Robots for trading in financial markets


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Messenger Inthe world fretted that algorithms how you can make money using the site know us better than we know ourselves. No concept captures this better than surveillance capitalisma term coined by American writer Shoshana Zuboff to describe a bleak new era in which the likes of Facebook and Google provide popular services while their algorithms hawk our digital traces.

Automated algorithmic trading took off around the beginning of the 21st century, first in the US but soon in Europe as well. One important driver was high-frequency trading, which runs at blinding speeds, down to billionths of a second.

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It offered investors the prospect of an edge over their rivals, while helping to provide liquidity to a market by ensuring there was always someone willing to buy and sell at a particular price. High-frequency trading is now behind ddu option than half of the volumes in both the stock and futures markets. In other markets, such as foreign exchangealgorithms have a smaller but still significant presence, with no signs that they will wane in future.

The vices of devices Humans still program the algorithms and design their trading strategies, though the rise of deep learning is putting even this role under threat.

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  • Financial trading bots have fascinating similarities to people – we need to learn from them

But the moment the algorithms go live on markets, they act on their own accord without human intervention, dancing with each other in dizzying and often unexpected ways. At first glance, they have little in common with us.

I coded a stock market trading bot. This is how much it made in a week.

Like human traders, however, they make decisions, observe others making decisions, and adjust their behaviour in response. Fooled you.

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The first algorithm will then quickly cancel its orders, having hopefully tricked its rival into making the wrong bet about which way the market is heading. Interestingly, sociologists consider this sort of mutual anticipation to be a central feature of what it means for humans to be social.

One end result: Successfully trading the stock market will become even tougher for most. And, we have to somehow assimilate that into our trading plans and figure out how we deal with that from a risk management standpoint. The rise of algorithmic trading programs has led traders away from past practices of running simple valuation-based scans for overvalued and undervalued stocks. In the place of those seemingly primitive exercises are programs that scan real-time news feeds and trade stocks based on headlines. As we have learned over the past ten years, those crowded trades could instantly unwind as the machines assess a single new headline.

They have long seen markets as highly social arenas. How it once was. Everett Collection But if machines can be social, how similar or different is it to how humans socialise really?

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There are obvious differences, of course. While the human traders of the past often knew one another well, and often hung out together after work, algorithms trade anonymously. Indeed, this is precisely why they are programmed to form expectations about one another.

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Facial cues are no longer available, but entire strategies have been developed that seek to find out whether a number of orders might have been placed by one and the same algorithm — robots for trading in financial markets then try to predict what its next moves might be.

To evade such attempts, algorithms are often designed so as not to be recognised as algorithms by other algorithms.

An AI Robot in the Service of the Stock Market

These are again attributes that sociologists have long considered key aspects of metropolitan life. Together with colleaguesI have spent the past robots for trading in financial markets years in major financial hubs interviewing traders, programmers, regulators, exchange officials and other finance professionals about these trading algorithms.

This has drawn out some other interesting similarities between human and automated traders. Programmers readily admit that once their algorithms start interacting with others, they get carried away and act unpredictably, as if they were in a mob. Certainly no trader or programmer had planned on creating this massive shift in prices, but decades of sociological research tell us that this sort of behaviour is expected in large groups.

We need to understand how our financial algorithms interact in concert before our own tools become our undoing.

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Unstoppable momentum. Lysogor Roman Of course, not all forms of social interaction are admirable or beneficial. Like humans, algorithms interact with each other in ways that range from caring and peaceful to cold and violent: from providing liquidity and maintaining market stability to making manipulative orders and triggering wild trading activity. Getting to grips with these interactions is not only key to understanding modern trading and trying to prevent future flash crashes.

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Algorithms talk to one another in more and more fields today. Understanding how they behave as crowds will hopefully shed light in areas where they are just starting to come into their own — think self-driving traffic systems or automated warfarefor instance.

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It may even alert us to the avalanches that lie in wait, too.