MetaTrader 5 Python User Group - the summary - page 31

[Sergey Golubev]

Build Self Optimizing Expert Advisors With MQL5 And Python (Part II): Tuning Deep Neural Networks

Members of our community are keen to integrate AI into their trading strategies, which requires tuning AI models for specific markets. Each AI model has adjustable parameters that significantly influence its performance; optimal settings for one market may not work for another. This article will show how to customize AI models to outperform default settings using optimization algorithms, specifically the Nelder-Mead algorithm. We'll apply this algorithm to fine-tune a deep neural network using data from the MetaTrader5 terminal and then export the optimized model in ONNX format for use in an Expert Advisor. For those unfamiliar with these concepts, we will provide detailed explanations throughout the article.

[Sergey Golubev]

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.

[Sergey Golubev]

Developing a robot in Python and MQL5 (Part 1): Data preprocessing

We will look at the development cycle of a trading robot: data collection, processing, sample expansion, feature engineering, model selection and training, creating a trading system via Python, and monitoring trades.

Working in Python has its own advantages: speed in the field of machine learning, as well as the ability to select and generate features. Exporting models to ONNX requires exactly the same feature generation logic as in Python, which is not easy. That is why I have selected online trading via Python.

[Sergey Golubev]

MQL5 Integration: Python

In this new article, I'm going to give you an ultimate introduction to an important tool that will add value to your programming skills. We'll look at Python Integration. When it comes to how this can be useful for us as developers it will depend on your objectives of software as Python is a high-level programming language that is easy to read, and it is simple as well. Python is a programming language that provides extensive libraries for areas such as data analysis, statistical computing, and machine learning. So, the integration of Python and MQL5 can provide better insights that can be helpful for financial market participants to improve their results through data processing and predictive analytics.

[Sergey Golubev]

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.

[Evgeni Gavrilovi]

If possible, add here https://www.mql5.com/en/docs/python_metatrader5 new function to get this news feed, headline and full text of the news from python

[Sergey Golubev]

Self Optimizing Expert Advisor with MQL5 And Python (Part III): Cracking The Boom 1000 Algorithm

We will analyze all of Deriv’s synthetic markets individually, starting with their best known synthetic market, the Boom 1000. The Boom 1000 is notorious for its volatile and unpredictable behavior. The market is characterized by slow, short and equally sized bear candles that are randomly followed by violent, skyscraper sized bull candles. The bull candles are especially challenging to mitigate because the ticks associated with the candle normally aren’t sent to the client terminal, meaning that all stop losses are breached with guaranteed slippage every time.

Therefore, most successful traders have created strategies loosely based on only taking buy opportunities when trading the Boom 1000. Recall that the Boom 1000 could fall for 20 mins on the M1 chart, and retrace that entire movement in 1 candle! Therefore, given its overpowered bullish nature, successful traders look to use this to their advantage by attributing more weight to buy setups on the Boom 1000, than they would to a sell setup.

[Sergey Golubev]

Applying Localized Feature Selection in Python and MQL5

In this article, we explore a feature selection algorithm introduced in the paper 'Local Feature Selection for Data Classification' by Narges Armanfard, James P. Reilly, and Majid Komeili. This method aims to identify predictive features that are often overlooked by traditional selection techniques due to their limited global utility. We will begin with a general overview of the algorithm, followed by its implementation in Python to create classifier models suitable for export to MetaTrader 5.

[Sergey Golubev]

Self Optimizing Expert Advisor With MQL5 And Python (Part IV): Stacking Models

In this series of articles, we will discuss different ways of building trading applications capable of dynamically adjusting themselves to evolving market conditions. There are potentially infinite ways we can approach this problem but, it is unlikely that all possible solutions will be valid. Therefore, our goal today is to demonstrate and empirically analyze the merits and shortcomings of different possible solutions, to help you improve your trading strategies.

[Sergey Golubev]

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.