Simply put, quantitative trading is the practice of using computer analysis of the stock market to make informed decisions about which stocks to buy and sell. A lot of trading on the stock market can revolve around emotional reactions to stocks rising and falling, and while some brokers swear by their intuition or gut feelings, quantitative trading introduces the scientific method to the market. This allows quantitative traders (you might see the term “quants” to refer to traders who practice this method) to create a probability-based system for decision making, using data from systems such as StockOdds.
Quantitative trading – the basics
There is a reason why many of the major stock trading organizations are able to use quantitative trading practices more effectively than home based stock traders: professional stock trading generates huge amounts of historical data that quantitative traders can use to build models from. It still requires a human with knowledge of a wide variety of stock trading strategies to design the computer program that will make past predictions based on past trends, but having a large training set means that the power of the computer analysis can be harnessed to pick out even the smallest patterns. Quantitative traders will also backtest their programs on the data set to make sure the program performs optimally before applying it to the real life market.
To really get an idea of how quantitative trading can help you conquer the stock market, you can think about the way in which meteorologists predict the weather. They have complicated computer simulations that take in as much historical data about the weather conditions; temperature, air speed, atmospheric pressure and so on. The analytical part shows both the conditions and the result which allows the program to make a statistical guess about what weather will come from a certain set of conditions. The same applies to quantitative analysis of the market, and the more information that the computer program has about market conditions, the more accurate it will be.
All In Good Time
As with other stock trading strategies, there are subtle sub groups within the world of quantitative trading and they each have their pros and cons. It’s worth knowing about each of the types so that you can find the best piece of quantitative trading software that matches your trading style. The different strategies are all to do with the timing and length of trades:
- Low frequency trading – this uses historical end of day data rather than the small ticks that happen during the course of a day. Computer analysis of longer term trends and patterns remove the vulnerability to the daily news cycle, but requires much more data and can easily be impacted by sudden volatile changes to the local or global economy. A popular method of low frequency quantitative trading is to use mean reversion data to develop a swing trading strategy for a set class of stocks.
- Medium frequency trading – this is a classic day trader strategy, where the data collected and analyzed happens over the course of minutes and hours. While it allows models to be affected by minor rises and falls, it allows statistical modelling of the fluctuations of the stock market day, where there are high and low frequency periods of trading. The more common models look for stocks that have significant price moves meaning that investors require less capital per trade to turn a profit.
- High frequency trading – when most people here about quantitative trading, they will think about recent scandals involving high frequency trading; the practice of buying and selling shares in a speed that is not achievable by the human mind or by most computer connections. This day trading strategy is best left to the professionals who have direct market access, the fastest computing powers and the most complex algorithms that take advantage of even the smallest price fluctuations. The goal here is to turn a tiny profit over a high volume of trades which will slowly accumulate over the course of a day.
Pros and Cons of Quantitative Trading
Obviously, most at-home traders won’t have the technology or the computing speed to engage in most high frequency trading practices, but there are some benefits of learning how to use statistics in trading stocks. Firstly, it takes the mantra “make a plan and stick to it” and turns it into a firm set of rules to follow throughout the day. Even if you can’t automate your buying and selling, it’s much more relaxing relying on a program that tells you the optimum way to manage your portfolio. Quantitative trading practices also remove the emotions of a good or bad day from your decision making process. It’s easy to get sucked into a spiral of chasing bigger profits or sending good money after bad; statistical analysis of the data will tell you when to stop.
One of the main drawbacks of quantitative trading is that unless you have the time and effort to keep your model fed with current market data, it can quickly become outdated. For example, you could have a medium frequency program that studies day trading data over the previous month for a particular set of stocks. It will make predictions for the best day trading strategies based on this data, but will ignore any new information that could drastically change the value of the company. You would either need to feed daily data and update the model, which is incredibly time consuming for the at-home trader, or develop a new model frequently to make use of the most recent data.
The other big problem with quantitative trading is that you need a huge data set to really be able to train your program to find the patterns in the noise. As a general rule for statistical analysis of the market, the more historical data you have, the better your future predictions will be. Having access to a regularly updated database of stock market data will dramatically increase your chances of success as a quantitative trader.
Disclaimer: This content does not necessarily represent the views of IWB.