🧑🏫 Automated & Algorithmic Trading Tutorial
The Foreign Exchange market is a fully decentralized marketplace where international currencies are traded over the counter. Since there is no central authority controlling currency transactions, there is no single exchange rate for each pair. However, due to currency arbitrage, exchange rates tend to remain very close to one another. Additionally, the Forex market is extremely liquid, with daily volumes exceeding 7 trillion US dollars (BIS, 2024). This combination of a decentralized structure and vast liquidity creates an ideal environment for the development of automated trading systems.
⚙️ Automated and Systematic Trading
Automated trading refers to the process of trading global financial markets without any human intervention. It is a branch of systematic trading, meaning all automated trading systems are systematic by nature. Systematic trading assumes:
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A rules-driven trading strategy based on objectively computable inputs
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Implementation of the strategy by eliminating human emotional factors
According to Mitra, di Bartolomeo, and Banerjee (2011), automated trading can be classified into five main categories:
(i) Algorithmic Executions (the category that interests us the most)
(ii) Statistical Arbitrage (exploiting trading opportunities arising from market inefficiencies)
(iii) Predatory Trading (entering thousands of orders while expecting to execute only a small fraction)
(iv) Crossing Transactions (trading directly with another entity without exposing orders to the wider market)
(v) Electronic Liquidity Provision
🔄 How Automated Trading Differ from Algorithmic Trading?
Automated trading is often considered synonymous with algorithmic trading, but there is a difference in how these two approaches interact with the market. Automated trading refers to the automation of everyday manual trading tasks. It typically focuses on predicting asset price movements based on recognizable price trends, macroeconomic indicators, news releases, and other events.
In contrast, algorithmic trading involves researching and analyzing market conditions and trading data to develop efficient rules and instructions. It incorporates a wide range of parameters such as price, time, and volume.
👉 In summary:
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Automated trading focuses on automating the trading process, particularly execution.
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Algorithmic trading focuses on automating research and analysis, with an integrated execution component.
🖥️ Algorithmic Trading
Algorithmic trading, or algo trading, uses computer algorithms that follow a defined set of rules and instructions to trade global markets. These algorithms analyze demand and supply dynamics to create market and pending orders. The entire process operates without human intervention and is based on the following fundamental principles:
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Financial markets are not perfectly efficient, at least for short periods
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Financial markets have finite depth
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Historical results have some predictive power (Sharpe, 1994)
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Financial data (price and quantity) are driven by human psychology and are therefore random and unstable
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Regularities in financial data exist, but only for short periods; windows of opportunity close quickly
An algorithmic system consists of two main components:
(1) The Forecasting Module
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Forecasts based on analyzing trends and changes in demand and supply dynamics
(2) The Action Module
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Executes pending and market orders at selected prices and times, including opening, modifying, and closing trades
Modules for creating forecasting indicators include:
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Dynamic changes in demand and supply, such as analyzing order volumes
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Volume clusters, where changes can signal upcoming shifts in demand and supply
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Asset pricing inefficiencies, identified by divergences between an asset’s price and related key assets
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Intermarket correlations, like the relationship between AUDUSD and gold
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News effect, where market reactions to news can form predictable patterns
🔨 Advanced Tools for Creating and Optimizing Algorithmic Trading Systems
- Volume Breakout Analysis
- Order Book Analysis
- Time Series Analysis
- Pattern Recognition
- Market Sentiment Measures (using data mining)
- Intermarket Correlations Analysis
- Historical Backtesting
- Monte-Carlo Simulation (using random sampling to solve deterministic problems)
- Walk-Through Optimization
- Sharpe/Sortino Ratios
- Hamilton–Jacobi–Bellman (HJB) Equation
- Queuing Theory
🏦 Algorithmic Trading and the Role of Institutional Players
Algorithmic strategies are employed by many hedge funds and other specialized financial firms. Institutional traders use a wide range of sophisticated systems for various purposes, such as profiting from arbitrage. According to the Bank of England (2017), two major trends are identified:
(i) Data-driven modeling techniques that combine pattern recognition, computational statistics, predictive analytics, and artificial intelligence.
(ii) A rapidly growing volume of detailed data, often referred to as Big Data.
📉 Quantitative analysis
Quantitative analysis uses diverse market data to develop models capable of identifying trading opportunities. The forecasting modules primarily analyze fundamental and statistical data (such as mean reversion). Backtesting with historical data allows for optimizing results.
Machine learning is a more complex task. It refers to using statistical tools and techniques to enable computer systems to ‘learn.’ Learning means improving the system’s performance without direct human intervention. A machine learning system includes the following components:
- Specific problems to be solved
- Data sources
- Models that analyze data
- Optimization algorithms
- Validation and backtesting modules
📊 Why Use an Automated Trading System?
An automated system can handle the entire trading process, from analysis-based decision-making to market execution. The multitasking capability of an auto-trading system allows for continuous 24/7 analysis of a wide range of Forex exchange rates across multiple timeframes. Additionally, trading decisions are made without emotion, stress, or fatigue. This means that by developing an automated trading system, you can save significant time and improve overall results by removing the human factor from the decision-making process.
Using Forex Robots (Expert Advisors)
Retail traders use automated strategies through simple Expert Advisors (EAs), also known as Forex robots. A Forex robot is a small piece of software designed to operate on a specific trading platform. The most popular auto-trading platforms for retail traders include MetaTrader 4, MetaTrader 5, cTrader, TradeStation, and NinjaTrader. An Expert Advisor uses algorithms to analyze the market and identify opportunities based on price movements and volume. It also applies money management rules that determine position sizes and limit risk. Various filters protect the trading account from choppy market conditions, such as high slippage, wide spreads, market correlations, and upcoming news releases.
🏢 Building a Custom Automated Trading System
Nowadays, building an automated trading system is easier and more affordable than ever. Many applications enable the transformation of ideas into fully functioning trading systems without requiring programming skills.
» Expert Advisor Builders for MT4
» Advanced Strategy Building for Algo Traders
These applications offer a graphical user interface (GUI) that can directly create executable files for platforms such as MetaTrader, NinjaTrader, cTrader, and TradeStation. As there is no need for programming skills anymore, all retail traders can get involved in building automated systems.
🛠️ Minimum Configuration for Retail Traders
The minimum requirements for using an Expert Advisor are:
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A dedicated ECN trading account that allows auto-trading and scalping
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Competitive pricing with tight spreads and low commissions
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Access to MetaTrader or a similar auto-trading platform
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A minimum deposit of $500 is required to open the ECN account
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Installation of an Expert Advisor (commercial or custom-made)
■ Automated and Algorithmic Trading Guide
George M. Protonotarios
ForexRobots.net (c)
RESOURCES:
- «Automated Analysis of News to Compute Market Sentiment: Its Impact on Liquidity and Trading» -G. Mitra, D. di Bartolomeo, and A. Banerjee (2011)
- «Building Automated Trading Strategies» -George Protonotarios (2018)
- Automated Trading with Machine Learning on Big Data, Dymitr Ruta, Conference Paper · June 2014
- Professional Automated Trading Theory and Practice (Eugene A. Durenard)
