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Key Features

Algorithmic trading is distinguished by several essential features that make it unique compared to other investment strategies:

  • Trading Frequency: One of the standout features is high trading frequency. Algorithms can execute multiple trades within seconds, allowing for capitalising on small price fluctuations in assets.

  • Short Holding Periods: Algorithmic trading often involves holding positions for very short periods, sometimes even fractions of a second. This differs from long-term investment strategies.

  • Clear Objectives: The primary goal of algorithmic trading is to profit through buying and selling financial assets. Algorithms are designed to maximise returns and minimise risks.

How to Create a Trading Algorithm: Step by Step

Algorithmic trading involves using computational algorithms to make buying and selling decisions in financial markets. Creating a successful trading algorithm follows several key steps:

Step 1: Data Analysis. The process begins with gathering and analysing financial data, including historical asset prices, economic news, corporate reports, and other relevant indicators. This data analysis aims to identify patterns, trends, and trading opportunities in the target market. If the algorithm focuses on forex, it is not the same as indices, stocks, or cryptocurrencies. Data analysts use technical, fundamental, and quantitative analysis tools.

Step 2: Strategy Design. Based on the results of data analysis, strategy developers design a clearly defined trading strategy that will set the foundation and rules the algorithm will follow in making trading decisions. The strategy specifies what the algorithm will execute. For example, the strategy determines the financial markets it will trade, the specific technical indicators that will signal when to buy or sell an asset, and the volume and price ranges within which it will trade.

Step 3: Algorithm Development. Subsequently, algorithm programmers begin creating the computer code that automates the decision-making process defined by the strategy. This code must be precise and efficient and is typically written in programming languages like Python, Java, or C++. The algorithm must be capable of processing real-time data and applying the strategy rules to determine when to execute trades.

Step 4: Testing and Optimisation. After developing the algorithm, it's crucial to conduct extensive testing on historical and real-time data to assess its performance. During this stage, adjustments and optimisations can be made to enhance profitability and reduce risk. Testing should consider various market conditions and extreme scenarios. While the future cannot be predicted, past data can be used to avoid undesirable events in cases of less unusual events for which we have a history.

Step 5: Implementation and Execution. Once the algorithm has been tested and optimised, it is implemented in a production environment. This involves connecting the algorithm to a trading platform or a stock exchange API, allowing real-time order execution. The algorithm continuously monitors the market and makes trading decisions based on the established strategy.

Step 6: Monitoring and Risk Management. Algorithmic trading requires constant supervision to ensure the algorithm functions correctly and does not generate significant losses. Additionally, robust risk management must be implemented to protect the investment. This includes setting loss limits and the ability to halt the algorithm if abnormal behaviour is detected.

Step 7: Ongoing Evaluation and Improvements. Creating and improving a successful trading algorithm continues after its implementation. Algorithmic traders continue to evaluate the algorithm's performance and make improvements as more data is obtained and areas of opportunity are identified. This may involve adding new rules or optimising existing parameters.

Creating a successful trading algorithm is an ongoing process that requires data analysis, programming skills, and a deep understanding of financial markets. Successful algorithmic traders combine technology with a solid grasp of trading strategies to make informed and automated market decisions.

Pros and Cons of Algorithmic Trading


  • Speed: Instant order execution can capitalise on rapid market opportunities.

  • Discipline: Algorithms follow a predefined strategy without emotions or biases.

  • Automation: Allows for 24/7 trading without the need for constant supervision.


  • Technical Risk: Technical issues or algorithm errors can result in significant losses.

  • Data Dependency: Algorithm accuracy depends on the data quality used.

  • Competition: Markets are filled with algorithmic competitors, making consistent profits challenging.

Who Uses Algorithmic Trading?

Algorithmic trading can be suitable for various investors, but it's essential to consider risk tolerance, experience, and financial goals. It attracts institutional investors and hedge funds looking to capitalise on small market fluctuations.

However, due to the complexity and potential risks of algorithmic trading, individual investors should approach it with caution and, if necessary, seek professional advice.

Choosing the right trading strategy is a crucial decision for any investor. There is no universally superior strategy, as what works for one person may not be suitable for another. Your choice should be based on your financial goals, risk tolerance, and lifestyle.

Choosing the right trading strategy is a crucial decision for any investor. There is no universally superior strategy, as what works for one person may not be suitable for another. Your choice should be based on your financial goals, risk tolerance, and lifestyle.

Example of Opening and Closing Orders in Forex Algorithmic Trading

Imagine you are developing a trading algorithm for the Forex market and want to take advantage of price discrepancies between two related currency pairs, EUR/USD and GBP/USD. Your algorithm is designed to operate when the relationship between the prices of these currency pairs deviates significantly from their historical average value. Here is a step-by-step example of how your algorithm could work:

  1. Data Analysis. Your algorithm begins by analysing historical price data for the EUR/USD and GBP/USD currency pairs and the historical relationship between their prices. This involves collecting and processing a large amount of data to identify patterns and trends.

  2. Set the Trading Criteria. Define specific criteria that will trigger a trade. For example, you might open a position when the price relationship between EUR/USD and GBP/USD deviates by more than 1% from its 30-day historical average.

  3. Real-Time Monitoring. Your algorithm runs in real-time, constantly observing prices for both currency pairs. As prices change, the algorithm calculates and updates the current relationship between the currencies.

  4. Trading Signal. A trading signal is generated when the price relationship reaches or exceeds the predefined 1% deviation. This indicates a potential arbitrage opportunity since prices have deviated significantly from their expected historical value.

  5. Order Generation. Your algorithm automatically generates buy and sell orders. You'll buy EUR/USD, which you consider undervalued, and sell GBP/USD, which you consider overvalued. Orders are sent to the Forex market instantly.

  6. Automated Execution. Orders are executed automatically and quickly in the Forex market, taking advantage of the price discrepancy. This can happen in milliseconds.


The rationale behind this arbitrage strategy in the Forex market is similar to the previous example. Historically, the EUR/USD and GBP/USD currency pairs tend to move together due to their economic and trade relationships. However, when this relationship deviates significantly from its expected historical value, it will likely return to its average level over time. Therefore, the algorithm seeks to capitalise on these temporary deviations to profit by buying the undervalued currency and selling the overvalued one.

An Hourly Review of a Trader in this Strategy

Algorithmic trading is a strategy that operates relentlessly in the financial markets, and a trader dedicated to it goes through a series of key activities during the day:

  • Start of the Day: The trader begins by reviewing financial and economic news to stay informed about events that could affect the markets.

  • Data Analysis: They dedicate time to analysing historical and real-time data to identify patterns and trading opportunities.

  • Algorithm Configuration: If an algorithmic trader adjusts and verifies their algorithms to ensure they are ready to trade.

  • Opening Trades: When opportunities are identified, the algorithm opens buy or sell positions according to its strategy. The trader monitors its performance, ensuring it stays within the expected ranges. Additionally, they constantly supervise their open positions and adjust stop-loss or take-profit orders as necessary.

  • Market Monitoring: They constantly monitor market movements and news that could change conditions.

  • Closing Trades: At the end of the day, the trader closes trades according to their strategy, ensuring daily/monthly/yearly profit or loss goals are met.

  • Preparing for the Next Day: Before signing off, the trader prepares for the next day by reviewing positions and reconfiguring algorithms if necessary.

Most Used Indicators in Algorithmic Trading

The indicators used in algorithmic trading may vary depending on the specific strategy being implemented, but here are some common indicators that may be relevant:

  1. Moving Averages: Moving averages are used to smooth price data and help identify trends. Simple moving averages (SMA) and exponential moving averages (EMA) are common in algorithmic strategies.

  2. Relative Strength Index (RSI): RSI measures the speed and change of price movements. It determines if an asset is overbought or oversold, influencing trading decisions.

  3. Moving Average Convergence Divergence (MACD): MACD is a trend-following indicator that shows the relationship between two price-moving averages. It is used to identify changes in trend direction.

  4. Bollinger Bands: Bollinger Bands consist of a central band (SMA) with upper and lower bands representing standard deviations of the price. They can be used to identify overbought or oversold levels.

  5. Stochastic Oscillator: The Stochastic Oscillator measures the speed of price change and is used to identify overbought and oversold conditions.

  6. Volume Weighted Average Price (VWAP): VWAP calculates the volume-weighted average price over a specific period. It is important in algorithmic trading strategies based on volume.

  7. Average True Range (ATR): ATR measures asset volatility. It can help establish stop-loss and take-profit levels in algorithmic strategies.

  8. Ichimoku Cloud: This indicator provides information on trends, support and resistance levels and can be useful in trend-following algorithmic strategies.

  9. Fibonacci Retracement: Fibonacci retracement levels are used to identify potential support and resistance levels based on the Fibonacci sequence.

  10. Correlation Coefficient: The correlation coefficient measures the relationship between two assets. It can be useful in strategies involving asset pairs.

  11. Volatility Indicators: Volatility indicators like the Average True Range (ATR) or the Volatility Index (VIX) can be crucial in algorithmic trading strategies.

  12. Event-Driven Indicators: Specific indicators related to economic events or news can be used for event-driven algorithmic strategies, such as arbitrage.

Risk Management

Risk management is crucial in algorithmic trading to protect capital and minimise losses. Here are some risk management strategies that traders use in this strategy:

  • Diversification: Spreading capital across multiple assets or strategies to reduce exposure to a single risk.

  • Stop-Loss: Setting stop-loss orders limits losses if the market moves against the position.

  • Position Size: Determining position size based on the total allowable risk in the portfolio.

  • Continuous Monitoring: Keeping constant track of positions and adjusting stop-loss and take-profit orders as needed.

  • Contingency Plan: A contingency plan for unexpected situations, such as extremely volatile market events, as a tool to "turn off" the algorithm and close open positions immediately.


Algorithmic trading is a strategy that has revolutionised trading in financial markets. Its ability to operate automatically and at high speed offers exciting opportunities for investors. However, it has risks, and proper risk management is essential for long-term success.

Traders must understand this strategy deeply, from its definition and characteristics to its daily running and risks. Only those who master this strategy can fully leverage its potential and minimise associated risks. Ultimately, algorithmic trading is a powerful investment tool that must be used with care and wisdom.

Best Algorithmic Trading-Friendly Trading Apps

Algorithmic trading, or algo trading, represents an investment strategy that relies on algorithms and mathematical models to execute buying and selling decisions for financial assets. Its primary aim is to seize trading opportunities in an automated and highly efficient manner, reducing human decisions (and emotions) to a minimum.

This approach starkly contrasts manual trading strategies, as it enables instantaneous decision-making devoid of direct human intervention. Algorithms are meticulously crafted to analyze a spectrum of historical and real-time datasets, discern patterns, and execute buy or sell orders contingent upon market conditions.

In this article, we shall comprehensively explore all you need to comprehend about this formidable trading strategy.



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