An in-depth analysis focuses on Deriv's Boom 1000 synthetic market. This market is marked by its volatile bull and bear candles, making it notoriously unpredictable. The method of sending limited tick data results in breached stop losses with guaranteed slippage, hence many consider buy opportunities in trading strategies given its dominant bullish movements.

Machine learning techniques were employed to model relations and predict price movements. 100,000 rows of M1 data with RSI indicator readings were collected. Exploratory data analysis revealed an 83% correlation between falling RSI readings and falling prices, but the RSI had a weak correlation of 0.016 with price levels. The dataset was split for training and validation.

Two neural network classifiers were used: one for predicting price changes and another for RSI changes. The RSI model achieved an ac...
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Discover the intricacies of the Smart Money Optimal Control (SMOC) advisor, designed for advanced Forex trading. Leveraging stochastic modeling and control optimization, SMOC uses Model Predictive Control (MPC) to anticipate market moves. Key features include adaptive forecast horizons and lot sizes tailored to market volatility and drawdown, dynamic stop loss and take profit based on the Average True Range (ATR), and a blend of technical indicators like SMA, RSI, and ATR for trend and volatility analysis. While offering sophisticated risk management, SMOC also tackles computational demands and the risk of overfitting, making it a potent tool for traders.
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Algorithmic trading efficiency demands seamless communication between trading systems and human administrators. This article presents methods to enhance the Administrator Messaging Interface using MQL5, emphasizing quick messaging and responsive interfaces.

Define responsiveness within the MQL5 GUI, focusing on immediate feedback, smooth performance, dynamic updates, scalability, and error handling. These components are crucial for timely trading actions and an intuitive interface.

Quick messages facilitate standard responses, error notifications, and routine commands. Include buttons in the Admin Panel for predefined messages, reducing manual input and ensuring rapid, consistent communication.

Implement responsive control buttons—minimize, maximize, and close—using the CButton class. Enhance flexibility through customizable quick messages, managing cha...
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A new library for multicurrency Expert Advisors helps accurately determine new bar events across different instruments. In a multicurrency scenario, new bars form at varying times for each instrument. Since the EA operates on a single instrument's chart, it is crucial to identify whether a new bar has formed in the OnTick() event handler for other instruments. This library offers a solution by storing the time of the first tick for each required instrument at every tick. When a new bar starts, this time updates and remains constant until the next bar.

The library simplifies the process by managing the necessary variables for different instruments and time periods. Users only need to call `UpdateNewBar()` at the beginning of OnTick() and use `IsNewBar(symbol, timeframe)` to check for new bar events. The library must be included by adding `NewBarEvent.mqh`. ...
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MetaTrader 5 developers, elevate your trading strategies with a sophisticated trailing stop mechanism using the Parabolic SAR indicator. This method automates StopLoss adjustments, enhancing trade protections without premature exits. Learn how to integrate a specialized trailing function in MQL5, leveraging Parabolic SAR for automatic price adaptation. Delve into creating an Expert Advisor to handle StopLoss via precise criteria checks, maintaining StopLevel and FreezeLevel constraints. Utilize \CTrade\ class for streamlined trade functions, ensuring seamless StopLoss modifications. This approach not only shields gains but also optimizes position management in trending markets. Enhance your algorithmic trading efficiency with these advanced techniques!
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The Patch Time Series Transformer (PatchTST) offers an innovative method for time series forecasting. By segmenting time series into patches, it leverages Transformer architecture to capture complex semantic relationships between data points more effectively than traditional point-level analysis. The approach reduces computational complexity, enhances learning from extended data windows, and improves representation learning. PatchTST treats multivariate time series as independent univariate sequences, optimizing analysis while maintaining efficiency. The article also discusses practical implementation in MQL5, emphasizing efficient data preparation through OpenCL for improved algorithm performance. PatchTST showcases its potential in anomaly detection, classification, and long-term forecasting, making it a robust solution for advanced time series a...
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The ATR Bands by AlexanderTeaH, originally from TradingView, have been successfully translated to MQL4. In scenarios where the entry is not a reversal, the Average True Range (ATR) can effectively guide stop loss or exit points. This tool also serves well for trailing stops, similar to the chandelier exit strategy. The conversion ensures that MQL4 users can now apply ATR Bands in their trading strategies, enhancing risk management and exit planning. This integration aims to provide traders with more precise control over their trades, leveraging the versatility of ATR-based indicators.
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Discover the power of Ant Colony Optimization (ACO) for solving complex combinatorial problems in algorithmic trading. Belgian researcher Marco Dorigo's ACO models the foraging behavior of ants, leveraging pheromone-based stigmergy to find optimal paths. This population-based method excels in path planning by simulating how ants leave pheromone trails that guide others, creating a feedback loop to refine solutions.

Key variants of ACO, such as Ant System (AS), Ant Colony System (ACS), and Max-Min Ant System (MMAS), enhance its capabilities through optimized pheromone updates and path selection strategies. Tailoring these algorithms for trading can revolutionize neural network training and financial instrument optimization.
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Smart MA Trailing Stop EA is an effective tool for trending markets, automating stop loss adjustments. It adjusts stop losses based on the selected Moving Average (MA) period and the distance between the price and the MA.

Automated Stop Loss Adjustments: The EA automatically adjusts stop losses in line with the trend, securing profits as long as the trend persists.

Customizable MA Period and SL Distance: Users can set their preferred MA period and stop loss distance in pips.

Designed for Trending Markets: The EA places stop losses at a safe distance, protecting positions in momentum-driven markets.

Compatibility with 4 and 5-Digit Brokers: The EA adapts to the correct decimal placement, ensuring accuracy without extra effort.

This tool provides precise stop loss management, securing profits during strong market movements, and is user-friendly.
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For those looking to automate opening buy and sell positions in MetaTrader 5 at the current market price with specific stop loss and take profit parameters, below is a simplified script. This script ensures the automatic capture of the current price and sets the trade parameters without displaying any dialog windows upon execution. Initial values for lot size, stop loss, and take profit should be defined in the script, which can later be modified via global variables in the MT5 menu.

```mql5
//+------------------------------------------------------------------+
//| Expert initialization function |
//+------------------------------------------------------------------+
void OnStart()
{
// Define initial parameters
double lotSize = 0.1;
int stopLossPips = 20;
int takeProfitPips = 40;

// If global variables exist...
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In algorithmic trading, choosing the right indicators is crucial. Traditional feature selection methods often fall short in non-stationary environments due to their global bias. The "Local Feature Selection" (LFS) algorithm offers a solution by focusing on locally optimal feature subsets, making it ideal for data with shifting patterns. This method emphasizes minimizing intra-class distances while maximizing inter-class distances through a weighting scheme that adapts dynamically to data variations. Implementing LFS in Python allows traders and developers to build more adaptive and robust models, ultimately enhancing predictive performance on MetaTrader 5. Discover how LFS can streamline your trading strategy!
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Struggling with non-stationary financial time series? Dive into our deep-dive analysis on achieving stationarity using first differences and empirical distribution functions. Learn how stationarity ensures accurate calculations in algorithms and why it matters for both regression and neural network analyses. Discover the Kolmogorov-Smirnov test’s role in detecting data homogeneity, pivotal for precise trading strategies and forecasts. Explore practical applications with real data cases, including EURUSD and XAUUSD minute bars, and understand how this statistical approach ensures reliable, error-free predictions even with dependent data. Perfect for enhancing your algorithmic trading models.
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Bollinger Bands, created by John Bollinger in 1987, quantify market volatility and identify overbought/oversold conditions. Consisting of three lines, they expand and contract with volatility changes, serving as dynamic support/resistance levels. This article dives into 8 Bollinger Bands signals, explaining their integration into a custom signal class for algorithmic trading in MetaTrader 5. The goal is to optimize and test different pattern weights, leveraging classes from MQL5's standard library for efficient implementation. Using these signals, traders can develop robust strategies, anticipating trend reversals and breakouts for enhanced trading decisions in volatile markets.
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MetaQuotes Language 5 (MQL5) Expert Advisor enhances interactivity with Telegram via responsive inline buttons. Inline buttons, defined using JSON, offer streamlined user actions within messages. The article covers their integration in MQL5, including creating a dedicated message class to manage interactions and a callback query class for handling specific user actions triggered by these buttons. By incorporating these elements, the bot processes user inputs dynamically, improving the overall user experience. Guidance on testing the bot ensures seamless functionality, making trading bots more responsive and user-centric for optimal trading efficiency.
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A new script provides a graphical panel of information to display the maximum lot size permitted, calculated based on account size, leverage, and free margin. Unlike traditional scripts that rarely use dialogs, this script offers a visual representation without needing to load an expert advisor. This functionality is particularly useful for those who prefer quick access to essential trading information directly through a script, streamlining the process of fetching free margin-related data. Efficient and straightforward, this script meets a specific need by delivering critical trading metrics with minimal setup.
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Auto-optimizing forex trading algorithms offer significant advantages for automated traders. An Expert Advisor (EA) can adjust its strategy based on market conditions post-trade. This adaptive feature allows EAs to refine their approach over time.

Key components to set up include necessary libraries, input parameters, global variables, and optimization settings. The core functions of the EA—initialization, deinitialization, and tick processing—are essential for maintaining operational stability.

Optimization logic involves evaluating parameter combinations to find the best-performing settings. Techniques like multi-criteria optimization and walk-forward optimization reduce the risk of overfitting and improve robustness.

Best practices include balancing optimization frequency and maintaining computational efficiency. Comprehensive logging and effective use of d...
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Developing an automated trading application (EA) involves multiple stages where each generates critical information. The information is saved in a database with results stored from various strategy tests.

A challenge arises in creating trading strategy objects from this saved data. Direct object storage in the database is not an option. Converting object properties to a string for storage ensures data can be read back and objects recreated. This can be managed through methods or constructors that parse these strings to assign values to object properties.

Creating complex objects, such as groups of trading strategies or an EA itself, demonstrates the need for a systematic approach to string parsing and object reconstruction.

A practical implementation requires classes to be "factoryable." This means developing a base class, CFactorable, which can be initi...
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## Introduction

Enhancing the Admin Panel GUI in MQL5 includes essential improvements such as toggling between dark and light themes, adding stylish buttons, and customizing fonts.

## Basic Admin Panel Achievements

Previously accomplished:
- Created an Admin Panel with a Messaging Interface and Telegram Integration.
- Added buttons for minimize, maximize, close, and quick messaging.

## Applying MQL5 GUI Styling Features

MQL5 offers various tools for GUI customization, including color and font adjustments. These enable creating a visually cohesive and functional interface.

## Customizing Colors and Fonts

Define an array of available fonts and manage their selection through an index. Implement a button for users to cycle through fonts, ensuring dynamic and cohesive panel appearance.

## Theme Management Logic

Set up a system with light and dark themes. ...
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Announcing the updated NoLoss EA, improved significantly through community feedback. Unlike most lagging indicators, this EA eliminates dependency on indicators, candlestick patterns, chart patterns, or price action analysis. It uses a progressive strategy verified over a year of use and four years of backtesting.

**Preferred Pair**: EURUSD

**Setup**: Compatible with any timeframe due to its non-reliance on traditional indicators and patterns.

**Parameters**:
- **Initialsize**: Set your initial lot size based on your account balance.
- **MaxBuy/Sell**: Define the maximum number of open buy/sell positions.
- **OverallBuy/SellProfit**: Establish the combined profit target for all buy/sell orders.
- **Slippage**: Enter your slippage value.
- **DistanceBetweenOrders**: Set the pip distance to trigger new orders in the same direction.
- **NumberofBuy/Sell**: Sp...
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Category Theory, a powerful mathematical framework, can be leveraged in algorithmic trading using MQL5 to understand the relationships between trading strategies, instruments, and market conditions. By classifying and analyzing these relationships through concepts like domains and morphisms, traders can identify patterns and create adaptable, reliable trading algorithms. This approach ensures consistency, verifies system correctness, and enhances communication of complex trading strategies. Practical applications include constructing unique trading domains, enforcing data integrity, and visualizing morphisms, leading to sophisticated expert advisors in MetaTrader 5. This methodology aids MQL5 programmers in developing robust, scalable trading systems.
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The Trend Direction Force Index v2 (TDFI) indicator has been successfully converted from TradingView to MQL4. Originally designed for identifying market trend direction and magnitude, this indicator is now available for MetaTrader 4 users. The TDFI calculates a standardized measure of price movement, offering enhanced insight into market dynamics. This version maintains the original algorithm, ensuring consistency in its output while leveraging the MQL4 environment. The conversion facilitates seamless integration into existing trading strategies for algorithmic traders and technical analysts working within the MetaTrader 4 platform.
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