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Larry Williams Market Secrets (Part 15): Trading Hidden Smash Day Reversals with Market Context
Larry Williams Market Secrets (Part 15): Trading Hidden Smash Day Reversals with Market Context
Build an MQL5 Expert Advisor that automates Larry Williams Hidden Smash Day reversals. It reads confirmed signals from a custom indicator, applies context filters (Supertrend alignment and optional trading‑day rules), and manages risk with stop‑loss models based on smash‑bar structure or ATR and a fixed or risk‑based position size. The result is a reproducible framework ready for testing and extension.
MQL5 Trading Tools (Part 24): Depth-Perception Upgrades with 3D Curves, Pan Mode, and ViewCube Navigation
MQL5 Trading Tools (Part 24): Depth-Perception Upgrades with 3D Curves, Pan Mode, and ViewCube Navigation
In this article, we enhance the 3D binomial distribution graphing tool in MQL5 by adding a segmented 3D curve for improved depth perception of the probability mass function, integrating pan mode for view target shifting, and implementing an interactive view cube with hover zones and animations for quick orientation changes. We incorporate clickable sub-zones on the view cube for faces, edges, and corners to animate camera transitions to standard views, while maintaining switchable 2D/3D modes, real-time updates, and customizable parameters for immersive probabilistic analysis in trading.
Feature Engineering With Python And MQL5 (Part IV): Candlestick Pattern Recognition With UMAP Regression
Feature Engineering With Python And MQL5 (Part IV): Candlestick Pattern Recognition With UMAP Regression
Dimension reduction techniques are widely used to improve the performance of machine learning models. Let us discuss a relatively new technique known as Uniform Manifold Approximation and Projection (UMAP). This new technique has been developed to explicitly overcome the limitations of legacy methods that create artifacts and distortions in the data. UMAP is a powerful dimension reduction technique, and it helps us group similar candle sticks in a novel and effective way that reduces our error rates on out of sample data and improves our trading performance.
MQL5 Trading Tools (Part 25): Expanding to Multiple Distributions with Interactive Switching
MQL5 Trading Tools (Part 25): Expanding to Multiple Distributions with Interactive Switching
In this article, we expand the MQL5 graphing tool to support seventeen statistical distributions with interactive cycling via a header switch icon. We add type-specific data loading, discrete and continuous histogram computation, and theoretical density functions for each model, with dynamic titles, axis labels, and parameter panels that adapt automatically. The result lets you overlay distribution models on the same sample and compare fit across families without reloading the tool.
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.
Introduction to MQL5 (Part 43): Beginner Guide to File Handling in MQL5 (V)
Introduction to MQL5 (Part 43): Beginner Guide to File Handling in MQL5 (V)
The article explains how to use MQL5 structures with binary files to persist Expert Advisor parameters. It covers defining structures, accessing members, and distinguishing simple from complex layouts, then writing and reading entire records using FileWriteStruct and FileReadStruct in FILE BIN mode. You will learn safe patterns for fixed-size data and how shared storage (FILE COMMON) enables reuse across sessions and terminals.
Neural Networks in Trading: Adaptive Detection of Market Anomalies (DADA)
Neural Networks in Trading: Adaptive Detection of Market Anomalies (DADA)
We invite you to get acquainted with the DADA framework, which is an innovative method for detecting anomalies in time series. It helps distinguish random fluctuations from suspicious deviations. Unlike traditional methods, DADA is flexible and adapts to different data. Instead of a fixed compression level, it uses several options and chooses the most appropriate one for each case.
Neuro-Structural Trading Engine — NSTE (Part II): Jardine's Gate Six-Gate Quantum Filter
Neuro-Structural Trading Engine — NSTE (Part II): Jardine's Gate Six-Gate Quantum Filter
This article introduces Jardine's Gate, a six-gate orthogonal signal filter for MetaTrader 5 that validates LSTM predictions across entropy, expert interference, confidence, regime-adjusted probability, trend direction, and consecutive-loss kill switch dimensions. Out of 43,200 raw signals per month, only 127 pass all six gates. Readers get the complete QuantumEdgeFilter MQL5 class, threshold calibration logic, and gate performance analytics.
Trend Criteria. Conclusion
Trend Criteria. Conclusion
In this article, we will consider the specifics of applying some trend criteria in practice. We will also try to develop several new criteria. The focus will be on the efficiency of applying these criteria to market data analysis and trading.
MQL5 Trading Tools (Part 26): Integrating Frequency Binning, Entropy, and Chi-Square in Visual Analyzer
MQL5 Trading Tools (Part 26): Integrating Frequency Binning, Entropy, and Chi-Square in Visual Analyzer
In this article, we develop a frequency analysis tool in MQL5 that bins price data into histograms, computes entropy for information content, and applies chi-square tests for distribution goodness-of-fit, with interactive logs and statistical panels for market insights. We integrate per-bar or per-tick computation modes, supersampled rendering for smooth visuals, and draggable/resizable canvases with auto-scrolling logs to enhance usability in trading analysis.
Pair Trading: Algorithmic Trading with Auto Optimization Based on Z-Score Differences
Pair Trading: Algorithmic Trading with Auto Optimization Based on Z-Score Differences
In this article, we will explore what pair trading is and how correlation trading works. We will also create an EA for automating pair trading and add the ability to automatically optimize this trading algorithm based on historical data. In addition, as part of the project, we will learn how to calculate the differences between two pairs using the z-score.
Predicting Renko Bars with CatBoost AI
Predicting Renko Bars with CatBoost AI
How to use Renko bars with AI? Let's look at Renko trading on Forex with forecast accuracy of up to 59.27%. We will explore the benefits of Renko bars for filtering market noise, learn why volume is more important than price patterns, and how to set the optimal Renko block size for EURUSD. This is a step-by-step guide on integrating CatBoost, Python, and MetaTrader 5 to create your own Renko Forex forecasting system. It is ideal for traders looking to go beyond traditional technical analysis.
Building a Volume Bubble Indicator in MQL5 Using Standard Deviation
Building a Volume Bubble Indicator in MQL5 Using Standard Deviation
The article demonstrates how to build a Volume Bubble Indicator in MQL5 that visualizes market activity using statistical normalization. It covers how to work with tick and real volume, compute the mean and standard deviation over a rolling window, and normalize volume values to identify relative strength. You will implement chart objects to display bubbles with dynamic size and color, providing a clear representation of volume intensity directly on the chart.