MetaTrader 5 Python User Group - the summary - page 35

[Sergey Golubev]

Price Action Analysis Toolkit Development (Part 31): Python Candlestick Recognition Engine (I) — Manual Detection

Candlestick charts are a fundamental tool used by financial analysts and traders to visualize and interpret price movements over time. Originating from Japanese rice merchants centuries ago, these charts have evolved into a vital component of technical analysis across various financial markets, including stocks, forex, and futures.

 

[Sergey Golubev]

Implementing Practical Modules from Other Languages in MQL5 (Part 02): Building the REQUESTS Library, Inspired by Python

An ability to send HTTP requests to the web directly from MetaTrader 5 is one of the best things that's ever happened to the MQL5 programming language. With this ability, traders can communicate with their external websites, servers, trading apps, etc.

[Sergey Golubev]

Forum on trading, automated trading systems and testing trading strategies

Market Condition Evaluation based on standard indicators in Metatrader 5

Sergey Golubev, 2025.07.19 03:58

Price Action Analysis Toolkit Development (Part 32): Python Candlestick Recognition Engine (II) — Detection Using Ta-Lib

In my previous article, I introduced the use of Python for candlestick pattern detection. We began by employing manual detection techniques, where each candlestick pattern was coded explicitly. In this approach, whenever MQL5 sends data to Python—including open, close, high, low, and timestamp—Python processes this information to identify the pattern through calculations.

[Sergey Golubev]

Data Science and ML (Part 46): Stock Markets Forecasting Using N-BEATS in Python

N-BEATS (Neural Basis Expansion Analysis for Time Series) is a deep learning model specifically designed for time series forecasting. It provides a flexible framework for univariate and multivariate forecasting tasks.

It was introduced by researchers at Element AI (now part of ServiceNow) in 2019, with the paper N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.

Developers at Element AI developed this model to challenge the dominance of classical statistical models like ARIMA and ETS in time series, while compromising the capabilities offered by classical machine learning models.

[Sergey Golubev]

MetaTrader tick info access from MetaTrader services to python application using sockets

Sometimes everything is not programmable in the MQL5 language. And even if it is possible to convert existing advanced libraries in MQL5, it would be time-consuming. A better option is to integrate or utilize the existing libraries to achieve a task. For example, there are numerous machine-learning-libraries in python. It's not the best option for us to create identical copies in MQL5 form just to achieve a machine learning task for trading. It is better to export the data needed for that python machine learning library, do the necessary process in the python environment and import the result back into the MQL5 program. This article is about transporting such data from the MetaTrader terminal to the python environment.

[Sergey Golubev]

Price Action Analysis Toolkit Development (Part 35): Training and Deploying Predictive Models

This article is organized as follows:

• Introduction
• Recap of the Ingestion Pipeline
• MQL5 and Python Implementation
• Model training in Python
• Model deployment and real-time inference
• Conclusion

[Sergey Golubev]

Python-MetaTrader 5 Strategy Tester (Part 01): Trade Simulator

The MetaTrader5-Python package is a beneficial module that enables Python developers to develop their trading applications for the MetaTrader 5 platform. It grants developers access to the trading platform for receiving data, sending, and monitoring trades.

This module revolutionized the way we think of the MetaTrader 5 desktop application, it is not a one-dimensional app restricted to its native programming language for building trading robots known as MQL5. This trading application is flexible enough and capable of receiving trade commands from an external programming language apart from MQL5.

[Sergey Golubev]

Price Action Analysis Toolkit Development (Part 36): Unlocking Direct Python Access to MetaTrader 5 Market Streams

Our previous article demonstrated how a straightforward MQL5 script could transfer historical bars into Python, engineer features, train a machine learning model, and then send signals back to MetaTrader for execution—eliminating manual CSV exports, Excel-based analysis, and version control issues. Traders gained an end-to-end pipeline that transformed raw minute-bar data into statistically driven entry points, complete with dynamically calculated stop-loss (SL) and take-profit (TP) levels.

[Sergey Golubev]

Multi-module trading robot in Python and MQL5 (Part I): Creating basic architecture and first modules

Do you know what's most interesting? This system really works in the market. It is not just a textbook example, but an actual trading tool. Of course, we will start with the basic version, but even that is impressive. We have a great journey ahead of us. We will create a system capable of learning and adapting. We will improve it step by step. For now, let's start with the most important thing - building a solid foundation.

[Sergey Golubev]

Quantum computing and trading: A fresh approach to price forecasts

Imagine a world where every market transaction is analyzed through the lens of possibilities that exist simultaneously — like famous Schrödinger's cat who is both alive and dead until we open the box. This is how quantum trading works: it looks at all potential market states at once, opening up new horizons for financial analysis.