From Novice to Expert: Implementation of Fibonacci Strategies in Post-NFP Market Trading
From Novice to Expert: Implementation of Fibonacci Strategies in Post-NFP Market Trading
In financial markets, the laws of retracement remain among the most undeniable forces. It is a rule of thumb that price will always retrace—whether in large moves or even within the smallest tick patterns, which often appear as a zigzag. However, the retracement pattern itself is never fixed; it remains uncertain and subject to anticipation. This uncertainty explains why traders rely on multiple Fibonacci levels, each carrying a certain probability of influence. In this discussion, we introduce a refined strategy that applies Fibonacci techniques to address the challenges of trading shortly after major economic event announcements. By combining retracement principles with event-driven market behavior, we aim to uncover more reliable entry and exit opportunities. Join to explore the full discussion and see how Fibonacci can be adapted to post-event trading.
Mastering Quick Trades: Overcoming Execution Paralysis
Mastering Quick Trades: Overcoming Execution Paralysis
The UT BOT ATR Trailing Indicator is a personal and customizable indicator that is very effective for traders who like to make quick decisions and make money from differences in price referred to as short-term trading (scalpers) and also proves to be vital and very effective for long-term traders (positional traders).
Creating Custom Indicators in MQL5 (Part 4): Smart WaveTrend Crossover with Dual Oscillators
Creating Custom Indicators in MQL5 (Part 4): Smart WaveTrend Crossover with Dual Oscillators
In this article, we develop a custom indicator in MQL5 called Smart WaveTrend Crossover, utilizing dual WaveTrend oscillators—one for generating crossover signals and another for trend filtering—with customizable parameters for channel, average, and moving average lengths. The indicator plots colored candles based on the trend direction, displays buy and sell arrow signals on crossovers, and includes options to enable trend confirmation and adjust visual elements like colors and offsets.
Optimizing Trend Strength: Trading in Trend Direction and Strength
Optimizing Trend Strength: Trading in Trend Direction and Strength
This is a specialized trend-following EA that makes both short and long-term analyses, trading decisions, and executions based on the overall trend and its strength. This article will explore in detail an EA that is specifically designed for traders who are patient, disciplined, and focused enough to only execute trades and hold their positions only when trading with strength and in the trend direction without changing their bias frequently, especially against the trend, until take-profit targets are hit.
MQL5 Trading Tools (Part 11): Correlation Matrix Dashboard (Pearson, Spearman, Kendall) with Heatmap and Standard Modes
MQL5 Trading Tools (Part 11): Correlation Matrix Dashboard (Pearson, Spearman, Kendall) with Heatmap and Standard Modes
In this article, we build a correlation matrix dashboard in MQL5 to compute asset relationships using Pearson, Spearman, and Kendall methods over a set timeframe and bars. The system offers standard mode with color thresholds and p-value stars, plus heatmap mode with gradient visuals for correlation strengths. It includes an interactive UI with timeframe selectors, mode toggles, and a dynamic legend for efficient analysis of symbol interdependencies.
Reimagining Classic Strategies (Part 21): Bollinger Bands And RSI Ensemble Strategy Discovery
Reimagining Classic Strategies (Part 21): Bollinger Bands And RSI Ensemble Strategy Discovery
This article explores the development of an ensemble algorithmic trading strategy for the EURUSD market that combines the Bollinger Bands and the Relative Strength Indicator (RSI). Initial rule-based strategies produced high-quality signals but suffered from low trade frequency and limited profitability. Multiple iterations of the strategy were evaluated, revealing flaws in our understanding of the market, increased noise, and degraded performance. By appropriately employing statistical learning algorithms, shifting the modeling target to technical indicators, applying proper scaling, and combining machine learning forecasts with classical trading rules, the final strategy achieved significantly improved profitability and trade frequency while maintaining acceptable signal quality.
Overcoming The Limitation of Machine Learning (Part 5): A Quick Recap of Time Series Cross Validation
Overcoming The Limitation of Machine Learning (Part 5): A Quick Recap of Time Series Cross Validation
In this series of articles, we look at the challenges faced by algorithmic traders when deploying machine-learning-powered trading strategies. Some challenges within our community remain unseen because they demand deeper technical understanding. Today’s discussion acts as a springboard toward examining the blind spots of cross-validation in machine learning. Although often treated as routine, this step can easily produce misleading or suboptimal results if handled carelessly. This article briefly revisits the essentials of time series cross-validation to prepare us for more in-depth insight into its hidden blind spots.
MQL5 Trading Tools (Part 12): Enhancing the Correlation Matrix Dashboard with Interactivity
MQL5 Trading Tools (Part 12): Enhancing the Correlation Matrix Dashboard with Interactivity
In this article, we enhance the correlation matrix dashboard in MQL5 with interactive features like panel dragging, minimizing/maximizing, hover effects on buttons and timeframes, and mouse event handling for improved user experience. We add sorting of symbols by average correlation strength in ascending/descending modes, toggle between correlation and p-value views, and incorporate light/dark theme switching with dynamic color updates.
Central Force Optimization (CFO) algorithm
Central Force Optimization (CFO) algorithm
The article presents the Central Force Optimization (CFO) algorithm inspired by the laws of gravity. It explores how principles of physical attraction can solve optimization problems where "heavier" solutions attract less successful counterparts.
Developing Trend Trading Strategies Using Machine Learning
Developing Trend Trading Strategies Using Machine Learning
This study introduces a novel methodology for the development of trend-following trading strategies. This section describes the process of annotating training data and using it to train classifiers. This process yields fully operational trading systems designed to run on MetaTrader 5.
MetaTrader 5 on Linux
MetaTrader 5 on Linux
In this article, we demonstrate an easy way to install MetaTrader 5 on popular Linux versions — Ubuntu and Debian. These systems are widely used on server hardware as well as on traders’ personal computers.
Automating Trading Strategies in MQL5 (Part 47): Nick Rypock Trailing Reverse (NRTR) with Hedging Features
Automating Trading Strategies in MQL5 (Part 47): Nick Rypock Trailing Reverse (NRTR) with Hedging Features
In this article, we develop a Nick Rypock Trailing Reverse (NRTR) trading system in MQL5 that uses channel indicators for reversal signals, enabling trend-following entries with hedging support for buys and sells. We incorporate risk management features like auto lot sizing based on equity or balance, fixed or dynamic stop-loss and take-profit levels using ATR multipliers, and position limits.
MetaQuotes ID in MetaTrader Mobile Terminal
MetaQuotes ID in MetaTrader Mobile Terminal
Android and iOS powered devices offer us many features we do not even know about. One of these features is push notifications allowing us to receive personal messages, regardless of our phone number or mobile network operator. MetaTrader mobile terminal already can receive such messages right from your trading robot. You should only know MetaQuotes ID of your device. More than 9 000 000 mobile terminals have already received it.
Neuroboids Optimization Algorithm 2 (NOA2)
Neuroboids Optimization Algorithm 2 (NOA2)
The new proprietary optimization algorithm NOA2 (Neuroboids Optimization Algorithm 2) combines the principles of swarm intelligence with neural control. NOA2 combines the mechanics of a neuroboid swarm with an adaptive neural system that allows agents to self-correct their behavior while searching for the optimum. The algorithm is under active development and demonstrates potential for solving complex optimization problems.
From Novice to Expert: Creating a Liquidity Zone Indicator
From Novice to Expert: Creating a Liquidity Zone Indicator
The extent of liquidity zones and the magnitude of the breakout range are key variables that substantially affect the probability of a retest occurring. In this discussion, we outline the complete process for developing an indicator that incorporates these ratios.
Neuroboids Optimization Algorithm (NOA)
Neuroboids Optimization Algorithm (NOA)
A new bioinspired optimization metaheuristic, NOA (Neuroboids Optimization Algorithm), combines the principles of collective intelligence and neural networks. Unlike conventional methods, the algorithm uses a population of self-learning "neuroboids", each with its own neural network that adapts its search strategy in real time. The article reveals the architecture of the algorithm, the mechanisms of self-learning of agents, and the prospects for applying this hybrid approach to complex optimization problems.
Creating a Trading Administrator Panel in MQL5 (Part IX): Code Organization (IV): Trade Management Panel class
Creating a Trading Administrator Panel in MQL5 (Part IX): Code Organization (IV): Trade Management Panel class
This discussion covers the updated TradeManagementPanel in our New_Admin_Panel EA. The update enhances the panel by using built-in classes to offer a user-friendly trade management interface. It includes trading buttons for opening positions and controls for managing existing trades and pending orders. A key feature is the integrated risk management that allows setting stop loss and take profit values directly in the interface. This update improves code organization for large programs and simplifies access to order management tools, which are often complex in the terminal.
Using Deep Reinforcement Learning to Enhance Ilan Expert Advisor
Using Deep Reinforcement Learning to Enhance Ilan Expert Advisor
We revisit the Ilan grid Expert Advisor and integrate Q-learning in MQL5 to build an adaptive version for MetaTrader 5. The article shows how to define state features, discretize them for a Q-table, select actions with ε-greedy, and shape rewards for averaging and exits. You will implement saving/loading the Q-table, tune learning parameters, and test on EURUSD/AUDUSD in the Strategy Tester to evaluate stability and drawdown risks.