What is automated trading and how does it work?

What is automated trading and how do I use it?

Traders at all levels can use this guide to deepen their understanding of automated trading. Combined with our detailed exploration of algorithmic trading, it offers the knowledge and tools to refine your approach and incorporate effective strategies into your trading plan.

How does automated trading differ from algorithmic trading?

This is critical and often misunderstood. Algorithmic and Automated trading are not the same, though they are closely related and frequently overlap.

The terms "algorithmic trading" and "automated trading" are frequently used interchangeably and can also loosely be described as BOTs (effectively a trading roBOT). Still, they have very distinct meanings and differences:

Algorithmic trading

• Definition: Algorithmic trading involves using complex mathematical models via algorithms to execute trades based on predefined rules and criteria. These criteria are often based on technical indicators, price, volume, and other quantitative methods.
• Purpose: The focus is on creating algorithms to make decisions based on data analysis and market conditions, such as identifying trends, arbitrage opportunities, or high-frequency trades.
• Customisation: Traders can create sophisticated algorithms involving custom coding, backtesting, and extensive data analysis to optimise performance and trading results.
• Examples: These include high-frequency trading (HFT), trend-following, mean-reversion strategies, and statistical arbitrage.

Automated trading

• Definition: Automated trading, on the other hand, refers to the broader process of using technology to execute trades automatically using pre-defined buy or sell orders at specific prices or times with minimal manual intervention. It can also include monitoring market conditions and handling post-trade activities.
• Purpose: The primary goal is to streamline the execution process, reducing the need for manual order placements and potentially minimising human error and delays.
• Complexity: It can be as simple as setting up automatic buy and sell orders with stop-loss and take-profit limits. However, applying more advanced setups that incorporate ‘simpler’ algorithmic strategies is also possible.
• Example: Using a trading platform's built-in features to automatically execute buy and sell orders when certain conditions are met, such as price reaching a specific level. This setup also includes stop losses, limit orders, and trailing stops.

In summary, all algorithmic trading is automated and focused on using advanced, data-driven strategies to make trading decisions. Automated trading encompasses any system that executes trades without human input with simple rule-based actions. The critical difference lies in the sophistication and customisation of the strategies used.

The benefits and risks of using automated trading systems

The benefits of automated trading

• Maintains Discipline: Removes emotional and impulsive decisions, ensuring trades are executed based on pre-planned strategies.
• Time-Efficient: Saves time by automating trades, fitting seamlessly into any trader's schedule, whether during the day or night.
• Expands Opportunities: Enables traders to explore multiple strategies and market opportunities without risking human burnout.
• Optimised Execution: Improves trade accuracy by entering and exiting positions at optimal prices to maximise profits and limit losses.
• Simultaneous Trading: This allows the execution of multiple trades across different markets in real time, eliminating the need for manual effort.
• Comprehensive Analysis: Utilises various indicators to identify new opportunities and analyse trends effectively.

The risks of automated trading

• Reliance on Technology: Overdependence on systems can lead to devastating consequences during mechanical or connectivity failures.
• Over-Optimisation Risks: Systems that perform well in backtesting may fail in live markets because they are too tailored to historical data.
• Monitoring Requirements: Regular oversight is needed to catch issues like connectivity problems or system malfunctions promptly.
• Human Input Errors: Misconfigured parameters can result in poorly aligned strategies that fail to match actual market conditions.
• Compounded Losses: The speed and volume of automated trades can amplify losses if the strategy encounters unfavourable market conditions.

Platforms available and coding skills that ‘might’ be needed.

You don’t necessarily need to know how to code to use automated trading systems, but it depends on the platform and the level of customisation you want.

When you do not need to code

• Pre-Built systems: Many trading platforms, such as Metatrader (MT4/MT5), offer pre-built automated trading systems or "plug-and-play" Expert Advisors (EAs). You can use these without any coding knowledge.
• Drag-and-drop tools: Some platforms provide user-friendly interfaces where you can create automated strategies by setting parameters through a graphical interface, avoiding the need for coding.
• Third-party solutions: Many third-party providers sell or share trading algorithms that you can integrate directly into your trading platform.

When coding might be needed

• Custom strategies: If you want to design unique strategies or tailor existing ones to your preferences, you may need basic knowledge of programming languages such as Python, MQL (for MetaTrader), or Java.
• Advanced features: Integrating data feeds, APIs, or machine learning algorithms often require coding skills.
• Optimisation and debugging: Modifying or debugging an algorithm for better performance may require some coding expertise.

Alternatives to coding

• Hiring developers: You can hire programmers to build or modify automated systems based on your strategy.
• Online templates and communities: Many platforms have extensive libraries of shared algorithms and community support to help you implement strategies without coding.

Integrating Artificial Intelligence (AI) into automated trading

Artificial intelligence (AI) dramatically impacts automated trading, pushing it well beyond the capabilities of traditional algorithmic systems. AI trading systems may execute pre-defined rules and adapt, learn, and optimise tactics using real-time and historical data. Here are the primary roles AI plays in automated trading:

Enhanced Data Analysis

AI allows trading algorithms to analyse massive volumes of organised and unstructured data at rates far exceeding human capabilities. This includes:

• Market trends: Recognising patterns in price or volume trends across several marketplaces.
• Sentiment analysis: Using natural language processing (NLP) to assess market sentiment by analysing news, social media, and other textual information.
• Fundamental data integration incorporates macroeconomic indicators, financial reports, and geopolitical events into decision-making models.

Predictive modelling

AI, particularly machine learning (ML), excels in creating prediction models from historical data. These models are crucial for:

• Forecasting price movements: Making accurate predictions about short- or long-term trends.
• Risk assessment: Determining the possibility of unfavourable market conditions and modifying tactics proactively.

Adaptive strategies

Unlike traditional algorithms that follow static rules, AI-powered systems can:

• Learn from experience: They may update their techniques by recognising what works and what doesn't via reinforcement learning.
• Respond dynamically: Adjust to changing market circumstances without manual intervention, making real-time judgements.

Anomaly detection

Artificial intelligence systems may continually monitor markets for abnormalities, such as:

• Market manipulation: Identifying anomalous trade patterns that may suggest spoofing or manipulative behaviour.
• Volatility spikes: Recognising aberrant price fluctuations to avoid or capitalise on high-risk circumstances.

Portfolio optimisation

AI algorithms can:

• Diversify portfolios: Maximise asset allocation across numerous assets and markets to attain a certain risk-reward ratio.
• Rebalance dynamically: Adjust portfolios in reaction to market changes or new opportunities, maximising profits while minimising risk.

Execution optimisation

AI can enhance trade execution through:

• Minimising slippage: This involves predicting market liquidity to execute trades at the best prices.
• Real-time Order adjustments: Orders are modified in response to changing market depth or transaction costs.

Trade automation latency

Trade automation latency is the time difference between an automated trading system initiating a trade and its actual execution in the market. It is essential in automated and algorithmic trading, especially high-frequency trading (HFT), where even microseconds can substantially impact profitability. Latency can also impact transaction accuracy and efficacy, particularly in fast-moving markets.

Components of trade automation latency

• Order Processing Time: The time it takes for the trading algorithm to construct and submit an order based on its predefined criteria.
• Network Transmission Time: The time it takes to send an order from a trader's system to an exchange or broker's server.
• Exchange Processing Time: The time required for the exchange to process, match, and execute an incoming order.
• Acknowledgement Time: The time it takes for the exchange to confirm trade execution in the trader's system.

Factors that contribute to latency

• Distance to Exchange: The physical distance between the trader's server and the exchange can cause latency issues.
• Network Quality: The speed and reliability of an internet connection influence transmission times.
• Hardware Limitations: Trading systems with slower CPUs or inadequate memory may experience delays.
• Software Efficiency: Poorly optimised algorithms or platforms can lengthen order processing times.
• Market Conditions: High volumes and significant volatility might cause congestion and delays at the exchange.

Latency matters

• High-Frequency Trading (HFT): Low latency is critical, as profits depend on executing transactions faster than competitors.
• Volatile markets: Due to quick market fluctuations, even small delays can result in missed opportunities or execution at unfavourable pricing.
• Slippage: High latency raises the risk of slippage, which occurs when deals are performed at prices other than intended.

Reducing latency

• Consider colocation: Placing trading servers close to the exchange's data centres to reduce physical distance.
• Direct market access (DMA):Thisis the process of connecting directly to an exchange's order book without the need for intermediaries.
• Optimised algorithms: Using lightweight and efficient algorithms to accelerate order generation.
• High-speed networks: Moving to low-latency network connections such as fibre optics.
• Hardware upgrades:Investing in faster processors and more efficient systems.

The mysterious ‘black box’ system!

A 'black box' system is an automated trading system in which users cannot see or access the internal workings, algorithms, or decision-making processes. Traders rely on these systems to conduct transactions based on preprogrammed logic without knowing or regulating how the system makes trading decisions. The name "black box" emphasises the system's lack of transparency, as it operates as an opaque entity.

Black box systems characteristics

• Opacity: Users must learn or understand the reasoning or algorithms that govern trading decisions.
• Pre-built and private: Third parties or private corporations frequently create these systems, which they then sell or lease to traders.
• Ease of use: Black box systems are designed to be plug-and-play, with minimal human input or configuration.
• High dependence: Because users cannot change or troubleshoot the system, they must rely on the developers' performance claims.

How a Black Box System Differs from Other Automated Trading Systems

Black box systems advantages

• Simplicity: Traders with limited technical or programming knowledge will find it easy to utilise.
• Speed: Rapid deployment, saving time on development and backtesting.
• Performance: Typically created by experienced developers or organisations with vast databases and resources.

Black Box Systems Disadvantages

• Lack of control: Users cannot customise or adjust the system to meet specific goals or market developments.
• Trust dependency: Success is determined by the developer's or provider's reliability.
• Over-optimisation risk: Black box systems can become too optimised on historical data, making them less effective in live markets.

Backtesting automated strategies before going live

Backtesting is an essential stage in building automated trading strategies. Here's how traders generally approach it.

Define the strategy

• Set rules: Clearly outline entry and exit criteria, risk management rules (e.g., stop-loss and take-profit), and position sizing.
•  Identify indicators: Choose the technical or fundamental indicators the approach will employ (such as moving averages and RSI).
•  Determine objectives: Define success (such as a certain return-on-risk ratio or consistent profitability).

Gather historical data

• Quality matters: Use accurate and reliable historical market data relevant to the strategy's asset class and time frame.
•  Granularity:Choose data with appropriate granularity (e.g., tick data for high-frequency trading and daily data for long-term strategies).
•  Diverse market conditions: Test the strategy's robustness with data from high volatility, low liquidity, and stable markets.

Choose a Backtesting platform

• Platformslike MetaTrader (MT4/MT5) offer built-in backtesting features.
• Custom frameworkslike Python libraries (Backtrader, Zipline) provide greater flexibility and control.
• Broker tools: Some brokers offer built-in backtesting capabilities within their trading platforms.

Configure the backtest

Simulate realistic conditions:

• Incorporate transaction costs, spreads, and slippage to simulate real-world trading.
• Consider latency and market impact if applicable.
• Set parametersandapply your strategy's rules to the chosen historical dataset.

Execute the backtest

• Use the backtesting algorithmto model trades based on historical data.
• Ensure proper trade execution logic, including order matching and position closure.

Analyse performance metrics

Key Metrics:

• Net profit/loss.
• Use sharpe ratioor other risk-adjusted return measures.
• Maximum drawdown(most significant peak-to-trough decline).
• Calculate the win/loss ratioand average trade size.
• Equity curve: Identify poor performance or instability periods using cumulative return analysis.

Refine and optimise

• Parameter tuning: Adjust variables like indicator thresholds, stop-loss distances, and time frames to optimise performance.
• Prevent overfitting: Ensure the strategy is not overly optimised for historical data, which may reduce its effectiveness in live markets.

Validate using walk-forward testing

• Divide data: Separate historical data into in-sample (for optimisation) and out-of-sample (for validation) datasets.
•  Iterative testing: Confirm the resilience and flexibility of the method through iterative testing across multiple out-of-sample periods.

Forward trading (Simulated Live Testing)

• Paper Trading: Test the strategy in a simulated live environment without risking real money. This aids in identifying any faults that may not have been obvious during backtesting.
• Monitor performance: Continuously monitor the strategy's performance and adapt as needed.

 Deployment

• Live trading: After comprehensive testing and optimisation, implement the technique in the market. Continue to evaluate its performance and adjust as necessary.

How can I get the most out of automated trading?

Automated trading provides traders a potent and efficient method of executing strategies with speed, precision, and minimal emotional interference. Traders can enhance overall trading discipline, streamline execution, and explore diverse opportunities across multiple markets by leveraging technology.

Nevertheless, the strategy necessitates meticulous planning, rigorous backtesting, and continuous monitoring to reduce risks such as market volatility, technical malfunctions, and over-optimisation.

Automated trading can be a valuable instrument for traders at all levels, allowing them to navigate markets confidently and consistently, if they maintain a proper balance of adaptability and preparation.