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  1. Neural Networks Made Easy (Part 97): Training Models With MSFformer

    by , 02-21-2025 at 05:37 AM
    In the previous article we built the main modules of the MSFformer model, including CSCM and Skip-PAM. The CSCM module constructs a feature tree of the analyzed time series, while Skip-PAM extracts information from time series at multiple scales using attention mechanism based on a temporal feature tree. In this article, we will continue that work by training the model and evaluating its performance on real-world data using the MetaTrader 5 Strategy Tester.
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  2. MQL5 Wizard Techniques you should know (Part 52): Accelerator Oscillator

    by , 02-20-2025 at 05:24 AM
    The Accelerator Oscillator is another Bill Williams Indicator that is designed to keep track of momentum and though common as it is derived from another oscillator that we covered in an earlier article, we take look at how various patterns exhibited by it could be used or even combined in ways that may give one an edge. This indicator specifically aims to track acceleration or deceleration of the momentum, which can be used in providing signals for possible trade entries and exits. Acceleration
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  3. Mini-articles with good indicators - go to subscribe (for free)

    by , 02-20-2025 at 02:47 AM
    Mini-articles with technical and fundamental analysis on the way as forecasts and levels -
    https://www.mql5.com/en/channels/forecast
  4. Custom Indicator: Plotting Partial Entry, Exit and Reversal Deals for Netting Accounts

    by , 02-12-2025 at 02:45 AM
    When we talk about indicators, we can think of different functions: plotting (histograms, trend lines, arrows or bars), calculating data based on price and volume movements, and observing statistical patterns in our trades. However, in this article we will consider another way of constructing an indicator in MQL5. We will talk about how to manage your own positions, including entries, partial exits, etc. We will make extensive use of dynamic matrices and some trading functions related to
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  5. Developing a trading robot in Python (Part 3): Implementing a model-based trading algorithm

    by , 01-11-2025 at 01:26 PM
    In the first article of the series, we loaded the dataset, placed labels, enriched the dataset and also performed dataset labeling. The second article was devoted to the creation and training of the model, as well as implementation of cross-validation and bagging.

    Now that our model is trained and tested, it is time to start real trading using the MetaTrader 5 library for Python. This powerful library allows us to automate trading directly through Python using the functions
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