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Profitable Forex Trading During Boston’s Lunch Hour

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What Is Forex Trading? And Should You Be Doing It?

Author: 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 / Modified: April 3, 2020 / Approved: April 17, 2020 / Published: April 26, 2020

Machine learning paradigms promise to help traders reduce uncertainty through better predictions made through more complex algorithms. We ask about the detectable consequences of both uncertainty and complexity at the aggregate market level. Our analysis of nearly 1 billion trades (2007-2017) for eight currency pairs shows that the rise in algorithmic trading is associated with more complex subsequences and more predictable structures of bid-ask spreads. . However, algorithmic intervention is also related to future uncertainty, which at first glance appears contradictory. At a micro level, traders use algorithms to reduce local uncertainty by creating more complex algorithmic patterns. This entails a more predictable structure and more complexity. At a macro level, increased overall complexity means more combination possibilities and therefore greater uncertainty about the future. The cascade law of entropy dictates that uncertainty decreases when trading at the fourth digit level behind the dollar, while new uncertainty begins to arise at the fifth digit behind the dollar (aka ‘pip trading’). In sum, our information theory analysis helps clarify that the seeming contradiction between reducing micro-level uncertainty and increasing macro-level uncertainty is a consequence of the inherent relationship between complexity and uncertainty.

The information revolution has revolutionized business, economic, political, and socio-cultural behavior, as well as the way financial markets operate. If we compare the bustle that was still going on on exchanges just a decade ago with the smooth hum of today’s electronic exchanges, we can see that algorithmic trading has had a huge impact on the way financial assets change hands. In this study, we find observable signatures that demonstrate changes in the nature of trading dynamics over the past decade. We find that overall emerging trading dynamics have become more predictable, more complex and more uncertain at the same time. We chose the foreign exchange market for our analysis. The reason is that the forex market has experienced a clear and distinct growth in algorithmic trading.

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Typically, traders use algorithmic automation to make local dynamics more stable and predictable. Although a large literature has also shown that transaction speeds increase, this aspect is not of interest in this study. Prediction accuracy is the name of the game in the currently dominant machine learning paradigm [1]. Most digitally automated information processes, including bots, trading algorithms, and artificial intelligence (AI) of all kinds, follow a set of deterministic 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-trained (so-called machine learning). The algorithm reliably and predictably buys or sells based on the current version of the step-by-step recipe. An algorithm is defined as “an ordered set of clear, executable steps that define a final process” [2]. So, by definition, an algorithm predictably executes a (given or self-learned) recipe to reach an inevitable conclusion that deterministically defines its behavior.

In our analysis, we found evidence that the algorithmic machines used in foreign exchange markets are associated with an additional level of predictable structure to the evolving dynamics of bid-ask spreads. Complex new subsequences provide dynamism by increasing predictable complexity. We show that algorithmic trading is one of the key explanatory variables for this trend. We also show that algorithmic trading is correlated with increased predictability because it reduces future uncertainty about the next bid-ask spread. However, this only applies if you look at the dynamics at a detailed level for transactions that occurred 10 years ago. All uncertainty in the market has been removed using algorithms from the same grainy lens traders used to look at reality 10 years ago. In the fourth digit after the dollar, profits are no longer traded. Algorithms replaced surprises with predictable structures (more complex, but more predictable).

At the same time, the level of fragmentation of trading dynamics has also increased over the past decade. Algorithms are used to tap into a more detailed level of reality that humans cannot reach. At this new level of tick trading, we discover unprecedented amounts of predictable complexity and unpredictability simultaneously. From the perspective of the fifth behind the dollar (where the profits are made these days), uncertainty has never been greater. Algorithmization of foreign exchange trading is associated not only with a reduction in uncertainty, which was the norm a decade ago, but also with a new, more detailed outlook on a much more uncertain reality.

Our research shows that traders have adopted automated algorithms to make their daily lives more predictable, and that they might have succeeded in taming the markets if the world had stayed in the harsh black-and-white it used to be. But in doing so, they opened up an unprecedented new level of shades of gray, which ultimately made the entire market system less predictable than before.

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The paper proceeds as follows. Based on the existing literature, we develop two complementary hypotheses regarding changes in trade dynamics in foreign exchange markets. We then propose to use measurement devices from dynamical systems theory to quantify market dynamics and present three older and complementary measurement methods. We obtain nearly 1 billion tick-level trades in eight currency pairs over an 11-year period from 2007 to 2017 and calculate the proposed measure over 528 bimonthly periods. We then use simple multiple linear regression to test hypotheses regarding the increasing role of algorithms and the nature of changes in trade dynamics. Finally, we interpret our findings with the help of existing theorems and literature from information theory approaches to dynamical systems. Our results demonstrate a methodology that assesses dynamic changes induced by algorithms and contributes to the ongoing debate on the impact of more sophisticated algorithmic trading on market dynamics.

Trading markets have been called “the world’s largest and most powerful techno-social system” [3]. A major 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 specific variables in the decision-making process such as time, price, volume, and other historical and simulated patterns.

Farmer and Skouras [4] divide trading algorithms into three broad groups: The first is the execution algorithm (also called ‘algos’ in the literature). It consists of instructions that allow humans to set parameters for trade execution, such as determined time frames, trading volume patterns, risk-adjusted real-time market conditions, relative prices between selected stocks, etc. Their primary goal is not necessarily to execute a trade. You gain additional trading profits, but minimize costs and risks and ensure the reliability of order execution according to the set strategy. It’s as simple as an ‘execution algorithm’ and automates various aspects of trading. A decade ago, investors looking to buy a significant amount of stock would have to hire a floor broker to quietly process their orders, using human judgment to buy a portion of the entire transaction to avoid the stock price rising. Running algorithms can be bought or sold in increments of days or months. Many of these use machine learning to understand market patterns.

The second group refers to:

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