Articles on the MQL5 programming and use of technical indicators
Technical indicators constitute an important part of trading, and every trader tries to find something new. The authors of these articles talk about econometrics and machine learning, automatic marking of Elliott Waves and digital filters - everything from the classical methods of analysis to the modern interpretation of classical ideas.
The category features articles that will teach you how to develop a custom indicator and contains unique collections of ready-to-use mathematically explained William Blau's trading ideas.
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Rolling Sharpe Ratio with Statistical Significance Bands in MQL5
This article presents a custom MetaTrader 5 indicator that computes a rolling annualized Sharpe ratio and plots configurable z-score significance bands based on Lo's asymptotic standard error. It uses a circular return buffer with incremental variance to keep O(1) updates. We explain the n^(-1/2) uncertainty scaling, the inflation of intervals at high Sharpe values, and how to set per-instrument annualization for correct deployment.
Beyond GARCH (Part VII): Monte Carlo Volatility Forecasting in MQL5
We implement the CMonteCarlo module that turns the fitted MMAR parameters into a volatility forecast via Monte Carlo. It runs N independent simulations over a chosen horizon and reports mean, median, standard deviation, and a percentile-based 95% confidence interval, with access to per-run values if needed. Adaptive cascade depth selects the minimal k such that b^k covers the horizon, keeping the run fast and consistent.
Implementing Anchored VWAP Indicator in MQL5: A Step-by-Step Guide
A step-by-step guide to building an anchored VWAP indicator with an interactive draggable anchor line in MQL5. The article covers the complete implementation, including calculation methodology, session resets, standard deviation bands, and custom visualization. Learn the architectural design decisions behind stateless boundary detection, multi-instance support, and cross-asset volume handling to build a versatile indicator with benchmarking, technical, and analytical capabilities.
Detecting Structural Breakpoints in Price Series Using CUSUM in MQL5 (Part 2): Implementing the Detector as a Native MQL5 Indicator
This article benchmarks CUSUM_Breakpoint.mq5 against the Siegmund ARL₀ prediction on live‑like data. The empirical false‑alarm rate is about five times higher than theory for all tested symbols and timeframes, and confirmations show sensitivity to variance changes over mean changes. Practitioners should calibrate h and k on the target instrument's history and apply the signal to manage volatility regimes, not to infer directional shifts.
Feature Engineering for ML (Part 8): Entropy Features in MQL5
An MQL5 port of four entropy estimators — Shannon, Plug-In, Lempel-Ziv, and Kontoyiannis — operating on the intrabar tick-rule sequence. CopyTicksRange() limits data to the broker's cached tick window, so features apply to recent bars only. The implementation encodes bid-direction ticks from MqlTick, replaces NumPy-dependent steps with array-based methods, and ships CEntropyFeatures.mqh and EntropyViewer.mq5 for EA and indicator use.
Implementing and Benchmarking Bag-of-SFA-Symbols (BOSS) Against Dynamic Time Warping (DTW)
This article implements BOSS from scratch in MQL5 and applies it to regime classification: SFA turns windows into words, bags record word frequencies, and an ensemble over window lengths votes on labels. We cover the encoding steps, the BOSS distance, training with auto-generated regime labels, and practical parameters. A BTCUSD benchmark versus DTW shows higher macro accuracy on clean data and markedly faster inference.
Trends and Traditions: Using Rademacher Functions in Trading
Although the functions we will discuss have been known for quite some time, their application in the field of trading remains terra incognita to this day. In this article, we will explore some of the opportunities these old-but-new functions offer for developing trading strategies and assess their potential.
Bloch's Relative Moving Average (RMA) Framework Implementation In MQL5
We port Daniel Bloch's Relative Moving Average framework into a complete MetaTrader 5 system. Instead of smoothing price, the RMA measures where price sits inside its own recent distribution on a [0,1] fractile scale, and drives four cross-strategies with a regime-adaptive exit. Includes the engine, indicators, and a backtested Expert Advisor.
Market Heat Map Indicator Based on Prime-Number Density
An innovative indicator based on prime number theory helps identify strong reversal levels that other traders overlook. Testing on 10 assets showed that reversals in mathematically significant zones occur 1.5 to 1.8 times more frequently. Five practical application scenarios with specific rules for filtering out false breakouts and making precise market entries.
Learnable Curves, Not Weights: A Kolmogorov-Arnold Network from Scratch
This article builds a Kolmogorov–Arnold Network (KAN) in MQL5, where every edge carries a learnable B‑spline curve rather than a scalar weight. We construct the spline basis, assemble edges and a layer, and fit all coefficients by ridge‑regularized least‑squares in a single solve. The model is delivered as an indicator that visualizes the learned curves and an Expert Advisor that acts on the prediction, providing an interpretable, reusable codebase.