Articles
Reimagining Classic Strategies (Part 20): Modern Stochastic Oscillators for MetaTrader 5
This article demonstrates how the stochastic oscillator, a classical technical indicator, can be repurposed beyond its conventional use as a mean-reversion tool. By viewing the indicator through a different analytical lens, we show how familiar strategies can yield new value and support alternative
Overcoming The Limitation of Machine Learning (Part 9): Correlation-Based Feature Learning in Self-Supervised Finance for MetaTrader 5
Self-supervised learning is a powerful paradigm of statistical learning that searches for supervisory signals generated from the observations themselves. This approach reframes challenging unsupervised learning problems into more familiar supervised ones. This technology has overlooked applications
Reimagining Classic Strategies (Part 19): Deep Dive Into Moving Average Crossovers for MetaTrader 5
This article revisits the classic moving average crossover strategy and examines why it often fails in noisy, fast-moving markets. It presents five alternative filtering methods designed to strengthen signal quality and remove weak or unprofitable trades. The discussion highlights how statistical
Overcoming The Limitation of Machine Learning (Part 8): Nonparametric Strategy Selection for MetaTrader 5
This article shows how to configure a black-box model to automatically uncover strong trading strategies using a data-driven approach. By using Mutual Information to prioritize the most learnable signals, we can build smarter and more adaptive models that outperform conventional methods. Readers
Overcoming The Limitation of Machine Learning (Part 7): Automatic Strategy Selection for MetaTrader 5
This article demonstrates how to automatically identify potentially profitable trading strategies using MetaTrader 5. White-box solutions, powered by unsupervised matrix factorization, are faster to configure, more interpretable, and provide clear guidance on which strategies to retain. Black-box
Self Optimizing Expert Advisors in MQL5 (Part 17): Ensemble Intelligence for MetaTrader 5
All algorithmic trading strategies are difficult to set up and maintain, regardless of complexity—a challenge shared by beginners and experts alike. This article introduces an ensemble framework where supervised models and human intuition work together to overcome their shared limitations. By
Reimagining Classic Strategies (Part 18): Searching For Candlestick Patterns for MetaTrader 5
This article helps new community members search for and discover their own candlestick patterns. Describing these patterns can be daunting, as it requires manually searching and creatively identifying improvements. Here, we introduce the engulfing candlestick pattern and show how it can be enhanced
Reimagining Classic Strategies (Part 17): Modelling Technical Indicators for MetaTrader 5
In this discussion, we focus on how we can break the glass ceiling imposed by classical machine learning techniques in finance. It appears that the greatest limitation to the value we can extract from statistical models does not lie in the models themselves — neither in the data nor in the
Self Optimizing Expert Advisors in MQL5 (Part 16): Supervised Linear System Identification for MetaTrader 5
Linear system identifcation may be coupled to learn to correct the error in a supervised learning algorithm. This allows us to build applications that depend on statistical modelling techniques without necessarily inheriting the fragility of the model's restrictive assumptions. Classical supervised
Overcoming The Limitation of Machine Learning (Part 6): Effective Memory Cross Validation for MetaTrader 5
In this discussion, we contrast the classical approach to time series cross-validation with modern alternatives that challenge its core assumptions. We expose key blind spots in the traditional method—especially its failure to account for evolving market conditions. To address these gaps, we
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