Discover a specialized approach to calculate the pseudoinverse in MetaTrader 5 using matrix factorization without general-purpose algorithms. This article highlights the efficiency of transforming arrays into a simple 2x2 matrix, enabling faster execution than generic methods. While traditional computations rely on libraries using matrices, this guide details implementing pseudoinverse directly in arrays, providing practical insights for developers. By mimicking matrix operations, programmers can optimize neural network calculations, offering a path toward efficient execution. Intended for educational use, it also hints at potential hardware implementations for scaling computations, catering to both traders and developers engaged in algorithmic trading.
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The article highlights key insights for developing effective trading strategies in MetaTrader 5. It advises on spotting unreliable Expert Advisors (EAs) by examining claims about AI integrations and mentions the pitfalls of look-ahead bias and misrepresented AI usage. To avoid overfitting, it suggests utilizing longer data periods and limiting tunable parameters during optimization. The piece stresses the importance of considering commission and spread impacts in backtesting, especially for high-frequency strategies. It also recommends in-sample and out-of-sample testing to ensure model robustness, focusing on maintaining a consistent profit factor and keeping drawdown within acceptable limits.
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Recent research has highlighted limitations in deep learning-based time series modeling, noting that shallow networks or linear models often outperform them on certain benchmarks. As foundational models in NLP and CV advance, their adaptation to time series data remains a challenge. TEMPO is introduced as a generative pre-trained transformer model focused on time series forecasting. It uses GPT and a two-component analytical framework to address specific temporal patterns like trends and seasonality. TEMPO enhances prediction accuracy by decomposing time series data into components, enriching model performance through an innovative prompt-based approach. This approach combines trend, seasonality, and residuals for a cohesive forecasting solution.
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The article explores the Traj-LLM algorithm, designed to enhance trajectory prediction using Large Language Models. Developed initially for autonomous vehicle applications, Traj-LLM harnesses Sparse Contextual Joint Encoding, high-level interaction modeling, and Lane-aware probabilistic learning, ensuring improved prediction accuracy. By utilizing pre-trained LLMs, the model overcomes traditional constraints of feature engineering, providing a robust approach to model temporal dependencies and interactions among traffic elements. The article also discusses implementing Traj-LLM in algorithmic trading using MQL5, highlighting modifications to existing neural network components for improved data processing efficiency and accuracy.

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Autonomous driving shares challenges with trading, notably in navigating dynamic environments. An autonomous vehicle's task of predicting future road events is complex due to unknown goals of other road users. Multi-agent traffic scenarios involve intricate interactions further complicated by rule-based constraints. Recent research adopts a vectorized approach for compact scene representation. However, real-time motion prediction remains difficult due to computational demands. The paper "HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction" introduces a method that uses a hierarchical model to manage interactions and dependencies, addressing computational efficiency and accuracy in motion prediction for large numbers of agents. It applies a Transformer architecture for improved scene comprehension.

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Algorithmic traders frequently encounter challenges when relying on RSI (Relative Strength Index) due to its sensitivity to parameters like period, timeframe, and market-specific factors. Traditional guidelines (e.g., levels of 70 and 30) may not yield consistent signals across different contexts. To address these inconsistencies, a more dynamic approach involves examining the true range of the indicator and adjusting the midpoint based on observed data, rather than preset ranges.

Implementing this in MQL5 offers advantages, incorporating a flexible RSI class to handle multiple periods and levels. This facilitates analysis across varied market conditions, enabling traders to empirically assess profitability of different RSI deviations and optimize periods through systematic testing rather than static assumptions.

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Deepening the understanding of DDPG is pivotal for those navigating reinforcement learning. The initialization of the DDPG-Agent class highlights essential components: dual network architecture, separate optimizers, and efficient experience management. Dual architecture facilitates stable learning through separate actor and critic networks, with target networks maintaining weight consistency. The experience management ensures memory efficiency via a fixed-size replay buffer for off-policy learning.

Incorporating key mechanisms such as state-processing, exploration strategies, and device-management in action selection enhances algorithm robustness. Learning updates demand sufficient experiences, allowing meaningful batch statistics; the critic networks update using stable Q-targets, while actor network updates focus on maximizing Q-values.

Target net...

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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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Price forecasting and market trend prediction are essential tasks in trading and risk management. Traditional machine learning models often face challenges in volatile markets. Moving from training from scratch to pretraining on large, unlabeled data sets, followed by fine-tuning specific tasks, enhances forecasting accuracy. Models like the Transformer architecture, when adapted for financial data, leverage asset correlations and temporal dependencies for improved predictions. Implementing alternative attention mechanisms accounts for market dependencies, enhancing model performance.

The R-MAT model is one such example, incorporating relative Self-Attention for processing molecular graphs. Its adaptability and accuracy across various tasks present new opportunities for developing trading strategies.

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In our latest exploration of MetaTrader 5 development, we focus on implementing a robust Zone Recovery System combined with an Envelopes-based trend-trading strategy using MQL5. This system intelligently manages trades by employing a "zone" strategy to recover from potential losses, automatically placing counter-trades when needed. By integrating Relative Strength Index and Envelopes indicators, the strategy identifies optimal entry and exit points, maintaining low risk and maximizing profitability. Designed for dynamic market conditions, this setup offers flexibility and precision for both novice and seasoned developers looking to automate and enhance their trading algorithms. Dive into the intricacies of our structured MQL5 implementation for deeper insights.

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Discover the SAMformer framework, an innovative approach to overcoming traditional Transformer limitations in long-term multivariate time series forecasting. By leveraging a shallow architecture, SAMformer reduces computational complexity and addresses overfitting. The core feature, Sharpness-Aware Minimization (SAM), enhances model robustness against parameter variations, significantly improving prediction quality. SAMformer's high accuracy with fewer parameters supports efficient deployment in resource-limited environments, finding applications in finance, healthcare, and more. Recently, SAM optimization was integrated into the convolutional layer, simplifying implementation while retaining functionality. This progress marks a significant step toward advanced, scalable Transformer models.

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Discover an innovative approach to market analysis through the lens of language and AI. Our study transforms price movements into binary sequences, then into mnemonic phrases using the BIP39 protocol, akin to cryptocurrency wallets. By applying a transformer architecture typically used in natural language processing, we develop a system to translate market data into human-readable language. This method captures hidden market patterns, achieving noteworthy prediction accuracy. Our findings suggest the market speaks a nuanced "language" with its vocabulary, providing unique insights for traders and developers. This groundbreaking approach opens new avenues for understanding and engaging with financial markets.

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Explore the innovative Market DNA concept, which creates a unique price-action signature for each currency pair, using metrics like volatility and session rhythms. The MQL5 implementation builds a "DNA passport" on MetaTrader 5, compressing market data into actionable insights. This robust system quantifies market regimes, aids in strategic adaptation, and flags significant shifts through mutation detection. Designed for both developers and traders, it automates analysis, enhances decision-making, and records signals for further use. By integrating Market DNA into trading workflows, gain a strategic edge with data-driven adaptability, consistent entries, risk-aware sizing, and efficiency in trade selection.

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ETARE presents a modern approach to algorithmic trading by integrating genetic optimization, machine learning, and advanced risk management. This system continually adapts to market conditions through an innovative trading loop, periodically refining strategies every 50 trades. Each trading instrument receives individual analysis based on recent data, ensuring precise position management. The strategies employ a descending volume approach to manage risk and maximize profits. A comprehensive trading decisions database ensures continuous learning, allowing the system to optimize for future trades. Through rigorous testing, ETARE has demonstrated adaptability across various market conditions, embodying a resilient and evolving trading system.

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In our previous technical development updates, we integrated an advanced ChatGPT module into MetaTrader 5 using MQL5, enhancing the user interface with scrollable chat and timestamp features. This time, our focus shifts to overcoming multiline input limitations and ensuring efficient chat history storage using AES256 encryption and ZIP compression.

Implementation involves refining multiline text rendering, integrating a sidebar for navigating preserved chat histories, and leveraging AI for real-time trade signal generation. We've modularized our MQL5 code and enhanced the bitmap handling, ensuring a seamless integration of UI elements like scalable icons and images.

Additionally, we improved prompt handling to accept multiline inputs effectively, using logic to append or create new paragraphs based on user input. This is crucial for detailed market queri...

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