Daniel Opoku / Perfil

The article develops a volume-change strategy that transforms tick or real volume with Ln(volume+1), differences it bar to bar, and applies arctan to produce a bounded, scale‑independent signal. A threshold drives entries, with selectable gates for positive, negative, or bi‑directional signals and candle‑based confirmation. An MQL5 Expert Advisor implements the logic and supports fixed TP/SL, N‑candle exits, or both, enabling straightforward testing and deployment.

An on-chart MQL5 indicator generates a live stream of encoded candlestick symbols and a frequency report for the last N bars. It supports color customization and type filtering, and uses A/a, H/h, E/e, G/g, D, and N/n classifications. Traders can quickly review occurrences and common sequences directly on the chart.

This article extends the candlestick encoding framework by separating previously unclassified bullish and bearish candlesticks into distinct symbols. Using an MQL5 script, historical data were automatically encoded, sequential patterns were extracted, and frequency statistics were generated. The results show that these newly identified candles represent a significant portion of market activity, revealing structural information previously hidden by the generic underscore notation. The expanded taxonomy improves the completeness of the symbolic representation while preserving an objective, quantitative framework for large-scale market structure analysis.

This article extends single-candlestick analysis to ordered double-candlestick patterns using an MQL5 script. The script encodes candles into symbols, extracts every consecutive two-symbol sequence (treating Aa and aA as different), counts occurrences and percentages, and writes sorted frequency tables to a text file. Readers can quickly identify the most recurrent transitions by symbol, timeframe, and lookback for further statistical testing.

This article introduces a frequency-analysis framework for encoded candlestick patterns in MQL5. By transforming candlesticks into alphabetic symbols, historical price action can be analyzed as a statistical sequence rather than a visual chart. Using GBPUSD and Gold across multiple timeframes, the study examines the occurrence frequency of individual candlestick types, identifies dominant market structures, and reveals the symmetry between bullish and bearish price movements. The results establish a quantitative foundation for pattern discovery and prepare the way for analyzing multi-candlestick sequences and their predictive potential in algorithmic trading systems.

Developing permutation-based tools in MQL5 provides a systematic way to analyze candlestick pattern combinations for trading strategies. This article introduces a permutation calculator and generator designed to compute and enumerate all possible ordered candlestick sequences from bullish and bearish sets, with or without repetition. By generating exhaustive pattern combinations, traders can perform data-driven analysis to identify high-probability market patterns and improve decision-making in automated trading systems.

We present a rule‑based alphabet for candlestick price action that maps measurable shape and direction to letter codes (A/a, H/h, E/e, G/g, D). The article shows an MQL5 implementation: classifying candles, building two‑bar sequences via permutations, and scanning charts with an indicator and alerts. Readers gain a practical template for objective pattern detection and systematic testing.

In the world of technical analysis, price often takes center stage. Traders meticulously map out support, resistance, and patterns, yet frequently ignore the critical force that drives these movements: volume. This article delves into a novel approach to volume analysis: the Volume Boundary indicator. This transformation, utilizing sophisticated smoothing functions like the butterfly and triple sine curves, allows for clearer interpretation and the development of systematic trading strategies.

The relentless quest to decode market rhythms has led traders and quantitative analysts to develop countless mathematical models. This article has introduced the Flower Volatility Index (FVI), a novel approach that transforms the mathematical elegance of Rose Curves into a functional trading tool. Through this work, we have shown how mathematical models can be adapted into practical trading mechanisms capable of supporting both analysis and decision-making in real market conditions.

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.

In this article, we demonstrated how the fascinating mathematical concept of the Butterfly Curve can be transformed into a practical trading tool. We constructed the Butterfly Oscillator and built a foundational trading strategy around it. The strategy effectively combines the oscillator's unique cyclical signals with traditional trend confirmation from moving averages, creating a systematic approach for identifying potential market entries.

For many traders, it's a familiar pain point: watching a trade come within a whisker of your profit target, only to reverse and hit your stop-loss. Or worse, seeing a trailing stop close you out at breakeven before the market surges toward your original target. This article focuses on using multiple entries at different Reward-to-Risk Ratios to systematically secure gains and reduce overall risk exposure.

Muchos traders se han encontrado en esta situación: a menudo se ciñen a sus criterios de entrada, pero les cuesta gestionar las operaciones. Incluso con las configuraciones de entrada adecuadas, la toma de decisiones basada en las emociones —como cerrar posiciones por pánico antes de que alcancen sus niveles de take profit o de stop loss— puede provocar una curva de capital descendente. ¿Cómo se puede superar este problema y mejorar los resultados? En este artículo se abordarán estas cuestiones analizando las tasas de aciertos aleatorias y demostrando, mediante una simulación de Monte Carlo, cómo los traders pueden perfeccionar sus estrategias tomando beneficios a niveles razonables antes de alcanzar el objetivo inicial.

Este artículo presenta el oscilador ParaFrac y su modelo V2 como herramientas de negociación. En él se describen tres estrategias de negociación desarrolladas a partir de estos indicadores. Cada estrategia se sometió a pruebas y se optimizó para identificar sus puntos fuertes y débiles. El análisis comparativo puso de manifiesto las diferencias de rendimiento entre el oscilador Parafrac original y su modelo V2.

El oscilador Parafrac V2 es una herramienta avanzada de análisis técnico que integra Parabolic SAR con Average True Range (ATR) para superar las limitaciones de su predecesor, que se basaba en fractales y era propenso a que los picos de señal eclipsaran las señales anteriores y actuales. Aprovechando la medida de volatilidad que ofrece ATR, la versión 2 proporciona un método más fluido y fiable para detectar tendencias, cambios de tendencia y divergencias, lo que ayuda a los traders a reducir la saturación de los gráficos y la parálisis del análisis.

El objetivo último de todo trader es la rentabilidad, por lo que muchos fijan objetivos de beneficio específicos que desean alcanzar en un periodo de trading definido. En este artículo, utilizaremos simulaciones de Monte Carlo para determinar el porcentaje óptimo de riesgo por operación necesario para alcanzar los objetivos de trading. Los resultados ayudarán a los traders a evaluar si sus objetivos de beneficio son realistas o demasiado ambiciosos. Por último, analizaremos qué parámetros se pueden ajustar para establecer un porcentaje práctico de riesgo por operación que esté alineado con los objetivos de trading.

Exploraremos cómo se pueden combinar el indicador Parabolic SAR y el indicador Fractal para crear un nuevo indicador de tipo oscilador. Al integrar las ventajas únicas de ambas herramientas, los operadores pueden aspirar a desarrollar una estrategia de trading más refinada y eficaz.