Learn how to design a trading system with MQL5 using popular technical indicators. This series covers the Gator Oscillator, its application, and how to create a trading system on MetaTrader 5.

Gator Oscillator Definition:
Created by Bill Williams, the Gator Oscillator helps identify market trends and trading opportunities by measuring the convergence and divergence of balance lines derived from the Alligator indicator.

Gator Oscillator Trading Strategies:
1. Gator Status Strategy: Identifies market phases based on bar colors.
2. Gator Signals: Provides signals for entry, holding, and exit positions.
3. Gator with MA: Combines Gator indicator with Moving Average signals for buy/sell positions.

Blueprints and MQL5 code implementation for these strategies can efficiently enhance trading systems. Before using any strategy, ensure thorough testing on demo ac...
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The Predictive Moving Average, developed by John Ehlers and featured in "Rocket Science For Traders" (page 212), is an early leading indicator. It provides actionable insights for buy and sell decisions based on color-coded signals. When the indicator line turns green, it signals a buying opportunity. Conversely, a red line indicates a selling point. This method aims to offer precise market timing by anticipating future price movements. Integrating this indicator into trading strategies can enhance decision-making processes and optimize trade execution by leveraging its predictive capabilities.
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Q-learning was initially introduced in an article about Deep Q-Learning (DQN), which approximates the Q-function—a dependency of rewards on system states and actions. The real world, however, presents multifaceted challenges that affect the accuracy of these estimations due to outliers and incomplete parameter consideration. In 2017, new algorithms were proposed to study reward value distributions, improving Q-learning application in Atari games.

Distributed Q-learning offers a significant enhancement by approximating the reward value distribution instead of a single value. This method involves splitting possible rewards into quantiles, leveraging parameters like Vmin, Vmax, and the number of quantiles. Unlike classic Q-learning, it transforms the problem into a standard classification problem, using LogLoss instead of standard deviati...
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Switching Templates Efficiently in MT4

To switch sequentially between multiple templates, modify the mq4 file in Metaeditor. Change the template names to your preference, separated by commas. Example: extern string Templates = "1.tpl, 2.tpl, 3.tpl";

Set ApplyAllCharts to true to apply the template to all charts, or false to apply it to the current chart only. Ensure template files are in your Data Folder/templates folder. At least two templates are required for switching.

Set a hotkey by right-clicking the script in your navigator and choosing desired keys. This enables quick switching.

The script uses Global Variables to track which template corresponds to each Chart ID. To reset, delete all variables starting with "AbiroidTemplate" from Global Variables.

Error Codes:
- Error Code 0: No error, but issue with MT4. Refresh Scripts in navigator or re...
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In our previous article, we covered the process of sending chart snapshots with captions from MetaTrader 5 to Telegram. The method we used, though effective, was straightforward and somewhat inflexible, leading to repetitive and unmanageable code. To enhance this, transitioning to a more modular codebase is advisable.

In this fourth part, the focus will be on improving reusability through code modularization. Detailed discussions will be held on the principles of code modularization and their application to our project. We'll reorganize our existing MQL5 script into separate, well-defined functions.

By the end, you'll have the option of using either the old monolithic program or a new modular Expert Advisor (EA) providing the same output. We'll break the code into discrete functions for tasks like sending messages, taking screenshots, and en...
#MQL5 #MT5 #Telegram #CodeModularization

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For those utilizing the Ichimoku indicator and seeking Tenkan and Kijun cross signals, consider this Expert Advisor (EA). After download, compile it in Meta Editor. Then, on MT4, drag and drop it onto your chart. Adjust the timeframe to match your trading style and enable notifications. In the EA's dialog box, two parameters determine operational hours, set in a 24-hour format ranging from 0-24. Specify these according to your trading schedule. Ensure your Meta Quotes ID is updated in the notification settings of your MT4. Maintain effective trading practices.
#MQL4 #MT4 #Ichimoku #ForexEA

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In our latest article, we explore the Conformer method for weather forecasting, adapted for improving trading models using MetaTrader 5. Through timeseries data input, the Encoder model optimizes the Actor's policy. To enhance this process, we introduce Reversible Instance Normalization (RevIN), a simple yet effective normalization technique that addresses distribution shifts by normalizing input sequences and denormalizing output sequences. This technique enhances model accuracy by aligning mean and variance, thus improving timeseries forecasting. We also delve into practical implementation in MQL5, using neural layers for normalization and denormalization, enhancing the robustness and adaptability of trading algorithms.
#MQL5 #MT5 #forecasting #RevIN

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Reviewing the code example for generating a histogram of momentum offset reveals several key components. The primary function captures the momentum calculation and its corresponding histogram generation. The `hist()` function from a data visualization library is utilized to plot the momentum data.

Steps involved:
1. Momentum data is derived from a price series.
2. The offset value is computed to adjust the histogram.
3. The histogram plot is generated to visualize the momentum distribution.

To enhance clarity and maintainability, consider adding comments to the code. This will provide additional context for each step, improving the overall readability and usability for other developers.
#MQL4 #MT4 #trading #forex

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Discover the transformative impact of deep learning on timeseries forecasting with the introduction of the Client method. By blending the trend-capturing prowess of linear models with the intricate pattern recognition of enhanced Transformer architectures, Client offers a robust solution to multivariate long-term forecasting challenges. Key to this innovation is the strategic use of Reversible Normalization and transposed data processing to harness inter-variable dependencies. Practical applications in MQL5 highlight the method’s efficiency, providing a comprehensive guide to implementing a custom neural layer. Elevate your trading strategies with this sophisticated approach to predictive modeling.
#MQL5 #MT5 #forecasting #deeplearning

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Examining the integration of Q-Learning and Markov Chains to refine the learning process of a multi-layer perceptron (MLP) network reveals potential improvements in models for Expert Advisors. Q-Learning, a reinforcement learning algorithm, quantifies actions as rewards during training rounds, referred to as episodes.

Reinforcement learning, often categorized alongside supervised and unsupervised learning, balances exploration and exploitation, updating a Q-Learning map to track suitable actions in different states. This map utilizes a learning rate, reward, and discount factor.

Markov Chains, supplementing Q-Learning, transition between states based on probabilities. Transition matrices calculate state importance, memorylessly transitioning from current states. This approach aids in training the Q-Learning map and updating actions efficiently....
#MQL5 #MT5 #machinelearning #trading

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Since 2019, this personal library has been developed to facilitate position manipulation and retrieve various information. Key functions include:

- `SymbolOpenPipsMagic`: Returns open pips from all positions by magic number.
- `SymbolOpenPercentageMagic`: Returns percentage from open positions by magic number.
- `SymbolOpenPoints`: Returns open points/pipets trades.
- `SymbolOpenPositionsClose`: Closes all open positions.
- `SymbolOpenPositionsSpecific`: Returns the number of specific open trades by comment.
- `SymbolPositionResultMoney`: Returns the result from a position at a specified price target in money.

Other utilities handle pending orders, calculated margins, risk/reward ratios, and normalize volumes. Chart manipulation and candle data extraction are also streamlined. Additional functions support account status checks, managing historical data, a...
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Explore the potential of AI in trading with this comprehensive guide on algorithmic strategies for MetaTrader 5. Discover how to uncover key relationships between currency and precious metals markets, leveraging xauusd, xpdusd, and usdcad pair data. Learn how to fetch and preprocess this data using MQL5 and Python, ensuring clean datasets for accurate modeling.

Dive into various forecasting models, including Linear Regression and Linear Support Vector Regressor (LSVR), and understand their performance using 5-fold cross-validation. Finally, export your finely-tuned model to ONNX format and integrate it with your MetaTrader 5 Expert Advisor for smarter trading decisions.
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The U-shaped Transformer algorithm enhances the vanilla Transformer architecture for time series forecasting by retaining high frequency context. The innovative approach involves a two-stage training process: pre-training with diverse datasets to learn dependencies and reduce noise, followed by fine-tuning on specific problems. This method ensures efficiency, requiring less computational power for fine-tuning. Key features include patch-based embedding for extracting multi-scale features, convolutional layers, and skip-connections for fast data transfer, enhancing model robustness and generalization. Implementing these techniques in MQL5 involves advanced neural network layer handling and dynamic positional encoding, providing traders and developers a powerful tool for improved time series analysis.
#MQL5 #MT5 #Timeseries #AI

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Discover how to enhance your trading charts with custom objects in MQL5! This article discusses how to create and modify chart objects to provide visual clarity and actionable insights for traders and developers. Learn to use objects like rectangles, trend lines, and text labels to highlight key price levels, monitor trades, and display real-time performance data. This practical guide walks you through building an Expert Advisor that visualizes entry points, stop losses, and take profits directly on the chart, dynamically updates with market changes, and provides historical trade analysis. Perfect for those looking to deepen their algorithmic trading skills with MQL5.
#MQL5 #MT5 #MQL5 #Trading

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New feature added to allow direct specification of the starting point for the Parabolic SAR. Users can now set the initial value for more precise control over the Parabolic SAR calculation. Parameters include Trailing Mode options: None, Trailing Fixed, and Trailing Fixed Parabolic SAR. This enhancement enables tailored adjustments in your technical analysis, offering customizable options to fit diverse trading strategies. This update aims to provide advanced users with greater flexibility in setting and refining their indicators, contributing towards more accurate market predictions and better-informed decision-making.
#MQL5 #MT5 #Indicator #AlgoTrading

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Streamline Your Algorithmic Trading with Advanced EA Integration

Harness the power of efficient EA design in MetaTrader 5! Our strategy-centric approach seamlessly integrates multiple trading strategies into a single Expert Advisor (EA), optimizing performance and managing drawdown risks. We employ genetic optimization to fine-tune strategy parameters across symbols and timeframes, ensuring robust trading results.

Utilize optimization cache files for storing intermediate results and leverage SQLite for scalable database management. Our systematic process, including event-specific handlers and data frames, ensures precise data collection from MetaTrader 5 testing agents. This comprehensive framework is ideal for both seasoned developers and those new to EA development. Dive in and elevate your trading strategies!
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Part 5 of the series continues the integration of MetaQuotes Language 5 (MQL5) with Telegram, focusing on allowing the Expert Advisor (EA) in MetaTrader 5 (MT5) to receive and interpret commands from Telegram users. The objective is to enable two-way communication, where the EA not only sends messages but also intelligently responds to user commands.

First, the necessary environment is set up by including essential MQL5 libraries for trade management and data handling. This prepares the EA to manage complex data structures received from Telegram.

Subsequently, classes are created to retrieve and process chat updates from JSON data returned by the Telegram API. The classes parse the JSON data to extract chat updates and user commands, essential for dynamic communication between MT5 and Telegram.

The EA decodes and interprets incoming data from the Telegr...
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Enhance your trading strategy with a dynamic multi-pair Expert Advisor (EA) designed to capitalize on correlations and inverse correlations between currency pairs. By defining a primary currency pair to signal trades for other pairs, this EA optimizes performance during high-impact market events. The system is flexible, allowing traders to input and modify currency pairs and uses the RSI indicator to generate trading signals. Efficiently trade correlated pairs dynamically, adjusting for market conditions. This innovative approach not only boosts consistency but also leverages relationships between currency pairs to maximize trading opportunities and manage risks effectively. Explore smarter trading today.
#MQL5 #MT5 #Algorithm #EA

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Dive into the world of Gaussian Process Kernels in algorithmic trading with this insightful exploration of linear and Matérn kernels! Linear kernels are ideal for capturing long-term trends with computational efficiency, making them perfect for assets with clear upward or downward price movements. On the other hand, the Matérn kernel, with its adjustable smoothness, excels at modeling both trending and volatile data, providing nuanced forecasts. By implementing these kernels in MetaTrader 5 via MQL5, traders and developers can leverage sophisticated models to navigate the complexities of financial time series, optimize performance, and handle noisy data with precision.
#MQL5 #MT5 #EA #Algorithm

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Potentially infinite ways exist for modern investors to integrate AI into their trading strategies. In a series of articles, we explore the vast range of AI applications in trading. Our goal is to identify a strategy suitable for specific investor profiles.

The Financial Times Stock Exchange 100 (FTSE100) tracks the performance of the 100 largest companies on the London Stock Exchange. Created in 1984 with 1,000 points, it trades around 8,000 points. Companies within the index are weighted based on market capitalization.

UK government issues debt instruments called gilts. Gilts are fixed-income securities available in two types: conventional and index-linked. Conventional gilts pay a fixed coupon, while index-linked adjust for inflation. Gilts offer predictable income and low default risk.

Bonds and stocks have an inverse relationship: when stock ...
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