Discrete Hartley transform
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In 1942,
Ralph Hartley proposed an analogue of the Fourier transform in his article "
A More Symmetrical Fourier Analysis Applied to Transmission Problems".
Just like Fourier transform (
FT), Hartley transform (
HT) turns the original signal into a sum of trigonometric functions. But there is one significant difference between them.
FT converts real values to
complex numbers, while
HT provides only real results. Because of this difference, the Hartley transform did not become popular - scientists and technicians did not see any advantages in it and continued to use the usual Fourier transform. In 1983,
Ronald Bracewell presented a discrete version of the Hartley transform.
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How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 5): Bollinger Bands On Keltner Channel — Indicators Signal
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The definition of a multi-currency Expert Advisor in this article is an Expert Advisor or trading robot that can trade (open orders, close orders and manage orders, for example: Trailing Stop Loss and Trailing Profit) for more than 1 symbol pair from only one symbol chart, where in this article Expert Advisor will trade for 30 pairs.
In this article we will use signals from two indicators, in this case Bollinger Bands® On Keltner Channel.
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Integrating ML models with the Strategy Tester (Conclusion): Implementing a regression model for price prediction
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In the
previous article, we completed the implementation of a CSV file management class for storing and retrieving data related to financial markets. Having created the infrastructure, we are now ready to use this data to build and train a machine learning model.
Our task in this article is to implement a regression model that can predict the closing price of a financial asset within a week. This forecast will allow us to analyze market behavior and make informed decisions when trading financial assets.
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Developing a multi-currency Expert Advisor (Part 2): Transition to virtual positions of trading strategies
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In the previous
article, we started developing a multi-currency EA that works simultaneously with various trading strategies. At the first stage there were only two different strategies. They represented the implementation of the same trading idea, worked on the same trading instrument (symbol) and chart period (timeframe). They differed from each other only in the numerical values of the parameters.
We are now only interested in testing the suitability of this approach, and not the efficiency of its implementation. Therefore, within the framework of this article, we will try to develop at least some working implementation of this approach, which later will help us build a more beautiful one from an architectural point of view since we will already have knowledge on how to avoid mistakes.
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Developing a multi-currency Expert Advisor (Part 3): Architecture revision
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In the previous articles, we started developing a multi-currency EA that works simultaneously with various trading strategies. The solution provided in the
second article is already significantly different from the one presented in the
first one. This indicates that we are still in search of the best options.
Let's try to look at the developed system as a whole, abstracting from the small details of the implementation, in order to understand ways to improve it. To do this, let us trace the albeit short, but still noticeable evolution of the system.
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Data Science and ML(Part 30): The Power Couple for Predicting the Stock Market, Convolutional Neural Networks(CNNs) and Recurrent Neural Networks(RNNs)
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In the previous articles, we have seen how powerful both
Convolutional Neural Networks (CNNs) and
Recurrent Neural Networks (RNNs) are and how they can be deployed to help beat the market by providing us with valuable trading signals.
In this one we are going to attempt combining two of the most powerful techniques CNN and RNN and observe their predictive impact in the stock market. But before that let us briefly understand what CNN and RNN are all about.
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Neural Network in Practice: Pseudoinverse (II)
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In the previous article "
Neural Network in Practice: Pseudoinverse (I)", I showed how you can use a function available in the MQL5 library to calculate the pseudoinverse. However, the method present in the MQL5 library, as in many other programming languages, is intended to calculate the pseudoinverse when using matrices or at least some structure that may resemble a matrix.
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Custom Indicator: Plotting Partial Entry, Exit and Reversal Deals for Netting Accounts
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When we talk about indicators, we can think of different functions: plotting (histograms, trend lines, arrows or bars), calculating data based on price and volume movements, and observing statistical patterns in our trades. However, in this article we will consider another way of constructing an indicator in MQL5. We will talk about how to manage your own positions, including entries, partial exits, etc. We will make extensive use of dynamic matrices and some trading functions related to trade history and open positions.
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