Enhance analytical strategies with the Moving Average (MA) crossover indicator. This tool precisely identifies potential buy and sell signals by observing the intersection of different moving averages. Typically, a short-term MA crossing above a long-term MA signifies a buying opportunity, whereas crossing below may suggest a selling point. Implement this concept to refine strategic entries and exits in trading activities.
Ensure your chosen timeframe aligns with strategy objectives, as this affects signal reliability. Regular review of market conditions in conjunction with the indicator optimizes decision-making. Analyze and adjust the parameters to meet specific trading requirements. Proper implementation can lead to better market insight and improved trading outcomes.
π Read | AlgoBook | @mql5dev
#MQL4 #MT4 #Algorithm
Ensure your chosen timeframe aligns with strategy objectives, as this affects signal reliability. Regular review of market conditions in conjunction with the indicator optimizes decision-making. Analyze and adjust the parameters to meet specific trading requirements. Proper implementation can lead to better market insight and improved trading outcomes.
π Read | AlgoBook | @mql5dev
#MQL4 #MT4 #Algorithm
β€51π9π¨βπ»5
Explore the world of algorithmic optimization with the Black Hole Algorithm (BHA), inspired by the cosmic phenomena of black holes. This algorithm excels by attracting optimal solutions while discarding less effective ones, providing robust solutions for complex function optimization and machine learning hyperparameter tuning. It offers simplicity with minimal parameters, making it accessible yet powerful for developers. Test results show it outperforms its initial version, eliminating local optima traps for reliable convergence. This innovative approach balances exploration and exploitation, making it suitable for intricate multi-extremal problems, promising enhanced efficiency and application in diverse computational scenarios.
π Read | Signals | @mql5dev
#MQL5 #MT5 #Algorithm
π Read | Signals | @mql5dev
#MQL5 #MT5 #Algorithm
β€35π¨βπ»6π3π₯3π2
The financial markets rely heavily on understanding what lies beneath price charts. Order books reveal the realities of buy and sell pressures, impacting price movements. Retail traders often lack access to these insights due to platform limitations, generally relying on meta-platforms like MetaTrader 5. The Slippage Tool bridges this gap.
Designed for MetaTrader 5, it uses tick data to simulate essential order book signals. The tool reconstructs insights like VWAP for trend identification, tackles imbalance to reveal directional pressures, evaluates spreads to understand cost and liquidity, and analyzes flow for short-term sentiment. These features equip traders with actionable data for better entry timing and risk assessment, aiding more precise trading decisions.
π Read | VPS | @mql5dev
#MQL5 #MT5 #Trading
Designed for MetaTrader 5, it uses tick data to simulate essential order book signals. The tool reconstructs insights like VWAP for trend identification, tackles imbalance to reveal directional pressures, evaluates spreads to understand cost and liquidity, and analyzes flow for short-term sentiment. These features equip traders with actionable data for better entry timing and risk assessment, aiding more precise trading decisions.
π Read | VPS | @mql5dev
#MQL5 #MT5 #Trading
β€70β‘13π8π₯8β6π¨βπ»5π€1
Logify is a logging library tailored for MQL, facilitating efficient debugging, tracking, and monitoring in expert advisors (EAs) and indicators. It delivers structured and customizable logs either directly on the chart or within the terminal, supporting varied log levels such as DEBUG, INFO, ALERT, ERROR, and FATAL. The library allows users to manage log formats flexibly and choose from multiple handlers including charts, console, files, and databases.
The library's modular architecture makes integration straightforward. To incorporate Logify into your MQL project, copy the Logify folder and include it in your EA, indicator, or script. Follow quick start examples for implementation with default or custom settings. Diverse handlers like Comment, Console, File, and Database ensure adaptable log display and storage. Logify supports token-based log format...
π Read | Freelance | @mql5dev
#MQL5 #MT5 #Logging
The library's modular architecture makes integration straightforward. To incorporate Logify into your MQL project, copy the Logify folder and include it in your EA, indicator, or script. Follow quick start examples for implementation with default or custom settings. Diverse handlers like Comment, Console, File, and Database ensure adaptable log display and storage. Logify supports token-based log format...
π Read | Freelance | @mql5dev
#MQL5 #MT5 #Logging
β€37π5π2π¨βπ»1π1
Explore the practical implementation of the MVC pattern in MetaTrader 5, focusing on the separation of code into Model, View, and Controller components. The article introduces the Model as the decision-making core, processing input data and maintaining independence from frequent changes. The View handles visualization, showcasing how data is represented on charts. The Controller bridges the user interaction with the Model and View, ensuring smooth data flow and integrity. Learn how to manage input parameters effectively, validate them, and design interfaces that facilitate seamless information exchange between components. Discover the potential for expanding functionalities, adhering to best practices in code organization, and enhancing the robustness of trading applications.
π Read | AlgoBook | @mql5dev
#MQL5 #MT5 #MVC
π Read | AlgoBook | @mql5dev
#MQL5 #MT5 #MVC
β€57π7π¨βπ»6π5π₯1
Automated trading systems often leverage strategies like the average crossover to identify potential entry and exit points in the market. This technique involves monitoring the interaction between moving averages, typically a short-term and a long-term average, to generate buy or sell signals. Such EAs execute trades based on predefined parameters, which removes emotional decision-making from the trading process. It's crucial for traders to test these systems in different market conditions to ensure their robustness. While average crossover EAs can be effective, they require fine-tuning and optimization to align with individual trading styles and risk management preferences.
π Read | Docs | @mql5dev
#MQL4 #MT4 #EA
π Read | Docs | @mql5dev
#MQL4 #MT4 #EA
β€46π10π¨βπ»1
The article explores the implementation of a trading strategy using the Momentum indicator within MetaTrader 5. Momentum, an important tool for traders, measures price velocity, aiding in identifying trend strength and direction. The article outlines the calculation methods for Momentum, both by subtraction and division, with emphasis on the latter used in MetaTrader 5. A simple Momentum strategy is proposed, focusing on crossover signals around the 100 level for buying and selling. It includes a detailed blueprint for coding this strategy into a trading system using MQL5, enabling automated signal generation, enhancing trading efficiency and decision-making.
π Read | AlgoBook | @mql5dev
#MQL5 #MT5 #Momentum
π Read | AlgoBook | @mql5dev
#MQL5 #MT5 #Momentum
β€58π13π3π¨βπ»3π€2
MetaTrader 5 build 5260 provides improvements to MQL5 Algo Forge, our Git-based project management platform: faster Git operations, more reliable modification checks, and new comprehensive documentation to help you explore, fork, and collaborate on algorithmic trading projects.
The MQL5 update introduces expanded OpenBLAS support with new matrix balancing methods and stricter inheritance rules, which make code safer and more predictable.
We've also added terminal translation into Irish, making the platform accessible and user-friendly for native speakers.
Read more...
The MQL5 update introduces expanded OpenBLAS support with new matrix balancing methods and stricter inheritance rules, which make code safer and more predictable.
We've also added terminal translation into Irish, making the platform accessible and user-friendly for native speakers.
Read more...
β€56π12π₯10π€£7π6β‘4π2
Implementing code to draw triangles on a chart assists in detecting harmonic patterns and visually plotting them. This technique can be extended to include the plotting of rhombuses and parallelograms, providing additional flexibility in geometric representation. Utilizing these shapes enhances the ability to clearly display detected patterns on charts, aiding in pattern analysis and verification processes. By adapting this approach, developers can accurately reflect complex structures, which can be crucial for detailed analytical frameworks and systems. Efficient code execution ensures reliable visualization of both triangular and quadrilateral figures, supporting advanced pattern detection methodologies in technical analysis.
π Read | NeuroBook | @mql5dev
#MQL4 #MT4 #AlgoTrading
π Read | NeuroBook | @mql5dev
#MQL4 #MT4 #AlgoTrading
β€42π5π¨βπ»2π€―1
Discover the groundbreaking exploration of markets through the lens of 3D bars, transcending traditional analysis by constructing a tensor model that integrates price, volume, and time. This innovative approach reveals powerful predictive patterns heralding trend reversals with remarkable accuracy, transforming market data into a vibrant, dynamic structure. By employing Gann's methodological principles for data normalization, coupled with a unique volumetric profile and the development of a trend strength integral indicator, the article demonstrates how these insights can enhance decision-making. The adaptable application of these findings within MetaTrader 5 underscores a significant leap in algorithmic trading, offering advanced foresight for traders and developers alike.
π Read | AppStore | @mql5dev
#MQL5 #MT5 #Indicator
π Read | AppStore | @mql5dev
#MQL5 #MT5 #Indicator
β€57π5π¨βπ»3π3π2
The Laguerre Filter is a technical analysis tool that employs Laguerre polynomials to provide a smoothing effect on price data. This indicator assists in identifying both short-term and long-term market trends. Traders can interpret signals as follows: a green indicator line suggests a buying opportunity, whereas a red line advises selling actions. The tool focuses on delivering clear and concise trend data, optimizing decision-making processes. Availability for this indicator exists outside this communication, with further resources accessible through designated channels. Subscription details are subject to specific distribution platforms.
π Read | Docs | @mql5dev
#MQL5 #MT5 #Indicator
π Read | Docs | @mql5dev
#MQL5 #MT5 #Indicator
β€39π16π7π¨βπ»1
Explore the advanced development of composite graphical objects in MetaTrader 5. Discover enhanced functionality as the author delves into event handling, focusing on real-time creation of these objects through drag-and-drop mechanics. The introduction of form objects for each anchor point allows precise positioning, facilitating seamless attachment to base objects. Unique methods for handling price and time coordinates ensure accurate tracking and management. The custom toolkit, embedded within base object classes, enables efficient form creation, object manipulation, and event response. This structured approach promises to optimize algorithmic trading strategies by offering practical insights for both traders and developers seeking refined control within the MetaTrader 5 environment.
π Read | Calendar | @mql5dev
#MQL5 #MT5 #GraphObj
π Read | Calendar | @mql5dev
#MQL5 #MT5 #GraphObj
β€53π7π5π2π1π¨βπ»1
The Adaptive CCI indicator offers significant improvements over the traditional Commodity Channel Index by utilizing dynamically adjusting thresholds that respond to market volatility. Unlike fixed limits, these thresholds, calculated through an Exponential Moving Average of detected peaks and troughs, automatically determine optimal overbought and oversold levels. This comprehensive update significantly enhances signal accuracy, particularly by eliminating false signals in sideways markets and ensuring asset-specific calibration.
Key functional aspects include volatility-based smoothing, which fine-tunes responsiveness through the Average True Range, allowing for greater sensitivity in volatile markets and stability during quieter periods. Additionally, the indicator is fully compatible with both real-time trading and historical backtesting, ensur...
π Read | Freelance | @mql5dev
#MQL5 #MT5 #Indicator
Key functional aspects include volatility-based smoothing, which fine-tunes responsiveness through the Average True Range, allowing for greater sensitivity in volatile markets and stability during quieter periods. Additionally, the indicator is fully compatible with both real-time trading and historical backtesting, ensur...
π Read | Freelance | @mql5dev
#MQL5 #MT5 #Indicator
β€51π5π5β3π3π¨βπ»3π±1
The ZigZag WaveSize indicator has undergone significant enhancements. Key improvements include adapting the code for MetaTrader 5 and optimizing interactions with graphical objects. New features have been added, such as horizontal levels on extrema with options for type selectionβhorizon, beams, or sectionsβand a liquid levels filter that remains unaffected by price. The breakout buffer now allows setting sensitivity to false breakouts. Enhanced label customization includes options for number, appearance, and automated deletion of outdated labels. Alerts have been introduced for structure breakdowns and changes in motion patterns. Optimization efforts focus on refining extremum update logic and enabling dynamic updates for new objects, reducing load when new bars surface. A centralized label system is now in place to manage positioning effectively, ...
π Read | VPS | @mql5dev
#MQL5 #MT5 #Indicator
π Read | VPS | @mql5dev
#MQL5 #MT5 #Indicator
β€43π10π¨βπ»4π€¨3π€―1
Explore the comprehensive guide to harnessing linear regression models in MetaTrader 5. The article delves into using statistical methods, such as Pearson's correlation coefficient, to evaluate market data effectively. Learn to implement simple and multiple linear regression models using MQL5 and Python to predict stock price changes based on key indicators. Uncover how to visualize data and measure predictive accuracy with R-squared, ensuring robust algorithmic trading strategies. The piece also highlights the importance of data quality and assumption validations, focusing on enhancing trading outcomes through informed decision-making. A perfect read for traders and developers seeking a deep dive into algorithmic trading solutions.
π Read | AlgoBook | @mql5dev
#MQL5 #MT5 #DataScience
π Read | AlgoBook | @mql5dev
#MQL5 #MT5 #DataScience
β€71π16π5π¨βπ»5
The Fractal with CCI Filter Indicator is designed to improve the performance of the classic fractal indicator using a CCI-based filter. By integrating the Commodity Channel Index, this version aims to reduce false signals, particularly in sideways markets, enhancing signal reliability through confirmation of the movement's strength.
Key features include a CCI Smart Filter, which activates fractal signals when the CCI reaches extreme levels, and timing accuracy by utilizing the CCI value on the signal candle. Additionally, the CCI period and filtering threshold offer full configurability. Visual aids include red arrows for sell signals and green arrows for buy signals, compatible with any timeframe.
To utilize effectively, monitor for sell signals when a red arrow is above a candle and for buy signals with a green arrow below. For increased reliabilit...
π Read | Freelance | @mql5dev
#MQL5 #MT5 #Indicator
Key features include a CCI Smart Filter, which activates fractal signals when the CCI reaches extreme levels, and timing accuracy by utilizing the CCI value on the signal candle. Additionally, the CCI period and filtering threshold offer full configurability. Visual aids include red arrows for sell signals and green arrows for buy signals, compatible with any timeframe.
To utilize effectively, monitor for sell signals when a red arrow is above a candle and for buy signals with a green arrow below. For increased reliabilit...
π Read | Freelance | @mql5dev
#MQL5 #MT5 #Indicator
β€42β6π3π₯2π2π¨βπ»2π2
Simplified CSV file writing using a straightforward class is achievable without extensive preparation or type casting declarations. Consider implementing a minimalistic class where you define a method for writing CSV files efficiently.
```python
import csv
class SimpleCSVWriter:
def __init__(self, filename):
self.filename = filename
def write(self, data):
with open(self.filename, mode='w', newline='') as file:
writer = csv.writer(file)
writer.writerows(data)
csv_writer = SimpleCSVWriter('output.csv')
data = [
['Name', 'Age', 'City'],
['Alice', 30, 'New York'],
['Bob', 25, 'Los Angeles'],
['Charlie', 35, 'Chicago']
]
csv_writer.write(data)
```
Instantiate the `SimpleCSVWriter` with the desired filename, prepare your list of lists containing rows, and utilize the `write` method to generate your...
π Read | CodeBase | @mql5dev
#MQL5 #MT5 #CSV
```python
import csv
class SimpleCSVWriter:
def __init__(self, filename):
self.filename = filename
def write(self, data):
with open(self.filename, mode='w', newline='') as file:
writer = csv.writer(file)
writer.writerows(data)
csv_writer = SimpleCSVWriter('output.csv')
data = [
['Name', 'Age', 'City'],
['Alice', 30, 'New York'],
['Bob', 25, 'Los Angeles'],
['Charlie', 35, 'Chicago']
]
csv_writer.write(data)
```
Instantiate the `SimpleCSVWriter` with the desired filename, prepare your list of lists containing rows, and utilize the `write` method to generate your...
π Read | CodeBase | @mql5dev
#MQL5 #MT5 #CSV
β€34β3π3π¨βπ»3β‘2π2π2
The article delves into the development of a custom Heikin Ashi indicator using MQL5, crucial for traders and developers focused on algorithmic trading. It provides an intricate breakdown of the Heikin Ashi candles, explaining their advantage in smoothing out market trends, enhancing clarity over traditional candlestick charts. The guide details the calculation methods for Heikin Ashi's open, high, low, and close values and the logic behind integrating these calculations into MQL5. The article emphasizes modular programming for maintaining clean, understandable code and explains setting up visual elements, such as color buffers, to improve indicator readability. It equips developers to create more refined trading tools.
π Read | AppStore | @mql5dev
#MQL5 #MT5 #Indicator
π Read | AppStore | @mql5dev
#MQL5 #MT5 #Indicator
β€31π4π₯3π¨βπ»3π€2
In delving into advanced machine learning, this article addresses a critical but often overlooked component: irreducible error. Beyond the inherent variability and model bias commonly acknowledged, it introduces a third source of error, obscured by abstraction. By examining models from a geometric perspective, practitioners can better understand the manifold mismatch, where predictions poorly align with target realities in their own domains. This discussion extends into practical trading applications, demonstrating enhanced profitability and reduced risk using an improved feedback controller approach. Both developers and traders can benefit from these insights, offering a more intelligent application of machine learning in trading strategies.
π Read | Forum | @mql5dev
#MQL5 #MT5 #ML
π Read | Forum | @mql5dev
#MQL5 #MT5 #ML
β€27β8π4π₯2π¨βπ»2πΎ2
Explore the groundbreaking Multi-Agent and Self-Adaptive portfolio optimization framework integrated with Attention mechanisms and Time series (MASAAT) for dynamic trading. This sophisticated framework harnesses attention mechanisms to extract trend features from noisy time series, utilizing Cross-Sectional Attention (CSA) and Temporal Attention (TA) for superior asset and temporal insight. Agents analyze market trends at various granularities, enhancing portfolio adaptability in volatile conditions. An MQL5 implementation showcases its practical application, emphasizing modular object structure for parallel operation. Leveraging OpenCL, multi-agent trend detection is optimized, paving the way for refined portfolio strategies in computational finance. Ideal for developers keen on algorithmic trading innovations.
π Read | Signals | @mql5dev
#MQL5 #MT5 #Portfolio
π Read | Signals | @mql5dev
#MQL5 #MT5 #Portfolio
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