Quantitative Trading: The Ultimate Guide

HFT or high-frequency trading has increased in the past few years. HFT is a trading strategy that involves using computer programs to execute a high number of deals in a short amount of time.

In reality, many hedge funds are switching from traditional to high-frequency trading.

Quantitative trading, which uses mathematical and statistical data to locate trading opportunities, is one of the HFT methods.

This guide will explain what quantitative trading is, how it works, and some of the quantitative trading strategies you can apply.

What is quantitative trading?

Quantitative trading is a method of identifying trading opportunities based on mathematical and statistical data.

Quantitative analysis is the source of the term quantitative trading. Quantitative analysis in financial markets gives traders the ability to convert complex patterns into numerical values that can understand market movements.

Quant or quant jockeys are traders who rely on quantitative analysis.

In the financial markets, Harry Markowitz was the one who used mathematical models. In addition, he mentioned mathematical models in his doctoral thesis, which appeared in the Journal of Finance.

How does quantitative trading work?

Quantitative trading uses two common data points: price and volume. Quantitative traders can use these points to make trading strategies based on mathematical databases.

Quantitative trading relies on creating a trading model using mathematical calculations and developing a computer program to apply the model to the market data. This model applies to backtest, and if it produces positive results, it is then applied to real-world market data.

For example, during the New York trading day, a trader notices that the price of a certain currency pair rises. Therefore, he or she will create software that searches the whole historical data of a currency pair during the New York session for this situation.

If the software discovered that a currency pair increased 80% during the New York session, a trader's model would predict that the currency pair would rise 80% of the time during the New York session.

It is only a basic illustration of how Quantitative Trading works. For example, quantitative traders often select a collection of assets to research complicated historical data and apply it to real-world markets.

What is a quant trader, and what do they do?

A quant is someone who works with numbers. The name quant comes from the word quantitative, which means "numbers."

Quants are traders who use complicated mathematical and statistical models to examine a large quantity of market data to locate trading opportunities in the markets.

The advancement of computer algorithms for analyzing large amounts of data, especially among large trading firms that can afford the high computational power required for such analysis, and quants are the human element behind those analyses.

Quants use self-developed computer programs to mine price and volume data, investigate the available data, find profitable trades, and develop suitable trading strategies to capitalize on such possibilities.

As a result, a quant trader should have a well-rounded knowledge of mathematics and statistics, computer skills, and actual trading experience. Quants vary from ordinary retail traders and investors in that they take a different strategy to trade.

Rather than depending on their knowledge of the financial markets, quants scan the markets for opportunities using algo-based, complicated mathematical models.

Large investment institutions, such as hedge funds and banks undertake most quant trading. These organizations frequently have a dedicated quant team that develops computer algorithms that evaluate datasets using mathematical models to identify new possibilities and then develop strategies around them.

Quants with a degree in math, statistics, or software engineering and an MBA in financial modeling are sought by these firms.

How to become a quant trader?

Even though most quant traders’ work for large organizations that can afford the supercomputers and data needed for research, an increasing number of them are now trading on their own. In general, the abilities needed to start quant trading on your own are the same as those needed to work for a hedge fund.

As a result, if you want to try your hand at quant trading, you'll need extraordinary mathematics skills to construct and evaluate your statistical models. You'll also need a lot of programming expertise to build your system from the ground up.

History of quant

Due to the complexity of statistical data, huge financial organizations used to undertake Quantitative Trading.

Computing advancements in the late 1970s and 1980s aided quant trading's mainstreaming. The designated order turnaround (DOT) system is one of them since it allowed the New York Stock Exchange (NYSE) to take electronic orders for the first time. Another was the first Bloomberg terminals, which provided traders with real-time market data.

Then, in the 1990s, algorithmic trading techniques became more popular, and more hedge fund managers adopted them.

On the other hand, the dot-com bubble proved to be a watershed moment, with quant techniques proving less vulnerable to the irrational buying of unknown internet companies and the eventual crash.

The growth of high-frequency trading in the new century introduced more individuals to the notion of quant. By 2009, high-frequency traders using mathematical models had conducted 60% of US stock deals.

Quantitative vs. Algorithmic trading

Algorithmic trading and quantitative trading are sometimes confused. Algorithmic trading involves using computerized systems to discover trading opportunities and execute trades on behalf of the trader.

Quantitative Trading calculates an asset's historical data using mathematical models. It does not, however, execute trades on the trader's behalf.

Examples of Quantitative Trading

Let's illustrate Quantitative trading with examples.

Let's say a trader applies forex momentum trading. They can code a simple program that picks out the winners during an upward momentum in the markets. During the next market uptrend, the program will identify those forex pairs.

Let's use a more complex example:

Assume you manage the XYZ fund. To choose and pick stocks, you employ a quantitative approach. To choose equities, the algorithm examines more than 50 factors in five categories: momentum, value, earnings, and volume. The algorithm assigns each variable a value, and you select the ones with the highest ratings.

Elements of Quantitative trading

To apply quantitative trading strategies, there are four main elements in a quantitative system. Let's explain them:

1. Strategy Identification

The research step of the quantitative trading process involves establishing a trading strategy and determining if it is compatible with other methods.

Many of the tactics you'll examine fall into the mean reversion or trend following categories. We'll discuss Quantitative strategies later.

This phase aims to collect all of the data needed to optimize the strategy for maximum profits with the least amount of risk in the market. It essentially converts a plan into a mathematical model.

2. Backtesting

The purpose of strategy backtesting is to determine if the first-step method is lucrative when applied to historical data. It establishes the benchmark for how well the approach will perform in the "real world."

Backtesting a system needs the ability to measure its performance. The maximum drawdown and the Sharpe Ratio are the "industry standard" quantitative strategy measures.

3. Execution

The execution system is the method through which a strategy generates a list of trades then executed by a broker.

Automated or semi-automated execution systems are available. In addition, the interface to the brokerage decreased transaction costs, and performance divergence of the live system from the backtested performance are all important factors to consider while developing an execution system.

Trading fees (spreads, charges, or tax), slippage, and the broker interface are all important issues for execution. A trading system's optimal performance recognizes by good execution, which ensures that the best prices are always attained in the market.

4. Risk Management

Quantitative trading has several risks. It takes into account brokerage risks, technology weaknesses, and any backtesting biases.

Risk management includes the concept of optimum capital allocation as well. It's the average of how much money goes into various strategies and trades within those strategies.

Quantitative Trading strategies

Due to the character of Quantitative Trading, when practiced, it yields profitable results. Here are some of the popular Quantitative trading strategies you can apply:

Momentum trading

Momentum trading, often known as trend-following, is a straightforward strategy that involves riding the trend as long as it lasts. Traders utilize quantitative research to forecast the market's overall motion.

Let's say a trader is looking for GBP/USD market sentiment to identify profitable trades during an upswing. During an uptick, he'd create a model that solely looked at the winners. As a result, he or she will be able to forecast market emotion better than others.

HFT trading

HFT (High-Frequency Trading) will apply formulae to generate many trading opportunities for tiny price movements. To identify the future price movement, HFTs typically employ tick data or at most one-minute periods. This technique is most likely to be used by hedge funds, CTAs, and financial institutions.

Price action patterns are defined using mathematical equations in HFT methods. They are rarely linked to technical indicators, and the models have maintained a closely guarded secret among the institutions or traders who created them.

Statistical principles such as normal distribution, standard deviation, and mean are commonly used in HFT methods. In addition, there are other typical probability distributions included.

These variables are frequently connected to short periods to provide a statistical and probabilistic picture of the upcoming price movement.

Mean reversion

Mean reversion is a strategy for predicting when a market's present price trend will reverse. Again, a set of technical indicators, such as the RSI or the Stochastic Oscillator, can be used to determine formulas.

The goal is to figure out when pricing reaches a point where the following move reverses the previous one.

Arbitrage

In New York, a stock may be priced in US dollars, whereas it may be quoted in London in British pounds. As a result, it may lead to arbitrage possibilities. Arbitrage is the practice of profiting from price differences between two same or comparable assets.

In the case above, there might be price disparities owing to different demand in the two centers, or the GBP/USD FX rate could shift rapidly, causing a mismatch in pricing for the asset on the two exchanges.

Algorithmic trading

Algorithm trading is a type of trading that uses an automated algorithm to locate trading opportunities. However, this technique does not include the use of an algorithm. Instead, it involves developing a model to predict when major institutions will trade, allowing traders to trade against them.

Consider the case of a trader who developed a model that projected XYZ business would buy thousands of units of a currency pair. He may acquire that currency pair ahead of schedule and then sell it for a greater price later.

To mask their intentions, huge corporations now trade via many brokers and across networks. In this scenario, quantitative analysis comes in helpful.

Quantitative trading platforms

There is a wide array of online platforms where you can implement your quantitative strategies.

Backtesting and model construction, for example, may be done with several script languages or simply with the click of a mouse. Some are costly and targeted at institutional or professional traders.

The most often used platforms are MT4 and MT5.

Depending on the version, these systems allow for limitless backtesting across a variety of time ranges.

You'll need to learn MQL4 or MQL5, depending on the platform. Backtesting across a variety of time frames yields quantitative data such as the Sharpe ratio and drawdown.

You need trading platforms to try out your quantitative trading strategies.

Your questions about Quantitative trading

We know you have a lot of questions in mind after reading about Quantitative trading. So, let's answer them:

Is quantitative trading profitable?

Quantitative trading systems were developed using pure mathematics and statistics to create a trading system that can be traded without the trader's involvement. Hedge funds and institutional investors are growing interested in it.

Although this form of trading can be beneficial, it is not a "set it and forget it" technique, as some traders assume.

Even with quantitative trading, the trader must be very active, constantly tweaking the trading algorithm as the markets move.

How do I become a quant?

A prospective quant trader must have great mathematical skills and a strong interest in mathematics in general.

A bachelor's degree in mathematics, a master's degree in financial engineering or quantitative financial modeling, or an MBA can all help you get a position; many analysts additionally hold a Ph.D. in these or related subjects.

A quant should have expertise and understanding with data mining, research methodologies, statistical analysis, and automated trading systems in addition to an advanced degree.

What are the benefits of quantitative trading?

The option to assess an unlimited number of markets over an infinite number of data points is the most significant advantage of quantitative trading.

Quantitative traders use mathematics to expand their trading perspectives to include the whole financial market.

Another advantage of quantitative trading is that it removes emotion from the equation instead of relying on data-driven conclusions devoid of the bias introduced by human traders.

Finally, when designed correctly, quantitative traders' automated systems may be extremely profitable.

Can an Individual run a Quantitative trading strategy?

Yes, an individual can. A single individual can operate a quantitative trading strategy using inexpensive software and data. However, because of the high expenses and technological requirements, a single individual cannot manage a high-frequency trading strategy.

What's the difference between Qualitative and Quantitative trading?

Qualitative traders make trading decisions based on their intuition and pattern detection skills, as well as looser criteria.

Quantitative traders still use intuition and pattern identification skills. Nonetheless, they frequently employ them to develop hypotheses, which they then test across various asset classes, time frames, and periods to assess the strategy's resilience.

When a trader sees a lot of momentum flowing into this ticker and a trend is forming, he/she will wait for the next pullback and buy.

Buying trend pullbacks within momentum names seems like a good strategy, a quantitative trader would think. I'm going to do some backtesting to determine whether it's profitable.

Advantages and Disadvantages of Quantitative Trading

Let's discuss some of the pros and cons of quantitative trading.

Advantages

• The goal of quantitative trading is to determine the likelihood of a profitable trade.

• It allows for effective asset monitoring, analysis, and trading decisions on a given collection of assets.

• Quantitative trading approaches employ computer algorithms to assess and create successful trading decisions, resulting in more effective trading evaluations.

• It eliminates fear and greed as emotions and encourages rational decision-making rather than relying on guesswork or chance.

Disadvantages

• Because financial markets are volatile, algorithmic models must develop regularly.

• The majority of quantitative systems are successful exclusively for the market type. Therefore, they will need some tweaking when market conditions change.

Conclusion

Quantitative trading marks as the use of mathematical and statistical models to forecast trading opportunities.

Quantitative trading is difficult to master since it necessitates great mathematics and coding abilities. However, quantitative trading can be very profitable if a trader has a thorough understanding of these topics.