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Algorithmic Trading In The Forex Market
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Advantages Of Algorithmic Trading In Forex
By Martin Hilbert Martin Hilbert Scilit Preprints.org Google Scholar 1, * and David Darmon David Darmon Scilit Preprints.org Google Scholar 2
Received: February 1, 2020 / Revised: April 3, 2020 / Accepted: April 17, 2020 / Published: April 26, 2020
The machine learning paradigm promises traders to reduce uncertainty through better predictions made by increasingly complex algorithms. We ask about detectable outcomes of both uncertainty and complexity at the aggregate market level. We analyzed nearly one billion trades of eight currency pairs (2007-2017) and showed that the rise of algorithmic trading is associated with more complex subsequences and more predictable structures in bid-ask spreads. However, algorithmic involvement is also associated with more future uncertainty, which seems contradictory, at first glance. At the micro level, traders use algorithms to reduce their local uncertainty by creating more complex algorithmic patterns. This implies a more predictable structure and more complexity. At the macro level, increased overall complexity implies more combinatorial possibilities and thus more uncertainty about the future. The entropy chain rule reveals that uncertainty was reduced when trading at the fourth digit level behind the dollar, while new uncertainty began to emerge at the fifth digit behind the dollar (also known as “pip trading”). In summary, our information theoretic analysis helps us clarify that the apparent contradiction between decreasing micro-level uncertainty and increasing macro-level uncertainty is the result of the inherent relationship between complexity and uncertainty.
The information revolution has not only revolutionized business, economics, politics and socio-cultural conduct, but also the modus operandi of financial markets. Looking at the hustle and bustle that was still ongoing on trading floors just a decade ago, and comparing it to the gentle hum of today’s computational trading floors, suggests that algorithmic trading has had a major effect on the way financial assets change hands. In this study, we look for observable signatures that evidence changes in the nature of trade dynamics over the past decade. We find that the overall emerging business dynamic has become more predictable, more complex and more uncertain at the same time. We chose the forex market for our analysis, as it has seen a clear and delineable growth of algorithmic trading.
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In general, marketers use algorithmic automation to make their local dynamics more reliable and predictable. A large literature also shows that it makes trade faster, but we are not concerned with this aspect in this study. Prediction accuracy is the name of the game for the currently dominant machine learning paradigm [1]. Most digitally automated information processes, including bots, trading algorithms, and all forms of artificial intelligence (AI), follow a deterministic set of local rules that respond to programmed instructions or learned patterns. For example, a forex algorithm can buy or sell in response to a set of inputs, which can be predefined (so-called expert systems) or self-learned (so-called machine learning). The algorithm will reliably and predictably buy or sell according to the current version of the step-by-step recipe. Algorithms are defined as “an ordered set of unambiguous, executable steps that define a termination process” [2]. So, by definition, algorithms predictably execute a recipe (given or self-learned) to arrive at an inevitable conclusion that defines their behavior deterministically.
In our analysis, we find evidence that the algorithmic machinery employed in foreign exchange markets is associated with an additional level of predictable structure to the evolving dynamics of bid-ask spreads. New intricate sub-sequences give the dynamics a predictable increase in complexity. We show that algorithmic trading is one of the main explanatory variables of this trend. We also show that algorithmic trading correlates with greater predictability, because it reduces future uncertainty about the next bid-ask spread. However, this is only true if we look at the dynamics at the level of detail on which trade occurred a decade ago. Putting on the coarse-grained lenses that traders used to look at reality a decade ago, algorithms have removed all uncertainty from the market. There are no more profits trading in the fourth digit behind the dollar. Algorithms have replaced surprise with predictable structure (more complex, but more predictable).
At the same time, the last decade has also seen an increase in the level of fine grain in the trading dynamics. Algorithms were used to exploit a more detailed level of reality, where humans do not reach. In this new level of tick trading, we encounter an unprecedented amount of predictable complexity and unpredictability at the same time. From the perspective of the fifth digits behind the dollar (where profits are made today), uncertainty is greater than ever. The algorithmization of currency trading is linked to the reduction of uncertainty as was the norm a decade ago, but also to a new and more detailed perspective of reality, which is much more uncertain.
Our findings suggest that traders introduce automated algorithms to make their daily routines more predictable and that they would succeed in taming the markets if the world remained at the level of thick black and white it used to be. However, in doing so, they opened up an unprecedented level of shades of gray and ended up making the entire market system less predictable than ever.
A Guide To Algorithmic Trading
The paper proceeds as follows. Based on the existing literature, we formulate two complementary hypotheses about the dynamics of changing trade in foreign exchange markets. We then propose to quantify market dynamics with the measurement apparatus based on dynamical systems theory and present three complementary and long-standing measures. We obtain almost one billion tick-level trades of eight currency pairs over the eleven years between 2007 and 2017 and calculate the proposed measures for 528 bimonthly periods. Next, we use multiple linear regression analysis to test our hypotheses about the growing role of algorithms and our signatures of changing trading dynamics. Finally, we interpret our findings with the help of existing theorems and literature from information theoretic approaches to dynamical systems. Our results show a methodology for assessing dynamic changes brought about by algorithms and contribute to ongoing discussions about the effect of increasingly sophisticated algorithmic trading on market dynamics.
Commercial markets have been called “the largest and most powerful technosocial system in the world” [3]. The main driver of technological change over the past decade has been the introduction of algorithmic trading (AT). AT can be broadly defined as a set of automated trading strategies that follow certain variables in their decision-making process, such as time, price and volume, and other historical and simulated patterns.
Farmer and Skouras [4] distinguish between three large groups of trading algorithms. The first are execution algorithms (often referred to as “algos” in the literature). They consist of instructions that allow humans to set the parameters for trade execution, such as a specific time frame, volume patterns, risk-adjusted real-time market conditions, relative prices between selected stocks, etc. Its main objective is not necessarily to make an extra trading profit, but to minimize costs and risks and ensure reliability in the execution of an order in accordance with an established strategy. As simple as “execution-somethings” are, they automate many different aspects of trading. A decade ago, an investor trying to buy a sizeable amount of stock had to hire a floor broker to work the order quietly, using human judgment to buy chunks of the total trade to avoid driving up the stock price. Execution algorithms can buy or sell on the time scale of days or even months. Many of them use machine learning to understand market patterns.
The second group refers to the
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