Quant trading is a trading strategy that relies on quantitative analysis, employing statistical and mathematical models to find profitable trades.
Quantitative analysis takes advantage of the massive amount of market data, as well as recurring trends, to offer investment insights and evaluate stock performance. As a strategy, quant trading uses that analysis of a given stock’s metrics, including price and volume, to predict performance and make bets based on those predictions.
What is Quantitative Trading?
Historically, quant trading has been the province of large, institutional investors and hedge funds, who have had access to sophisticated research and computer models that make it easier to use technical analysis to research stocks. But that’s starting to change, with more individuals taking advantage of the tools that the internet has provided to engage in a host of quantitative trading strategies.
Some of the most common quantitative trading strategies include statistical arbitrage, high-frequency trading and algorithmic trading. Most of those tactics involve trades with very short time horizons.
What different quant strategies have in common is that they use data-based models to locate trading opportunities, and to calculate the likelihood of a positive outcome for those opportunities. Unlike some investment strategies, it doesn’t rely on deep research of the companies underlying the securities themselves. Rather, it looks to statistical methods and computer models to find promising trades.
How Quant Traders Track Data Points
Most quant traders start by tracking specific data points. While most commonly tracked data points are price and volume, any metric can be used to build a strategy. There are some traders who even build programs to monitor social media for investor sentiment.
Quant traders use that data to discover trends or correlations that have proven to be predictive of certain outcomes, such as a stock going up or down. Then they will build a model to identify those trends and correlations as they occur. Some investors, especially high-volume investors, will even go so far as to automate their trading to execute purchases and sales whenever those conditions arise.
For example, a quant trader who believes in the power of market momentum might write a computer program that teases out stocks that have won in previous upward market swings. When the markets begin another bull run, a simple version of that program will either alert the trader to those stocks, or buy them directly. A more complex version of the program might identify a common metric for the stocks that had excelled during the last runup, and then build a repository of those stocks for when the next upward swing.
That example could equally apply to stocks in a down market, or stocks during sinking interest rates, or stocks during periods of persistently low unemployment. A quant trader looks at the math to anticipate the next market moves.
Getting Started With Quant Trading
For an investor who is looking to build their own models for quant trading, they need to find the right software to get started. Some of these programs can be expensive, and many require a major time investment to use them well. So it’s helpful to do some research before choosing a software package.
If an investor is looking for software that will help them build models, spot opportunities, and execute trades, then the stakes of choosing the right software are even higher. These software packages are typically provided by brokerages, or from specialized software firms. Most ready-made quant trading software suites will offer free trial versions that allow customers to try them out. But they can come with blind spots, or shortfalls that can cost an investor real money. That’s why some more tech-savvy and adventurous investors will go so far as to build their own software to identify—and act on—investment opportunities.
Features to Look for in Quant Trading Software
Most ready-made trading software packages offer real-time market data and price quotes. Quant traders want access to company fundamentals such as P/E ratios, earnings and other metrics updated in real time. And lacking that, they should look for software programs that allow them to easily integrate outside data sources, which can open up new and unique possibilities for research and discovery.
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Depending on the breadth of their outlook, quant traders may want to trade across several different markets. But each exchange might provide data via a different digital language. Be sure that any software package can integrate feeds in these different formats, or that it has access to popular third-party data purveyors such as Bloomberg or Reuters.
While those capabilities will help quant traders focus on the right data and build the right models, there’s another side, namely trading on those models. This is where finding or building the right software can make or break a quant trader.
For quant traders, especially traders who make many short-term trades in the course of a day, one vital feature for software comes down to latency. If it takes 0.2 seconds for a price quote to get to your software vendor’s data center from the exchange, and it takes 0.3 seconds for it to get from there to your screen, and then 0.1 seconds for the trading software you use to process the data, and then another 0.3 seconds for the trading software to receive the data, analyze it, and make a trade, that matters. Especially in quant trading, time is money. But the lag continues. It may take 0.2 seconds for a trade order to get to a broker, and another 0.3 seconds for the broker to deliver that trade order to the exchange.
Especially in a stock hat’s seeing heavy volume, that 1.4 seconds could mean the difference between a successful and unsuccessful trade. That means that any delay in a software constitutes a real disadvantage to a quant trader, and should be considered when buying software.
Pros and Cons of Quant Trading
Emotion can be one of the biggest obstacles to successful trading. Investors may hold onto losing positions too long, thinking they’ll turn around. And they may let winning investments run too long, and lose money when they take a turn. But computer models have no emotions. That’s one reason why quantitative trading is so popular.
That said, quantitative trading can come with its own unique problems. The main one is that the financial markets are always changing. The rules, trends, correlations, cycles and even fundamental logic of the markets often seem to change with dizzying speed. As a result, even the most back-tested and seemingly promising quantitative trading model will occasionally fail. And while many models and trading programs may be profitable for a time, a successful quant trader is always looking for the next big change.
Some investors may find that using fundamental analysis on stocks offers a bit of the best of both worlds. Fundamental analysis incorporates both quantitative and qualitative analysis, in an effort to create a better overall picture of a given stock.
Quant trading—once the province of institutions and hedge funds—has gone mainstream. Individuals are getting in on this strategy, using data to try and predict the markets, rather than relying on emotion and instinct.
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