Machine Learning for Trading

Technology and investment are the most interesting domains for people across the world. Everyone wants to get a verse in the technical domain and get a job as well as an earn side income. Trading is one of the best ways to earn a lot of money even without investing much time and money. Nowadays, trading is one of the most competitive domains, and with machine learning algorithms, it has become a new wonder weapon for everything across the globe. Machine learning has a crucial role in trading as it extracts signals from financial and alternative data to design and backtest systematic strategies. In this topic, we will discuss various key aspects related to trading and how Machine learning can be used for trading, along with the advantages of using ML for Trading.

Let's start with an introduction to Machine Learning and Trading first.

What is Machine Learning?

Machine Learning is a subset of Artificial intelligence which allows machines to learn and predict through past experience and predict accurate results without much human intervention. It is widely being used in almost every field, including Healthcare, defense, education, finance, etc.

How does machine learning work with data?

Machine learning applies a process to detect the hidden patterns in data sets from various data sources. Further, it helps to train models with past experience and gives computers the ability to learn without being explicitly programmed. Experience is nothing but the training data required for algorithms. The main difference between machines built earlier to solve a problem and today's machine learning systems is that earlier machines were programmed by humans to solve a specific problem, whereas now, machines are using algorithms that make decisions by learning from the data.

Types of Machine Learning

Machine learning is categorized mainly into three types as follows:

1. Supervised Machine Learning

Supervised Learning uses a labeled dataset to train the model, and on the basis of the training, the model makes the predictions. Here, the labeled dataset means the input is already tagged with the correct outputs, which helps the model to predict accurately with the test/new dataset. It is named supervised learning, as it is based on supervision, which supervises the learning model.

This learning technique maps the input variable(x) with the output variable(y). It is currently widely used for multiple applications; some of them include Risk Assessment, Fraud Detection, Spam filtering, etc.

Supervised learning can be used to solve two types of problems as follows:

• Classification
• Regression

2. Unsupervised machine learning

Unsupervised learning works opposite to that supervised learning technique as it takes an unlabeled dataset as input and aims to find an association between input values. It finds the hidden insights and patterns in the input dataset and, on that basis, makes the prediction. Although it finds the underlying pattern within the dataset, it requires human intervention to validate the predicted output, and it is less accurate compared to supervised learning methods. It can be widely used for complex real-world applications, such as Anomaly detection, recommendation engine, etc.

Unsupervised learning can be used to solve 2 types of problems as follows:

• Clustering
• Association

3. Reinforcement learning

The reinforcement learning technique is different from supervised and unsupervised learning techniques as it does not take any labeled or unlabeled dataset; rather agent (intelligent computer program) of RL explores the environment, performs an action, receives feedback, and learns from it. As there is no labeled data, so the agent is bound to learn by its experience only. While performing the actions, the RL agent receives the feedback in the form of rewards (positive or negative), and the main aim of the agent is the maximize the positive rewards.

Reinforcement learning is mainly used to solve the problems of sequential decision-making and long-term goal. E.g., Chess, Robotics, etc.

Introduction to Trading

Trading is defined as the act or process of exchanging goods or services within a country or between trading nations. It is a primary economic term that involves buying and selling of commodities and services, along with compensation paid by a buyer to a seller.

Types of Trading Strategies

Trading strategies are mainly categorized into 4 types with different timeframes and duration of the trade. These are as follows:

Machine Learning in Trading

Detecting patterns is the key factor for successful trading, and machine learning is the key player for traders across the world. Initially, traders observe trends or past day's market data pattern, and based on that; they start trading for maximum return in comparison to others. These are called trading strategies that can be expressed as a set of rules that trigger buys and sells when certain conditions are met.

Traders work on meaningful data patterns in the movement of technical trading indicators: mathematical calculations based on information about prices, volatility, and so on. However, trading is also possible without using machine learning, i.e., manual trading, but we know humans are slow and inconsistent compared to a machine. On the other hand, Machines are faster and more accurate for bulk processing of data, so machine learning is more advantageous to manual trading. Further, ML algorithms can spot patterns in large volumes of data.

Machine Learning Models for Algorithmic Trading

Algorithmic Trading is based on computer programs that execute algorithms to automate some or all elements of trading. Machine learning uses various algorithms that learn from data, build the model, and achieve the goal with minimum prediction errors. Supervised and unsupervised machine learning models are much helpful for trading. There are some important machine learning models that are generally used in trading, which are as follows:

• Linear models: These models are used for regression and classification of cross-section, time series, and panel data.
• Generalized additive models: These models generally include non-linear tree-based models, e.g., decision trees.
• Ensemble models: These models include random forest and gradient boosting machines.
• Unsupervised models: Unsupervised methods for linear and non-linear models are useful for dimensionality reduction and clustering.
• Neural Network models: These models are useful for understanding the recurrent and convolution architectures.
• Reinforcement model: It helps to solve various complex, partially observable problems in trading with the help of the Markov Decision Process and Q-learning.

Ways to use Machine Learning in Trading

Below are some important ways that let us know how exactly ML is used in trading:

• Pattern Formation

Machine Learning is one of the great technologies that analyze huge amounts of data within seconds. In the same way, it can also detect the trading patterns rapidly, are historical, and replicate for smart trading.

• Predictive Trading (Sentiment Based)

On the basis of different analyses, including news headlines, media comments, and other platforms, ML models can forecast the movement of other traders along with the direction of stock using sentiment analysis.

• Increase Trading Speed

With Machine Learning and AI, trading has become as fast as possible such as it can be facilitated in milliseconds. Further, this high-speed trading is automatic and does not require any human intervention.

Advantages of Machine Learning in Trading

Although it's possible to watch the market and make trades based on strategies in manual ways, in this competitive technology world, we cannot follow classical approaches. Machine Learning has the power to solve large-scale problems like optimization, analysis, and forecasting in the trading domain and has replaced all classical ways of trading with algorithmic trading. Machine learning, in contrast, has several benefits compared to traditional methods, such as:

• Detect patterns: The definition of machine learning has evolved around finding meaningful patterns in data. ML algorithms are helpful in detecting patterns in large volumes of data. If the volume of data is more, then the spotting pattern will also be more which can be more significant for identifying those trading patterns rapidly, which are historical and replicating for smart trading. Whereas humans cannot identify and build patterns with such speed.
• Prediction of stock prices

Machine learning algorithms feed training data into systems so that they can predict accurate outcomes in the future. Similarly, machine learning is also significant in predicting stock prices, which helps traders to invest in stocks. This is known as the target variable, and sample data is called the predictor variable. Hence, machine learning algorithms learn to apply predictor variables for forecasting the target variables.

• Accelerates the Search for Effective Algorithmic Trading Strategies

ML is also significant in searching for effective algorithmic trading strategies. Since ML is an automation technique, so it is more beneficial than other traditional approaches. These trading strategies help marketers by optimizing their profits and minimizing the risk chances to a great extent. Further, there are various trading strategies that work as optimizing algorithms for ML, such as linear regression, deep learning, neural network, etc.

• Increase the number of markets to monitor

Machine learning is used to increase the number of marketers to monitor, which helps to increase the chances of profit for traders. Further, machine learning is used by various trading organizations for investment decision-making.

Conclusion:

Similar to other industries, machine learning is much useful for trading and stock marketing also. In this article, from a trading point of view, we first understood the basic concepts of machine learning, types of ML, algorithms used in ML, application of ML, and advantages of machine learning in trading, and how machine learning could be implemented in trading.