Algorithmic trading is the most popular kind of trading nowadays, and it is more advantageous than manual trading because it allows for faster and more accurate trading practice.

During the projection period, the worldwide algorithmic trading market is expected to increase at a CAGR of roughly 10%. (2021-2026).

As a result, whether you're a data analyst, a retail trader, or an engineer, practically everyone wants to understand algorithmic trading these days. Let us go over the five reasons for this devotion to mastering algorithmic trading:

• Obtaining a dream career in the burgeoning FinTech sector

• To establish a more data-driven trading strategy

• to establish one's own algo trading desk or consulting firm

• To decrease trading-related manual hurdles

• To manage risks more effectively and conveniently

To get dream job in the growing FinTech domain

Because of the quick, precise, and secure procedures, the Fintech domain is the fastest expanding in the world. Algorithmic trading is a subset of the Fintech industry that draws people looking for high-paying jobs and bonuses.

Many people want to learn algorithmic trading because of this well-known fact, but they must keep in mind that there are many career responsibilities such as Quantitative analyst, Quantitative developer, and so on.

Regardless of the role you choose, there are some things you should understand about algorithmic trading. These include:

• Language of programming

• Financial market experience

• Management of data

• Quantitative analysis

To establish a more data-driven trading strategy

Data-driven trading is more accurate and has a lower risk of loss. You can do the following with the help of data:

• Examine the market data from the past.

• Trading tactics should be backtested.

• Monitor the financial markets at the same time to identify the best trades.

The most important aspect of the algorithmic trading process is data. Data analysis can identify trends in financial markets, providing a foundation for all trading ideas and strategy development. As a result, data is the fuel for financial market trading.

Furthermore, it has often been about "one who has rapid access to data in trading." Not only does rapid access work, but you must also be educated about data management, since both are essential.

As a result, with rapid access to data, effective data management, and proper data implementation, one may trade in the financial market considerably ahead of others and get an advantage.

Data can be retrieved quickly and converted into usable information with the aid of artificial intelligence or machine learning.

The five categories of data products are as follows:

• Real-Time Data (Level1, Level 2, Level 3, and tick by tick data)

• Snapshot Data

• End of Day (EOD) Data

• Corporate Data

• Historical Data

To establish one's own trading desk or consulting firm

To establish your own trading desk or consultancy company, you must first have a thorough understanding of algorithmic trading, without which you would be unable to proceed.

Once you have a good understanding of algorithmic trading, you will be able to follow the method in the most efficient way possible. Because it is a type of business with a bright future, a trading desk may help you get an advantage.

With the fast growth of algorithmic trading throughout the world, owning a trading desk or consulting business may be extremely useful and profitable. Setting up your own trading desk or consultancy firm, like any other business, needs cash, effort, and vast expertise.

Furthermore, traders rely on trading desks because of their subject understanding and performance.

To eliminate trading-related manual obstacles

When it comes to algorithmic trading surpassing conventional trading, it has been observed that trading via algorithms is faster and more accurate, since there are no human emotions to cause errors. Furthermore, transaction costs are reduced since algorithms only perform as taught.

Let's take a look at what all hurdles are removed by algorithmic trading:

Emotions such as fear, greed, or ecstasy do not dominate.

In algorithmic trading, one can be certain that emotions will not be used to make poor decisions while developing trading strategies or executing transactions. Algorithmic trading is entirely based on logic and seeks to maximize profits.

Reduce trade transaction expenses.

Reduce trade transaction expenses.

The transaction costs are lowered to a minimum with the aid of algorithmic trading since algorithms do not leap to different transactions in a short period of time due to emotional influences. As a result, an algorithm operates in the manner specified.

Time management when trading

When it comes to conserving time, algorithmic trading is the ideal option since it watches and trades in many financial markets at the same time. Algorithms execute trades based on changing market circumstances, trends, and instructions such as stop loss, stop limit, and so on.

It aids in becoming future-ready.

Algorithmic trading is a fantastic tool for being future-ready, as we have highlighted how fast algorithmic traders are becoming more powerful over time.

To handle risks more effectively and conveniently

Trading risk management is critical for avoiding the danger of incurring losses from stock market trades. Risk management includes identifying, evaluating, and mitigating risks that often occur when the market swings in the opposite direction of expectations.

As a result, it is critical to base your expectations on a comprehensive market research and after anticipating all hazards.

The following risk management methods may be implemented with the aid of algorithmic trading:

Portfolio optimization

Portfolio optimization entails examining portfolios with varying investment allocations. The optimisation of investments occurs by assessing the risk and return for each of the portfolios.

For example, a portfolio may be analyzed using the Sharpe ratio, which calculates the excess return over additional risk incurred for each investment in the portfolio.

As a result, the portfolio may be optimized such that equities with a higher Sharpe ratio outnumber those with a lower Sharpe ratio.

Hedging

Hedging is an investing technique used to counter a possible loss or, in other words, future price changes. Financial products such as insurance, future contracts, swaps, options, and so on can be used to hedge.

For example, futures contracts for grade A rice are exchanged on a commodities market, with each contract weighing 100 kg. Mani wishes to purchase 5,000 kgs of grade A rice during the month's final week, whereas Russell wishes to sell 5,000 kgs of grade A rice during the month's final week.

Now, the futures contract is acceptable for both parties, since a trade for 50 contracts on the market may be completed between the two parties.

The 1% rule and the 2% investment rule

The 1% and 2% trading rules state that the maximum amount of risk that can be taken on a single deal should be either 1% or 2%. This allows you to avoid the enormous loss that would otherwise occur.

For example, by utilizing Beta, a measure of volatility, one might avoid trading if the risk of investing in a stock exceeds 2%.

Keeping an eye on the financial markets while utilizing cutting-edge technologies

To discover the greatest trading chances, trades should be tracked using artificial intelligence such as machine learning. For example, using machine learning techniques, the algorithms monitor various financial markets and financial assets to identify the most profitable trading possibilities.

Avoiding ambiguous trading setups

When you use moving indicators such as EMA, MA, and so on, and one of them indicates a clear trade setup but does not agree with the trade setups of other indicators, it leads to confusion.

In such a case, it is advisable to wait for the proper deal and avoid making any judgments if you are unsure. For example, if the EMA does not agree with the MA, one must wait for the proper trade circumstance before engaging in trades.

Stop loss

A stop loss order is a purchase or sell order that is triggered when the stock price hits a predetermined price known as the stop price. This allows the trader to avoid constant market monitoring.

For example, if you purchased the stock XYZ for $50 a share and were concerned that its price might fall, you could utilize a stop loss order. If the price falls below a specific level, the stop loss order might suggest that it is time to sell. Assume you set the price at $45 per share, below which the algorithm will sell the asset to protect itself from a bigger loss.

In conclusion

This post intended to simply highlight the five most important reasons why algorithmic trading is becoming popular these days. In addition, the essay sought to teach you what distinguishes this sophisticated trading approach from traditional manual trading.

If you want to learn algorithmic trading, you can check the Hafizebot guides.