What Are Quantified Strategies? – [With Examples]

Last Updated on November 25, 2020 by Oddmund Groette

This website is all about quantified strategies, often called algorithmic strategies. But what exactly are quantified strategies?

Quantitative/quantified strategies defined:

Quantitative strategies are strategies based on finding inefficiencies in financial markets based on numbers, math and statistics. This is done by studying historical data from the past, mostly time series of the price of financial instruments, and the aim is to detect patterns and relationships that are unlikely to occur by chance. This is called backtesting. The rules of the trading strategy are 100% quantified and thus based on the scientific method. The rules of the strategy are strict and not open to discretionary judgments. The trading signals are mostly passed on to a trading software/platform that executes the trading automatically, also called a trading robot, but you can of course enter buy and sell orders manually.

Other labels for quantitative strategies are algorithmic strategies or “quant” strategies. Basically, all labels involve the same elements in their methods.  A “quant” is simply a trader that is relying on quantified strategies and automatic trading.

The aim of this article is to explain the basic concepts of a quantitative trading strategy and how you develop a trading system. We at Quantified Strategies have build trading strategies based on our own hypothesis for close to twenty years. The strategies are developed on strict mechanical rules that have been successful in past data, and which we believe offer profitable opportunities in the future.

The development of quantitative strategies is not something new but has risen with steady improvements in computer power. Even Benjamin Graham, the founder of value investing, to some extent used strict quantitative models to find undervalued securities. Since then the advent of computers has made Graham’s methods almost useless because computer power finds those mispricings in seconds. Sooner or later, most strategies get “arbed” into oblivion. Any quantified strategy that gets too popular is destined for the graveyard at some point in the future.

You can make your trading strategies complex or simple. We believe in simplicity. However, it takes a significant amount of time to develop the skills to both test and trade strategies. Patience is required. This is a skill like all other skills required for a job, and just like no one expects a recruit to become a carpenter overnight, you can’t become a trader overnight. You can expect years of trial and error to really get good at what you are doing.

Some elements of a quantified strategy:

So what exactly do you need to do to develop a quantified strategy? Below is a list of what is most relevant to look at:

  1. Make sure you have many hypotheses/ideas to test at all times. Brainstorm and write down ideas regularly.
  2. Which markets are you going to trade? Why do you want to trade this market?
  3. Which timeframe should you trade? Scalping, daytrading, swing-trading (1-10 days), or perhaps monthly trading? You decide, but it normally pays off to be an investment agnostic.
  4. Find variables to test for each idea you want to test. When you formed your hypothesis, most likely you had some variables in mind. Make sure you don’t curve fit your strategy with many variables. The importance of simplicity can’t be stressed enough.
  5. Test different ways to enter a trade: enter at close? Enter at open? Enter at market? Enter with a limit order?
  6. Likewise, when do you exit the trade? Are you using a stop-loss or target? Should you exit at close or on open?
  7. How many shares/contracts per trade? Quant traders like to call this risk-analysis or money management. This is a pretty wide topic in itself. For example, two stocks trading at 50 dollars might have completely different volatility and thus risk. A 1 000 share position in a low volatility stock, might be the same as a 200 share position in a high volatility stock.
  8. Does the strategy add correlation? Does its trading signals overlap with your other strategies? Does it add significant value to your profits? Usually, it’s a good idea to trade several markets to get diversification and potentially smooth your overall returns. The idea is that losses in one strategy are offset by gains in another quantified strategy. You aim for little correlation between your quantified strategies and it’s absolutely crucial to investigate how a strategy performs alongside your other strategies. The lack of correlation among the trading strategies is very underappreciated! It’s more likely that you’ll perform better the more systems/stocks you trade. Many “suboptimal” strategies are much better than one “best” strategy. The reason is diversification and because many strategies go through periods of mediocre profits, even losses, and most strategies simply stop working after some time.
  9. Last but not least: whenever you develop a quantified strategy, make sure you test “out of sample”. Out of sample simply means you test your strategy on future unknown data. It’s important you commit to this procedure because of the risk of curve-fitting your strategy to the past data. The best way to do this is to trade with “paper money” in a virtual account for several months before you go live.

Example of a quantitative strategy:

To let you get a better grasp of what a quantitative strategy is, we can use one of our strategies published several years ago. This is a simple trading strategy, but simplicity is the way to go. The quantified strategy has four rules and is meant to be traded on Nasdaq futures or the ETF with ticker code QQQ, but it works on many other stock indices as well:

  1. RSI(2) must be lower than 10.
  2. Internal bar strength (IBS) must be lower than 0.2 (read here for an explanation of IBS).
  3. If both 1 and 2 are true, then enter at the close.
  4. Exit at the close when today’s close is higher than yesterday’s high.

Despite its simplicity, the strategy has performed remarkably well for over two decades in both bull and bear markets:

RSI(2) and IBS have performed well in both bull and bear markets.

The strategy returned almost 14% annually, compared to only 7.9% for “buy and hold”.

Advantages with quantified strategies:

One of the main obstacles to generate acceptable returns is yourself. Anyone who has traded can agree that we easily fall prey to behavioral mistakes, for example selling in a panic and buying when markets are euphoric. Quantified strategies are per definition mechanical and not open to judgment from you, and thus it doesn’t let you form any opinions on where the market is heading. You can simply focus on just executing the strategy. The temptation to twist your strategies lurks on every corner, but hopefully, a quantified strategy can help you limit this risk.

When a new trading day starts, you simply initiate your programs and let them run. It enables you to overcome greed, fear and frustration. You simply take the emotions out of the trade. Of course, this requires that you have a good deal of faith in the system. But if you are confident it makes money in the long run, it should be relatively easy to implement. Consistency is important in order to have faith in your systems. Our experience indicates that the traders succeeding in the long-run have quantified rules when trading.

Another advantage of quantified rules is that you can regularly do analysis to determine what works and what don’t. It’s also a lot easier to trade many more strategies at the same time. Done correctly, this smooths your equity curve. You simply take advantage of the law of big numbers and at the same time develop a mindset of thinking in terms of probabilities. When thinking in probabilities, you better grasp the concept of the law of big numbers. Over a large sample of trades, where one trade is very uncertain, the variability of the end result can be drastically reduced if you have many trades and strategies.

By quantifying and automating your strategies you free up time to explore and research other strategies. By enabling a trading platform (see below) you can practically trade as many strategies as you like, something which of course is impossible if you enter trades manually. You can even have a full-time job and let your strategies run in the background, or only at certain intervals, for example at the open or the close. By programming, you can pretty easily automate all these procedures. Time is money!

Trading software:

How can you trade many quantified strategies? A quant trader uses a computer to execute his or her strategies live in the market. This is best done via a trading platform like for example Amibroker, Tradestation, Multicharts, etc. We in Quantified Strategies use Amibroker and Tradestation:

All trading platforms let you connect to a broker to allow for live trading.

However, don’t underrate the classical spreadsheet. Most traders have at least some basic knowledge of how to use a spreadsheet, and this is a great way to start. You can for example code a simple script in Visual Basic to generate orders from different cells in a spreadsheet. But to trade multiple strategies, it’s recommended that you get some basic understanding of a trading platform.

The biggest restraint is not computational power, but more likely limits on the programming abilities of the trader, or how to generate ideas to test. As a quant-trader, you are dependent on having ideas to test and trade. In order to generate ideas to test we recommend you start live trading. It’s when you trade real money you force yourself to think, and it forces you to pay attention. As always: trade small and within your comfort zone. Testing strategies are called backtesting:

Backtesting:

You can perform backtesting in either Excel or a trading platform, like for example Amibroker, as already mentioned. Backtesting involves testing the hypothesis on historical data, but the most important aspect of testing is out of sample testing. Out of sample is when you test the strategy on data not included in the backtest. You can do this by splitting your dataset into two parts, one for creating the strategy, and one part where you test the strategy out of sample. Even better is to test the strategy on live data for several months on a day to day basis.

Successful algorithmic trading involves alpha:

One of the main goals of any quantified trader or investor is to generate alpha. Alpha is simply the return you get when beating “the market” or a relevant benchmark. You must, as an individual trader, compare the relevant investment opportunities you have. The opportunity cost should be your benchmark. An option is for example to invest in mutual funds, which historically has risen about 6-7% annually in real terms. Thus, your aim as a trader could for example be 10% annually before taxes. However, you have to factor in costs. Trading involves time and stress. Long-term investing (in most cases) defer taxes until you realize the gains, while trading involves annual taxes. In the long-run, taxes are a headwind to consider.

There is no denying that alpha is difficult to generate. Market participants compete with each other, it’s a battleground. The market is a zero-sum competition in relation to an index, and only a small group of participants can generate excess return (alpha) above the index. The traders or funds who beat the index typically vary over longer time periods. Very few manage to beat the market over long periods of time.

Nothing lasts forever:

No strategy lasts forever. Something that gets too popular will eventually get “arbed” away. Any strategy that looks good on paper and in backtesting, might turn out to be a trap in the real world. Why is that? That is because traders often curve fit their data to fit the past, and that is of course unlikely to replicate in the future.

Both the world and markets change and evolve. How can you factor in that terrorists hijack planes and fly them into skyscrapers? How about the Covid-19? The fact is that markets are quite random and the focus changes from year to year. Any aspiring trader is recommended to read the books by Nassim Nicholas Taleb to better understand this.

Yet another issue is the natural market cycles of the markets. Any super-performing strategy in the past could happen just as well because of the ebb and flow of the markets. Any profitable algorithmic trading system could produce profits due to a bull market, while it’s unlikely to perform well in a bear market. The anatomy of bull and bear markets are usually significantly different from each other, something we will get back to in later articles.

Conclusion:

Quantified strategies are trading signals that are 100% mechanical and without judgment from your side. We believe this can help a lot of aspiring traders because most traders fail due to a lack of a plan. Quantitative strategies are mechanical rules based on past data that help you eliminate behavioral mistakes, let you trade multiple strategies to reduce correlation, and force you to think in terms of probabilities. These are important aspects of successful trading and help you find niches in the marketplace.

 

Disclosure: We are not financial advisors. Please do your own due diligence and investment research or consult a financial professional. All articles are our opinion – they are not suggestions to buy or sell any securities.