Is Algorithmic Trading Legal and Profitable?

In today's fast-paced financial markets, Algorithmic Trading or Algo Trading has become a powerful tool for traders who want to gain an advantage. Algorithmic trading is based on pre-defined rules based on price, volume or indicators, which are then automated using computer programs to perform automatic trades. This automation helps eliminate emotional decision-making and enables traders to work at greater speed, accuracy and stability.

Algorithmic trading has revolutionised how markets work. In the global markets - it is responsible for significant ownership of the total trade volume - and India is no exception. With the increase in digital infrastructure, better access to trade APIs and advanced analysis tools, traders can quickly use this technology to compete in markets that require accuracy and speed.

Over the years, algorithmic trading in India has seen explosive growth among institutions, hedge funds, retailers and small proprietary companies. Many brokers, including Groww, have introduced APIs that have made it easier for retail traders to build and automate strategies without knowing a whole programming language. algorithmic trading is no longer a niche. A normal retail trader can create data-driven decision-making, backtest ideas, and easily automate strategies. 

Legality of Algorithmic Trading in India

Algorithmic Trading or algo trading has changed the global landscape in the financial markets, including India. To ensure fair and transparent practice, the Securities and Exchange Board of India (SEBI) is the primary regulating authority overseeing algorithmic trading. SEBI recognises the growing importance of algorithmic trading and aims to facilitate the safe and effective integration of algorithms in the Indian securities market. SEBI emphasises the need for a strong risk management and compliance structure to maintain investors' safety and market integrity.

In February 2025, SEBI released a circular titled "Safe Partnership of Retail Investors in Algorithmic Trading". This initiative aims to regulate and monitor the increasing participation from retail investors in algorithmic trading. Some of the major guidelines included were:

• Approval of algorithms: Brokers must receive pre-approval from stock exchanges for each algorithm they provide to retail investors.
• Unique identification marking: All algorithmic orders require a unique identifier, and there should be an audit to increase openness.
• Coordination of algorithm suppliers: Institutions and portals delivering algorithmic trading facilities will have to register with the respective stock exchanges.
• Retail Investor-developed algorithms: Algorithms developed by retail investors who exceed a specified order frequency should be registered on stock exchanges.

There are key compliance requirements for brokers and traders as well. Brokers and traders who are engaged in algorithmic trading must follow the following compliance goals:

• Algorithm approval: Algorithmic traders must secure the necessary permits from stock exchanges for each algorithm they plan to go live in the market.
• Audit: The system should have a process to mark the orders with unique identifiers to ensure traceability and accountability.
• Risk management system: Install effective and strong risk management protocols to monitor and control algorithm trading activities.
• Investors education: Provide extensive information to customers about the risk and functionality associated with algorithmic trading strategies.

While SEBI encourages technological progress in trading, it imposes some restrictions to prevent manipulation in the market. SEBI monitors OTR (order-to-trade ratio) to prevent excessive order placement that may interfere with the market balance. Moreover, access to collection facilities,  which allows traders to keep their servers close to exchanging the server for quick execution, is regulated to ensure fairness and prevent unfair advantage.

As of the latest news, Sebi has expanded the timeline for the construction of the implementation standards related to the role in February 2025 on the safe participation of retail investors in Algorithmic Trading. This extension provides extra time for brokers and exchanges to develop and use the framework required to follow new guidelines. These regulatory measures reflect Sebi's obligation to promote innovation in business technologies and secure investors' conservation and market integrity. Market participants should be informed of the ongoing regulatory updates to maintain compliance.

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

How Algorithmic Trading Works in India?

The performance of any algorithmic trading system begins with sound logic. The logic or rules can be made using technical indicators, price action, volume or statistical models. These algorithmic programs continuously scan market data and punch orders to buy or sell after completing specific criteria - all without manual intervention. When the logic is triggered, the order is routed to the exchange (e.g. NSE or BSE) through the broker's system, which ensures fast and efficient execution, often within milliseconds. This speed and precision are important in markets where opportunities last for some time only. 

API (Application Programming Interface) plays a vital role in connecting the algorithms to traders with brokers and exchanges. Brokers such as Groww offer API that allows developers to automate orders, obtain live market data and manage portfolios. Many programming languages can integrate the algorithm with brokers' APIs.

While algorithms provide speed and stability, no strategy is riskless. Risk management in algorithmic trading is necessary to prevent large, unexpected losses. This includes using a stop loss order, setting maximum loss limits, monitoring, and fine-tuning the strategy for continuous improvements. Traders are recommended to monitor automated systems regularly, as market conditions can change rapidly, and incorrect data feed or other errors can lead to significant losses. 

Traders use a variety of algorithmic trading strategies based on capital, risk tolerance and market sentiments. Popular strategies include trend following (moving average crossovers), mean reversion, arbitrage (cash-future), option-based strategies and price action ideas. Some traders also use news-based analysis or machine learning models for predictions.

Read More : SEBI Regulations on Algorithmic Trading in India

Profitability of Algorithmic Trading in India

The profitability of algorithmic trading is strongly influenced by market conditions. Trending markets often benefit from speed and breakout strategies, while range-bound conditions favour mean reversion systems. Factors such as company financials, RBI policy updates, global signals and Fii Dii activity can quickly alter the market sentiment. The algorithm must be able to adapt itself to remain relevant under different conditions. A strategy that works well during a trending period might underperform when the markets are dull.

In algorithmic trading, speed can create a big difference. The speed includes the time needed to get the live data, analyse the logic, and place the order with the broker, which then sends the order to the exchange. With the increase of API-based trading and colocation services, the execution time in India has dropped significantly. Retailers using broker APIs such as Groww may experience quick execution, although it is not the same as an institutional HFT setup. Minor delays ensure that the orders are near the desired price, reduce smoothly and improve the possibility of capturing profitable trades.

The basic understanding is that a system is profitable because of its strategy. A well-designed algorithm should have a clear rule, backtested results and risk-management parameters. Effective strategies often combine with market structure concepts such as support/resistance or price mantle analysis using technical indicators (e.g. RSI, MACD, Bollinger band). Efficiency also depends on how well a strategy handles losses and drawdowns. The trader should appreciate that no strategy will work every day and should be willing to take losses for overall trading profits.

The major factors that affect profitability are transaction costs, including brokerage fees, exchange costs, STT (securities transactions), and slippage. These costs can eat significantly in profits, especially for strategies that take many trades. Choosing the right strategy with low orders and avoiding overfitting the rules can help increase net returns. In addition, trading only in liquid stocks can help traders control slippage costs. 

How to Get Started with Algorithmic Trading in India?

The first step to enter the world of algorithmic trading in India is choosing a broker that supports API-based or platform-based algorithm trading. Most of the top brokers in India provide strong APIs for retailers. Some third-party platforms also allow you to create, test and automate strategies even without coding skills. When choosing the broker, you should consider factors such as API reliability, transaction costs, access to historical data and customer assistance.

If you plan to create custom strategies, choosing the right programming language is important. Python is the most popular option among traders because of its simplicity. Many libraries, such as Panda, Numpy, and TA-LIB, are very compatible with broker API. R is also used, especially for statistical or machine learning models. If you are a beginner, Python is a great entrance point with many free resources and easy integration with Groww API. 

Before you make your strategy live, it is important to understand the guidelines of SEBI (Securities and Exchange Board of India) that regulate algorithmic trading in India. In 2025, SEBI has given guidelines that the traders must receive approval of their algorithms from the brokers. Moreover, algorithmic trades must contain unique identifiers for openness. Being compliant with regulations protects traders not only legally but also creates confidence in retail market participation.

Backtesting in algorithmic trading is a crucial step. This involves testing your strategy on historical market data to assess how it will work under real circumstances. Many tools such as Tradingview, Amibroker, or python libraries can be used to backtest the strategies. A well-defined logical strategy that has been backtested provides confidence and statistical support, which helps you fine-tune entry/exit points. Backtesting also helps finalise risk parameters before deploying real money in live markets.

Even the best strategies can fail if good risk management is not followed. Set clear stop losses and take profit levels, have good position sizing and monitor your portfolio's exposure in stocks to have higher chances of profitability. The platforms and Python often allow you to integrate these rules directly into the script. Also, the traders can define the maximum loss for the day, and if that loss is triggered, they should stop trading for that day. Protecting your capital is more important than chasing profits - especially in volatile markets where sudden news can trigger wild swings.

Read More: How to Start Algorithmic Trading? Complete Guide

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