Professional Automated Trading

Theory and Practice

Eugene A. Durenard

• Publisher: Wiley
• Year: 2013
• Print ISBN: 9781118129852
• eBook ISBN: 9781118419298
• Edition: 1
• Topic: Business
• Subtopic: Finance
• Index: Business

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.

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:

1. The existence of a rules-driven trading strategy that is based on objectively reproducible (computable) inputs.

2. The application of that strategy with discipline and outside of the human emotional context.

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 s...

Table of contents

• Cover
• Series Page
• Title Page
• Copyright Page
• Preface
• Chapter 1: Introduction to Systematic Trading
• Part One: Strategy Design and Testing
• Part Two: Evolving Strategies
• Part Three: Optimizing Execution
• Part Four: Practical Implementation
• Appendix: Auxiliary LISP Functions
• Bibliography
• Index