Better NN EA development - page 86

[Sergey Golubev]

Neural networks made easy (Part 46): Goal-conditioned reinforcement learning (GCRL)

"Goal-conditioned reinforcement learning" sounds a little unusual or even strange. After all, the basic principle of reinforcement learning is aimed at maximizing the total reward during the interaction of the agent with the environment. But in this context, we are looking at achieving a specific goal at a specific stage or within a specific scenario.

[Sergey Golubev]

Neural networks made easy (Part 47): Continuous action space

The EA assessed the market situation at each new trading candle and made a decision on a trading operation. But every upcoming bar carries risks for our account. Price movement within a bar can be detrimental to our balance. This is why it is always recommended to use stop losses. This simple approach allows us to limit risks per trade.

[Sergey Golubev]

Neural networks made easy (Part 48): Methods for reducing overestimation of Q-function values

As you might remember, in DDPG, the Critic model learns the Q-function (prediction of expected reward) based on the results of interaction with the environment, while the Agent model is trained to maximize the expected reward, based only on the results of the Critic’s assessment of actions. Consequently, the quality of the Critic’s training greatly influences the Agent’s behavioral strategy and its ability to make optimal decisions.

[Sergey Golubev]

Neural networks made easy (Part 49): Soft Actor-Critic

In this article, we will focus our attention on another algorithm - Soft Actor-Critic (SAC). It was first presented in the article "Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor" (January 2018). The method was presented almost simultaneously with TD3. It has some similarities, but there are also differences in the algorithms. The main goal of SAC is to maximize the expected reward given the maximum entropy of the policy, which allows finding a variety of optimal solutions in stochastic environments.

[Sergey Golubev]

Data Science and Machine Learning (Part 15): SVM, A Must-Have Tool in Every Trader's Toolbox

Support Vector Machine (SVM) is a powerful supervised machine learning algorithm used for linear or nonlinear classification and regression tasks, and sometimes outlier detection tasks.

Unlike Bayesian classification techniques, and logistic regression which deploy simple mathematical models to classify information, The SVM has some complex mathematical learning functions aimed at finding the optimal hyperplane that separates the data in an N-dimensional space.

Support vector machine is usually used for classification tasks, something we'll also do in this article.

[Sergey Golubev]

Data Science and Machine Learning (Part 20) : Algorithmic Trading Insights, A Faceoff Between LDA and PCA in MQL5 

LDA is a supervised generalization machine learning algorithm that aims to find a linear combination of features that best separates the classes in a dataset.

Just like the Principal Component Analysis(PCA), it is a dimension reduction algorithm, These algorithms are a common choice for dimensionality reduction, in this article we are going to compare them and observe in what situation each algorithm works best. We already discussed the PCA in the prior articles of this series, Let us commence by observing what the PCA algorithm is all about as we will discuss it mostly, finally we will compare their performances on a simple dataset and in the strategy tester, make sure you stick to the end for awesome data science stuff.

[Sergey Golubev]

Quantization in machine learning (Part 1): Theory, sample code, analysis of implementation in CatBoost 

The article considers the theoretical application of quantization in the construction of tree models. No complex mathematical equations are used. While writing the article, I discovered the absence of established unified terminology in the scientific works of different authors, so I will choose the terminology options that, in my opinion, best reflect the meaning. Besides, I will use the terms of my own in the matters left unattended by other researchers. This article will use terms and concepts I have previously described in the article "CatBoost machine learning algorithm from Yandex without learning Python or R". Therefore, I recommend that you familiarize yourself with it before reading the current article.

[Sergey Golubev]

The Disagreement Problem: Diving Deeper into The Complexity Explainability in AI

The disagreement is an open area of research in an interdisciplinary field known as Explainable Artificial Intelligence (XAI). Explainable Artificial Intelligence attempts to help us understand how our models are arriving at their decisions but unfortunately everything is easier said than done. 

We are all aware that machine learning models and available datasets are growing larger and more complex. As a matter of fact, the data scientists who develop machine learning algorithms cannot exactly explain their algorithm’s behaviour across all possible datasets.  Explainable Artificial Intelligence (XAI) helps us build trust in our models, explain their functionality and validate that the models are ready to be deployed in production; but as promising as that may sound, this article will show the reader why we cannot blindly trust any explanation we may get from any application of Explainable Artificial Intelligence technology.

[Sergey Golubev]

Sergey Golubev #:

Quantization in machine learning (Part 1): Theory, sample code, analysis of implementation in CatBoost 

The article considers the theoretical application of quantization in the construction of tree models. No complex mathematical equations are used. While writing the article, I discovered the absence of established unified terminology in the scientific works of different authors, so I will choose the terminology options that, in my opinion, best reflect the meaning. Besides, I will use the terms of my own in the matters left unattended by other researchers. This article will use terms and concepts I have previously described in the article "CatBoost machine learning algorithm from Yandex without learning Python or R". Therefore, I recommend that you familiarize yourself with it before reading the current article.

Quantization in machine learning (Part 2): Data preprocessing, table selection, training CatBoost models 

The article considers the practical application of quantization in the construction of tree models. No complex mathematical equations are used. This is the second part of the article "Quantization and other methods of preprocessing input data in machine learning", so I strongly recommend starting your acquaintance with it. Here we will talk about the following:

• In the first part, we will consider the methods for preprocessing sample data implemented in MQL5.
• In the second part, we will conduct an experiment that will provide information on the feasibility of data quantization.

[Sergey Golubev]

Forum on trading, automated trading systems and testing trading strategies

MetaTrader 5 Python User Group - the summary

Sergey Golubev, 2024.05.08 12:51

Data Science and ML (Part 22): Leveraging Autoencoders Neural Networks for Smarter Trades by Moving from Noise to Signal

In this article, we will see how we can use an autoencoder neural network in the financial space to help us remove noise in the market so that we can discover trading opportunities.

This article is an easy read if you have a basic understanding of ONNX, PCA, and Neural Networks in general.