Understanding algorithmic trading: An overview for beginners

Algorithmic trading explained

Algorithmic trading is an advanced method that relies on advanced coding and formulas based on a mathematical model. However, compared to traditional trading methods, the process differs by being automated.

Moreover, no human emotions are involved that would otherwise compromise the potential outcome. Investors will typically utilize this strategy when attempting to sell or buy assets rapidly to earn from short-term price movements.

Consequently, each trader can participate in multiple trades within a 24-hour interval and capitalize on even the smallest increments in value. Aside from the stock market, the algorithmic trading approach is used in many other industries, the most notable being Forex and crypto.

Related terms for beginners

There are key terms that each beginner will come across when entering the algorithmic trading space, and these include black box, dark pool, back-testing, and optimization.

• Black Box - refers to a trading strategy where a computer program monitors the market. Its objective is to spot signals for trading and initiate buy/sell orders. The underlying algorithms and logic are generally not disclosed or revealed to investors/traders, hence the term "black box"

• Dark Pool - refers to a privately organized exchange for trading securities that would not be accessible by the investing public. They are intended to enable institutional investors to trade without exposure until after the trade has been executed and then reported

• Back-Testing - the performance of a trading strategy can be evaluated through simulations. As a result, investors can expose their strategy algorithm to a stream of historical data. This enables investors the opportunity to measure various performance metrics. These include risk-adjusted returns, win rates, maximum drawdown, and profit and loss

• Optimization - the process of improving a portfolio, algorithm, or trading system. The objective is to reduce costs while also increasing its efficiency. Any portfolio can be optimized through the reduction of risks and the increase of expected returns or the change in the frequency of rebalancing

How does algorithmic trading work?

To truly understand how algorithmic trading works, it's first essential to understand the economic theories on which it is based.

The usage of algorithms within financial markets originated back in the 1970s. They initially had simple rule-based systems, which were utilized to execute trades based on stock exchanges. These were basic but used for executing trades at the best available prices.

Let’s explore the mechanics of algorithmic trading and see why it's a topic of interest among traders.

Trading algorithms that are well-programmed can run on their own. Some human oversight will always be recommended. However, they are designed to operate as efficiently as possible. Typically, what will be required from trades is to pick a time frame and trade frequency that they can monitor. Financial constraints will need to be implemented for an algorithm to be used as efficiently as possible. Then, investors will need to create or fine-tune a strategy.

As the technology evolved, they have become far more sophisticated and now utilize artificial intelligence (AI) elements like machine learning and deep learning. Some even use large language models and analyze financial news and social media.

Example of algorithmic trading

Please, be aware that this example is for demonstration purposes only and cannot be used for taking your trading decisions.

Here's an example of a very simple algorithmic trading strategy based on two very popular technical indicators - Moving Average Convergence Divergence (MACD) and Relative Strength Index (RSI).

Let’s define that a Buy signal is generated when the MACD line crosses above the signal line, and the RSI value is above 50, indicating a potential increase of the asset price. The algorithm will monitor the chart and send a buy order when conditions are met.

The signal for closing this long position would be when the MACD line crosses below the signal line and the RSI value is in the overbought zone above 70, indicating potential reversal. The sell order would be sent, thus exiting the trade.

In the following example of the AAPL chart we can see what the trade would look like.

Benefits and challenges of algorithmic trading

Numerous benefits make algorithmic trading superior to manual trading. For example, each investor can automate their trading methods while eliminating human error. In addition, it offers much better speed and efficiency. Computer programs can analyze vast amounts of market data in real-time, benefiting those with high-frequency trading.

Algorithmic trading is also superior in terms of its objectivity. The programs are based on rules and strategies that have been defined upfront. As a result, they can eliminate the emotional biases that could impact human decision-making.

These algorithms can also benefit those who want to implement complicated strategies involving many different asset classes or advanced risk management techniques.

Some risks associated with algorithmic trading include system failures or technical risks. In addition, there can potentially be more significant losses compared to manual trading.

Also, expert advisors can assist traders and are between fully algorithmic trading and manual trading. Investors can get in-depth data as to how they can trade and learn about the various rules of testing EAs and evaluating their effectiveness.

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