How AI Algorithms Work for Trading

AI algorithms define the process that artificial intelligence systems use to learn, enabling AI to solve problems, classify data, and make predictions.

Gone are the days when people analyzed data or generated invoices themselves.

Over the past few years, businesses across various industries have embraced artificial intelligence (AI). From manufacturing to stock trading, AI algorithms are now everywhere, functioning differently depending on the industry.

For example, an automotive parts manufacturing plant might leverage AI algorithms to improve accuracy or reduce task time, while an AI trading bot may use generative AI to create novel trading strategies.

In this guide, we’ll discuss the ins and outs of AI algorithms, including how they work and their types. We’ll also discover the most popular AI algorithm on the market.

What is artificial intelligence?

Before defining an AI algorithm, reviewing its fundamental concept, which is AI, is essential.

When most people think about AI, they imagine all-powerful supercomputers. In reality, you can find AI at work in multiple products and services, including natural language processing (NLP) programs, robotics, and facial recognition software.

AI refers to software or machine intelligence (i.e., non-human intelligence). While definitions of AI vary, most modern interpretations label AI as machines that can synthesize information and perform advanced functions without human interference.

It’s an umbrella term with numerous AI subfields, with each field distinguishing itself from the others based on the tools it uses and the goals it aims to achieve.

Here are the four main AI subfields:

Reactive machines: Reactive AI systems use present data to complete specific tasks. For example, IBM’s supercomputer Deep Blue uses reactive AI to identify chess pieces and predict its opponent’s next move.
Limited memory: Limited-memory AI leverages past data to make predictions and classify random data. Self-driving cars use limited-memory AI to observe their environment, add object classifications, and decide when to stop, turn, or change lanes.
Theory of mind: This AI field can understand human emotions and motivations. While currently only theoretical, future theory of mind AI systems may simulate human behavior.
Self-awareness: Self-awareness represents peak AI. It involves machines that understand their internal thoughts, conditions, and traits. Presently, self-aware AI exists only in science fiction.

What is an AI algorithm?

All AI systems use algorithms. AI algorithms are like operational manuals that tell a machine how to complete a calculation, react to inputs, and improve on existing processes. AI programs can’t think or grow with algorithms, meaning algorithms put the "intelligence" in artificial intelligence.

Put another way, if AI exists to solve problems, algorithms determine how those problems are solved.

For example, mathematics and computer science define an algorithm as an instructional sequence tasked to solve a problem or complete a computation. Algorithms range from simple instructions designed to perform calculations to advanced decision trees that leverage conditionals to draw inferences from a dataset.

How do AI algorithms work?

Algorithms vary in complexity from basic "if/then" statements to intricate mathematical equations. AI algorithms, specifically, use supplied data to complete tasks. Here’s how they work:

Data input: AI algorithms start with data input, which can be in various forms like numbers, text, images, or audio. This data is preprocessed to convert it into a format the algorithm can understand.
Model training: In machine learning, a subset of AI, algorithms are "trained" to use large datasets. Training involves adjusting the algorithm's parameters until it recognizes patterns or makes decisions.
Feature extraction and selection: Next, AI algorithms identify and select relevant features (characteristics) from the input data crucial for the task at hand.
Algorithmic models: Different models are used depending on the task.
Validation and testing: The algorithm is validated and tested using new datasets to check its accuracy and reliability.
Iteration and optimization: AI algorithms often go through multiple iterations, where they’re refined and optimized for better performance.

How do AI algorithms work in trading?

Algorithmic (or algo) trading involves using AI algorithms to execute trades at high speeds and volumes, which is impossible for human traders. These algorithms analyze market data, predict trends, and make trading decisions.

For example, machine learning algorithms can analyze a historical market dataset to identify trends and make predictions. NLP algorithms, on the other hand, conduct sentiment analysis by scanning reports, social media platforms, and other online traffic to determine how investors value specific assets.

You can find AI algorithms everywhere on modern trading platforms, from high-frequency trading systems to chatbots that can create unique investment strategies based on user input.

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Types of AI algorithms

AI algorithms can be broadly classified into three main types, each with a unique methodology and application area. Understanding these types provides insight into how AI systems are trained and function in various settings.

Below is an overview:

1. Supervised learning algorithms

Supervised learning algorithms attempt to create functions that can connect labeled data and desired outputs using training data. Data scientists and programmers often use supervised learning algorithms to solve classification tasks or regression problems like linear regression and logistic regression. Classification aims to allocate data using either/or logic, while regression estimates a possible outcome using dependent and independent variables.

Commonly used supervised learning algorithms include decision trees, random forests, Naive Bayes probabilistic classifiers, support vector machines (SVM), and neural networks. You can find these AI algorithms in applications ranging from speech recognition to sentiment analysis.

2. Unsupervised learning algorithms

Unlike supervised algorithms, unsupervised learning algorithms learn to recognize patterns using unlabeled training data. Unsupervised learning algorithms try to recognize or imagine outcomes by mimicking provided datasets and fixing output errors.

Clustering is one of the most prominent methods used in these algorithms. A branch of machine learning, clustering involves grouping unlabeled data into predefined classifications––or clusters––to find commonalities.

Popular clustering approaches include k-means clustering, hierarchical clustering, and probabilistic mixture models. Given their ability to classify unlabeled data, programmers frequently use unsupervised learning algorithms in systems that enhance features, recommend products, or detect anomalies.

3. Reinforcement learning algorithms

Like unsupervised algorithms, reinforcement learning algorithms don’t require explicitly labeled inputs and outputs. Instead, reinforcement learning algorithms explore known and unknown knowledge to find the most potentially efficient solution for a problem.

In reinforcement learning systems, an environment signals an agent to perform a specific action and sends a feedback reward signal to the agent. This reward can be positive or negative and determines whether the agent will improve upon winning solutions or try a different approach.

Policy gradient, Q-learning, and Monte Carlo are commonly used in reinforcement learning algorithms. Many methods combine deep learning and reinforcement techniques to maximize a system’s learning potential. Reinforcement learning applications include gaming AIs, image processing, and robotics.

What are generative AI and LLMs?

Also known as GenAI, generative AI uses generative machine learning models to create text, images, video, and music. These models rely on deep neural networks to structure patterns in large training datasets, which they use to form the basis for their generated media. The more these models receive data and create media, the more advanced they become.

Let’s suppose you provide a generative AI system with millions of illustrations. Over time, the system will learn to analyze and reproduce images according to their style, shading, composition, lighting, or theme. The same goes for models that analyze fiction writing, code, or videos.

Among generative AI types, large language models (LLMs) rank among the most well-known and popular. These models aim to produce understandable text by learning about semantics, syntax, and other aspects inherent in human language. Using regressive supervised learning algorithms, LLMs leverage massive training datasets to predict output text.

Notable LLM examples include chatbots such as OpenAI’s ChatGPT. These chatbots can take user inputs and generate everything from simple poetry to complex Python code.

What is the most popular AI algorithm?

LLMs, like GPT-4, rank among the most popular AI algorithms––thanks to their user-friendliness and flexibility. Their popularity extends to algorithmic trading, which increasingly leverages LLMs to generate novel trading strategies and scan market research to identify trends.

A prominent example is Composer. With Composer, you can use a trading-optimized GPT4 version to create algorithmic trading strategies. All you need to do is input your desired design using natural language and watch as Composer’s built-in AI chatbot constructs trading use cases for your symphony.

Up your trading game with Composer

Trading assets is one thing, but giving commands and letting an AI do the talking is another. Yes, with Composer’s AI, you can develop your own strategies without the hassle of learning coding.

Head to composer.trade, and sign up for a free 14-day trial today.