What is Quantitative trading?

Quantitative trading involves trading strategies and decisions based on mathematical computations, historically present data, number-crunching and constant hypotheses of future events and their impact on the financial markets. In simpler terms, the meaning of quantitative trading is trading based on quantitative analysis. The two common inputs used in quantitative trading are price and volume as they are the main inputs to mathematical models.

Quantitative trading is gradually becoming a household individual investor name but it is a strategy carried out by institutional investors and hedge funds. This also contributes to the large volume and prices of transactions that usually occur as part of a quantitative trading strategy.

Quantitative traders take advantage of modern technology, mathematical models and widely available comprehensive data to make rational trading decisions.

The key components of Quantitative trading include the following:

  1. Identifying a strategy

    A quantitative trading plan involves an extensive period of planning and research during which traders identify market strategies, realize market opportunities and narrow down the frequency of trade. Moving ahead this strategy undergoes major scrutiny and upgrades to increase the returns while simultaneously decreasing the risks associated with the trade.

  2. Backtesting the strategy

    Backtesting the software doesn’t always comment on the viability and the success rate of the strategy when used in the current market environment or applied to future hypotheticals and trend based trading cycles. However, it can be beneficial and give a certain quality and viability check to the strategy when applied to historically present and out-of-sample data and works in the actual market. Backtesting is also subject to the transaction costs involved as well as the availability of historical data amongst other such factors.

  3. System of execution

    An execution system is either a semi-manual or fully automated approach towards the execution of a set of trades per trading strategy. The ideal path when considering an execution system would be to automate the execution mechanism of one trade accurately to minimize the transaction cost. This deals with the major concerns of execution systems in quantitative trading which are brokerage and transaction costs.

  4. Risk Management

    Quantitative trading risk management deals with all the possible risks or events that may hinder a trade such as the biases of technology risk, brokerage risk - the bankruptcy of the broker, and more.

Advantages of Quantitative trading:

  • Quantitative trading helps make effective trading judgements on a set of stocks along with effective monitoring and analysis of the stock trends and movements.
  • Quantitative trading aims to calculate the probability of a profitable trade.
  • Promotes rational decisions by weeding out emotions of fear, greed and other irrationalities.
  • Methods of Quantitative trading are known to enhance effective trading decisions through mathematics and computer algorithms by eliminating or minimizing human error.

Disadvantages of Quantitative trading:

  • Algorithmic models are required to adapt and evolve regularly due to volatile financial market conditions.
  • Most quantitative trading models are built and sustained according to a particular market type or market condition. Therefore, they need to be revised and redeveloped as market conditions change or new market types are entered.

Effective quantitative trading strategies:

The current market conditions are constantly changing and evolving. A few quantitative trading strategies that are effective in the current markets include the following :

  • Alternative data strategies

    Generally, the data fed into the mathematical models for quantitative analysis are price or volume. The alternative data includes non-traditional data that has predictive value in the financial markets.

    The most popular types of alternative data are:

    • Location data
    • Consumer expenditure data
    • Satellite imagery
    • Weather data
    • Web-scraped data
  • Obscure and small markets

    Obscure markets are less popular and regulated whereas small markets indicate markets that can only take up smaller volumes of trade as larger volume trades would directly create a price movement.

  • High-Frequency Trading (HFT)

    The primary characteristics associated with HFT are high communication and computation speeds, a larger volume of trades with lower profit per trade and an expensive software interface.

  • Machine Learning

    Machine learning is a form of AI-based trading where the computer learns from historical data. It learns from its own historical experiences to the point where eventually it no longer has to be told how to function or what activities to undergo as it can compute and complete its tasks with self-sufficiency.

Examples of Quantitative trading:

Let’s say John trades an XYZ fund. To select and pick his stocks for maximum returns and profitability, he uses an algorithmic system.

The system will now scrutinize all of the presently available data and scan a range of variables over a wide category range, i.e. momentum, volume, value, earnings and so on. This will help accurately pick the right stocks and apply weightage to them based on John's preferences.

Now, the system will put a weight or a value on each variable and place the option available before John to pick the ones that have the highest ratings as they meet his set of criteria and requirements with the greatest accuracy.

Where can I learn algorithmic or quantitative trading for free?

To be a quantitative trader, you need a particular skill set and the right tools before commencing with the operations. This includes the mastery of mathematics, science, finance and programming. Thus, it is unrealistic to assume that with a few books or short tutorials someone may become an effective and efficient quantitative trader. Quantitative traders invest a hefty amount of time and money into their education and pursuit towards becoming analytical quant traders - keeping in mind that the set-up costs of trading systems and infrastructure are also highly capital intensive.


Quantitative trading is a computer software oriented trading strategy. It uses historically present data and mathematical models to compute optimal trading patterns that follow the trends and the movement of stocks to maximize the returns of an investment in a particular stock. It is highly capital intensive to not only function as a quantitative trader, but also to gain the appropriate education required to become a quantitative trader.

However, that does not mean that you cannot earn and accurately capitalize on trading opportunities in the market. Open a Demat account with IIFL Securities today to start trading and capitalizing on potentially large and fleeting financial opportunities.

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Frequently Asked Questions Expand All

Algorithmic trading includes trading through algorithms that analyze charts, read data and then open and close a position on behalf of the trader. Quantitative trading includes using mathematical models and statistical figures to identify a trading opportunity, but not necessarily execute it. These two concepts are similar and overlapping, but they are not the same.

John trades an XYZ fund using an algorithmic system. The system will scrutinize all of the presently available data and scan a range of variables over varied categories i.e. momentum, volume, value, earnings and so on.

The system will put a weight or a value on each variable and the option available in front of John and John can pick the ones that have the highest ratings as they meet his set of criteria and requirements with the greatest accuracy.

Yes, this form of trading is usually carried out by institutional investors or hedge funds because the required resources, cost of education and infrastructure are highly capital intensive, but, if conducted with the right knowledge and with the proper equipment, this form of trading is very profitable.