A Beginners Guide to Machine Learning for Trading

Artificial Intelligence (AI) and Machine Learning (ML) are currently permeating every aspect of our lives. The term artificial intelligence refers to the intelligence displayed by machines. Machine learning is a subfield of Artificial Intelligence that studies computer algorithms that are trained and improved over time.

Trading occurs in a highly competitive environment because traders are under constant pressure to make wise choices that will maximize their profits. To produce accurate predictions, machine learning algorithms make extensive use of both structured and unstructured data. In this article, you’ll learn how to use machine learning for trading, some of the pros and cons of doing that, and the reasons for making this move.

What is Machine Learning?

Machine learning is the ability of machines to make certain decisions or perform actions, based on the analysis, observations, and experiences within a given set of data. It allows the system to perform a certain task without any particular instructions assigned to them.

It is common to confuse Machine Learning, Artificial Intelligence, and Deep Learning while learning machine learning basics. Below is a diagram that illustrates the concept of machine learning.

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The system will be able to perform simple classification tasks as well as complex mathematical computations such as regression, just like humans. Perform classification or regression, which entails the construction of mathematical models.

Depending on the training data they use, mathematical models are split into two categories: supervised learning models and unsupervised learning models.

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Image Source: QuantInsti

Also Read: How Machine Learning can Influence the Stock Markets?

Role of Machine Learning in finance

Machine learning has already established a solid foundation for its application and role in industries other than just finance. Multiple industries are utilizing machine learning, including healthcare, e-commerce, virtual assistance, social media, transportation, and financial services.

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In finance, it is the use of a variety of techniques to expertly handle and process enormous amounts of complex data, and learn from it how to carry out a particular task, like telling fake legal documents from real ones.

The industry generates a large amount of historical financial data, and ML has found many practical applications in finance. Technology now plays a crucial part in many aspects of the financial ecosystem, from loan approval and credit scoring to portfolio and risk management.

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Machine learning, specifically machine learning in Python for Algorithmic trading, has recently become a hot topic among both retail and institutional traders. A forecast by Valuates Reports states that “The global algorithmic trading market size was valued at USD 12,143 Million in 2020 and is expected to reach USD 31,494 Million by 2028, represents a CAGR of 12.7% from 2021 to 2028”

Artificial Intelligence and Machine Learning offer tremendous economic development opportunities, which is why it is growing in importance. A project undertaken by Price Waterhouse Coopers (PWC) predicted that “Artificial intelligence technologies could boost global GDP by $15.7 trillion, a full 14%, by 2030.

There are several applications of Machine Learning for Trading. Some popular ones are:

  • Stock price prediction using historical data
  • Improves the effectiveness of algorithmic trading strategies
  • Monitoring a large number of markets

Why use Machine Learning for Trading?

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Finding patterns is the secret to profitable trading. In the past, traders looked for patterns in market data and made predictions based on those patterns to increase the profit from their trading activities. These tactics can be described as a set of guidelines that, when certain conditions are met, cause buys and sells to occur.

Traders frequently look for patterns in the movement of technical trading indicators: mathematical calculations based on price, volatility, and other data.

Although it is possible to monitor the market and place trades using these types of strategies, humans are slow and erratic. For a high-frequency trading platform that can process thousands of transactions per second, it is frequently advantageous to encode strategies in an “if this happens, do that” algorithm because machines are far more faster and accurate than humans.

A recent survey by Cornell University – arxiv, concludes that “In contrast to classical stochastic control theory and other analytical approaches for solving financial decision-making problems that heavily reply on model assumptions, new developments from reinforcement learning (RL) are able to make full use of the large amount of financial data with fewer model assumptions and to improve decisions in complex financial environments.

We can use data, such as OHLC or price data, fundamental data, or alternative data such as tweets and news data about a specific asset, to create a machine learning model and then use it to predict the future.

The machine learning model will provide us with signals on whether to buy or sell an asset to maximize our profits when we say that it can predict the future. Machine learning has a long way to go before it can reliably predict the future, of course.

Compared to conventional algorithmic trading, machine learning, however, has several advantages. Large amounts of data can be analyzed by machine learning algorithms to find patterns.

In order to use algorithmic trading strategies, they are used to identify associations in historical data. Machine learning gives traders the ability to speed up and automate one of the trickiest, most time-consuming, and most difficult aspects of algorithmic trading, giving them an edge over rule-based trading.

Trading through machine learning through high-speed transactions allows for substantial gains in profits, which have attracted both retail and institutional traders. So, why shouldn’t a trader use Machine Learning to upgrade their trading skills when every industry uses it in some way?

Also Read: Machine Learning Algorithms For Trading

Benefits of Machine Learning for Trading

Some of the major advantages of using Machine Learning for Trading:

  • ML is used to speed up the search for efficient algorithmic trading strategies. It is significantly superior to the manual process because it offers an automated approach. By maximizing profits and simulating risks, these algorithmic trading strategies assist traders.
  • Machine Learning also increases the number of markets that an individual can monitor and engage with. A trader’s likelihood of selecting the most lucrative market increases with the number of markets available. As a result, this application of machine learning can increase your opportunities.
  • Machine learning uses historical data, also known as predictor variables, to forecast stock prices. To do so, the ML algorithm learns to use predictor variables to forecast the target variables.
  • Detecting suspicious activity and fraud faster for enhanced security
  • The use of machine learning algorithms in predicting financial trends is crucial. FinTech businesses can use ML algorithms to predict market risk, identify potential future financial opportunities, reduce fraud, etc.

Drawbacks of Machine Learning for Trading

Some of the challenges faced while using Machine Learning for Trading are:

  • Automation, no matter how intelligent, is not foolproof.
  • The foundation of ML analysis is the idea that the stock market is dynamic and constantly shifting. Natural disasters are an example of a factor that cannot be considered in advance. As a result, they are prone to irrational behavior and by definition cannot be included in ML analysis.
  • Overfitting in machine learning refers to building a statistical model with more data than is required. Trading algorithms frequently receive an excessive amount of historical data. While this isn’t necessarily a bad thing, overfitting can make trading strategies less adaptable to both current and future conditions. As a result, backtesting is useful but not entirely reliable. In a live market, this bias creates the impression that a strategy will perform exactly as predicted.
  • It’s important to note that ML algorithms are very good at providing precise predictions for changes in stock prices. The bias, however, becomes more pronounced as the forecast horizon gets longer; the further into the future you attempt to predict, the less likely it is that your predictions will be accurate.
  • Other agents create different machine learning models that quickly adjust to the shifting market circumstances. In actuality, this means that in the comparatively short term, some of the stock market’s unpredictable nature can be negated. This is because only recent news can be used to gain a competitive advantage.
  • While there appears to be more than enough historical data for ML algorithm training, the truth is that it is insufficient as density increases slowly. Scale-based predictions come with a price: a risk of erroneous decisions and a reduction in accuracy.

Most of these challenges can be overcome with the right knowledge and gaining the right skills under expert guidance.


The financial industry has undergone significant change since the advent of AI and machine learning. To recognize market trends and evaluate the risks associated with investments, machine learning algorithms can be used. The level of competition has increased significantly, which has led to an increase in the number of traders using ML for trading.

Look out for some courses that teach machine learning using Python, and you can take your trading to the next level with the latest expertise, knowledge, and technology.

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