Modern financial markets operate a lot differently today than they did even 20 years ago. Experienced trader Daniel Calugar says that a lot has changed in recent years, and it continues to change almost daily.
Algorithmic trading is one of the biggest trends in trading, and professional and casual investors alike are taking advantage of advanced technologies such as machine learning, artificial intelligence, and big data to draw new insights on the market and even execute trades automatically.
Algorithmic trading — the process of using high-powered computers and complex mathematical equations to identify trading opportunities and execute them more accurately — is certainly changing the way investors approach the market.
However, while algo trading is changing the market itself, many of the building blocks of the modern market have remained the same over the years. In fact, the intricate world of market microstructure has a profound influence on algorithmic trading strategies.
In this article, Dan Calugar will discuss concepts such as order types, liquidity dynamics and market impacts as the essential building blocks for success in today’s financial markets.
What is Market Microstructure?
Market microstructure is the practice of studying the basics of financial markets as well as how they work. The term was first coined back in 1976 by Mark Garman, a professor at UC Berkeley, when describing all the minutiae of trading activity that occurred in asset markets.
Studies of market microstructure involve experimental, empirical, and theoretical research that’s focused on the economics behind security markets. At a basic level, it helps investors better understand what makes people who participate in the markets tick.
By discovering those answers, traders are better able to identify the best investment opportunities and take advantage of them.
Traders have researched market microstructure for decades, and today, it’s a vital component in algorithmic trading.
How Algo Traders Use Market Microstructure
Security markets don’t just reflect simple supply and demand. If they did, predicting where markets are going would be much easier.
Markets today are multifaceted and very complex. This results in prices that can deviate substantially from their equilibrium values and from economic theories that are considered standard for the industry.
Thanks in large part to the prevalence of algorithmic trading, the volume of trades has skyrocketed in recent years, and the actual trading process has evolved substantially as well. This has, in turn, caused financial markets to change even more and make them even more unpredictable.
Algo traders rely on market microstructure so they can try to understand the ever-changing financial markets. They use it to try to identify things such as optimal market prices that lead to the greatest returns.
Daniel Calugar says that big data is essential to market microstructure for algorithmic trading, as it focuses on how various sources of information get conveyed through liquidity, transaction type, and costs and movements in price. By putting all of these elements together, algo traders can glean valuable insights on the overall market — or specific assets within the market — at any set time.
This information can then be applied to traditional financial theories and trading strategies. It can also be used to analyze alternative data, such as breaking news events and their effects on the market in real-time.
Understanding the different types of market microstructures helps algo traders get to the nitty gritty of pricing assets, which in turn helps them make more informed investment decisions. Integrating this information into their algorithms also helps them make trades more quickly, efficiently, and effectively.
Why Market Microstructure is Important
There is a lot of friction in today’s financial markets. Market microstructure analyses help traders make conclusions about how market participants might behave based on how the market is responding.
Using the example above, when political news breaks, how are traders going to react? Being able to more accurately predict the changing behaviors of the market as a whole can help traders make better investment decisions for themselves. Dan Calugar points out that it’s these nuances that market microstructure can highlight, in addition to simple market liquidity and price discovery.
This is necessary today because modern markets don’t revolve around a group of professional traders who are present every day on a trading floor. Traders literally come in many different forms — from professionals to casual, from on-site to at-home, from traditional to advanced, and much, much more.
This means that today’s markets are much more complex than they once were. Trading is not only more accessible to people around the world but there are many different types of traders who employ various strategies as they try to generate big returns.
This can make predicting where markets are going more challenging, emphasizing the importance of relying on tried-and-true analyses and approaches. Market microstructure is central to helping algo traders gain insights into all the different nuances that can affect the market.
Order Types and Algorithmic Trading
Order types are one of the basics of market microstructure. They are tools that investors use to determine what to do with an order. In essence, they are the constraints that dictate how orders are executed — or, rather, what the order actually does.
One example is a market order, which will sell or buy an asset at the current price for that asset. Limit orders will define either a minimum or maximum price that a trader would be interested in selling or buying an asset at.
If orders were that simple, there really would be no need for in-depth market microstructure analyses. However, there are not only much more complex orders but a whole library of different types of orders. This means that understanding the various nuances of order types is crucial for algorithmic traders.
Two Basic Order Types for Algo Trading
Daniel Calugar says that all algo traders should start with two basic order types — limit orders and stop orders.
Limit orders were mentioned briefly above. These orders attempt to execute trades at opportune times based on in-depth predictive analysis of where the market is headed.
Buy limits, then, are executed below where a market price currently is, anticipating that the price of the asset will rebound in the future. Sell limits do the exact opposite — they are positioned above current prices in anticipation of the price rising to a defined threshold before dropping again.
Limit orders are not new, of course, but algorithmic trading is certainly changing how they’re used.
Market microstructure analyses form the backbone of the research that helps to determine market predictions. Investors can use the treasure troves of data available to them to more accurately predict the behavior of the market so they can then implement limit orders that are more successful.
Limit orders of the past were rather challenging for average traders to execute, if only because they needed to constantly monitor the market’s movements. Even when they were able to correctly identify the opportune time to execute the trade, they might miss out on the best price due to the manual work required.
Now, algorithmic trading can execute these limit orders automatically based on the parameters put in place by investors.
Another basic order type for algo trading is stop orders. They serve as ways to mitigate and manage risk.
Stop-loss orders are one subset of this order type. They are set in place to trigger anytime the market is moving in a way that’s against the position you have in an asset. Based on certain parameters that you put in place, stop-loss orders will automatically be placed at a certain price or threshold to sell your position. In essence, they help to cap your potential losses.
Take-profit orders work in the opposite way. They guarantee you a certain amount of profit for an asset once it reaches or crosses a certain threshold. Dan Calugar explains that take-profit orders will automatically execute a sale of an asset once it reaches a certain price, thereby securing positive returns for you.
While both of these order types would be considered basic in algo trading, they do highlight the important role that market microstructure plays in their success. Traders need to be able to correctly predict where the market is going in order to take advantage of these order types, and that revolves around the different microstructures of the market.
Liquidity Dynamics and Algorithmic Trading
Liquidity didn’t used to be as big of a focal point of trading as it is today. In the U.S., for instance, most trading has historically been done on the New York Stock Exchange. The exchange provides traders with enhanced liquidity, which brings peace of mind, knowing that securities could be easily converted into cash or cash could easily be converted into securities.
Traders prefer markets with greater liquidity because it results in immediacy. When an investor wants to sell an asset, they want to do so immediately so they can mitigate their losses or capture their profit — as mentioned above.
In this example, if the market doesn’t have enough liquidity, an investor may need to hold onto an asset longer than they want. That, of course, could result in bigger losses or lower profits. Illiquid markets, then, have additional risk — something that traders are usually looking to reduce.
The multiple markets that exist today provide a lot of profitable trading opportunities, but they also present additional risk in terms of liquidity, or lack thereof.
How Algo Trading Uses Liquidity
In terms of market microstructure, algorithmic trading takes into consideration the liquidity of markets — both current and predicted future liquidity.
During the financial crisis of the early 2000s, there was an extreme overload of inventory as people were in “sell mode.” That caused quite the imbalance of liquidity, which in turn caused major issues for traders who were looking to sell.
The financial crisis, of course, was an extreme event that few people could have predicted.
However, there are plenty of other less extreme instances in market microstructure where liquidity could be affected. If traders are able to predict possible imbalances in liquidity, they can either take advantage or avoid certain aspects of the market.
Algorithmic trading can analyze various information sources to help predict future liquidity. One of the most common forms is alternative data, such as breaking news information.
Jobs reports, for instance, can provide insight into not only the current status of the economy but also where it might be going. And while that’s not always a great predictor of how the stock market will perform, it does provide insight into how the general public might approach financial markets.
That’s not the only way algo traders attempt to predict market liquidity, but it does serve as an easy-to-understand example.
The reason liquidity matters so much, according to Daniel Calugar, is that there is a direct correlation between liquidity and risk. The more liquid a market is, the less inherently risky it is. The less liquid it is, the more inherently risky it is.
Market Impact and Algorithmic Trading
When assets are traded, their prices are affected. Sometimes, these changes in price are minimal, while other times, they can be quite significant.
No matter how large or small the impact is, it’s generally referred to as market impact.
In basic terms, when assets are purchased, their price goes up. When they are sold, the price goes down. How much it goes up or down, though, depends heavily on how liquid it is. When an asset is considered liquid, a single trade will likely only have minimal impact on its price — depending on the size and scope of the trade, of course.
Because large trades can have a huge market impact, algo trading has sometimes been criticized as causing market volatility. In fact, there have been “flash crashes” that a lot of pundits have blamed on algo trading.
That being said, many trading algorithms are actually designed to minimize market impact and, as a result, market volatility. This is done by breaking large orders down into smaller ones and executing them little by little over a longer period of time.
By executing the overall trade this way, instead of in one large single trade, the market impact is more likely to be reduced. This is important for algo traders because they ultimately want a market that is more stable and predictable. If they’re the ones who are actually causing the unpredictability and volatility, then they could be negatively affecting their own investments.
How Algo Trading Uses Market Impact
In theory, using an advanced order strategy such as the one described above is simple enough to understand. However, there are nuances to it, according to Dan Calugar.
For example, how many smaller orders should the large order be broken down into? Should each smaller order be of equal size? When should the orders be executed, i.e., how much time should pass in between each? Should the algorithm automatically execute each smaller trade regardless of the current price, or should it re-analyze the current price and only execute if it meets the original parameters?
These are all things that are vital to take into consideration. Ultimately, of course, the goal is to maximize profit as much as possible. That’s why the size and timing of the orders are so crucial.
In order for these trades to be executed with the utmost efficiency and effectiveness, complex analyses must be done well in advance of that first trade being executed.
The market microstructure in place here, then, will help algo traders predict what the market impact might be not just after their entire trade is executed but actually while each individual trade is being executed.
There are two reasons for this.
First, knowing the market impact after the entire trade is executed will help to predict where the market as a whole could head and where, specifically, the value of the asset being traded. This helps to guide the investor in deciding the appropriate time — or price — at which to sell the asset.
Second, predicting the market impact of each individual smaller trade will help the investor structure the trade properly to minimize volatility.
Dan Calugar says that larger trades are broken down into smaller ones that are executed over time for the express purpose of minimizing volatility. But, in order to do that, they must be sized and spaced properly. If they aren’t, the market could still experience volatility that could significantly affect the profitability of the trade.
Knowing this will help investors determine whether the larger trade should be broken down into five or 10 smaller trades and whether they should be spaced 10 minutes apart or 20 minutes apart, for instance.
By conducting extensive market microstructure analyses, algo traders can ensure that each of these trades is executed at the same price — or at least at negligible price differences.
About Daniel Calugar
Daniel Calugar is a versatile and experienced investor with a background in computer science, business, and law. He developed a passion for investing while working as a pension lawyer and leveraged his technical capabilities to write computer programs that helped him identify more profitable investment strategies. When Dan Calugar is not working, he enjoys spending time working out, being with friends and family, and volunteering with Angel Flight.