Simple Vs. Complex Trading Strategies: Keep It Simple
Simple vs complex trading strategies have a clear winner: Simple trading strategies trump complex trading strategies. Trading needs to be simple and easy. Our aim is to build and create simple automated trading strategies and trading edges that you can use in your trading. You need just a few variables in your trading strategies. Street smarts trump book smarts in trading. In this article, we explain simple vs complex trading strategies (simple trading vs complex trading).
Simple trading strategies with few variables or parameters trump complex strategies. Complex strategies are more likely to go wrong. Instead, try to create many strategies that complement each other. Less is more in trading!
Key takeaways
- The article Simple Vs. Complex Trading Strategies emphasizes the advantages of employing simple trading strategies over complex ones. It argues that strategies with fewer variables or parameters are generally more effective and less prone to errors compared to their complex counterparts.
- Key takeaways from the article include:
- Simplicity in Trading: Simple trading strategies are easier to implement and less likely to fail. The article advocates for creating numerous straightforward strategies that complement each other, adhering to the principle that “less is more” in trading.
- Risk of Complexity: Complex strategies increase the risk of curve-fitting, where a strategy is overly tailored to historical data and may not perform well in live markets. Simplifying strategies helps mitigate this risk.
- Occam’s Razor in Trading: The article references Occam’s Razor, suggesting that among competing strategies that predict equally well, the simplest one should be selected. This principle supports the preference for simplicity in trading strategies.
- Cognitive Bias Toward Complexity: Traders may have a cognitive bias toward adding complexity, believing it will enhance performance. However, the article suggests that removing unnecessary elements often leads to better solutions in trading.
The power of simple trading strategies
Many believe in complexity.
The more complex and the more advanced, the better. We are used to seeing “advanced course in xyz” in everyday life.
Fortunately, complexity has no place in trading. Finding trading strategies isn’t about how complex things you build but how you can brainstorm many simple ideas. Go for a walk, get ideas, form a hypothesis, backtest it on a computer, and conclude.
Yes, markets are complex, very complex. But (perhaps) counterintuitive, it makes sense to solve complex problems with simplicity.
Do you need exceptional coding skills? No
Do you need computing power? Yes, but not much.
Do you need complex formulas? No.
The power of finding ideas to test comes with experience. Once an experienced trader has the minimum information and capacity necessary to form a hypothesis, adding additional variables generally does not improve its accuracy.
Quite the contrary, additional complexity does, however, lead to overconfidence and curve-fitting. The trader gets more confident in his or her ability to predict, but overconfidence bias is pretty well-documented. See the graph further down in the article.
The focus should be on overcoming your biases, avoiding your weaknesses, and building on your strengths.
In trading, it pays to remove variables, not add
The two researchers hypothesized that there might be a psychological explanation: when faced with a problem, people tend to select solutions that involve adding new elements rather than taking existing components away.
Did you know that removing traffic lights and road signs leads to fewer accidents? Why do we always want to add more complexity to a solution? Why don’t we remove items of information when solving a problem? Why don’t we remove laws and regulations to solve problems? Why do we add legislation instead of removing it?
I recently came across an interesting article in Scientific American written by Diana Kwon called Our Brain Typically Overlooks This Brilliant Problem-Solving Strategy. The article explains how we can remove elements instead of adding and still get a better solution. Removing elements is for many a radical idea, but it shouldn’t be.
This is very relevant for traders and those interested in quantifying problems. Most of us make strategies based on many variables, yet it’s often the few and simple models that turn out to be both better and more reliable. Especially beginners believe they need to find something that is advanced to succeed. But it’s often the opposite. Profitable trading is more about finding simple strategies and following the plan. It doesn’t need to be the best or optimal plan, but the “simplest” plan that you are able to follow and execute.
Why is trading difficult?
Trading is difficult because the markets are complex and depend on an almost unlimited number of factors.
Morgan Housel writes in the brilliant book The Psychology of Money that an engineer can have a successful career knowing not much else than engineering. The same goes for a veterinarian – he or she doesn’t need to know much else.
But in business, trading, and investing it’s not like this. Trading is a little math, psychology, game theory, history etc. Perhaps this doesn’t make it harder, but it certainly makes it much more prone to change and uncertainty. Imagine all the criteria you can put into a trading strategy! It’s endless.
Less is more in trading
Less is more in trading because you need to keep things simple, not complicated, which in most cases leads to curve fitting.
Rory Sutherland wrote in his book Alchemy that bullshit multiplied by bullshit means bullshit squared. He’s right. The easiest and best solution in many cases is to remove information or criteria. A good quant makes things simple.
The chart below explains why Sutherland is right (the chart is taken from Psychology of Intelligence Analysis by Richards Heuer):
Once you have a minimum of information to form a judgment, adding more information doesn’t improve the outcome. Nassim Taleb once wrote that the easiest way to bankrupt a fool is to give him lots of information.
Jim Simons has made a fortune using quantified strategies in the markets, but in the book The Man Who Solved The Market the author argues the strategies are neither novel nor sophisticated. We have covered how Jim Simons trading strategies made 66% a year.
Why do traders make trading complicated?
Traders make trading complicated because traders tend to believe that complexity is more likely work well, when in reality it makes strategies perform worse and more likely to be based on curve fitting.
Most likely, traders have an in-built bias toward complexity. This is what Diana Kwon writes in an article we found on the internet:
While the propensity for businesses and organizations to opt for complexity rather than simplification was previously known, the novelty of this paper is that it shows that people tend toward adding new features, “even when subtracting would clearly be better,” he adds. Meyvis also notes that other reasons for this effect may be a greater likelihood that additive solutions will be appreciated or the so-called sunk-cost bias, in which people continue investing in things for which time, money or effort has already been spent.
Curtis Faith on simple vs complex trading strategies
Many have not heard about Curtis Faith, but he was one of the original “Turtle Traders” in Richard Dennis‘ successful trend-following project back in the 1980s.
Mr. Faith has written one of the best trading books out there: an easy-to-read book about the most important aspects of trading. Here is Curtis’ view on complexity:
It takes a lot of time and study before one realizes just how simple trading is, but it takes many years of failure before most traders come to grips with how hard it can be to keep things simple and not lose sight of the basics (page 115)…..Keep it simple. Simple time tested methods that are well executed will beat fancy complicated methods every time (page 131)……In a similar manner, simple rules make systems more robust because those rules work in a greater variety of circumstances (page 212)…….People have a tendency to believe that complicated ideas are better than simple ones….Some of us thought that trading successfully couldn’t possibly be that simple; that there must be something else to it (page 224).
Complexity increases the risk of curve-fitting:
Complexity increases the risk of curve-fitting. Simple systems are more robust because the rules work in a wider variety of circumstances.
Traders should keep in mind Occam’s Razor
William of Ockham (1285-1347) argues the simplest of two theories should always be preferred to a more complex one. This is something all traders should have in the back of their heads.
2 Reasons Why Less Is More In Trading: Keep It Simple
Most people instinctively add features and variables in most areas of life. If your trading strategy seems to stop working, you instinctively add one or more variables to the equation. But what if the right course of action is to do the opposite, to remove variables(s)? Less is more in trading!
This section gives you 2 reasons why less is more in trading and why you need to keep trading and investing simple. Adding variables typically increase complexity and you risk curve fitting the strategies to the data. Not only in trading but also in everyday life.
Simplicity is beauty and beauty is simplicity, nothing more, nothing less.”
Oscar Wilde
What is complexity?
Complexity is a group of of things that are connected in complicated ways – difficult to analyze, understand, or explain.
This certainly applies to all financial markets. The factors influencing the price of financial assets are endless and even worse, we don’t know the future factors and variables. Complexity compounds – it has exponential growth as you add variables. You have to prepare for the unknown unknowns.
What is simplicity?
Simplicity is defined as uncomplicated, or uncompounded by Merriam-Webster. For example, treat others as you would like them to treat you. That is an amazingly simple rule to live by (yet many totally ignore it). You don’t need a myriad of legislation when you need one rule (albeit as a naive example).
Why traders are inclined to add variables to a strategy and thus increase complexity: complexity bias
Traders are inclined to add variables to a strategy because they believe a more complex strategy will perform better than a simple one. However, a complex strategy is much more likely to be curve fitted.
What would you like to do more of? This is a frequent question most people ask themselves. But have you ever tried to ask yourself what you should do less of? You automatically have free time for other endeavors if you do less of one thing.
The natural inclination in most aspects of life is to add complexity. When facing a problem, we tend to choose the most complex solutions – utterly opposite of Occam’s Razor (which states that of two competing theories, the simpler explanation is to be preferred).
Why do we add complexity?
First, complexity sells better. Who wants to buy a trading strategy with two variables when you can have one with eight? Presumably, you get more for your money (keep reading, and you’ll understand this fallacy).
Second, when things are simple, they become dull. Many trade for the excitement, not money (believe it or not).
Third, the more we know about a subject, the more complexity we prefer. Complexity is a moving goalpost that is constantly changing.
Fourth, hardly anyone wants to hear simple solutions. The more complexity we add, the better it sounds.
Adding increases complexity
Think about the legislation: the length and number of bills grow longer every day, and our lives and societies become more complex every day.
But all legislation has unintended consequences. As you add complexity, you increase the risk of bigger systematic risks. Just think about the EU-wide consequences of the phase-out of all German nuclear power by 2022. Because of the centralized grid, it has created ripple effects all over Europe.
As of writing, Facebook is down for the 7th hour. This has major consequences for small businesses that rely on their services. Facebook might be perfect for your business (disclosure: we own shares in Facebook), but a centralized system is vulnerable to systematic risk. It increases complexity as the equation gets bigger and bigger.
This is no different in trading. We add features, variables, and computer power, believing that this will set us apart from the competition.
But less is more in trading. Instead of adding more, most traders are probably better off by subtracting.
Why it’s tempting to add variables in trading
It’s tempting to add variables in trading because of the endless noise and opinions in the markets. When we have unlimited information, it’s tempting to look for patterns and explanations when there are none. We look for solutions and strategies that are more complex than it needs to be.
Let’s look at 2 simple reasons why less is more in trading:
Why less is more in trading: Reason no. 1
If you’re a systematic trader you are always at the risk of curve fitting your strategies. If you added one more variable, you would have reduced the drawdown from 25% to 10% in 2008…..
However, in trading, there is one rule that should always be in the back of your head:
The more filters and variables you add, the more your risk curve fits your results.
The instinct is to believe that more complexity and logic add value. All of our monthly Trading Edges are not particularly advanced, but they do work (and have done so for a long time, albeit this is no guarantee it will continue to do so).
Many traders think that a strategy should be complex. The more, the better, they believe. The backtest might be better, but it’s unlikely to hold in an out-of-sample backtest.
If you add many variables, a tiny change in one of them influences your result. It’s multiplicative. If you have 5 variables, only one change in one can make your strategy worthless.
Why less is more in trading: Reason no. 2
When you add a variable or filter, you remove trades from your backtest, discarding some and keeping others.
What does this mean? In practice, you are fitting your variables to the dataset. By all means, this could be smart, but in most cases, it’s not. You really have to know what you’re doing the more variables you add.
When you form a hypothesis and start backtesting, you don’t know if your thinking is correct. Thus, you should always start with the simplest ones and only later add variables by scrutinizing the trades removed from the backtest.
Why do traders increase complexity?
Traders tend to increase complexity because of a psychological bias: they prefer complexity, according to research. The more complex, the more impressive.
People and traders tend to choose solutions that involve adding new elements rather than removing existing components. This phenomenon is applicable not only in trading but also in various problem-solving scenarios.
The more complex – the more impressive. But think about the Turtle Trader project by Richard Dennis in the 1980s. None of the strategies are for rocket scientists. The strategies are very simple, yet we tend to overcomplicate things by adding variables.
We tend to add variables when things are not going as planned. “If we only add this variable.” But you need to be very careful to avoid this mindset. Of course, some changes might be warranted, but you need a rational reason for changing a strategy. And above all, you need to think long term.
Removing Elements for Better Solutions
Removing elements, a seemingly radical idea for many, can lead to better solutions. This principle is highly relevant for traders and those interested in quantifying problems.
The most effective trading strategies often rely on a few simple models, debunking the misconception that success requires complexity.
The Wisdom of Simplicity
In trading, as Rory Sutherland emphasizes in his excellent book called Alchemy, simplicity is key. Multiplying complexity often results in compounded problems. We tend to get better confidence the more information we have, but this is often flawed.
Simple Vs. Complex Trading Strategies – Conclusion
In the world of trading, simplicity is supreme. Seasoned traders advocate for simple, time-tested methods over intricate and complicated strategies.
The goal is not to add complexity but to remove complexity: to strip away unnecessary variables, creating robust and reliable trading strategies. As the age-old saying goes, “Keep it simple, and success will follow.”
We wrote earlier about why programmers and coders are bad traders. Simplicity in trading requires common sense and being street smart – not book smart. One tool is handy in that regard: remove variables from the equation.
When we look to improve a trading problem, adding more variables is natural. However, that is often the wrong approach. By subtracting or removing variables you can improve the result and at the same time reduce the risk of curve fitting. Simple trading strategies beat complex strategies. Always keep this in mind when you are evaluating simple vs complex trading strategies. Many sub-optimal strategies that might not be the best on their own, can increase the return of a portfolio of strategies.
FAQ:
What is the key principle when choosing between simple and complex trading strategies?
The key principle when choosing between simple and complex trading strategies is that simple trading strategies with few variables or parameters tend to outperform complex strategies. The focus is building and creating simple automated trading strategies and trading edges with just a few variables.
Why do simple trading strategies trump complex ones in the world of trading?
Simple trading strategies trump complex ones because they are more likely to be effective and less prone to going wrong. Complexity in trading can lead to overconfidence, curve-fitting, and increased risk. The aim is to create many simple strategies that complement each other, adhering to the principle that less is more in trading.
What is the power of simple trading strategies, and how do they contribute to success in trading?
Despite the complexity of markets, simple trading strategies offer a powerful approach. The emphasis is on brainstorming simple ideas, forming hypotheses, and backtesting them. The experience is key to finding effective and reliable strategies, and additional complexity often leads to overconfidence and curve-fitting.
Why is simplicity important in trading?
Simplicity is important in trading to avoid unnecessary complexity and the risk of curve-fitting strategies to historical data. The article suggests that adding variables increases complexity, potentially leading to less effective strategies.
Why might traders be tempted to add complexity to their strategies?
Traders may be tempted to add complexity because it tends to sell better, creating a perception that more variables and features offer better value. Additionally, complexity can be seen as more interesting and is often preferred when traders have more knowledge about a subject.
What is the recommended approach when forming a hypothesis and backtesting a trading strategy?
When forming a hypothesis and backtesting a trading strategy, it’s recommended to start with simple variables and gradually add complexity, scrutinizing the impact on removed trades from the backtest to ensure a more informed and cautious approach.
Why are simple trading strategies considered better than complex ones?
Simple trading strategies are preferred because they have fewer variables, reducing the chances of errors and curve fitting. Complexity often leads to overfitting datasets, making strategies less adaptable to changing market conditions.
How does experience play a role in the success of simple trading strategies?
Trading success is not about the complexity of strategies but about generating and testing simple ideas. Experience plays a crucial role in building and implementing simple automated trading strategies. Seasoned traders understand that solving complex market problems requires simplicity, not intricate formulas or exceptional coding skills.
What is the psychological explanation for the preference for complexity in trading?
Research suggests a psychological tendency to choose solutions that involve adding new elements rather than removing existing components. Traders may overcomplicate strategies by adding variables when facing challenges. The key is to overcome biases, avoid weaknesses.
Why is trading difficult?
Trading is difficult because it demands a diverse skill set, unlike careers in engineering or veterinary science, for example. Trading involves elements of math, psychology, game theory, history, and more.
The complexity arises from the multitude of criteria that can be incorporated into a trading strategy, making it highly susceptible to change and uncertainty. But again, the more variables you put in, the less likely is the strategy to work in the future.
Why does experience trumps complexity in trading?
Experience trumps complexity in trading because seasoned traders understand that solving complex market problems requires simplicity, not intricate formulas or exceptional coding skills.
Contrary to common belief, trading success is not about the complexity of strategies.
Instead, it’s about generating and testing simple ideas. Experience plays a crucial role in this process. Building trading strategies involves some element of curve fitting, so you need some experience. Backtesting is very much a trial-and-error task where you build experience. This is a long-term process – we are talking many years. Rome was not built in a year, which applies very much to trading.