What is Algo Trading?

• In algorithmic trading or algo trading, computer programs are used to automate the execution of buy or sell transactions. 

• The trade is executed when the pre-defined parameters, such as price, volume, or moving average are met. 

• You need a defined strategy, real-time market data feed, and sophisticated API, among other things to effectively use algo trading. 

• The risks of algo trading include technical failures, market volatility, operational monitoring, and more. 

Understanding Algorithmic Trading 

An automated computer program designed to execute market trades only when a series of specific variables are met is called Algorithmic Trading. As per the Securities and Exchange Board of India (SEBI), algo trading “includes any type of automated rule-based trading where decision-making is delegated to a computer model.” 
It simply means, instead of a manual order placed by a trader, an automated software monitors the financial markets in real time on multiple crucial variables such as price action, volume, time, order books and trends. Once these specific rules align with the coded program, the software executes a trade that is completely free of human emotions, such as greed or fear.  

• Key Components of Algorithmic Trading 

  Algo trading is a broad spectrum that constitutes several aspects to make it a successful trading mechanism. Listed below are some core components that are required for effective algorithmic trading: 

  • Trading strategy: Having a fixed set of rules is the most critical element of any algo trading model. A series of precise and analytical instructions stating when to buy, sell or exit a position is the crux of all algo trading. These commands would typically include quantitative parameters like price action, different types of moving averages, time, volume, lot size, commodity type and other vital indicators.  

  Here’s an example: The algo would execute a buy order when a stock falls by over 2% in a timeframe of 10 mins and volume is above 100 shares.  

  • Market monitoring and data feed: The algorithm requires historical as well as real-time market data like volumes, last traded price, moving averages, price volume trend, order books, among other significant variables. The software continuously monitors this data and looks out for stocks that match all parameters to execute a buy, sell, or exit position. An algo trade can only be successful if the algorithm can pick on this data at the correct time and make timely trades. 

  • Order execution: Once the algorithm fulfils all the coded parameters, an order is executed. This order is sent to the exchange via an Application Programming Interface (API). The API must be highly sophisticated and seamlessly communicate with the trading platform and the stock exchange in real time. It must monitor and access critical data while performing key functions such as executing limit orders and setting stop losses without human intervention.  

  • Risk management: Most people believe Algorithmic Trading means more profit because it is automated. Algo trading is fundamentally more of a risk management programme that eliminates human emotions and can be customised as per the trader's risk profile. While some retail traders may want a system that is more oriented towards capital preservation, institutional traders could opt for an algorithm that is more dynamic and execute volume orders for maximum profit booking. Ideally, an algo trading program must cater to both kinds of traders while working seamlessly with the API. 

  The Algorithms must also have inbuilt risk management mechanisms like exiting positions when the circuit is hit, abnormal market fluctuations, or any other risks that can be mitigated right at the onset, saving the trader from booking a loss. SEBI has several guidelines around risk management in algorithmic trading.  

  • Back testing and validation: An algorithm must be back-tested for a substantial period of time, under various scenarios. Simulated market conditions, such as bull runs, price crashes, circuit breakers, gap-up or gap-down openings, news-driven market fluctuations, and other real-world scenarios, must be tested to see how the algorithm would react. Back testing data and validating the output of the algorithm is vital to predict the accuracy of algo trading once the program is live.  

  • Infrastructure and latency management: Infrastructure is the backbone of any algorithmic trading. It mainly consists of two parts: The first is the software where the program runs from, and the second is the broker site that is linked with the algo trading API. A robust infrastructure with minimal latency is vital for smooth functioning for booking profits, especially while making heavy volume and high frequency trades (HFT) through algo trading. Secure servers with minimal downtime are crucial for HFT, any kind of technical glitch or failure can leave a huge financial impact on the trader.  

  • Monitoring algorithmic trading: While algo trading is completely automated and free of human bias, it cannot just be connected to a trading account and left unchecked. The trading account should be monitored regularly for bugs, abnormal activity and risk parameters to adapt to current market conditions. Regular checks on the algo trading account are essential for optimal resource utilisation and profit booking.   

  • Audit trail and compliance: SEBI has mandated that all algorithmic trading must strictly adhere to its guidelines for compliance and regulatory reasons. All algo trades must be logged and traceable. All algorithms must be coded with unique signatures that can be traced easily during audits of trading accounts, brokers, or exchanges.  

  Example: A trader has coded an algo to execute a trade in Bank Nifty stocks, implementing the moving average crossover strategy. The condition is to buy when the 5-day average of the stock crosses above the 30-day moving average, and sell when it falls below.  

  The algo will monitor Nifty stocks constantly and execute the trade as soon as the criteria are met. The algorithm can also include certain risk parameters, such as a stop loss of 2% and a cap of ₹50,000.  

  Understanding the workings of algo trading is important for any trader looking to switch to automated trading for maximum benefits while lowering their risk profile. 

  Also, learn What is Slippage Trading here. 

  How Does Algo Trading Work? 

  Algorithmic trading is a seamless collaboration of sophisticated software programming skills and financial markets expertise. Successful algo trading requires clear and effective trading strategy, a fundamental understanding of math, and a strong coding background.  

• It is a computer programme that is built specifically for executing buy, sell and exit market positions during trading hours when a stock meets all pre-defined parameters. These algorithms are developed for more accurate trading results while eliminating the scope for human error.  

• An important aspect of understanding how algorithmic trading works is that it must function in real time, and seamlessly while working simultaneously on the broker’s trading account, the API, and the exchanges for the trader to benefit from algo trading. 

  Difference Between Algo Trading Vs Manual Trading

  Algo trading is an automated computer program used to execute trades, and humans do manual trading. The table below illustrates the difference between the two: 

  Quality	Algo Trading	Manual Trading
  Decision Making	Completely automated by a program with predefined parameters and rules	Heavily dependent on human analysis and news-driven speculations
  Speed	Trades are executed in real time	Typically takes longer, since orders are placed manually
  Emotional Bias	Decisions are only data-driven	Human emotions such as greed, fear and risk-taking behaviours often come into play
  Consistency	Algo trades are based on the same strategy.	Decisions can vary and several strategies can be applied in manual trading
  Accuracy	Comparatively higher than manual trading with no human errors	Several variables such as human biases, calculation errors, speculative decisions
  Monitoring Trading Account	Must be monitored regularly for bugs and system maintenance	Constant monitoring of trades during trading hours.

• Simply put, algorithmic trading vs manual trading highlights how automating trades can reduce errors and improve accuracy, while manual trading is time-intensive and heavily dependent on the personal skills of a trader. 

• Also, Learn the DIfference Between Equity Shares and Preference Shares here. 

• Difference Between Algo Trading Vs High Frequency Trading [H2] 

• Algorithmic trading and high-frequency trading both rely on computer-automated execution of trades but are very different. They differ in the frequency of trades, speed, and type of trading. The table below will show you the distinct differences in algo trading vs high frequency trading in detail: 

  Variable	Algo Trading	High Frequency Trading
  Meaning	A program made of set rules to execute trades	A highly sophisticated form of algo trading that focuses on high-speed frequency trades
  Execution Frequency	Will execute trades only when set parameters are met in real-time	Executes thousands of sell, buy and exit positions within seconds
  Main Objective	Higher accuracy trading eliminating human biases	Books profit from high-volume trades within seconds
  Software	Runs on broker portals via APIs	Needs advanced infrastructure, minimal latency, and dedicated servers to handle  extremely high volume trading within seconds
  Trader	Typically, retail and institutional traders use algo trading	Proprietary trade firms and large institutional players use HFT trading regularly

• To sum it up, algo trading vs high frequency trading highlights how HFT is algorithmic, but algo trading doesn’t necessarily become high frequency. 

  Also, Learn the Difference Between Order Book and Trade Book here. 

  Understanding Algo Trading Benefits 

  Algorithmic trading is a modern way of trading, and like any upgrade, it has several benefits too. Let us discuss some of the important ones: 

  • Speed and efficiency: The algo can assess, analyse and decide within seconds, adapting to the ever-evolving market conditions in real-time, making it one of the most significant algo trading benefits.  

  • Accuracy and discipline: Algo trading benefits bring out a refined way of trading that has rules and discipline. It eliminates human intervention, which boosts accuracy manifolds.  

  • Back-testing capabilities: One of the vital advantages algorithmic trading offers is using historical and real-time market data to back-test new trading strategies before implementing them in the real markets.   

  • Cost and time savings: Trading accounts need not be closely monitored, as they will run on algorithmic trading, which not only saves time but also reduces overall costs and effort of executing trades manually.   

  • Diversification: Algo trading can be deployed across multiple trading instruments, using different strategies for each. This not only reduces the risk profile but also allows for portfolio diversification. 

  These points tell us the main advantages of algorithmic trading, and it has brought about a better change in the way people trade.   

  Algo Trading Strategies  

  All algo strategies are designed to follow a set of rules that must be met to take market positions and book maximum profits. Let us discuss some popular algorithmic trading strategies:   

  Trend-Following: This is arguably one of the most popular algo trading strategies. The algorithm is built to track trends and momentum in the markets, like dips or rallies, and take positions accordingly to optimise trades and book profits.   

  Arbitrage: In this algo trading strategy, the algorithm uses the price difference on different exchanges to pick a more profitable trade. For example, if a stock is cheaper on BSE than on NSE, the algo will take a purchase position in the cheaper stock and a sell position in the higher-priced exchange simultaneously.  

  Mean Reversion:  It is among the widely used algorithmic trading strategies that, when a stock price dips or rallies far from its mean average price, it will lead to a market correction and return to the average price. When the algorithm detects such a stock, it takes a position that can make a profit.  

  Market Making: This is a fairly simple algo trading strategy where the algorithm will list a buy and sell price on the same asset to book small profits in a bid-ask trading style. This strategy is typically used for HFT trades. 

  Most algorithmic trading strategies depend on speed, historical and real-time market data, and technical indicators that assist traders to optimise profits and reduce the risk of financial loss. 

  Algo Trading in India and Securities and Exchange Board of India (SEBI) Regulations 

  Listed below are some key SEBI guidelines for algorithmic trading: 

  • SEBI mandates that brokers and trading institutions must be approved by the National Stock Exchange (NSE) and the Bombay Stock Exchange (BSE) for offering retail participants algorithmic trading. 

  • Any and all orders that are executed via an algorithm must consist of a unique marker that is easily traceable to the algo that initiated it from start to finish.  

  • One of the crucial algo trading rules by SEBI is that it categorically mandates that the NSE and BSE must implement systems that identify the order-to-trade ratio (OTR) and identify any abnormality in algorithmic activities.  

  • All algo trading servers and systems must run on platforms and software that are verified and approved by the two main stock exchanges of India.  

  SEBI has these guidelines in place to protect the interests of the retail trader while maintaining market integrity, transparency, and fairness for all. So, make sure that the broker adheres to these SEBI algo trading rules. 

  Also, read SEBI Guidelines on Mutual Funds here. 

  Risks Associated with Algo Trading 

  Algo trading is an upgrade that brings about speed, discipline, and accuracy to trading like never before, but traders must consider some important issues too. Like all things in the world, algorithmic trading has its advantages and associated risks; let us look at some important ones:  

  1. Technical failures: Easily one of the biggest risks, algo trading is susceptible to server breakdowns, technical errors, system glitches, and coding mistakes. This could lead to the failed execution of trades and incorrect positions taken that could lead to huge financial loss.  

  2. Market volatility: The only certain thing in financial markets is its uncertainty. No algorithm can accurately predict which way the market will definitely move. Sudden fluctuations can lead the algo to make multiple trades within seconds without using human instincts and seeing how the market move plays out. This could rake up losses.  

  3. Overconfidence: Traders tend to get overconfident about the algorithm’s capabilities during paper trading, but this often changes in real-market conditions due to several uncontrollable variables.  

  4. Regulatory Non-compliance: Traders should be aware of SEBI-prescribed guidelines. Trade only with brokers and API providers that strictly meet all regulatory compliances. This significantly mitigates the chances of fraud and loss of capital.  

  5. Operational risks: While algo trading is automated, regular monitoring is needed to ensure there are no irregularities, coding errors, or software bugs that are impacting the algorithm’s ability to perform optimally. Human supervision is essential to ensure the smooth functioning of the algorithm in real-time market conditions. 

  While automating trades makes them more accurate, speedy, and free of human intervention, it is important to know the limitations and associated risks of algo trading. 

  How to Get Started with Algorithmic Trading? 

  Any trader who is keen to begin their algo trading journey must have a fundamental grasp of financial markets and basic computer programming. Traders should be in a position to test these automated trading strategies, assess their risk profile, set risk parameters accordingly, and monitor progress closely to ensure maximum profit booking.  

• Here are some steps on how to do algorithmic trading: 

  1 - Learn the Basics 

  The trader must have some experience in manual trading in different instruments: 

  • Traders must be aware of different kinds of trading, such as equities, futures & options, commodities, and others 

  • It is also ideal if the trader understands market concepts like trading management systems, moving averages and other technical indicators, order types, stop loss, risk-to-reward ratio, etc.  

  • Have a fair understanding of how software programs work to identify bugs or abnormal behavior by the algo to mitigate financial risks.  

  2 - Define Your Trading Strategy 

  Algorithmic trading at its core is a set of pre-decided parameters that must be met for a successful trade. 

  • Lock in the asset and market instrument you want to trade in. 

  • Set your entry/exit parameters, stop loss, and risk profile, and allocate capital for the trade. 

  For instance : An algorithm can exit a position in an equity stock when the price falls by 2% with the all-time low of a 30-day moving average.  

  3 - Select a Platform and Broker 

  To begin your  algo trading journey, select a broker that supports trading via APIs 

  • Ensure to avail algo trading facilities with a broker that is compliant with SEBI guidelines. 

  • Beginners must start with paper trading and back testing strategies before diving into financial markets.  

  4 - Back-test, and Optimise

  Successful algo trading can be achieved by using historical data alongside real-time market movements: 

  • Ideal for back-testing the algo with several market-like simulations to avoid unpleasant surprises in real market conditions.   

  • Identifying gaps in the algo and tweaking it for optimal results.  

  • Maximise profit bookings by identifying the correct risk-to-reward ratio, technical indicators and risk profile settings.  

• 5 - Deploy and Monitor 

  Once the algorithm is thoroughly back-tested, it’s time to deploy it in real markets. 

  • Monitor all algo trades to ensure that the algorithm is working seamlessly while it is live in your trading account 

  • It is prudent to keep an audit on all trades, the risk profile, order books and instruments traded. This is also mandated in SEBI’s algo trading rules.  

  • Traders can also change strategies by using different, updated and new software for their algo trading.   

  • Use different strategies, stop loss limit, volume, and other technical averages to find the one that aligns with your trading goals and profit  

  6 - Maintain and Update 

  Indian financial markets are ever-evolving, and your algorithm must adapt to them as soon as possible.  

  • Updating and upgrading your algo trading account is crucial for long-term and sustainable profit booking.  

  • Algo trading doesn’t guarantee success and high returns in the markets, but it definitely boosts profit and provides a smarter way of trading.  

  Anyone looking to start algorithmic trading or wondering how to do algo trading must keep these points in mind for an optimal trading experience.  

  Conclusion 

  Algorithmic trading has changed the way trades are executed at NSE and BSE. An impactful shift has occurred where trading is no longer speculative, emotionally driven, and biased but is backed with data and made with surgical precision.  

  The ability of algorithms to execute HFTs and retail trades at the same time with higher accuracy within seconds will eventually lead to more liquidity in the Indian financial market. This will also have a direct impact on fair pricing and equipping traders to deal with market fluctuations better.   

  India is seeing a steady rise in traders adapting to algo trading. As per a report issued by the National Stock Exchange, algo trades have steadily risen in the last decade, especially in the equity derivative segment, in daily turnover. Retail traders are opting for brokers that have access to APIs, dedicated servers for HFTs, cloud portals, essential tools needed for algo trading and most importantly, brokers that are aligned with the SEBI compliance regulations.  

  Algo trading isn’t just about speed and accuracy; it is slowly and steadily changing the way India trades. While algorithm-based trading is gaining popularity, it also needs to be monitored and needs to be accountable. SEBI and other regulatory bodies are ensuring this for the new-age Indian trader, bringing transparency and uniformity to this new form of trading.  

  As this technology advances, it will be a huge challenge to provide its access to a much larger reach while ensuring it maintains integrity and is entirely compliant with all government compliances. Algorithmic trading will soon make India a dynamic, technology-driven exchange in the world.