Basics of Machine Learning in Trading Strategy Design
Artificial Intelligence and Machine Learning have become the buzzwords of transformative technologies hype in recent years. It is an integral part for many businesses already, with their main goal to substitute human intervention by adopting cutting-edge technologies. Automated cars, automated customer service, face and voice recognition, chatbots and many other features have become a routine occurrence now. Machines take huge amounts of data and quickly “learn” to make decisions.
One of the best use cases of machine learning is the trading industry, where large data volume analysis and market data monitoring is required. Being a perfect fit for such tasks, machine learning is a promising and advanced method for decision-making regarding investments and risk assessment.
It’s not a secret that assets trading always carries a chance of losing big money. Prices either go up or down, and such behavior depends on many factors, both internal and external. Understanding the complex market’s performance principles is extremely important to keep one’s head above water. Therefore, the primary task for a trader is to understand the price movement and catch the right moment to buy or sell.
For years, technical analysis has been one of the most common tools for decision making along with various types of indicators, such as EMA, MACD, RSI and many others. The large variety of indicators is intended to use statistical dependencies that appear during the historical data analysis. You can observe then, that same market patterns can be repeated. However, being a lot more complicated system, the market cannot be described by one or more relatively simple statistical models or patterns.
Application of Machine Learning in Trading Strategies
By leveraging the methods of machine learning for trading strategy designing, in particular, deep learning approaches and neural networks, traders ensure more efficient performance.
Trading strategies development with machine learning is able to provide insights into one or more of the following matters:
- whether it is a good time for opening a position;
- whether it should be Long or Short position;
- which position quantity fits the best under given conditions;
- what is the right moment for closing a position;
- what is forecasted price range over a certain period in the future.
Well-defined and mathematically described tasks provide us with the target function. One of the simplest target functions can be a function that returns the position direction that needs to be opened at a given time: y (t) = position_direction (t).
In addition to the target function, the machine learning system requires the determination of several more components.
- the set of input features;
- the criterion of system / strategy effectiveness evaluation;
- the set of input data (training and testing data);
- the selection of machine learning system model – the neural networks as an example.
Let’s take a closer look at these components.
The selection of input features set has a direct impact on the efficiency of the whole further process. The set may include, for example, Price, AskPrice, BidPrice, Volume, Fundamental Data, Leve2 Data, and other features, as well as derivative calculations using technical analysis tools. Input features are primarily the quantities that have a particular impact on the target value. The set of features can be adjusted manually using expert knowledge or either automatically. It should be well balanced as a too large/small or irrelevant set may lead to the inability to determine the suitable model parameters in the following steps.
Next key ingredient is the criterion of effectiveness evaluation which can be expressed as Profit & Loss, or the accuracy of the future price evaluation as the mean square error.
Another component is input data (training and testing). Usually, the more input historical data you have (e.g. historical value of price change, fundamental data, etc.), the more complex machine learning models can be used, the greater number of input features can be applied and the greater is the accuracy of the system as a whole. The input data is divided into two components:training data and testing data. In turn, these can be divided into two sets:
- set of adjustments of the various performance parameters;
- set of the final assessment of the effectiveness.
And the last component of the machine learning system is the model of machine learning itself. There are extremely a lot of them, but in the last few years, it’s become popular to use models based on neural networks. Such a model is based on the perceptron concept which is a very simplified mathematical form of a neuron in the human brain.
What Neural Networks Do
In fact, the perceptron is a module that summarizes all of its inputs with certain weighting factors and applies a certain output conversion function depending on the type of task being solved. One perceptron is not able to search for complex statistical dependencies, so they’re being integrated together in levels. These levels are processed one by one as input data. The inputs reach the first level of neurons, then being transformed and passed to the next level all the way to output level that produces the result of the target function.
Through learning, the neural network forms a certain internal representation of the input data at each level based on the automatically detected statistical dependencies between the input and output data.
The process of Learning looks like multiple calculations for each set of input data of target function, calculation of its deviation from the correct value and, on this basis, updating of the neural network internal parameters.
Incorporating Machine Learning is an innovative and smart approach to analyze the market and build effective profitable trading strategies. Fintatech team will continue an in-depth overview of how using machine learning in trading strategy development can benefit your trading efforts substantially.
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