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.
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.
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 for our objective as a community of algorithmic traders. Our discussion, therefore, aims to give the reader an approachable bridge into the open research area of self-supervised learning and offers practical applications that provide robust and reliable statistical models of financial markets without overfitting to small datasets.
This article builds an order-flow footprint indicator in MQL5 that aggregates tick-by-tick volume into quantized price levels and supports Bid vs Ask and Delta display modes. A canvas overlay renders color-scaled volume text aligned with the candles and updates on every tick. You will learn sorting of price levels, max-value normalization for color mapping, and responsive redraws on zoom, scroll, and resize to read volume distribution and aggressor dominance inside each bar.
This article implements a regime-adaptive grid trading EA based on the PhD research of Aldo Taranto. It presents a regime‑adaptive grid trading EA that constrains risk through restartable cycles and equity‑based safeguards. We explain why naive grids fail (variance growth and almost‑sure ruin), derive the loss formula for real‑time exposure, and implement regime‑aware gating, ATR‑dynamic spacing, and a live kill switch. Readers get the mathematical tools and production patterns needed to build, test, and operate a constrained grid safely.
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.
This article integrates the Optuna hyperparameter optimization (HPO) backend into a unified ModelDevelopmentPipeline. It adds joint tuning of model hyperparameters and sample-weight schemes, early pruning with Hyperband, and crash-resistant SQLite study storage. The pipeline auto-detects primary vs. secondary models, prepends a fitted column-dropping preprocessor for safe inference, supports sequential bootstrapping, generates an Optuna report, and includes bid/ask and LearnedStrategy links. Readers get faster, resumable runs and deployable, self-contained models.
The article enhances an MQL5 footprint indicator with a compact box above each candle that summarizes net delta, total volume, and buy/sell percentages. We implement supersampled anti‑aliased rendering, rounded corners via arc and quadrilateral rasterization, and per‑pixel alpha compositing. Supporting utilities include ARGB conversion, scanline fills, and box‑filter downsampling. The box delivers fast sentiment reads that stay legible across zoom levels.
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.
In this article, we explore practical and robust risk management techniques specifically tailored for liquidity-based trading. You will learn how to protect positions during retests, handle false breakouts with confidence, and identify signs of potential level manipulation. By the end, you will have built an adaptive Expert Advisor capable of managing zone flips and executing strategic pending orders with integrated risk control.
The Fibonacci retracement tool is an essential component of price action analysis, providing critical levels for potential market reactions. However, its effectiveness is often limited by the need for continuous human monitoring, which can lead to missed setups. In this part of our series, we introduce a tool that synchronizes and actively monitors manually drawn Fibonacci levels using MQL5, combining discretionary insight with automated oversight.
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.
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.
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.
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.
Unlike MQL5, Python programming language offers control and flexibility when it comes to dealing with and manipulating time. In this article, we will implement similar modules for better handling of dates and time in MQL5 as in Python.
Fixed fractions and raw probabilities misallocate risk under overlapping labels and induce overtrading. This article delivers four AFML-compliant sizers: probability-based (z-score → CDF, active-bet averaging, discretization), forecast-price (sigmoid/power with w calibration and limit price), budget-constrained (direction-only), and reserve (mixture-CDF via EF3M). You get a signed, bounded position series with documented conditions of use.
Candlestick patterns help traders understand market psychology and identify trends in financial markets, they enable more informed trading decisions that can lead to better outcomes. In this article, we will explore how to use candlestick patterns with AI models to achieve optimal trading performance.
This article presents an MQL5 Expert Advisor that upgrades raw swing detection to a rule-based Structural Validation Engine. Swings are confirmed by a break of structure, displacement, liquidity sweeps, or time-based respect, then linked to a liquidity map and a structural state machine. The result is context-aware entries and stops anchored to validated levels, helping filter noise and systematize execution.
The article describes a complete pipeline that uses data analysis for finding low-frequency lead/lag trading opportunities. It goes into building a cross-correlation-based Lead/Lag analyser step-by-step, with special attention to the most common errors beginners may commit while developing cross-asset diffusion queries. After screening dozens of cointegrated and correlated pairs, a trading candidate pair is chosen, and its tradeability is evaluated in a pure SQL backtest. Once it is qualified, the strategy is backtested on the MetaTester for parameter optimization. The Expert Advisor with respective backtest settings and optimization inputs is provided, along with Python and SQL scripts.
Most algo traders optimize Expert Advisors individually but never measure how they behave together on a single account. Correlated strategies amplify drawdowns instead of reducing them, and coverage gaps leave portfolios blind during entire trading sessions. This article builds a complete portfolio scorer in MQL5 that reads daily P&L from backtest CSV files, computes a full Pearson correlation matrix, maps trading activity by hour and weekday, evaluates asset class diversity, and outputs a composite grade from A+ to F. All source code is included; no external libraries are required.
The bet-sizing signal from Part 10 is concurrency-corrected but carries no payoff-ratio adjustment, no response to a hard drawdown budget, and no validation across combinatorial paths. This article covers three additions: a two-stage architecture in which a Kelly payoff multiplier is applied on top of get_signal, preserving the concurrency correction while incorporating win/loss asymmetry; a prop firm integration layer that calibrates the sigmoid w parameter continuously from the remaining drawdown budget under FundedNext Stellar 2-Step rules; and a CPCV backtest framework that simulates a fresh account state across all φ[N, k] paths, producing a Sharpe distribution and a PBO audit.
This article extends the MQL5 footprint chart with market-structure and order-flow layers: volume-profile bars, point of control, value-area highlighting, stacked imbalance detection, absorption zones, and single-print/unfinished markers. We expand bar data structures, add functions for POC/value area, imbalance, and absorption, and build a fixed-order rendering pipeline. You will get ready-to-use inputs, metadata, and drawing utilities to integrate and customize these layers in your indicator.
Build a rule-based on-chart risk management panel in MetaTrader 5 using the MQL5 Standard Library. The guide covers a CAppDialog-based GUI, manual event routing, and an automated update loop. You will bind UI events to CTrade to execute conditional closures, show net floating P/L, and read automated targets directly from the chart.
Before moving forward with the development of multi-currency EAs, let's try to switch to creating a new project using the developed library. This example will demonstrate how to best organize source code storage and how using the new code repository from MetaQuotes can help us.
This is an improved chaotic optimization algorithm (COA) that combines the effects of chaos with adaptive search mechanisms. The algorithm uses a set of chaotic maps and inertial components to explore the search space. The article reveals the theoretical foundations of chaotic methods of financial optimization.
This article presents a Time-of-Day capital rotation engine for MQL5 that allocates risk by trading session instead of using uniform exposure. We detail session budgets within a daily risk cap, dynamic lot sizing from remaining session risk, and automatic daily resets. Execution uses session-specific breakout and fade logic with ATR-based volatility confirmation. Readers gain a practical template to deploy capital where session conditions are statistically strongest while keeping exposure controlled throughout the day.
Tree-based classifiers are typically overconfident: true win rates near 0.55 appear as 0.65–0.80 and inflate position sizes and Kelly fractions. This article presents afml.calibration and CalibratorCV, which generate out-of-fold predictions via PurgedKFold and fit isotonic regression or Platt scaling. We define Brier score, ECE, and MCE, and show diagnostics that trace miscalibration into position sizes, realized P&L, and CPCV path Sharpe distributions to support leakage-free, correctly sized trading.
We revamp our earlier articles on testing trade setups with the MQL5 Wizard by putting a bit more emphasis on input data quality, cleaning, and handling. In the earlier articles we had looked at a lot of custom signal classes, usable by the wizard, so we now shift our focus to a custom trailing class, given that exiting is also a very important part in any trading system. Our broad theme for this particular piece data-efficiency and the O(1) range-query; the core ‘tech’ is MQL5, SQLite, Python-Polars; the Algorithm is the Sparse-Table while we will seek validation from the ATR Indicator.
Build a local, bidirectional voice interface for MetaTrader 5 using MQL5 WebRequest and two Python services. The article implements offline speech recognition with Vosk, wake‑word detection, an HTTP command endpoint, and a text‑to‑speech server on localhost. You will wire an Expert Advisor that fetches commands, executes trades, and returns spoken confirmations for hands‑free operation.
In this article, we explore the butterfly curve, a parametric mathematical equation, and render it visually on a MQL5 canvas. We build an interactive display with a draggable, resizable canvas window, supersampled curve rendering, gradient backgrounds, and a color-segmented legend. By the end, we have a fully functional visual tool that plots the butterfly curve directly on the MetaTrader 5 chart.
The article explores the possibility of improving price forecasting based on trading volume analysis by integrating technical analysis principles with LSTM neural network architecture. Particular attention is paid to the detection and interpretation of anomalous volumes, the use of clustering and the creation of features based on volumes and their definition in the context of machine learning.
Hidden Markov Models (HMMs) are a powerful class of probabilistic models designed to analyze sequential data, where observed events depend on some sequence of unobserved (hidden) states that form a Markov process. The main assumptions of HMM include the Markov property for hidden states, meaning that the probability of transition to the next state depends only on the current state, and the independence of observations given knowledge of the current hidden state.
In this article we introduce Python-MetaTrader5-like ways of handling trading operations such as opening, closing, and modifying orders in the simulator. To ensure the simulation behaves like MetaTrader 5, a strict validation layer for trade requests is implemented, taking into account symbol trading parameters and typical brokerage restrictions.
This article presents an EA that automates the previously introduced Market Entropy methodology. It computes fast and slow entropy, momentum, and compression states, validates signals, and executes orders with SL/TP and optional position reversal. The result is a practical, configurable tool that applies information-theoretic signals without manual interpretation.
The article presents a new metaheuristic method based on a fractal approach to partitioning the search space for solving optimization problems. The algorithm sequentially identifies and separates promising areas, creating a self-similar fractal structure that concentrates computing resources on the most promising areas. A unique mutation mechanism aimed at better solutions ensures an optimal balance between exploration and exploitation of the search space, significantly increasing the efficiency of the algorithm.
The Camel Algorithm, developed in 2016, simulates the behavior of camels in the desert to solve optimization problems, taking into account temperature, supply, and endurance. This article also presents a modified version of the algorithm (CAm) with key improvements: the use of a Gaussian distribution in generating solutions and the optimization of the oasis effect parameters.
We design a simple external trade analytics pipeline for MetaTrader 5 and implement its backend in Python with Flask and SQLite. The article defines the architecture, data model, and versioned API, and shows how to configure the environment, initialize the database, and run the server locally. As a result, you get a clean base to capture closed-trade records from MetaTrader 5 and store them for later analysis.
Head and Shoulders patterns are difficult to identify consistently in live market data due to noise and structural ambiguity. This article presents a structured, triangle-based MQL5 indicator that isolates pattern components, constructs the neckline, and validates formations using ATR, symmetry, and slope constraints. The system detects and draws standard and inverse patterns, assigns a quality score, and confirms breakouts with optional alerts, enabling consistent and rule-based chart analysis.
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.
