Machine learning: self-learning algorithms dominate retail


    Systematic traders have been using statistical models as the basis for their trading strategies for many years. F9news has covered deep learning methods and so-called neural networks, which are increasingly used in retail, go one step further.

    Machine learning is a branch of artificial intelligence. The term is used very broadly and refers to techniques that identify patterns in large data sets. With the help of algorithms, an attempt is made to discover laws that the human brain cannot recognize.

    The systems learn from their experience and are constantly developing. The aim is to intelligently link data with one another, to recognize relationships and to make predictions on the basis of these. Given the high volume of data, long historical records, and quantitative nature, financial markets are ideal for machine learning applications.

    Wide range of approaches

    The machine learning algorithms can be broken down into learning styles, methods and implementation approaches:

    Source: 4besnews

    Learning styles

    Supervised learning:

    The system is populated with known input and output data and trained so that it can predict future expenses.

    Unsupervised learning:

    The system should find hidden patterns or structures in input data independently.



    The system divides values ​​into classes, for example whether an e-mail is genuine or spam.


    The system is able to predict continuous reactions, typical application is systematic trading.


    Clustering is used for exploratory data analysis to find hidden patterns or groupings in data. Applications include gene sequence analysis, market research, and object recognition.

    Examples of implementation approaches

    Linear regression:

    A type of supervised algorithm that tries to explain an observable dependent variable in terms of one or more independent variables.

    Decision tree:

    A type of supervised algorithm. It consists of decision nodes in which tests for certain attributes are carried out.

    Neural network:

    The neural network consists of interconnected nodes. You can take in information from outside or from other nodes and forward it in modified form or output it as the end result.

    Neural network as a super brain

    While linear regression and the decision tree are more traditional analysis methods, deep learning techniques based on complex neural networks are increasingly used today. These are in great demand in times of big data because they can process large amounts of data. At the same time, however, they also require immense computing power.

    The way neural networks work is modeled on the human brain. The neurons are an important part of the brain. These nerve cells have processes that transmit information from one cell body to the next and establish or break connections. In the neural network, this function is assigned to the hidden layers.

    You are responsible for evaluating cause-effect relationships and calculating probabilities.

    The system is constantly evolving as it takes past experience into account and makes adjustments based on previous trading experience.

    Neural networks are used, for example, for image and word recognition, but also for the development of trading strategies. Just as deep learning recognizes certain features in a photo, it can analyze the market environment and provide information.

    The ability to detect nonlinear relationships in the input data makes it an ideal method for modeling dynamic systems such as financial markets.

    By analyzing and interacting with historical data, it knows how the market reacted to past events and learns to be productive in a wide variety of market conditions.

    The future belongs to the machines

    However, if we leave all skepticism aside, then the constantly improved computing power coupled with ever more comprehensive data sets provide an excellent basis for systematic trading strategies. More data can be analyzed and more accurate predictions can be made with better techniques.

    Machine learning enables working with non-linear data and identifying patterns based on complex interactions where people would be overwhelmed.

    That is why the trend towards machine trading will continue over the next few years. We humans play an ever smaller role. In the future, we will no longer solve the problems of financial market forecasting, we will only define them. Maybe that’s not bad at all, because:

    In the long run, we are all dead but the machines will keep on trading.

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