What is Automated Trading?

Introduction to Automated Trading

 

What is Automated Trading?

Automated trading, or algorithmic trading, refers to the use of hardware and software to create computer systems that autonomously generate trading orders and submit them to the market without human intervention. These systems can automatically open, modify, and close trading positions around the clock.

Automated trading systems can handle the entire trading process, from decision-making to market execution. Their immense multitasking capability allows them to analyze hundreds of financial markets simultaneously. Additionally, automated systems are emotion-free and can trade 24/7 without experiencing stress or fatigue. These advantages make the development of a successful automated trading system the “holy grail” for any ambitious trader.

Core Advantages of Automated Trading Systems

⚡ Speed & Efficiency

Algorithms can process massive volumes of market data within milliseconds—far outpacing human capabilities. This speed enables strategies like high-frequency trading (HFT), which exploit fleeting price discrepancies that exist for mere microseconds.

🧠 Emotionless Execution

Unlike human traders, algorithms aren’t swayed by emotions such as fear, greed, or fatigue. They operate with unwavering discipline, executing trades solely based on logic, math, and statistical models.

🌐 Round-the-Clock Trading

Automated systems can operate 24/7, monitoring global markets and executing trades even during off-hours. This ensures that no opportunity is missed—whether during a late-night news cycle or a sudden market shift.

📊 Simultaneous Multi-Market Analysis

A single algorithm can track and trade across hundreds of instruments—stocks, forex, crypto, commodities—on multiple exchanges in real time, enabling comprehensive diversification and market coverage.

🔍 Backtesting & Strategy Optimization

Before going live, strategies can be rigorously tested against historical data. This allows traders to fine-tune parameters, validate assumptions, and enhance performance without risking real capital.

 

 

Automated Trading in Institutional vs. Retail Markets

 

Institutional Traders: Leveraging Cutting-Edge Algorithmic Systems

Institutional players—such as hedge funds, investment banks, and asset managers—utilize highly sophisticated automated trading platforms that blend advanced quantitative techniques with vast computational resources. According to a 2017 Bank of England report, two transformative trends are reshaping institutional algorithmic trading:

1. Advances in Data-Driven Modeling

Modern institutional systems combine computational statistics, mathematical optimization, and machine learning to build predictive models. Cutting-edge techniques such as pattern recognition, deep learning, and reinforcement learning enable these algorithms to adapt dynamically to evolving market conditions. Artificial intelligence (AI) further empowers these systems to detect complex, non-linear patterns in data that traditional models might overlook.

2. The Rise of Big Data

The proliferation of granular, high-frequency datasets—including tick-level trade information, order book dynamics, and alternative data sources like satellite imagery and social media sentiment—offers institutions unparalleled insights into market behavior. Leveraging cloud computing and distributed processing frameworks (e.g., Apache Spark, Hadoop), institutions efficiently handle massive data volumes in real time. Additionally, natural language processing (NLP) tools parse earnings reports, news articles, and central bank communications to inform algorithmic decisions.

Together, these technologies enable institutions to implement high-frequency trading (HFT), statistical arbitrage, and smart order routing at massive scale—often driving the majority of equity market volume worldwide.

 

Retail Traders: Accessible Automation with Expert Advisors (EAs)

In contrast, retail traders typically use more accessible and standardized automation tools—most notably Expert Advisors (EAs), which are pre-programmed trading bots available on widely used platforms like:

• MetaTrader 4 (MT4) & MetaTrader 5 (MT5) — Popular for forex and CFDs, supporting scripting in MQL4/MQL5.

• TradeStation & NinjaTrader — Favored for U.S. equities and futures, with integration for Python and C#.

How Retail EAs Work:

• 24/7 Market Monitoring: EAs continuously analyze price charts and technical indicators such as moving averages, RSI, and Fibonacci retracements to generate trade signals.

• Strategy Types: Common automated approaches include:

  • Trend-following (e.g., breakout systems)

  • Mean-reversion (e.g., Bollinger Bands)

  • Volatility-based (e.g., forex straddle strategies)

• Built-in Risk Management: Most EAs incorporate basic money management features—fixed lot sizes, stop-loss/take-profit orders, or volatility-adjusted position sizing—to help limit downside risk.

Limitations of Retail Automation:

• Heavy Reliance on Technical Analysis: Many retail EAs focus solely on technical indicators, lacking integration of fundamental or sentiment analysis.

• Overfitting Risks: Inadequate or improper backtesting can produce strategies that perform well historically but fail in live markets.

• Latency and Infrastructure Constraints: Retail traders don’t have access to the ultra-low-latency hardware and network infrastructure that institutions use, which can impact execution speed and slippage.

Basics Concepts of Automated Trading

Automated trading is a method of trading global financial markets using a combination of computer software and hardware. It is a sophisticated branch of systematic trading, and all automated trading systems are inherently systematic.

Major Assumptions of Systematic Trading

Systematic trading is based on the following assumptions:

• The existence of a rules-driven trading strategy grounded in objectively reproducible (computable) inputs

• The disciplined application of that strategy, free from human emotional influence

When we talk about automated trading, we refer to how trading orders are executed. An automated trading system must be capable of placing trades without human intervention, including limit orders such as take-profit and stop-loss

 

General Categories of Automated Trading

According to Mitra, di Bartolomeo, and Banerjee (2011), automated trading can be divided into five main categories:

(i) Algorithmic Executions

Opening and closing speculative positions based on mathematical algorithms.

(ii) Statistical Arbitrage

Trading strategies that automate the investment decision process using statistical models.

(iii) Crossing Transactions

A market participant seeks a counterparty to take the other side of a trade without exposing the order to the broader market.

(iv) Electronic Liquidity Provision

Electronic liquidity providers are willing to buy or sell assets upon request but often differ from traditional market makers by not openly identifying the assets they trade.

(v) Predatory Trading

This involves placing thousands of simultaneous orders while expecting to execute only a small fraction. The “place and cancel” process serves two main purposes: first, to gather information by observing which orders execute, gaining insight into the trading intentions of large participants such as institutional asset managers; second, to artificially create abnormal trading volume or price trends in a security to mislead other traders and gain an advantage.

 

 

Advantages & Disadvantages of Automated Trading

 

Major Advantages of Automated Trading

The greatest advantage of automated trading is its ability to minimize emotions throughout the trading process. Human emotions—such as fear, over-optimism, and other impulses—often disrupt decision-making in financial markets. These emotional responses work against logic and reduce the odds of success. In fact, the emotional part of the brain is the worst trader there is.

Manual trading is also limited by human stamina, but an automated trading system never gets tired. By using a VPS hosting service, you can even turn off your PC and continue trading 24 hours a day.

Automated trading also allows for easy and quick backtesting of trading ideas, which is very difficult with manual strategies.

Additionally, automated systems can analyze multiple financial markets simultaneously and respond to trading opportunities much faster.

 

Major Disadvantages of Automated Trading

The main disadvantage of automated trading systems is that they can only handle the market conditions they were programmed for. This means they cannot interpret or adapt to new conditions resulting from major fundamental changes.

For example, a significant event like a country defaulting on its payments usually triggers extreme market volatility, which can drastically alter market behavior. Semi-automatic systems may adapt better to such changes since they can be adjusted manually.

Furthermore, some experts argue that automated systems can be inefficient because they rely on backward-looking indicators to generate trading signals.

  

■ What is Automated Trading

George Protonotarios, financial analyst

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