Object segmentation in 3D scenes involves providing precise masks for detected objects in point clouds. Modern methods are grouped into assumption-based and clustering-based approaches. Assumption-based methods work top-down, first proposing regions and then determining masks, but struggle with point cloud sparsity and object complexity. Clustering-based methods adopt a bottom-up approach, assigning semantic labels and predicting instance centers but suffer from inaccuracies and extended processing times.

The Superpoint Transformer (SPFormer) combines both approaches, utilizing a sparse 3D U-Net for point-level feature extraction and grouping points into superpoints. SPFormer introduces a Transformer decoder that predicts instances utilizing cross-attention with superpoints, streamlining the segmentation process by eliminating redundant steps.

Imple...

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Transfer learning is a machine learning approach where a model trained for one task is applied to another related task. Instead of building a model from scratch, a pre-trained model's knowledge is adapted to a new task. This is useful when there's limited data for the new task. For instance, developing a cat vs. dog classifier with few images can leverage a model like ResNet50 trained on ImageNet.

In financial markets, similar approaches apply. Despite different price scales, features like percentage change and stationary indicators offer consistency across various instruments. Techniques to handle continuous variables involve calculating percentage changes and employing stationary indicators, ensuring models generalize effectively across different markets.

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The text underscores the significance of comprehending arrays and strings within statically typed languages compared to their dynamically typed counterparts. While languages like Python and JavaScript handle arrays and strings seamlessly, C/C++ provides challenges due to its handling of these structures internally. MQL5, positioned between these realms, simplifies some concepts, yet still demands understanding of its underlying principles for effective data manipulation.

Emphasizing practical learning through experimentation, it suggests modifying existing code snippets to grasp conceptually how different outcomes can be achieved. It introduces the integration of operator precedence and variables, laying a foundation for understanding more intricate data types.

Addressing string handling, it details how MQL5 strings are special arrays with an en...

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The angle of the moving average for the current and adjacent candle can provide valuable insights into market trends. This parameter helps in identifying the momentum and direction of price movements. Calculating the angle involves comparing the slope between the moving average line of two consecutive timeframes. A steeper angle may indicate stronger momentum, while a flatter angle might signal consolidation or weaker trends. Monitoring the angle change in real-time can assist developers and traders in making more informed decisions regarding their strategies. Analyzing this data could be a component in crafting a robust technical analysis toolkit.

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Dive into the development of a professional-grade Trade Assistant Tool for MetaTrader 5, crafted using MQL5. This tool facilitates efficient placement of Buy/Sell Stop and Limit orders with an intuitive graphical interface. Developers can explore the architecture involving metadata definition, constants, and the integration of essential libraries, like "CTrade". Key features involve a responsive control panel with buttons for order types, integrated lot sizing, and real-time price adjustments via draggable interactive elements on the chart. This project exemplifies the practical application of advanced object handling, enabling precise trading actions, thus forming a solid foundation for further enhancements in risk management.

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The article outlines challenges in algorithmic trading, emphasizing the inadequacies of commonly used regression metrics like RMSE in predicting market returns. Despite following "best practices," traders often face unexpected risks due to models that predict average returns, which minimizes statistical errors but fails in real trading scenarios. The piece highlights reward hacking, where models appear proficient by merely clustering around statistical means, misleading practitioners. To address these issues, the article suggests the adoption of new evaluation frameworks that incorporate profit and loss metrics alongside novel model architectures like Dynamic Regime Switching Models for a more robust trading strategy.

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In the exploration of algorithmic trading setups, innovative combinations like the DeMarker oscillator and the Envelopes Channel can help traders navigate range-bound markets. By avoiding traditional trend indicators, this approach minimizes lag and focuses on mean reversion and momentum divergences. Key patterns include the "Bullish Fake Out," where the price recovery signals seller exhaustion, and the "Bearish Fake Out," signaling buyer exhaustion. These setups optimize entry points and manage risks effectively, providing practical applications for skilled traders and developers using MetaTrader 5, particularly those trading GBP/USD on smaller time frames or seeking to refine trading strategies.

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The foreign exchange market offers unique opportunities for algorithmic strategies. Python and MetaTrader 5 have been instrumental in developing an arbitrage trading system, focusing on identifying price imbalances. A key aspect involved calculating synthetic cross rates, surpassing a thousand in number.

Implementing this system required robust risk management alongside a well-designed architecture, algorithms, and decision-making processes. Technologies such as Python and the MetaTrader 5 API facilitated real-time trading and technical analysis. Handling multiple currency pairs and calculating synthetic prices were crucial components of the strategy.

With the help of backtesting and live trading, system efficiency and potential improvements were evaluated, underscoring the need for careful analysis and continual optimization.

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Dive into Forex pattern analysis with Python and MetaTrader 5 for a systematic approach to identifying trading opportunities in currency markets. This article explores how to transform complex Forex market data into simpler trend indicators using an innovative algorithm for detecting repeating patterns. Key steps include setting up the Python environment, managing libraries like MetaTrader 5 and pandas, and connecting to trading platforms. The article explains processing OHLC data into directional patterns and building a comprehensive system for evaluating win rates and pattern frequencies. This analytical method offers practical forecasting tools for traders looking to enhance their algorithmic trading systems.

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Recursive Moving Averages, like DEMA and TEMA, improve data smoothing by calling their own calculations multiple times. They are designed to enhance traditional exponential moving averages. The objective is to eliminate any loop limits while maintaining performance speed. Key parameters involve the regular period, number of iterations for smoother results, and the chosen smoothing method, either exponential or another form, to optimize speed. Additionally, a trigger line generates trading signals by averaging the sum of smoothing iterations, with crossing lines indicating potential trades. One optional feature is the visual display of arrows at signals. When setting parameters, ensure they provide clarity without cluttering the chart with multiple signals. This careful balance is essential for effective analysis and decision-making.

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The MetaTrader 5 terminal is expanding its functionality with integrated chat capabilities, allowing traders to communicate more effectively within the MQL5 ecosystem. This addition enhances the trading experience, enabling private messaging, group discussions, and channel subscriptions directly through the terminal. While current communication remains internal, the API's flexibility, paired with WebRequest functions, can bridge MetaTrader 5 to external platforms like Telegram and WhatsApp.

Our focus is on developing a robust Communications Panel, drawing from popular messaging app features. Key characteristics include a minimalist design, centralized message thread area, interactive buttons, and support for multimedia sharing. The design will prioritize functionalities such as quick messaging, two-way communication, screenshot sharing, and contact managemen...

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Unlock the potential of economic forecasting with Python, leveraging historical data and indicators to predict market movements. This guide explores setting up an analytical environment with pandas, obtaining data from the World Bank via wbdata, and connecting with MetaTrader 5 for real-time market insights. Harness CatBoost Regressor for machine learning, training models on exchange rates and indicators for precise predictions. By integrating economic indicators into currency data, discover patterns and potential trades. Although forecasts aren't guarantees, combining data, analysis, and machine learning enhances decision-making for traders and developers keen on algorithmic trading.

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Binary classification models are useful for predicting whether tomorrow's closing price will be higher than today's. Logit and probit regression, common techniques within supervised learning, provide a foundation for this analysis. These models utilize price patterns and standardized price increments as predictors to form a training dataset. The resulting trained classifiers are implemented within trading algorithms like LogitExpert EA.

The process begins with data preparation, where features are defined, standardized, and structured for optimal parameter estimation. Maximum likelihood estimation, often combined with methods like L-BFGS optimization and L2 regularization, helps minimize the loss function, mitigating overfitting risks.

Once parameters and covariance matrices are estimated, prediction occurs, generating buy or sell signals. These signals determine ...

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Explore the latest insights into refining the MetaTrader 5 control indicator system. Delve into optimization challenges and improve code efficiency by refining object handling through the C_DrawImage class. Leverage embedded resources to reduce code complexity and enhance long-term usability by passing pointers wisely. Uncover practical improvements for handling bitmap images, ensuring robust initialization procedures, and adopting memory-efficient practices. Boost the sustainability of trading applications with safer inheritance structures, allowing seamless extensions. Understand the intricacies of utilizing standard MQL5 library functions, optimizing processes without compromising on performance. Aimed at traders and developers eager to advance their algorithmic trading strategy development skills.

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A robust Stochastic Crossover Strategy efficiently enters trades based on %K and %D line movements. Users can adjust periods for %K, %D, and slowing to align with their strategy. The Entry Cooldown feature minimizes impulsive entries and whipsaws, with the default cooldown preventing new trades shortly after the last one.

Standardized risk measures include adjustable Stop Loss and Take Profit settings, currently set at 300 points each. Trades use a fixed lot size of 0.1 for simplified risk management. The strategy incorporates automatic position closure upon opposing signals and prevents duplicate positions in the same direction. By leveraging the Trade.mqh library, execution is seamless.

Users can customize various input parameters related to risk and the Stochastic calculations. It's imperative to place the EA file correctly within the MT5 platfo...

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The combination of the DeMarker indicator with Envelopes in Python provides a strategic edge in market analysis. By converting these MQL5 indicators into Python, leveraging libraries like MetaTrader 5 and pandas, traders can seamlessly access price data and implement technical strategies. One approach involves constructing custom functions for each indicator, optimizing speed and module dependency. This method allows for the creation of robust trading systems that benefit from reduced computational overhead.

The DeMarker indicator measures momentum and provides insights into asset overbought or oversold conditions. With Python, implementing features like DeMax and DeMin offers enhanced modularity and reusability in technical analysis. Price fluctuations over specified periods reveal potential market trends, with values normalized to facilitate straightforward inte...

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The enhancement of the Trade Assistant Tool in MetaQuotes Language 5 (MQL5) introduces dynamic visual feedback mechanisms for MetaTrader 5, optimizing pending order placement efficiency. Enhancements include a draggable control panel, intuitive hover effects, and real-time order validation capabilities. These features improve interface interaction, reduce navigation errors, and ensure order precision by logically aligning entry, stop-loss, and take-profit levels with current market prices.

Implementation in MQL5 involves defining panel objects and user interaction variables. Advanced functions such as "isOrderValid" and "updateRectangleColors" are incorporated to ensure trade alignment and visual feedback accuracy. The "updateButtonHoverState" manages interactivity, enhancing user experience through clear button and element feedbacks.

Enhanced OnChartEve...

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Optimizing code for algorithmic trading in MetaTrader 5 enhances back-test accuracy and execution. Key improvements include modular code design, efficient use of technical indicators, and an event-driven execution model. These strategies reduce computational load and prevent false signals, improving the Expert Advisor's performance under heavy back-test conditions. Integrating RSI and MACD indicators with candlestick patterns aligns trade decisions with broader market trends, improving consistency in back-tests. Further enhancements suggest dynamic SL/TP adjustments and multi-timeframe analysis to refine trade accuracy and robustness. This comprehensive approach creates a disciplined, context-aware trading system.

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Reversing candle patterns are used to identify potential trend reversals in charts. Start by setting the input for the sequence of bulls and bears. Determine how many consecutive bullish candles are required before a bearish pattern can indicate a reversal, or vice versa. Configure the Stop Loss (SL) and Take Profit (TP) levels to manage risk and maximize potential gains. Ensure the input of row mode instructions allows for flexibility in pattern recognition. Implement these setups within a full Expert environment to automate the identification. This can improve efficiency in tracking potential trade opportunities based on trend reversals and help in making data-driven trading decisions.

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Custom indicators can transform a MetaTrader 5 Expert Advisor from a rigid tool into a dynamic trading assistant. By planning and coding effectively, developers can access custom data like the VWAP directly within an EA. This approach requires naming custom indicators correctly and using functions like iCustom and CopyBuffer to integrate them into the EA. This allows for tailored trading strategies beyond standard indicators. Developers can program indicators with parameters, like moving averages, and enable the EA to handle multiple scenarios, leveraging MetaTrader 5's capabilities. This flexibility paves the way for more refined and powerful algorithmic trading systems.

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