QuantFACTORY can communicate with any market data prov
ins can easily be written to connect to new providers.
can import historical data and store real-time data in the datacentre server to run back
can also store low latency market data, third party vendors’ data (e.g. Bloomberg,
and be connected to liquidity platforms (e.g. Currenex, Hotspot
he data capture configuration is managed through a web interface (instruments
timescales). QuantFACTORY can handle different timescales, ranging from daily to intraday bars,
offer and order-book updates or custom data.
D E V E L O P M E N T P L A T F O R M
QuantFACTORY development platform is a Visual Studio add-in and as such benefits from its
It provides several tools to ease the model development process. This combination
allows clients to manage referential and historical market data and to develop and test models.
QuantFACTORY then analyzes the performance of clients’ models and exports them to a live
test your alpha models
a list of instruments (equities, derivatives, bonds, swaps,
instruments, FX, commodities etc.) and trading rules (mono- or multi-asset)
the ‘Indicator Editor’ window contains a library with more than 60 listed indicators
can also customize and add indicators. The QuantFACTORY development platform is
based application, providing convenient and sophisticated ways of writing
any .Net language within the Visual Studio environment. Using the libraries and third
July 2011
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22
in architecture, QuantFACTORY can communicate with any market data provider or
datacentre server to run back-
(e.g. Bloomberg,
Currenex, Hotspot, EBS or
hrough a web interface (instruments and
erent timescales, ranging from daily to intraday bars,
and as such benefits from its
It provides several tools to ease the model development process. This combination
allows clients to manage referential and historical market data and to develop and test models.
xports them to a live
a list of instruments (equities, derivatives, bonds, swaps, multi-
asset) are available.
60 listed indicators
QuantFACTORY development platform is
of writing models in
third party add-
Automated FX Trading July 2011
© 2011 QuantHouse. All rights reserved. Reproduction of this report by any means is strictly prohibited.
23
ons (for MATLAB, R…), clients can import mathematical and statistical functions that will be
integrated in to the model.
Debug and improve models
To further enhance models, a debugging mode is included. Alpha models will run at a time step
interval to trace internal events, signals and execution flow with high resolution, allowing clients
to easily detect any bugs.
Simulate model results
Continue by choosing the timescales, parameters for different models and the historical data
chosen to back-test models. Models will then be executed. The application has a range of
different back-testing statistics views: Performance Summary for Curves and Indicators, Portfolio,
Bar Chart, Global Trade Statistics and Equity Curve Statistics.
Optimize models
With an optimization procedure clients can define and test parameter values in order to obtain
the best results and determinate the appropriate levels of risk.
E X E C U T I O N P L A T F O R M
Produce and launch model
To start trading, clients simply load their precompiled strategy component (generated in the
development platform) into the execution platform, configure the adapter that will be used to
connect the model to the market data provider and broker or EMS. In addition, clients can create
their own alert conditions to warn about position items based on P&L value, set-up notification
mode (auditory alerts, via email, color codes), and program actions. From there, clients can
monitor orders and observe in real-time the full cycle of orders’ execution, from order sending
to position updates in the model.
Q UA NTFEED – ULTR A LOW LATE NCY MARKET DATA
http://www.quanthouse.com/low-latency-market-data
To enable our customers to manage the ever-increasing demand for low latency market data and
to meet the changing requirements of today’s trading environment with new trading venues,
fragmentation of liquidity and rapidly increasing volumes of data, QuantHouse has developed an
end-to-end product offering encompassing data capture within the exchange, ultra-fast data
normalization and dissemination over QuantHouse’s proprietary fibre optic network. Since we
design, implement and maintain every single element of the market data chain, we can offer our
customers an ultra-low-latency solution, accessible through one single connection to our
network, using a unique API, making it very easy to access any market using the same
application.
Automated FX Trading July 2011
© 2011 QuantHouse. All rights reserved. Reproduction of this report by any means is strictly prohibited.
24
Q UA NTLINK – ULTR A LOW LATENCY TR ADING INFRASTR UCTURE
http://www.quanthouse.com/QuantLink
QuantLINK is QuantHouse’s trading infrastructure service to help buy side and sell side trading
firms improve their infrastructures to keep up with market demand. Whether you use Smart
Order Routing or algorithmic trading applications, your overall performance is linked to the
quality of your trading infrastructures. QuantLINK combines QuantHouse’s proprietary fibre
optic network interconnecting the heart of the exchanges with proximity hosting at the source.