This is the first in a series of posts in which we will change gears slightly and take a look at some of the fundamentals of algorithmic trading. So far, Robot Wealth has focused on machine learning and quantitative trading research, but I had several conversations recently that motivated me to explore some of the fundamental questions around algorithmic trading. In the next few posts, we will investigate questions such as:
So without further ado, let’s dive in!
What is Algorithmic Trading?
At its most basic level, algorithmic trading is simply the automated buying and selling of financial instruments including stocks, bonds and futures. It requires a networked connection to an electronic exchange, broker or counterparty. In addition, you need a means of programmatically buying, selling and performing other tasks related to trading, such as monitoring price action and market exposure.
Algorithmic trading is enabled thanks to the rise of electronic exchanges – a relatively recent phenomenon. Once upon a time, financial products were traded in the so-called ‘pit’ inside the exchange building using the ‘open outcry’ method. This consisted of brokers and traders being physically present in the pit and shouting prices at which they were willing to buy and sell. Participants even used hand signals to convey their intentions. This gradually began to give way to telephone trading and eventually to electronic trading. The shift started sometime in the 1980s and continues to this day, however the vast majority of exchanges around the world are now electronic.
Naturally, this evolution changed the dynamics of the trading process. Anecdotally, pit traders could sometimes read each other’s intentions through the physical contact that comes with being in the pit – obviously this is incredibly implausible when market participants trade electronically and can be separated by potentially vast spaces. Stories of life in the pit makes for interesting and often amusing reading. Some curated links:
It is also worth noting that algorithmic trading is not just for exchange-traded markets: over-the-counter (OTC) markets are also traded algorithmically. An OTC market is one where orders are not executed through a central exchange, but rather between two parties. OTC algorithmic trading typically takes place via an Electronic Communication Network (ECN) or dark pool. The former is typically used by market makers to disseminate and match orders with their network of counterparties. The latter is more like a private execution venue where liquidity is provided by the participants of the dark pool, away from the exchange.
Advocates of electronic trading point out the attendant increased market efficiency and reduced opportunity for manipulation. Electronic trading is also typically less expensive and with the advent of cheap Internet, is accessible to anyone with a decent connection. This means that an individual can buy or sell a financial product from their living room.
It must also be pointed out that as electronic trading has taken off, the instance of ‘flash crashes‘– huge spikes in volatility over short periods of time – has also increased. A case can be made that suggests that algorithms exacerbate such a crash because they act much faster than a human can intervene. But on the other hand, exchange operators are finding ways to handle this new environment in safer ways, for example electronic mechanisms to curb extreme volatility, order routing co-ordination between exchanges and re-thinking the role of market makers. Whether it is fair to blame flash crashes on electronic trading is a huge and sometimes contentious topic.
In order to execute trades algorithmically, we use a computer program connected to the exchange (either directly or via a broker) that executes our desired trading behavior on our behalf. Such a program or algorithm is simply a set of detailed instructions that a computer understands. A trivial example might go something like “read some price data, calculate its mean and standard deviation, and then if the most recent value of the price data is above its average and the standard deviation is less than some threshold, send a buy order to market.” Of course most trading algorithms are much more complex, but you get the idea.
The simple algorithm described above had some of the common aspects of an algorithmic trading system:
Other common components of such systems include:
Stay tuned for in-depth info on why people care about Algorithmic Trading. In the next post, Kris will also discuss 3 different types of algo trading:
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