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Ko'proq ko'rsatish๐ Telegram kanali MQL5 Algo Trading analitikasi
MQL5 Algo Trading (@mql5dev) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 513 431 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 151-o'rinni va Birlashgan Qirollik mintaqasida 5-o'rinni egallagan.
๐ Auditoriya koโrsatkichlari va dinamika
ะฝะตะฒัะดะพะผะพ sanasidan buyon loyiha tez oโsib, 513 431 obunachiga ega boโldi.
24 Iyun, 2026 dagi oxirgi maโlumotlarga koโra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 8 575 ga, soโnggi 24 soatda esa 389 ga oโzgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya oโrtacha 3.48% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.75% ini tashkil etuvchi reaksiyalarni toโplaydi.
- Post qamrovi: Har bir post oโrtacha 17 875 marta koโriladi; birinchi sutkada odatda 8 978 ta koโrish yigโiladi.
- Reaksiyalar va oโzaro taโsir: Auditoriya faol: har bir postga oโrtacha 41 ta reaksiya keladi.
- Tematik yoโnalishlar: Kontent indicator, chart, mql5, candle, range kabi asosiy mavzularga jamlangan.
๐ Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taโriflaydi:
โThe best publications of the largest community of algotraders.
Subscribe to stay up-to-date with modern technologies and trading programs development.โ
Yuqori yangilanish chastotasi (oxirgi maโlumot 25 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boโlib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim taโsir nuqtasiga aylantirishini koโrsatadi.
513 431
Obunachilar
+38924 soatlar
+1 9847 kunlar
+8 57530 kunlar
Postlar arxiv
513 535
Machine learning continues to evolve rapidly, propelled by data growth and complex algorithms. Optimization remains crucial, with the ADAM algorithm emerging as a particularly effective method since its introduction in 2014. It was developed by D. P. Kingma and J. Ba for optimizing neural network weights, owing to its adaptive moment estimates.
ADAM's design offers several advantages: simplicity, computational efficiency, low memory requirements, and independence from gradient rescaling. However, its reliance on analytical gradients limits its application scope. A new proposal suggests a population-based adaptation, extending its use beyond neural networks to broader optimization challenges.
This approach involves transitioning ADAM into a stochastic, population-based algorithm. The C_AO_ADAM class implements a version that integrates randomness and hybrid...
๐ Read | Calendar | @mql5dev
#MQL5 #MT5 #Algorithm
513 535
Explore the intersection of algorithmic trading and machine learning with a focus on enhancing the Awesome Oscillator and Envelope Channels using advanced CNN techniques. This approach leverages a dynamic CNN architecture guided by dot product cross-time attention, optimizing kernel and channel sizes. The method captures dependencies across time, crucial for non-stationary financial data. While offering significant adaptability, it incurs computational cost and the risk of overfitting. Alternatives like dilated or adaptive convolutions are also considered for efficiency. Detailed Python implementations illustrate encapsulating Awesome Oscillator and Envelopes Channels, showcasing practical applications for traders and developers.
๐ Read | Forum | @mql5dev
#MQL5 #MT5 #Indicator
513 535
In the latest article, the development of a London Session Breakout System in MetaTrader 5 (MQL5) takes center stage. This system is designed to harness the volatility of the London market open by pinpointing pre-session price ranges and executing pending orders. It integrates advanced risk management tools such as risk-to-reward ratios, drawdown limits, and a real-time monitoring control panel. Detailed implementation steps are provided, including range calculation, order placement, and creation of a dynamic user interface for traders. The article culminates in a robust algorithm ready for backtesting and further refinement, offering significant practical benefits for traders and developers alike.
๐ Read | Forum | @mql5dev
#MQL5 #MT5 #TradeSystem
513 535
An Expert Advisor showcases functionality for order placement followed by modifications to set Stop Loss and Take Profit values. It features a mechanism where any open order is closed precisely 30 seconds after execution. This behavior suits strategies that rely on predefined trade lifespan, using exact timing for entries and exits. The application of fixed-duration trades helps in managing exposure with time-sensitive precision. Automating such processes can aid in backtesting strategies reliant on short-term price movements, enhancing decision-making and operational efficiency.
๐ Read | VPS | @mql5dev
#MQL4 #MT4 #EA
513 535
Explore the power of algorithmic trading with innovative techniques for combining strategies in MetaTrader 5. Harness the potential of a genetic optimizer to refine trading strategies collectively, boosting profitability through collaborative decision-making. However, this approach revealed an unexpected outcome: strategies with high correlation might inadvertently be selected, highlighting the need for revising selection methods. Delve into Python for data analysis using libraries like Pandas and scikit-learn, where statistical models can enhance strategy validation. Furthermore, leverage ONNX for neural network deployment, ensuring model independence and easy integration. Although initial results were suboptimal, careful revision promises enhanced trading performance through uncorrelated strategy analysis.
๐ Read | Forum | @mql5dev
#MQL5 #MT5 #Strategy
513 535
In Part 5 of our MQL5 series, we focus on creating a rolling ticker tape for the Multi-Timeframe Scanner Dashboard. This feature allows for real-time monitoring of multiple symbols, displaying bid prices, spreads, and daily percentage changes dynamically, thus providing essential information swiftly without overwhelming charts.
The architecture involves structured scrolling lines for symbols, prices, and spreads, utilizing arrays for data and timers for smooth performance. Customization options include speed, coloring for price movements, and layout adjustments. We implement this in MQL5 by defining input parameters, creating text labels with specific fonts and sizes, and using bitmap images for symbols.
The update mechanism relies on timers instead of tick-based updates, ensuring consistent refresh rates. Functions manage background adaptation to charts' r...
๐ Read | CodeBase | @mql5dev
#MQL5 #MT5 #Ticker
513 535
The "2 Moving Averages with Bollinger Bands" is a custom indicator for MetaTrader 5 designed for technical analysis in trading. It combines configurable fast and slow moving averages with optional Bollinger Bands to generate real-time Buy and Sell signals. These signals are triggered by crossover events, with additional options for alerts through charts, sound, and email notifications. This indicator is versatile across all symbols and timeframes and is built on a non-repainting logic.
Key features include customizable periods and methods for the moving averages, such as SMA, EMA, SMMA, and LWMA, and the inclusion of upper, middle, and lower Bollinger Bands. Suitable for scalping, swing, or trend-following strategies, it aids in identifying trend reversals and entry points. Though it's a powerful tool, it's essential to pair it with sound risk mana...
๐ Read | NeuroBook | @mql5dev
#MQL5 #MT5 #Indicator
513 535
The 2 Moving Averages with Bollinger Bands indicator for MetaTrader 5 offers a comprehensive approach to technical analysis by merging two well-established tools. This combination facilitates traders' ability to pinpoint trend shifts and potential entry points. It features customizable fast and slow moving averages, allowing users to select period, calculation method, and price application. The indicator generates Buy and Sell signals based on moving average crossovers. Integrated Bollinger Bands provide clear volatility and price channel visualization, adjustable in multiple parameters. Additionally, the tool supports various alert systems, including chart alerts and notifications through email.
This indicator is adaptable to any symbol and timeframe, holding value in scalping, swing trading, or trend-following methodologies. Users can enhance setups...
๐ Read | Calendar | @mql5dev
#MQL4 #MT4 #Indicator
513 535
Multivariate time series forecasting is a machine learning task focusing on predicting future trends from historical data. This task is difficult due to feature correlations and temporal dependencies and finds real-world applications in sectors like healthcare and finance. Transformer-based architectures, although impactful in NLP and computer vision, face challenges in time series forecasting due to training instability, especially with smaller datasets. The "SAMformer" framework addresses this by using simplified architecture, incorporating Sharpness-Aware Minimization and channel-wise attention to improve training stability and generalization. SAMformer optimizes Transformers to perform competitively by tackling entropy and loss sharpness issues, introducing novel strategies to enhance model efficiency and reliability.
๐ Read | Signals | @mql5dev
#MQL5 #MT5 #TimeSeries
513 535
Explore a revolutionary approach to market analysis with the innovative concept of 3D bars in MetaTrader 5. These bars integrate price, volume, and time into a single snapshot, offering a comprehensive view of market structure and dynamics. By visualizing data as 3D figures, traders can identify volume-supported movements and detect potential reversals early. This approach addresses the signal lag issue of classic technical analysis by utilizing real-time metrics. Through advanced algorithms and Python integration, traders and developers can harness this model to improve predictive accuracy and decision-making. Discover how 3D analysis transforms traditional chart reading into robust market insights.
๐ Read | Freelance | @mql5dev
#MQL5 #MT5 #Algorithm
513 535
N-BEATS offers a sophisticated deep learning approach for time series forecasting, standing out with its blend of simplicity and accuracy. It challenges traditional models such as ARIMA by employing modular neural networks that provide interpretable results. This model excels in capturing complex patterns, offering adaptability for both univariate and multivariate tasks. N-BEATS supports multi-series forecasting, making it an optimal choice for financial data with multiple features. It boasts fast, scalable training and requires no domain-specific tweaks, learning patterns directly from data. Practical for traders and developers, it integrates seamlessly with platforms like MetaTrader 5, enhancing decision-making through precise forecasts.
๐ Read | Signals | @mql5dev
#MQL5 #MT5 #NBEATS
513 535
A new script is available to streamline the setup process when creating charts. Time spent on adjusting color themes is minimized by offering three pre-configured options: Green/Red, Aqua/Pink, and Black/White. These themes can be customized according to your preferences by modifying the color variables, which are clearly annotated in the script. The variables start with 'clr' followed by the color name. To apply your selected theme, create a new chart and drag the script onto it, then simply choose your desired theme and confirm. This script enhances efficiency by automating a repetitive task, allowing for more focus on analysis and strategy development.
๐ Read | Calendar | @mql5dev
#MQL5 #MT5 #script
513 535
The book "MQL5 Programming for Traders" is now available in four languages. In addition to English and Russian, translations into Spanish and Chinese have been released. Learn algorithmic trading in your native language. The book is available online, as well as in PDF and CHM.
Whether you're completely new to programming or already have some experience, this book is suitable for all skill levels. Beginners will learn fundamental concepts, the development environment, and the basics of OOP, while more advanced users will explore practical solutions and advanced APIs.
The book also includes numerous source code examples to help you both learn the language and start building your own applications.
Start learning MQL5 today and step into the world of professional algorithmic trading. The skills you gain will allow you to bring your own trading ideas to life โ and potentially turn them into a business by selling applications on the Market or taking Freelance orders.
Read the Book...
513 535
Explore the fundamentals of the Average True Range (ATR) indicator and its impact on trading strategies with this in-depth guide. ATR, a staple for measuring market volatility, is delved into from its calculation and interpretation to practical applications. Discover how to integrate ATR into MetaTrader 5 using MQL5, enabling precise volatility assessments and strategic trading decisions. Gain insights into simple yet effective ATR-driven strategies, such as dynamic stop-loss and take-profit levels, that enhance trading efficiency. Whether you're a seasoned developer or new to algorithmic trading, this well-crafted breakdown equips you with the knowledge to optimize your trading system's performance using ATR.
๐ Read | Freelance | @mql5dev
#MQL5 #MT5 #ATR
513 535
A service has been developed to create custom symbols featuring non-standard timeframes. This enables developers and traders to configure and visualize data beyond conventional timeframe limitations. Custom symbols enhance the flexibility of data analysis and strategy testing by allowing tailored timeframe definitions. This functionality caters to the needs of users seeking more detailed or unique periodic data breakdowns. It serves as a valuable tool for those refining trading strategies or conducting precise backtesting. The adaptability of custom timeframes is a significant advancement, offering more control and customization in data representation and analysis tasks.
๐ Read | Quotes | @mql5dev
#MQL5 #MT5 #AlgoTrading
513 535
In recent developments in the library's graphical class improvements, emphasis has been placed on refining functionality for easier manipulation of pivot points in composite graphical objects. Enhancements were introduced to streamline the management of form objects that handle these pivot points, ensuring they can be conveniently relocated alongside their graphical counterparts.
To facilitate debugging, modifications in error-handling during graphical resource creation were integrated, aiding in rapid error identification. Improvements in class file implementation involved optimizing method structures, adjusting object naming conventions, and refining coordinate calculations to ensure precise rendering on charts.
Advanced methods now provide better management of form objects, offering the capability to redraw these forms in sync with graphical ob...
๐ Read | Docs | @mql5dev
#MQL5 #MT5 #Indicator
513 535
The EA implements a Relative Vigor Index (RVI) crossover strategy, providing an example of how such crossovers can be automated within a trading environment. It features a trailing stop mechanism to manage trade exits efficiently. This addition helps to lock in profits as market conditions fluctuate, offering a pragmatic approach to risk management. The EA serves both as a tool for executing the RVI crossover strategy and as a learning aid for incorporating trailing stops into algorithmic trading systems, encouraging developers to integrate similar techniques into their own projects for enhanced trading performance and security.
๐ Read | Forum | @mql5dev
#MQL4 #MT4 #EA
513 535
The introduction of automation using TA-Lib significantly enhances candlestick pattern detection in Python. TA-Lib, with its ability to automatically identify over 60 candlestick patterns, integrates seamlessly into Python, making it a preferred tool for many developers. The library, originally crafted in ANSI C, supports more than 200 technical indicators, facilitating complex trading strategies.
Combining TA-Lib with mplfinance and matplotlib allows for the automatic plotting of financial charts. This system leverages a Flask web service to facilitate real-time data processing and visualization. Its integration with MQL5 ensures chart I/O remains streamlined, focusing on efficient data processing and pattern recognition.
Understanding the roles of essential libraries such as Flask, NumPy, Pandas, and Matplotlib is crucial for developers aiming to c...
๐ Read | Docs | @mql5dev
#MQL5 #MT5 #TA-Lib
513 535
Integration of automated trade execution in the News Headline Expert Advisor (EA) seeks to fill a critical functionality gap by employing the CTrade class. This advances the EA from merely providing alerts and insights to executing trade orders autonomously during news events. Previous iterations already delivered features such as direct chart display of economic events, real-time news headlines, technical indicator insights, and AI-driven market analysis.
The introduced strategy implements time-based pending orders, allowing for reaction to market volatility driven by economic news. Key changes include leveraging the economic calendar to time trade entry, using pip-accurate price calculations, and deploying CTrade for streamlined order management, which covers packaging, tracking, and error handling.
Enhanced risk management is crucial, incorporating...
๐ Read | Signals | @mql5dev
#MQL5 #MT5 #Strategy
513 535
Reducing lag in moving average crossover trading strategies can be achieved without advanced modeling tools. One approach uses different periods on price types rather than price prediction. A promising strategy is the dual time-frame moving average crossover. It involves observing higher timeframe crossovers and aligning trades on the lower timeframe. The strategy employs trend-following and mean-reverting entry modes, with exits determined by higher or lower timeframe signals.
Challenges remain in achieving stable strategies, as initial backtest results often do not translate to forward tests. Improved stability might require allowing optimizers to adjust risk parameters dynamically. While this strategy shows potential, repeated optimizations with varying parameters could yield more stable results.
๐ Read | Signals | @mql5dev
#MQL5 #MT5 #Algorithm
