What is Algorithm Trading? : Definition, How It Works, Pros & Cons

An algorithm is a set of instructions a computer follows to solve a problem or perform a task. Trading involves the exchange of financial instruments between individuals or institutions. When these two ideas come together, we arrive at algorithmic trading, often referred to as algorithmic trading.

Essentially, algorithmic trading uses computer programs and logic to automatically execute trades on broker terminals. These trades are based on a set of rules, based on factors such as price, timing, volume, or other market conditions. One of the major reasons why traders are shifting to algorithmic trading is because it can not only process vast amounts of data, but also analyse and place trades at lightning speed. 

Algorithmic trading in India has a relatively young history compared to global markets. The foundation started in the mid-1990s, with the installation of electronic trading systems in the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE).

However, algorithmic trading received only official recognition when the Securities and Exchanges Board of India (SEBI) allowed it in 2008, followed by the development of important infrastructure such as NSE collocalization services and smart order routing in 2011. The advent of these new technologies led to good adoption among institutional investors and proprietary trading firms.

With the introduction of APIs by the brokers, algorithmic trading contributes to substantial trade volume on Indian stock exchanges, thereby affecting market structure and efficiency due to the increase in retail participation. With increased participation by all kinds of traders, algorithmic trading has improved liquidity, reduced spreads and enabled more efficient price discovery.

What is Algorithmic Trading

Algorithmic trading, at its core, means writing a code that tells a computer when to buy or sell securities such as stock, futures or options. These decisions are based on predefined criteria such as value, time, volume or other mathematical models. When the criterion logic is triggered, the algorithm automatically performs the trade without human intervention. This automated process reduces the time interval and removes emotional bias from the trading process. 

Pre-programmed instructions are the heart of algorithmic trading. These instructions, also known as trading strategies, are designed using a mixture of technical indicators, historical data and quantitative models.

For example, it may be a simple instruction such as "If the 5-minute moving average crosses over the moving average of 20 minutes, you can buy 100 shares of a particular company." When such a rule is coded into the system, the algorithm scans the market according to the desired frequency or timeframe, and when the conditions are true, the trade is triggered within milliseconds!

Here are the key components that are required to start algorithmic trading:

• Market Understanding: There is a misconception that algorithmic trading will automatically generate profits. In fact, algorithmic trading starts with having a good grasp of how financial markets operate. The trader should be able to design strategies based on his risk appetite and psychology. 
• Programming Skills: There are some third-party softwares that can assist in algorithmic trading. However, they may lack flexibility. Hence, if the trader is proficient in languages like Python, Java, C++, or Javascript, they can turn their trading ideas into automated codes. 
• Data Analysis: The idea is often very abstract, and it is important to refine it. That is where data analysis helps us take the historical data, do data analysis and try to find trends, patterns, and irregularities that can drive a strategy.
• Math & Statistics: Since most algorithms rely on mathematical models, having a good understanding of concepts like probability, regression, and statistical inference is key to building smarter, data-driven systems.
• Trading Strategy: Once we have the idea in place, we create a complete trading strategy around it. A trading strategy essentially has all the rules clearly written down. It underlines the rules for enter and exit trades, how to manage risk, and what metrics you will use to measure success. If the strategy has re-entry or partial exits, the same is also defined during this step.
• Backtesting: Before going live, it's always a good idea to backtest the strategy on historical data. This can again be done using third-party software or using Python. The code for backtesting is often more complex than coding the live strategy. Also, there is a possibility that you might have to pay for the historical data on which you want to perform the backtest. 
• Live Feeds: Every strategy requires access to a live feed so that the strategy can keep checking if the logic is getting triggered. Hence, it is very important to have reliable and timely data feeds. Your algorithm is only as good as the information it receives.
• Execution Infrastructure: There are 2 elements in the infrastructure. The first is the broker API through which the algorithm will be executed. The second part is the software or platform that will run the algorithmic code. It is important that both are very stable for successful algorithmic trading.
• Risk Management: Even after doing backtesting and coming up with great strategies, there is still a risk of loss in the stock market. Hence, every trading algorithm should have built-in risk management. This includes position sizing, stop losses, and maximum drawdown limits which helps to protect against unexpected losses.
• Monitoring: It is important to understand that algorithmic trading does not mean that we can just run the algorithm and forget about it. We should do regular checks to ensure they’re working as expected and to make adjustments when the market shifts.
• Regulatory Awareness: Algorithmic traders must stay within the legal and regulatory framework. Understanding and adhering to these rules is crucial to avoid penalties.

• Testing & Fine-tuning: Finally, we should constantly test and optimise the strategies and its performance. Always remember that no strategy works forever. So we have to keep finding new strategies and discarding the bad ones. 

Difference Between Algorithmic Trading and Manual Trading

There is a big difference between algorithmic trading and manual trading. One of the most-significant benefits of algorithmic trading is the speed. Unlike manual trade, algorithmic trading performs trades within milliseconds.

Another significant difference lies in emotions. Algorithmic trading is run with zero emotional intervention and ensures that the trade is purely based on logic, while manual traders are receptive to emotional factors such as fear, greed or nervousness.

Consistency in algorithmic trading is another strong point as it follows the same predetermined strategy rules each time, while manual trading may be affected by a trader's mood or prevailing market sentiment, leading to discrepancies.

Scalability is also a big asset. Algorithmic systems can handle several strategies and asset classes at the same time. On the other hand, individual manual traders will find it challenging to analyse multiple symbols at the same time. 

However, when it comes to ease of access, manual trading has the upper hand. Manual trading is easily accessible through mobile or web platforms, while algorithmic trading often requires coding expertise or access to platforms that provide no code solutions. This makes algorithmic trading a little less acceptable for beginners. However, it can prove to be quite powerful for those who can use its ability.

How Does Algorithmic Trading Work?

Algorithmic trading combines financial expertise with programming skills. To start algorithmic trading, one must not only have the trading logic and the strategy, but also have a good understanding of the programming language.

Algorithmic trading works by automating the process of buying and selling securities using a set of predetermined terms and conditions. These rules are coded in a computer program that interacts with the stock exchange infrastructure to place trades with minimal human intervention. 

The process starts by defining a few key elements of the trading domain such as the asset the trader wants to trade, the rules of the strategy, the trading objectives and how much risk he/she is willing to take.

Once the strategy is finalised, an appropriate algorithm is designed which will receive real time data, perform analysis and then check for logic when the entry or exit is to be executed. 

Difference Between High Frequency Trading and Algorithmic Trading

It is important to differentiate between High Frequency Trading (HFT) and algorithmic trading. While both of them rely on automation, they vary greatly in terms of scale, speed and infrastructure needs. 

HFT is known for its ultrafast design, which is capable of placing thousands of trades in microseconds. In contrast, traditional algorithmic trading usually works slowly, often performing trades based on a minute timeframe or subsecond intervals.

The HFT infrastructure is far more sophisticated and demanding and requires a colocation server with direct access to exchange systems to reduce the delay. However, traditional algorithmic trading works effectively through broker APIs and cloud-based systems, making it more accessible to retailers.

When it comes to capital and costs, HFT requires high investment in infrastructure and is generally available to institutions with large capital. On the other hand, traditional algorithmic trading is relatively affordable for retailers, thanks to online brokers which offer APIs to connect to the broker seamlessly. 

The kind of strategies used also differ significantly. HFT strategies often focus on arbitrage and mispricing and can involve market making as well. On the other hand, traditional algorithmic trading is better suited for strategies such as trend following, mean reversion, momentum trading and indicator based strategies.

Overall, HFT is mainly used by institutions due to the costs of high infrastructure and regulatory restrictions. On the other hand, traditional algorithmic trading is becoming increasingly popular with retail investors and traders, thanks to widespread use of APIs that require very little or no coding knowledge.

Programming languages play a major role in algorithmic trading. Python is probably leading the list currently when it comes to creating algorithmic strategies because of its simplicity and huge ecosystem of economic libraries. Here are some reasons why traders choose Python to do algorithmic trading:

• Ease of use: Even beginners can learn Python quickly, making it accessible to traders with limited coding experience.
• Library: Powerful libraries such as pandas, numpy, ta zipline help with data analysis, technical indicators, backtesting and business design.
• Integration: Python is easily integrated with broker API and enables real -time data access and order placement.
• Community Support: A large developer community means learning from the treasure of training programs, forums and shared strategies.

Many retailers are not only using Python to do automation, but also using no-code platforms that translate visual strategies based on their strategy logic. 

Algorithmic Trading Benefits

The fact that algorithmic trading is gaining popularity is due to the number of advantages it offers. Here are some of the benefits of algorithmic trading: 

1. Faster Execution
   With the number of traders and sophistication of strategies increasing, it is imperative to be very quick in trading. Algorithmic trading is designed for exactly that. Algorithms can process data, analyse patterns, come up with logic and execute trades in a fraction of a second. This can help traders to make trades even at the smallest price changes.
2. Improved Accuracy
   Since the system follows predetermined rules and does not depend on emotions, it significantly reduces the possibility of human error. The algorithm does what it is supposed to do as per the predefined rules every time.
3. Handles Multiple Trades at Once
   The Algorithmic system can run multiple strategies at the same time without slowing down or getting overwhelmed. It opens more opportunities without losing control or speed.
4. Backtesting Strategies

Before trading with real money, you can test your strategy using previous market data. This usually helps traders understand how their strategies can perform under different circumstances, and allows them to fine-tune them before making them live.

5. Lower Screen Time and Costs
   Since the system can run trades on its own without continuous supervision, it saves time and reduces the costs that come with manual trade and monitoring. Also, the speed helps in reducing slippage which is a hidden cost in trading. 

Read more: Benefits of Algorithmic Trading in Stock Market

Algorithmic Trading Strategies

Algorithmic trading is not just about automation - it's about automating smart strategies that can utilise market patterns to generate profit. Here are some of the most widely used strategies: 

1. Trend Following Strategies 

These strategies are based on technical indicators such as moving average, MACD or other speed indicators. The logic is simple: Buy when the indicator shows signs of upward momentum and sell when it shows signs of reversal.

Example: Buy Nifty 50 Futures when the 50-day moving average crosses over the 200-day moving average (Golden Cross).

2. Arbitrage strategies

Arbitrage involves taking advantage of the price difference between two markets or securities. While there used to be good arbitrage opportunities in NSE and BSE earlier, now traders often use cash and future arbitrage based on their idea of backwardation or contango.

Example: If Infosys stock is trading at ₹1,500 in the cash market and ₹1,530 in the futures market, the algorithm can buy and sell futures simultaneously to lock in spread profit.

3. Mean Reversion strategies

These strategies are based on the fact that a stock price will return to their historical average over time. Indicators such as Bollinger Band or RSI are usually used to identify overbought or oversold conditions.

Example: Sell ​​HDFC Bank. If it crosses RSI 80 (overbought), it is expected to return to a low price.

4. Scalping

Scalping includes multiple trades in a day to benefit from small price movements. This requires fast execution which can be achieved using an algorithm and strong risk management.

Example: Buy and sell Bank Nifty options based on 1-minute breakout to catch small but frequent profits.

5. Option strategies

The options are ideal for algorithmic trading strategies due to their volatility. Strategies can be made based on VIX, IV, greeks and open interest as well.

Example: Sell ​​ATM straddle at 9:30 with a predefined stoploss and profit. More complexity can be added such multiple profit levels and re-entries. 

Algorithmic Trading in India and SEBI Regulations

While algorithmic trading has been unregulated so far for the retailers, Securities and Exchange Board of India (SEBI) is now trying to regulate algorithmic trading to ensure openness, justice and market integrity.

SEBI has stated that all algorithmic trading strategies used by retail investors through brokers should be approved by exchanges and tagged with unique Algorithmic IDs. Brokers should also ensure appropriate risk control, audit paths and the customer's consent to distribute such strategies.

In addition, the SEBI discourages the use of irregular third-party Algorithm suppliers and emphasises the direct responsibility of the brokers for algorithms offered on the platforms. The purpose of these rules is to protect retailers from black box strategies and ensure a level of trust among the growing retailers who are trying algorithmic trading systems. 

Read more: SEBI's New Rules for Retail Investors in Algo Trading

Algorithmic Trading Risks

While algorithmic trading has a lot of benefits, it also comes with some downsides that traders should be aware of:

1. High Development Costs
   It can be expensive to create and maintain a solid algorithmic trading system. This often requires technical expertise, access to quality data and powerful infrastructure - nothing that every trader cannot accommodate.
2. Heavy Dependence on Technology
   Algorithmic trading depends completely on stable internet and computer systems. If your network goes down or has a technical root, it can't be known as expected - which can cause serious damage.
3. No Human Judgement
   Algorithms strictly follow the programming code. They cannot "think" or adapt as a human being, especially in unexpected or rapidly changing market conditions.
4. Limited Customization
   When a trade sale is formed, it can be difficult to change on the fly. Unlike manual trade, it is not always flexible or comfortable.
5. Regulatory Hurdles
   Algorithmic traders should follow specific market rules. They should know the latest updates in algorithmic trading and understand that they should only work within the guidelines as specified by SEBI.
6. Overfitting the Strategy
   Sometimes the algorithm is very nicely set to previous data and does not work well when the market behaves differently. The work done in the past cannot guarantee profits in the future. So it is important that fine tuning is done up to a certain level, otherwise overfitted strategies do not work during live trading. 
7. Data Delays
   The algorithmic system is highly dependent on real -time data. Even small delays or bad data can cause the wrong decisions and potential losses.
8. Poor Handling of Rare Events
   At the time of unexpected market incidents (such as sudden accidents or news), the algorithm may not know how to react and do irrational trades.

Read more: Is Algorithmic Trading Legal and Profitable?

How to Get Started with Algorithmic Trading? 

Starting with algorithm trading is now easier than ever before, thanks to the growing number of platforms and brokers. You can use the Groww API to automate trades and build customised trading strategies. Here is a step-by-step guide to help kick start your algorithmic trading journey with Groww:

1. Understand the basics of algorithmic trading

Make sure you understand the process involved in algorithmic trading - from writing a trading strategy, coding it and performing trades through the API. You do not necessarily have to be an expert programmer, but a basic understanding of trade logic, market data and automation will help a lot.

2. Choose a broker which supports algorithmic trading

Not all brokers in India offer API access. Groww is one of the few brokers now providing API to retailers, making it possible to connect your proprietary algorithms to Groww’s trading platforms. Some reasons why you may like to choose the Groww API are:

• Easy API Integration: Groww’s API is suitable and well documented for developers, suitable for both beginner and experienced coders.
• Live and historical data access: The API provides access to real-time market data and historical prices to create your strategies.
• Order execution: The API offers quick order execution. You can also modify or cancel orders via API.
• Free API: The API is free and low brokerage fees make it attractive for retail traders.

3. Design your trading strategy

The next step is to create a rule-based strategy that can be coded. For example: "When RSI falls below 30 and sells when it rises over 70, you can buy Nifty futures." Once the strategy is defined, use Python or any other programming language to connect your strategy with the Groww API. 

4. Backtesting

Since you can get historical data using Groww API, you can easily test the performance of your strategy over time. Backtesting helps you understand profitability, risk and reward before putting the actual money at risk.

5. Go live with paper trading or small capital

It is always good to start with paper trading to get confidence. Once your code is working fine, you can make your algorithm live with little capital to assess its working in the live market conditions. You should also keep tracking the performance, updating risk control (e.g. stop loss, maximum trades per day) to finetune and optimise your strategy.

Read More: How to Start Algorithmic Trading?

Conclusion

Algorithmic trading has changed the financial markets by offering unmatched speed, accuracy and efficiency. It allows traders to automate strategies, reduce emotional prejudices and perform more trades in milliseconds. However, it does come with its own set of challenges. From technical disorders and high growth costs to the absence of human decisions and regulatory complications, traders should approach algorithmic trading with a balanced perspective.

Success in algorithmic trading comes from a concrete understanding of both its abilities and its boundaries. Traders should understand that just having the fastest code may not generate profits. It also depends on designing well-tested strategies, handling risks with care and being ready to change with market conditions.

Whether you are a beginner looking for an early automatic trade or an experienced businessman looking for a scale operation, it is important to understand both the allocation and the risk. Algorithm trading can be a powerful addition to your trading journey, with proper preparation, equipment and mentality.