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Overcoming The Limitation of Machine Learning (Part 6): Effective Memory Cross Validation
Overcoming The Limitation of Machine Learning (Part 6): Effective Memory Cross Validation
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 introduce Effective Memory Cross-Validation (EMCV), a domain-aware approach that questions the long-held belief that more historical data always improves performance.
Developing Advanced ICT Trading Systems: Implementing Signals in the Order Blocks Indicator
Developing Advanced ICT Trading Systems: Implementing Signals in the Order Blocks Indicator
In this article, you will learn how to develop an Order Blocks indicator based on order book volume (market depth) and optimize it using buffers to improve accuracy. This concludes the current stage of the project and prepares for the next phase, which will include the implementation of a risk management class and a trading bot that uses signals generated by the indicator.
From Basic to Intermediate: Template and Typename (V)
From Basic to Intermediate: Template and Typename (V)
In this article, we'll explore one last simple use case for templates, and discuss the benefits and necessity of using typename in your code. Although this article may seem a bit complicated at first, it is important to understand it properly in order to use templates and typename later.
Statistical Arbitrage Through Cointegrated Stocks (Part 6): Scoring System
Statistical Arbitrage Through Cointegrated Stocks (Part 6): Scoring System
In this article, we propose a scoring system for mean-reversion strategies based on statistical arbitrage of cointegrated stocks. The article suggests criteria that go from liquidity and transaction costs to the number of cointegration ranks and time to mean-reversion, while taking into account the strategic criteria of data frequency (timeframe) and the lookback period for cointegration tests, which are evaluated before the score ranking properly. The files required for the reproduction of the backtest are provided, and their results are commented on as well.
Implementing a Scalping Market Depth Using the CGraphic Library
Implementing a Scalping Market Depth Using the CGraphic Library
In this article, we will create the basic functionality of a scalping Market Depth tool. Also, we will develop a tick chart based on the CGraphic library and integrate it with the order book. Using the described Market Depth, it will be possible to create a powerful assistant tool for short-term trading.
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.
Black-Scholes Greeks: Gamma and Delta
Black-Scholes Greeks: Gamma and Delta
Gamma and Delta measure how an option’s value reacts to changes in the underlying asset’s price. Delta represents the rate of change of the option’s price relative to the underlying, while Gamma measures how Delta itself changes as price moves. Together, they describe an option’s directional sensitivity and convexity—critical for dynamic hedging and volatility-based trading strategies.
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.
From Novice to Expert: Revealing the Candlestick Shadows (Wicks)
From Novice to Expert: Revealing the Candlestick Shadows (Wicks)
In this discussion, we take a step forward to uncover the underlying price action hidden within candlestick wicks. By integrating a wick visualization feature into the Market Periods Synchronizer, we enhance the tool with greater analytical depth and interactivity. This upgraded system allows traders to visualize higher-timeframe price rejections directly on lower-timeframe charts, revealing detailed structures that were once concealed within the shadows.
Self Optimizing Expert Advisors in MQL5 (Part 16): Supervised Linear System Identification
Self Optimizing Expert Advisors in MQL5 (Part 16): Supervised Linear System Identification
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 learning algorithms have many needs that may be supplemented by pairing these models with a feedback controller that can correct the model to keep up with current market conditions.
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.
Polynomial models in trading
Polynomial models in trading
This article is about orthogonal polynomials. Their use can become the basis for a more accurate and effective analysis of market information allowing traders to make more informed decisions.
MetaTrader 5 Machine Learning Blueprint (Part 5): Sequential Bootstrapping—Debiasing Labels, Improving Returns
MetaTrader 5 Machine Learning Blueprint (Part 5): Sequential Bootstrapping—Debiasing Labels, Improving Returns
Sequential bootstrapping reshapes bootstrap sampling for financial machine learning by actively avoiding temporally overlapping labels, producing more independent training samples, sharper uncertainty estimates, and more robust trading models. This practical guide explains the intuition, shows the algorithm step‑by‑step, provides optimized code patterns for large datasets, and demonstrates measurable performance gains through simulations and real backtests.
Circle Search Algorithm (CSA)
Circle Search Algorithm (CSA)
The article presents a new metaheuristic optimization Circle Search Algorithm (CSA) based on the geometric properties of a circle. The algorithm uses the principle of moving points along tangents to find the optimal solution, combining the phases of global exploration and local exploitation.
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.
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.
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.
Reimagining Classic Strategies (Part 18): Searching For Candlestick Patterns
Reimagining Classic Strategies (Part 18): Searching For Candlestick Patterns
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 for more profitable trading applications.
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.
Mastering Log Records (Part 10): Avoiding Log Replay by Implementing a Suppression
Mastering Log Records (Part 10): Avoiding Log Replay by Implementing a Suppression
We created a log suppression system in the Logify library. It details how the CLogifySuppression class reduces console noise by applying configurable rules to avoid repetitive or irrelevant messages. We also cover the external configuration framework, validation mechanisms, and comprehensive testing to ensure robustness and flexibility in log capture during bot or indicator development.
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.