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Experiments with neural networks (Part 4): Templates
Experiments with neural networks (Part 4): Templates
In this article, I will use experimentation and non-standard approaches to develop a profitable trading system and check whether neural networks can be of any help for traders. MetaTrader 5 as a self-sufficient tool for using neural networks in trading. Simple explanation.
Neural networks made easy (Part 34): Fully Parameterized Quantile Function
Neural networks made easy (Part 34): Fully Parameterized Quantile Function
We continue studying distributed Q-learning algorithms. In previous articles, we have considered distributed and quantile Q-learning algorithms. In the first algorithm, we trained the probabilities of given ranges of values. In the second algorithm, we trained ranges with a given probability. In both of them, we used a priori knowledge of one distribution and trained another one. In this article, we will consider an algorithm which allows the model to train for both distributions.
Neural networks made easy (Part 36): Relational Reinforcement Learning
Neural networks made easy (Part 36): Relational Reinforcement Learning
In the reinforcement learning models we discussed in previous article, we used various variants of convolutional networks that are able to identify various objects in the original data. The main advantage of convolutional networks is the ability to identify objects regardless of their location. At the same time, convolutional networks do not always perform well when there are various deformations of objects and noise. These are the issues which the relational model can solve.
Population optimization algorithms: Monkey algorithm (MA)
Population optimization algorithms: Monkey algorithm (MA)
In this article, I will consider the Monkey Algorithm (MA) optimization algorithm. The ability of these animals to overcome difficult obstacles and get to the most inaccessible tree tops formed the basis of the idea of the MA algorithm.
Population optimization algorithms: Harmony Search (HS)
Population optimization algorithms: Harmony Search (HS)
In the current article, I will study and test the most powerful optimization algorithm - harmonic search (HS) inspired by the process of finding the perfect sound harmony. So what algorithm is now the leader in our rating?
Take a few lessons from Prop Firms (Part 1) — An introduction
Take a few lessons from Prop Firms (Part 1) — An introduction
In this introductory article, I address a few of the lessons one can take from the challenge rules that proprietary trading firms implement. This is especially relevant for beginners and those who struggle to find their footing in this world of trading. The subsequent article will address the code implementation.
Data Science and Machine Learning (Part 11): Naïve Bayes, Probability theory in Trading
Data Science and Machine Learning (Part 11): Naïve Bayes, Probability theory in Trading
Trading with probability is like walking on a tightrope - it requires precision, balance, and a keen understanding of risk. In the world of trading, the probability is everything. It's the difference between success and failure, profit and loss. By leveraging the power of probability, traders can make informed decisions, manage risk effectively, and achieve their financial goals. So, whether you're a seasoned investor or a novice trader, understanding probability is the key to unlocking your trading potential. In this article, we'll explore the exciting world of trading with probability and show you how to take your trading game to the next level.
Dealing with Time (Part 2): The Functions
Dealing with Time (Part 2): The Functions
Determing the broker offset and GMT automatically. Instead of asking the support of your broker, from whom you will probably receive an insufficient answer (who would be willing to explain a missing hour), we simply look ourselves how they time their prices in the weeks of the time changes — but not cumbersome by hand, we let a program do it — why do we have a PC after all.
Population optimization algorithms: Gravitational Search Algorithm (GSA)
Population optimization algorithms: Gravitational Search Algorithm (GSA)
GSA is a population optimization algorithm inspired by inanimate nature. Thanks to Newton's law of gravity implemented in the algorithm, the high reliability of modeling the interaction of physical bodies allows us to observe the enchanting dance of planetary systems and galactic clusters. In this article, I will consider one of the most interesting and original optimization algorithms. The simulator of the space objects movement is provided as well.
Alan Andrews and his methods of time series analysis
Alan Andrews and his methods of time series analysis
Alan Andrews is one of the most famous "educators" of the modern world in the field of trading. His "pitchfork" is included in almost all modern quote analysis programs. But most traders do not use even a fraction of the opportunities that this tool provides. Besides, Andrews' original training course includes a description not only of the pitchfork (although it remains the main tool), but also of some other useful constructions. The article provides an insight into the marvelous chart analysis methods that Andrews taught in his original course. Beware, there will be a lot of images.
How to use ONNX models in MQL5
How to use ONNX models in MQL5
ONNX (Open Neural Network Exchange) is an open format built to represent machine learning models. In this article, we will consider how to create a CNN-LSTM model to forecast financial timeseries. We will also show how to use the created ONNX model in an MQL5 Expert Advisor.
Category Theory in MQL5 (Part 5): Equalizers
Category Theory in MQL5 (Part 5): Equalizers
Category Theory is a diverse and expanding branch of Mathematics which is only recently getting some coverage in the MQL5 community. These series of articles look to explore and examine some of its concepts & axioms with the overall goal of establishing an open library that provides insight while also hopefully furthering the use of this remarkable field in Traders' strategy development.
Learn how to design a trading system by Gator Oscillator
Learn how to design a trading system by Gator Oscillator
A new article in our series about learning how to design a trading system based on popular technical indicators will be about the Gator Oscillator technical indicator and how to create a trading system through simple strategies.
Moral expectation in trading
Moral expectation in trading
This article is about moral expectation. We will look at several examples of its use in trading, as well as the results that can be achieved with its help.
Neural networks made easy (Part 35): Intrinsic Curiosity Module
Neural networks made easy (Part 35): Intrinsic Curiosity Module
We continue to study reinforcement learning algorithms. All the algorithms we have considered so far required the creation of a reward policy to enable the agent to evaluate each of its actions at each transition from one system state to another. However, this approach is rather artificial. In practice, there is some time lag between an action and a reward. In this article, we will get acquainted with a model training algorithm which can work with various time delays from the action to the reward.
Creating an EA that works automatically (Part 07): Account types (II)
Creating an EA that works automatically (Part 07): Account types (II)
Today we'll see how to create an Expert Advisor that simply and safely works in automatic mode. The trader should always be aware of what the automatic EA is doing, so that if it "goes off the rails", the trader could remove it from the chart as soon as possible and take control of the situation.
Category Theory in MQL5 (Part 4): Spans, Experiments, and Compositions
Category Theory in MQL5 (Part 4): Spans, Experiments, and Compositions
Category Theory is a diverse and expanding branch of Mathematics which as of yet is relatively uncovered in the MQL5 community. These series of articles look to introduce and examine some of its concepts with the overall goal of establishing an open library that provides insight while hopefully furthering the use of this remarkable field in Traders' strategy development.
Data Science and Machine Learning (Part 13): Improve your financial market analysis with Principal Component Analysis (PCA)
Data Science and Machine Learning (Part 13): Improve your financial market analysis with Principal Component Analysis (PCA)
Revolutionize your financial market analysis with Principal Component Analysis (PCA)! Discover how this powerful technique can unlock hidden patterns in your data, uncover latent market trends, and optimize your investment strategies. In this article, we explore how PCA can provide a new lens for analyzing complex financial data, revealing insights that would be missed by traditional approaches. Find out how applying PCA to financial market data can give you a competitive edge and help you stay ahead of the curve
MQL5 Cookbook — Macroeconomic events database
MQL5 Cookbook — Macroeconomic events database
The article discusses the possibilities of handling databases based on the SQLite engine. The CDatabase class has been formed for convenience and efficient use of OOP principles. It is subsequently involved in the creation and management of the database of macroeconomic events. The article provides the examples of using multiple methods of the CDatabase class.
Data Science and Machine Learning (Part 12): Can Self-Training Neural Networks Help You Outsmart the Stock Market?
Data Science and Machine Learning (Part 12): Can Self-Training Neural Networks Help You Outsmart the Stock Market?
Are you tired of constantly trying to predict the stock market? Do you wish you had a crystal ball to help you make more informed investment decisions? Self-trained neural networks might be the solution you've been looking for. In this article, we explore whether these powerful algorithms can help you "ride the wave" and outsmart the stock market. By analyzing vast amounts of data and identifying patterns, self-trained neural networks can make predictions that are often more accurate than human traders. Discover how you can use this cutting-edge technology to maximize your profits and make smarter investment decisions.
Category Theory in MQL5 (Part 3)
Category Theory in MQL5 (Part 3)
Category Theory is a diverse and expanding branch of Mathematics which as of yet is relatively uncovered in the MQL5 community. These series of articles look to introduce and examine some of its concepts with the overall goal of establishing an open library that provides insight while hopefully furthering the use of this remarkable field in Traders' strategy development.
Creating an EA that works automatically (Part 03): New functions
Creating an EA that works automatically (Part 03): New functions
Today we'll see how to create an Expert Advisor that simply and safely works in automatic mode. In the previous article, we started to develop an order system that we will use in our automated EA. However, we have created only one of the necessary functions.
Experiments with neural networks (Part 3): Practical application
Experiments with neural networks (Part 3): Practical application
In this article series, I use experimentation and non-standard approaches to develop a profitable trading system and check whether neural networks can be of any help for traders. MetaTrader 5 is approached as a self-sufficient tool for using neural networks in trading.