Algorithmic Trading Explained: What Is Algo Trading?

Algorithmic trading has revolutionised the financial markets by automating the process of executing trades with minimal human intervention. 

By leveraging advanced algorithms and high-speed computing, traders manage large volumes of transactions while prioritising trade execution. 

This method, which first gained traction in the 1980s, has since become a cornerstone for institutional investors and trading firms. 

However, despite its popularity on the stock market and in other spheres, algorithmic trading is prohibited when trading CFDs with Plus500. 

Therefore, this article is intended to educate the trader about a key mechanism affecting today’s market landscape, although it will not be directly encountered on the Plus500 trading platform.

Here’s what you need to know about the intricacies of algorithmic trading, including its history, impact, and popular associated strategies: 

What Is an Algorithm?

In order to form a deeper understanding of algorithmic trading, we’ll first need to define what an algorithm itself is. An algorithm in trading is a set of programmed instructions designed to solve specific tasks or make decisions automatically. 

Similar to a recipe that guides the preparation of a dish, trading algorithms use predefined rules to execute trades, analyse market conditions, or price complex financial instruments like financial derivatives. These algorithms are crucial in automated and high-frequency trading, enabling the rapid buying and selling of securities at speeds far beyond human capability. By quickly processing vast amounts of data, algorithms allow traders to respond instantly to market movements, accounting for a significant portion of global trading volume.

What Is Algorithmic Trading?

Algorithmic trading can be understood as using advanced computer formulas and algorithms to execute financial trades automatically. 

Types of Algorithmic Trading

Three algorithm types generally fall under the trading umbrella: execution algorithms, black-box algorithms, and high-frequency trading (HFT) algorithms. 

These systems analyse price, timing, and volume to make trading decisions that traditionally require human expertise. 

History of Algorithmic Trading

Since the early 1990s, when it was invented, algorithmic trading has fundamentally changed the financial industry's daily operations. by enabling faster, more efficient, and precise trading decisions. This evolution can be traced through several distinct phases, each marked by technological advancements and new trading strategies.

The origins of algorithmic trading can be found in the creation of early automated systems designed to streamline stock trading by reducing human error. Companies like Island ECN, founded in 1997, played a pioneering role by using algorithms to facilitate direct trading between participants on their electronic communication network (ECN), eliminating the need for traditional brokers.

The early 2000s marked the advent of high-frequency trading, which introduced sophisticated algorithms capable of executing trades in fractions of sections to capitalise on small price discrepancies. Fueled by improvements in computing power and data processing, HFT allowed firms to gain a competitive advantage. However, this rapid trading also sparked concerns about market stability and fairness.

In recent years, algorithmic trading has embraced machine learning and artificial intelligence (AI), enabling the creation of dynamic algorithms that adapt to market changes and analyse large datasets for hidden patterns. These advancements have expanded opportunities in areas like quantitative trading, portfolio management, and risk management.

How Does Algo Trading Work?

Algorithmic trading, in essence, connotes computer programs doing the work of trade execution according to preset criteria. These programs, or algorithms, are designed to analyse large amounts of market data and identify "signals" that suggest potential trading opportunities. A signal could be any pattern or trend in the data, such as price movements, trading volumes, or even external factors like economic indicators. The challenge is to find reliable signals that consistently lead to profitable trades.

A common approach in algorithmic trading is to use strategies like "mean reversion," which assumes that prices will eventually return to their historical averages. An algorithm might rank stocks by how much they deviate from their average prices and then buy undervalued stocks and sell overvalued ones. The idea is that over time, prices will adjust toward the mean, allowing the trader to capture profits.

To build and implement a trading algorithm, traders set various parameters such as leverage limits, the number of positions, and risk controls. These parameters help manage risks associated with factors like market volatility and sector exposure. The algorithm is then tested extensively using historical data to simulate its performance under different market conditions. Once optimised, the algorithm is deployed in real-time markets to execute trades automatically, aiming to maximise returns while minimising risks.

Algorithmic Trading Example

Imagine a stock trader has created an algorithm to trade shares of Apple (AAPL) stock. The algorithm is programmed to buy 150 shares whenever the 50-day moving average of the stock price exceeds the 150-day moving average—a technical signal known as a bullish crossover, often indicating a potential price increase. The algorithm continuously tracks these moving averages and automatically executes the trade when the crossover occurs. This eliminates the need for the aforementioned trader to monitor the market and ensures that trades are made based on specific, pre-established rules. This is the essence of algorithmic trading.

The Pros and Cons of Algorithmic Trading

Since its emergence in the 1980s, algorithmic trading has become widely adopted by institutional investors and large trading firms due to its ability to enhance trading efficiency, reduce costs, and quickly trade at volume.

However, algorithmic trading also has drawbacks, such as the potential to cause market instability through flash crashes and liquidity issues. Algorithm usage is not allowed when trading CFDs with Plus500, and due to its widespread nature, algorithmic trading may affect the underlying shares or forex pairs linked to CFDs. (Source: Investopedia)

Algorithmic Trading Strategies

Traders across different markets utilise a variety of different strategies when engaging in algorithmic trading, with several combining computer power with technical indicators. Let’s take a look at a few of these:

• Trend-Following Strategies: These strategies focus on identifying and trading based on established market trends, such as moving averages or price level movements. They are straightforward to implement because they don't require predicting future prices; instead, they execute trades when specific trends are detected, like the popular 50—and 200-day moving averages.

• Arbitrage-Related Strategies: Exploits price differences of the same asset in different markets by buying low in one market and selling high in another, capturing the spread as a risk-free profit. Algorithms can efficiently identify and execute these opportunities, often in real-time, across various markets, including stocks and futures.

• Index Fund Rebalancing Strategies: These strategies take advantage of predictable market movements during periods when index funds adjust their holdings to match their benchmark indices. Algorithms can predict and trade ahead of these rebalancing events, capturing small, reliable profits from price movements associated with the adjustments.

• Mathematical Model-Based Strategies: These strategies use complex mathematical models, such as the delta-neutral strategy, which balances opposing positions in options and their underlying assets to minimise risk. These strategies rely on precise mathematical relationships, allowing for sophisticated risk management and profit generation through algorithmic trading.

• Mean Reversion Strategies: Based on the principle that asset prices will revert to their historical average after deviating significantly. Algorithms can automatically buy or sell when prices move outside a defined range, capitalising on the expected return to the mean.

• Volume-Weighted Average Price (VWAP) Strategies: This strategy breaks down large orders into smaller ones, executing them throughout the day to achieve an average price that aligns with the market's volume profile. This strategy reduces the market impact of large trades, aiming to minimise costs by matching the average market price.

• Time-Weighted Average Price (TWAP) Strategies: Similar to VWAP, but focuses on time rather than volume, dividing orders evenly across a specified period to avoid market impact. TWAP is ideal for executing large trades gradually, ensuring the average price paid is close to the overall market price during the trade window.

• Percentage of Volume (POV) Strategies: These strategies adjust the size of partial orders based on a predefined ratio of the total market volume, allowing for dynamic participation as market conditions change. This strategy can be fine-tuned to increase or decrease participation in response to price movements, ensuring optimal trade execution.

• Implementation Shortfall Strategies: These strategies aim to minimise the total cost of executing an order by balancing the immediate cost against potential future gains or losses. The algorithm adjusts its trading pace based on real-time market conditions, increasing participation when prices are favourable and slowing down when not.

Algorithmic Trading vs Automated Trading

Automated trading and algorithmic trading are often confused, but they fill different roles on the financial markets. Automated trading refers to systems that execute trades automatically based on pre-set conditions, without the need for ongoing human intervention. These systems typically rely on straightforward criteria, such as buying or selling when a specific price is reached. The execution is automated, but the trade signals often come from manual input or simpler indicators involved in technical analysis.

In contrast, algorithmic trading is more technically sophisticated. It involves programming algorithms to identify optimal trade setups and make decisions based on a variety of factors, such as historical data, real-time market conditions, and complex mathematical models. These algorithms not only execute trades but also continuously analyse market data to adapt to changing conditions, making them more flexible and dynamic than automated trading systems. The primary difference lies in the complexity and adaptability, with algorithmic trading offering a more nuanced and data-driven approach to executing trades.

Conclusion

Algorithmic trading represents a significant advancement in the world of finance, enabling faster, more accurate, and often more profitable trading strategies. Its ability to analyse vast amounts of data in real time and execute trades has changed the way markets function. However, it also presents challenges, including reliance on technology, potential market instability, and high development costs. As financial markets continue to evolve, algorithmic trading will likely remain a crucial tool, but market actors will continue to carefully weigh its benefits against the risks and complexities it introduces.

FAQs

Is algorithmic trading different from high-frequency trading?

Yes, algorithmic trading is a broader concept that encompasses various strategies, including high-frequency trading (HFT), which specifically focuses on executing large numbers of trades at extremely high speeds.

Is algorithmic trading legal?

Algorithmic trading’s legality and the specific regulatory measures in place regarding its practise depend on the sphere of trading and jurisdiction. This type of trading is not allowed on the Plus500 CFD platform.

What is the most commonly used strategy for algorithmic trading?

There is no single strategy that outweighs the others when it comes to algorithmic trading, but popular strategies used by market actors around the world include trend-following, arbitrage, and mean reversion.

How successful is algorithmic trading?

The success of algorithmic trading varies based on the quality of the algorithms, market conditions, and execution, but it has proven to be highly successful for many institutional investors and trading firms.