Automated FX Trading QuantHouse

Automated Trading: Fueling Global FX Market Growth© 2011 QuantHouse. All rights reserved. Reproduction of this report by any means is strictly prohibited. Automated FX Trading July 2011 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 © 2011 QuantHouse. All rights reserved. Reproduction of this report by any means is strictly prohibited. 2 Given that markets remain fragmented. electronic trading accounted for 68% of all FX trading. At the end of 2010. automated trading accounted for 29% of overall trade volume. 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 trading in FX is poised to grow quite rapidly over the next few years. the need to source multiple liquidity pools simultaneously has only strengthened the overall position of electronic trading. Electronic trading adoption continues to increase in the global FX market. 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. as the first-generation automated trading firms are joined by an influx of nextgeneration equity and futures automated trading firms looking to capture uncorrelated alpha in FX. Key takeaways from the study include the following: • During the latter part of 2008 and well into 2009. consolidated view into the entire market. 3) largely unregulated market with lack of an industry benchmark and best execution obligation. customers faced a much different market from previous years. trading from non-bank financial institutions increased to 48% from 20% in 1995. The study concludes with a case study. This figure is expected to hit more than 40% by the end of 2012 Challenges in automated FX trading include 1) latency.7 trillion. . During the same time period. average daily trade volume returned to earth in 2009. In recent years. thereby becoming the largest counterparty in the FX market. the global FX market bounced back quite nicely with a strong volume growth in 2010. At the end of 2010. banks accounted for 64% of all trading. a new breed of actively trading market participants has driven significant innovation in trading technology. 5) lack of a comprehensive. 2) decentralized. However. analyzes the rapidly evolving automated trading in the global FX market. All rights reserved.1 trillion. In 1995. standing at approximately US$3. 3 • • • • • © 2011 QuantHouse. highly fragmented across numerous single bank and ECN execution venues. As traders navigate through growing complexity within the marketplace. and 6) dispersed price discovery process.Automated FX Trading July 2011 EXECUTIVE SUMMARY Automated Trading: Fueling Global FX Market Growth commissioned by QuantHouse and produced by Aite Group. development and implementation of automated trading strategies have become vital part of some of the more sophisticated FX traders. but that figure declined to below 40% by 2010. Reproduction of this report by any means is strictly prohibited. marked by wider spreads and declining liquidity. Consequently. reaching US$4. 4) venue-specific market structure and communication standards. Automated FX Trading July 2011 • Despite existing challenges. which could potentially pave the way for the next phase in automated trading. taking with them not only their quantitative skills. but many more remain before the real winners can be determined in this rapidly evolving marketplace. In recent months. 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. in which non-bank trading firms play a larger role in liquidity provision. 4 . © 2011 QuantHouse. 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. Reproduction of this report by any means is strictly prohibited. several traders from major banks have left to start their own firms. The first round may be over in FX automated trading. but also their market structure knowledge. In fact. All rights reserved. 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. © 2011 QuantHouse. lower volume. The global FX market remains highly fragmented with single bank. 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. Reproduction of this report by any means is strictly prohibited. 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. On the exchange-traded market. In the meantime. The global FX market fared much better in 2010. In recent years. large FX banks have pressed on. increasingly relying on internalization to manage their P&L. resuming its overall growth and seeing volume levels skyrocket.Automated FX Trading July 2011 INTRODUCTION After robust market growth in 2007 and 2008. and in most cases. 5 . returning to close to its record 2008 level. multi-bank. development and implementation of automated trading strategies have become vital part of some of the more sophisticated FX traders. the CME Group has developed a formidable FX futures franchise that currently has a virtual monopoly. much focus has been placed on the dynamic changes within the different client segments. All rights reserved. maintaining the global FX market’s overall market dominance. As traders navigate through growing complexity within the marketplace. 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. wider spreads in 2009. the global FX market experienced lower volatility. this study analyzes the rapidly evolving automated trading in the global FX market. With the client-to-dealer market outpacing the growth of inter-dealer market in terms of overall trading volume. Under this competitive environment. a new breed of actively trading market participants has driven significant innovation in trading technology. and interbank execution venues vying for precious market share. .7 trillion.000 $4.000 $1. other financial institutions in Figure 2). During the same time period.500 $2.000 $2. Reproduction of this report by any means is strictly prohibited. thereby becoming the largest counterparty in the FX market. Figure 1: Growth of Global FX Market Average Daily Trade Volume (In US$ Billions) $5. Tokyo Foreign Exchange Joint Standing Committee.000 $500 $1998 2001 2004 2007 2008 2009 2010 Source: BIS. banks accounted for 64% of all trading.500 $3. 2008 yielded historical highs in terms of overall trading volume. © 2011 QuantHouse.e.New York Foreign Exchange Committee. Bank of England Foreign Exchange Joint Standing Committee (JSC). followed by an inevitable decline in 2009. trading from non-bank financial institutions increased to 48% from 20% in 1995. the global FX market bounced back quite nicely with a strong volume growth in 2010.500 $1. During the latter part of 2008 and well into 2009. standing at approximately US$3.000 $3.3 trillion in daily trading volume in 2008 compared to about US$4 trillion in 2007. However. reaching US$4. but that figure declined to below 40% by 2010.Automated FX Trading July 2011 MARKET TRENDS G LO BA L FX M A R K E T G ROW T H Thanks in large part to high volatility. average daily trade volume returned to earth in 2009.1 trillion (Figure 1). customers faced a much different market from previous years. Consequently. 6 . The industry averaged approximately US$4. In 1995. Canadian Foreign Exchange Committee. marked by wider spreads and declining liquidity. and Aite Group D I VE RS I F I C AT I O N O F M A R K E T PART I C I PA N 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. Singapore Foreign Exchange Market Committee. All rights reserved.500 $4. In comparison. the interbank market represented close to 60% of the marketplace in 2001. All rights reserved. By the end of 2010. Interbank 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 1998 2001 2004 2007 2008 2009 2010 64% 59% 53% 43% 41% 40% 39% Client-to-dealer Interbank 36% 41% 47% 57% 59% 60% 61% Source: BIS M A R K E T FR AG ME NTAT I O N © 2011 QuantHouse. the client-to-dealer market has increased its overall market share over the last few years at the expense of the interbank market.Automated FX Trading July 2011 Figure 2: Diversification of Market Participants Reported FX Market Turnover by Counterparty 70% 60% 50% 40% 30% 20% 10% 0% 1995 Dealers 1998 2001 2004 2007 2010 Other Financial Institutions Non-Financial Customers Source: BIS Following the theme of declining bank transactions. Reproduction of this report by any means is strictly prohibited. the client-to-dealer market accounted for 61% of overall FX trading while the interbank stood at 39%. Inter-Dealer Client-to-Dealer vs. 7 . as banks continue to fine-tune their ability to manage their FX risk books in real-time. Figure 3: Client-to-Dealer vs. All rights reserved. driven by early acceptance in the interbank market. EBS currently holds the top spot with 23% market share followed very closely in second place by Thomson Reuters. as well as numerous electronic venues. and interbank venues. While the CME Group is an exchange with its FX futures product compared to the OTC products of the other FX ECNs. the need to source multiple liquidity pools simultaneously has only strengthened the overall position of electronic trading. the term FX ECNs has emerged to describe mixture of multi-bank and interbank venues that support ECN-like functionality.Automated FX Trading July 2011 The global FX market remains highly fragmented. it is clear from a trading value perspective that the CME Group has become a major player in the global FX market (Figure 4). electronic trading adoption continues to increase in the global FX market. Figure 4: Market Fragmentation in FX ECN Market FX Venues Competitive Landscape (As of January 2011) Hotspot FX 7% Fxall 10% ICAP (EBS) 20% FX Connect 13% Thomson Reuters 19% CME Group 17% Currenex 14% Source: ECNs. represented by the still significant voice market. multi-bank. In recent years. including streaming live quotes. including single bank. electronic trading accounted for 68% of all FX trading. © 2011 QuantHouse. Aite Group I NC RE AS E D A D OP T I O N I N E LE C T RO N I C T R A D I N G Unlike other over-the-counter (OTC) markets. At the end of 2010. Reproduction of this report by any means is strictly prohibited. Examining the FX ECN market alone. Given that markets remain fragmented. This figure is expected to reach more than 70% by end of 2012 (Figure 5). 8 . automated trading accounted for 29% of overall trade volume. At the end of 2010. as the firstgeneration automated trading firms are joined by an influx of next-generation equity and futures automated trading firms looking to capture uncorrelated alpha in FX. © 2011 QuantHouse.Automated FX Trading July 2011 Figure 5: Projected Electronic Trading in FX Projected Electronic Trading in FX 80% 70% 60% 50% 40% 30% 20% 10% 0% Source: Aite Group I NC RE AS E D A D OP T I O N I N AUTOM AT E D T R A D I NG Automated trading in FX is poised to grow quite rapidly over the next few years. formed by FX quants and traders who have left large banks looking to capture new opportunities on the other side of the market. This figure is expected to hit more than 40% by the end of 2012 (Figure 6). In addition. All rights reserved. Reproduction of this report by any means is strictly prohibited. 9 . new automated trading firms have emerged in recent months. 10 . 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. All rights reserved.Automated FX Trading July 2011 Figure 6: Projected Adoption of Automated Trading in FX Estimated Automated Trading in FX 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% 2003 2004 2005 2006 2007 2008 2009 2010 e2011 e2012 Source: Aite Group I NC RE AS E D I N T E R N A L I Z AT I O N BY S I N G L E BA NK P LAT FOR M S 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. In a way. © 2011 QuantHouse. Consequently. By utilizing sophisticated pricing engine and real-time internalization capabilities. Reproduction of this report by any means is strictly prohibited. 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). utilize traditional interbank markets to lay off their risk. 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. Price aggregation is a difficult proposition in the FX market due to the differences that exist across multiple venues and how they operate. plenty of opportunities currently exist for those firms looking to capture alpha in the global FX market. however.Automated FX Trading July 2011 NEW OPPORTUNITIES AND CHALLENGES Against these diverse market realities. In cases of single bank platforms. obvious challenges exist. the traditional FX venues. Constant upgrades: Venues are constantly going through enhancements of new functionality. Different frequency of price distribution: Each venue has its own timeframes in terms of price distribution. 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. Reproduction of this report by any means is strictly prohibited. coinciding with declining market share of EBS and Reuters have made it essential that firms attempt to source liquidity from various venues. From strategy development to actual execution. At the same time. in addition to their own in different latency. However. . O P P ORT U N I T I E S I N P R I C E AG G R EG AT I O N From a technology perspective. they will be distributing prices to many different FX ECNs. recent efforts made by third-party vendors hold hope for those smaller players looking for a level playing field. Depth of book: Wide differences exist in terms how much of the order book is actually represented in each venue. While the larger FX players have developed price aggregation capabilities internally. adding another layer complexity in terms of firms trying to figure out the best location for execution at any given time. 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. All rights reserved. getting the price aggregation piece right is the first important steps towards developing a winning automated trading operations. 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. making it less cumbersome for traders to figure out where the market is headed. such as EBS and Reuters dominated the market. order types. driven by the OTC nature of the FX market. reduction in latency and more. C H AL LE N G E S I N M A N AG I N G M U LT IP L E T R A D I N G VE N U E S Not so long ago. the recent trend of growing market presence of newer ECNs as well as single bank platforms. Tick size and order types: FX venues also support different tick sizes and order types. 11 • • • • © 2011 QuantHouse. L I F E O F T R AD I NG ST R AT EG I E S While there are long-term quant strategies that have generated great returns. Since 2002. In fact. however. the FX market continues to evolve with new types of customer segments beyond the traditional corporate and asset manager customer base. some would argue that the large FX banks learned a painful lesson between 2002 and 2006. As a result. This requires constant monitoring of the strategies. the pressure to come up with the next big quant model has grown exponentially. actively trading hedge funds and proprietary trading firms have made a huge impact in the FX market. the search for a single unifying platform that can create a more centralized and streamlined process and functionality appears inevitable.Automated FX Trading July 2011 C H A NG I NG R E LAT I O N SH I P B E T W E E N BA N KS A N D AUTOM AT E D T R A D I NG FI R M S While the banks have gone through a series of consolidations. If non-bank automated trading firms can become direct clearing members for OTC products. automated trading firms have come to realize that banks have a vital position in the FX market. As banks continue to increase their internalization efforts. potential liquidity from automated trading firms has become more attractive. and also illustrate their commitment to taking more risk as a legitimate liquidity provider. leading to fewer banks making markets. One potential change that could change the balance in the market is successful implementations of centralized clearing in the OTC marketplace. 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. in order to ensure continued success. On average. rapid construction and implementation of alpha models become that much more urgent. the banks initiated massive overhaul of their trading infrastructure. 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. driven by a robust IT infrastructure and development of automated trading strategies. but also on developing more efficient pricing engine and internalization capabilities to better manage their risk books against different types of customer segments. not only focusing on drastically lowering latency levels within the single bank platforms. the entire process of idea generation to implementation can take anywhere from 10 to 28 weeks. All rights reserved. © 2011 QuantHouse. co-opetition has become a competitive necessity. As a direct consequence of this experience. On the other hand. Since 2008. D EC LI N I NG SH E LF . banks and certain segments of the automated trading community are attempting to peacefully co-exist. With a notable increase in the total number of quantitatively-driven funds. Reproduction of this report by any means is strictly prohibited. driven by latency arbitrage strategies in the first wave of automated trading firms. 12 . It was also during this time that the banks decided to kick out those automated trading firms whose relationships they deemed unprofitable. Given the fact that certain short-term strategies only remain effective for three to four months. banks’ stranglehold in the FX market could be weakened and hence open up a new phase of intense competition. there is a growing group of high-frequency trading shops relying on implementation of short-term alpha discovery strategies. Reproduction of this report by any means is strictly prohibited. © 2011 QuantHouse. there is growing recognition that the links between alpha discovery and execution cannot be ignored. All rights reserved. 13 .Automated FX Trading July 2011 And as the shelf life of alpha strategies continues to decrease driven by competition and changing market conditions. Java or other languages. analysts and managers are contemplating basic questions like instrument alternatives. and acceptable level of latency in testing and analysis. the quants will also either acquire or code various mathematical.Automated FX Trading July 2011 KEY INGREDIENTS IN AUTOMATED TRADING U ND E RSTA N D I NG T H E ST R AT EGY D E V E LO P M E N T WO R K F LOW There is no industry standard when it comes to strategy analysis workflow. All rights reserved. in order to accommodate testing and analysis of a given model. Figure 7: Alpha Generation Workflow Source: Aite Group A high-level. relational databases. quants will mine the data sets to identify patterns. types of data would include fundamental and technical data as well as historical and real-time tick data. Typically using C. value versus growth. 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. Data acquisition and preparation: Perhaps the most time-consuming and tedious part of the entire workflow. statistical. All funds are built around a specific alpha philosophy. In addition. and market conditions that may lead to alpha discovery. size of data sets. domestic versus international and other items such as decisioning variables. C++. . typically transformed and normalized. events. 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. Depending on the type of firm. and technical functions and routines to be used to formulate their alpha models. proprietary programs. During this phase. appropriate data must be acquired. Alpha discovery: Once the data has been loaded properly. Reproduction of this report by any means is strictly prohibited. During this abstract phase. firms have used a wide variety of data storage options (including Excel. and then stored and ready to be mined and manipulated. C#. 14 • • © 2011 QuantHouse. and high performance databases) depending on the type of data. C H A L LE N G E S I N FX FOR AU TO M AT E D T R AD I NG While there is certainly a growing market demand for low latency trading infrastructure within the FX market. latency levels can vary widely from less than 15 milliseconds (i. the model also needs to communicate with execution management systems (EMS) typically via FIX. Production and live launch: Once the alpha model has been validated. Most quants use popular third-party statistical packages (i.. Reproduction of this report by any means is strictly prohibited. multiple combinations of parameters. As a result. The quant manager tests the investment model for validation in light of ever-changing market conditions. Simulations: Once the alpha model has been tightened up. often the same package they have used during their academic training. Unfortunately.. intensive simulation takes place using either historical or live data under varying known and hypothetical market conditions. Use of visualization and various portfolio optimization tools would be used during this phase to ensure appropriate risk parameters and diversifications. and Tokyo — has made it tough to alleviate latency caused by physical distance. 15 . As a result. 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. In order to ensure automated trading. and R) during this process. different portfolio attributes and more to validate or debunk the various alpha models. it will be coded into production. a system of continuous finetuning. These simulations determine the appropriate levels of risk and investment parameters to ensure effectiveness and profitability of given alpha models. most firms have focused on addressing the three major technology areas to address the latency issue: © 2011 QuantHouse. In the short-term. MATLAB.e. London. cash equities market (where the accepted level of latency has fallen below hundreds of microseconds). and new factors are added and/or additional coding takes place to fine-tune and optimize the alpha model. • Back-testing: Using relevant sets of historical data. Analysis and optimization: The results from back-testing are analyzed. All rights reserved. the FX market is relatively slow compared to the U.Automated FX Trading July 2011 quants will create specific parameters for the models. after which integration work takes place to create an automated investment environment. Still. typically operating in the hundreds-of-milliseconds range. basic latency issue is faced by all market participants.e.S. • • • The entire workflow in quantitative modeling is an iterative process. S+. quants will back-test alpha models through varied market and economic conditions. the globally distributed nature of the FX market with three major FX centers — New York. local transaction) to close to 300 milliseconds (cross-border transactions). depending on the location of the trader and the matching engine. For most of the large automated trading firms. Some of the key components of an automated FX trading infrastructure include the following: © 2011 QuantHouse. 16 . consolidated view into the entire market. traders in the global FX market face more pressing issues. highly fragmented across numerous single bank and ECN execution venues. Unregulated for the most part. Lack of a comprehensive. All rights reserved. driven largely by the fact that the FX market is still an OTC marketplace. and Colocation/proximity solution. Reproduction of this report by any means is strictly prohibited. with lack of an industry benchmark and best execution obligation. In recent years. firms engaged in automated FX trading must overcome the following barriers to ensure that their trading needs are met: • • • • • Decentralized. Consequently. however.Automated FX Trading July 2011 • • • Network optimization. Balancing between the inherent distance latency hurdles and decentralized nature of the FX market is not a simple chore. Venue-specific market structure and communication standards. 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. solving the market fragmentation and latency issues has meant mostly internal development with close cooperation between FX ECNs. 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. In addition to the obvious latency struggles. Low latency connectivity. and Dispersed price discovery process. AUTOM AT E D FX T R A D I NG I N F R AST R UC T U R E Despite these obstacles. a few technology service providers have emerged to provide trading infrastructure to facilitate those firms looking to jumpstart in automated FX trading. Key features of this would include the following: • Support for industry standard development languages standar • Seamless workflow from development and testing to optimization and production • Support for multiple asset classes and currencies © 2011 QuantHouse. store. Key features of this would include the following: • Ability to capture. and optimizing strategies prior to going into back-testing. access to accurate real time and historical real-time data drives the development of strategies as well as live implementation of those strategies to capture alpha. framework firms can start developing.Automated FX Trading July 2011 Figure 8: Automated FX Trading Infrastructure : Source: Aite Group • Access to real-time and historical data: Considered to be the most important piece time data: of the automated trading environment. 17 . In today’s automated trading technology market. . prohibited. Reproduction of this report by any means is strictly prohibited. replay and update live data • Support for handling different time slices and types • Integration with market leading data vendors and feeds egration • Robust strategy development framework: Once normalized data can be acquired. All rights reserved. live production. there are viable trading third-party alpha generation platforms that help quants move from idea generation party and development to production in a seamless fashion. back testing. 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 © 2011 QuantHouse.Automated FX Trading July 2011 • Full integration with external applications. All rights reserved. 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: • Seamless transition from development to production • Ability to start. 18 . traders can put it into production and leverage the trading engine to send out and manage orders to various execution venues. modify. including leading statistical packages. 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. Intraday trade analytics can provide real-time feedback on overall execution quality so that the trader can make necessary adjustments to underlying strategy assumptions. Reproduction of this report by any means is strictly prohibited. firms can utilize various trade analytics to measure overall performance of automated trading strategies. such as MATLAB • Robust back-testing and optimization environment • Trading engine: Once the strategy has been thoroughly back-tested and optimized. Focusing on the automated FX market. All rights reserved. Helps Fisycs Capital to quickly improve trading ideas. 19 . Current list of strategies implemented by Fisycs Capital include the following: • • • • FX Intraday U. Fisycs Capital is currently authorized and regulated by the AMF (Autorité des Marchés Financiers) in France. Fisycs Capital examined many different options. Reproduction of this report by any means is strictly prohibited. Fisycs selected the QuantHouse’s QuantFACTORY platform for various reasons: • • • Ability to connect strategy to multiple venues and brokers at the same time. Fairly shallow learning curve in grasping the code needed to test and very easy to use. • • • • • © 2011 QuantHouse. Fisycs Capital is a Paris-based systematic. All strategies’ modules are separated but can communicate with each other. thus shortening the time from research or recalibration to live execution.Automated FX Trading July 2011 CASE STUDY Founded in November 2009. Openness of the platform. At the end of its analysis. Equity Market Neutral Global Macro FX Fundamental In the process of setting up its investment and trading technology infrastructure. can take in multiple data sources very quickly. enabling Fisycs Capital to link their own execution algos.S. quantitative hedge fund focusing exclusively on liquid markets. Fisycs Capital believes that the ultimate benefits of leveraging the QuantFACTORY platform are the following: • As a multi-strategy hedge fund. QuantFACTORY is very good at handling data. very easy to support multi-desk trading activities and also very easy to support risk management and asset allocation off the same platform. both in-house and vendor supplied platforms. 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. but many more remain before the real winners can be determined in this rapidly evolving marketplace. in which non-bank trading firms play a larger role in liquidity provision. enabling firms to quickly develop and launch new strategies to take advantage of changing market conditions. but also their market structure knowledge. In fact. © 2011 QuantHouse. In this rapidly evolving automated trading marketplace.. willing to take wider spreads to get the trades done). the FX market remains very much a two-tiered market. technology has become a competitive differentiator.Automated FX Trading July 2011 CONCLUSION Despite the influx of automated trading flow. the banks still maintain enough market clout to hold onto their competitive edge. most automated trading is occurring on FX ECNs. In recent months. The first round may be over in FX automated trading. which could potentially pave the way for the next phase in automated trading. single bank platforms tend to interact against customers who can be perceived either as less sophisticated or less sensitive to explicit transaction costs (i. the availability of cost effective. taking with them not only their quantitative skills. Reproduction of this report by any means is strictly prohibited.e. While the initial stage of technology development has been driven by large firms with significant in-house application development capabilities. 20 . several traders from major banks have left to start their own firms. 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. robust third-party solutions will go a long way in leveling the playfield for the rest of the marketplace moving forward. On the other hand. All rights reserved. ) and runs multiple models on different timescales. and order routing services to help hedge funds. trading infrastructure latency Platform. bonds etc. forex. from data acquisition to alpha discovery and from back-testing to production.quanthouse. proprietary desks and low-latency low latency-sensitive sell side firms take the lead. Its foundation layer provides a powerful Application Programming Interface (API) to build computerized quantitative trading systems. Reproduction of this report by any means is strictly prohibited. prohibited. is an Integrated algo Development Environment (IDE) designed to help clients significantly optimize each step of the automated trading development cycle. Q UA N T FAC TO RY – AUTO M AT E D TR A D I N G P L AT FO R M AU TO FOR http://www. QuantHouse clients interconnected benefit from a leading global trading infrastructure for ultimate results. allowing them to focus on business development. QuantFACTORY handles all asset classes (futures. These include low latency ultra-low-latency market data technologies.com/?q=node/19 QuantFACTORY. All rights reserved. The framework’s openness and industry standard language enables quant traders. equities. researchers and developers to quickly build and deploy alpha re models. © 2011 QuantHouse. 21 .Automated FX Trading July 2011 ABOUT QUANTHOUSE: QuantHouse is an independent global provider of low-latency trading solutions. QuantFACTORY is a suite of products. the firm’s advanced algo-trading development tool. Automated Trading Platform. With more than 14 international hosting facilities within or near more than 45 exchanges all interconnected by our proprietary fibre optic network. . designed to efficiently handle the different phases of the trading alpha generation workflow process. Currenex. Using the libraries and third party add- © 2011 QuantHouse. The QuantFACTORY development platform is an event-based application. Develop. All rights reserved. EBS or Hotspot Trayport). The data capture configuration is managed through a web interface (instruments and he hrough timescales).or multi-asset) are available. DEVELOPMENT PLATFORM Alpha discovery The QuantFACTORY development platform is a Visual Studio add-in and as such benefits from its powerful IDE.Automated FX Trading July 2011 DATACENTER SERVER Data acquisition and preparation reparation With its plug-in architecture. providing convenient and sophisticated ways of writing models in based provid any . Reproduction of this report by any means is strictly prohibited.g.g. asset) In addition. multilegged instruments. This combination allows clients to manage referential and historical market data and to develop and test models. Hotspot. It provides several tools to ease the model development process. ins Clients can import historical data and store real real-time data in the datacentre server to run backback tests. ranging from daily to intraday bars. a list of instruments (equities. best bid-offer and order offer order-book updates or custom data. Reuters RMTD and IDC) and be connected to liquidity platforms (e. the ‘Indicator Editor’ window contains a library with more than 60 listed indicators but clients can also customize and add indicators. and new plug-ins can easily be written to connect to new providers. QuantFACTORY then analyzes the performance of clients’ models and exports them to a live exports execution platform. . QuantFACTORY can communicate with any market data provider or in prov broker. commodities e etc. different tick-data. third party vendors’ data (e. QuantFACTORY can handle different timescales. FX. swaps. Bloomberg. Clients can also store low latency market data. bonds. 22 .Net language within the Visual Studio environment.) and trading rules (mono. prohibited. record and bask-test your alpha models test Within the same application. derivatives. Since we design. via email. making it very easy to access any market using the same application. parameters for different models and the historical data chosen to back-test models. configure the adapter that will be used to connect the model to the market data provider and broker or EMS. allowing clients to easily detect any bugs. EXECUTION PLATFORM Produce and launch model To start trading. R…). All rights reserved. using a unique API. Simulate model results Continue by choosing the timescales. The application has a range of different back-testing statistics views: Performance Summary for Curves and Indicators. color codes). 23 .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. and program actions. © 2011 QuantHouse. QuantHouse has developed an end-to-end product offering encompassing data capture within the exchange. from order sending to position updates in the model.quanthouse. Models will then be executed. we can offer our customers an ultra-low-latency solution. Reproduction of this report by any means is strictly prohibited. Global Trade Statistics and Equity Curve Statistics. Debug and improve models To further enhance models. 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. ultra-fast data normalization and dissemination over QuantHouse’s proprietary fibre optic network. signals and execution flow with high resolution. fragmentation of liquidity and rapidly increasing volumes of data. set-up notification mode (auditory alerts. clients can import mathematical and statistical functions that will be integrated in to the model. Portfolio. clients simply load their precompiled strategy component (generated in the development platform) into the execution platform.Automated FX Trading July 2011 ons (for MATLAB. Bar Chart. Alpha models will run at a time step interval to trace internal events. a debugging mode is included. implement and maintain every single element of the market data chain. In addition. Q UA N T FE E D – U LT R A LOW LAT E NC Y M A R K E T DATA http://www. clients can create their own alert conditions to warn about position items based on P&L value. From there. clients can monitor orders and observe in real-time the full cycle of orders’ execution. accessible through one single connection to our network. Reproduction of this report by any means is strictly prohibited.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. All rights reserved.Automated FX Trading July 2011 Q UA N T LI N K – U LT R A LOW L AT E N C Y T R A DI N G I N F R A ST R UC T U R E http://www. Whether you use Smart Order Routing or algorithmic trading applications.quanthouse. 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. 24 . © 2011 QuantHouse.