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Machine Learning Blueprint (Part 4): The Hidden Flaw in Your Financial ML Pipeline — Label Concurrency
Machine Learning Blueprint (Part 4): The Hidden Flaw in Your Financial ML Pipeline — Label Concurrency
Discover how to fix a critical flaw in financial machine learning that causes overfit models and poor live performance—label concurrency. When using the triple-barrier method, your training labels overlap in time, violating the core IID assumption of most ML algorithms. This article provides a hands-on solution through sample weighting. You will learn how to quantify temporal overlap between trading signals, calculate sample weights that reflect each observation's unique information, and implement these weights in scikit-learn to build more robust classifiers. Learning these essential techniques will make your trading models more robust, reliable and profitable.
Big Bang - Big Crunch (BBBC) algorithm
Big Bang - Big Crunch (BBBC) algorithm
The article presents the Big Bang - Big Crunch method, which has two key phases: cyclic generation of random points and their compression to the optimal solution. This approach combines exploration and refinement, allowing us to gradually find better solutions and open up new optimization opportunities.
Price Action Analysis Toolkit Development (Part 48): Multi-Timeframe Harmony Index with Weighted Bias Dashboard
Price Action Analysis Toolkit Development (Part 48): Multi-Timeframe Harmony Index with Weighted Bias Dashboard
This article introduces the “Multi-Timeframe Harmony Index”—an advanced Expert Advisor for MetaTrader 5 that calculates a weighted bias from multiple timeframes, smooths the readings using EMA, and displays the results in a clean chart panel dashboard. It includes customizable alerts and automatic buy/sell signal plotting when strong bias thresholds are crossed. Suitable for traders who use multi-timeframe analysis to align entries with overall market structure.
The MQL5 Standard Library Explorer (Part 3): Expert Standard Deviation Channel
The MQL5 Standard Library Explorer (Part 3): Expert Standard Deviation Channel
In this discussion, we will develop an Expert Advisor using the CTrade and CStdDevChannel classes, while applying several filters to enhance profitability. This stage puts our previous discussion into practical application. Additionally, I’ll introduce another simple approach to help you better understand the MQL5 Standard Library and its underlying codebase. Join the discussion to explore these concepts in action.
Reimagining Classic Strategies (Part 17): Modelling Technical Indicators
Reimagining Classic Strategies (Part 17): Modelling Technical Indicators
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 complexity of the algorithms — but rather in the methodology we use to apply them. In other words, the true bottleneck may be how we employ the model, not the model’s intrinsic capability.
Optimizing Long-Term Trades: Engulfing Candles and Liquidity Strategies
Optimizing Long-Term Trades: Engulfing Candles and Liquidity Strategies
This is a high-timeframe-based EA that makes long-term analyses, trading decisions, and executions based on higher-timeframe analyses of W1, D1, and MN. This article will explore in detail an EA that is specifically designed for long-term traders who are patient enough to withstand and hold their positions during tumultuous lower time frame price action without changing their bias frequently until take-profit targets are hit.
Statistical Arbitrage Through Cointegrated Stocks (Part 7): Scoring System 2
Statistical Arbitrage Through Cointegrated Stocks (Part 7): Scoring System 2
This article describes two additional scoring criteria used for selection of baskets of stocks to be traded in mean-reversion strategies, more specifically, in cointegration based statistical arbitrage. It complements a previous article where liquidity and strength of the cointegration vectors were presented, along with the strategic criteria of timeframe and lookback period, by including the stability of the cointegration vectors and the time to mean reversion (half-time). The article includes the commented results of a backtest with the new filters applied and the files required for its reproduction are also provided.
Price Action Analysis Toolkit Development (Part 49): Integrating Trend, Momentum, and Volatility Indicators into One MQL5 System
Price Action Analysis Toolkit Development (Part 49): Integrating Trend, Momentum, and Volatility Indicators into One MQL5 System
Simplify your MetaTrader  5 charts with the Multi  Indicator  Handler EA. This interactive dashboard merges trend, momentum, and volatility indicators into one real‑time panel. Switch instantly between profiles to focus on the analysis you need most. Declutter with one‑click Hide/Show controls and stay focused on price action. Read on to learn step‑by‑step how to build and customize it yourself in MQL5.
Developing a Trading Strategy: The Butterfly Oscillator Method
Developing a Trading Strategy: The Butterfly Oscillator Method
In this article, we demonstrated how the fascinating mathematical concept of the Butterfly Curve can be transformed into a practical trading tool. We constructed the Butterfly Oscillator and built a foundational trading strategy around it. The strategy effectively combines the oscillator's unique cyclical signals with traditional trend confirmation from moving averages, creating a systematic approach for identifying potential market entries.
From Novice to Expert: Forex Market Periods
From Novice to Expert: Forex Market Periods
Every market period has a beginning and an end, each closing with a price that defines its sentiment—much like any candlestick session. Understanding these reference points allows us to gauge the prevailing market mood, revealing whether bullish or bearish forces are in control. In this discussion, we take an important step forward by developing a new feature within the Market Periods Synchronizer—one that visualizes Forex market sessions to support more informed trading decisions. This tool can be especially powerful for identifying, in real time, which side—bulls or bears—dominates the session. Let’s explore this concept and uncover the insights it offers.
Formulating Dynamic Multi-Pair EA (Part 5): Scalping vs Swing Trading Approaches
Formulating Dynamic Multi-Pair EA (Part 5): Scalping vs Swing Trading Approaches
This part explores how to design a Dynamic Multi-Pair Expert Advisor capable of adapting between Scalping and Swing Trading modes. It covers the structural and algorithmic differences in signal generation, trade execution, and risk management, allowing the EA to intelligently switch strategies based on market behavior and user input.
Self Optimizing Expert Advisors in MQL5 (Part 10): Matrix Factorization
Self Optimizing Expert Advisors in MQL5 (Part 10): Matrix Factorization
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 how you can use it within the MetaTrader 5 terminal, through the MQL5 API, to gain more profound insights into your market data.
Developing a Trading Strategy: The Triple Sine Mean Reversion Method
Developing a Trading Strategy: The Triple Sine Mean Reversion Method
This article introduces the Triple Sine Mean Reversion Method, a trading strategy built upon a new mathematical indicator — the Triple Sine Oscillator (TSO). The TSO is derived from the sine cube function, which oscillates between –1 and +1, making it suitable for identifying overbought and oversold market conditions. Overall, the study demonstrates how mathematical functions can be transformed into practical trading tools.
Automating Trading Strategies in MQL5 (Part 40): Fibonacci Retracement Trading with Custom Levels
Automating Trading Strategies in MQL5 (Part 40): Fibonacci Retracement Trading with Custom Levels
In this article, we build an MQL5 Expert Advisor for Fibonacci retracement trading, using either daily candle ranges or lookback arrays to calculate custom levels like 50% and 61.8% for entries, determining bullish or bearish setups based on close vs. open. The system triggers buys or sells on price crossings of levels with max trades per level, optional closure on new Fib calcs, points-based trailing stops after a min profit threshold, and SL/TP buffers as percentages of the range.
From Novice to Expert: Time Filtered Trading
From Novice to Expert: Time Filtered Trading
Just because ticks are constantly flowing in doesn’t mean every moment is an opportunity to trade. Today, we take an in-depth study into the art of timing—focusing on developing a time isolation algorithm to help traders identify and trade within their most favorable market windows. Cultivating this discipline allows retail traders to synchronize more closely with institutional timing, where precision and patience often define success. Join this discussion as we explore the science of timing and selective trading through the analytical capabilities of MQL5.
Markets Positioning Codex in MQL5 (Part 1): Bitwise Learning for Nvidia
Markets Positioning Codex in MQL5 (Part 1): Bitwise Learning for Nvidia
We commence a new article series that builds upon our earlier efforts laid out in the MQL5 Wizard series, by taking them further as we step up our approach to systematic trading and strategy testing. Within these new series, we’ll concentrate our focus on Expert Advisors that are coded to hold only a single type of position - primarily longs. Focusing on just one market trend can simplify analysis, lessen strategy complexity and expose some key insights, especially when dealing in assets beyond forex. Our series, therefore, will investigate if this is effective in equities and other non-forex assets, where long only systems usually correlate well with smart money or institution strategies.
Self Optimizing Expert Advisors in MQL5 (Part 17): Ensemble Intelligence
Self Optimizing Expert Advisors in MQL5 (Part 17): Ensemble Intelligence
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 aligning a moving average channel strategy with a Ridge Regression model on the same indicators, we achieve centralized control, faster self-correction, and profitability from otherwise unprofitable systems.
Understanding functions in MQL5 with applications
Understanding functions in MQL5 with applications
Functions are critical things in any programming language, it helps developers apply the concept of (DRY) which means do not repeat yourself, and many other benefits. In this article, you will find much more information about functions and how we can create our own functions in MQL5 with simple applications that can be used or called in any system you have to enrich your trading system without complicating things.
Overcoming The Limitation of Machine Learning (Part 7): Automatic Strategy Selection
Overcoming The Limitation of Machine Learning (Part 7): Automatic Strategy Selection
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 solutions, while more time-consuming, are better suited for complex market conditions that white-box approaches may not capture. Join us as we discuss how our trading strategies can help us carefully identify profitable strategies under any circumstance.
Developing Trading Strategy: Pseudo Pearson Correlation Approach
Developing Trading Strategy: Pseudo Pearson Correlation Approach
Generating new indicators from existing ones offers a powerful way to enhance trading analysis. By defining a mathematical function that integrates the outputs of existing indicators, traders can create hybrid indicators that consolidate multiple signals into a single, efficient tool. This article introduces a new indicator built from three oscillators using a modified version of the Pearson correlation function, which we call the Pseudo Pearson Correlation (PPC). The PPC indicator aims to quantify the dynamic relationship between oscillators and apply it within a practical trading strategy.
Markets Positioning Codex in MQL5 (Part 2): Bitwise Learning, with Multi-Patterns for Nvidia
Markets Positioning Codex in MQL5 (Part 2): Bitwise Learning, with Multi-Patterns for Nvidia
We continue our new series on Market-Positioning, where we study particular assets, with specific trade directions over manageable test windows. We started this by considering Nvidia Corp stock in the last article, where we covered 5 signal patterns from the complimentary pairing of the RSI and DeMarker oscillators. For this article, we cover the remaining 5 patterns and also delve into multi-pattern options that not only feature untethered combinations of all ten, but also specialized combinations of just a pair.
Neural Networks in Trading: Hierarchical Dual-Tower Transformer (Hidformer)
Neural Networks in Trading: Hierarchical Dual-Tower Transformer (Hidformer)
We invite you to get acquainted with the Hierarchical Double-Tower Transformer (Hidformer) framework, which was developed for time series forecasting and data analysis. The framework authors proposed several improvements to the Transformer architecture, which resulted in increased forecast accuracy and reduced computational resource consumption.
From Novice to Expert: Predictive Price Pathways
From Novice to Expert: Predictive Price Pathways
Fibonacci levels provide a practical framework that markets often respect, highlighting price zones where reactions are more likely. In this article, we build an expert advisor that applies Fibonacci retracement logic to anticipate likely future moves and trade retracements with pending orders. Explore the full workflow—from swing detection to level plotting, risk controls, and execution.
Automating Trading Strategies in MQL5 (Part 41): Candle Range Theory (CRT) – Accumulation, Manipulation, Distribution (AMD)
Automating Trading Strategies in MQL5 (Part 41): Candle Range Theory (CRT) – Accumulation, Manipulation, Distribution (AMD)
In this article, we develop a Candle Range Theory (CRT) trading system in MQL5 that identifies accumulation ranges on a specified timeframe, detects breaches with manipulation depth filtering, and confirms reversals for entry trades in the distribution phase. The system supports dynamic or static stop-loss and take-profit calculations based on risk-reward ratios, optional trailing stops, and limits on positions per direction for controlled risk management.
Developing a Trading Strategy: The Flower Volatility Index Trend-Following Approach
Developing a Trading Strategy: The Flower Volatility Index Trend-Following Approach
The relentless quest to decode market rhythms has led traders and quantitative analysts to develop countless mathematical models. This article has introduced the Flower Volatility Index (FVI), a novel approach that transforms the mathematical elegance of Rose Curves into a functional trading tool. Through this work, we have shown how mathematical models can be adapted into practical trading mechanisms capable of supporting both analysis and decision-making in real market conditions.
Overcoming The Limitation of Machine Learning (Part 8): Nonparametric Strategy Selection
Overcoming The Limitation of Machine Learning (Part 8): Nonparametric Strategy Selection
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 will also learn to avoid common pitfalls like overreliance on surface-level metrics, and instead develop strategies rooted in meaningful statistical insight.