Algorithmic trading is the most preferred form of trading in today’s time and is more beneficial than manual trading since it enables faster and more accurate trading practice.
The global algorithmic trading market is forecast to grow at a CAGR of around 10% during the forecast period (2021-2026).
Hence, be it a data analyst, a retail trader or an engineer, almost everyone is aiming to learn algorithmic trading these days. Let us discuss the 5 reasons for this commitment to learning algorithmic trading, which are:
- To get a dream job in the growing FinTech domain
- To develop a more data-driven approach to trading
- To set up ones own algo trading desk or consultancy firm
- To reduce manual related obstacles in trading
- To manage risks better and with convenience
To get dream job in the growing FinTech domain
The Fintech domain is the fastest growing in the world owing to the quick, accurate and safe methods. Algorithmic trading is a part of the Fintech domain that attracts individuals who aim to earn lucrative salaries and bonuses.
Owing to this well-known fact, many aspire to learn algorithmic trading but they must keep in mind that there are various job roles such as Quantitative analyst, Quantitative developer etc.
There are different aspects that you need to learn in algorithmic trading irrespective of the role you opt for and these aspects are:
- Programming language
- Experience in financial markets
- Data management
- Quantitative analysis
To develop a more data-driven approach to trading
Data driven trading is more accurate and holds less chances of incurring losses. With the help of data, you can:
- Study the historical market data
- Backtest your trading strategies
- Monitor the financial markets simultaneously for finding the best trades
Data is the most significant part of the algorithmic trading process. Analysing data can reveal the trends in financial markets, creating a base for all the trading ideas and strategy creation. Hence, data is the fuel to financial market trading.
Also, it has usually been about “one who has quick access to data in trading”. Not only does the quick access work, you need to be knowledgeable with regard to the data management as well as as these are both integral.
Hence, with the quick access to data, good data management as well as right implementation of data, one can trade in the financial market much before others and gain an edge.
With the help of artificial intelligence or machine learning, data can be extracted faster and be transformed into useful information.
The 5 different types of data products are:
- 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 set up ones own trading desk or consultancy firm
In order to set up your own trading desk or consultancy firm you need to have the required knowledge of algorithmic trading without which it is not possible to move forward.
Once you understand algorithmic trading well, you will be able to follow the procedure in the most efficient manner possible. Trading desk helps give you an edge since it is a kind of business that has a wonderful future.
With the rapidly growing algorithmic trading across the world, having a trading desk or a consultancy firm can be highly beneficial and successful. Just like any other business, setting up your own trading desk or consulting firm requires capital, dedication and immense knowledge.
Also, the traders rely on trading desks owing to their domain expertise and performance.
To reduce manual related obstacles in trading
Speaking about algorithmic trading outperforming traditional trading, it has been witnessed that trading via algorithms is much faster and accurate with no human emotions that can lead to errors. Moreover, transaction costs reduce because algorithms only perform in the instructed manner.
Let us see what all obstacles are removed with algorithmic trading:
Emotions such as fear, greed or over excitement don't take over
In algorithmic trading, one can be sure of not getting driven by the emotions that lead to wrong decision making with regard to creating trading strategy or execution of trades. Algorithmic trading is completely on the basis of logic and aims at best returns.
Reduce transaction costs in trading
With the help of algorithmic trading, the transaction costs are reduced to being minimal since algorithms do not jump to different trades within a short span by coming under the influence of emotions. Hence, an algorithm works in the instructed manner.
Time-efficiency while trading
When it comes to saving time, algorithmic trading works the best because it monitors and trades simultaneously in different financial markets. Algorithms base the trade execution on the changing market conditions, the trends and instructions such as stop loss, stop limit etc.
Helps to be future-ready
Algorithmic trading is a powerful tool for being future-ready since we already discussed in the beginning how rapidly algorithmic traders are growing stronger with time.
To manage risks better and with convenience
Risk management in trading is essential for averting the risk of bearing the losses arising from stock market trade. Risk management involves identification, evaluation and mitigation of risks which usually arise when the market moves in the opposite direction from the expectations.
So, it is really important to set your expectations on the basis of a thorough analysis of the market and after anticipating all the risks.
With the help of algorithmic trading, following are the measures for risk management:
Portfolio optimization
Portfolio optimization implies analyzing portfolios with different proportions of investments. By calculating the risk and the return for each of the portfolios, the optimisation of investments takes place.
For instance, a portfolio can be analysed using the Sharpe ratio with which one can find the ratio of excess return over additional risk taken for each of the investments in the portfolio.
Hence, the portfolio can be optimised in a way that the stocks with a higher Sharpe ratio are more than the ones with a lower Sharpe ratio.
Hedging
Hedging is an investment strategy designed to offset a potential loss or, in other words, anticipated price fluctuations in the future. For hedging, financial instruments like insurance, future contracts, swaps, options etc. can be used to hedge.
For instance, Futures of grade A rice are traded on a commodities exchange and each contract is for 100 kgs. Mani wants to buy 5,000 kgs of grade A rice during the last week of the month while Russell seeks to sell 5,000 kgs of grade A rice during the last week of the month.
Now, the futures contract is suitable for both parties, as a trade can be executed between the two parties for 50 contracts on the exchange.
1% rule and 2% investing rule
1% and 2% rule in trading imply the maximum amount of the risk which is feasible on per trade should be either 1% or 2%. This helps you to avoid the excessive loss that may happen otherwise.
For instance, by using Beta which is the measure of volatility, one can avoid trading if the risk of investing in a stock shows going beyond 2%.
Monitoring the financial markets while utilising advanced technology
Trades should be monitored using artificial intelligence such as machine learning so as to find out the best trading opportunities. For instance, by utilizing machine learning techniques, the algorithms monitor different financial markets as well as financial assets to find the trading opportunities that can be most beneficial.
AI in trading enhances this process by providing real-time insights and adaptive strategies. With the ability to analyze large datasets quickly, AI-driven systems identify profitable patterns and adjust trading strategies accordingly, offering a significant edge in a competitive market.
Avoiding unclear trade setups
If you are using the moving indicators like EMA, MA, etc. and one of them shows a clear trade setup but does not agree with the trade setups of other indicators, it creates confusion.
In such a scenario, it is best to wait for the right trade and not make any decisions when you are not sure about it. For instance, if EMA does not comply with MA, one must be patient for the right trade scenario before indulging in trades.
Stop loss
Stop loss is a buy or sell order which gets triggered when the stock price reaches a specified price known as the stop price. This helps the trader avoid continuous monitoring of the market.
For instance, if you buy the stock XYZ at $50 per share that you feared might drop in price, you could use a stop loss order. The stop loss order can indicate to sell if the price dropped below a certain price. Say, you decide that price to be $45 per share below which the algorithm will sell the stock in order to safeguard against a larger loss.
Conclusion
This article aimed at briefly mentioning the 5 most crucial reasons algorithmic trading is trending these days. Also, the article aimed to help you learn what sets this advanced trading technique apart from conventional manual trading.
If you wish to start learning algorithmic trading, you can enrol in the course Getting Started with Algorithmic Trading! to begin from scratch.
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