There is good reason for all the hype about quantitative trading. It’s because quantitative trading strategies effectively combine historical market data with precise mathematical formulas.
Basically, by analyzing historical data of a market, quantitative investors are able to build formulas that have proven to be effective at accurately predicting future market movement.
Measurements that quantitative trading analysis take into account when building their program strategies are things such as the Relative Strength Index, the Sharpe Ratio and the Average True Range (ATR).
But hold up. No, this is NOT the Holy Grail of market strategies, and YES there are limitations.
However, there is a great amount of success in quantitative trading. This is because it relies on the thorough development of specific formulas. And by applying these formulas to automated algorithms, it carefully avoids the psychological defects of being human.
What’s the catch?
The catch is that quantitative trading is a full-time job. So if you are just looking to improve your personal investment strategies, then the easiest way to take advantage of quantitative trading is to talk to your favourite brokerage firm. They undoubtedly use quantitative trading strategies.
Still, the complexity of quantitative trading should not deter you from wanting to know more about how it works. There is certainly no harm in a better understanding of what goes on behind the curtain of your investments.
In this article we go through the fundamentals of quantitative trading:
- Necessary Skills
- The Wizards Behind the Data
- Algorithms and High-Frequency Trading
- The Major Ingredients of Quantitative trading
- Quantitative strategies
- Kinds of Trading
- Concepts for Quantitative trading
- Quality Data for Backtesting Needs
- Research Hot Spots
- Who are the Quant Traders?
Quant Trading in 2 Minutes
- The most simple explanation of quantitative trading is: It’s a trading strategy that applies mathematical formulas to automated trading. This requires significant research and backtested data to predict profitable opportunities in the market.
- Investors who effectively implemented quantitative trading rely on massive amounts of data, which can involve multiple markets and securities. This is only possible with the combination of mathematical formulas and data analysis of historical market data.
- The great advantage of quant trading is the use of backtesting, which develops effective market predictions and then automates trading.This process human error and emotional trading.
- The challenge of quantitative trading is in order to develop successful algorithmic models which takes a lot of work. Also, once the market changes the model must also change so that out of date models are not in use. That means constantly updating your models.
- To be a successful quant trader you need to be a skilled mathematician. If numbers are not your thing then this is not the gig for you! This is a niche job that requires multiple degrees in math or engineering or both. Many analysts hold graduate degrees and Ph.Ds in a related field.
- Quantitative trading is a competitive field because it requires a combination of advanced degrees and the ability to work long hours in a stressful environment. These are highly coveted positions. Naturally, successful performers are very well compensated.
Example of Quantitative Trading
Quantitative trading algorithms are customized to appraise the different parameters of multiple stocks and markets. If the trader is keen on momentum (or trend) investing, then they may make a program that follows winning stocks.
A program like this simply follows the upward movement of the market. So that when the market is up, then the program buys the asset.
However, it is more realistic to include multiple strategies in quantitative trading. And even if the program is just using momentum, it must follow multiple markets to be effective. It also needs to be adjusted regularly to reflect real-world outcomes.
The Necessary Skills of a Quant Trader
Market insight and originality:
- Quantitative traders must build unique trading strategies based on their own research and statistical knowledge. The reason you cannot simply download some software onto your computer is that these are valuable strategies that traders charge for.
- If the quant trader is merely relying on canned models things will end badly.
- This is because the programs need to respond to real markets, which means that they need to updated and revised constantly.
- Because the maintenance of these strategies requires a lot of work, quant traders are not giving their work away for free. So, to take full advantage of these strategies you will need to work for a brokerage or hedge fund.
Programming skills and language savvy:
- Quant traders are very comfortable with data mining, research, analysis, and automated trading systems. That typically also means applying high-frequency trading or algorithmic trading.
- Extensive programming knowledge is key.
Computer usage:
- Quantitative traders rely on data feeds with prices and quotes in order to develop unique, competitive algorithms. So they rely on charting and analysis software and orders are placed with a broker platform.
The Wizards Behind the Math
Quantitative traders also called “quant traders”, need to be skilled at multiple tasks related to trading. A good trader needs to be able to:
- Know how to mine quality market research to access price and quote data.
- Know how to identify profitable trading opportunities.
- Understands how assets are valued so they can identify new players.
- Create suitable trading strategies, tailored for the traded markets.
- Have access to high computational power in order for their formulas to be competitive.
- Must be a mathematician, economist, computer wiz with seasoned insight into the markets to be original and relevant.
Quantitative mathematicians are looking for are things like:
- A decrease in value, and the potential environmental causes.
- An increase in value, as well as the potential causes.
- Patterns of trade, both for when the asset was bought and sold.
- Any other information that seems pertinent to the specific market.
Quantitative finance and trading use applied mathematical and statistical methods to different markets. The reason this strategy is effective is that it uses historical market data to find market patterns. These patterns are based on the analysis of the market’s prices.
Using this data they can find averages and means of the value of the asset. And, because they are considering mass data, they must work out the unique conditions of the target market into their formulas. That means correcting for biases and eliminating dead stocks.
As with any other strategy, there is going to be a certain amount of success and failure. The idea behind any trading strategy is to find useful models to understand the movement of different markets. The formula is useful because it allows investors to make strategic decisions about price movement.
The formulas are applied to future markets. These formulas are used to place many kinds of market and buy or sell orders. These strategies are applied to several kinds of trading. That includes large firms and hedge funds, day-traders, and high-frequency trading.
Algorithms and High-Frequency Trading
Quantitative trading has been around for a long while. Economists and mathematicians have used data to predict financial outcomes for the last 100 years.
However, quantitative trading has really made head-away in the digital age. It is because of the computing power currently available which has made algorithmic trading and high-frequency trading thrive.
All of these strategies rely on historical market data. Then the information is carefully mined for profitable investment opportunities.
Another reason that this is not a part-time job, is that it requires a significant investment in research in order to build functioning algorithms. But there are no pre-made strategies for the rookie. Essentially, a quantitative trader combines in-depth mathematics knowledge, practical trading exposure, and computer skills to custom make their own programs.
The Major Ingredients of Quantitative trading
Successful quantitative traders are skilled computer programmers. To create functioning models, rely on programming knowledge of MATLAB, R, Python or C/C++.
Quantitative traders rely on the success of their formulas and algorithms. And that means a high competency when it comes to programming languages. When relying on automated systems, the code executes buys and sells. That means that accuracy is absolutely crucial!
At this point, you may still wonder: what kind of necessary information goes into designing a successful program?
Here are a few of the fundamental elements that go into building a program:
- Identify a Strategy
- This includes, what the trading frequency will be, what statistical models to implement, what kind of data to use for the model. All of this needs to be collected before the next step can begin.
- Strategy Backtesting
- Backtesting is the backbone of any functioning automated trade. Backtesting uses historical market information to make accurate future predictions.The data collected from backtesting is the practice round, to see how the algorithms play out in the real market. This practice round is key!Predictions will be more accurate with more data, so the longer the asset or market has been around, the more information there is to apply intelligently. Intelligent decision making includes: obtaining a wholistic data set, analyzing the strength of the strategy’s performance. And looking for and eliminating biases that will affect the accuracy of the results. Backtesting is CRAZY important! I will go into a little more detail later on.
Execution Systems
The execution system is the actual filling of the trade orders. This is done by the broker. Even with automated trades, there are still manual tasks for the broker. Low-frequency-trades still require manual practices. While high-frequency-trades are fully automated, as they take place in fractions of seconds; way too fast for any human.
Both will need the support and infrastructure of a brokerage firm. Especially HFT, these are incredibly efficient, which means they require incredibly expensive computational power. HFT is essentially a competition of speed.
The success of quantitative formulas will then depend on the following: the quality of the algorithm, the speed of the automation, and avoiding transaction costs and slippage.
Requisite Strategies
- Mean-reverting strategy
- A mean analyzes both the long-term mean (average) of an asset’s price. Included in the analysis is the short-term deviation from the average price. Mean-reverting then uses the spread (or difference) between two similar assets to determine when the asset will revert to its mean.
- Momentum strategy
- This is basically “trend” following. By taking into account current market trends, the investor exploits both the momentum of the asset, and the investor psychology that will continue this upward trend. It is also referred to as “hitching a ride.”
Kinds of Trading that use Quant Trading
- Low-frequency trading: This refers to any strategy that holds assets for longer than a trading day.
- High-frequency trading: This refers to a strategy that holds assets for no more than one trading day.
- Ultra-high frequency trading: Assets are held for seconds or milliseconds. This kind of trade only works completely automated.
Concepts for Quant Trading
Relative Strength Index (RSI)
RSI classifies the momentum of price movements. That means that it charts the historical strength and weakness of an asset or market. The prices used are from recent trading periods.
A stock it a higher RSI is the result of positive changes in price. While a lower RSI is indicative of more negative movement of the asset.
Most often RSI is determined based with a 14-day timeframe. The scale is a measurement from 0 to 100. High levels are marked at 70, while low levels sit at 30. Extremes are considered in 80/90s and 10/20s. Lower levels are typically less frequent.
Moreover, RSI measures the speed and size of price movements. The speed, or momentum, is determined by the rate of peaks and drops in price. It also compares higher closes of sales to lower closes.
Sharpe Ratio
The Sharpe ratio is the reward-to-variability ratio or the reward v. risk ratio. It is named after William F. Sharpe, the economist who developed it.
That means that it measures the return on risk. It is generally believed that higher risk stocks have a greater potential return. The Sharpe ratio measures such a return.
This information is gathered using the performance of a high-risk investment compared to a risk-free asset. Then, the difference between the two is divided by the volatility of the high-risk asset.
Average True Range (ATR)
ATR is a measurement of the degree of price volatility. The average true range is the average of the volatility over at least a 14-period time-frame. It is not a measure of a price, rather it measures volatility only.
The true range is determined by the largest of the following data points:
- The most recent period’s high, minus the most recent period’s low
- The absolute value of the most recent period’s high minus the previous close
- And the absolute value of the most recent period’s low minus the previous close
Quality Data for Backtesting
Backtesting is central to quantitative trading. This accuracy of the program will only improve with increased data sets, that are derived from quality data.
These are two crucial points: large data sets, and quality data.
Along with powerful math programming skills, these two things will make or break an algorithm. Even with the abundance of information available, it is still a challenge to find good quality data.
Maybe if you are a bit of a nerd and want to work through some of your own findings. Or maybe you want to incorporate some quality information into your trading bot. Then follow the links below and get started on your research.
Research Hot Spots
Here is a small list of places to begin looking for strategy ideas from: Beginner’s Guide to Quantitative Trading
- arXiv Quantitative Finance – arxiv.org/archive/q-fin
- Seeking Alpha – www.seekingalpha.com
- Elite Trader – www.elitetrader.com
- Nuclear Phynance – www.nuclearphynance.com
- Quantivity – quantivity.wordpress.com
Quality Data
Once good data is selected, it is necessary to filter out certain spikes. These spikes are going to affect the outcome of the prediction. This is important and difficult work. And this may or may not be able to account for those infamous “Black Swans.”
Another important tool is “survivorship bias.” This filters out dead markets that are no longer active. That means that their data is not included in the analysis.
This is important, as only active markets get included. That means the dataset is more accurate when working in the real world.
Executive Break Down for Quant Traders
- Quantitative trading is a niche skill, which requires excellent math skills and knowledge of the markets. These traders design original strategies using economic models. These strategies are constantly revised to remain accurate and effective. There is no auto-pilot.
- Most quant traders also work with algorithmic trading and high-frequency trading. That means that while humans are designing programs, computers are responsible for heavy computational work. They are also responsible for executing buy and sell orders at competitive speeds. And to remain competitive, speed is everything.
- Quantitative strategies rely on applied economic theorems and savvy of the real world. If the strategies that measure volatility and means are not applied to real-world data then they are not relevant. Only relevant strategies can be successful.
- More and more trading is being automated. The primary reasons are accuracy and speed. Automated trading use algorithms derived from data. But the actual trade is executed by a computer program. Doing so leaves less room for human error or the pitfalls of human emotion.
- Backtesting is the backbone of quantitative trading. Quality strategies are developed with data from historical market performance. The better and more accurate the information is the better the chance for the trader’s success. This means knowing how to remove biases, sort through dead data, and incorporate effective time periods. Once strategies are devised, they are backtested to ensure they are ready to be deployed in the real world. If there is little data for an asset, accurate backtesting will be a challenge.
Who are the Quant Traders?
As an individual investor, you are most likely going to benefit from the unique skills of professional quantitative traders, rather than get into quant traders for yourself. As I mentioned earlier, this is not a part-time gig and requires specialized skills.
And, if you are still interested in becoming a quantitative trader you will need to get a degree in finance or maths. And you will also need to have a very strong competency in multiple programming languages.
Wrap Up
Quantitative trading is a fascinating and complicated field. This article has only sketched an outline of what a quantitative trader does.
To be an effective quantitative trader, an assortment of parameters are necessarily combined and applied. And this includes information gathered from a combination of markets to maximize profits. Trading is dynamic and volatile. That means these qualities need to be taken into consideration. Algorithms that work effectively in a real-world environment know that volatility and change are real considerations.
Volatility and dynamism also mean that once the market has shifted the algorithm must be adjusted. Algorithms always need to include this new information.
Additional resources:
And if you want to nerd out on more trading strategies, then check out Fibonacci Retracement and The Elliot Wave Theory.
The key proposition
A major reason that quantitative trading and algorithmic trading have become so popular, is because they are not humans. When it comes to the market, the ideal is to have rational actors, that are making decisions based on sound data, rather than “gut reactions.”
The key to effective automation is combining human intelligence and artificial intelligence. AI is faster and acts based on the word of the programming code. Computers do not make fear-based decisions.
All that being said, the automation of investment does not give any of us license to be lazy when it comes to savvy investment strategies. Arm yourself with knowledge of the markets and the technology in the driver’s seat. The more you know, the smarter your investment decisions will be.
And when it comes to quantitative trading, I say, leave some things to the experts.
If you'd like to learn more about this topic, and if you're thinking about taking it to the next level, I'd recommend checking out a book called 'Quantitative Trading: How to Build Your Own Algorithmic Trading Business' which is available on Amazon here - https://amzn.to/3DNJU9s
