Quantitative Trading Definition

Quantitative trading consists of trading strategies based on quantitative analysis, which rely on mathematical computations and number crunching to identify trading opportunities. Price and volume are two of the more common data inputs used in quantitative analysis as the main inputs to mathematical models.

As quantitative trading is generally used by financial institutions and hedge funds, the transactions are usually large and may involve the purchase and sale of hundreds of thousands of shares and other securities. However, quantitative trading is becoming more commonly used by individual investors.

Price and volume are two of the more common data inputs used in quantitative analysis as the main inputs to mathematical models.

Quantitative trading techniques include high-frequency trading, algorithmic trading and statistical arbitrage. These techniques are rapid-fire and typically have short-term investment horizons. Many quantitative traders are more familiar with quantitative tools, such as moving averages and oscillators.

Quantitative traders take advantage of modern technology, mathematics and the availability of comprehensive databases for making rational trading decisions.

Quantitative traders take a trading technique and create a model of it using mathematics, and then they develop a computer program that applies the model to historical market data. The model is then backtested and optimized. If favorable results are achieved, the system is then implemented in real-time markets with real capital.

The way quantitative trading models function can best be described using an analogy. Consider a weather report in which the meteorologist forecasts a 90% chance of rain while the sun is shining. The meteorologist derives this counterintuitive conclusion by collecting and analyzing climate data from sensors throughout the area.

A computerized quantitative analysis reveals specific patterns in the data. When these patterns are compared to the same patterns revealed in historical climate data (backtesting), and 90 out of 100 times the result is rain, then the meteorologist can draw the conclusion with confidence, hence the 90% forecast. Quantitative traders apply this same process to the financial market to make trading decisions.

Depending on the trader's research and preferences, quantitative trading algorithms can be customized to evaluate different parameters related to a stock. Consider the case of a trader who believes in momentum investing. She can choose to write a simple program that picks out the winners during an upward momentum in the markets. During the next market upturn, the program will buy those stocks. This is a fairly simple example of quantitative trading. Typically an assortment of parameters, from technical analysis to value stocks to fundamental analysis, are used to pick out a complex mix of stocks designed to maximize profits. These parameters are programmed into a trading system to take advantage of market movements.

The objective of trading is to calculate the optimal probability of executing a profitable trade. A typical trader can effectively monitor, analyze and make trading decisions on a limited number of securities before the amount of incoming data overwhelms the decision-making process. The use of quantitative trading techniques illuminates this limit by using computers to automate the monitoring, analyzing, and trading decisions.

Overcoming emotion is one of the most pervasive problems with trading. Be it fear or greed, when trading, emotion serves only to stifle rational thinking, which usually leads to losses. Computers and mathematics do not possess emotions, so quantitative trading eliminates this problem.

Quantitative trading does have its problems. Financial markets are some of the most dynamic entities that exist. Therefore, quantitative trading models must be as dynamic to be consistently successful. Many quantitative traders develop models that are temporarily profitable for the market condition for which they were developed, but they ultimately fail when market conditions change.