Is algorithmic trading right for you? Explore automated systems

An algorithmic trading system is a set of predefined rules or criteria that, based on incoming data, triggers and executes trades according to those established instructions. Trading systems vary widely, ranging from those based on fundamental analysis to those utilising technical analysis, statistical data, or a combination of these methods. A trading system doesn't predict price movements; instead, it reacts to price changes based on predefined rules and strategies.

Building an algorithmic automated trading system demands both market understanding and practical experience. Essentially, traders utilise their expertise to identify recurring market patterns, reactions to specific events, and behaviours of various currency pairs. This experience refines the system by accounting for key elements, including market liquidity, order execution, different order types, economic releases, and fundamental analysis. Traders can use this knowledge in instrument selection, trading hours, or potentially to activate or deactivate the system as needed.

Building an algorithmic trading system used to be an intimidating process for the average retail trader; however, we live in an era where we all have access to free programming tools. It has become far more accessible for traders to utilise an Artificial Intelligence (AI) model to aid in coding a strategy. Many of the commonly used AI tools can help a trader get started. By providing the strategy parameters to the AI model, a coded trading system file can then be downloaded and applied to a trading platform.

For example, an AI-generated trading algorithm written in MQL can be downloaded, applied to the MetaTrader platform, and enabled to execute trades.

The AI’s ability to code enables traders to create and test more strategies, saving them time and effort to focus on improving their trading plans.

• Emotions and biases: An automated trading system can help traders eliminate emotional trading decisions or biases they may have towards a specific market move or a fundamental expectation. This may hold as long as the emotions and biases are excluded when designing the system itself.
• Data processing: Algorithms can process vast amounts of data and execute trades much faster than humans. Designing and testing different algorithmic trading systems enables traders to explore and consider market approaches they may not have previously considered. It enables traders to delve deeper into various technical indicators, allowing them to gain a better understanding of how to incorporate different indicators and tools within the same strategy. Using an automated system will enable traders to approach markets with various methods. A simple example is utilising a trend-following strategy alongside a range trading strategy.
• Backtesting: Algorithmic trading strategies can be tested using historical data to evaluate their performance before being implemented in live trading. The backtesting process relies heavily on the quality of data for the instrument being traded. Backtesting helps traders validate and optimise their strategies. It allows traders to test their methods on different types of datasets, for example, by testing on shorter timeframes or by dividing the testing data into periods that correspond to specific market events. It can also enable traders to employ or apply strategies to various market instruments that exhibit distinct price action behaviour.
• For example, suppose the US dollar's price action is trending due to a fundamental market event, such as a change in interest rate expectations. In that case, it is possible to observe price movements for cross-pairs, which do not include the US dollar, which may provide trading ranges, thus creating an environment for applying a trend-following strategy along with a range-trading one.
• 24/5 Operation: Algorithms can run continuously, allowing for trading around the clock.
  It goes without saying that having a 24/5 active trading system escalates the risk exposure, as the system becomes vulnerable to any market-impacting event.

• System failures: Generally speaking, all software is susceptible to technical glitches, bugs, or network issues that can result in errors or data loss. And that's why traders need to pay attention to such matters and have an understanding of how the platform and their software work.
• Market volatility: Sudden market changes can trigger unexpected algorithm behaviour and losses. An example can be a surprise tariff announcement by the US president or an unexpected interest rate decision by a major central bank, such as the Federal Reserve. A trader employing a mean reversion strategy that seeks small market movements may yield negative results if inadequate risk management procedures are in place.
• Over-optimisation: Algorithms that are too closely fitted to historical data may look great in backtesting, but may not perform well in live trading. It is easy for many to create a chart, apply a moving average, and optimise the average parameters so that it fits the data set perfectly. This is called 'curve fitting,' as it essentially learns the random fluctuations of past data instead of identifying true market patterns.
• Regulatory risks: Rules and regulations related to algorithmic trading or trading in general differ between jurisdictions; these differences may include margin requirements, instrument availability, intermarket access, or trade entry and exit rules.
  One of the most commonly known regulatory requirements that applies only in the US that algorithmic traders must follow and incorporate into their design is the “First In First Out” (FIFO) rule. As the name implies, the rule requires that traders exit their equal-sized trades on the same currency pair in the same order in which they were initially opened.

• Trend following: Trend -following trading strategies or systems are one of the most commonly used approaches, primarily employed on more extended time frames, such as daily or weekly, to allow for potentially larger price movements. Trend following strategies or systems may come with lower transaction costs, however, it can involve other types of costs such as finance fees and rollover. Which is why traders need to be selective of which currency pair or instrument they are trading.
  
  Although a trend -following strategy can generate significant price moves, it should also include parameters to preserve profits made from larger moves. Generally speaking, long-term trends may consist of multiple price dips along the way, which can erase or negate any profits made.
• Moving averages trading systems: Another standard trading system approach is by using price interaction with a moving average or a combination of multiple moving average crosses, for example, a fast average crossing above or below a slower one. Traders can utilise the crosses as points of entry and exit. These types of strategies can be vulnerable to whipsaws; some traders may use filters to minimise their impact.
  
  Moving averages and trading systems can be customised for longer or shorter trading ideas, depending on the trader's choice, and can be implemented on any timeframe. However, it goes without saying that the shorter the timescale, the more frequent the trades are executed, and thus, higher trading costs, which traders need to incorporate into any system or take into account when creating a trading system.
• Technical patterns or breakout systems: It was previously difficult to program an automated system based on a technical chart pattern, as patterns can take many different forms with varying angles, degrees, or price move percentages, making it challenging to code. However, that’s not the case anymore, with the abundance of advanced software solutions currently available. Chart patterns and breakout systems are becoming increasingly accessible. An example of this is the Autoc Chartist MT4 Plugin¹ or the TradingViewChart pattern indicator².

Market inefficiencies – arbitrage: Algorithmic trading offers strategies that aim to capitalise on market inefficiencies. Arbitrage trading strategies involve algorithms that detect and aim to profit from price discrepancies of the same asset or its derivative across different markets.

There are many types of trading systems, and the ideas can be limitless, especially with AI technologies. Advanced algorithmic trading systems incorporate AI into their design, enabling the system to adapt itself to changing market conditions. The above types of trading systems are the basic ones, and as traders continue to develop more advanced strategies, the possibilities are endless.

Numerous platforms can be used for algorithmic trading. These platforms offer various tools and ways for traders to create, optimise, and test their trading systems before making any real investments.

One of the most commonly used algorithmic trading platforms is MetaTrader. The platform offers an easy-to-use interface and programming language that enables traders around the world to create algorithmic trading robots known as “Expert Advisers” or EAs.

The advancement of artificial intelligence has made it a much easier task to create an EA now, as it utilises many of the available AI platforms. Traders don’t need to learn coding anymore; instead, they can input their trading parameters and rules to an AI platform in plain English and download a ready-made EA file, which can be applied to MetaTrader for algo trading.

There are many other key considerations that traders should take into account when building a trading system. By no means should this blog post be considered inclusive of all considerations; however, we have attempted to cover the basics and some of the most important considerations.

Market data feeds: Real-time data feeds provide the most up-to-date market prices, volumes, and other relevant information. This data is crucial for algorithms to make decisions.

Infrastructure: Servers, networks, and software are required to run the trading algorithms and connect to trading platforms and data feeds.

Strategy development: Defining a robust and tested trading strategy is crucial for success.

Risk management: Implementing stop-loss orders and other risk management measures is essential.

Monitoring and maintenance: Algorithms require regular monitoring and maintenance to ensure they function as intended.

Evaluating the results of a trading system is crucial for determining its effectiveness and making necessary adjustments. Traders typically look at several key metrics to assess performance. Profit/loss is the most basic, showing the overall gain or loss. The Sharpe ratio measures risk-adjusted return, indicating how much return is earned for each unit of risk taken. Drawdown shows the maximum decline from a peak, highlighting potential exposure. The win rate, average profit, and average loss help analyse the consistency and quality of trades. By combining these metrics, traders can gain a comprehensive understanding of their system's strengths and weaknesses, enabling them to refine strategies and manage risk effectively.

Footnotes:

¹ https://autochartist.com/metatrader_en/

² https://www.tradingview.com/scripts/chartpattern/