What is Quantitative Trading?
Quantitative trading is a computer software-based trading strategy that uses mathematical models and calculations to assess patterns and trends in the movement and behavior of stocks with the aim to pick undervalued stocks at the right time and make a profitable trade execution. It is usually based on inputs like price and volume at which they are traded. Stocks most often do not have a fixed pattern and have cyclical patterns where quantitative trading techniques help in cashing in on those trends.
The main aim behind this is to pick stocks that are under-priced and to find assets that are priced above their actual worth. It eliminates human intervention out of investment decision making. The reason for being a computer program based model may pick up a trend where a human mind may miss.
Quantitative trading strategies are in modern days, also used by retail investorsRetail InvestorsA retail investor is a non-professional individual investor who tends to invest a small sum in the equities, bonds, mutual funds, exchange-traded funds, and other baskets of securities. They often take the services of online or traditional brokerage firms or advisors for investment decision-making.read more.
Explanation
The quantitative trading models have price and volume as their core inputs for mathematical model building.
These trading methods have algorithmic and complex statistical models. They are fast-paced and short term trading goals. The quantitative trader is better versed with numerical tools like moving averages is one of the statistical tools. The traders capitalize on technology, mathematical and statistical models for making sharp trading strategies. Quantitative traders take a trading strategy and build a mathematical model based on historical data.
The model is then tested and evaluated. If the model yields results, the model is then used for real capital and market trading. The operation of these models is analogous to climate forecasting, where probabilistic techniques are used based on historical data to predict the weather. The same method is used by traders to market data to make investing decisions.
Examples of Quantitative Trading
Let’s say Bob runs an XYZ fund. He uses an algorithmic system to select and pick stocks.
- The system scans more than 50 variables in five categories being momentum, value, earnings, and volume to pick and choose stocks.
- The system puts a value to each variable, and Bob chooses the ones with the highest ratings.
Components of Quantitative Trading
It broadly consists of four major components.
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For eg:
Source: Quantitative Trading (wallstreetmojo.com)
#1 – Strategy Identification
It starts with finding a strategy, exploiting a market opportunity, and narrowing down on the trading frequency. Any quantitative trading plan starts with an extensive period of research. The process includes devising a strategy, assessing whether the strategy is suitable with the current set of strategies, and gathering any data required to test the strategy and trying to upgrade the strategy to get greater returns and reduce the risk.
A retail investor will have to determine his or her capital requirement and how transaction costs would affect his decisions. Various public forums provide profitable trading strategies. There are various sources where trading results, including transaction costs, are available. Quantitative write-ups discuss various trading strategies in depth. Trade magazines disclose strategies devised by fund houses.
#2 – Strategy Backtesting
Backtesting aims to give proof that the technique used is profitable when used on historical and out of sample data. It gives evidence of how the strategy will work in the actual market. Backtesting is not conclusive evidence of how successful the strategy will be. It is subject to numerous biases that may be removed as far as possible. Other factors of backtesting in backtesting include the availability of historical records, transaction costs involved, and deciding a suitable backtesting method. Once the strategy is decided, it is required to gather historical records to perform testing. There are many data sources for the same. Their costs vary based on the quality of data.
#3 – Execution Systems
An execution system is a way a set of trades generated by the trading strategy is executed. The trade execution may be semi-manual or automated. The major concern when devising an execution system is the interface with the brokerage and minimization of transaction costs. The ideal path will be to automate the execution mechanism of one trades. It allows you to focus on research and run strategies of higher frequency.
#4 – Risk Management
The final issue in quantitative trading is risk management. It includes biases like technology risk, brokerage risk that is the bankruptcy of the broker. In brief, it includes everything possible that may hinder trading.
How to Become a Quantitative Trader?
A Potential Quant Trader has to have proper skills and tools before getting into quantitative trading. A strong background in finance, mathematics, and computer programming are some of the pre-requisites for an aspirant to be a quantitative trader.
Advantages
- The aim of trading is to calculate the probability of a gainful trade.
- It enables effective monitoring, analysis, and making trading judgments on a given set of stocks.
- Quantitative trading methods enhance effective trading decisions through the use of computer algorithms to analyze and make profitable trading decisions.
- It weeds out the emotion of fear and greed and promotes rational decisions, not leaving things to surmise or chance.
Disadvantages
- The financial markets are volatile, requiring the algorithmic models to evolve consistently.
- Most Quantitative models are profitable only for a particular market type or condition for which the model was made. They need to be redeveloped as the market conditions evolve over time.
Conclusion
Quantitative trading has technology as its cornerstone. It enables a faster and profitable trade execution. But, blindly following quantitative models may be foolhardy.
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