Robin Sharpe provides an excellent introduction to algorithmic trading in Automated Trading and the New Markets. For more information, see John Bates in Dr. Dobbs, and wikipedia.
For example, trader A (or their software) notices a periodic spike in the price of equity X; trader B may be trying to buy a very large position of X in small portions so as not to push up the price too much. Trader A (or the software) buys stock X just moments before each predicted spike, selling it to trader B at a higher price once B enters the market. Trader B (or their software) notices the run-up, and changes their buying rhythm to disrupt trader A. And on it goes. . .
Such market interactions aren’t new, but software can execute the trades faster than humans can respond to them. Rather than duels between traders in real time, it becomes a duel between traders’ models of how the market functions. These are contests between world theories, where the theories themselves constitute the world. Kyril calls this the “reflectivity” of the market.
Douglas Hofstadter introduced the term “strange loop” in Gödel, Escher, Bach to describe a series of steps through a hierarchical system which take one back to the beginning. His new book I Am a Strange Loop uses this concept to explain self-awareness. In a New Scientist interview, Hofstadter says the brain, and the self, is like a smile because it’s a process rather than a thing. (Extending the metaphor: Software is to hardware as a smile is to a body.)
A market is observable through its behavior, that is, how it responds to stimuli. When the responses happen faster than humans can follow, and involve the integration of more variables than humans can handle on their own, the market is less a social interaction among people than an environment in which people act. A market is neither a place, nor a group of traders, nor the sequence of trades, nor a reflection of an outside commercial reality; it is the self-perpetuating process that involves all of them. The system’s behavior becomes a subconscious expression of the cumulative conscious plans of many people – subconscious because the mechanism is not directly available to human introspection.
Markets are examples of distributed cognition, that is, cognition which occurs in an ensemble of people and tools, rather than in a single brain; see Giere (2002, PDF) for a good survey. What’s striking about algorithmic trading is that the amount of cognition occurring outside human brains is growing rapidly.
Robin Sharp (ibid) points out that trading is increasingly hands-off, since humans can’t cope with the reaction times required.
“Whilst the theory behind program trading is fairly simple, the software reality is that program trading operates at a different time-scale to even the fastest human trading. . . It’s worth understanding that the brain brings experience and subjectivity to the table and software brings speed and objectivity to the table. For most types of trading experience beats speed but there is a lot of noise in the market in the sub-second region where the brain simply can’t compete with a computer. . . At the moment the volume of trading at these sub-second time scales is not be great (less than 5%) and is held back because the coarse granularity of ticks and price data has been designed for human interaction. However this will change.”He also notes that algorithms can exploit the capacity limitations of human traders: “A large number of trades are difficult for traders to juggle in their heads. When such large events occur is small time frames computers can predict irrational behaviour of traders, again for a profit.”
The kinds of problems that traders face are not only analytical or cognitive; they’re also social. Sharp notes the organizational impediments to certain kinds of trades: “One of the reasons traders don’t trade cross market is that they cannot price the instruments fast enough, and – again – is that traditional corporate management structures and regulatory structures impede cross-market trading.”
Sharp tabulates the latencies in a trading cycle. The computer processing is 200 milliseconds, quick compared to 1,250 milliseconds for human perception, evaluation and response. The network latency is about 400 milliseconds. It’s worth noting that these network applications reinforce old geographic patterns rather than abolishing distance. Kyril tells me that the NYSE is doing a good business selling rack space at the Exchange to big trading houses; because every millisecond counts, their computers need to be on the same LAN as the Exchange or else they’ll lose to faster arbitrageurs.
I’m struggling with the question of whether automated trading leads to a qualitative change in the markets, rather than simply a quantitative one. While algorithms that respond to changes in the market begin to constitute the market, this kind of loop applies to traditional human-only trading, too.
Sure, the feedback loop is much faster with automated trading. But is it a difference in degree, or a difference in kind? It’s different from the human perspective; stuff now happens too quickly to follow consciously, and the role of humans changes. However, it might simply be a change in time scale, not a change in process.
The increasing complexity of the market may be even more important. Arbitrage can link markets, which generates more correlated variables than traders can juggle in their heads. An automated trading strategy could link current and futures markets, on different exchanges (New York and London), for different instruments (equities and foreign exchange), and different data types (Reuters news feed, GOOG and MSFT stock prices, S&P500 index, the 15 minute volume weighted price of GOOG). As Robin Sharp points out: “Eventually arbitrage will force separate markets to revaluate their relationships. It only takes one successful arbitrage engine to forever link two previously unrelated markets. Anybody in the business will know how deeply this will be felt.”
Perhaps integrating more streams of information in more complex and rapid ways creates a new kind of market causality. When humans were trading with each other, it was a social process. Now, from the human perspective, it’s more like experimenting on the world than dealing with people. “Hard intangibles” come into play because this new world is not the one humans evolved in. Humans still set the goals and strategies, but the parameters of this world interact in unexpected ways. And genetic algorithms will lead to algorithmic trades that are profoundly alien to human intuition. Trading is another activity, like large software projects, where the abstractions we’ve created are beginning to outstrip our ability to understand them.
Ronald Giere, “Scientific cognition as distributed cognition,” in The Cognitive Basis of Science, Cunningham, Stich & Siegal (eds.), Cambridge University Press, 2002