Better NN EA development - page 87
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Forum on trading, automated trading systems and testing trading strategies
MetaTrader 5 Python User Group - the summary
Sergey Golubev, 2024.06.05 05:02
Data Science and ML (Part 23): Why LightGBM and XGBoost outperform a lot of AI models?
Gradient Boosted Decision Trees (GBDT) are a powerful machine learning technique used primarily for regression and classification tasks. They combine the predictions of multiple weak learners, usually decision trees, to create a strong predictive model.
The core idea is to build models sequentially, each new model attempting to correct the errors made by the previous ones.
Have gained much popularity in the machine learning community as the algorithms of choice for many winning teams in machine learning competitions. In this article, we are going to discover how we can use these accurate models in our trading applications.
Forum on trading, automated trading systems and testing trading strategies
MetaTrader 5 Python User Group - the summary
Sergey Golubev, 2024.06.25 12:35
Data Science and Machine Learning (Part 24): Forex Time series Forecasting Using Regular AI Models
Time series forecasting is the process of using past data to predict future values in a sequence of data points. This sequence is typically ordered by time, hence the name time series.
Forum on trading, automated trading systems and testing trading strategies
MetaTrader 5 Python User Group - the summary
Sergey Golubev, 2024.07.06 15:10
Data Science and Machine Learning (Part 25): Forex Timeseries Forecasting Using a Recurrent Neural Network (RNN)
Recurrent Neural Networks (RNNs) are artificial neural networks designed to recognize patterns in sequences of data, such as time series, language, or video. Unlike traditional neural networks, which assume that inputs are independent of each other, RNNs can detect and understand patterns from a sequence of data (information).
A basic understanding of Python, ONNX in MQL5, and Python machine learning is required to understand the contents of this article fully.Forum on trading, automated trading systems and testing trading strategies
MetaTrader 5 Python User Group - the summary
Sergey Golubev, 2024.07.26 14:47
Data Science and ML (Part 26): The Ultimate Battle in Time Series Forecasting — LSTM vs GRU Neural Networks
Long Short-Term Memory(LSTM), is a type of recurrent neural network designed for sequence tasks, excelling in capturing and utilizing long-term dependencies in data. Unlike vanilla Recurrent Neural Networks(simple RNNs) discussed in the previous article of this series (a must-read). Which can't capture long-term dependencies in the data.
Forum on trading, automated trading systems and testing trading strategies
MetaTrader 5 Python User Group - the summary
Sergey Golubev, 2024.07.26 14:50
Data Science and ML (Part 27): Convolutional Neural Networks (CNNs) in MetaTrader 5 Trading Bots — Are They Worth It?
Convolutional Neural Networks (CNNs) are a class of deep learning algorithms specifically designed to process structured grid-like data, such as images, audio spectrograms, and time-series data. They are particularly well-suited for visual data tasks because they can automatically and adaptively learn spatial hierarchies of features from input data.
CNNs are the extended version of artificial neural networks (ANN). They are predominantly used to extract the feature from the grid-like matrix dataset. For example, visual datasets like images or videos where data patterns play an extensive role.
Forum on trading, automated trading systems and testing trading strategies
MetaTrader 5 Python User Group - the summary
Sergey Golubev, 2024.08.06 19:42
Data Science and ML (Part 28): Predicting Multiple Futures for EURUSD, Using AI
Multi-step forecasting is crucial in various domains, such as finance, weather prediction, supply chain management, and healthcare. For instance, in financial markets, investors need to forecast stock prices or exchange rates for several days, weeks, or even months ahead. In weather prediction, accurate forecasts for the upcoming days or weeks can help in planning and disaster management.
This article assumes you have a basic understanding of machine learning and AI, ONNX, How to Use ONNX models in MQL5, Linear Regression, LightGBM, and Neural Networks.Forum on trading, automated trading systems and testing trading strategies
MetaTrader 5 Python User Group - the summary
Sergey Golubev, 2024.08.19 17:11
Data Science and ML (Part 29): Essential Tips for Selecting the Best Forex Data for AI Training Purposes
With all the trading data and information such as indicators (there are more than 36 built-in indicators in MetaTrader 5), symbol pairs (there are more than 100 symbols) that can also be used as data for correlation strategies, there are also news which are valuable data for traders, etc. The point I'm trying to raise is that there is abundant information for traders to use in manual trading or when trying to build Artificial Intelligence models to help us make smart trading decisions in our trading robots.
Out of all the information we have at hand, there has to be some bad information (that is just common sense). Not all indicators, data, strategy, etc. are useful for a particular trading symbol, strategy, or situation. How do we determine the right information for trading and machine learning models for maximum efficiency and profitability? This is where feature selection comes into play.
Forum on trading, automated trading systems and testing trading strategies
MetaTrader 5 Python User Group - the summary
Sergey Golubev, 2024.10.02 14:41
Data Science and ML(Part 30): The Power Couple for Predicting the Stock Market, Convolutional Neural Networks(CNNs) and Recurrent Neural Networks(RNNs)
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.
Forum on trading, automated trading systems and testing trading strategies
MetaTrader 5 Python User Group - the summary
Sergey Golubev, 2024.10.16 16:34
Data Science and ML (Part 31): Using CatBoost AI Models for Trading
CatBoost is an open-source software library with gradient-boosting algorithms on decision trees, it was designed specifically to address the challenges of handling categorical features and data in machine learning.
It was developed by Yandex and was made open-source in the year of 2017, read more.
Despite being introduced recently compared to machine learning techniques such as Linear regression or SVM's, CatBoost gained massive popularity among AI communities and rose to the top of the most used machine learning models on platforms like Kaggle.
What made CatBoost gain this much attention is its ability to automatically handle categorical features in the dataset, which can be challenging to many machine learning algorithms.