Why Choose Quantitative Trading?

Strictly speaking, quantitative trading uses complex statistical methods and mathematical models to identify “high-probability” events that can generate excess returns from vast amounts of historical data, uncover patterns among them, and develop strategies.

These patterns and strategies can be validated and formalized through data models, and then executed efficiently and rigorously by computers.

Put simply, quantitative trading uses statistics, mathematics, computer technology, and modern financial theory to help investors make money more effectively.

These quantitative methods can not only be used to analyze vast amounts of historical data, but can also generate specific trading signals, control position sizes, manage risk, and more.

Therefore, we can see that quantitative trading is no longer completely opposed to traditional discretionary trading.

For example, common intermarket futures arbitrage strategies and options volatility arbitrage strategies are both forms of semi-automated trading. These types of trading require traders to combine historical mean reversion with subjective judgments about macroeconomic policies, adjust the parameters in advance, and then let the computer execute the strategies rigorously.

Overall, quantitative trading is an enhancement of traditional discretionary trading. It removes some of the unstable human factors, enabling users to focus on pursuingexcess returns (that is, Alpha).

Therefore, quantitative trading is bound to become a trend in future development.

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Why Choose Quantitative Trading?

Quantitative trading is often compared with discretionary trading, and people are always debating which one is better. To explain their differences more clearly, let’s take a brief look below.

Let’s start by looking at discretionary trading.

Investors who trade subjectively rely primarily on their own judgment of the market. They pay attention to international affairs, financial news, brokerage research reports, companies’ financial data, stock price trends, various market information, and may even refer to the sentiment in their social circles or listen to rumors.

Investors then analyze the information based on these factors.

Qualitative analysis may involve trying a company’s products or conducting on-site visits; quantitative analysis involves using various metrics to score stocks according to personally established stock-selection criteria, then deciding to buy stocks with high scores and sell stocks with low scores, thereby forming trading decisions.

When placing orders, investors who trade manually must be especially careful to avoid entering the wrong stock symbol or making a slip of the finger, which could result in an incorrect order. If the transaction amount is large, they also need to split a large order into smaller ones to reduce costs. After opening a position, investors must remain alert to risk, strictly follow their preset take-profit and stop-loss strategies, and prevent losses from becoming excessive.

It is not difficult to see from this that subjective trading relies heavily on “people.” That is, under the same stock-selection strategy, with 100 individuals trading, there can be 100 different results. Based on the law of large numbers and an approximately normal distribution, we can see that a small group of people have very smooth and attractive equity curves, but the vast majority of people will fall below the moving-average level. Here, “moving average” refers to the equity curve produced by a computer strictly executing a trading strategy. The reason is often that rationality is always defeated by emotion.

Opposed to subjective trading is quantitative trading.

Quantitative trading relies not only on historical market data and fundamental indicators but also uses some nontraditional data.

For example, market sentiment and keywords from financial news can be converted into indicators that machines can understand. The more raw the data, the better.

For example, raw, unprocessed data provided directly by an exchange can be purchased. Although third-party data providers offer relatively affordable prices, their data-cleaning process may remove information that appears useless but actually contains hidden profit opportunities.

Once the data is available, mathematical and statistical methods, such as unit root tests, linear regression, and machine learning, can be used to identify “high-probability” events that can generate excess returns from large amounts of data.

When selecting stocks, for example, quantitative trading evaluates them across multiple dimensions. Traditionally, stock-selection factors are divided into seven major categories:

1. Profitability
2. Valuation
3. Cash flow
4. Growth
5. Asset allocation
6. Price momentum
7. Technical factors

Modern statistical techniques are used to screen for effective factors, determine factor weights, and score core factors, ultimately generating trading signals. Automated trading is then conducted through an API, while the trading system uses a risk-control module to manage positions.

One major advantage of quantitative trading is that computers can execute tasks efficiently, freeing people from tedious, repetitive work and allowing traders to spend more time developing better strategies.

In addition, quantitative trading can approach problems from a faster and more nuanced perspective.

Human reaction time, from seeing data to processing it in the brain and then operating the keyboard, takes hundreds of milliseconds, while computers can execute tasks at the nanosecond level. Therefore, in the realm of high-speed trading, quantitative trading can earn profits that discretionary trading cannot capture.

Of course, quantitative trading also has its drawbacks.

Precisely because machines can execute strategies perfectly, a company's core profit-making strategies must be kept strictly confidential.

For example, if a highly profitable strategy were leaked and many companies used the same strategy, only the fastest companies could make money, while most of the others might make no money or even suffer losses. If everyone pursued speed, the competition would evolve into a contest over hardware. Maintaining and upgrading equipment that costs tens of millions of yuan each year is not something every company can afford.

By comparison, discretionary traders have less need to keep their strategies confidential. Even if an excellent strategy were given to 100 individuals, perhaps only two or three people would make money, while the others would lose money. Discretionary trading relies more on human factors, such as unique insights into macroeconomic policies and a feel for the market built up over many years.

In addition, discretionary traders also have an advantage when responding to sudden changes. For example, in the futures market, when faced with a rapidly changing quote board, an experienced discretionary trader may be able to identify the counterparty as a high-frequency trading firm and then strike it with a large order, forcing it to close its positions.

Overall, both discretionary trading and quantitative trading can ultimately achieve success. The difference is that discretionary trading appears flat but becomes increasingly steep the further you go; the process is full of subtleties and depends on talent, luck, and moments of insight. Quantitative trading, on the other hand, is more like a modern competitive sport with systematic training: build a solid foundation, proceed steadily, and you will always be able to see the direction of progress.

That concludes this article. Thank you for reading! AllTick will continue to bring you the latest quantitative-trading insights and knowledge. Our real-time market data APIs are designed for quantitative traders and cover tick data for A-shares, Hong Kong stocks, U.S. stocks, forex, futures, and cryptocurrencies. Start a free trial today.real-time market Tick data, and free trials are welcome.

In the next article, we will introduce the legends in the history of quantitative trading:Legends of Quantitative Trading: The Experts Who Changed the Market