Time Segmented Volume (TSV) is a technical analysis indicator developed by Worden Brothers Inc. TSV segments a stock’s price and volume data according to specific time intervals, allowing for a detailed comparison to highlight periods of accumulation (buying) and distribution (selling). The methodology aims to identify leading market indicators by evaluating different time segments of price and volume metrics. This approach can offer valuable insights when analyzing market trends and investor behavior. Understanding TSV can enhance one's ability to forecast market movements based on detailed time-based segmentation of trading activities.

Source: Worden Brothers Inc.
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Machine learning in trading: theory, models, practice and algo-trading

There are innumerable ways AI can enhance trading strategies, yet evaluating each one before adoption is impractical. Let's revisit the strategy of multiple symbol analysis to see how AI can improve it.

Trading strategies with multiple symbol analysis rely on correlations between symbols. Correlation measures linear dependency between variables but does not imply a relationship. For instance, the USDZAR currency pair correlates with oil and gold prices. Understanding these correlations helps gauge risk and guide decisions.

For this analysis, market data from MetaTrader 5 was exported and used to train models with OHLC quotes and a combination of oil and gold prices. Results showed oil having a stronger correlation with USDZAR than gold. Models like linear regression and KNeighborsRegressor performed best, with hyperparameter tuning improving accurac...
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Regularization is crucial in machine learning algorithms to enhance the performance of neural networks. It addresses the issue of biasing towards specific parameters, which can hinder network performance on out-of-sample data.

The primary methods of regularization include Lasso (L1), Ridge (L2), Elastic-Net, and Drop-Out. Each method introduces different benefits when correctly paired with activation and loss functions, thus preventing exploding or vanishing gradients.

For classifier networks, L1 regularization (Lasso) is useful as it promotes sparsity. For regressor networks, L2 regularization (Ridge) is favored for balanced weight distribution.

Drop-out regularization is beneficial for deeper networks as it improves generalization by forcing the network to learn redundant representations, making it robust and resilient to noisy data.

When testing o...
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Dynamic Time Warping (DTW) offers powerful pattern recognition in financial time series, surpassing traditional methods like Euclidean distance. Originating from speech recognition, DTW aligns sequences with flexible timing, ideal for non-linear financial data.

DTW's strength lies in its ability to account for temporal distortions, enabling identification of unique patterns. The MQL5 implementation focuses on calculating distance scores with customizable step patterns, distance measures, and global constraints, improving alignment accuracy.

Key features include:
- Versatile alignment of sequences with different rhythms.
- Application-specific constraints for meaningful analysis.
- Essential class structures (CStepPattern, CConstraint, Cdtw) for DTW calculations.

This technique provides a robust tool for sophisticated financial market analysis.
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A notable advancement in trading automation: the same code is compatible with both MT4 and MT5 platforms. This code fetches news automatically from FXStreet servers, allowing for thorough filtering by importance level, currency, and keyword. Key features include filtering news by High, Low, or Medium volatility, currency-specific filtering, keyword-based filtering, and an auto GMT offset check. It also includes a customizable dashboard to display upcoming news, which can be enabled or disabled as required. Additionally, lines indicating upcoming events are displayed on the chart. The code is flexible and can be modified to place trades at specific times related to events or to suspend trading during those periods. Access the MT5 version at the provided link.
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Discover the power of non-linear methods in financial time series analysis! This article delves into innovative techniques for creating non-linear indicators on MetaTrader 5, transforming linear indicators using logarithmic price transformations to provide robust solutions where linear indicators fall short. Learn to utilize measures like the median and Bayesian smoothing to enhance sensitivity to price changes. Explore practical applications like robust trend analysis with the Weighted Median Ichimoku and improved pattern recognition using ordinal statistics. Ideal for both traders and developers, this comprehensive guide enhances algorithmic trading strategies and indicator accuracy comprehensively.
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This indicator offers clear visualization of bid and ask prices, aiding in trade entry and exit decisions. It includes custom bid and ask lines that are labelled for easy identification. Unlabeled lines can cause confusion, making this a useful addition for clarity.

A buy order will execute at the current ask price, while a sell order will execute at the current bid price. Seeing the ask line clearly allows for more strategic buying near the low of a candle rather than the high. Similarly, a labelled bid line assists in selling near the high of a candle, maximizing potential gains. This utility simplifies the trading process and enhances decision-making efficiency.
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Concept:

Continuing from the previous article, the development of the ProgressBar control advances. The control comprises an underlay and a progress bar. Future iterations will support additional design elements on the underlay, such as texts and images. Currently, it exists as a static WinForms object.

Today, programmatic control functionality for the ProgressBar will be developed. This allows usage in applications to indicate progress during extensive operation sequences. Visual effects, such as moving glare, can provide indications of process continuation or completion.

Once complete, the ProgressBar control may remain visible until manually closed, enabling observation of process completion through ongoing visual effects. Multiple progress bars can indicate subprocess completion while the main process continues.

Visual effects will be ...
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The code in discussion is compatible with the mt4 platform. It automatically retrieves news from FXStreet servers, allowing users to filter by importance level, currency, and keywords. The mt4 version is available at the provided link.

Key features include:
- Filtering news by high, low, or medium volatility.
- Filter by currencies.
- Filter by news keywords.
- Automatic GMT offset check.
- A dashboard to show upcoming news (optional).
- Lines on the chart for all upcoming events.

The code can be easily modified to place trades at specific times before or after events or to halt trading during events.
#MQL5 #MT5 #FXNews #TradingTech

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Bird Swarm Algorithm (BSA) is a bioinspired evolutionary algorithm developed by Meng and colleagues in 2015. It utilizes swarm intelligence based on social interactions and behavior of bird flocks.

The algorithm integrates three aspects of bird behavior: flight, foraging, and vigilance. Individual birds employ unique strategies, leading to effective cooperation and support in optimizing solutions. Different individuals within the BSA algorithm can switch between these behaviors to balance exploration and exploitation, aiming to avoid premature convergence.

Flight behavior and foraging are dependent on global and individual fitness. BSA models flock behavior, where birds exhibit adaptations such as flocking, communication, adaptability, and leadership-following dynamics to find optimal solutions.

BSA combines these principles for an optimization ap...
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This indicator provides alerts on MT4, mobile, and email, enhancing signal management efficiency. Press "N" to generate a new signal, and "D" to delete a signal. Customize your message preferences directly. The MT5 version is available at the provided link for enhanced compatibility.

Stay updated without constant screen monitoring, leveraging advanced alert functionalities. This tool is designed for streamlined communication and better trading decisions. Access the MT5-compatible version to optimize your trading strategy and ensure timely alerts across platforms.

For additional details, visit the link for comprehensive information on functionality and customization options.
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Explore how Multiple Time-Frame Analysis and AI converge in MetaTrader 5. By integrating EURUSD price predictions, this series evaluates the predictive power of AI across various strategies. Data and models built with open, high, low, and close price information revealed that while Linear Regression provided the best benchmark, the Gradient Boosting Regressor (GBR) showed potential after optimization. Despite challenges in achieving superior accuracy, this study illustrates AI's capabilities and provides practical tools and scripts for developers. Implement and visualize AI in your trading strategies with clear guidance on exporting models to ONNX and integrating them into MQL5 Expert Advisors.
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Dive into the future of trading with machine learning and predictive analytics, now seamlessly integrated with MetaTrader 5! By leveraging Python’s powerful libraries like scikit-learn and combining them with MQL5, traders can transition from static rule-based systems to dynamic, data-driven models that adapt to market fluctuations.

The step-by-step guide covers gathering historical data, processing it with Jupyter Lab, training a Random Forest model, and deploying it within MQL5 using ONNX for enhanced decision-making. Experience improved prediction accuracy, robust trade execution, and real-time adaptability. This revolutionary approach bridges financial analysis with AI, automating strategies that respond dynamically to market conditions.
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Discover invaluable insights into regression analysis for time series trading with our latest guide. We demystify the impacts of non-stationarity on regression models, using Monte Carlo simulations to highlight the pitfalls of spurious correlations. Learn how to leverage MQL5's powerful statistical and graphics libraries for precise calculations. We showcase the correct application of ordinary least squares and matrix algebra to estimate model parameters and how to diagnose model misspecification using residual analysis and auto-correlation functions. Enhance your algorithmic trading strategies by ensuring your regression models meet stationarity requirements for reliable predictions. Perfect for both seasoned and aspiring MetaTrader 5 developers!
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Explore the power of Gaussian Process (GP) Kernels in algorithmic trading without diving into machine learning. This post simplifies how GP Kernels measure relationships in time series data, producing forecasts with confidence estimates - a key advantage over traditional methods like ARIMA.

Learn about the Radial Basis Function (RBF) kernel, its flexibility in modeling non-linear relationships, and how it handles noisy data with accuracy. Also, discover practical MQL5 coding for leveraging RBF kernels in predicting financial metrics.

Step up your trading strategy by incorporating uncertainty quantification from GP Kernels, enhancing decision-making and managing risks effectively. Perfect for both novice and expert MetaTrader 5 developers.
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Stay informed with this useful MT5 indicator that sends alerts to your mobile and email. Easily manage signals by pressing "N" for new alerts and "D" to delete them. Tailor your alert preferences for seamless integration into your workflow. MT4 version is available at mql5.com/en/code/40575. Operate efficiently without the need to constantly monitor your screen. Ideal for those requiring reliable updates on market movements without direct supervision. Ensure you stay ahead with timely and customized notifications.
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This Expert Advisor (EA) originated as a task to trail equity. Subsequent additions included managing individual trades and manually setting prices to initiate a grid of pending orders. This EA does not contain conditions to open trades autonomously. Users must identify market areas where they would manually open a grid of orders. Upon identifying such an area, input the desired price for the EA to open a grid of pending orders. When the market reaches the specified price, the EA will send the grid and manage trades according to user-defined parameters, including Stop Loss and trailing actions. As of 13 Sep 2022, the EA compiles without errors.
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Optimizing trading strategies across multiple time periods helps assess their robustness. The period of 2018-2022 includes diverse market conditions and is sufficiently short for practical testing. MetaTrader 5’s forward period testing divides this span into main and forward sections, effectively evaluating strategies on unseen data.

Instances are optimized on past data and tested against future data. This serves as Out-Of-Sample (OOS) testing. Although initially overlooked, forward period analysis helps verify strategy performance beyond the optimization phase.

Observations indicate performance degradation in EAs during the forward period. Increased drawdown and elongated recovery times are common but manageable, suggesting room for further parameter refinement. Future work should include parameter clustering to mitigate these issues and ensure more r...
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