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3 년도
경험
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14
제품
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37
데모 버전
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2
작업
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0
거래 신호
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구독자
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In this article, triangular arbitrage is presented as a problem of finding cycles in a directed graph, where the vertices are currencies and the edges are currency pairs with weight rates. Profitable cycle: product of weights >1. Our Floyd-Warshall and DFS algorithms find optimal currency exchange paths that return to the starting point with a profit.
The article presents a complete implementation of TimeGPT, a specialized Transformer-based architecture for forecasting financial time series on the MetaTrader 5 platform. Adaptation of the attention mechanism to financial data, selective tokenization of price changes, hardware-aware optimizations, and advanced learning techniques are discussed. Included are practical testing results showing 87% forecast accuracy over a 24-bar horizon with a training time of 15 minutes on the CPU. We also present a ready-made trading EA with automatic retraining.
Implementation of the N-BEATS architecture for Forex trading in MetaTrader 5 with quantile forecasting and adaptive risk management. The architecture is adapted through bilinear normalization and specialized loss functions for financial data. Backtesting on 2025 data shows inability to generate profits, confirming the gap between theoretical achievements and practical trading performance.
The article presents the revolutionary architecture of PatchTST, a tailored transformer for financial time series analysis that breaks market data into 16-bar patches for efficient processing. We will discuss the full implementation of a trading robot in MQL5 covering everything from mathematical fundamentals and data structures to a ready-made EA with risk management and continuous learning systems.
The article presents an innovative hybrid system for forecasting exchange rates that combines a linear autoregressive model with a U-Transformer architecture for residual analysis. The system automatically switches between signal sources depending on their quality and includes complete trading logic with averaging/pyramiding strategies. The key advantage of this approach is that the neural network is trained on the residuals of the linear model, which simplifies the task and reduces the risk of overfitting. The implementation is done entirely in MQL5 and is ready for use in real trading with automatic adaptation to changing market conditions.
A revolutionary approach to machine learning in trading through quantum computing. The article demonstrates a practical implementation of an adaptive QRC system with continuous retraining for predicting market movements in real time.
