Algorithmic Trading Articles — Guides & Examples

Reimagining Classic Strategies (Part 19): Deep Dive Into Moving Average Crossovers

This article revisits the classic moving average crossover strategy and examines why it often fails in noisy, fast-moving markets. It presents five alternative filtering methods designed to strengthen signal quality and remove weak or unprofitable trades. The discussion highlights how statistical models can learn and correct the errors that human intuition and traditional rules miss. Readers leave with a clearer understanding of how to modernize an outdated strategy and of the pitfalls of relying solely on metrics like RMSE in financial modeling.

From Novice to Expert: Developing a Geographic Market Awareness with MQL5 Visualization

Trading without session awareness is like navigating without a compass—you're moving, but not with purpose. Today, we're revolutionizing how traders perceive market timing by transforming ordinary charts into dynamic geographical displays. Using MQL5's powerful visualization capabilities, we'll build a live world map that illuminates active trading sessions in real-time, turning abstract market hours into intuitive visual intelligence. This journey sharpens your trading psychology and reveals professional-grade programming techniques that bridge the gap between complex market structure and practical, actionable insight.

Automating Trading Strategies in MQL5 (Part 45): Inverse Fair Value Gap (IFVG)

In this article, we create an Inverse Fair Value Gap (IFVG) detection system in MQL5 that identifies bullish/bearish FVGs on recent bars with minimum gap size filtering, tracks their states as normal/mitigated/inverted based on price interactions (mitigation on far-side breaks, retracement on re-entry, inversion on close beyond far side from inside), and ignores overlaps while limiting tracked FVGs.

Mastering Fair Value Gaps: Formation, Logic, and Automated Trading with Breakers and Market Structure Shifts

This is an article that I have written aimed to expound and explain Fair Value Gaps, their formation logic for occurring, and automated trading with breakers and market structure shifts.

Fortified Profit Architecture: Multi-Layered Account Protection

In this discussion, we introduce a structured, multi-layered defense system designed to pursue aggressive profit targets while minimizing exposure to catastrophic loss. The focus is on blending offensive trading logic with protective safeguards at every level of the trading pipeline. The idea is to engineer an EA that behaves like a “risk-aware predator”—capable of capturing high-value opportunities, but always with layers of insulation that prevent blindness to sudden market stress.

Simplifying Databases in MQL5 (Part 2): Using metaprogramming to create entities

We explored the advanced use of #define for metaprogramming in MQL5, creating entities that represent tables and column metadata (type, primary key, auto-increment, nullability, etc.). We centralized these definitions in TickORM.mqh, automating the generation of metadata classes and paving the way for efficient data manipulation by the ORM, without having to write SQL manually.

$Developing Trading Strategies with the Parafrac and Parafrac V2 Oscillators: Single Entry Performance Insights$

This article introduces the ParaFrac Oscillator and its V2 model as trading tools. It outlines three trading strategies developed using these indicators. Each strategy was tested and optimized to identify their strengths and weaknesses. Comparative analysis highlighted the performance differences between the original and V2 models.

Mastering Kagi Charts in MQL5 (Part 2): Implementing Automated Kagi-Based Trading

Learn how to build a complete Kagi-based trading Expert Advisor in MQL5, from signal construction to order execution, visual markers, and a three-stage trailing stop. Includes full code, testing results, and a downloadable set file.

Developing a multi-currency Expert Advisor (Part 24): Adding a new strategy (I)

In this article, we will look at how to connect a new strategy to the auto optimization system we have created. Let's see what kind of EAs we need to create and whether it will be possible to do without changing the EA library files or minimize the necessary changes.

Overcoming The Limitation of Machine Learning (Part 9): Correlation-Based Feature Learning in Self-Supervised Finance

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.

Adaptive Smart Money Architecture (ASMA): Merging SMC Logic With Market Sentiment for Dynamic Strategy Switching

This topic explores how to build an Adaptive Smart Money Architecture (ASMA)—an intelligent Expert Advisor that merges Smart Money Concepts (Order Blocks, Break of Structure, Fair Value Gaps) with real-time market sentiment to automatically choose the best trading strategy depending on current market conditions.

Automated Risk Management for Passing Prop Firm Challenges

This article explains the design of a prop-firm Expert Advisor for GOLD, featuring breakout filters, multi-timeframe analysis, robust risk management, and strict drawdown protection. The EA helps traders pass prop-firm challenges by avoiding rule breaches and stabilizing trade execution under volatile market conditions.

Codex Pipelines: From Python to MQL5 for Indicator Selection — A Multi-Quarter Analysis of the FXI ETF

We continue our look at how MetaTrader can be used outside its forex trading ‘comfort-zone’ by looking at another tradable asset in the form of the FXI ETF. Unlike in the last article where we tried to do ‘too-much’ by delving into not just indicator selection, but also considering indicator pattern combinations, for this article we will swim slightly upstream by focusing more on indicator selection. Our end product for this is intended as a form of pipeline that can help recommend indicators for various assets, provided we have a reasonable amount of their price history.

Price Action Analysis Toolkit Development (Part 38): Tick Buffer VWAP and Short-Window Imbalance Engine

In Part 38, we build a production-grade MT5 monitoring panel that converts raw ticks into actionable signals. The EA buffers tick data to compute tick-level VWAP, a short-window imbalance (flow) metric, and ATR-based position sizing. It then visualizes spread, ATR, and flow with low-flicker bars. The system calculates a suggested lot size and a 1R stop, and issues configurable alerts for tight spreads, strong flow, and edge conditions. Auto-trading is intentionally disabled; the focus remains on robust signal generation and a clean user experience.

How to build and optimize a cycle-based trading system (Detrended Price Oscillator - DPO)

This article explains how to design and optimise a trading system using the Detrended Price Oscillator (DPO) in MQL5. It outlines the indicator's core logic, demonstrating how it identifies short-term cycles by filtering out long-term trends. Through a series of step-by-step examples and simple strategies, readers will learn how to code it, define entry and exit signals, and conduct backtesting. Finally, the article presents practical optimization methods to enhance performance and adapt the system to changing market conditions.

Automating Trading Strategies in MQL5 (Part 46): Liquidity Sweep on Break of Structure (BoS)

In this article, we build a Liquidity Sweep on Break of Structure (BoS) system in MQL5 that detects swing highs/lows over a user-defined length, labels them as HH/HL/LH/LL to identify BOS (HH in uptrend or LL in downtrend), and spots liquidity sweeps when price wicks beyond the swing but closes back inside on a bullish/bearish candle.

From Novice to Expert: Trading the RSI with Market Structure Awareness

In this article, we will explore practical techniques for trading the Relative Strength Index (RSI) oscillator with market structure. Our focus will be on channel price action patterns, how they are typically traded, and how MQL5 can be leveraged to enhance this process. By the end, you will have a rule-based, automated channel-trading system designed to capture trend continuation opportunities with greater precision and consistency.

Building AI-Powered Trading Systems in MQL5 (Part 7): Further Modularization and Automated Trading

In this article, we enhance the AI-powered trading system's modularity by separating UI components into a dedicated include file. The system now automates trade execution based on AI-generated signals, parsing JSON responses for BUY/SELL/NONE with entry/SL/TP, visualizing patterns like engulfing or divergences on charts with arrows, lines, and labels, and optional auto-signal checks on new bars.

Codex Pipelines, from Python to MQL5, for Indicator Selection: A Multi-Quarter Analysis of the XLF ETF with Machine Learning

We continue our look at how the selection of indicators can be pipelined when facing a ‘none-typical’ MetaTrader asset. MetaTrader 5 is primarily used to trade forex, and that is good given the liquidity on offer, however the case for trading outside of this ‘comfort-zone’, is growing bolder with not just the overnight rise of platforms like Robinhood, but also the relentless pursuit of an edge for most traders. We consider the XLF ETF for this article and also cap our revamped pipeline with a simple MLP.

From Novice to Expert: Automating Trade Discipline with an MQL5 Risk Enforcement EA

For many traders, the gap between knowing a risk rule and following it consistently is where accounts go to die. Emotional overrides, revenge trading, and simple oversight can dismantle even the best strategy. Today, we will transform the MetaTrader 5 platform into an unwavering enforcer of your trading rules by developing a Risk Enforcement Expert Advisor. Join this discussion to find out more.

Introduction to MQL5 (Part 32): Mastering API and WebRequest Function in MQL5 (VI)

This article will show you how to visualize candle data obtained via the WebRequest function and API in candle format. We'll use MQL5 to read the candle data from a CSV file and display it as custom candles on the chart, since indicators cannot directly use the WebRequest function.

Creating Custom Indicators in MQL5 (Part 1): Building a Pivot-Based Trend Indicator with Canvas Gradient

In this article, we create a Pivot-Based Trend Indicator in MQL5 that calculates fast and slow pivot lines over user-defined periods, detects trend directions based on price relative to these lines, and signals trend starts with arrows while optionally extending lines beyond the current bar. The indicator supports dynamic visualization with separate up/down lines in customizable colors, dotted fast lines that change color on trend shifts, and optional gradient filling between lines, using a canvas object for enhanced trend-area highlighting.

Larry Williams Market Secrets (Part 1): Building a Swing Structure Indicator in MQL5

A practical guide to building a Larry Williams–style market structure indicator in MQL5, covering buffer setup, swing-point detection, plot configuration, and how traders can apply the indicator in technical market analysis.

Pure implementation of RSA encryption in MQL5

MQL5 lacks built-in asymmetric cryptography, making secure data exchange over insecure channels like HTTP difficult. This article presents a pure MQL5 implementation of RSA using PKCS#1 v1.5 padding, enabling safe transmission of AES session keys and small data blocks without external libraries. This approach provides HTTPS-like security over standard HTTP and even more, it fills an important gap in secure communication for MQL5 applications.

Statistical Arbitrage Through Cointegrated Stocks (Part 9): Backtesting Portfolio Weights Updates

This article describes the use of CSV files for backtesting portfolio weights updates in a mean-reversion-based strategy that uses statistical arbitrage through cointegrated stocks. It goes from feeding the database with the results of a Rolling Windows Eigenvector Comparison (RWEC) to comparing the backtest reports. In the meantime, the article details the role of each RWEC parameter and its impact in the overall backtest result, showing how the comparison of the relative drawdown can help us to further improve those parameters.

Creating Custom Indicators in MQL5 (Part 2): Building a Gauge-Style RSI Display with Canvas and Needle Mechanics

In this article, we develop a gauge-style RSI indicator in MQL5 that visualizes Relative Strength Index values on a circular scale with a dynamic needle, color-coded ranges for overbought and oversold levels, and customizable legends. We utilize the Canvas class to draw elements like arcs, ticks, and pies, ensuring smooth updates on new RSI data.

From Novice to Expert: Navigating Market Irregularities

Market rules are continuously evolving, and many once-reliable principles gradually lose their effectiveness. What worked in the past no longer works consistently over time. Today’s discussion focuses on probability ranges and how they can be used to navigate market irregularities. We will leverage MQL5 to develop an algorithm capable of trading effectively even in the choppiest market conditions. Join this discussion to find out more.

Reimagining Classic Strategies (Part 20): Modern Stochastic Oscillators

This article demonstrates how the stochastic oscillator, a classical technical indicator, can be repurposed beyond its conventional use as a mean-reversion tool. By viewing the indicator through a different analytical lens, we show how familiar strategies can yield new value and support alternative trading rules, including trend-following interpretations. Ultimately, the article highlights how every technical indicator in the MetaTrader 5 terminal holds untapped potential, and how thoughtful trial and error can uncover meaningful interpretations hidden from view.

The article presents a new metaheuristic algorithm, Chaos Game Optimization (CGO), which demonstrates a unique ability to maintain high efficiency when dealing with high-dimensional problems. Unlike most optimization algorithms, CGO not only does not lose, but sometimes even increases performance when scaling a problem, which is its key feature.

Statistical Arbitrage Through Cointegrated Stocks (Part 5): Screening

This article proposes an asset screening process for a statistical arbitrage trading strategy through cointegrated stocks. The system starts with the regular filtering by economic factors, like asset sector and industry, and finishes with a list of criteria for a scoring system. For each statistical test used in the screening, a respective Python class was developed: Pearson correlation, Engle-Granger cointegration, Johansen cointegration, and ADF/KPSS stationarity. These Python classes are provided along with a personal note from the author about the use of AI assistants for software development.

Building a Trading System (Part 4): How Random Exits Influence Trading Expectancy

Many traders have experienced this situation, often stick to their entry criteria but struggle with trade management. Even with the right setups, emotional decision-making—such as panic exits before trades reach their take-profit or stop-loss levels—can lead to a declining equity curve. How can traders overcome this issue and improve their results? This article will address these questions by examining random win-rates and demonstrating, through Monte Carlo simulation, how traders can refine their strategies by taking profits at reasonable levels before the original target is reached.

MQL5 Wizard Techniques you should know (Part 82): Using Patterns of TRIX and the WPR with DQN Reinforcement Learning

In the last article, we examined the pairing of Ichimoku and the ADX under an Inference Learning framework. For this piece we revisit, Reinforcement Learning when used with an indicator pairing we considered last in ‘Part 68’. The TRIX and Williams Percent Range. Our algorithm for this review will be the Quantile Regression DQN. As usual, we present this as a custom signal class designed for implementation with the MQL5 Wizard.

Introduction to MQL5 (Part 22): Building an Expert Advisor for the 5-0 Harmonic Pattern

This article explains how to detect and trade the 5-0 harmonic pattern in MQL5, validate it using Fibonacci levels, and display it on the chart.

Introduction to MQL5 (Part 24): Building an EA that Trades with Chart Objects

This article teaches you how to create an Expert Advisor that detects support and resistance zones drawn on the chart and executes trades automatically based on them.

MQL5 Wizard Techniques you should know (Part 84): Using Patterns of Stochastic Oscillator and the FrAMA - Conclusion

The Stochastic Oscillator and the Fractal Adaptive Moving Average are an indicator pairing that could be used for their ability to compliment each other within an MQL5 Expert Advisor. We introduced this pairing in the last article, and now look to wrap up by considering its 5 last signal patterns. In exploring this, as always, we use the MQL5 wizard to build and test out their potential.

Self Optimizing Expert Advisors in MQL5 (Part 15): Linear System Identification

Trading strategies may be challenging to improve because we often don’t fully understand what the strategy is doing wrong. In this discussion, we introduce linear system identification, a branch of control theory. Linear feedback systems can learn from data to identify a system’s errors and guide its behavior toward intended outcomes. While these methods may not provide fully interpretable explanations, they are far more valuable than having no control system at all. Let’s explore linear system identification and observe how it may help us as algorithmic traders to maintain control over our trading applications.

MQL5 Wizard Techniques you should know (Part 85): Using Patterns of Stochastic-Oscillator and the FrAMA with Beta VAE Inference Learning

This piece follows up ‘Part-84’, where we introduced the pairing of Stochastic and the Fractal Adaptive Moving Average. We now shift focus to Inference Learning, where we look to see if laggard patterns in the last article could have their fortunes turned around. The Stochastic and FrAMA are a momentum-trend complimentary pairing. For our inference learning, we are revisiting the Beta algorithm of a Variational Auto Encoder. We also, as always, do the implementation of a custom signal class designed for integration with the MQL5 Wizard.

From Novice to Expert: Higher Probability Signals

In high-probability support and resistance zones, valid entry confirmation signals are always present once the zone has been correctly identified. In this discussion, we build an intelligent MQL5 program that automatically detects entry conditions within these zones. We leverage well-known candlestick patterns alongside native confirmation indicators to validate trade decisions. Click to read further.

Data Science and ML (Part 47): Forecasting the Market Using the DeepAR model in Python

In this article, we will attempt to predict the market with a decent model for time series forecasting named DeepAR. A model that is a combination of deep neural networks and autoregressive properties found in models like ARIMA and Vector Autoregressive (VAR).

Creating a mean-reversion strategy based on machine learning

This article proposes another original approach to creating trading systems based on machine learning, using clustering and trade labeling for mean reversion strategies.