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Pattern recognition has always been a valuable tool for traders. Whether it's identifying unique combinations of candlesticks or drawing imaginary lines on a chart, these patterns have become an integral part of technical analysis. Humans have always excelled at finding and recognizing patterns—so much so that it is often said we sometimes see patterns where there are none. Therefore, it would benefit us to apply more objective techniques when identifying potentially profitable patterns in

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This article continues our look at reinforcement learning by considering another algorithm, namely the Monte-Carlo. This algorithm is very similar and in fact arguably encompasses both Q-Learning and SARSA in that it can be either on-policy or off-policy. What sets it apart though is the emphasis on episodes. These simply are a way of batching the reinforcement learning cycle updates, that we introduced in this article, such that the updating of the Q-Values of the Q-Map happens less frequently.

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In the 20th century, Richard Donchian established a trend-following strategy through his studies of financial markets, which later evolved into the Donchian Channels. We briefly discussed his work in a previous article, but today we will focus on implementing the strategies associated with his theory. According to various sources, the channels are believed to encompass multiple strategies within their framework. The abundance of literature on Donchian Channels suggests the continued effectiveness

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In the previous article Neural Network in Practice: Secant Line, we began to discuss applied mathematics in practice. However, this was only a short and quick introduction to the topic. We have seen that the basic mathematical operation to be used is the trigonometric function. And, contrary to what many think, this is not a tangent function but a secant function. Although this may all seem quite confusing at first, you will soon find that everything is much simpler than it seems. Unlike

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Custom signal classes for wizard assembled Expert Advisors can take on various roles, that are worth exploring, and we continue this quest by examining how the Q-Learning algorithm when paired with Markov Chains can help refine the learning process of a multi-layer-perceptron network. Q-Learning is one of the several (approximately 12) algorithms of reinforcement-learning, so essentially this is also a look at how this subject can be implemented as a custom signal and tested within a wizard

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