MetaTrader 5 Python User Group - the summary - page 37

[Sergey Golubev]

Python-MetaTrader 5 Strategy Tester (Part 04): Tester 101

In previous articles of this series, we laid the groundwork for building a MetaTrader 5–like strategy tester from scratch. With the core structure in place, several critical components are still missing in our project.

To this stage, we are yet to process ticks and bars sequentially, we lack mechanisms for monitoring open orders and the simulated trading account, and we do not have performance metrics such as profit and loss, drawdown, win rate, risk–reward ratios, and detailed trade statistics in the simulator.

[Sergey Golubev]

Python-MetaTrader 5 Strategy Tester (Part 05): Multi-Symbols and Timeframes Strategy Tester

In the previous article, we were able to utilize real ticks, generated ticks, and bars obtained from the MetaTrader 5 terminal in our custom strategy tester. Despite ending up with a first successful strategy tester action, we are yet to fully handle and process data across various instruments and timeframes (something the MetaTrader 5  strategy tester does really well).

[Sergey Golubev]

Quantitative Analysis of Trends: Collecting Statistics in Python

Quantitative trend analysis is an approach that transforms chaotic market movements into an orderly system of numbers and patterns. In a world where most traders rely on intuition and visual assessment of charts, mathematical analysis of trend movements provides an undeniable advantage. Instead of subjective feelings, you receive precise data: the average trend duration in days, its typical value in points, and characteristic patterns of development and completion.

It is this objectivity that makes quantitative analysis the cornerstone of professional trading. William Eckhardt, a famous trader, rightly noted that trading is not a field of psychology, but a field of statistics. When you know that uptrends on the EURUSD pair statistically last longer than downtrends, or that 70% of trends on GBPUSD end before reaching 200 points, this is not just information, but a specific guide to action.

[Sergey Golubev]

CFTC Data Mining in Python and Building an AI Model

The proposed approach combines COT/TFF data and market quotes into a single Python model with automated trading via MetaTrader 5. This allows us to move from analysis to action without delays and human intervention.

[Sergey Golubev]

Python + MetaTrader 5: Fast Research Framework for Data, Features, and Prototypes

In this article, we will show:

• how Python is integrated with MetaTrader 5;
• how to use it to analyze financial data and test hypotheses;
• how to build and train a small model and then transfer the trained result to an EA using ONNX.

This will allow us to move from a research experiment to practical implementation in a trading system.

[Sergey Golubev]

Beyond the Clock (Part 1): Building Activity and Imbalance Bars in Python and MQL5

Four production problems in the pipeline are addressed explicitly: loading multi-year tick data without exceeding memory (partitioned Parquet storage read via Dask), cleaning broker-feed artifacts that corrupt bar construction (zero spreads, duplicate timestamps, NaT indices), calibrating the adaptive threshold for imbalance bars automatically so the first bar does not bias the EWM history, and persisting the EWM tracker across EA restarts so the threshold does not reset mid-session. A short parity test at the end verifies that the two implementations, fed the same tick stream, produce identical bars.

[Sergey Golubev]

Downloading International Monetary Fund Data Using Python

Over the decades of its existence, the IMF has accumulated a unique database covering economic indicators of 190 countries. This data includes not only traditional metrics like GDP and inflation, but also complex structural indicators that allow us to see the real state of economies long before markets begin to react. The problem is that this data is presented in raw form and requires significant processing to become of practical value.

[Sergey Golubev]

Detecting and Classifying Fractal Patterns Using Machine Learning

This article presents an original approach to identifying and classifying fractal patterns. The analysis will be conducted in Python, with the ability to export final models to the MetaTrader 5 terminal in ONNX format.

[Sergey Golubev]

Feature Engineering for ML (Part 5): Microstructural Features in Python

The preceding articles in this series treated time as a feature in its own right: fractional differentiation preserves memory across a stationary series, and cyclical encoding embeds the Fourier structure of the trading calendar into the feature matrix. Both operate on bar-level data. Microstructural features work differently. They treat each bar not as a single observation but as a compressed summary of many individual trades, and they ask what those trades reveal about the market's internal state at the moment the bar closed.

[Sergey Golubev]

Why MetaTrader 5 Is the Ideal Platform for AI Trading: MQL5 + Python + ONNX + AI Assistant in a Unified Algorithmic Trading Ecosystem

In this article, AI is not considered as a magical Make Money button, but as a layer that enhances analysis, helps formalize trading ideas, and integrates into verifiable algorithmic logic. The main goal is to show MetaTrader 5 as an infrastructure where AI progresses from a hypothesis into a controllable trading robot.