Why do quantitative funds need human traders

Quantitative trading

Quantitative trading is the use of sophisticated mathematical and statistical models and calculations to identify profitable opportunities in the financial markets.

Quantitative trading is known for implementing advanced modern technology on huge databases to produce comprehensive analysis of the opportunities present in the market.

For quantitative traders, price and volume are the most important variables and the larger the data set, the better.

Trading has always been likened to predicting the weather and if so, then quantitative traders are the equivalent of modern real-time meteorologists who use the latest state-of-the-art equipment to determine the weather in a specific location at a specific time.

Granted, the results may not always be accurate, but the success rate is usually more than decent and all predictions are based on both a huge historical database and a current one.

How quantitative trading works

Quantitative trading is largely data driven and uses purely statistical and mathematical models to determine the likelihood of certain outcomes. It requires a lot of computing power for extensive investigations and creates conclusive hypotheses from numerous numerical data sets.

For this reason, quantitative trading has long been reserved for top financial institutions and wealthy individuals. In the last few days, however, it has also been increasingly used by private investors.

An example of a quantitative model would be to analyze the upward pressure on McDonald’s stock (MCD) on the NYSE during midday.

A quant would then develop a program to analyze this pattern over the entire history of the stock. If this pattern is found to occur over 90% of the time, then the quantitative trading model developed will predict that the pattern will repeat 90% of the time in the future.

Quantitative vs. Algorithmic Trading

The idea of ​​quantitative trading is to generate solid trading ideas using purely mathematical models. A quantitative trader researches and analyzes historical data and then applies advanced mathematical and statistical models to identify trading opportunities in the market. The trading ideas can then be executed manually or automatically in the market.

As mentioned earlier, quantitative trading has always been more widespread among financial institutions due to the computing power required. But the advancement of technology, particularly in cloud computing resources, has opened the doors for the average retailer to try their hand at this as well.

In contrast, algorithmic trading involves the use of algorithms to identify and exploit trading opportunities in the market.

This basically means that algorithmic trading converts a trading idea into a trading strategy with the help of coded algorithms. So algorithms have the task of automating trading strategies.

Algorithmic traders can automate all aspects of trading activity, from market scanning and signal generation to order execution and market exit. No human intervention is required in either phase.

While quantitative traders use mathematical models to generate trading signals, algorithmic traders often use traditional technical analysis methods, such as candlestick patterns and a combination of technical indicators.

In addition, quantitative traders employ sophisticated methods, while algorithmic traders can implement both simple and advanced strategies in the market.

There's an obvious overlap between quantitative and algorithmic trading, but the subtle differences can play an important role.

Then there are HFT (high frequency trading), which is about taking advantage of the speed of execution with the help of cutting-edge technologies.

In essence, HFT aims to gain a mechanical advantage in the market. HFT is basically a subset of quantitative trading, but it is very fast.

However, quantitative trading is not tied to a super fast execution speed of orders. Instead, it can be slow, medium-paced, or fast. It is not uncommon for quantitative traders to place positions in the market that can last up to a year.

Quantitative trading systems

Quants develop systems to help them find the best math probabilities on the market. There are numerous different quantitative trading systems, but they all have 4 core components: strategy, backtesting, execution and risk management.

Strategy identification

This is essentially the research phase of a quantitative trading system. The type of strategy must match the portfolio the trader wants to use.

For example, a stock trader can implement a medium term strategy that seeks to capitalize on earnings and dividend reports, while a forex trader can use a short term strategy. The frequency of trading is an important aspect of quantitative trading.

There are several types of strategies that can be developed, such as: B. Mean reversion, trend following or momentum trading.

The intention of this phase is to collect all the necessary data to optimize the strategy for maximum returns and minimum risk in the market. It's about effectively turning a strategy into a mathematical model.


Backtesting is done to qualify the identified strategy. This is where the collected data is used.

In backtesting, the strategy is applied to past data to determine how reliably it would have performed in the market.

Granted, success here is not a guarantee of future performance, but it is a good indicator of the type of returns that can be expected from the strategy in the real market.

Backtesting makes it possible to improve and optimize the strategy, as it can reveal inherent weaknesses.

Errors can be unpredictable drawdown levels or even high volatility in the performance levels. In order to achieve accurate backtesting results, the data available must be of high quality, as must the software platform used.


Every trading system must have an execution element, i.e. how the generated trading signals are placed on the market.

Execution can be manual (every detail is entered by the trader), semi-manual (one-click trade prompt) and automated (no human intervention required).

The most important aspects of execution include trading costs (spreads, commissions or taxes), slippage, and the broker interface.

Good execution enables a trading system to work optimally and get the best prices on the market at all times.

Risk management

Trading the financial markets is inherently risky. Risk management is therefore an important part of quantitative trading systems.

Risk is essentially anything that can affect the successful performance of a quantitative trading system.

In the marketplace, quants face different types of risk. There is of course market risk, which means that changes in the price of the underlying financial assets can be rapid and dynamic, resulting in losing deals.

This type of risk is what traders focus on the most and it can be mitigated by installing parameters such as stop losses, bet size, trading hours, tradable markets or even leverage levels. But that's not the only risk quants face.

There is also an efficient allocation of capital to various assets, a technology risk, a broker risk, and even a personality risk (but that can be mitigated with automation).

As in any business, tight risk management in quantitative trading will ensure that you are protected but still sufficiently open to numerous profitable opportunities in the long run.

Advantages and disadvantages of quantitative trading

The main advantage of quantitative trading is that its massive computing power gives investors access to wider market opportunities.

Investors can trade numerous markets at the same time using multiple trading strategies without sacrificing quality or consistency.

You don't have to worry about not being able to actively monitor or track trading risks in the market.

Quantitative trading also eliminates the risk of subjective trading in the market. The risk of human emotion and bias is eliminated through the use of mathematics in trading activity.

Traders should find confidence in a trading system that has been thoroughly tested to ensure that it is making objective trading decisions in the market at all times.

At a time when data is flowing freely, it is difficult to keep track of how this can affect our portfolios. Quantitative trading ensures that you don't have to monitor every single element of data, while at the same time ensuring that every trading opportunity is most likely to be fully exploited in the fast and dynamic financial markets.

But there are also some downsides. Up until now, quantitative trading was reserved for institutional traders in particular, as setting up a reliable trading system is associated with high costs.

From researching to collecting high quality data to testing and optimizing, developing a good quantitative trading system is a time-consuming and capital-intensive endeavor.

It also requires a high level of math and programming knowledge and skills that the average retail investor does not always have.

While there are beginner-friendly templates, such solutions may not be enough.

It's also worth noting that a quant trading system is only as good as its creator. Automating a profitable strategy can improve its performance, but it will be difficult to improve a mediocre strategy in a market that is constantly fast, dynamic, and unpredictable.

Quantitative trading strategies

Here are some of the most common strategies quantitative traders use:

Statistical arbitrage

This is a strategy designed to take advantage of the mispricing of assets in the market. Statistical arbitrage trades happen within seconds or minutes when an underlying exchange or service has not valued an asset according to its true value. The trades must be done in a short period of time so that the market risk is lower.

Market making

This is a strategy aimed at making money out of the bid / ask spreads. Market making is simply buying the best bid and selling the best ask. Market makers thus act as wholesalers in the financial markets, with their prices reflecting the demand and supply in the market. They are not necessarily brokerage firms, but rather large market players who provide a more liquid market for investors.

Mean inversion

Mean Reversion is based on the idea that extreme prices are rare and temporary and that the prices of financial assets will always tend towards average prices over the long term. Defined deviations from the average prices represent an opportunity to trade the underlying market. A mean can be represented by a complex math formula or simply the average of prices over the last X periods, like the Simple Moving Average. If the prices are below the average price by the specified deviation, this is an invitation to buy; sales opportunities also arise if the prices are a specified deviation above the average.

Directional strategies

These are strategies aimed at taking advantage of the final direction of the market. In markets like long-term bonds and select stocks or cryptocurrencies, quantitative trading systems can determine when there is real upward or downward momentum so they can ride the wave. The market direction can be forecast based on price information and volume data from the past; then the appropriate directional strategies can be implemented in the market.

Event arbitrage

Economic events, such as stock market mergers and acquisitions, can create short-term opportunities that quantitative traders can take advantage of. In the case of mergers and acquisitions, the idea is usually to sell the shares in the buying company while buying the company to be acquired. The danger of event arbitrage is that there is market risk in the event a deal is abandoned, which can happen due to legal challenges or other complications.


This is a controversial trading technique that continues to this day, despite being considered an outlawed. It involves placing limit orders outside the bid-ask spread with no intention of executing them. For example, if the price of EURUSD is 1.2000 / 1.2005, an order can be placed at 1.2010 for a long position.

This creates the illusion of increased market demand, but the offending algorithm will cancel the trade before it is executed.

The intention was simply to get a selling price higher than prevailing prices. This is usually associated with large institutions in some traditional markets, but in modern markets it is difficult for a single entity to manipulate prices.

Final word

Quantitative trading is becoming increasingly popular in financial markets as technology becomes more democratic. It is a holistic way to act objectively in a fast and dynamic market. When you're ready to trade quantitatively, AvaTrade is here to give you access to intuitive trading platforms.

Open a demo account now and start trying out your quantitative trading strategies risk-free.

Quantitative Trading FAQ

  • Is Quantitative Trading Profitable?

    Quantitative trading systems use pure math and statistics to develop a trading system that can be traded without any input from the trader. Also known as algorithmic trading, it has become increasingly popular with hedge funds and institutional investors. This type of trading can be profitable, but it is not a "set it and forget it" strategy as some traders believe. Even with quantitative trading, the trader must be active in the market and make adjustments to the trading algorithm when the markets change.

  • How do you become a quantitative trader?