Algo (Algorithmic) Trading: Definition, How It Works & Strategies
Have you ever invested in a mutual fund scheme through a Systematic Investment Plan (SIP)? The Asset Management Company (AMC) or the fund house deducts a fixed amount from your bank account at regular intervals. The mutual fund provider depends on a predefined algorithm to deduct a fixed amount from your bank account. Similarly, algorithms are used to buy and sell securities in financial markets. Let us delve deeper and understand the concept of algo trading.
What is Algo Trading?
Algorithms are everywhere around us. The functions in our smartphones are based on algorithms. Popular search engines are based on algorithms. You can also find algorithms in navigation applications and online games. Similarly, algorithms are also used in the investment or trading sector. Algo or algorithm trading is the use of pre-programmed instructions to execute orders. Algo trading executes orders at a high speed, which is impossible for humans to achieve. Pre-determined instructions are fed into a trading system, which executes orders on behalf of the investor.
A trading platform with pre-programmed algorithms will wait for the desired conditions. Once the predetermined conditions are met, the trading system or platform executes orders at a super-high speed. Financial institutions, AMCs, hedge funds, and other entities actively indulge in algo trading to execute orders in the share market. Retail investors usually do not have the infrastructure and expertise required to indulge in algorithmic trading. However, many stockbrokers have started offering algorithmic trading tools and automation facilities to retail investors in the past few years. Retail investors might use pre-built algorithms to execute their orders.
How Does it Work?
Investors indulged in this phenomenon need to develop a trading strategy first. The trading strategy will be based on rules like order quantity and risk level. Based on the trading strategy, an algorithm is developed. All conditions for executing the order are mentioned in the algorithms. The trading algorithm monitors the market prices and movements at all times. When the predetermined conditions are met, the algorithm executes the order automatically. There is minimal or no manual interruption in algorithmic trading.
Let us understand how algo trading works with a real-life example. A fund house can use an algorithm to implement the mean reversion strategy. The mean reversions strategy states that the price of an asset will revert to its historical average with time. In such a case, the fund house can develop an algorithm to buy 100 shares when their prices are below the historical average. The algorithm can also execute a sell order when the price of shares is above the historical average.
Strategies of Algo Trading
Now that you understand what algo trading is, here are the common strategies used by investors:
Investors try to follow trends with the help of pre-programmed algorithms. Moving averages, momentum trading, channel breakouts, and other trends are used. The algorithm will execute the order when the market achieves the desired trend.
Investors might buy securities in one market and sell them in another to make a profit. Arbitrage trading strategies can be implemented with the help of pre-programmed algorithms. It allows investors to make profits on price differences in two markets for the same asset.
This strategy works on the principle that the price of an asset will revert to its historical mean over time. Pairs trading and Bollinger bands are commonly used mean reversion strategies in algo trading.
Mathematical or Statistical Models
Many investors indulge in quantitative or machine learning-based trading. Such investors will use mathematical or statistical models for algorithmic trading.
Investors might execute orders based on different events. For instance, earnings surprise strategies are used by several algo traders. Orders are executed based on the earnings reports of the company. Pre-defined expectations regarding the earnings reports are fed into the trading system, and the algorithm executes the order accordingly.
Index Fund Rebalancing
Algorithms can be developed for index funds . These algorithms rebalance the fund according to the index it tracks. Algo trading can minimise the tracking error for index funds.
Advantages and Disadvantages of Algorithmic Trading
Now that you have understood algo trading strategies, let us discuss the pros and cons. Beginners or novices might start using pre-built algorithms without understanding the basics. Every trading strategy has some benefits and a few risks. It is essential to understand the pros and cons of algorithmic trading before making a trading decision. Here are the advantages and disadvantages you must know:
High Volume and Speed
Algo trading allows for the rapid execution of a high volume of orders, often faster than manual trading.
Orders executed by algorithms are highly accurate, minimising the chances of human error.
Pre-programmed algorithms follow exact instructions provided by the investor, reducing the need for manual intervention.
Algo trading can significantly reduce investment costs, particularly for institutional investors.
Algo trading is used to implement various risk management strategies, including stop-loss , fund rebalancing, and portfolio rebalancing.
These advantages make algorithmic trading a valuable tool for both individual and institutional investors seeking efficiency, precision and cost savings in their trading activities.
Technical glitches can affect algorithmic trading, leading to issues like order execution delays and software bugs.
Algorithms may struggle to adapt to rapidly changing or highly volatile market conditions, potentially leading to undesired outcomes.
Overly optimised algorithms that rely solely on past data can fail to adapt to current market conditions, resulting in potential losses.
Lack of Human Judgment
Algorithmic trading lacks real-time human judgment, making it less suitable for investment strategies requiring subjective decision-making.
The success of trading algorithms heavily depends on the accuracy of the input data; discrepancies can lead to algorithmic failures.
Institutional investors may face legal and regulatory challenges if algorithmic trading results in incorrect or problematic trades.
While algorithmic trading offers various advantages, these considerations and drawbacks highlight the need for careful monitoring, risk management, and regulatory compliance in algorithmic trading strategies.
Algo-Trading Time Scales
Algo trading is implemented across several time scales based on investment needs. Here are the different time scales in algorithmic trading:
HFT or high-frequency trading can execute orders between milliseconds and microseconds. Advanced algorithms are used to execute orders at such a high speed.
Some investors might want to place orders between milliseconds and seconds. Such investors use the low-latency time scale for algo trading.
Many intraday traders place orders between seconds and minutes through algorithmic trading. They might even use an hour-based time scale for executing orders. Intraday traders focus on taking advantage of short-term price movements within a given day.
Swing traders take advantage of the market’s short- to medium-term price movements. The time scale for swing trading can be between hours and days.
The time scale will also differ in position trading, arbitrage, event-based trading, and long-term investing.
Algo trading allows investors to execute orders at a super-high speed. Investors can take advantage of short-term price movements by executing orders in quick succession. However, a few risks are associated with algorithmic trading, like low human interruption, data discrepancies, and over-optimisation. Beginners in the stock market can use pre-built algo trading tools for faster trading. Start implementing algo trading strategies now!