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  1. How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 3): Added symbols prefixes and/or suffixes and Trading Time Session

    by , 08-24-2024 at 01:07 PM
    Several fellow traders sent emails or commented about how to use this Multi-Currency EA on brokers with symbol names that have prefixes and/or suffixes, and also how to implement trading time zones or trading time sessions on this Multi-Currency EA.

    Therefore, on this occasion I will explain and create a function to be implemented in the EA in the previous article FXSAR_MTF_MCEA by adding an automatic detection function for broker symbols with prefixes and/or suffixes and
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  2. Neural networks made easy (Part 79): Feature Aggregated Queries (FAQ) in the context of state

    by , 08-22-2024 at 12:32 PM
    Object detection in video has a number of certain characteristics and must solve the problem of changes in object features caused by motion, which are not encountered in the image domain. One of the solutions is to use temporal information and combine features from adjacent frames. The paper "FAQ: Feature Aggregated Queries for Transformer-based Video Object Detectors" proposes a new approach to detecting objects in video. The authors of the article improve the quality of queries for Transformer-based
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  3. Neural networks made easy (Part 71): Goal-Conditioned Predictive Coding GCPC)

    by , 08-22-2024 at 08:30 AM
    Goal-Conditioned Behavior Cloning (BC) is a promising approach for solving various offline reinforcement learning problems. Instead of assessing the value of states and actions, BC directly trains the Agent behavior policy, building dependencies between the set goal, the analyzed environment state and the Agent's action. This is achieved using supervised learning methods on pre-collected offline trajectories. The familiar Decision Transformer method and its derivative algorithms have demonstrated
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  4. MQL5 Wizard Techniques you should know (Part 16): Principal Component Analysis with Eigen Vectors

    by , 08-15-2024 at 07:09 PM
    Principal Component Analysis (PCA) is the focusing on only the ‘principal components’ among the many dimensions of a data set, such that one is reducing the dimensions of that data set by ignoring the ‘non-principal’ parts.

    PCA though, with eigen values & vectors, take on a slightly deeper approach. Typically, data sets that are handled under PCA are in a matrix format and the principal components, that are sought from a matrix would be a single vector column (or row) that
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  5. Neural networks made easy (Part 75): Improving the performance of trajectory prediction models

    by , 08-15-2024 at 08:03 AM
    Forecasting the trajectory of the upcoming price movement probably plays one of the key roles in the process of constructing trading plans for the desired planning horizon. The accuracy of such forecasts is critical. In an attempt to improve the quality of trajectory forecasting, we complicate our trajectory forecasting models.
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