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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Serie: Wiley Trading Series
- Sprache: Englisch
An insider's view of how to develop and operate an automated proprietary trading network Reflecting author Eugene Durenard's extensive experience in this field, Professional Automated Trading offers valuable insights you won't find anywhere else. It reveals how a series of concepts and techniques coming from current research in artificial life and modern control theory can be applied to the design of effective trading systems that outperform the majority of published trading systems. It also skillfully provides you with essential information on the practical coding and implementation of a scalable systematic trading architecture. Based on years of practical experience in building successful research and infrastructure processes for purpose of trading at several frequencies, this book is designed to be a comprehensive guide for understanding the theory of design and the practice of implementation of an automated systematic trading process at an institutional scale. * Discusses several classical strategies and covers the design of efficient simulation engines for back and forward testing * Provides insights on effectively implementing a series of distributed processes that should form the core of a robust and fault-tolerant automated systematic trading architecture * Addresses trade execution optimization by studying market-pressure models and minimization of costs via applications of execution algorithms * Introduces a series of novel concepts from artificial life and modern control theory that enhance robustness of the systematic decision making--focusing on various aspects of adaptation and dynamic optimal model choice Engaging and informative, Proprietary Automated Trading covers the most important aspects of this endeavor and will put you in a better position to excel at it.
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Seitenzahl: 470
Veröffentlichungsjahr: 2013
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Contents
Cover
Series Page
Title Page
Copyright Page
Preface
Chapter 1: Introduction to Systematic Trading
1.1 DEFINITION OF SYSTEMATIC TRADING
1.2 PHILOSOPHY OF TRADING
1.3 THE BUSINESS OF TRADING
1.4 PSYCHOLOGY AND EMOTIONS
1.5 FROM CANDLESTICKS IN KYOTO TO FPGAs IN CHICAGO
Part One: Strategy Design and Testing
Chapter 2: A New Socioeconomic Paradigm
2.1 FINANCIAL THEORY VS. MARKET REALITY
2.2 THE MARKET IS A COMPLEX ADAPTIVE SYSTEM
2.3 ORIGINS OF ROBOTICS AND ARTIFICIAL LIFE
Chapter 3: Analogies between Systematic Trading and Robotics
3.1 MODELS AND ROBOTS
3.2 THE TRADING ROBOT
3.3 FINITE-STATE-MACHINE REPRESENTATION OF THE CONTROL SYSTEM
Chapter 4: Implementation of Strategies as Distributed Agents
4.1 TRADING AGENT
4.2 EVENTS
4.3 CONSUMING EVENTS
4.4 UPDATING AGENTS
4.5 DEFINING FSM AGENTS
4.6 IMPLEMENTING A STRATEGY
Chapter 5: Inter-Agent Communications
5.1 Handling Communication Events
5.2 EMITTING MESSAGES AND RUNNING SIMULATIONS
5.3 IMPLEMENTATION EXAMPLE
Chapter 6: Data Representation Techniques
6.1 DATA RELEVANCE AND FILTERING OF INFORMATION
6.2 PRICE AND ORDER BOOK UPDATES
6.3 SAMPLING: CLOCK TIME VS. EVENT TIME
6.4 COMPRESSION
6.5 REPRESENTATION
Chapter 7: Basic Trading Strategies
7.1 TREND-FOLLOWING
7.2 ACCELERATION
7.3 MEAN-REVERSION
7.4 INTRADAY PATTERNS
7.5 NEWS-DRIVEN STRATEGIES
Chapter 8: Architecture for Market-Making
8.1 TRADITIONAL MARKET-MAKING: THE SPECIALISTS
8.2 CONDITIONAL MARKET-MAKING: OPEN OUTCRY
8.3 ELECTRONIC MARKET-MAKING
8.4 MIXED MARKET-MAKING MODEL
8.5 AN ARCHITECTURE FOR A MARKET-MAKING DESK
Chapter 9: Combining Strategies into Portfolios
9.1 AGGREGATE AGENTS
9.2 OPTIMAL PORTFOLIOS
9.3 RISK-MANAGEMENT OF A PORTFOLIO OF MODELS
Chapter 10: Simulating Agent-Based Strategies
10.1 THE SIMULATION PROBLEM
10.2 MODELING THE ORDER MANAGEMENT SYSTEM
10.3 RUNNING SIMULATIONS
10.4 ANALYSIS OF RESULTS
10.5 DEGREES OF OVER-FITTING
Part Two: Evolving Strategies
Chapter 11: Strategies for Adaptation
11.1 AVENUES FOR ADAPTATIONS
11.2 THE CYBERNETICS OF TRADING
Chapter 12: Feedback and Control
12.1 LOOKING AT MARKETS THROUGH MODELS
12.2 FITNESS FEEDBACK CONTROL
12.3 ROBUSTNESS OF STRATEGIES
12.4 EFFICIENCY OF CONTROL
Chapter 13: Simple Swarm Systems
13.1 SWITCHING STRATEGIES
13.2 STRATEGY NEIGHBORHOODS
13.3 CHOICE OF A SIMPLE INDIVIDUAL FROM A POPULATION
13.4 ADDITIVE SWARM SYSTEM
13.5 MAXIMIZING SWARM SYSTEM
13.6 GLOBAL PERFORMANCE FEEDBACK CONTROL
Chapter 14: Implementing Swarm Systems
14.1 SETTING UP THE SWARM STRATEGY SET
14.2 RUNNING THE SWARM
Chapter 15: Swarm Systems with Learning
15.1 REINFORCEMENT LEARNING
15.2 SWARM EFFICIENCY
15.3 BEHAVIOR EXPLOITATION BY THE SWARM
15.4 EXPLORING NEW BEHAVIORS
15.5 LAMARK AMONG THE MACHINES
Part Three: Optimizing Execution
Chapter 16: Analysis of Trading Costs
16.1 NO FREE LUNCH
16.2 SLIPPAGE
16.3 INTRADAY SEASONALITY OF LIQUIDITY
16.4 MODELS OF MARKET IMPACT
Chapter 17: Estimating Algorithmic Execution Tools
17.1 BASIC ALGORITHMIC EXECUTION TOOLS
17.2 ESTIMATION OF ALGORITHMIC EXECUTION METHODOLOGIES
Part Four: Practical Implementation
Chapter 18: Overview of Scalable Architecture
18.1 ECNs AND TRANSLATION
18.2 AGGREGATION AND DISAGGREGATION
18.3 ORDER MANAGEMENT
18.4 CONTROLS
18.5 DECISIONS
18.6 MIDDLE AND BACK OFFICE
18.7 RECOVERY
Chapter 19: Principal Design Patterns
19.1 LANGUAGE-AGNOSTIC DOMAIN MODEL
19.2 SOLVING TASKS IN ADAPTED LANGUAGES
19.3 COMMUNICATING BETWEEN COMPONENTS
19.4 DISTRIBUTED COMPUTING AND MODULARITY
19.5 PARALLEL PROCESSING
19.6 GARBAGE COLLECTION AND MEMORY CONTROL
Chapter 20: Data Persistence
20.1 BUSINESS-CRITICAL DATA
20.2 OBJECT PERSISTENCE AND CACHED MEMORY
20.3 DATABASES AND THEIR USAGE
Chapter 21: Fault Tolerance and Recovery Mechanisms
21.1 SITUATIONS OF STRESS
21.2 A JAM OF LOGS IS BETTER THAN A LOGJAM OF ERRORS
21.3 VIRTUAL MACHINE AND NETWORK MONITORING
Chapter 22: Computational Efficiency
22.1 CPU SPIKES
22.2 RECURSIVE COMPUTATION OF MODEL SIGNALS AND PERFORMANCE
22.3 NUMERIC EFFICIENCY
Chapter 23: Connectivity to Electronic Commerce Networks
23.1 ADAPTORS
23.2 THE TRANSLATION LAYER
23.3 DEALING WITH LATENCY
Chapter 24: The Aggregation and Disaggregation Layer
24.1 QUOTES FILTERING AND BOOK AGGREGATION
24.2 ORDERS AGGREGATION AND FILLS DISAGGREGATION
Chapter 25: The OMS Layer
25.1 ORDER MANAGEMENT AS A RECURSIVE CONTROLLER
25.2 CONTROL UNDER STRESS
25.3 DESIGNING A FLEXIBLE OMS
Chapter 26: The Human Control Layer
26.1 DASHBOARD AND SMART SCHEDULER
26.2 MANUAL ORDERS AGGREGATOR
26.3 POSITION AND P & L MONITOR
Chapter 27: The Risk Management Layer
27.1 RISKY BUSINESS
27.2 AUTOMATED RISK MANAGEMENT
27.3 MANUAL RISK CONTROL AND THE PANIC BUTTON
Chapter 28: The Core Engine Layer
28.1 ARCHITECTURE
28.2 SIMULATION AND RECOVERY
Chapter 29: Some Practical Implementation Aspects
29.1 ARCHITECTURE FOR BUILD AND PATCH RELEASES
29.2 HARDWARE CONSIDERATIONS
Appendix: Auxiliary LISP Functions
Bibliography
Index
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Cover image: © iStockphoto.com / ideiados Cover design: Paul McCarthy
Copyright © 2013 by Eugene A. Durenard. All rights reserved.
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ISBN 978-1-118-12985-2 (Hardcover)
ISBN 978-1-118-41902-1 (ePDF)
ISBN 978-1-118-41929-8 (ePub)
Preface
Professional Automated Trading, Theory and Practice: comes from several years of research and practice of successful implementation of automated systematic trading strategies in the context of proprietary trading at a few major financial institutions and my own firm.
On one hand, trading is a science that is based on a variety of techniques coming from mathematics, physics, psychology, biology, and various computer science techniques. On the other hand it is an art of knowing and respecting the market and equally importantly of knowing oneself. But foremost it is a business that hinges on a carefully understood discipline and process of seeking reward under risk.
This book presents some of the science and some of the process involved in building a scalable diversified systematic trading business. The art is mostly left to the reader: You are encouraged to find your own way of crystallizing your intuition about the external world and translating it into trading models and risk management that fit best your psychology, capital, and business constraints.
The aim is to provide a set of tools to build a robust systematic trading business and is mostly directed toward proprietary trading groups, quantitative hedge funds, proprietary desks, and market-making businesses at investment banks, and asset management companies, as well as ambitious individual traders seeking to manage their own wealth on such principles.
The book is divided into an introductory section and four parts, each coming with a specific sub-goal. The introductory chapter aims at comparing the systematic and discretionary trading disciplines from several angles. They are discussed in the philosophical, business, and psychological contexts. It is an important analysis as it shows that the two disciplines are equally valid as far as their raison d’être and business efficiency are concerned. Hence it is argued that the choice between the two hinges on the psychological makeup of the trader. An overview of various types of systematic market players and specific techniques are presented in the historical context.
The book’s central idea is to frame systematic trading in the framework of autonomous adaptive agents. This framework comes from recent studies in robotics and artificial life systems. It opens the avenue to implement concepts of adaptation, evolution, and learning of trading agents. It also aims at bridging the gap between systematic and discretionary trading by making those robotic trading agents acquire some animal-like traits. It is very much research in progress and a fascinating area with a lot of future potential.
Part One introduces the basic conceptual and programmatic framework for the design of trading strategies as trading agents. The representation of the agent’s core decision making by way of reactive finite-state machines is introduced. The framework also allows the trading agents to communicate and signal to each other either in a parallel or sequential computational cycle.
The discussion continues with a set of broad types of automatic decision-making models that perform in various market regimes. In particular basic trend-following, breakout, mean-reversion, acceleration, and conditional market-making strategies are discussed.
As diversification of markets and models is most important for success, a fair amount of emphasis is put on designing tools that enable efficient treatment of portfolios. The concept of an aggregate agent is introduced for this purpose.
Detailed implementations of back- and forward-testing engines are presented and pitfalls associated with curve-fitting and statistical insignificance are discussed.
The goal of Part Two is to give the reader insights into building robust trading systems that can gracefully withstand changes of regime. The agent-based representation of strategies is a handy and natural framework for making progress toward this goal.
Trading strategies can be seen as filters that help discover implicit market regimes and changes of regime. When the market regime changes, strategies that were compatible with the old regime may lose performance whereas other strategies may start performing better with the arrival of the next regime. In order to quantify strategy performance and exploit its variability, absolute and relative fitness measures are introduced.
One avenue to adaptation comes from studying the persistence of performance of parametric sets of nonadaptive strategies. It is realized via the implementation of an automatic choice mechanism that switches between a set of nonadaptive strategies. The book introduces swarm systems that are aggregate agents that embed various types of switching mechanisms.
The discussion encompasses the robustness and effectiveness of the choice mechanisms underlying the swarm systems. Measures of efficiency are introduced and ideas from reinforcement learning are used to train the parameters of the choice algorithms.
This paradigm for adaptation can be explained in the following terms. The collection of nonadaptive strategies is a set of potential behaviors of the aggregate adaptive agent. The adaptive agent runs all those potential behaviors in parallel in a simulation mode. It is endowed with criteria to choose a subset of behaviors that is expected to produce a positive performance over the next foreseeable future. This is the behavior that the agent implements in real trading. As time unfolds, the agent learns from experience to choose its behavior more effectively. Effectiveness means that as the market goes through various cycles of regime changes, the performance during those change periods does not degrade.
From this it is evident, because history tends to repeat itself, that it is wise to endow the swarm system with a large enough set of potential behaviors that have proved to be effective at some periods of the past. However a degree of innovation is also needed, akin to the exploration versus exploitation in reinforcement learning. This aspect is touched upon and constitutes an active area of my research at present.
Part Three focuses on the important aspect of trading costs and slippage. It starts with the analysis of the intra-day bid-offer and volume seasonality in major markets, then explores the volume-price response functions. It discusses several algorithmic execution strategies designed to help reduce market impact once a decision to trade had been made.
Part Four presents the implementation of a scalable and efficient low-latency trading architecture that is able to support a set of signals generated by a swarm of adaptive models. The complexity of dealing with real-time swarm systems leads to design the whole trading architecture on the basis of feedbacks between a set of distributed concurrent components.
The discussion encompasses design patterns for data and state persistence, the advantages of designing different components in different appropriately chosen languages, and a domain model that allows seamless communications between these components via message-passing algorithms.
Efficiency constraints on data representation and complexity for internal and external communications and various protocols are touched upon. Details of the order management system (OMS) with its architecture of a recursive controller are given. The OMS is further optimized by allowing order aggregation.
Solving the inverse problem of dis-aggregation at the middle-office level an infrastructure able to support a “dark pool” is discussed.
A variety of real-time human interface controllers that are necessary to complete various feedbacks in the system are presented. In particular the real-time risk, P & L, position managers, and the model controllers are discussed.
As the whole architecture itself is designed to be robust and resilient to various bottleneck or disconnect issues, emphasis is on advice to architect the system in a way that ensures fast self-recovery and minimal downtime.
Robustness and continuity also need to be achieved at the level of releases and patches. Solutions are suggested from standpoints of software and hardware.
Finishing with hardware, the state of my own practical research is discussed. It applies techniques from parallel processing on one hand and designer chips on the other hand to further improve the efficiency of the original design in certain appropriate situations.
The theoretical Parts One to Three can be read independently of the practical Part Four yet there is a definite logical thread running through them. Many designs in Part Four were introduced to specifically address the concepts introduced in Parts One to Three in concrete implementations that I and my teams performed in practice.
This book aims to provide a methodology to set up a framework for practical research and selection of trading models and to provide tools for their implementation in a real-time low-latency environment. Thus it requires readers to have some knowledge of certain mathematical techniques (calculus, statistics, optimization, transition graphs, and basic operations research), certain functional and object-oriented programming techniques (mostly LISP and Java), and certain programming design patterns (mostly dealing with concurrency and multithreading). Most modern concepts coming from research in evolutionary computing, robotics, and artificial life are introduced in an intuitive manner with ample references to relevant literature.
As one sees from this overview this book is first of all a synthesis of many concepts coming from various domains of knowledge. As an academic environment, I always feel the importance to institute a creative environment with little legacy or a priori dogmas that allow for the confluence of various ideas to bear fruit.
I hope that this book will provide an inspiration to creatively compete in the fascinating world of automated trading of free markets. In the same sense that trading is a means-ends process that maximizes the reward-to-risk ratio, the design of the architecture of the trading technology is a means-ends process that maximizes the throughput-to-downtime ratio. I aim to demonstrate that the two concepts are intimately, linked in the modern world.
I am dedicating this book to my parents Alexis and Larissa, who encouraged me to start it and to my soulmate Caroline who has supported me throughout the process of writing it.
I would like to thank the whole team at Wiley, and in particular Bill Falloon, Meg Freeborn, Tiffany Charbonier and Vincent Nordhaus for all their help and guidance during the writing and editing process.
CHAPTER 1
Introduction to Systematic Trading
Systematic trading is a particular discipline of trading, which is one of the oldest human activities. Trading and the associated arena set by the marketplace coevolved in time to become one of the dominant industries on the planet. At each stage of their development, new efficiencies were introduced.
Starting as barter where goods were exchanged “on sight, ” the first major evolutionary step was the introduction of a numeraire (be it gold or fiat money) that literally allowed comparison between apples and oranges. It also allowed the storage of value in a compact way. Then the first organized exchanges in Flanders and Holland introduced several key concepts: first and foremost the concept of the exchange as a risk disintermediator, then the concept of standardization so important in comparing bulk commodities, and finally the technique of open outcry—the famous Dutch Auction at the basis of the modern exchange mechanism. Despite the fact that the concept of interest (via grain loans) was introduced by the Egyptians, the effective leverage in the marketplace only came with the growth of the stock markets and commodity futures markets in the United States in the early twentieth century. Also at that point the nascent global banking system spurred the creation of the money market where short-term loans are traded in a standardized fashion and help to transfer leverage between counterparties. An important factor in the stabilization of the market process was the introduction of floor specialists or market-makers who ensured orderly matching of buyers and sellers. With the advent of increasing computing power, the co-evolution of the marketplace and the trading associated with it has accelerated further. Not only has the banking system evolved into a global network of compensating agents where money can be transferred at the speed of light, but the whole flow of information has become available to a much larger group. The marketplace and trading have become truly global and gradually more electronic. This evolution has taken its toll on the open outcry system and on specialists, with some of them being gradually crowded out by robotic market-making computer programs and the increasing importance of semi-private matching engines like dark pools and electronic commerce networks (ECNs).
And this is where we are right now, a world some would say of information overflow, of competition for microseconds, of over-leverage and over-speculation. Each evolutionary stage comes with its share of positives and negatives. A new organism has to keep searching for its boundaries independently of its forebears and try to learn from its rewards and mistakes so as to set the stage for its own progress.
This book focuses on a subset of trading techniques that applies to a subset of the marketplace. It explores the systematic automated trading of liquid instruments such as foreign exchange, futures, and equities. It is an activity on the edge of the evolutionary path that also tries to find its current boundaries, technologically and conceptually.
This introductory chapter sets the philosophical context of trading and puts on equal footing the seemingly contradictory approaches of systematic and discretionary trading. They are compared as business activities by presenting a cost-benefit analysis of each, concluding with the viability and similarity of both. The psychological implications of choosing one path over the other is analyzed and it is argued that it is the defining criterion from a rational trader’s perspective. The chapter concludes by putting the theoretical Parts One to Three and the practical Part Four of the book into the historic context and showing how the evolution of systematic trading is intimately related to the progress in technology and science.
1.1 DEFINITION OF SYSTEMATIC TRADING
The majority of successful traders design their trading strategy and trading discipline in the most objective way possible but cannot be qualified as systematic, because many of their decisions are based on their perceived state of the world, the state of their mind, and other factors that cannot be computationally quantified. The type of trading that is relying on noncomputable processes will be qualified as discretionary in this book.
As opposed to the discretionary, the qualifier systematic encompasses the following two concepts:
Systematic trading implies the construction of a mathematical model of a certain behavior of the market. This model is then encompassed in a decision-making algorithm that outputs continuously the allocation of exposure to such a model in the context of the trader’s other models’ behavior, total risk allocation, and other objective and reproducible inputs. The continuous running of such an algorithm is oftentimes best left to a robot.
Before making further comparisons let us now explore the two trading approaches in a broader philosophical context of the perceived behavior of the market and its participants.
1.2 PHILOSOPHY OF TRADING
The philosophy of trading derives from a set of beliefs about the workings of the human mind, the behavior of crowds of reward-seeking individuals, and the resulting greed-fear dynamics in the market. Trading is a process, a strategy, a state of mind. It is the mechanism by which a market participant survives and thrives in the marketplace that itself is composed of such participants and constrained by political and regulatory fads and fashions.
Choosing a trading style is as much about knowing and understanding the workings of the market as it is knowing and understanding oneself. The nonemotional self-analysis of behavior under stresses of risk, reward, and discipline are part of the personal effort any trader has to evolve through, most often by trial and error. I will defer comments on this self-analysis to later and will now focus on the more objective and observable part related to the market.
1.2.1 Lessons from the Market
Let us first see what conclusions we can derive from observing the market as a whole and the behavior of its participants. The most relevant observations can be summarized as follows:
Macroeconomic information unfolds gradually, therefore prices do not discount future events immediately. Why is it the case that at the peak of the business cycle asset prices do not discount its next through and vice versa? Because no one knows when the next through is coming despite the seeming regularity of business cycles. Things always look so optimistic on the top and so pessimistic at the bottom. This is why we observe long-term trends in all asset prices and yields.The leverage in the market yields a locally unstable system because individuals have finite capital and are playing the game so as to survive the next round. This instability is increased by the asymmetry between game-theoretic behaviors of accumulation and divestment of risky positions. When you accumulate a position you have all the incentive in the world to tell all your friends, and it is a self-fulfilling virtuous circle as people push prices in “your” direction, thus increasing your profit. This is the epitome of a cooperative game. On the other hand, when you divest, you have no incentive to tell anyone as they may exit before you, pushing prices away from you. This is a classic Prisoner’s Dilemma game where it is rational to defect, as it is not seen as a repeated game. This is why we observe a great deal of asymmetry between up and down moves in prices of most assets, as well as price breakouts and violent trend reversals.There is a segmentation of market participants by their risk-taking ability, their objectives, and their time frames. Real-money investors have a different attitude to drawdowns than highly leveraged hedge funds. Pension fund managers rotate investments quarterly whereas automated market-makers can switch the sign of their inventory in a quarter of a second. In general, though, each segment reacts in a similar way to price movements on their particular scale of sampling. This explains the self-similarity of several patterns at different price and time scales.The market as a whole has a consensus-building tendency, which implies learning at certain timescales. This is why some strategy classes or positions have diminishing returns. When people hear of a good money-making idea, they herd into it until it loses its money-making appeal.The market as a whole has a fair amount of participant turnover, which implies un-learning at certain longer timescales. A new generation of market participants very rarely learns the lessons of the previous generation. If it were not the case why are we going through booms and busts with the suspicious regularity commensurate to a trading career lifespan of 15 to 20 years?There is no short-term relationship between price and value. To paraphrase Oscar Wilde, a trader is a person who knows the price of everything but the value of nothing.1.2.2 Mechanism vs. Organism
The above observations do not reflect teachings of the economic orthodoxy based on the concept of general equilibrium, which is a fairly static view of the economic landscape. They become more naturally accepted when one realizes that the market itself is a collection of living beings and that macro-economics is an emergent property of the society we live in. The society, akin to an organism, evolves and so does the market with it. The complexity of the macroeconomy and of the market is greater than what is implied by overly mechanistic or, even worse, static models.
In thinking about the market from this rather lofty perspective, one is naturally drawn into the debate of mechanism versus organism, the now classic debate between biology and physics. The strict mechanistic view of economics, where the course of events is determined via an equilibrium concept resulting from the interaction of a crowd of rational agents, has clearly not yielded many robust predictions or even ex post explanations of realized events in the last 100 years of its existence. Thus despite the elaborate concepts and complicated mathematics, this poor track record causes me to reject the mechanistic view of the world that this prism provides.
The purely organistic view of the market is probably a far fetch from reality as well. First of all, the conceptual definition of an organism is not even yet well understood, other than being a pattern in time of organized and linked elements where functional relationships between its constituents are delocalized and therefore cannot be reduced to the concept of a mechanism (that is, a set of independent parts only linked by localized constraints). There are clearly delocalized relationships in the market, and stresses in one dimension (whether geographic location, asset class, regulatory change, etc.) quickly propagate to other areas. This is in fact one of the sources of variability in correlations between different asset classes as well as participants’ behaviors. On the other hand, on average these correlation and behavioral relationships are quite stable. Also, unlike in a pure organism, the removal or death of a “market organ” would not necessarily imply the breakdown of the organism (i.e., market) as a whole. For example, the various sovereign debt defaults and write-downs in the past did not yield the death of the global bond market.
1.2.3 The Edge of Complexity
So, intuitively the market is not as simple as Newton equations nor is it as complicated as an elephant or a mouse. Its complexity lies somewhere in between. It has pockets of coherence and of randomness intertwined in time. A bit like a school of silverside fish that in normal circumstances has an amorphic structure but at the sight of a barracuda spontaneously polarizes into beautiful geometric patterns.
The good thing is that the market is the most observable and open human activity, translated into a series of orders, trades, and price changes—numbers at the end of the day that can be analyzed ad nauseam. The numeric analysis of time series of prices also yields a similar conclusion. The prices or returns do not behave as Gaussian processes or white noise but have distributional properties of mild chaotic systems, or as Mandelbrot puts it, turbulence. They are nonstationary, have fat tails, clustering of volatility that is due to clustering of autocorrelation, and are non-Markovian. A very good overview of the real world properties of price time series is given in Theorie des Risques Financiers by Bouchard and Potters.
1.2.4 Is Systematic Trading Reductionistic?
As per the definition above, systematic trading is essentially a computable model of the market. Via its algorithmic nature it can appear to be a more reductionistic approach than discretionary trading. A model reduces the dimensionality of the problem by extracting the “signal” from the “noise” in a mathematical way. A robotic application of the algorithm may appear overly simplistic.
On the other hand, discretionary traders often inhibit their decision making by strong beliefs (“fight a trend”) or do not have the physical ability to focus enough attention on many market situations thus potentially leaving several opportunities on the table. So discretionary trading also involves an important reduction in dimensionality but this reduction is happening differently for different people and times.
1.2.5 Reaction vs. Proaction
A common criticism of systematic trading is that it is based on backward-looking indicators. While it is true that many indicators are filters whose calculation is based on past data, it is not true that they do not have predictive power. It is also true that many systematic model types have explicitly predictive features, like some mean-reversion and market-making models.
At the same time one cannot say that discretionary trading or investing strategies are based solely on the concept or attempts of prediction. Many expectational models of value, for example the arbitrage pricing theory or the capital asset pricing model, are based on backward-looking calculations of covariances and momentum measures. Despite the fact that those models try to “predict” reversion to some normal behavior, the predictive model is normally backward-looking. As Niels Bohr liked to say, it is very difficult to predict, especially the future.
1.2.6 Arbitrage?
Many times I’ve heard people arguing that the alpha in systematic strategies should not exist because everyone would arbitrage them away, knowing the approximate models people use. The same could be argued for all the discretionary strategies as most of the approaches are well known as well. Thus the market should cease trading and remain stuck in the utopian equilibrium state. Yet none of this happens in reality and the question is why? Probably exactly because of the fact that people do not believe that other people’s strategies will work. So as much as it is seemingly simple to arbitrage price discrepancies away, it is less simple to arbitrage strategies away. Having said that, the market system in itself is cyclical and, as mentioned above, strategies get arbitraged away temporarily, until the arbitrageurs blow up all at the same time because of their own sheer concentration of risk and the cycle restarts with new entrants picking up the very valuable mispriced pieces.
1.2.7 Two Viable Paths
Viewing trading and the market from this level yields a positivist view on the different ways to profit from it. The discretionary traders see in it enough complexity to justify their approach of nonmechanizable intuition, insight, and chutzpah. The systematic traders see in it enough regularity to justify their approach of nonemotional pattern matching, discipline, and robotic abidance to model signals.
Which approach is right then becomes a matter of personal taste, as the edge of complexity the market presents us with does not allow for a rational decision between the two. In fact both approaches are right, but not necessarily all the time and not for everyone. Of course the Holy Grail is to be able to combine the two—to become an übertrader who is as disciplined as a robot in its mastery of human intuition.
This book of course does not offer the Holy Grail to trading; intuition and insight are quite slippery concepts and highly personal. There is no one way. But this work is not interested either in focusing on the same old mechanistic techniques that appeared at numerous occasions in books on systematic trading. It aims at moving further afield toward the edge of complexity, by giving enough structure, process, and discipline to manage a set of smarter, adaptive, and complex strategies.
1.3 THE BUSINESS OF TRADING
If, as was derived in the last section, there is no a priori rational way to choose between discretionary and systematic trading paths, one should then aim at objectively comparing the two approaches as business propositions. Seeing it this way will lead naturally to a choice based on the trader’s own psychology; that is, which of the two business propositions is the most compatible with the inner trust of his own ability to sustain and stand behind that business activity over time.
The goal of a business is to produce a dividend to its stakeholder. Any sustainable business is built on four pillars:
Both discretionary and systematic trading businesses should be seen in the context of those necessary contexts. Of course trading is not per se manufacturing of anything other than P & L. So the product is the trader’s edge or algorithm and the factory is the continuous application of such trading activity in the market. Marketing is the ability to raise more capital or assets under management based on performance, regulatory environment, or good looks. Here the trader can mean an individual, a group, or a corporate body.
So let us do a comparison between systematic and discretionary trading, keeping in mind the above concepts.
1.3.1 Profitability and Track Record
Before one even starts looking at the individual pillars of business, can one say anything about the long-term profitability of the two trading styles? This is an important question as it may provide a natural a priori choice: If one type of business is dominantly more profitable than the other then why bother with the laggard?
Interestingly it is a hard question to answer as the only objective data that exists in the public domain is on hedge fund and mutual fund performance. Any of the profitability data of bank proprietary desks is very hard to come by as it is not usually disclosed in annual reports. Also the mutual funds should be excluded on the basis of the fact that their trading style is mostly passive and index-tracking. This leaves us with comparing discretionary to systematic hedge funds.
In both camps there is a wide variety of underlying strategies. In the discretionary camp the strategies are long-short equity, credit, fixed-income relative value, global macro, special situations, and so on. On the systematic side the strategies are commodity trading advisors (CTAs), statistical arbitrage, high-frequency conditional market-makers, and so on. What is the right comparison: absolute return, assets under management (AUM)–weighted return, return on shareholders equity? Because private partnership is the dominant corporate structure for hedge funds, the return on shareholders equity is not a statistically significant comparison as far as publicly available data is concerned. Hence one has no choice but to compare strategy returns. As on average the fee structure is similar in both camps, one may as well compare net returns to investors.
Figure 1.1 shows the comparative total return on the Hedge Fund Research CTA Index and the total return on the SP500 stock index. Table 1.1 shows the comparative statistics of major Hedge Fund Research strategy indices from 1996 to 2013.
FIGURE 1.1 HFR CTA Index versus SP500 Total Return Index
Some of the earliest hedge funds were purely systematic and have survived until now despite the well-known attrition in the hedge fund industry as a whole. Many commodity trading advisors and managed account firms have been involved in the systematic trading business for at least 40 years. Their track record represents an interesting testament to the robustness of the systematic approach, from the performance and process perspective. Also systematic strategies have in general low correlation to discretionary strategies and to other systematic strategies, especially classified by time frame.
In conclusion one sees that the major strategy types tend to be quite cyclical and that there are sizable up-runs and drawdowns in each class, be it in the discretionary or systematic camps. Thus it is difficult to draw any conclusions on the dominance of either style on the basis of profitability alone.
This brings us back to our exploration of how the two styles compare in the context of the four business pillars mentioned above, in the order of product, factory, marketing, and capital.
1.3.2 The Product and Its Design
Research and information processing are the crux of the product’s edge for the trader. A trading strategy is first and foremost an educated idea on how to profit from certain situations, be they ad hoc or periodic, and how to mitigate losses from unexpected events. It requires an ability to gather, process, and research a large quantity of information.
Information
In the discretionary world, this information is categorized into the following seven areas and the trader forms an intuiton based on this set in order to pull the trigger:
The majority of the time in the systematic world, the information required is limited to the price and transactional and in rarer occasions on the holdings and flows (such as the Commitment of Traders report in the futures markets). Most of the systematic models base their decision making on the extraction of repeatable patterns from publicly available data on prices and executions. The statistical significance of such patterns is derived from simulation (the action of back- and forward-testing).
Both activities are clearly information-intensive but this intensity manifests itself in quite different dimensions. The discretionary style requires processing of a broad scope of nonnumerical data, and traders read and rely on a range of broker and analyst research along with continuous news and political analysis. A lot of useful information is also seen in the flow and holdings that are obtained via brokers, that is, who are the transacting participants and how much. This in itself implies that discretionary trading is difficult to do solo and often requires teams of people to digest all the information flow. Interestingly, some firms have started creating numerical sentiment indices based on textual and voice news flows, a technique used initially by intelligence agencies to discover subtle changes in political rhetoric.
For the systematic style, the dimensionality of the information is much lower; the models are in general only interested in price or tick data but they require a continuous feed and automated processing of this data at high speeds, especially in the current context of the ECNs. This means that from a technological perspective, especially for high-frequency business, the required connectivity and throughput needs to be large. This in general has cost implications.
Most systematic models also require prior and continuous recalibration, thus large databases of historical data need to be kept for research purposes.
Research
Information is useless if it cannot be interpreted in context, be it intuitive or model based. To be able to form such an educated view, some research needs to be performed on the relevant data.
In the discretionary context, most useful research falls into (1) political and regulatory analysis, (2) macroeconomic analysis, (3) asset-specific research, or (4) quantitative research. Many investment banks and institutions have large departments focused on macroeconomic analysis and asset-specific research. Discretionary traders or teams have access to such research via prime brokerage relationships and those costs are implicitly absorbed into trading and clearing fees. A few smaller private firms run by former bank or government institutions officials provide political and regulatory analysis and macroeconomic analysis for fees and also use their former contacts to introduce clients to current central bankers, finance ministers, and other officials. Such relationships are invaluable for certain strategies such as global macro, where fund managers constantly try to read between the lines for changes of moods or rhetoric in order to form their own expectations on upcoming policy moves. Thus a lot of research that is valuable for discretionary trading is already out there. It needs to be gathered, filtered, read, and distilled to be presented to the portfolio managers. Large discretionary hedge funds hire in-house economists and analysts to do such work but many operate just using publicly available and broker research.
There is a subset of discretionary strategies that is driven by quantitative modeling. Fixed-income relative value, long-short equity, and volatility strategies are such areas, for example. Each require a fair amount of advanced mathematical techniques, pricing tools, and risk management tools. Although there is commercially available software with standard libraries for pricing options, interpolating yield curves, or handling large-scale covariance analysis, the vast majority of quantitative discretionary operations employ in-house quants to write a series of models and pricing tools as well as to maintain the relevant data and daily process. This has clear cost implications on such businesses.
The systematic approach is entirely research-driven and in a very direct sense research innovation is the backbone of the business. The principal areas of research fall into the following four categories:
These four categories are closely intertwined in automated systematic trading and demonstrating this concretely is an important feature of this book.
1.3.3 The Trading Factory
Process
Designing and implementing a disciplined trading process on the basis of either computable or subjective signals is key to the success of the business of trading. The process presupposes an infrastructure and a technology optimized for the production of the trading widget. It is not enough to have a good widget idea; one also has to be able to manufacture it efficiently. Of course, having a great factory producing widgets that no one wants is a waste of time and money. But as much as great trade ideas or strategies are necessary, they are not sufficient if not implemented correctly. The underlying processes of discretionary and systematic businesses present many similarities but also major differences, as we will show now.
In the discretionary world, choosing the winning set of human traders is key. The traders have to have at least the following four features, with the last three criteria being essentially a strong self-discipline:
Several successful traders have published honest and objective self-analyses of their occasional failings in instituting such discipline, courage, or focus and drew lessons for the benefit of the whole trading community. Of course longer-term survival let alone profitability hinges on the discipline of applying the trading process as per the last three criteria.
As mentioned in the previous section, the systematic business is research-driven. The principal goal of that research is to produce a portfolio of profitable models. It implies that the continuous fostering of innovative research is a key element to the process and to the success of the business. Finding a set of robust models in the systematic world is equivalent to hiring a desk of good traders in the discretionary world.
The systematic approach a priori formalizes a lot of the individual trader’s discipline as models are run continuously, have embedded stop-losses and profit targets, and can be scheduled to be turned on or off during certain periods. The trading process is thus run as an algorithm. The key four features for success are similar in nature to those mentioned above:
The systematic trading process is much more involved than the discretionary one as by its nature it is automated. The increased complexity comes from the fact that many things that are second nature to humans are actually hard to implement in software (for example, automatic recovery mechanisms from data disconnects or loss). It is a technology-driven process as the technology implements the factory element. Thus from the technology perspective, the systematic business requires an investment into software and hardware much larger than for the discretionary business. We focus in Part Four on the analysis of the various key elements one needs to master to put such a process in place.
Cooperation
We have come to another important aspect of the nature of communication and cooperation within the two businesses. In a discretionary hedge fund, especially in areas like global macro, there is a tendency to encourage trade diversification by discouraging communication between various traders. This is a noncooperative game scenario and some funds push it even further by encouraging traders to compete for the biggest risk allocation from the same pot thus creating potential friction, jealousy, and mutual dislike between people.
Interestingly, on the systematic side such selection is done implicitly by the higher-order model feedback mechanism. So the noncooperative game is left to the machine and one does not hear models screaming or squealing when they get demoted. The research process, though, has to be a cooperative game where cooperation between team members serves the exact purpose of creating a diversified portfolio of models. Efficient systematic research has to be run on the examplar of academic institutions where people are given enough leeway to innovate and learn from communication with each other, and are driven by the common good of cooperative success.
There is another important cooperative game going on in the systematic trading business. It is the natural synergy between the research, development, technology, infrastructure, and monitoring teams. Research needs an optimized implementation that in turn needs efficient technology run on a robust infrastructure that is being monitored continuously. All areas need research to come up with money-making models to produce cash and sustain the whole food chain. The success of some large systematic funds is corroborated by my own knowledge of the way such cooperation had been instituted within them.
1.3.4 Marketing and Distribution
The differences in the products and processes discussed above imply differences in the approach to marketing and branding of discretionary and systematic strategies. One could say that the brand of a discretionary trading business falls more into the craft category, whereas the brand of a systematic trading business falls more into an industry category. The last remark could be justified from our analysis of the process, not the product. Both product design processes are crafts, coming from accumulated intuition of traders on one hand and researchers on the other. From a marketing perspective, the element of skill is crucial in both worlds.
One could argue that it is somewhat easier for a newcomer to launch a systematic fund rather than a discretionary fund. The crucial point that comes in all capital raising discussions is the ability to produce a credible track record. It is difficult for discretionary traders to have a track record unless they have traded before, which is of course possible only if they traded their own money or could take their track record from a previous firm (a very tricky exercise in itself). Thus the majority of discretionary traders start in market-making and other sell-side careers then graduate to a proprietary trader status. Only then can they start to build their independent track record.
The situation is quite different in systematic trading as there is a reasonable degree of acceptance among allocators of back-tested and paper-traded track records. This of course supposes that the simulated net asset value (NAV) contains a realistic (or, even better, pessimistic) assessment of transaction costs, scalability, and sustainability of the market access and the trading process in general. The discussion then focuses on how this track record was generated and whether there was a risk of over-fitting and using future information in the process of building the models.
Once the fund has been launched, let us compare the hypothetical clues to answer the four main types of questions clients would usually ask while doing their due-diligence assessment:
In conclusion, I believe that it is somewhat easier to start a systematic fund but it requires a similar marketing effort as for a discretionary business.
1.3.5 Capital, Costs, and Critical Mass
Enlightened by the comparison of the three functional parts of the business, we now come to the crucial questions of necessary initial capital and of running capital for operations. Of course we need to compare the two businesses pari passu as far as size and revenue goals are concerned. We use the example of hedge funds because they are stand-alone entities where all costs and revenues can be objectively estimated.
How much is needed to start the business?
In 2010, the realistic critical mass of initial capital needed to start a hedge fund business is north of $50 million and better at $100 million. The main reason is the structure of allocators—funds of hedge funds, asset managers, and family offices. Most of them will rarely look at a target with AUM below $50 million because they do not want to participate more than 10 percent in any fund. This helps them to reduce the risk of concentration of other clients in the fund if, of course, the other clients are also invested less than 10 percent each. As they get lesser fees than hedge funds themselves, an investment of less than $5 million is not worth the costs and time of due-diligence process.
It is actually not a bad thing for the fund itself as it forces it to be diversified in its client base, so that losing one client will not put the fund in jeopardy. But then the question comes down to the classic chicken-and-egg: How would one start a fund in this difficult environment? One needs to find a set of seed investors, hopefully all at the same time, a lot of performance luck, and a lot of marketing effort! This is the same across various strategies and the systematic business is no different from the discretionary in this respect. Thus the barriers of entry are quite high for either type of stand-alone trading business.
How much is needed to maintain the operations?
As the seeders invariably take a cut of the economics, the resulting revenue is probably not the usual 2 percent management:20 percent performance fees structure but closer to 1 percent:15 percent. Assuming raising $50 million of AUM the first year, the realistic management fee revenue is around $500, 000.
Certain types of trading styles can be perfectly run on minimal infra-structure consisting of the head trader (Chief Investment Officer), a middle-office person (Chief Operating Officer and Chief Risk Officer), and a marketing and client relationship person who can also hold the title of Chief Compliance Officer. Those four functions combined into three people tick the minimally accepted boxes as far as institutional allocators are concerned in their goal toward reducing operational and key-man risks. Other functions can be outsourced, in particular many back-office functions of control, fund administration, IT support, and legal support. The costs of renting a furnished office space plus utilities of course varies but be it offshore or onshore, it comes roughly to at least $50, 000 per year. The IT and legal support costs, communications (phone and Bloomberg feed), and directors’ and officers’ insurance also come to at least $50, 000 a year but may be larger. Adding travel and entertainment costs puts the total pre-salaries expenses at around $150, 000 conservatively. The salary expenses then pretty much take up the rest of the fees, with usually $150, 000 to the COO, $150, 000 to the marketing person, and the rest to the head trader, who probably is the sole partner working for the upside call option. The business can survive one or two years on this without making extra trading revenue but if it does not, clients will usually pull the capital anyway. So the $50 million is indeed the low end, the necessary but not always sufficient critical initial mass.
