What is Algo Trading?

Last Updated: 18 Nov 2024

When you unlock your phone and open any application, it functions based on algorithms. What you do, what you see, and how the application is customised to match your preferences the reason is algorithms. With the advent of advanced technologies, almost every sector is basing its effectiveness on this piece of logical code. Algorithms leverage user data, past patterns, and a designated set of prespecified instructions to achieve the set goals. For example, an algorithm is used by Mutual Fund companies to deduct the set amount from your bank account every month towards a SIP.

Algorithms are not new to the Indian financial market as they are used in the virtual transaction system to ensure trading transparency, effective user experience, and the mitigation of lags or glitches. However, using algorithms is not limited to depositories or stockbrokers.

Strategies of Algo Trading

There are numerous strategies investors use to undertake Algorithmic Trading. The most common are listed below:

• Index Fund Rebalancing: Index Funds continuously rebalance their portfolio to match the underlying asset’s current market price. In this way, they create opportunities for algo traders to capitalise on the expected trades and make profits from the difference of 20-80 basis points. These trades triggered by the index fund rebalancing are majorly undertaken through the use of algorithmic trading.
• Trend Following: This type of algorithmic trading is the most common among algo traders. In the process, they use moving averages, price movements, channel breakouts, etc., to prepare a set of instructions for the algorithmic trading software. Once the set trend is achieved, the software executes the order for the investor.
• Arbitrage: Arbitrage is when you buy a lower-priced stock from one market and sell it simultaneously in another market where the stock price is high, making a profit from the price difference. Investors leverage data to identify such stocks that are trading at different prices and then use algorithmic trading to implement buy and sell orders in both markets.
• Mathematical Model: Investors use proven mathematical models to simultaneously trade on the same underlying asset’s stock and derivative. Since it can be a complex set of transactions, they use algorithmic trading to identify such assets and execute orders among various asset classes based on price fluctuations.
• Mean Reversion: This strategy promotes the temporary highs and lows of an asset, and if given needed time, the asset price will always revert to the mean value (average price). The investors use algo trading to define the asset’s price range and ensure they buy/sell the asset automatically if it breaks in or out of the defined range.

Advantages and Disadvantages of Algorithmic Trading

Advantages:

• Speed and Precision: Unlike human beings, algorithms execute trades in milliseconds, which allows traders to react quickly to market conditions. This exceptional pace helps in capitalising on short-term opportunities.
• Lower Transaction Costs: Algorithmic trading automates procedures that streamline trade execution. It helps minimise the necessity for human intervention and decrease associated charges, particularly in high-frequency trading.
• Removal of Emotional Biases: Algorithmic trading decreases the impact of human sentiments that generally impact trading choices. The algorithm adheres to a set strategy consistently, ensuring discipline amidst the volatile nature of financial markets.
• Algo Backtesting for Optimisation: Prior to implementation, algorithms can be rigorously examined against historical information to assess performance and refine strategies. This assists in pinpointing potential risks and rewards.
• Multiple Market Access: Algorithms can simultaneously track and trade across numerous markets, capturing price gaps and diversifying risk. This permits complex strategies like arbitrage and global asset allocation that demand coordination across many trading venues.

Disadvantages:

• High Initial and Maintenance Costs: The cost of setting up and maintaining algorithmic trading systems involves significant investment in technology, infrastructure, and expert knowledge, which makes it a huge hurdle for smaller traders.
• Dependency on Technology: Algorithmic trading is based on technology and, therefore, is vulnerable to technical problems like power outages, software bugs, or internet disruptions that could bring about enormous losses.
• Amplified Market Volatility: In times of economic turbulence, algorithms may increase unpredictability by executing huge volumes of transactions swiftly, impacting prices and leading to market disruptions.

How Algorithmic Trading Works 

Algorithmic trading operates by employing computer systems to automate the buying and selling of securities, with algorithms preset to trigger transactions based on certain conditions, including price action, timing signals, or trade volume. Traders and firms design these algorithms to scrutinise real-time market behaviour for trends and patterns that could provide clues about potentially lucrative opportunities.

The approach involves algorithms sifting through huge archives of historical and live market data to derive predictions. Guided by predefined filters, the algorithm then independently makes trades without human interference, which can reduce emotionally driven choices and boost efficiency. For example, if a certain stock reaches a predefined price point, the algorithm might immediately launch a buy or sell order.

Algorithms frequently exploit various strategies like arbitrage, momentum investing, or mean reversion tactics. They can react within milliseconds, allowing trades to benefit from even minor cost fluctuations in the market.

Moreover, algorithms can multitask multiple trades simultaneously, scaling operations that would be impractical for manual trading. The speed and efficiency of algorithmic trading make it an alluring choice for hedge funds and institutional investors aiming to optimise returns and mitigate risk in high-frequency, data-driven markets.

Technical Requirements for Algorithmic Trading 

Implementing the algorithm utilising a computing device is the ultimate part of algorithmic trading. It is accompanied by testing it on prior periods of market effectiveness to determine if employing it would be rewarding. The challenge is to transform the detected strategy into an incorporated computerised approach that has accessibility to a trading account for placing instructions.

Here are the necessities for algorithmic trading:

• Computing-programming expertise to program the needed investing approach hired programmers or pre-built investing software.
• Network connectivity and access to exchange platforms to place instructions.
• Accessibility to market information feeds that will be checked by the algorithm to spot opportunities to place buy and sell orders.
• The capacity and infrastructure to test the framework once it is constructed prior to using it on genuine markets.
• Availability of past data for testing based on the intricacy of guidelines set within the algorithm.

Types of Algorithmic Trading 

• Arrival Price Algorithms: These are designed to process transactions at rates as proximate to the order’s initiation as realistically possible. By prioritising swift execution, they minimise the timeframe between deciding to deal and actually performing, which decreases vulnerability to possible unfavourable price variations.
• Basket Algorithms: Also termed portfolio algorithms, these are intended to jointly oversee multiple securities within an investment simultaneously. They distribute the effect of each order across the portfolio, considering factors like cash flow, financing limitations, and participation boundaries.
• Implementation Shortfall Algorithms: They focus on minimising the gap between the intended or decision price (the price when the choice to deal was made) and the actual execution price. This strategy aims to reduce the “slippage” or costs linked to timing discrepancies.
• Percentage of Volume: This type of algorithmic trading adjusts trade size dynamically using live trading figures. The aim is to sustain a steady percentage of overall market activity, assisting timing without impacting the stock’s cost.
• Single-Stock Algorithms: These are customised for exchanging solitary securities instead of portfolios. They consider factors like market state, liquidity, order size, and timing to streamline every exchange’s execution.
• Volume-Weighted Average Price (VWAP): These algorithms intend to execute orders at costs that approximate the volume-weighted normal cost of the stock over a particular period. This sort of calculation is particularly handy for merchants hoping to maintain a strategic distance from market effect, as it spreads orders according to market volume over time.
• Time-Weighted Average Price (TWAP): These algorithms aim to evenly distribute trades across a predetermined timeframe, seeking an average execution cost that mirrors the time-weighted normal of the stock’s price. This tactic helps decrease market impact, as it prevents sudden huge trades that may move the price.
• Risk-Aversion Parameter: These can work together with other algorithms to change the aggressiveness of trades determined by the trader’s or customer’s risk tolerance. For example, a more risk-averse trader might favour slower, less impactful trades to circumvent substantial price changes, while a risk-tolerant trader could prioritise quick execution over reducing market impact.

Example of Algorithmic Trading

Let us consider a trader implementing two calls based on a stock’s moving averages. Moving averages are averages of previous prices that smooth volatility and uncover trends.

When the 50-day moving average crosses above the longer-term 200-day average, the trader would purchase 50 shares of that firm. Meanwhile, the algorithm would sell any remaining holdings should the 50-day average fall under the 200-day average.

Monitoring prices and averages, a computer program automatically places buy and sell orders without the trader’s constant vigilance. By programming only these two directives, the software analyses opportunities and executes trades according to the stipulations, freeing the individual from manually tracking charts and placing orders.

The system recognises and capitalises on prospects precisely as intended through algorithmic trading’s impartial implementation.

Future of Algorithmic Trading

The prospects for algorithmic trading appear quite promising, propelled forward by technological breakthroughs in artificial intelligence and machine learning. As these technologies continuously progress, algorithms will evolve to incorporate increasingly sophisticated strategies that dynamically react to real-time market fluctuations.

Enhanced analytic capacities will augment predictive precision, optimising the timing and exactness of trades. However, the buzz surrounding algorithmic trading has invited regulatory scrutiny as watchdogs aim to guarantee stable and equitable market conditions.

Moreover, considerations involving market manipulation from an ethical standpoint are anticipated to shape customary industry protocols. But algorithmic trading seems destined to spread further, offering novel prospects for traders while emphasising the importance of responsible and regulated practices under close watch.

Frequently Asked Questions

Does Algo Trading work?

Algo Trading is an effective trading style that numerous investors and big financial companies undertake to make trades every day. As you are the one who feeds the instructions to the software, you can ensure the execution remains safe and within your goals.

Is algo trading profitable?

Yes, algo trading can prove to be highly profitable if done with proper knowledge of the software’s features and the market. As algorithmic trading allows you to execute orders within seconds, you can better utilise the price movement and make high profits.

Is algo trading legal?

Yes. Algorithmic trading is entirely legal. As long as you do not break any rules set by the authorities, you can legally trade using various recognised algorithms.

Is algo trading allowed in India?

Yes, algo trading is legal and allowed in India. It was introduced in 2008, and SEBI still maintains its legality. Almost all stockbrokers, including IIFL, offer algo platforms to their customers for algorithmic trading.

Can you make money with algorithmic trading?

Certainly, generating income through algorithmic program trading is feasible for those with relevant expertise in quantitative analysis and market timing protocols. But while some traders might gain profits, losses are also possible, particularly in volatile markets. 

What programming language do algorithmic traders use?

Algorithmic traders frequently leverage various programming languages depending on their objectives, the platform they utilise, and the computational needs of their automated trading strategies. Some common languages used include R, Python, and C++.