High Frequency Trading (HFT) has continued to increase in the last few years. In fact, old hedge funds which previously focused on traditional methods of trading are now establishing their own quantitative trading divisions.
This is because most of the trading occurring today is done through quantitative robots. One of the leading hedge funds in the market is Citadel which is run by Ken Griffin. On a daily basis, the transactional arm of the hedge fund transacts more than $1 billion a few minutes after the market opens.
Another influential hedge fund managers in this industry is James Simmons who runs a hedge fund called Renaissance Technologies. These hedge funds have only a few employees who mostly are not financial professionals. They are either mathematicians or computer scientists whose work is to create algorithms to execute trades.
What is Quantitative Trading?
Quantitative trading is a day trading approach that involves using mathematical models to find trading opportunities. The idea is that several models, when carefully done, can help you predict the future. All day traders can use this approach today.
Those traders who are also excellent in mathematical modelling and coding can build their codes from scratch. At the same time, those who don’t have this knowledge can easily buy already-built robots in online marketplaces. One of the best-known marketplaces is the one run by MQL, which owns the popular MetaTrader 4 and 5.
Why quantitative trading is the future of trading and why you should learn it
#1 – Barriers Removed
In the past, to create your own robot, you needed to have a background in computer science or in software development.
This is because one needed to take time and develop the code which will execute trades. This prevented most people from developing these applications because not many financial professionals have experience in coding.
Today, most online brokers have developed platforms to help people with no coding experience to develop their robots. They have drag and drop tools and instructions which enables them to create robots within minutes.
#2 – Knowledgebase Available
In the past, to learn about quantitative trading, one needed to go to school and learn about coding. This was a major barrier to entry because many people saw no need for this training.
Today, traders have access to information on how to create trading bots. This information is available in various quant trading tutorials and videos which guide people on how to develop these codes.
There are also many online videos that guide people to develop the robots. In the past, this information was not available.
#3 – The Big Thing Now
As mentioned in the introduction, most hedge funds are now turning to automated trading. Most hedge funds are now experiencing a period of low growth and increased outflows.
On the other hand, automated hedge funds such as Betterment are experiencing a period of growth. Therefore, as the trend and the returns continues to grow, chances are that most people will focus on this new trend.
#4 – A Simple Process
Before you start practicing algorithmic trading, chances are that you feel that it is a difficult process. However, as you become more acquainted to the system, you will realize that it’s a simple process.
Once you have mastered the art and science of combining various indicators you will have a better time trading. Remember that the key to successful algo trading is to create a good system and backtest it for a period of time.
If you prove without any reasonable doubt that your system is good, then you will have an easy process of trading.
#5 – It Works
The last reason why algorithmic trading is the future is that it is an accurate method. The best way to look at this is to compare hedge funds that use the systems and compare it with those that don’t.
In the 2008 financial crisis, while most hedge funds closed shop, James Simmon’s firm reported its best year s far with an 80% return.
The fund has also never had any negative years. This means that when well-executed, algorithmic trading works. The key is to develop a good system and then backtest for a good period of time.
Why data matters
In quant trading, data is one of the most important parameter that must be gotten right. In fact, it has been argued that data is the backbone of any quantitative trading system. It’s the engine that powers any system. If a single digit or decimal point is left out when developing the system, chances of losing your trades are very high.
Price data and fundamental data
There are two main types of data when developing algorithms. These are: price data and fundamental data.
Price data includes a number of parameters such as the price of the asset, trading volumes of assets, size of the trade, and the information derived from transactions among others. In simple terms, price data refers to the entire order book which shows a continuous series of all bids and offers of an asset.
On the other hand, fundamental data are more complicated and refer to a number of data types that are difficult to categorize. They refer to any other data that is entered that is not related to the price of asset. Some of the good types of fundamental data are: price to book ratio, financial performance, and sentiment among others.
Macroeconomic data such as inflation and interest rates can also be said to be fundamental data.
Understand the data
To know how to use the data, one needs to understand where to get the data from. In quant trading and high frequency trading, the accuracy of the data must be accompanied by the timely delivery of the data. A microsecond in the financial market can mean huge losses.
There are many sources of data which include: regulators (filings relating to large owners), government agencies (mostly for fundamental data), news agencies (such as Bloomberg), proprietary data vendors (such as Markit), and corporations.
After getting the data, a common problem faced by many quantitative traders is on cleaning the data. This is a common problem that has led to the downfall of many quant traders. A common problem with quants is missing data especially where the data is not supplied at the given time by the data supplier.
This can be solved by building a system that understands when the data is missing. This system will not take irrational decisions that can lead to significant losses.
Another problem is what we call look-ahead bias. This is when you assume that you could have known something before it was possible to know it. As stated before, data is the machine that moves quant systems.
Hedge funds such as Renaissance technologies and Citadel have for years made more than 20% returns using quantitative systems. The LTCM mentioned above is a good example of what not to do when using quant systems. The fund almost lost 100% of its capital as a result of poor data sets combinations.
Therefore, you should carefully take your time when developing your system. You should back test and forward test the system to ensure that everything is right.
How to quantitative trade
There are several approaches to quantitative trading. But at the core, QT is just an automated method the manual trading. For example, if you use double moving averages to identify buying and selling opportunities, you can create a robot that will implement that when you are not around.
First, you need to have a trading strategy in mind. For example, if you are a scalper, you can find a quantitative robot that focuses on the scalping strategy. Similarly, if you are a trend follower, you can find a robot that is designed to follow this strategy.
Second, if you are a developer, you should focus on your strategy to build an algorithmic robot that is based on the strategy you have tried and tested over the years.
Finally, another approach is to buy a robot online, as we have described above. However, you should be extremely careful about buying robots online. For one, you should always take a free trial before you commit to spending money with a robot. Also, you should test the robot to see whether it works.
Quantitative trading is a relatively new approaches to the financial market. Indeed, the volume of trades executed algorithmically has increased substantially over the years. In fact, trillions of dollars-worth of trades are executed algorithmically every day.
Fortunately, anyone can use the strategy either by building his own algorithm or by buying an already-made product.