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This article focuses on Quantitative trading and how you can improve your profitability. In this article, you will understand all there is about quantitative trading. Let us first look at what it is.


Quantitative trading definition: It is a tactic that relies on mathematical calculations and number crunching to discover trading opportunities. Price and volume are the most commonly used information that goes into mathematical models in quantitative analysis.

Transactions are typically huge because financial institutions and hedge funds use quantitative trading. Many shares and securities are being bought and sold in large numbers, and at the same time, regular investors are starting to use quantitative trading more often.


Quantitative traders use current technology, mathematics, and the availability of large databases to make reasonable trading judgments.

Quantitative traders use mathematics to establish a model of a trading approach, then write a computer program which applies the model to historical market data. After that, they backtest and optimize the model. If the results are favorable, the traders use actual capital to apply the method in real-time markets.

An analogy is the best method to describe how quantitative trading algorithms work. Consider a weather forecast in which the meteorologist predicts a 90% likelihood of rain when the sun is shining. The meteorologist reached this surprising conclusion after collecting and analyzing climate data from sensors around the area.

A computerized quantitative analysis identifies patterns in data. When it compares these patterns to similar patterns shown in previous climate data, and 90 out of 100 times the result is rain, the meteorologist can reach a confident conclusion—hence the 90% forecast. Quantitative traders use the same method to make trading decisions in the financial market.


Quantitative trading, often known as quant trading, is a data-driven method of trading that makes trading choices using mathematical models and computer algorithms. Traditional trading, on the other hand, makes trading judgments based on human judgment and expertise. Quantitative trading has advantages over traditional trading: it is less emotional since it depends on objective data. Quantitative trading offers fast trade execution, which is good for volatile markets.

Traditional trading also has its advantages over Quantitative trading. Traditional trading is more flexible because you can adapt your trading strategies to the dynamic market. Traditional trading also requires less technical expertise. This quality makes it accessible to a wide range of traders.


Quantitative strategies include:

Trend following strategy

By trading with the trend, the trend-following strategy is a well-liked trading method. Identifying the currency pairs that exhibit the highest tendency to trend is accomplished by examining prior data. Once you have pinpointed these particular assets, the trades that you place depend primarily on the trend direction. In an uptrend, purchasing an asset is the aim, whereas, in a downtrend, the objective is to sell.

Using the trend-following method, traders search for indicators to determine if an asset is trending. Some indicators include the ADX (Average Directional Index), trend lines, and moving averages.

Mean reversion strategy

The mean-reversion technique is a common quantitative trading method based on the idea that prices revert to their mean over time. Traders using this technique examine the markets to find assets that have drifted greatly from their historical average values. These assets are likely to move back to these levels, which is the basis of this strategy.

For example, if the price of a stock has fallen sharply, a mean-reversion trader may enter a long position in the hope that the price will rebound back to its historical average.

Breakout strategy

The breakout strategy is dependent on explosive price movements. This kind of price movement happens when the price breaks levels it cannot pass. Traders use this method when they see a stock trading within a small price range. They expect the price will go up a lot when it breaks out of that range, so they buy it, hoping to make a profit.


You must remember three crucial points when incorporating quantitative trading risk management into your trading plan. These are some examples:

Market strategists frequently find that the best risk/reward ratio for their investments is around 1:3, or three units of projected return for every unit of added risk. Traders can more directly manage risk and return using stop-loss orders and derivatives such as put options.

  • Position sizing strategies - Position sizing is one of the most effective weapons in a trader's inventory but also one of the most difficult to master. You can choose various position sizing options based on your risk tolerance, such as set lots, dollar value, Kelly Criterion, and more.
  • Stop loss levels - Stop losses are critical for risk management and cash protection if your trades go against you. By placing appropriate stop losses, you can lessen the damage caused by big losing trades and, at the same time, prevent small losses from growing into bigger ones that could destroy your entire trading account.
  • Implementing money management principles - Another key aspect of risk management that every trader should grasp is money management. It assists you in determining how much money you are willing to lose on any single trade and then determining the size of your position accordingly.


In quantitative trading, technology is extremely important. Hardware and software are required to develop, backtest, and implement quantitative trading strategies.


Quantitative trading necessitates using sophisticated technology to run complex mathematical models and algorithms. High-performance servers, graphics processing units (GPUs), and field-programmable gate arrays (FPGAs) are examples of this hardware.


Quantitative trading software builds, backtests, and executes quantitative trading strategies. Programming languages, for example, Python and C++, as well as specialist trading platforms such as Bloomberg and Refinitiv, can be included in this software.


Quantitative trading is a good trading style that has numerous advantages. Despite these pros, it has several disadvantages.

Curve fitting

Curve fitting and optimization are the most dangerous pitfalls. The dependence on historical data inevitably leads to numerous techniques that result from randomness and chance. Out-of-sample tests minimize, but do not remove, the factor of curve fitting.

Depending on the time duration and quantity of observations, we recommend testing your quantitative trading techniques in a paper account for at least six months before going live. The shorter the testing period, the more observations necessary.

Difficulty in coding

Some tactics can be difficult, if not impossible, to code. They may be difficult and necessitate programming abilities that you need to improve, or you may need help to quantify the criteria. Nonetheless, complicated quantitative trading methods are only sometimes superior to simpler ones; the converse is true.

Making "simple" tactics with few variables is an undervalued trading ability. Evidence suggests that many programmers who become traders overcomplicate things.

Black swans

When you automate, many things can go wrong. Code errors might result in several trades, potentially destroying your account. Furthermore, even a minor error in the placement of parenthesis might significantly alter the strategy. "Black swans" do occur. You may believe you have everything planned, but you cannot prepare for what you do not know.

While computers allow you to automate all jobs, we do not encourage doing so. Both connectivity and power disruptions are potential black swan events. If you use a VPS, for example, an unexpected reboot on their end can wreak havoc on your systems.


Quantitative trading is fast changing. Here are some of the advancements that will grace this type of trading.

The expanding use of machine learning and artificial intelligence (AI) to analyze financial data and generate predictions is one of the most significant trends in quantitative finance. Traders can train machine learning algorithms to recognize patterns in vast datasets and forecast market moves in the future. AI may also automate finance industry processes such as risk assessment and portfolio management.

High-frequency trading (HFT) is another advancement in Quantitative trading. It is an automated trading in which you use complex algorithms to make quick trades based on market conditions. Because of the speed and accuracy of the algorithms used, HFT has grown in popularity in recent years. However, it has sparked debate, with some claiming that it gives certain traders an unfair advantage.

In addition, cryptocurrency and blockchain technology have substantially impacted quantitative finance. Cryptocurrencies like Bitcoin and Ethereum are digital money that use decentralized networks to check and record transactions. The blockchain technology underpins cryptocurrencies as a distributed ledger system, enabling many parties to record and verify transactions without a central authority.

Cryptocurrencies have the potential to destabilize established financial systems and alter the way you perform quantitative finance.


Quantitative trading is good for you. Quantitative trading is the future of online trading. This type of trading has numerous advantages. It has come to provide a solution to the challenges of traditional trading.


Quantitative trading is great for most traders. You need a bit of coding background, after which you can develop and code your strategy. Take the initiative to join Dominion Markets, the best trading company today, and try quantitative trading.