MetaTrader 5 Python User Group - the summary - page 36

[Sergey Golubev]

Neuro-symbolic systems in algorithmic trading: Combining symbolic rules and neural networks

In this article, I will explain how my team and I developed such a system in Python and show how to combine classical pattern analysis with modern machine learning methods. We will walk through the architecture, from basic components to complex decision-making mechanisms, and of course, I will share real code and test results.

[Sergey Golubev]

Price movement discretization methods in Python

Every trading system developer sooner or later faces a fundamental question: how to properly slice and dice market data for analysis? The conventional fixed-interval approach is like trying to measure an athlete's heart rate every 5 minutes, whether they are sprinting or resting. During periods of high activity, critical information is lost within a single bar, while during quiet hours we get dozens of empty bars, creating information noise.

[Sergey Golubev]

Creating volatility forecast indicator using Python

In this article, I will share my journey from despair to a working volatility forecasting system. No boring stuff or academic jargon – just real experience and working solutions. I will show you how I combined MetaTrader 5 with Python (spoiler: they did not get along right away), how I made machine learning work for me, and what pitfalls I encountered along the way.

[Sergey Golubev]

Implementing Practical Modules from Other Languages in MQL5 (Part 04): time, date, and datetime modules from Python

The MQL5 programming language has plenty of built-in functions for managing and evaluating the time, ensuring our algorithmic trading systems are aware and in touch with the time in the real world. However, our favorable programming language offers a very basic (simple) and sometimes not human-friendly way of working with time, dates, etc., compared to other languages such as Python which offers rich modules for the task such as datetime, calendar, time, zoneinfo, etc.

In this article, we are going to implement similar modules to those offered in Python for time handling in the MQL5 programming language.

[Sergey Golubev]

Implementing Practical Modules from Other Languages in MQL5 (Part 05): The Logging module from Python, Log Like a Pro

Logging is very crucial in any modern device, program, or software. It is simply the process of keeping records of everything that has happened in the lifetime of a particular operation.

• Computers keep records of software usage, connections, and system events.
• Our browsers keep the history of the sites we visit and how we interact with them.

Keeping these records is essential for many important reasons, including troubleshooting, debugging, auditing, monitoring performance, and understanding the behavior of our systems over time.

[Sergey Golubev]

Data Science and ML (Part 47): Forecasting the Market Using the DeepAR model in Python

Time series forecasting has never been an easy task in machine learning; several techniques and models have been introduced to tackle this problem, most without definitive success. Linear and non-linear models are often not capable of this task either, despite showing glimpses of decent predictions of time series data.

[Sergey Golubev]

Implementing Practical Modules from Other Languages in MQL5 (Part 06): Python-Like File IO operations in MQL5 

File operations are essential for any programming language. They help our programs interact with external files through code, helping us import and export bits of information. With hundreds, if not thousands, of file types available in modern software, we need better and more effective ways of handling (reading and writing) information to and from these files.

[Sergey Golubev]

Python-MetaTrader 5 Strategy Tester (Part 02): Dealing with Bars, Ticks, and Overloading Built-in Functions in a Simulator

In the previous article, we discussed and made a simulator class in Python called TradeSimulator, which relied heavily on information from MetaTrader 5, such as ticks, bar data, symbol information, and much more.

The first article laid the foundation for what's required in imitating the MetaTrader 5 client, and its strategy tester (simulator). In this article, we will introduce ticks and bars data, as well as functions similar to those provided by the Python-MetaTrader 5 module in the simulator, taking a step closer to replicating everything that MetaTrader 5 does and provides.

[Sergey Golubev]

Python-MetaTrader 5 Strategy Tester (Part 03): MT5-Like Trading Operations — Handling and Managing

In the previous article, we implemented similar syntax and functions to those offered by the Python-MetaTrader module in our simulator. With similar orders, deals, positions, and structures. In this post, we will implement a very close to MetaTrader 5 approach of handling such structures (trading operations). 

[Sergey Golubev]

Build a Remote Forex Risk Management System in Python

Our remote risk manager is not just a tool, it is your insurance against financial chaos in the unpredictable world of Forex trading. Are you ready to turn your trading from a risky gamble into a controlled process? Then buckle up — we're going on a journey through the world of smart risk management, where technology meets financial security.