Articles
Self Optimizing Expert Advisors in MQL5 (Part 14): Viewing Data Transformations as Tuning Parameters of Our Feedback Controller for MetaTrader 5
Preprocessing is a powerful yet quickly overlooked tuning parameter. It lives in the shadows of its bigger brothers: optimizers and shiny model architectures. Small percentage improvements here can have disproportionately large, compounding effects on profitability and risk. Too often, this largely
Overcoming The Limitation of Machine Learning (Part 3): A Fresh Perspective on Irreducible Error for MetaTrader 5
This article takes a fresh perspective on a hidden, geometric source of error that quietly shapes every prediction your models make. By rethinking how we measure and apply machine learning forecasts in trading, we reveal how this overlooked perspective can unlock sharper decisions, stronger returns
Self Optimizing Expert Advisors in MQL5 (Part 13): A Gentle Introduction To Control Theory Using Matrix Factorization for MetaTrader 5
Financial markets are unpredictable, and trading strategies that look profitable in the past often collapse in real market conditions. This happens because most strategies are fixed once deployed and cannot adapt or learn from their mistakes. By borrowing ideas from control theory, we can use
Reimagining Classic Strategies (Part 15): Daily Breakout Trading Strategy for MetaTrader 5
Human traders had long participated in financial markets before the rise of computers, developing rules of thumb that guided their decisions. In this article, we revisit a well-known breakout strategy to test whether such market logic, learned through experience, can hold its own against systematic
Self Optimizing Expert Advisors in MQL5 (Part 12): Building Linear Classifiers Using Matrix Factorization for MetaTrader 5
This article explores the powerful role of matrix factorization in algorithmic trading, specifically within MQL5 applications. From regression models to multi-target classifiers, we walk through practical examples that demonstrate how easily these techniques can be integrated using built-in MQL5
Self Optimizing Expert Advisors in MQL5 (Part 11): A Gentle Introduction to the Fundamentals of Linear Algebra for MetaTrader 5
In this discussion, we will set the foundation for using powerful linear, algebra tools that are implemented in the MQL5 matrix and vector API. For us to make proficient use of this API, we need to have a firm understanding of the principles in linear algebra that govern intelligent use of these
Self Optimizing Expert Advisors in MQL5 (Part 10): Matrix Factorization for MetaTrader 5
Factorization is a mathematical process used to gain insights into the attributes of data. When we apply factorization to large sets of market data—organized in rows and columns—we can uncover patterns and characteristics of the market. Factorization is a powerful tool, and this article will show
Reimagining Classic Strategies (Part 14): Multiple Strategy Analysis for MetaTrader 5
In this article, we continue our exploration of building an ensemble of trading strategies and using the MT5 genetic optimizer to tune the strategy parameters. Today, we analyzed the data in Python, showing our model could better predict which strategy would outperform, achieving higher accuracy
Self Optimizing Expert Advisors in MQL5 (Part 9): Double Moving Average Crossover for MetaTrader 5
This article outlines the design of a double moving average crossover strategy that uses signals from a higher timeframe (D1) to guide entries on a lower timeframe (M15), with stop-loss levels calculated from an intermediate risk timeframe (H4). It introduces system constants, custom enumerations
Self Optimizing Expert Advisors in MQL5 (Part 8): Multiple Strategy Analysis (3) — Weighted Voting Policy for MetaTrader 5
This article explores how determining the optimal number of strategies in an ensemble can be a complex task that is easier to solve through the use of the MetaTrader 5 genetic optimizer. The MQL5 Cloud is also employed as a key resource for accelerating backtesting and optimization. All in all, our
Forum
Has The Withdrawal Format Changed Again?
Normally, there's an option specifying which VISA card you'd like to withdraw to, after linking the card to your account. I can't find the option any more, additionally, when I'm making the withdrawal it warns me I'm about to withdraw to a new card, without informing me which card I'm withdrawing
Plotting into the future.
I've been working with ONNX models and I want to draw my model's forecast on to the chart so that as time goes on I can visually see the difference between what the model was expecting and what actually happened. As trivial as that sounds, I'm having a headache trying to plot into the future. I've
Plotting Indicator Values Into The Future For Begginers
I asked before for help on plotting future values directly on the chart, and I received informative help. I'd like to ask now, how can I plot into the future for your indicators. For example, I've got this indicator, shown in Fig 1, and I'd like to plot my predictions of what the indicator's value
ALGLIB For Beginners
I'm keen on learning how to use ALGLIB. However, all the articles I have read so far were teaching too many things at once for me to confidently follow along. Like there's theory in there about SVM, Neural Networks , Grokking Market Memory (I don't even know what that means bro). It's too much for
Failing to read a CSV file
Hi guys. I'm trying to read in a csv file with 2 rows and 8 columns and I keep failing. I'm able to open the file, however I cannot yet access the content of the file. Here is my code. //+------------------------------------------------------------------+ //|
Backtesting Libraries in MQL5
Given OHLC price data and a trading strategy , we can evaluate how profitable the strategy is. We could run a historical back test in MQL5 or if you have the data in CSV format you could use a library in Python or R to backtest your strategy. But how is this being done under the hood? I want to
The Bible of Money Management
Greenings I'd like to talk about money management and I'm keen to gain new perspectives. I trade the daily chart. What I do is, I pull up the chart and I've built an EA that opens a position on the minimum lot size and then places a stop loss 1.5 x the ATR. From there I check the value of the stop
Automating EA Optimization
Can someone please help me understand how to Automatically optimize my EA to a selected chart using a Python Script and the MT5 module for Python. Everything I've learned so far either 1) Simply didn't work, 2) Didn't make use of the MT5 Python module 3) Required me to learn C++ and build a DLL but