Technology is transforming the way we trade in the stock market quickly. Technology has opened several doors, including a new generation of traders considering trading as a full-time vocation, especially during the difficult times of the Covid-19 pandemic. Algorithmic trading is one such technology that has revolutionized stock market trading. It has provided traders with a competitive advantage to improve their skills and outperform traditional trading.

Algo trading in India

The Securities and Exchange Board of India (SEBI) permitted algorithmic trading in India in 2008. It began with Direct Market Access (DMA), which was limited to institutional investors at first, but the trading community embraced it because of the cost savings and improved execution. In June 2010, The National Stock Exchange (NSE) started offering additional 54 colocation server 'racks' on lease to broking firms to improve the speed in trading. And align with global markets.

What is algorithmic trading?

Algorithmic trading uses a computer program that follows a defined set of instructions (an algorithm) to place a trade. The illustrated instructions are based on timing, price, quantity, or any mathematical model. Apart from profit opportunities for the trader, algo-trading renders markets more liquid and makes trading more systematic by ruling out the impact of human emotions and errors on trading activities.

The benefit of Algo trading is that it includes features like back-testing, which allows users to run their strategy and observe how it performs. You might also use simulation in Algo trading to test your strategy in real-time but without having to make any actual deals. However, algorithms should only be used when the user is specific.

How Algo Trading Works

Here's a general overview of how algo trading works:

• Strategy Development: Traders or quantitative analysts (quants) develop trading strategies based on specific criteria, market indicators, technical analysis, or statistical models. These strategies aim to identify potential profitable trading opportunities.

• Coding the Algorithm: Once a trading strategy is defined, it needs to be translated into a computer program. Traders or quants write the algorithmic code using programming languages like Python, C++, or Java. The code specifies the conditions for entering or exiting trades, risk management rules, and other parameters.

• Market Data Feed: Algo trading systems require real-time market data to make trading decisions. Data feeds provide information on prices, order books, trade volumes, and other relevant market variables. Traders typically subscribe to market data providers or exchange feeds to receive the necessary information.

• Execution Platform: Algo trading systems connect to trading platforms or directly to exchanges to execute trades. These platforms provide access to the market and allow algorithms to place buy and sell orders automatically. They may also offer additional features like backtesting , simulation, and monitoring tools.

• Risk Management: Risk management is an essential aspect of algo trading. Traders need to define risk limits and implement safeguards to protect against unexpected market movements or technical glitches. This may include setting stop-loss orders, position sizing rules, or circuit breakers to prevent excessive losses.

• Order Placement: Based on the defined strategy and real-time market data, the algorithmic trading system generates buy or sell orders. These orders are automatically submitted to the market via the trading platform or exchange's API (Application Programming Interface).

• Trade Monitoring: Algo trading systems continuously monitor the market for changes in price, volume, or other relevant factors. They assess whether the conditions specified in the trading strategy are met. If the criteria are satisfied, the system will automatically execute the trade.

• Trade Execution: When the algo trading system identifies a trade opportunity, it submits the corresponding order to the market. The order is matched with available buy or sell orders from other market participants, and the trade is executed. The speed of execution is often a critical factor in algo trading.

• Post-Trade Analysis: After a trade is executed, algo trading systems can perform post-trade analysis. They analyze the trade's outcome, calculate performance metrics, and provide insights for further strategy improvement or adjustment.

It's worth noting that algo trading can be used for various types of financial instruments, including stocks, bonds, currencies, commodities, and derivatives. Additionally, different algorithms can be designed for different trading styles, such as high-frequency trading (HFT), statistical arbitrage, trend following, or mean reversion.

Algo trading has gained popularity due to its ability to execute trades at high speeds, remove human emotion from trading decisions, and handle large volumes of data. However, it also carries certain risks, including technological failures, model assumptions, and market volatility, which need to be carefully managed.

Benefits of Algo trading

The advantages of algorithmic trading over manual trading are one of the main reasons it has grown so popular. The benefits of algo trading include speed, accuracy, and cost savings.

1. Speed

The most significant benefit is speed because algorithms are written ahead of time and run automatically. These deals happen in fractions of a second, faster than humans perceive. Algorithmic trading offers the advantage of scanning and executing several indicators at a rate that no person could match. More chances are accessible at better pricing since trades can be assessed and done faster.

2. Accuracy

Another benefit of algorithmic trading is its precision. When a machine executes a transaction for you, you avoid the risks of mistakenly entering the erroneous deal that come with human trades. Compared to a computer program that has been carefully checked to ensure the correct order is submitted, manual submissions are considerably more likely to buy the wrong currency pair or for the wrong amount.

3. Reduction in transaction cost

Reduced transaction costs are another benefit of automated trading. Traders don't have to monitor the markets using algo trading because trades can be executed without constant oversight. Because of the decreased opportunity cost of constantly watching the markets, the considerable time reduction for trading lowers transaction costs.

Forms of Algo trading

1. Execution

When they don't want to impact stock prices with discrete, large-volume transactions - pension funds, mutual funds, and insurance firms deploy algo-trading to buy stocks in large amounts.

2. Arbitrage

Automated trade execution benefits market makers (such as brokerage firms), speculators, and arbitrageurs; also, algo-trading aids in creating sufficient liquidity for market sellers.

3. Systematic Trading

Trend watchers, hedge funds, and pairs traders find that programming their trading rules and automatically letting the software trade is far more efficient.

Types of algo strategies

Here are some of the common algo trading strategies :

1. Trend Following

This is the most extensively employed approach on the planet. Almost half of hedge funds' assets are invested in this age-old strategy. Has weathered the test of time. In theory, it's a simple notion, but putting it into practice is a challenge. Nevertheless, if done scientifically and with discipline, there is an ample alpha creation opportunity.

2. Smart Beta

Smart beta aims to mix the benefits of passive investment with the help of active investing. Smart beta indexes differ from typical market capitalization-based indices because they use different index construction rules. It focuses on collecting investing characteristics of market inefficiencies in a transparent, rules-based manner. Alternative weighing systems, such as volatility, liquidity, quality, value, size, and momentum, may be used in innovative beta strategies.

3. Means Reversion

This is the following most widely used technique. In range-bound markets, this strategy works effectively. Most of the time, needs are range-bound between 60% and 70%. The risk is more significant because it can be disastrous (i.e., When trend is strong). Stop losses must be rigorously followed. To get the best of both worlds, it can be used with a trend method.

4. Arbitrage

Arbitrage is a trade in which security, money, or commodity is bought and sold in different marketplaces almost simultaneously. The goal of arbitrage is to take advantage of the price differentials between other exchanges for the same financial product. Arbitrage is legal, but it is also beneficial to markets because it promotes market efficiency and provides trade liquidity.

5. Market Making

A market maker is only interested in earning a small profit margin (spread) between the prices at which they purchase and sell shares, and they want to do so as frequently as possible. On both sides of the book, market makers put buy and sell orders. Market makers use a variety of ways to make money that are dependent on price distribution.

To learn more, read What are the different types of Algo Trading?

Examples of Algo Tradings

There are numerous strategies utilized in algo trading, and here are a few examples:

One of the most popular strategies in algo trading is Mean Reversion. This strategy is based on the idea that prices and returns eventually move back towards their mean or average. Algorithms are designed to identify when a price has deviated significantly from its historical average and to then execute trades that anticipate a "reversion to the mean."

Another common example is Statistical Arbitrage, which leverages complex statistical models to identify trading opportunities. In this strategy, an algorithm may identify two stocks that are statistically correlated. If the prices diverge (i.e., one stock goes up while the other goes down), the algo would buy the lower-priced stock and sell the higher-priced one, expecting the prices to converge again.

Finally, the Momentum Strategy is another frequently used algo trading approach. It involves buying stocks that are trending up and selling those trending down. Algorithms monitor market data to identify significant upward or downward momentum in a stock's price and then execute trades accordingly.

Regulations for algo trading

SEBI enacted rules for investment advisors and financial planners in 2013. Following that, in 2014, SEBI passed requirements for research analysts. "Under IA Regulations, there is no stated ban for the use of automated advisory tools by SEBI registered investment advisers," according to the Consultation Paper on Amendments/Clarifications to the SEBI (Investment Advisers) Regulations, 2013, dated October 7, 2016. For a better understanding of the rules governing algo trading, you can check this link https://www.cskruti.com/regulations-for-algo-trading/

Wright's algo based products

The long-only products that we offer on smallcase are all based on data-driven smart beta algorithms, which fall into the low-frequency algorithmic trading category. Popular among these are:

• Momentum is a strategy that focuses on high momentum stocks that show strong growth prospects. The portfolio is carefully curated to optimize potential returns while minimizing risk. To learn more about momentum, read Complete guide to Momentum Investing & the Wright Momentum Portfolio

• Multi-Factor Balanced is a multi factor strategy that chases factors such as momentum, value, growth, quality, low volatility etc. Our AI & Quantitative models analyzes the stock universe based on several key factors. Employing sophisticated statistical concepts to fine-tune stock selection and optimise performance, 20-25 high-quality stocks are selected aiming for growth while minimising risk exposure. To learn more, read Complete guide to Factor Investing & Wright Balanced Multifactor Portfolio

We have also launched medium frequency algorithm products trading weekly options on an intraday basis. These algorithms can be found on Tradetron and will also soon be available on Algobulls.

The two newly launched products are:

1. Intraday Theta Decay ( Option Selling ) is an intraday options selling strategy traded on the weekly expiry. We will be short a naked option and a hedged option to gain from conditional market conditions the decay of theta. All positions are squared off intraday, and this is a highly leveraged strategy only suitable for people who understand derivatives.

Find more about the Option Selling algorithm, including the backtested performance, methodology, and how to invest.

2.Intraday Momentum ( Options Buying ) is an intraday options buying strategy traded on the hedged option strangles on weekly expiry. We will be buying a call strangle or put strangle based on momentum signals based on market conditions. All positions are squared off intraday, and this is a highly leveraged strategy only suitable for people who have an understanding of derivatives.

Find more about the Options Buying algorithm, including the backtested performance, methodology, and how to invest.

To learn more about Algo Trading check out these articles

1. What are the different types of Algo Trading?

2. Best Algo Trading Platforms in India: Our Recommendations

FAQs

1. Can algo trading be profitable in India?

Yes, algo trading can be profitable in India, but it depends on the effectiveness of the trading strategy, market conditions, risk management, and the trader's skill in developing and implementing algorithms.

2. How can I get started with algo trading?

To get started with algo trading, you first need to have a clear understanding of the financial markets and trading strategies. Knowledge in mathematics, statistics, and programming is also beneficial as developing and tweaking algorithms require these skills. Once you have a grasp on these areas, you can then choose a programming language to write your algorithms. Python and R are often popular choices due to their simplicity and extensive financial libraries. Afterward, you can backtest your algorithm on historical data to see how it would have performed in the past. Once you're satisfied with its performance, you can then implement your algorithm on a trading platform. Keep in mind, you should start small and use risk management practices, as algo trading can amplify both profits and losses.

3. What factors should be considered when implementing algo trading?

Important factors to consider when implementing algo trading include:

• Defining clear trading objectives and strategy.

• Obtaining reliable market data feeds.

• Developing robust algorithms and conducting thorough backtesting.

• Implementing risk management measures.

• Choosing the right trading platform or technology.

• Monitoring and evaluating the performance of the algorithms.

4. Are there any regulatory guidelines for algo trading in India?

Yes, the Securities and Exchange Board of India (SEBI) has issued regulatory guidelines for algo trading in India. These guidelines cover areas such as risk controls, testing requirements, co-location, algorithm approval process, and reporting obligations. It is important to comply with these guidelines when engaging in algo trading activities.

5. What are the potential risks and challenges associated with algo trading?

Some potential risks and challenges of algo trading include:

• Technical glitches or system failures.

• Inaccurate or incomplete data leading to faulty trades.

• Rapidly changing market conditions.

• Regulatory compliance and legal risks.

• Over-optimization of strategies leading to poor performance.

• High competition and limited opportunities for arbitrage.

6. Where to find the best algorithmic trading platform?

There are several algorithmic trading platforms available, and the best one for you depends on your specific needs and preferences. Wright Research is one such popular algorithmic trading platform. It's important to research and compare different platforms to find the one that suits your requirements in terms of features, data access, ease of use, and cost.